{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGoMEHsYXGj2"
      },
      "source": [
        "# **Assessing Ocean Circulation, Carbon Uptake, and Environmental Changes**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VezxCBlh1rtY"
      },
      "source": [
        "*How does the ocean’s circulation connect to carbon storage and environmental shifts?*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This notebook can be run with Binder by following the link:\n",
        "[polarin_bottles](https://mybinder.org/v2/gh/POLAR-RESEARCH-INFRASTRUCTURE-NETWORK/jupyter-notebooks/HEAD?urlpath=%2Fdoc%2Ftree%2Fnotebooks_binder%2Fpolarin_bottles.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w64_HtWpbu7N"
      },
      "source": [
        "The purpose of this notebook is to show the use of in situ observations as bottle data to assess ocean circulation, carbon uptake and environmental changes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JsUxfbXIW3wY"
      },
      "source": [
        "The tool uses the following product:\n",
        "\n",
        "- POLARIN's ERDDAP's dataset (https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_9c61_fff7_d03c.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMxVYTjjYFdd"
      },
      "source": [
        "The first code cell installs and imports into the notebook all the necessary libraries to run the code of the subsequent cells. The second one retrieves a list of platforms from the dataset, filtering them by latitude to include only those with latitude less than -55 or latitude greater than 55. A dropdown menu is created to allow the user to select a platform from this list. Upon selection, the code fetches the corresponding trajectory data and updates a plot to visualize the platform's trajectory based on latitude and longitude coordinates. It will also store in the notebook's memory some functions that will be used later."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T3hOC3bupSzE"
      },
      "outputs": [],
      "source": [
        "# @title\n",
        "# !pip install cartopy scipy seaborn\n",
        "from ipywidgets import Dropdown, Layout, HBox, VBox, Output\n",
        "from scipy.stats import linregress\n",
        "from io import BytesIO\n",
        "import cartopy.crs as ccrs\n",
        "import cartopy.feature as cfeature\n",
        "import matplotlib.ticker as ticker\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import requests\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477,
          "referenced_widgets": [
            "2a10df5465aa4ecab52fd5c3463e2d40",
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        "id": "LDSL0QDFoF-B",
        "outputId": "43b80b46-a558-4723-aa91-1142ff4a4714"
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2a10df5465aa4ecab52fd5c3463e2d40",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(Dropdown(description='Select a platform:', layout=Layout(width='300px'), options=('1', '2', '3'…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title\n",
        "BASE_URL_DATASET = 'https://erddap.s4polarin.eu/erddap/tabledap/cnr_iadc_9c61_fff7_d03c'\n",
        "\n",
        "north_lat_limit = 55\n",
        "south_lat_limit = -55\n",
        "\n",
        "coords_padding = 10\n",
        "\n",
        "def get_platforms():\n",
        "    profile_ids_query_north = f'.csvp?Bottle_No&latitude%3E={north_lat_limit}&distinct()'\n",
        "    profile_ids_query_south = f'.csvp?Bottle_No&latitude%3C={south_lat_limit}&distinct()'\n",
        "\n",
        "    resp_north = requests.get(BASE_URL_DATASET + profile_ids_query_north)\n",
        "    df_north = pd.read_csv(BytesIO(resp_north.content), encoding='utf-8', dtype=str)\n",
        "\n",
        "    resp_south = requests.get(BASE_URL_DATASET + profile_ids_query_south)\n",
        "\n",
        "    df_south = pd.read_csv(BytesIO(resp_south.content), encoding='utf-8', dtype=str)\n",
        "\n",
        "    frames = [df_north, df_south]\n",
        "    df_platforms = pd.concat(frames)\n",
        "\n",
        "    platforms = list(df_platforms['Bottle_No'])\n",
        "    return platforms, df_platforms\n",
        "\n",
        "def var_name_uom_map():\n",
        "    metadata_url = 'https://erddap.s4polarin.eu/erddap/info/cnr_iadc_9c61_fff7_d03c/index.csv'\n",
        "    resp_metadata = requests.get(metadata_url)\n",
        "    df_metadata = pd.read_csv(BytesIO(resp_metadata.content), header=0, encoding='utf-8')\n",
        "\n",
        "    df_metadata = df_metadata[~df_metadata['Variable Name'].str.contains('_error', na=False)]\n",
        "    df_metadata = df_metadata[~df_metadata['Variable Name'].str.contains('_qc', na=False)]\n",
        "    df_metadata = df_metadata[~df_metadata['Variable Name'].str.contains('_unk', na=False)]\n",
        "    df_metadata = df_metadata[~df_metadata['Variable Name'].str.contains('_68', na=False)]\n",
        "    df_metadata = df_metadata[~df_metadata['Variable Name'].str.contains('ref_', na=False)]\n",
        "    df_metadata = df_metadata[~df_metadata['Variable Name'].str.contains('rev_', na=False)]\n",
        "    df_metadata = df_metadata[~df_metadata['Variable Name'].str.contains('bottle_', na=False)]\n",
        "    df_metadata.reset_index(drop=True, inplace=True)\n",
        "\n",
        "    variable_long_name_map = {}\n",
        "    variable_units_map = {}\n",
        "\n",
        "    current_variable = None\n",
        "    for index, row in df_metadata.iterrows():\n",
        "        if row['Row Type'] == 'variable':\n",
        "            current_variable = row['Variable Name']\n",
        "            variable_long_name_map[current_variable] = current_variable\n",
        "            variable_units_map[current_variable] = ''\n",
        "\n",
        "        elif row['Row Type'] == 'attribute' and current_variable:\n",
        "            if row['Attribute Name'] == 'long_name':\n",
        "                variable_long_name_map[current_variable] = row['Value'].encode('utf-8').decode('unicode_escape')\n",
        "            elif row['Attribute Name'] == 'units':\n",
        "                unit = row['Value'] if row['Value'] != 'Unknown' else ''\n",
        "                unit = unit.encode('utf-8').decode('unicode_escape')\n",
        "                variable_units_map[current_variable] = unit\n",
        "\n",
        "    variable_units_map['time'] = ''\n",
        "    variable_units_map['ctd_salinity'] = 'psu'\n",
        "\n",
        "    return variable_long_name_map, variable_units_map\n",
        "\n",
        "def get_filtered_variable_map(selected_platform, variable_long_name_map, non_valid_vars=None, additional_vars=None):\n",
        "    data_query = f'.csv?&Bottle_No={selected_platform}'\n",
        "    resp = requests.get(BASE_URL_DATASET + data_query)\n",
        "    df = pd.read_csv(BytesIO(resp.content), header=0, encoding='utf-8')\n",
        "    df = df.iloc[1:]\n",
        "\n",
        "    df.dropna(axis=1, how='all', inplace=True)\n",
        "    df.dropna(axis=0, how='all', inplace=True)\n",
        "\n",
        "    all_vars = list(df.columns)\n",
        "\n",
        "    filtered_variable_long_name_map = {k: v for k, v in variable_long_name_map.items() if k in all_vars}\n",
        "\n",
        "    if additional_vars:\n",
        "        filtered_variable_long_name_map.update(additional_vars)\n",
        "\n",
        "    for non_valid_var in non_valid_vars:\n",
        "        filtered_variable_long_name_map.pop(non_valid_var, None)\n",
        "\n",
        "    long_name_to_variable_map = {v: k for k, v in filtered_variable_long_name_map.items()}\n",
        "\n",
        "    return filtered_variable_long_name_map, long_name_to_variable_map\n",
        "\n",
        "platforms, df_platforms = get_platforms()\n",
        "\n",
        "selected_platform = platforms[0]\n",
        "\n",
        "dropdown_platforms = Dropdown(\n",
        "    options=platforms,\n",
        "    value=platforms[0],\n",
        "    description='Select a platform:',\n",
        "    layout=Layout(width='300px'),\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "output_plot = Output()\n",
        "\n",
        "def update_plot():\n",
        "    with output_plot:\n",
        "        output_plot.clear_output(wait=True)\n",
        "        data_query = f'.csv?time%2Clatitude%2Clongitude&Bottle_No={selected_platform}'\n",
        "        resp = requests.get(BASE_URL_DATASET + data_query)\n",
        "        df = pd.read_csv(BytesIO(resp.content), header=0, encoding='utf-8')\n",
        "        if df.empty:\n",
        "            print(\"No data available for the selected platform.\")\n",
        "            return\n",
        "\n",
        "        df = df.iloc[1:]\n",
        "        df['latitude'] = df['latitude'].astype(float)\n",
        "        df['longitude'] = df['longitude'].astype(float)\n",
        "\n",
        "        min_lat, mean_lat, max_lat = df['latitude'].min(), df['latitude'].mean(), df['latitude'].max()\n",
        "        min_lon, mean_lon, max_lon = df['longitude'].min(), df['longitude'].mean(), df['longitude'].max()\n",
        "\n",
        "        lat_range = max_lat - min_lat\n",
        "        lon_range = max_lon - min_lon\n",
        "        max_range = max(lat_range, lon_range)\n",
        "\n",
        "        lat_buffer = (max_range - lat_range) / 2 + coords_padding\n",
        "        lon_buffer = (max_range - lon_range) / 2 + coords_padding\n",
        "\n",
        "        projection = ccrs.PlateCarree()\n",
        "\n",
        "        fig, ax = plt.subplots(figsize=(5, 5), subplot_kw={'projection': projection})\n",
        "\n",
        "        ax.add_feature(cfeature.LAND, edgecolor='black')\n",
        "        ax.add_feature(cfeature.OCEAN)\n",
        "        ax.add_feature(cfeature.COASTLINE)\n",
        "        ax.add_feature(cfeature.BORDERS, linestyle=':')\n",
        "\n",
        "        ax.plot(df['longitude'], df['latitude'], marker='.', linestyle='-', color='lime', transform=ccrs.PlateCarree())\n",
        "        ax.set_title(f'Trajectory for Platform: {selected_platform}')\n",
        "        ax.set_xlabel('Longitude')\n",
        "        ax.set_ylabel('Latitude')\n",
        "\n",
        "        ax.set_xticks([min_lon - lon_buffer, mean_lon, max_lon + lon_buffer])\n",
        "        ax.set_yticks([min_lat - lat_buffer, mean_lat, max_lat + lat_buffer])\n",
        "\n",
        "        ax.set_xlim(min_lon - lon_buffer, max_lon + lon_buffer)\n",
        "        ax.set_ylim(max(min_lat - lat_buffer, -90), min(max_lat + lat_buffer, 90))\n",
        "\n",
        "        plt.show()\n",
        "\n",
        "def on_change(change):\n",
        "    global selected_platform\n",
        "    if dropdown_platforms.value:\n",
        "        selected_platform = dropdown_platforms.value\n",
        "        update_plot()\n",
        "\n",
        "dropdown_platforms.observe(on_change, names='value')\n",
        "\n",
        "hbox = VBox([dropdown_platforms, output_plot])\n",
        "\n",
        "display(hbox)\n",
        "\n",
        "update_plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFgPO1AVjdx1"
      },
      "source": [
        "## **Visualize the correlation between two parameters for the selected platform**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFbuaETV2tLI"
      },
      "source": [
        "This code cell initializes and displays dropdown menus that allow the user to select variables, between all the ones available for the selected platform, for the X and Y axes of a scatter plot, as well as choose between linear and logarithmic scales for each. The selected variables and scales will be stored globally, and the plot will update according to the chosen values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "408c197e951c4beaa37554f83d4302d8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(VBox(children=(Dropdown(description='Y axis variable:', layout=Layout(width='300px'), options=(…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title\n",
        "variable_long_name_map, units_map = var_name_uom_map()\n",
        "non_valid_vars = ['time', 'latitude', 'longitude', 'Project', 'Cruise', 'Station', 'Type', 'line_id', 'Bottle N\\u00b0', 'sample', 'BottleNo', 'profile_type']\n",
        "filtered_variable_long_name_map, long_name_to_variable_map = get_filtered_variable_map(selected_platform, variable_long_name_map, non_valid_vars=non_valid_vars)\n",
        "\n",
        "first_var = 'Pressure'\n",
        "second_var = 'time'\n",
        "\n",
        "x_scale = 'Linear'\n",
        "y_scale = 'Linear'\n",
        "\n",
        "dropdown_1 = Dropdown(\n",
        "    options=list(long_name_to_variable_map.keys()),\n",
        "    value='maximum depth of the sensor\"',\n",
        "    description='Y axis variable:',\n",
        "    layout=Layout(width='300px'),\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "dropdown_2 = Dropdown(\n",
        "    options=list(long_name_to_variable_map.keys()),\n",
        "    value='T90 [°C]',\n",
        "    description='X axis variable:',\n",
        "    layout=Layout(width='300px'),\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "x_scale_dropdown = Dropdown(\n",
        "    options=['Linear', 'Logarithmic'],\n",
        "    value='Linear',\n",
        "    description='Choose the scale:',\n",
        "    layout=Layout(width='300px'),\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "y_scale_dropdown = Dropdown(\n",
        "    options=['Linear', 'Logarithmic'],\n",
        "    value='Linear',\n",
        "    description='Choose the scale:',\n",
        "    layout=Layout(width='300px'),\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "def on_change(change):\n",
        "    global first_var\n",
        "    global second_var\n",
        "    global x_scale\n",
        "    global y_scale\n",
        "    if dropdown_1.value and dropdown_2.value and x_scale_dropdown.value and y_scale_dropdown.value:\n",
        "        first_var = long_name_to_variable_map[dropdown_1.value]\n",
        "        second_var = long_name_to_variable_map[dropdown_2.value]\n",
        "\n",
        "        y_scale = y_scale_dropdown.value\n",
        "        x_scale = x_scale_dropdown.value\n",
        "\n",
        "dropdown_1.observe(on_change, names='value')\n",
        "dropdown_2.observe(on_change, names='value')\n",
        "\n",
        "x_scale_dropdown.observe(on_change, names='value')\n",
        "y_scale_dropdown.observe(on_change, names='value')\n",
        "\n",
        "vbox_1 = VBox([dropdown_1, dropdown_2])\n",
        "vbox_2 = VBox([y_scale_dropdown, x_scale_dropdown])\n",
        "\n",
        "hbox = HBox([vbox_1, vbox_2])\n",
        "display(hbox)\n",
        "\n",
        "on_change(None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "72WsSEYJ2zuW"
      },
      "source": [
        "Once the variables and scales have been selected, the next code cell generates a scatter plot to visualize the correlation between them. It fetches the data from the server, filters, and sorts it based on the chosen variables. It colors the data points by time, creating a color-coded legend for profile times. The plot will automatically adjust to these settings for accurate analysis.\n",
        "\n",
        "In this plot, a linear regression is also applied to show the relationship between the two selected variables. Linear regression is a statistical method used to model the relationship between two variables by fitting a straight line through the data. The line represents the best estimate of how one variable  changes in response to the other variable. Below the plot the R-squared and p-value obtained from the linear regression will be shown.\n",
        "\n",
        "- R-squared (R²): This value represents how well the regression line fits the data. It ranges from 0 to 1, where 1 means a perfect fit. A higher R² means that the line explains a larger portion of the variance in the data.\n",
        "\n",
        "- P-value: This tells us whether the relationship between the variables is statistically significant. A small p-value (typically less than 0.05) suggests that the correlation is likely not due to chance, implying a strong association between the variables."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 859
        },
        "id": "E-Kq9HUiYxmD",
        "outputId": "5fcd10f6-7e86-4768-8258-37ca48ed86cc"
      },
      "outputs": [
        {
          "data": {
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0er20vaCgQLi5uYmnnnpK2hYeHi4AiNTUVIN+93vppZeEubm5KCoqkrYFBgYKV1fXSn1TU1MFABEeHi5t69y5s7C3txe3b9+Wtp09e1bI5XIxYcIEaVtYWJgAIP773/8axBwxYoRo3LhxpbHu169fPwFALF26VNpWXFwsjV9SUiKEEOKbb74RcrlcHD582GD/tWvXCgDi6NGj0jYLCwsRFBRUaazAwEDRo0cP6fnIkSPFyJEjhYmJidi1a5cQQojTp08LAOKnn34SQghx9+5dYWNjI6ZMmWIQKz09XVhbWxtsHzRokOjYsaPBOdfr9aJXr16idevW0raKa+jr62twvWfOnClMTExETk5Otees4pzf/7Cxsan0mlu+fLkAIDZt2iRtKykpET4+PsLS0lLk5uYKIYTIzMwUAERYWFi1Y9cEgAgODq6y/bXXXhMAxNmzZ4UQdbuuFcd56tQpadu1a9eEmZmZGDFihLRt8eLFld4j98ZQKpXi8uXL0razZ88KAGLlypXVHpux916FitdxRESEtC0pKUkAEHK5XBw7dkzavmfPnkrvtwoV1zYzM7PauRh7zy9atEjIZDJx7do1aVtQUJAAIN59912Dvl26dBFeXl7S8+3btwsA4uOPP5a2lZWViT59+lQ516pUd/6XLl0qAFS63nPnzhUAxJAhQ6RtFhYWlf6uCCHEzp07jf59vV9Nr+c///xTnDhxQhQWFhpsnzlzpgAgzpw5U238+9X22hnrl5GRIZRKpfDz8xM6nU7avmrVKgFAfPXVV9K2itfa2rVrazWvitfA9OnTpW16vV4EBgYKpVJpMI/7z5mx11lsbKwAIL7++mtpW2RkpAAg9u/fb3R8CwsLg231+XnaoUMH6XNCCCHGjh0rZDKZCAgIMOjv4+NT6fOv4loYm3d1qvosJSIiIvq3YTkXIqJ/gYoSKrVdCfrrr78CQKWbIr7++usAUKl2upubG/z9/Y3GCgoKMqjnGh8fj0uXLmHcuHG4ffs2srKykJWVhfz8fAwaNAiHDh2CXq+vcm73xrp79y6ysrLQp08fFBQUICkpqVbHd6+0tDTEx8dj4sSJsLW1lbY/8cQTeOqpp6Rzca9p06YZPO/Tpw9u374tnefqmJqa4qWXXpKeK5VKvPTSS8jIyEBcXByA8pW3np6eaNu2rXR+srKyMHDgQADA/v37axynT58+OH36NPLz8wGUr0h++umn0blzZ6nMxOHDhyGTyfDkk08CAKKiopCTk4OxY8cajGtiYgJvb29p3OzsbERHR2P06NHSNcjKysLt27fh7++PS5cu4caNGwbzmTp1KmQymcH8dDodrl27VuOxAMAPP/yAqKgo7N27F+Hh4WjTpg2effZZxMTESH1+/fVXODo6YuzYsdI2hUKBV199FXl5eTh48GCtxqovFb9+uHv3LoC6X1cfHx94eXlJz5s3b45hw4Zhz549lcqTVMXX1xetWrWSnj/xxBPQaDS4cuXKQx/bvWVKPDw8YGNjA09PT4PyQBX//TDj3fuez8/PR1ZWFnr16gUhBM6cOVOpv7H3573j//rrrzA1NZVWpgPlZUumT5/+wHM0Zty4cbC2tsZ///tfREVF4erVq1i3bp10U897y9EUFhZCpVJVilFx/4b7S9fUlbOzM7p3717pfhB1/WyoD7/99htKSkowY8YMg5X3U6ZMgUajqfT5olKpMGnSpDqNERISIv13RWmkkpKSaksL3fs6Ky0txe3bt+Hu7g4bGxucPn26TuPfqz4/TydMmGCwSt/b2xtCCOlXTPdu/+OPP1BWViZtW7BgAYQQXIVORERE9IBYzoWI6F9Ao9EA+F8yrybXrl2DXC6Hu7u7wXZHR0fY2NhUSny6ublVGev+tkuXLgFAtaUTtFotGjVqZLQtISEB8+fPR3R0dKWktVarrTJmVSqO5d6fzFfw9PTEnj17kJ+fDwsLC2l78+bNDfpVzPXOnTvSua6Kk5OTQSyg/OaUQHm97p49e+LSpUu4cOGCQVmOe1XcrK86ffr0QVlZGWJjY+Hi4oKMjAz06dMHCQkJBkn0du3aSV8eVFybiqTu/SqO7fLlyxBC4O2338bbb79d5RydnZ2l59Wds9ro27evwY1FR40ahdatW2P69OnSlw/Xrl1D69atK5XEqCi5UNuEfX3Jy8sD8L8EZV2v6721qyu0adMGBQUFyMzMlOrCV+f+8w6Un/vanveqNGvWzOBLEQCwtraGi4tLpW1A7a+zMdevX8c777yDn3/+uVKc+9/zZmZmlc7v/cd77do1NG3atFKJJ2N/Ax6Go6Mjfv75Z4wfPx5+fn4Ayt9DK1euRFBQkMH4arXaaN3zoqIiqf1RqOtnQ32o6m+uUqlEy5YtK71PnZ2d63SjTrlcjpYtWxpsu/dvbFUKCwuxaNEihIeH48aNGwY11B/ks6VCfX6e3v9+rnh/GXvf6fV6aLXahyrbRERERET/wyQ6EdG/gEajgZOTE86fP1+n/e5PklWlugTP/W0Vq8wXL16Mzp07G92nqvrlOTk56NevHzQaDd599120atUKZmZmOH36NObMmVPtCvb6ZGJiYnS7qOHGdbWl1+vRsWNHfPLJJ0bb70+YGNOtWzeYmZnh0KFDaN68Oezt7dGmTRv06dMHq1evRnFxMQ4fPmxQu7zi/H3zzTdGE7QVN/Gs6Ddr1qwqV0zenzCq73NmaWkJb29v/PTTT5W+5HhcnD9/HiYmJlJSrD6ua109qtdqVXHrezydToennnoK2dnZmDNnDtq2bQsLCwvcuHEDEydOrPSer2r8htK3b19cuXIF586dQ35+Pjp16oSbN28C+F9iFwCaNm2KtLS0SvtXbHNycnok82vbti0A4Ny5c1X+PW5oj+oLhPtNnz4d4eHhmDFjBnx8fGBtbQ2ZTCbVk39Y9fF5+le974iIiIioMibRiYj+JYYMGYJ169YhNjYWPj4+1fZ1dXWFXq/HpUuXpFW8AHDr1i3k5OTA1dX1gedRUVpCo9HA19e3TvseOHAAt2/fxo8//oi+fftK21NTUyv1rW3CouJYkpOTK7UlJSWhSZMm9ZqgvXnzZqWk78WLFwFAumlnq1atcPbsWQwaNKjG46iqXalUokePHjh8+DCaN2+OPn36AChfoV5cXIzNmzfj1q1bBuex4trY29tXe20qVnkqFIo6X8P6VFGqIC8vDxYWFnB1dcXvv/8OvV5vsBq9osxPxbWu7WvjYVy/fh0HDx6Ej4+PtBK9LtcV+N8vA+518eJFmJubS6ut/4pjaUjnzp3DxYsXsXHjRkyYMEHaHhUV9cAxXV1dsW/fPuTl5Rl8YWfsb0B9MDExMUhQV5QUufe9U1Fm6f7X7vHjx2Fubm6QcK9PAQEBMDExwaZNmx7JzUWNufdv7r0rxktKSpCamvrQf1P0ej2uXLlicM7u/xtrzPfff4+goCAsXbpU2lZUVIScnByDfnV9zz3Kz1MiIiIi+uuwJjoR0b/EG2+8AQsLC7z44ou4detWpfaUlBSsWLECAPD0008DAJYvX27Qp2IFbWBg4APPw8vLC61atcKSJUukchf3yszMrHLfitV2966uKykpkWoM38vCwqJWP8Fv2rQpOnfujI0bNxokS86fP4+9e/dK56K+lJWV4fPPP5eel5SU4PPPP4ednZ1U/3r06NG4ceMGvvjii0r7FxYWSnXOgfLjvD/JU6FPnz44fvw49u/fLyXRmzRpAk9PT3z00UdSnwr+/v7QaDT44IMPUFpaWilexbWxt7dH//798fnnnxtdPVvdNawv2dnZiImJgaOjI+zt7QGUv27T09OxdetWqV9ZWRlWrlwJS0tL9OvXDwBgbm4OAEbPm1arRVJS0kOVb8jOzsbYsWOh0+nw1ltvSdvrcl0BIDY21qAW8x9//IGffvoJfn5+0nuh4suYql4Df3fG3vNCCOlv1YN4+umnUVZWhjVr1kjbdDodVq5c+eATraXMzEx89NFHeOKJJwySxaNGjcKtW7fw448/StuysrIQGRmJoUOHGq2XXh9cXFwwZcoU7N271+jx6/V6LF26FH/++We9jenr6wulUolPP/3U4Lp++eWX0Gq1D/X5UmHVqlXSfwshsGrVKigUCgwaNKjKfUxMTCqt3F65cmWl+w/U9T33KD9P6yIrKwtJSUkoKCj4S8YjIiIi+qfhSnQion+JVq1aISIiAs8//zw8PT0xYcIEdOjQASUlJYiJiUFkZCQmTpwIAOjUqROCgoKwbt06qYTKiRMnsHHjRgwfPhwDBgx44HnI5XKsX78eAQEBaN++PSZNmgRnZ2fcuHED+/fvh0ajwS+//GJ03169eqFRo0YICgrCq6++CplMhm+++cboT9a9vLywdetWhIaGonv37rC0tMTQoUONxl28eDECAgLg4+ODyZMno7CwECtXroS1tTUWLFjwwMdqjJOTEz766CNcvXoVbdq0wdatWxEfH49169ZJN4wbP348vvvuO0ybNg379+9H7969odPpkJSUhO+++w579uxBt27dpOP87bff8Mknn8DJyQlubm7SzRz79OmD999/H3/88YdBsrxv3774/PPP0aJFCzRr1kzartFosGbNGowfPx5du3bFmDFjYGdnh+vXr2Pnzp3o3bu3lJz67LPP8OSTT6Jjx46YMmUKWrZsiVu3biE2NhZ//vknzp49W6/n7fvvv4elpSWEELh58ya+/PJL3LlzB2vXrpVWhk6dOhWff/45Jk6ciLi4OLRo0QLff/89jh49iuXLl0srwtVqNdq1a4etW7eiTZs2sLW1RYcOHdChQwds27YNkyZNQnh4uPR+qM7FixexadMmCCGQm5uLs2fPIjIyEnl5efjkk08wePBgqW9drisAdOjQAf7+/nj11VehUqmkL4sWLlwo9an44uWtt97CmDFjoFAoMHTo0MeyvM2DaNu2LVq1aoVZs2bhxo0b0Gg0+OGHHx6qxvrQoUPRu3dvzJ07F1evXkW7du3w448/PtQXJ1Xp168ffHx84O7ujvT0dKxbtw55eXnYsWOHwYrzUaNGoWfPnpg0aRISExPRpEkTrF69GjqdzuB6PwpLly5FSkoKXn31Vfz4448YMmQIGjVqhOvXryMyMhJJSUkGN5F9WHZ2dpg3bx4WLlyIwYMH45lnnkFycjJWr16N7t274z//+c9DxTczM8Pu3bsRFBQEb29v7Nq1Czt37sSbb75Z5f0IgPJfa33zzTewtrZGu3btEBsbi99++61STfHOnTvDxMQEH330EbRaLVQqFQYOHCh9mXe/R/l5WherVq3CwoULsX///hpvLvr777/j559/BlB+DwytVov/9//+H4Dy46nqs5SIiIjoH00QEdG/ysWLF8WUKVNEixYthFKpFFZWVqJ3795i5cqVoqioSOpXWloqFi5cKNzc3IRCoRAuLi5i3rx5Bn2EEMLV1VUEBgZWGmf//v0CgIiMjDQ6jzNnzoiRI0eKxo0bC5VKJVxdXcXo0aPFvn37pD7h4eECgEhNTZW2HT16VPTs2VOo1Wrh5OQk3njjDbFnzx4BQOzfv1/ql5eXJ8aNGydsbGwEAOHq6iqEECI1NVUAEOHh4Qbz+e2330Tv3r2FWq0WGo1GDB06VCQmJhr0CQsLEwBEZmamwXZj8zSmX79+on379uLUqVPCx8dHmJmZCVdXV7Fq1apKfUtKSsRHH30k2rdvL1QqlWjUqJHw8vISCxcuFFqtVuqXlJQk+vbtK9RqtQAggoKCpLbc3FxhYmIirKysRFlZmbR906ZNAoAYP3680Xnu379f+Pv7C2tra2FmZiZatWolJk6cKE6dOmXQLyUlRUyYMEE4OjoKhUIhnJ2dxZAhQ8T3339f6dycPHmy0hj3XzNjKs75vQ8LCwvh4+Mjvvvuu0r9b926JSZNmiSaNGkilEql6NixY6VrLYQQMTExwsvLSyiVSgFAhIWFGczX2D73u3dOcrlc2NjYiC5duojXXntNJCQkGN2nttcVgAgODhabNm0SrVu3FiqVSnTp0sXo+XrvvfeEs7OzkMvlBq/Dihj3c3V1NXidGFPda7ridWwsrrG/BVXNo6r30/0SExOFr6+vsLS0FE2aNBFTpkwRZ8+erXSdgoKChIWFRZXj3Ov27dti/PjxQqPRCGtrazF+/Hhx5syZWl/7CosXL672vT9z5kzRsmVLoVKphJ2dnRg3bpxISUkx2jc7O1tMnjxZNG7cWJibm4t+/fpVet9U5d7X8IMoKysT69evF3369BHW1tZCoVAIV1dXMWnSJHHmzJlK/Wt77arrt2rVKtG2bVuhUCiEg4ODePnll8WdO3cM+lT1WqtKxWsgJSVF+Pn5CXNzc+Hg4CDCwsKETqcz6Hv/Obtz5470t8PS0lL4+/uLpKQko++XL774QrRs2VKYmJgY/B2r6jX4qD5Pq/r7auy8V2yr6W/uvXGNPWr620FERET0TyUTgnecISIiInqcyGQyBAcHG5Sl+Ctt2LABkyZNwunTp+Hi4oLGjRvXW/31oqIi5OXl4eOPP8bixYuRmZmJJk2a1EvsfyuZTIbZs2dLZbse1c04H/drN3HiRHz//fdGS4URERERET0M1kQnIiIiIqO6du0KOzs73L59u95irl27FnZ2dli8eHG9xaTyslR2dnb47LPPHtkYvHZERERE9G/FmuhEREREZMDf3x9RUVHSc2tr63qL/eyzz6JDhw6PJPa/1b3Xqk2bNo9sHF47IiIiIvq3YhKdiIiIiAw0bdoUTZs2fSSxXVxc4OLi8khi/1v5+vr+JePw2hERERHRvxVrohMRERERERERERERVYE10YmIiIiIiIiIiIiIqsAkOhERERERERERERFRFVgTvZb0ej1u3rwJKysryGSyhp4OERERERERERHVgRACd+/ehZOTE+RyrislotpjEr2Wbt68yRspERERERERERH9zf3xxx9o1qxZQ0+DiP5GmESvJSsrKwDlf2g1Gk0Dz4aIiIiIiIiIiOoiNzcXLi4uUo6HiKi2mESvpYoSLhqNhkl0IiIiIiIiIqK/KZbpJaK6YgEoIiIiIiIiIiIiIqIqMIlORERERERERERERFQFJtGJiIiIiIiIiIiIiKrAmuhERERERERERET/Inq9HiUlJQ09DaIGo1AoYGJiUuv+TKITERERERERERH9S5SUlCA1NRV6vb6hp0LUoGxsbODo6Firmw0ziU5ERERERERERPQvIIRAWloaTExM4OLiArmclZ7p30cIgYKCAmRkZAAAmjZtWuM+TKITERERERERERH9C5SVlaGgoABOTk4wNzdv6OkQNRi1Wg0AyMjIgL29fY2lXfh1ExERERERERER0b+ATqcDACiVygaeCVHDq/giqbS0tMa+TKITERERERERERH9i9SmBjTRP11d3gdMohMRERERERERERERVYFJdCIiIiIiIiIiIvrbWbBgARwcHCCTybB9+3ZMnDgRw4cPl9r79++PGTNmPJJxO3fuXO9xH8bVq1chk8kQHx/f0FOplfuv1eOOSXQiIiIiIiIiIiJ6JCZOnAiZTAaZTAalUgl3d3e8++67KCsre6i4Fy5cwMKFC/H5558jLS0NAQEBWLFiBTZs2FA/E/8/FQn6e82aNQv79u2r13Ho8Wba0BMgIiIiIiIiIiKif67BgwcjPDwcxcXF+PXXXxEcHAyFQoF58+ZV6ltSUlKrG5+mpKQAAIYNGybVtlapVPU78SpYWlrC0tLyLxmLHg9ciU5ERERERERERESPjEqlgqOjI1xdXfHyyy/D19cXP//8M4D/lfV4//334eTkBA8PDwDAuXPnMHDgQKjVajRu3BhTp05FXl4egPJyKkOHDgUAyOVyKYleU4mQ4uJizJo1C87OzrCwsIC3tzcOHDhQZf8WLVoAAEaMGAGZTCY9v7+cS8W4H3zwARwcHGBjYyOttp89ezZsbW3RrFkzhIeHG8T/448/MHr0aNjY2MDW1hbDhg3D1atXpfYDBw6gR48esLCwgI2NDXr37o1r167VdLol58+fR0BAACwtLeHg4IDx48cjKytLar979y5eeOEFWFhYoGnTpli2bFmlEjg1nbMNGzbAxsYGe/bsgaenJywtLTF48GCkpaVJfXQ6HUJDQ2FjY4PGjRvjjTfegBCi1sfxOGASnYiIiIiIiIiIiP4yarUaJSUl0vN9+/YhOTkZUVFR2LFjB/Lz8+Hv749GjRrh5MmTiIyMxG+//YaQkBAA5eVUKhLSaWlpBgnb6oSEhCA2NhZbtmzB77//jueeew6DBw/GpUuXjPY/efIkACA8PBxpaWnSc2Oio6Nx8+ZNHDp0CJ988gnCwsIwZMgQNGrUCMePH8e0adPw0ksv4c8//wQAlJaWwt/fH1ZWVjh8+DCOHj0qJaBLSkpQVlaG4cOHo1+/fvj9998RGxuLqVOnSl8Y1CQnJwcDBw5Ely5dcOrUKezevRu3bt3C6NGjpT6hoaE4evQofv75Z0RFReHw4cM4ffp0nc9ZQUEBlixZgm+++QaHDh3C9evXMWvWLKl96dKl2LBhA7766iscOXIE2dnZ2LZtm8E4GzZsqPWxNQSWcyEiIiIiIiIiIqJHTgiBffv2Yc+ePZg+fbq03cLCAuvXr5fKuHzxxRcoKirC119/DQsLCwDAqlWrMHToUHz00UfSam8AcHR0rNXY169fR3h4OK5fvw4nJycA5cn43bt3Izw8HB988EGlfezs7AAANjY2NY5ja2uLTz/9FHK5HB4eHvj4449RUFCAN998EwAwb948fPjhhzhy5AjGjBmDrVu3Qq/XY/369VLyODw8HDY2Njhw4AC6desGrVaLIUOGoFWrVgAAT0/PWh0rUH6+unTpYnBcX331FVxcXHDx4kU0bdoUGzduREREBAYNGiSNX3Fu6nLOSktLsXbtWmmeISEhePfdd6U4y5cvx7x58zBy5EgAwNq1a7Fnzx6D+VpbW0u/QngcMYlOREREREREREREj8yOHTtgaWmJ0tJS6PV6jBs3DgsWLJDaO3bsaFAH/cKFC+jUqZOUQAeA3r17Q6/XIzk5GQ4ODnWew7lz56DT6dCmTRuD7cXFxWjcuHHdD+o+7du3h1z+v6IfDg4O6NChg/TcxMQEjRs3RkZGBgDg7NmzuHz5MqysrAziFBUVISUlBX5+fpg4cSL8/f3x1FNPwdfXF6NHj0bTpk1rNZ+zZ89i//79Rmu3p6SkoLCwEKWlpejRo4e0/f5Edm3Pmbm5uZRAB4CmTZtKx6nVapGWlgZvb2+p3dTUFN26dTMo6TJixAiMGDGiVsfWEJhEJyIiIiIiIiIiokdmwIABWLNmDZRKJZycnGBqapiSvDdZ/qjk5eXBxMQEcXFxMDExMWirj5uEKhQKg+cymczoNr1eL83Hy8sLmzdvrhSrYgV8eHg4Xn31VezevRtbt27F/PnzERUVhZ49e9Y4n7y8PGnl/v2aNm2Ky5cv1ypGbc6ZseP8u9U8rwmT6ERERERERERERPTIWFhYwN3dvdb9PT09sWHDBuTn50sJ9qNHj0qlUh5Ely5doNPpkJGRgT59+tR6P4VCAZ1O90BjVqdr167YunUr7O3todFoquzXpUsXdOnSBfPmzYOPjw8iIiJqlUTv2rUrfvjhB7Ro0aLSlxYA0LJlSygUCpw8eRLNmzcHUL5q/OLFi+jbt6809oOcs3tZW1ujadOmOH78uBS3rKwMcXFx6Nq16wPFbAi8sSgRERERERERERE9Nl544QWYmZkhKCgI58+fx/79+zF9+nSMHz/+gUq5AECbNm3wwgsvYMKECfjxxx+RmpqKEydOYNGiRdi5c2eV+7Vo0QL79u1Deno67ty586CHVMkLL7yAJk2aYNiwYTh8+DBSU1Nx4MABvPrqq/jzzz+RmpqKefPmITY2FteuXcPevXtx6dKlWtdFDw4ORnZ2NsaOHYuTJ08iJSUFe/bswaRJk6DT6WBlZYWgoCDMnj0b+/fvR0JCAiZPngy5XC7VaH/Qc3a/1157DR9++CG2b9+OpKQkvPLKK8jJyTHos23bNrRt27bWMf9qTKITERERERERERHRY8Pc3Bx79uxBdnY2unfvjlGjRmHQoEFYtWrVQ8UNDw/HhAkT8Prrr8PDwwPDhw83WIltzNKlSxEVFQUXFxd06dLloca/l7m5OQ4dOoTmzZtj5MiR8PT0xOTJk1FUVASNRgNzc3MkJSXh2WefRZs2bTB16lQEBwfjpZdeqlV8JycnHD16FDqdDn5+fujYsSNmzJgBGxsbqXb7J598Ah8fHwwZMgS+vr7o3bs3PD09YWZmJsV5kHN2v9dffx3jx49HUFAQfHx8YGVlVan+uVarRXJycq1j/tVk4p9WoOYRyc3NhbW1NbRabbU/sSAiIiIiIiIioscPczvlN61MTU2Fm5ubQaKUCADy8/Ph7OyMpUuXYvLkyQ09nUeuLu8H1kQnIiIiIiIiIiIi+pc5c+YMkpKS0KNHD2i1Wrz77rsAgGHDhjXwzB4/TKITERERERERERER/QstWbIEycnJUCqV8PLywuHDh9GkSZOGntZjh0l0qpYQAtmlWSjWF0ElN4Otool0cwEiIiJ6NPj5S0REREREj1qXLl0QFxfX0NP4W2ASnaqUXnQD5++eRk5JNnQogwlMYaO0RQerrnA0c27o6REREf0j8fOXiIiIiIjo8SJv6AnQ4ym96AZisw8gqzgDSrkKVibWUMpVuF2cgdjsA0gvutHQUyQiIvrH4ecvERERERHR44dJdKpECIHzd0+jWF8EjakNFHIlZDI5FHIlrExtUKIvQsLdMxBCNPRUiYiI/jH4+UtERERERPR4YhKdKskuzUJOSTbMTSwq1V+VyWRQm1jgTsltZJdmNdAMiYiI/nn4+UtERERERPR4YhKdKinWF0GHMpjKjJfMN5GZQocyFOuL/uKZERER/XPx85eIiIiIiOjxxCQ6VaKSm8EEpigTZUbbdaL8JmcqudlfPDMiIqJ/Ln7+EhERERERPZ6YRKdKbBVNYKO0RaEuv1LdVSEECnX5aKRsDFtFkwaaIRER0T8PP3+JiIiIiIgeTw2aRF+zZg2eeOIJaDQaaDQa+Pj4YNeuXQZ9YmNjMXDgQFhYWECj0aBv374oLCyU2rOzs/HCCy9Ao9HAxsYGkydPRl5enkGM33//HX369IGZmRlcXFzw8ccf/yXH93clk8nQwaorlHIz3C3LQam+BHqhR6m+BHfLcqCSm6G9VZdK9VqJiIjowfHzl4iIiIjIuEWLFqF79+6wsrKCvb09hg8fjuTkZIM+RUVFCA4ORuPGjWFpaYlnn30Wt27dktrPnj2LsWPHwsXFBWq1Gp6enlixYoVBjLS0NIwbNw5t2rSBXC7HjBkzaj3Hzz77DC1atICZmRm8vb1x4sQJg/aUlBSMGDECdnZ20Gg0GD16tMH8qnL9+nUEBgbC3Nwc9vb2mD17NsrK/vfr1R9//BFPPfWUFNfHxwd79uypMW5t8qXLly+Hh4cH1Go1XFxcMHPmTBQVVV9esqZcbXJyMgYMGAAHBweYmZmhZcuWmD9/PkpLS6uNu27dOvTv3x8ajQYymQw5OTmV+jzzzDNo3rw5zMzM0LRpU4wfPx43b96s8VzURoMm0Zs1a4YPP/wQcXFxOHXqFAYOHIhhw4YhISEBQHkCffDgwfDz88OJEydw8uRJhISEQC7/37RfeOEFJCQkICoqCjt27MChQ4cwdepUqT03Nxd+fn5wdXVFXFwcFi9ejAULFmDdunV/+fH+nTiaOcPHtj8aq+xRoi9Gnk6LEn0xmqgc0NO2PxzNnBt6ikRERP84/PwlIiIior8DvV4gKT0Xx6/cRlJ6LvR6UfNOD+HgwYMIDg7GsWPHEBUVhdLSUvj5+SE/P1/qM3PmTPzyyy+IjIzEwYMHcfPmTYwcOVJqj4uLg729PTZt2oSEhAS89dZbmDdvHlatWiX1KS4uhp2dHebPn49OnTrVen5bt25FaGgowsLCcPr0aXTq1An+/v7IyMgAAOTn58PPzw8ymQzR0dE4evQoSkpKMHToUOj1+irj6nQ6BAYGoqSkBDExMdi4cSM2bNiAd955R+pz6NAhPPXUU/j1118RFxeHAQMGYOjQoThz5kyVcWuTL42IiMDcuXMRFhaGCxcu4Msvv8TWrVvx5ptvVnsuasrVKhQKTJgwAXv37kVycjKWL1+OL774AmFhYdXGLSgowODBg6sdf8CAAfjuu++QnJyMH374ASkpKRg1alS1cWtNPGYaNWok1q9fL4QQwtvbW8yfP7/KvomJiQKAOHnypLRt165dQiaTiRs3bgghhFi9erVo1KiRKC4ulvrMmTNHeHh41GleWq1WABBarbZO+/3d6fV6kVWcIW4UXhdZxRlCr9c39JSIiIj+8fj5S0RERFT//q25nXsVFhaKxMREUVhY+MAxTl29LaZHnBYByw+JQUv2i4Dlh8T0iNPi1NXb9TjT6mVkZAgA4uDBg0IIIXJycoRCoRCRkZFSnwsXLggAIjY2tso4r7zyihgwYIDRtn79+onXXnutVvPp0aOHCA4Olp7rdDrh5OQkFi1aJIQQYs+ePUIulxu89nJycoRMJhNRUVFVxv3111+FXC4X6enp0rY1a9YIjUZjkOu8X7t27cTChQurbK9NvjQ4OFgMHDjQYL/Q0FDRu3fvKuPWJldrzMyZM8WTTz5ZZfu99u/fLwCIO3fu1Nj3p59+EjKZTJSUlBhtr8v74bGpia7T6bBlyxbk5+fDx8cHGRkZOH78OOzt7dGrVy84ODigX79+OHLkiLRPbGwsbGxs0K1bN2mbr68v5HI5jh8/LvXp27cvlEql1Mff3x/Jycm4c+fOX3eAf1MymQyNlXZwMnNBY6Udf0JORET0F+DnLxERERE9juKuZeP9nRdw/oYWGjNTNGtkDo2ZKRJuavH+zguIu5b9l8xDq9UCAGxtbcvnFReH0tJS+Pr6Sn3atm2L5s2bIzY2tto4FTEeVElJCeLi4gzGlsvl8PX1lcYuLi6GTCaDSqWS+piZmUEulxvkOu8XGxuLjh07wsHBQdrm7++P3NxcqZLH/fR6Pe7evVvtcdUmX9qrVy/ExcVJZWmuXLmCX3/9FU8//bS0z4YNGwz+X6U2udr7Xb58Gbt370a/fv2kbQcOHIBMJsPVq1erPIaaZGdnY/PmzejVqxcUCsUDx6nQ4En0c+fOwdLSEiqVCtOmTcO2bdvQrl07XLlyBQCwYMECTJkyBbt370bXrl0xaNAgXLp0CQCQnp4Oe3t7g3impqawtbVFenq61OfeFxoA6XlFH2OKi4uRm5tr8CAiIiIiIiIiIvo30usFNsZcQ05BKVo0NoeFyhQmchksVKZwtTWHtrAUX8dce+SlXfR6PWbMmIHevXujQ4cOAMpzfEqlEjY2NgZ9HRwcqsz/xcTEYOvWrQalRh5EVlYWdDqd0fxjxdg9e/aEhYUF5syZg4KCAuTn52PWrFnQ6XRIS0urMvaD5DWXLFmCvLw8jB49+qHijhs3Du+++y6efPJJKBQKtGrVCv379zcop2JtbQ0PDw+DuDXlaiv06tULZmZmaN26Nfr06YN3331XajM3N4eHh8cDJb/nzJkDCwsLNG7cGNevX8dPP/1U5xjGNHgS3cPDA/Hx8Th+/DhefvllBAUFITExUaoH9NJLL2HSpEno0qULli1bBg8PD3z11VePfF6LFi2CtbW19HBxcXnkYxIRERERERERET2OLmbcxeWMPNhbqSr9UlImk8HOUoVLGXm4mHH3kc4jODgY58+fx5YtWx44xvnz5zFs2DCEhYXBz8+v1vsdPnwYlpaW0mPz5s212s/Ozg6RkZH45ZdfYGlpCWtra+Tk5KBr167SvR8DAgKkuO3bt3+g44qIiMDChQvx3XffVUpm19WBAwfwwQcfYPXq1Th9+jR+/PFH7Ny5E++9957UZ8SIEUhKSnqg+Fu3bsXp06cRERGBnTt3YsmSJVJbjx49kJSUBGfnut8Tavbs2Thz5gz27t0LExMTTJgwAUI8/Bc7pg8d4SEplUq4u7sDALy8vHDy5EmsWLECc+fOBQC0a9fOoL+npyeuX78OAHB0dJQK9FcoKytDdnY2HB0dpT733+m24nlFH2PmzZuH0NBQ6Xlubi4T6URERERERERE9K+kLShFSZkOZgqV0XYzhQmy8oqhLSh9ZHMICQmRblbZrFkzabujoyNKSkqQk5NjsBr91q1blfJ/iYmJGDRoEKZOnYr58+fXafxu3bohPj5eeu7g4ACVSgUTExOj+cd7x/bz80NKSgqysrJgamoKGxsbODo6omXLlgCA9evXo7CwEACkFdiOjo5SOZV741a03WvLli148cUXERkZaVBaxpja5EvffvttjB8/Hi+++CIAoGPHjsjPz8fUqVPx1ltvScn/++PWlKutUJFnbdeuHXQ6HaZOnYrXX38dJiYm1c69Jk2aNEGTJk3Qpk0beHp6wsXFBceOHYOPj89DxW3wlej30+v1KC4uRosWLeDk5ITk5GSD9osXL8LV1RUA4OPjg5ycHMTFxUnt0dHR0Ov18Pb2lvocOnQIpaX/ewNHRUXBw8MDjRo1qnIeKpUKGo3G4EFERERERERERPRvZG2ugNLUBEWlOqPtRaU6KE1NYG3+8PWn7yeEQEhICLZt24bo6Gi4ubkZtHt5eUGhUGDfvn3StuTkZFy/ft0geZqQkIABAwYgKCgI77//fp3noVar4e7uLj2srKygVCrh5eVlMLZer8e+ffuMJm6bNGkCGxsbREdHIyMjA8888wwAwNnZWYp7b+7z3LlzBonpqKgoaDQag4XH3377LSZNmoRvv/0WgYGBNR5HbfKlBQUFlRLlFQnuqlZ21yZXa4xer0dpaalUmaS+VMQrLi5+6FgNuhJ93rx5CAgIQPPmzXH37l1ERETgwIED2LNnD2QyGWbPno2wsDB06tQJnTt3xsaNG5GUlITvv/8eQPmq9MGDB2PKlClYu3YtSktLERISgjFjxsDJyQlAef2ehQsXYvLkyZgzZw7Onz+PFStWYNmyZQ156ERERERERERERH8bbeyt4G5viYSbWpgrTQxKugghkJlXjA5O1mhjb1XvYwcHByMiIgI//fQTrKyspPra1tbWUKvVsLa2xuTJkxEaGgpbW1toNBpMnz4dPj4+6NmzJ4DyEi4DBw6Ev78/QkNDpRgmJiaws7OTxqpYaZ6Xl4fMzEzEx8dDqVRWqpZxr9DQUAQFBaFbt27o0aMHli9fjvz8fEyaNEnqEx4eDk9PT9jZ2SE2NhavvfYaZs6caVBT/H5+fn5o164dxo8fj48//hjp6emYP38+goODpZuURkREICgoCCtWrIC3t7d0XBXnxZja5EuHDh2KTz75BF26dIG3tzcuX76Mt99+G0OHDpWS6du2bcO8efOkki61ydVu3rwZCoUCHTt2hEqlwqlTpzBv3jw8//zz0gr8EydOYMKECdi3b59U0iU9PR3p6em4fPkygPL7bFpZWaF58+awtbXF8ePHcfLkSTz55JNo1KgRUlJS8Pbbb6NVq1YPvQodACAa0H//+1/h6uoqlEqlsLOzE4MGDRJ79+416LNo0SLRrFkzYW5uLnx8fMThw4cN2m/fvi3Gjh0rLC0thUajEZMmTRJ379416HP27Fnx5JNPCpVKJZydncWHH35Y57lqtVoBQGi12rofKBERERERERERNSjmdoQoLCwUiYmJorCw8IH2P3X1thjx2RExcMl+8fzaGBH05XHx/NoYMXDJfjHisyPi1NXb9TzjcgCMPsLDw6U+hYWF4pVXXhGNGjUS5ubmYsSIESItLU1qDwsLMxrD1dW1xrHu72PMypUrRfPmzYVSqRQ9evQQx44dM2ifM2eOcHBwEAqFQrRu3VosXbpU6PX6GuNevXpVBAQECLVaLZo0aSJef/11UVpaKrX369fP6JyDgoKqjVtTvrS0tFQsWLBAtGrVSpiZmQkXFxfxyiuviDt37kh9wsPDxf3p5ZpytVu2bBFdu3YVlpaWwsLCQrRr10588MEHBq/J/fv3CwAiNTVV2lbV9at4Dfz+++9iwIABwtbWVqhUKtGiRQsxbdo08eeff1Z5DuryfpAJUQ+V1f8FcnNzYW1tDa1Wy9IuRERERERERER/M8ztAEVFRUhNTYWbmxvMzMweKEbctWxsjLmGyxl5KCkrL+HS2t4SE3q5wsvVtp5nTPTo1OX90OA3FqW/D73Q4+Kdi7hw+wIgA9rZtkPrRq0hlz12pfX/FvRCj8s5l5FbnAuNSgN3G3eeSyIiolri5ygRERFRw/BytUUXl0a4mHEX2oJSWJsr0MbeCnK5rOadif6mmESnWonPiMdn8Z8hISsBxbryYvwqExU6NOmAVzq/gs72nRt2gn8z8RnxiEiKQGpOKkr0JVDKlXCzccO4tuN4LomIiGrAz1EiIiKihiWXy9DW8d+5mp/+nbhch2oUnxGPhbELcTrjNEr1pVCbqmFmYoYSfQlOZ5zGwtiFiM+Ib+hp/m3EZ8RjyakluHD7AqyUVnC2dIaV0gpJt5Ow5NQSnksiIqJq8HOUiIiIiIj+akyiU7X0Qo/NFzYjLS8NJjITqE3VMJGbwNTEFBYKC8ggQ1p+Gr5N+hZ6oW/o6T729EKPiKQIaIu1aG7VHOYKc8hlcpgrzOFi5YLc4lyeSyIioirwc5SIiIiIiBoCk+hUrcs5l5GUnQQ99FDKlZDJDOtbqUxU0As9Em8n4nLO5Qaa5d/H5ZzLSM1JhZ3artK5lMlkaKJugis5V3guiYiIjODnKBERERERNQQm0alaucW5Ug10YzfrksvlgACKdcXILc79q6f3t5NbnIsSfQnMTI3f8VdlqkKJvoTnkoiIyAh+jhIRERERUUNgEp2qpVFpoDJRAYDRn0br9XpAVr4iXaPiDSVqolFpoJQrUVRWZLS9uKwYSrmS55KIiMgIfo4SEREREVFDYBKdquVu4462tm0hhxwl+hIIIQzai3XFkMvkaNe4Hdxt3Btoln8f7jbucLNxQ1ZhVqVzKYRAVmEWWtq05LkkIiIygp+jRERERETUEJhEp2rJZXK84PkCmlo2hV7oUVhWiDJ9Gcp0ZcgvzYeAQFOLphjbdqzRci9kSC6TY1zbcdCoNPjj7h8oKC2ATuhQUFqAP+7+AY1Kw3NJRERUBX6OEhERERFRQ+D/YVCNOtt3RphPGLrYd4FCrkBRWRGKdEVQyVXoat8VYT5h6GzfuaGn+bfR2b4zZnWbhbaN2+JuyV3czLuJuyV34dnYE7O6zeK5JCIiqgY/R4mIiIiI6K/GJDrVSmf7zvj8qc/x1eCvML/nfMz3mY+vBn+FtU+t5f+sPoDO9p3xYZ8P8X6f9/F2z7fxfp/3sajPIp5LIiKiWuDnKBEREdG/y6JFi9C9e3dYWVnB3t4ew4cPR3JyskGfoqIiBAcHo3HjxrC0tMSzzz6LW7duSe1nz57F2LFj4eLiArVaDU9PT6xYscIgRlpaGsaNG4c2bdpALpdjxowZtZ7jZ599hhYtWsDMzAze3t44ceKEQXtKSgpGjBgBOzs7aDQajB492mB+Vbl+/ToCAwNhbm4Oe3t7zJ49G2VlZVL7jz/+iKeeekqK6+Pjgz179lQbs6ioCBMnTkTHjh1hamqK4cOHV+pz5MgR9O7dG40bN4ZarUbbtm2xbNmyauNevXoVkydPhpubG9RqNVq1aoWwsDCUlJQY9BNCYMmSJWjTpg1UKhWcnZ3x/vvvVxt74sSJkMlkBo/Bgwcb9Dl9+jSeeuop2NjYoHHjxpg6dSry8vKqjVtbpvUShf4V5DI52tq2RVvbtg09lX8EuUyONo3aNPQ0iIiI/pb4OUpERETUcIQQyC0sQ2mZgMJUBo3aFDKZ7JGNd/DgQQQHB6N79+4oKyvDm2++CT8/PyQmJsLCwgIAMHPmTOzcuRORkZGwtrZGSEgIRo4ciaNHjwIA4uLiYG9vj02bNsHFxQUxMTGYOnUqTExMEBISAgAoLi6GnZ0d5s+fX2PC+F5bt25FaGgo1q5dC29vbyxfvhz+/v5ITk6Gvb098vPz4efnh06dOiE6OhoA8Pbbb2Po0KE4duwY5HLj65x1Oh0CAwPh6OiImJgYpKWlYcKECVAoFPjggw8AAIcOHcJTTz2FDz74ADY2NggPD8fQoUNx/PhxdOnSpcq4arUar776Kn744QejfSwsLBASEoInnngCFhYWOHLkCF566SVYWFhg6tSpRvdJSkqCXq/H559/Dnd3d5w/fx5TpkxBfn4+lixZIvV77bXXsHfvXixZsgQdO3ZEdnY2srOzazzPgwcPRnh4uPRcpVJJ/33z5k34+vri+eefx6pVq5Cbm4sZM2Zg4sSJ+P7772uMXROZuP+uTGRUbm4urK2todVqodFoGno6RERERERERERUB8ztlK9ATk1NhZubG8zMzB4oxu27JbiSXoC7hTro9AImchms1CZo6WiOxlbKep6xcZmZmbC3t8fBgwfRt29faLVa2NnZISIiAqNGjQJQntD19PREbGwsevbsaTROcHAwLly4ICW279W/f3907twZy5cvr3E+3t7e6N69O1atWgUA0Ov1cHFxwfTp0zF37lzs3bsXAQEBuHPnjvTa02q1aNSoEfbu3QtfX1+jcXft2oUhQ4bg5s2bcHBwAACsXbsWc+bMQWZmJpRK4+e7ffv2eP755/HOO+/UOPeJEyciJycH27dvr7HvyJEjYWFhgW+++abGvhUWL16MNWvW4MqVKwCACxcu4IknnsD58+fh4eFR6zg1zXPdunV4++23kZaWJn0pce7cOTzxxBO4dOkS3N3dK+1Tl/cDy7kQERERERERERFRjW7fLcG5a3eRk18GhYkMFio5FCYy5OSX4dy1u7h9t6TmIPVAq9UCAGxtbQGUrzIvLS01SEa3bdsWzZs3R2xsbLVxKmI8qJKSEsTFxRmMLZfL4evrK41dXFwMmUxmsHLazMwMcrkcR44cqTJ2bGwsOnbsKCXQAcDf3x+5ublISEgwuo9er8fdu3cf+rjud+bMGcTExKBfv37Stg0bNtT4C4T7z/Evv/yCli1bYseOHXBzc0OLFi3w4osvGqxEv3r1KmQyGQ4cOGAQ68CBA7C3t4eHhwdefvll3L59W2orLi6GUqk0WNWvVqsBoNpzXFtMohMREREREREREVG1hBC4kl6A0jIBC5UcpibldalN/y+ZXlpW3v6oi17o9XrMmDEDvXv3RocOHQAA6enpUCqVsLGxMejr4OCA9PR0o3FiYmKwdevWKkuT1FZWVhZ0Op1Bovv+sXv27AkLCwvMmTMHBQUFyM/Px6xZs6DT6ZCWllZl7PT0dKNxK9qMWbJkCfLy8jB69OiHOSxJs2bNoFKp0K1bNwQHB+PFF1+U2qytratdTX758mWsXLkSL730krTtypUruHbtGiIjI/H1119jw4YNiIuLk35BAAAKhQIeHh4wNzeXtg0ePBhff/019u3bh48++ggHDx5EQEAAdDodAGDgwIFIT0/H4sWLUVJSgjt37mDu3LkAUO05ri0m0YmIiIiIiIiIiKhauYVluFuog5lCXmn1sUwmg5lCjruFOuQWllURoX4EBwfj/Pnz2LJlywPHOH/+PIYNG4awsDD4+fnVer/Dhw/D0tJSemzevLlW+9nZ2SEyMhK//PILLC0tYW1tjZycHHTt2lVaOR0QECDFbd++/QMdV0REBBYuXIjvvvsO9vb2DxTjfocPH8apU6ewdu1aLF++HN9++63UNmLECCQlJRnd78aNGxg8eDCee+45TJkyRdqu1+tRXFyMr7/+Gn369EH//v3x5ZdfYv/+/dLNYp2dnZGUlIQePXpI+40ZMwbPPPMMOnbsiOHDh2PHjh04efKktFq9ffv22LhxI5YuXQpzc3M4OjrCzc0NDg4OVdacrwveWJSIiIiIiIiIiIiqVVomoNMLmCmMl+8wkQNFpQKlZY9uJXpISAh27NiBQ4cOoVmzZtJ2R0dHlJSUICcnx2A1+q1bt+Do6GgQIzExEYMGDcLUqVMxf/78Oo3frVs3xMfHS88dHBygUqlgYmKCW7duGfS9f2w/Pz+kpKQgKysLpqamsLGxgaOjI1q2bAkAWL9+PQoLCwGUr8SuOK4TJ05UilvRdq8tW7bgxRdfRGRkZJU11h+Em5sbAKBjx464desWFixYgLFjx1a7z82bNzFgwAD06tUL69atM2hr2rQpTE1N0aZNG2mbp6cnAOD69eu1rpPesmVLNGnSBJcvX8agQYMAAOPGjcO4ceNw69YtWFhYQCaT4ZNPPpHO8cPgSnQiIiIiIiIiIiKqlsJUBhO5DDq98XadHjCRy6Awrb5G9oMQQiAkJATbtm1DdHS0lNit4OXlBYVCgX379knbkpOTcf36dfj4+EjbEhISMGDAAAQFBeH999+v8zzUajXc3d2lh5WVFZRKJby8vAzG1uv12Ldvn8HYFZo0aQIbGxtER0cjIyMDzzzzDIDy1dcVcV1dXQEAPj4+OHfuHDIyMqT9o6KioNFo0K5dO2nbt99+i0mTJuHbb79FYGBgnY+rtipWkVfnxo0b6N+/P7y8vBAeHl5pFXjv3r1RVlaGlJQUadvFixcBQDru2vjzzz9x+/ZtNG3atFKbg4MDLC0tsXXrVpiZmeGpp56qddyqcCU6ERERERERERERVUujNoWV2gQ5+WWwkBuWdBFCoKhUDxsLU2jU9Z9uDA4ORkREBH766SdYWVlJ9cCtra2hVqthbW2NyZMnIzQ0FLa2ttBoNJg+fTp8fHzQs2dPAOUlXAYOHAh/f3+EhoZKMUxMTGBnZyeNVbHSPC8vD5mZmYiPj4dSqTRIWt8vNDQUQUFB6NatG3r06IHly5cjPz8fkyZNkvqEh4fD09MTdnZ2iI2NxWuvvYaZM2dWu/Laz88P7dq1w/jx4/Hxxx8jPT0d8+fPR3BwsHST0oiICAQFBWHFihXw9vaWjqvivFQlMTERJSUlyM7Oxt27d6Xj7ty5MwDgs88+Q/PmzdG2bVsAwKFDh7BkyRK8+uqrUoxt27Zh3rx5UkmXigS6q6srlixZgszMTKlvxcp5X19fdO3aFf/973+xfPly6PV6BAcH46mnnpJWp9+4cQODBg3C119/jR49eiAvLw8LFy7Es88+C0dHR6SkpOCNN96Au7s7/P39pTFWrVqFXr16wdLSElFRUZg9ezY+/PDDSrXyH4igWtFqtQKA0Gq1DT2Vx4deJ8TdNCGyr5T/q9c19IyIiIiIiIiIiIxibkeIwsJCkZiYKAoLCx9o/6zcYrH/XJbYeyZTHDp/WxxNvC0Onb8t9p7JFAfO3RZZucX1PONyAIw+wsPDpT6FhYXilVdeEY0aNRLm5uZixIgRIi0tTWoPCwszGsPV1bXGse7vY8zKlStF8+bNhVKpFD169BDHjh0zaJ8zZ45wcHAQCoVCtG7dWixdulTo9foa4169elUEBAQItVotmjRpIl5//XVRWloqtffr18/onIOCgqqN6+rqanS/Cp9++qlo3769MDc3FxqNRnTp0kWsXr1a6HT/y/+Fh4cb7FPxvLq4Qghx48YNMXLkSGFpaSkcHBzExIkTxe3bt6X21NRUAUDs379fCCFEQUGB8PPzE3Z2dkKhUAhXV1cxZcoUkZ6ebhB3/PjxwtbWViiVSvHEE0+Ir7/+utpzUJf3g0yIR3zL3H+I3NxcWFtbQ6vVQqPRNPR0Gl7ONeCPWCA/A9CXAXJTwMIecPEBbGr/0wsiIiIiIiIior8CcztAUVERUlNT4ebmBjMzsweKcftuCa6kF+BuoQ46vYCJXAYrtQlaOpqjsZWynmdM9OjU5f3Aci5UdznXgEu/AqUFgNIKMFEAulLg7s3y7a2fZiKdiIiIiIiIiOgfqLGVEraWCuQWlqG0TEBhKoNGbWpQ3oXon4ZJdKoboS9fgV5aAKgbAxV/IE1VgIkSKMwub7d2AWS8by0RERERERER0T+NTCaDtbmioadB9JdhlpPqJj+j/KG0+l8CvYJMBigt/9eHiIiIiIiIiIiI6G+OSXSqm9LC8hroJlV822iiKG8vLfxr50VERERERERERET0CDCJTnWjUJffRFRXarxdV1rerlD/tfMiIiIiIiIiIiIiegSYRKe6sbAvf5TkAUIYtglRvr2iDxEREREREREREdHfHJPoVDcyOeDiU77SvDAbKCsuv9loWXH5c4W6vJ03FSUiIiIiIiIiIqJ/AGY6qe5sXIHWTwNWTkBZEVB4p/xfK6fy7TauDT1DIiIiIiIiIiIionph2tAToL8pG1fA2gXIzyi/iahCXV7ChSvQiYiIiIiIiIiI6B+ESXR6cDI5YOnY0LMgIiIiIiIiIiIiemS4bJiIiIiIiIiIiIgeW4sWLUL37t1hZWUFe3t7DB8+HMnJyQZ9ioqKEBwcjMaNG8PS0hLPPvssbt26JbWfPXsWY8eOhYuLC9RqNTw9PbFixQqDGGlpaRg3bhzatGkDuVyOGTNm1HqOn332GVq0aAEzMzN4e3vjxIkTBu0pKSkYMWIE7OzsoNFoMHr0aIP5VeX69esIDAyEubk57O3tMXv2bJSVlRnte/ToUZiamqJz5841xv3999/Rp08fmJmZwcXFBR9//HGlPpGRkWjbti3MzMzQsWNH/Prrrw893wc9xzVd39qM/TCYRCciIiIiIiIiIqLa0+uBWwnA1aPl/+r1j3S4gwcPIjg4GMeOHUNUVBRKS0vh5+eH/Px8qc/MmTPxyy+/IDIyEgcPHsTNmzcxcuRIqT0uLg729vbYtGkTEhIS8NZbb2HevHlYtWqV1Ke4uBh2dnaYP38+OnXqVOv5bd26FaGhoQgLC8Pp06fRqVMn+Pv7IyMjAwCQn58PPz8/yGQyREdH4+jRoygpKcHQoUOhr+bc6XQ6BAYGoqSkBDExMdi4cSM2bNiAd955p1LfnJwcTJgwAYMGDapxvrm5ufDz84Orqyvi4uKwePFiLFiwAOvWrZP6xMTEYOzYsZg8eTLOnDmD4cOHY/jw4Th//vxDzfdBz3FN17cu5+pByIQQol4i/cPl5ubC2toaWq0WGo2moadDRERERERERER1wNxO+Wre1NRUuLm5wczM7MGCXD8OnFgHZCUDZcWAqQpo4gH0mAo0967fCVchMzMT9vb2OHjwIPr27QutVgs7OztERERg1KhRAICkpCR4enoiNjYWPXv2NBonODgYFy5cQHR0dKW2/v37o3Pnzli+fHmN8/H29kb37t2lhLxer4eLiwumT5+OuXPnYu/evQgICMCdO3ek155Wq0WjRo2wd+9e+Pr6Go27a9cuDBkyBDdv3oSDgwMAYO3atZgzZw4yMzOhVCqlvmPGjEHr1q1hYmKC7du3Iz4+vsr5rlmzBm+99RbS09OlGHPnzsX27duRlJQEAHj++eeRn5+PHTt2SPv17NkTnTt3xtq1ax96vkDtz3Ftrm9dxwbq9n7gSnQiIiIiIiIiIiKq2fXjwN63gLSzgJk1YONa/m/a7+Xbrx//S6ah1WoBALa2tgDKV5mXlpYaJKPbtm2L5s2bIzY2tto4FTEeVElJCeLi4gzGlsvl8PX1lcYuLi6GTCaDSqWS+piZmUEul+PIkSNVxo6NjUXHjh2lpDAA+Pv7Izc3FwkJCdK28PBwXLlyBWFhYbWac2xsLPr27WuQWPb390dycjLu3Lkj9bk/ue/v729wPhcsWIAWLVrUeb41OXDgAGQyGa5evQqgdte3vsauCpPoREREREREREREVD29vnwFemEOYNsSUFoCcpPyf23dgEItcPKLR17aRa/XY8aMGejduzc6dOgAANKKahsbG4O+Dg4OSE9PNxonJiYGW7duxdSpUx9qPllZWdDpdAbJ2/vH7tmzJywsLDBnzhwUFBQgPz8fs2bNgk6nQ1paWpWx09PTjcataAOAS5cuYe7cudi0aRNMTU1rNefaxK2qz73ns0mTJmjVqlWd4taGubk5PDw8oFAopH1rur71NXZVmEQnIiIiIiIiIiKi6mVeKC/hYuUAyGSGbTIZYGUPZCaV93uEgoODcf78eWzZsuWBY5w/fx7Dhg1DWFgY/Pz8ar3f4cOHYWlpKT02b95cq/3s7OwQGRmJX375BZaWlrC2tkZOTg66du0Kubw8PRsQECDFbd++fa3i6nQ6jBs3DgsXLkSbNm1qfRz1JSQkBPv27av3uD169EBSUhKcnZ3rPfaDqt3XE0RERERERERERPTvVZjzfzXQ1cbbTdVAWUZ5v0ckJCQEO3bswKFDh9CsWTNpu6OjI0pKSpCTk2OwWvnWrVtwdHQ0iJGYmIhBgwZh6tSpmD9/fp3G79atm0GtcQcHB6hUKpiYmODWrVsGfe8f28/PDykpKcjKyoKpqSlsbGzg6OiIli1bAgDWr1+PwsJCAJBWYDs6OuLEiROV4la03b17F6dOncKZM2cQEhICoHylvhACpqam2Lt3LwYOHFjpOBwdHY3Ot6Ktuj73n8/741Y33wdVm+v7qMauwJXoREREREREREREVD21TflNRMsKjbeXFZa3q23qfWghBEJCQrBt2zZER0fDzc3NoN3LywsKhcJgVXRycjKuX78OHx8faVtCQgIGDBiAoKAgvP/++3Weh1qthru7u/SwsrKCUqmEl5eXwdh6vR779u0zGLtCkyZNYGNjg+joaGRkZOCZZ54BADg7O0txXV1dAQA+Pj44d+4cMjIypP2joqKg0WjQrl07aDQanDt3DvHx8dJj2rRp8PDwQHx8PLy9jd/o1cfHB4cOHUJpaalBXA8PDzRq1Ejqc/8q86ioKKPHdG/c6ub7oGpzfR/V2BW4Ep2IiIiIiIiIiIiqZ+cJNPEov4morYVhSRchgLsZgFOn8n71LDg4GBEREfjpp59gZWUl1bi2traGWq2GtbU1Jk+ejNDQUNja2kKj0WD69Onw8fFBz549AZSXcBk4cCD8/f0RGhoqxTAxMYGdnZ00VsVK87y8PGRmZiI+Ph5KpbLaRGxoaCiCgoLQrVs39OjRA8uXL0d+fj4mTZok9QkPD4enpyfs7OwQGxuL1157DTNnzoSHh0eVcf38/NCuXTuMHz8eH3/8MdLT0zF//nwEBwdLNymtqAtfwd7eHmZmZpW236uiBMzkyZMxZ84cnD9/HitWrMCyZcukPq+99hr69euHpUuXIjAwEFu2bMGpU6ewbt06qc+qVauwbds2Kbldm/nW5hyfOHECEyZMwL59++Ds7Fyr61vbsR+YoFrRarUCgNBqtQ09FSIiIiIiIiIiqiPmdoQoLCwUiYmJorCw8MECXDsmxBeDhPi0mxDhTwvxzbPl/37aTYgvfMvbHwEARh/h4eFSn8LCQvHKK6+IRo0aCXNzczFixAiRlpYmtYeFhRmN4erqWuNY9/cxZuXKlaJ58+ZCqVSKHj16iGPHDM/FnDlzhIODg1AoFKJ169Zi6dKlQq/X1xj36tWrIiAgQKjVatGkSRPx+uuvi9LS0ir7h4WFiU6dOtUY9+zZs+LJJ58UKpVKODs7iw8//LBSn++++060adNGKJVK0b59e7Fz585KY91/bmoz35rO8f79+wUAkZqaKm2r6frWdux71eX9IPu/iVMNcnNzYW1tDa1WC41G09DTISIiIiIiIiKiOmBuBygqKkJqairc3NxgZmb2YEGuHwdOrCu/yWhZcXkJF7u2QPcpQHPj5UOIHkd1eT+wnAsRERERERERERHVTnNvoFl3IPNC+U1E1TblJVzkvPUi/XMxiU5ERERERERERES1J5cDDu0behZEfxl+RUREREREREREREREVAUm0YmIiIiIiIiIiIiIqsAkOhERERERERERERFRFZhEJyIiIiIiIiIiIiKqApPoRERERERERERERERVYBKdiIiIiIiIiIiIiKgKTKITEREREREREREREVWBSXQiIiIiIiIiIiIioiowiU5ERERERERERESPrUWLFqF79+6wsrKCvb09hg8fjuTkZIM+RUVFCA4ORuPGjWFpaYlnn30Wt27dktrPnj2LsWPHwsXFBWq1Gp6enlixYoVBjLS0NIwbNw5t2rSBXC7HjBkzaj3Hzz77DC1atICZmRm8vb1x4sQJg/aUlBSMGDECdnZ20Gg0GD16tMH8qnL9+nUEBgbC3Nwc9vb2mD17NsrKyoz2PXr0KExNTdG5c+dqYxYVFWHixIno2LEjTE1NMXz48Ep9Jk6cCJlMVunRvn37amNnZ2fjhRdegEajgY2NDSZPnoy8vDypPTk5GQMGDICDgwPMzMzQsmVLzJ8/H6WlpTWeCwAQQiAgIAAymQzbt2+Xtt++fRuDBw+Gk5MTVCoVXFxcEBISgtzc3FrFrQmT6ERERERERERERFR7Qg/kpQN3Usv/FfpHOtzBgwcRHByMY8eOISoqCqWlpfDz80N+fr7UZ+bMmfjll18QGRmJgwcP4ubNmxg5cqTUHhcXB3t7e2zatAkJCQl46623MG/ePKxatUrqU1xcDDs7O8yfPx+dOnWq9fy2bt2K0NBQhIWF4fTp0+jUqRP8/f2RkZEBAMjPz4efnx9kMhmio6Nx9OhRlJSUYOjQodDrqz53Op0OgYGBKCkpQUxMDDZu3IgNGzbgnXfeqdQ3JycHEyZMwKBBg2qcr06ng1qtxquvvgpfX1+jfVasWIG0tDTp8ccff8DW1hbPPfdctbFfeOEFJCQkICoqCjt27MChQ4cwdepUqV2hUGDChAnYu3cvkpOTsXz5cnzxxRcICwurcd4AsHz5cshkskrb5XI5hg0bhp9//hkXL17Ehg0b8Ntvv2HatGm1ilsTmRBC1Eukf7jc3FxYW1tDq9VCo9E09HSIiIiIiIiIiKgOmNspX4GcmpoKNzc3mJmZPViQnGvAH7FAfgagLwPkpoCFPeDiA9i41u+Eq5CZmQl7e3scPHgQffv2hVarhZ2dHSIiIjBq1CgAQFJSEjw9PREbG4uePXsajRMcHIwLFy4gOjq6Ulv//v3RuXNnLF++vMb5eHt7o3v37lJCXq/Xw8XFBdOnT8fcuXOxd+9eBAQE4M6dO9JrT6vVolGjRti7d2+Viexdu3ZhyJAhuHnzJhwcHAAAa9euxZw5c5CZmQmlUin1HTNmDFq3bg0TExNs374d8fHxNc4bKF9xnpOTY7Cq25jt27dj5MiRSE1Nhaur8et84cIFtGvXDidPnkS3bt0AALt378bTTz+NP//8E05OTkb3Cw0NxcmTJ3H48OFq5xAfH48hQ4bg1KlTaNq0KbZt22Z0FX2FTz/9FIsXL8Yff/xhtL0u7weuRCciIiIiIiIiIqKa5VwDLv0K3L0BmJoB6kbl/969Wb4959pfMg2tVgsAsLW1BVC+yry0tNQgGd22bVs0b94csbGx1capiPGgSkpKEBcXZzC2XC6Hr6+vNHZxcTFkMhlUKpXUx8zMDHK5HEeOHKkydmxsLDp27Cgl0AHA398fubm5SEhIkLaFh4fjypUrtV7N/SC+/PJL+Pr6GiTQN2zYYLAqPDY2FjY2NlICHQB8fX0hl8tx/Phxo3EvX76M3bt3o1+/ftK2AwcOQCaT4erVq9K2goICjBs3Dp999hkcHR1rnO/Nmzfx448/GsR9GEyiExERERERERERUfWEvnwFemkBoG4MmKoAmbz8X7UtUFpY3v6IS7vo9XrMmDEDvXv3RocOHQAA6enpUCqVsLGxMejr4OCA9PR0o3FiYmKwdetWg1IjDyIrKws6nc4g0X3/2D179oSFhQXmzJmDgoIC5OfnY9asWdDpdEhLS6sydnp6utG4FW0AcOnSJcydOxebNm2CqanpQx1LVW7evIldu3bhxRdfNNhubW0NDw8Pg/na29sb9DE1NYWtrW2l69CrVy+YmZmhdevW6NOnD959912pzdzcHB4eHlAoFNK2mTNnolevXhg2bFi1cx07dizMzc3h7OwMjUaD9evX1/l4jWESnYiIiIiIiIiIiKqXn1H+UFoB99eklskApeX/+jxCwcHBOH/+PLZs2fLAMc6fP49hw4YhLCwMfn5+td7v8OHDsLS0lB6bN2+u1X52dnaIjIzEL7/8AktLS1hbWyMnJwddu3aFXF6eng0ICJDi1nTzzgo6nQ7jxo3DwoUL0aZNm1ofR11t3LgRNjY2lUqnjBgxAklJSQ8Uc+vWrTh9+jQiIiKwc+dOLFmyRGrr0aMHkpKS4OzsDAD4+eefER0dXavSOsuWLcPp06fx008/ISUlBaGhoQ80v/s9mq8niIiIiIiIiIiI6J+jtLC8BrqJwni7iQIoySvv94iEhIRIN6ts1qyZtN3R0RElJSXIyckxWI1+69atSqU/EhMTMWjQIEydOhXz58+v0/jdunUzqDXu4OAAlUoFExMT3Lp1y6Dv/WP7+fkhJSUFWVlZMDU1hY2NDRwdHdGyZUsAwPr161FYWH7uKlZgOzo64sSJE5XiVrTdvXsXp06dwpkzZxASEgKgfKW+EAKmpqbYu3cvBg4cWKdjvJ8QAl999RXGjx9vUIPdGEdHR+lmqhXKysqQnZ1d6Tq4uLgAANq1awedToepU6fi9ddfh4mJSaW40dHRSElJqfRLg2effRZ9+vTBgQMHDObg6OiItm3bwtbWFn369MHbb7+Npk2b1uGoK2MSnYiIiIiIiIiIiKqnUJffRFRXWl7C5X660vJ2hbrehxZCYPr06di2bRsOHDgANzc3g3YvLy8oFArs27cPzz77LAAgOTkZ169fh4+Pj9QvISEBAwcORFBQEN5///06z0OtVsPd3b3Sdi8vL+zbt09aqa3X67Fv3z4psX2vJk2aAChPDGdkZOCZZ54BAGnV9b18fHzw/vvvIyMjQyqTEhUVBY1Gg3bt2kGhUODcuXMG+6xevRrR0dH4/vvvK52nB3Hw4EFcvnwZkydPrrGvj48PcnJyEBcXBy8vLwDlx6nX6+Ht7V3lfnq9HqWlpdDr9UaT6HPnzq1USqZjx45YtmwZhg4dWm1coLwm/cNiEp2IiIiIiIiIiIiqZ2Ff/rh7EzBRGpZ0EaJ8FbqVU3mfehYcHIyIiAj89NNPsLKykuprW1tbQ61Ww9raGpMnT0ZoaChsbW2h0Wgwffp0+Pj4oGfPngDKS7gMHDgQ/v7+CA0NlWKYmJjAzs5OGqtipXleXh4yMzMRHx8PpVKJdu3aVTm/0NBQBAUFoVu3bujRoweWL1+O/Px8TJo0SeoTHh4OT09P2NnZITY2Fq+99hpmzpxpUFP8fn5+fmjXrh3Gjx+Pjz/+GOnp6Zg/fz6Cg4Olm5RW1IWvYG9vDzMzs0rb75eYmIiSkhJkZ2fj7t270nF37tzZoN+XX34Jb29vo/G2bduGefPmSSVdPD09MXjwYEyZMgVr165FaWkpQkJCMGbMGDg5OQEANm/eDIVCgY4dO0KlUuHUqVOYN28enn/+eWkF/okTJzBhwgTs27cPzs7O0ury+zVv3lz6ouDXX3/FrVu30L17d1haWiIhIQGzZ89G79690aJFi2rPRW0wiU5ERERERERERETVk8kBFx/g0q9AYXZ5DXQTRfkK9JK88hXoLj7l/erZmjVrAAD9+/c32B4eHo6JEycCKK+FLZfL8eyzz6K4uBj+/v5YvXq11Pf7779HZmYmNm3ahE2bNknbXV1dcfXqVel5ly5dpP+Oi4tDREREpT73e/7555GZmYl33nkH6enp6Ny5M3bv3m1wU9Dk5GTMmzcP2dnZaNGiBd566y3MnDmz2uM2MTHBjh078PLLL8PHxwcWFhYICgoyuAnng3r66adx7do16XnFcQshpG1arRY//PADVqxYYTSGVqtFcnKywbbNmzcjJCQEgwYNkq7Hp59+KrWbmprio48+wsWLFyGEgKurK0JCQgzORUFBAZKTk1FaWlrr41Gr1fjiiy8wc+ZMFBcXw8XFBSNHjsTcuXNrHaM6MnHvmaEq5ebmwtraGlqtFhqNpqGnQ0REREREREREdcDcDlBUVITU1FS4ubnBzMzswYLkXAP+iC2/gai+rLyEi4V9eQLdxrV+J0z0CNXl/cCV6ERERERERERERFQ7Nq6AtUt5Er20sHwFuoX9I1mBTvS4aNBX95o1a/DEE09Ao9FAo9HAx8cHu3btAgBkZ2dj+vTp8PDwgFqtRvPmzfHqq69Cq9UaxLh+/ToCAwNhbm4Oe3t7zJ49G2VlZQZ9Dhw4gK5du0KlUsHd3R0bNmz4qw6RiIiIiIiIiIjon0UmBywdgUZu5f8ygU7/cA26Er1Zs2b48MMP0bp1awghsHHjRgwbNgxnzpyBEAI3b97EkiVL0K5dO1y7dg3Tpk3DzZs38f333wMAdDodAgMD4ejoiJiYGKSlpWHChAlQKBT44IMPAACpqakIDAzEtGnTsHnzZuzbtw8vvvgimjZtCn9//4Y8fCIiIiIiIiIiIiJ6zD12NdFtbW2xePFiTJ48uVJbZGQk/vOf/yA/Px+mpqbYtWsXhgwZgps3b0qF+teuXYs5c+YgMzMTSqUSc+bMwc6dO3H+/HkpzpgxY5CTk4Pdu3fXel6sm0VERERERERE9PfF3E491UQn+oeoy/vhsfmthU6nw5YtW5Cfnw8fHx+jfSr+yJmali+gj42NRceOHQ3udOvv74/c3FwkJCRIfXx9fQ3i+Pv7IzY29hEdCRERERERERERERH9UzT4jUXPnTsHHx8fFBUVwdLSEtu2bUO7du0q9cvKysJ7772HqVOnStvS09MNEugApOfp6enV9snNzUVhYSHUarXReRUXF6O4uFh6npub+2AHSERERERERERERER/Ww2+Et3DwwPx8fE4fvw4Xn75ZQQFBSExMdGgT25uLgIDA9GuXTssWLDgL5nXokWLYG1tLT1cXFz+knGJiIiIiIiIiIiI6PHR4El0pVIJd3d3eHl5YdGiRejUqRNWrFghtd+9exeDBw+GlZUVtm3bBoVCIbU5Ojri1q1bBvEqnjs6OlbbR6PRVLkKHQDmzZsHrVYrPf7444+HPlYiIiIiIiIiIiIi+ntp8CT6/fR6vVRGJTc3F35+flAqlfj5558rFXj38fHBuXPnkJGRIW2LioqCRqORSsL4+Phg3759BvtFRUVVWXe9gkqlgkajMXgQERERERERERER0b9Lg9ZEnzdvHgICAtC8eXPcvXsXEREROHDgAPbs2SMl0AsKCrBp0ybk5uZKdcnt7OxgYmICPz8/tGvXDuPHj8fHH3+M9PR0zJ8/H8HBwVCpVACAadOmYdWqVXjjjTfw3//+F9HR0fjuu++wc+fOhjx0IiIiIiIiIiIiIvobaNCV6BkZGZgwYQI8PDwwaNAgnDx5Env27MFTTz2F06dP4/jx4zh37hzc3d3RtGlT6VFRWsXExAQ7duyAiYkJfHx88J///AcTJkzAu+++K43h5uaGnTt3IioqCp06dcLSpUuxfv16+Pv7N9RhExERERERERERUS0tWrQI3bt3h5WVFezt7TF8+HAkJycb9CkqKkJwcDAaN24MS0tLPPvsswYlns+ePYuxY8fCxcUFarUanp6eBiWlASAtLQ3jxo1DmzZtIJfLMWPGjFrP8bPPPkOLFi1gZmYGb29vnDhxwqA9JSUFI0aMgJ2dHTQaDUaPHl2pBLUx169fR2BgIMzNzWFvb4/Zs2ejrKzMoE9xcTHeeustuLq6QqVSoUWLFvjqq68eOu6BAwfQtWtXqFQquLu7Y8OGDTXONzs7Gy+88AI0Gg1sbGwwefJk5OXlSe1FRUWYOHEiOnbsCFNTUwwfPrzGmAAghMA777yDpk2bQq1Ww9fXF5cuXarT2A+jQVeif/nll1W29e/fH0KIGmO4urri119/rbZP//79cebMmTrPj4iIiIiIiIiIiAzphR6Xcy4jtzgXGpUG7jbukMse3VrdgwcPIjg4GN27d0dZWRnefPNN+Pn5ITExERYWFgCAmTNnYufOnYiMjIS1tTVCQkIwcuRIHD16FAAQFxcHe3t7bNq0CS4uLoiJicHUqVNhYmKCkJAQAOXJaDs7O8yfPx/Lli2r9fy2bt2K0NBQrF27Ft7e3li+fDn8/f2RnJwMe3t75Ofnw8/PD506dUJ0dDQA4O2338bQoUNx7NgxyOXGz51Op0NgYCAcHR0RExODtLQ0TJgwAQqFAh988IHUryIh/+WXX8Ld3R1paWnQ6/VVzrc2cVNTUxEYGIhp06Zh8+bN2LdvH1588UU0bdq02sXJL7zwAtLS0hAVFYXS0lJMmjQJU6dORUREhDS2Wq3Gq6++ih9++KHW5/jjjz/Gp59+io0bN8LNzQ1vv/02/P39kZiYKJUAr2nshyETtclUE3Jzc2FtbQ2tVsv66EREREREREREfzPM7ZSvAk5NTYWbm1ulew/WVnxGPCKSIpCak4oSfQmUciXcbNwwru04dLbvXL8TrkJmZibs7e1x8OBB9O3bF1qtFnZ2doiIiMCoUaMAAElJSfD09ERsbCx69uxpNE5wcDAuXLggJbbv1b9/f3Tu3BnLly+vcT7e3t7o3r07Vq1aBaD8no8uLi6YPn065s6di7179yIgIAB37tyRXntarRaNGjXC3r174evrazTurl27MGTIENy8eRMODg4AgLVr12LOnDnIzMyEUqnE7t27MWbMGFy5cgW2trY1zrW2cefMmYOdO3fi/Pnz0n5jxoxBTk4Odu/ebTTuhQsX0K5dO5w8eRLdunUDAOzevRtPP/00/vzzTzg5ORn0nzhxInJycrB9+/Zq5yuEgJOTE15//XXMmjULQPn5c3BwwIYNGzBmzJg6jw3U7f3w2N1YlIiIiIiIiIiIiB4/8RnxWHJqCS7cvgArpRWcLZ1hpbRC0u0kLDm1BPEZ8X/JPLRaLQBISeO4uDiUlpYaJKPbtm2L5s2bIzY2tto4tU08V6WkpARxcXEGY8vlcvj6+kpjFxcXQyaTSfdwBAAzMzPI5XIcOXKkytixsbHo2LGjlOgGAH9/f+Tm5iIhIQEA8PPPP6Nbt274+OOP4ezsjDZt2mDWrFkoLCx8qLixsbGVkvv+/v4G53PDhg2QyWQGcW1sbKQkNgD4+vpCLpfj+PHjVc7nflevXoVMJsOBAwcAlK+KT09PN5iPtbU1vL29pfnU19hVYRKdiIiIiIiIiIiIqqUXekQkRUBbrEVzq+YwV5hDLpPDXGEOFysX5Bbn4tukb6EXVZcRqZd56PWYMWMGevfujQ4dOgAA0tPToVQqYWNjY9DXwcEB6enpRuPExMRg69atmDp16kPNJysrCzqdziAhff/YPXv2hIWFBebMmYOCggLk5+dj1qxZ0Ol0SEtLqzJ2enq60bgVbQBw5coVHDlyBOfPn8e2bduwfPlyfP/993jllVceKm5VfXJzc6UEvbW1NTw8PAzi2tvbG+xjamoKW1vbKq+DMQqFAh4eHjA3NzeYU3XnuL7GrgqT6ERERERERERERFStyzmXkZqTCju1ncHqYwCQyWRoom6CKzlXcDnn8iOdR3BwMM6fP48tW7Y8cIzz589j2LBhCAsLg5+fX633O3z4MCwtLaXH5s2ba7WfnZ0dIiMj8csvv8DS0hLW1tbIyclB165dpXroAQEBUtz27dvXek56vR4ymQybN29Gjx498PTTT+OTTz7Bxo0bq12NXh9GjBiBpKSkeo/r7OyMpKQk9OjRo95jP6gGvbEoERERERERERERPf5yi3NRoi+Bmanx2tEqUxVuF91GbnHuI5tDSEgIduzYgUOHDqFZs2bSdkdHR5SUlCAnJ8dgNfqtW7fg6OhoECMxMRGDBg3C1KlTMX/+/DqN361bN8THx0vPHRwcoFKpYGJiglu3bhn0vX9sPz8/pKSkICsrC6amprCxsYGjoyNatmwJAFi/fr2U9FYoFNJxnThxolLcijYAaNq0KZydnWFtbS318fT0hBACf/75J1q3bl3pOGoT19HR0egxaTQaqNVqo+fH0dERGRkZBtvKysqQnZ1d6TrURcW+t27dQtOmTQ3m07lz50c6dgWuRCciIiIiIiIiIqJqaVQaKOVKFJUVGW0vLiuGUq6ERlX/N20VQiAkJATbtm1DdHQ03NzcDNq9vLygUCiwb98+aVtycjKuX78OHx8faVtCQgIGDBiAoKAgvP/++3Weh1qthru7u/SwsrKCUqmEl5eXwdh6vR779u0zGLtCkyZNYGNjg+joaGRkZOCZZ54BUL76uiKuq6srAMDHxwfnzp0zSA5HRUVBo9GgXbt2AIDevXvj5s2byMvLk/pcvHgRcrnc4IuGe9Umro+Pj8ExVfQxdkz3xs3JyUFcXJy0LTo6Gnq9Ht7e3lXuVxM3Nzc4OjoazCc3NxfHjx+X5vOoxq7AJDoRERERERERERFVy93GHW42bsgqzIIQwqBNCIGswiy0tGkJdxv3eh87ODgYmzZtQkREBKysrJCeno709HSD2tyTJ09GaGgo9u/fj7i4OEyaNAk+Pj7o2bMngPISLgMGDICfnx9CQ0OlGJmZmQZjxcfHIz4+Hnl5ecjMzER8fDwSExOrnV9oaCi++OILbNy4ERcuXMDLL7+M/Px8TJo0SeoTHh6OY8eOISUlBZs2bcJzzz2HmTNnGtQUv5+fnx/atWuH8ePH4+zZs9izZw/mz5+P4OBg6Sal48aNQ+PGjTFp0iQkJibi0KFDmD17Nv773/9WuWK8NnGnTZuGK1eu4I033kBSUhJWr16N7777DjNnzpTibNu2DW3btpWee3p6YvDgwZgyZQpOnDiBo0ePIiQkBGPGjIGTk5PULzExEfHx8cjOzoZWq5XOeYUbN26gbdu20mp5mUyGGTNm4P/9v/+Hn3/+GefOncOECRPg5OSE4cOH12nsByaoVrRarQAgtFptQ0+FiIiIiIiIiIjqiLkdIQoLC0ViYqIoLCx8oP3P3DojXtj5ghj641AxadckMS1qmpi0a5IY+uNQ8cLOF8SZW2fqd8L/B4DRR3h4uNSnsLB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            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Linear Regression:\n",
            "R² = 0.21\n",
            "P-value = 0.11590559344129535\n"
          ]
        }
      ],
      "source": [
        "# @title\n",
        "if first_var != 'time' and second_var != 'time':\n",
        "    if first_var == second_var:\n",
        "        data_query = f'.csv?time%2C{first_var}%2CStation&Bottle_No={selected_platform}'\n",
        "    else:\n",
        "        data_query = f'.csv?time%2C{first_var}%2C{second_var}%2CStation&Bottle_No={selected_platform}'\n",
        "else:\n",
        "    if first_var == second_var:\n",
        "        data_query = f'.csv?{first_var}%2CStation&Bottle_No={selected_platform}'\n",
        "    else:\n",
        "        data_query = f'.csv?{first_var}%2C{second_var}%2CStation&Bottle_No={selected_platform}'\n",
        "\n",
        "resp = requests.get(BASE_URL_DATASET + data_query)\n",
        "df = pd.read_csv(BytesIO(resp.content), header=0, encoding='utf-8')\n",
        "\n",
        "df = df.iloc[1:]\n",
        "\n",
        "df[first_var] = pd.to_numeric(df[first_var], errors='coerce')\n",
        "df[second_var] = pd.to_numeric(df[second_var], errors='coerce')\n",
        "\n",
        "df = df.dropna(subset=[first_var, second_var])\n",
        "df = df.sort_values(by=[second_var, first_var])\n",
        "\n",
        "time_values = sorted(df['time'].unique())\n",
        "palette = sns.color_palette(\"tab20\", len(time_values))\n",
        "profile_colors = dict(zip(time_values, palette))\n",
        "\n",
        "plt.figure(figsize=(15, 8))\n",
        "\n",
        "for t in time_values:\n",
        "    mask = df['time'] == t\n",
        "    plt.scatter(\n",
        "        df.loc[mask, second_var],\n",
        "        df.loc[mask, first_var],\n",
        "        color=profile_colors[t],\n",
        "        label=t.replace('T', ' ').replace('Z', ''),\n",
        "        alpha=0.7\n",
        "    )\n",
        "\n",
        "if len(df) > 0:\n",
        "    slope, intercept, r_value, p_value, std_err = linregress(df[second_var], df[first_var])\n",
        "    x_range = np.linspace(df[second_var].min(), df[second_var].max(), 100)\n",
        "    regression_line = slope * x_range + intercept\n",
        "\n",
        "    plt.plot(x_range, regression_line, color='red')\n",
        "\n",
        "plt.ylabel(filtered_variable_long_name_map[first_var] + ' (' + units_map[first_var] + ')' if units_map[first_var] else filtered_variable_long_name_map[first_var])\n",
        "plt.xlabel(filtered_variable_long_name_map[second_var] + ' (' + units_map[second_var] + ')' if units_map[second_var] else filtered_variable_long_name_map[second_var])\n",
        "\n",
        "ax = plt.gca()\n",
        "ax.set_title(f'Correlation between {filtered_variable_long_name_map[first_var]} and {filtered_variable_long_name_map[second_var]} for platform: {selected_platform}')\n",
        "\n",
        "ax.set_xscale('log' if x_scale == 'Logarithmic' else 'linear')\n",
        "ax.set_yscale('log' if y_scale == 'Logarithmic' else 'linear')\n",
        "\n",
        "if x_scale == 'Logarithmic':\n",
        "    ax.xaxis.set_major_formatter(ticker.ScalarFormatter())\n",
        "    ax.xaxis.set_minor_formatter(ticker.NullFormatter())\n",
        "    ax.xaxis.set_major_locator(ticker.LogLocator(base=10.0))\n",
        "\n",
        "if y_scale == 'Logarithmic':\n",
        "    ax.yaxis.set_major_formatter(ticker.ScalarFormatter())\n",
        "    ax.yaxis.set_minor_formatter(ticker.NullFormatter())\n",
        "    ax.yaxis.set_major_locator(ticker.LogLocator(base=10.0))\n",
        "\n",
        "ax.yaxis.set_major_locator(ticker.MaxNLocator(integer=False, prune='lower'))\n",
        "ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=False, prune='lower'))\n",
        "\n",
        "plt.legend(title='Profile times legend:', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(f'Linear Regression:\\nR² = {r_value**2:.2f}\\nP-value = {p_value}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q9Z_0gQI6lf1"
      },
      "source": [
        "### **Additional resources**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcH6KyNp6ken"
      },
      "source": [
        "The Python libraries that have been used in this notebook are:\n",
        "- [cartopy](https://scitools.org.uk/cartopy/docs/latest/)\n",
        "- [ipywidgets](https://ipywidgets.readthedocs.io/en/stable/)\n",
        "- [scipy](https://scipy.org/)\n",
        "- [requests](https://requests.readthedocs.io/en/latest/)\n",
        "- [seaborn](https://seaborn.pydata.org/)\n",
        "- [numpy](https://numpy.org/)\n",
        "- [pandas](https://pandas.pydata.org/)\n",
        "- [matplotlib](https://matplotlib.org/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1lVzWt8E1rtd"
      },
      "source": [
        "This work has received funding from the European Union Horizon Europe project Polar Research Infrastructure Network (POLARIN) under grant agreement No. 101130949 (https://doi.org/10.3030/101130949).\n",
        "This notebook makes use of data available in the European Marine Observation and Data Network (EMODnet, https://emodnet.ec.europa.eu)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQJPALOH1rtd"
      },
      "source": [
        "| | | |\n",
        "|:-:|:-:|:-:|\n",
        "| <img src=\"https://ocean-ice.eu/wp-content/uploads/2025/02/TO-USE-RGB-for-digital-materials-V.png\" width=\"160\"/> | <img src=\"https://eu-polarin.eu/wp-content/uploads/2024/04/polarin-web1.svg\" width=\"140\"/> | <img src=\"https://emodnet.ec.europa.eu/sites/emodnet.ec.europa.eu/files/public/emodnet_logos/web/EMODnet_standard_colour.png\" width=\"140\"/> |\n",
        "\n"
      ]
    }
  ],
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TQWxCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFZIELoQQQlghSeBCCCGEFVJZOgBrt3LW17h5+ODg7Ia7dwDe/kF4+wei1thZOjQhhBBPMEngD+nw9mXk5uZiMBjAZAJAoVDiV6oswZVqE1i+BuWqNiAgqAIKhcLC0QohhHhSSAJ/SBcv7MfFxRmj0UhoaBiHDx/n8OETnDoVysl969m/ZSmYTDi6uFO5dnNKBlemfqtueHgHWDp0IYQQVkxhMv3TbBQFkpaWhqurK1evHsfFxfm2941GI2fOnGft2s38+edfxMcnmN9r/9Jwuvb/4FGGK4QQohhkZ6XzXo9qpKam4uLi8kjrlhZ4EdHpdMyatZD585dx7Xo0mZmZGHNzAbBRqfEPqkCJ0pUoV7U+jdr2sHC0QgghrJ0k8IfUr98wLlyIICEhGYNBj4u7N1XqtcU/sDx+geXxDyyPl28plDY2lg5VCCHEE0QS+EPat/8YwZXqULNFXWo0bEPpirVRKmV2nhBCiOIlCfwhfT3/CI7ObpYOQwghxFNGmooPSbrGhRBCWIIkcCGEEMIKSQIXQgghrJAkcCGEEMIKySA2IYQQD+Xm1YvcvHIBhVKJu5cffqXK4eDk+sjqz0xPJTLsOAa9DlcPH9y9/HH19H3il6+WBC6EEAKA3FwD8TevoFJrsLN3wtHF/b5JMEebxRdDn7ntuJOLB56+JfH0LYl/YAUq125GcOW6RTLNdufaOZw5vB2DPofMtBSir4VjNObmO8fW3pGv5x3GzsHpruWYTCZirl/CwckFVw/fh47rUZMELoQQgisXT/HNu13u+N5HU9cRVL46AAe3LWfu5PdxdfehSt0W1Gj4DL3e/ILF0z4DYPTod4iPT+TcuQvExMRy+ew1Th3YzPqFU3B286Ju806Ur96IMpXr4uLujVKpxGQy/fPPiMloxEalvusfDumpiSz5dQwAJUsGYG9nS7du7Rk4sBdeXp6sX7+VL7+cjIdPCTR2DubrjEYjkeePcWL/RrIz07C1c+TGlTAunj4AQOXazSlTpR7pKfFobO0pEVwZFzcvHJ3dcXJ1x9HFA1s7h8eqVS8JXAghnmJ6nZbYG5Ec3r7yrudsXTGTctUa4O0fRFTEWTCZSE2K5cCWpRzYstR8nlJpw0svdSUoqFS+6w0GA3Pn/sWcOYvYv3kxO9fO/ecdBXD7dhxOrp60f2kYrboMvK3F7uzqiaOzG7VqVGDdugX53tNqtUyZOhOAus07s33VH2SkJpGemsjF0wdIiLmGSq3G3s4OQ24uKpUN7du3wtPTg7VrtxAeehCNRoPBYECXk3NbXDY2KpzdPPH0LUWpslUpWaYKltxNRDYzKaRbm5lMXhaKvcPtm5kIIURx0+u0ZKQlY2OjwsXdu1BlDO0YdM/3lTY22Cht0Ov13CnZAnh4uFOrVjV69+5Ojx7P3bfOEyfOsHbtZqKibqJUKlAqbVAqFdj8s67G5s07iYmJQ2mjwtHZjfLVG1KhRmPKVKpD9LVwZn/3DoMG9eX778fmK/fq1es0aNgBg16P0WhEqVSiUmvQaNR4erox9I3+DBrUF5Xq/m3XxMQkIiOvceXKda5fv0lMTCwxMXFER8cRExNLXFwSOTla8/mW2MxEEnghSQIXQlhKSmIs86d+SNjJveQa9AC4evhQv1U3Wnbuj6dvSbTZmSTF3cDByRVHFzfUats7lnVo2wq2rfqdlMRY0lMS7ngOQKNGdRk1ajibNu3gwoUIYmLicHNzoW7dGowdO+qBkuKDMhqN/PrrHI4cOUFUVDQXL14mPSODW81dX18fQkN33bPOWwm8OGVkZJCYmEKtWq0kgVsTSeBCiP/KNejRZmeizcpAm52JMVePT4ky2P7nOWxRmf7FYE4d2ETr1s1p2LAO6ekZ7Nixl/NhlzAZjVSs2YSr4afJzkw3X+Ps6klAUEX8AssREFSRSrWb4RNQOl+5W1f8xvLfv7xjnfPn/0qnTm2L/F4eVHx8Ahs2bEOlUtOrV7fHZs+JtLR0goLqyHaiQghR1M4d20Xo0Z2o1RpKBFemXLUG6LRZHNy2gpTEGLIz08hKTyFHm4VvybIEla9B6Yq1KFW2Kge3LefIztV4+QXi5ReIp29JbGxsyEhNJub6JaKvXSQmKoLszHRzS/i/FAoF3gGlefWDHwmqUMN8XK/P4dyx3YSfPoCzuzfe/oGUq9rgvt3gep2WuZPe59SBTSgUCiZM+JiKFcuZ34+PT2D06K/YuXM/gSV9GTDgbZKTU7l5M4bw8MtcuxbBtfCTZGuzwWSidIWadB0wikq1mgHg6VsyX+wVKpTlxRe70LBhbWrVqnbfz9pgMBARcYW9ew9z5sw5dDo9NjZKVCoVrq7OBAT4ExhYgvr1a+Hp6XHf8v7L29uLfv1eKtA1TzppgReStMCFePzdvHrRPMVJo7FFp8vJG0WsUKBUKnF0cEBjmzeoydZWQ2xsAhkZmZhMRhQKBbd+Pdrb26M3GDDo85K0QqEETPz/r8//jlA2gbnL96Wh42n5XH+yM9PYsnwm21fPIic7E7VGQ25uLsbcXJRKG+o070Tb7q/nS/b/9ev41zhzaBt9+nRn/PiP8PBwK9TnEhsbz48//sb8+ctJT0/nlRHf07htDwDibkRydPdaLoUeJvLCCbRZGf/en1KJQqFEoVCY/+XdpgmjMe8+HoRarSYu7lyhYn/cSAtcCCGKgV+pcpQqW5XrEWcZPvxV+vTpztdf/0Rk5FVmzfqRwMASt12TlZXFxo3b2bJlFzt27MPWVsOePWtJT8+gRYuuJCUlYzIZ71ifm5cfXn6B2No7YmvrgKOLGxVrNsXF3Zs/J4/k5P6NaLWZ1KxRhZEjh9GpU1uMRiNhYZeYMOEHtm7dxNFda2nd7VW69Bt5W/f7+eN7qFu3Bj///PVDfS6+vt6UKVMayEu+OdkZZGelc3TnGs6f2IONSk356g1p9+JQ3L0DSIqNIjnhJtrsLHINOnINBgwGPcZcAwqlMi/p71pzWz0aOwdMRiNGY665h8LGxobx40c9VPyPi4yMDAYPft9i9UsLvJCkBS6EdZgyujcXTu1n0aIZtG/futDlvPbaCJYvX3fbcRsbFQqFAkOuAZ+AYHoOGYOLuxd+geXNA8c+erk+GamJVK9emSlTvqRmzap3rEOr1dK//1ts2bIbD58SvD56Wr7W+K/jBnHh5F5OndqOr2/hRp0DLF68kqFDR2Hv6EK7Hm8QH32VIztXodfl4OrqAijIyMgkN9dAqbJVeXHwGMpXb3jX8k4d2Mz0L16/7bhSqcTOzhZHRwdKlPDniy8+JDCwFCVL+j82z7ALa926zQwZ8gFZWVmAjEK3KpLAhbAOoUe2M/v7EeiyM9m4cRF169YsdFkREZG4uDjj7OyEnZ1dvvd+/30+oz78ApMxr3WuUmuo1+I5mrbvzaRRL9Kgfm02bVryQPVs27aH/v3fIjs7m+6vfkyb7q+hUCi4efUiX7/7HO6uzpw5sxONRlOo+5gzZwkjRnxqfq3WaGjcqA6ffz7S/PkYjUZ+/vkPvvt+GlmZ2Xw4ZTWB5arftcxcg54rF09x6ewRrl8K5Wr4aVISYzDodbeda2dnR1BQ3ngClSrvn42N6rav1Wo1NjZKtNoccnJyyM7OQafTkZOjQ6fTodPpSUpKJjtby6uv9mHSpHGF+jxuuXEjmm3bdhMbm4C3txcVKgRTsWI5MjIyOXjwOCdOnCYsLJzwS5FE34zFp0Qwfd/6mskf9pQEbk0kgQthPdKS4/li6DOYcnVMn/4tnTu3M7+XlJTCu+9+ws2bsZhMRoxGEyqVim7dOjBs2KsFqicjI4Nz58I5d+4ia9ZsZO/ew+j/SWA+vt5cCNtfoLK6dOnHiRNnqNeiMy+/+x22dg6EHtnBtLGvUrZcaQ7sX1+o6VsfffQFM2b8SVBQSfr27cE777x+xz8G0tLSeeaZHly8eJnew76iRaeXC1SPyWRCm51BenICWZlpZGemk5Ycx7Hd60iKv4ExN5fcXAPGXMM/YwEMecf+eZ5uNOZiMuZio9KgVmtQaWzRaOxQ29qi1tgRG3WZrIxUAIYM6cfXX39WoPg2btzOjBl/EhoaRlpaBjrd7Yu3/D97Jxd8S5ShdtMOtOoyAINBz3s9qkkCtyaSwIWwLlcunuL3CW+SGBfFc8+1488/fwHghx9mMH7890DeKOzE2CjzNZGRx3BzK/wvZZ1Ox+LFKzl9+jxvvTXothXKHsQnn0xg+vS5ePoF0rnvu5Sr1pALp/bz5+SRVKpUjn371hVbd/TIkWP544+81c5Uag09Xv+MUmWrYtDryMnOQqvNxJhrwNXDFw/vALz8AlH+sxiLMTeXnJwsbO0ciy2+zPRUvhrenuT4myiVSpQ2qn8WhlGiVNpgo1SgtFHmjYS3scHW1hYHB3syM7NISk4lKzMTtcaW8tUbUbJMFQKCKhAQVAFHFw+yM9JIS0kgLTkOldoWT9+SePuXxtE5/yYt2VnpksCtjSRwIayPQa9j1eyv2bbqDxwdnTDkGsjRalGpbRnxzWK8fEvxYd96AHz++UhGjBhi4YjzbNiwjTff/JCUlFRAQdV6LVFrNJzcv4m6dWuydeuyYqnXaDSydetuzp69wO+/z+fmzZh7nu9bsgzdB44mR5vJ3EnvmzcY0dja4+LuhX9QRUpXqEnzjn1xdvUsshgjzx/jcthxdDla9LocDPqcf/6rw6DP+69el0N2VjpZ6ak4OLni6VeK6g3aUKNhG9Qau/tXdBeSwK2QJHAhrJPJZOLgtuVcPneUq5fOYO/gRKXazfEvVR5XDx+mjXuVrIxUOrRvhZubK7a2tlSoUIbXX3/F4gOvwsLCmTp1JmvWbiErM9N8fOLET3jjjQHFXn90dCyHDh3H3d0VT08PvL09USoVhIaGcfToKX786Xcy0v+ddhYS0hh/f1+io+PYtevfxwf1Qrow6MOfij3eR0ESuBWSBC7E4y0q8jzbVv6OnYMTLu7eBJWvQXDFWtg75v2SPXNoG9PGPfgz7qlTv3xsFhIxGo0sXryK77//hcjIa4SENGHVqrn3v7CY6XQ6tm3bQ4kS/lSoUMY80G/Hjr288MKr5nnzDVp1J6hCTXxLBOMfVAEP7wBLhv1QJIFbIUngQjzeNi+bzspZE1Ha2KBAQW6uAbXGjvYvDaPt84OxUanYuXYu18JPE3ZyL2nJ/64D/vzznbh+/SZxcQlkZGTQsmVTZs6c9Mha4BkZGXzxxWR8fLx5//2hj6TO4nbjRjSTJv3Kpk07SE5OQ6vVmufTu3n5U7VuCO1fGoaXX6CFIy0YSeBWSBK4EI+3lIQYJrzVkcz0FCpXLsfQoQOYNm0O585doFLtZrzzVf6tKGd9+zZHdq7G39+XHTtWPtQ86/u5fv0mQ4d+QFjYJTKzsnFxduK33ybRpEl95sxZzKeffk3OP9tZHj26mbJlg+9ZnsFgYM+eg1y/foPY2ASqVatEhw5tii3+B6XT6UhOTiU+PpHLl69y5co1oqNjyc7WotPpiYy8yvnzF0lNzVuz3T+wPJ9P32rhqAtGErgVkgQuxOMvJTGWXev+ZMfq2RiNenq80JlFi1YA8GzPNwnp3A8XNy9sVGqSE6KZO+k9LpzaDyhwcHTA08OVSpXKM3Pm5Icajf7/PvxwPDNnzsPVw4fazTpycv9GUhJi8i3fCuDg6Ejk5cN3nOJlNBpZtGgFv/02n7PnLpqXeb0lMfHCI+kx0Ol0vPnmh2zavBNdTt42nkajEaPJyINslq20UeHk4oGrhw9efqVwcHJFpdbgW7IMdg7OGHMNmEwmqjdojauHb7HfT0FJArdCksCFsB4JMdeYN2UUF08fwM3NlfT0DHL/WbfbwdmNJs+8SJU6IZQsWwVtViZhJ/Zw5eIp4m5Ecvn8MVRqFcOHvcro0W8XybaZ7du/xKFDxxn04c/UC3kObVYGZw5vIz01iZLBlQgPPcy6+ZOpVq0SdevWRKlUotfriYtLICEhkevXb5KUnEauQY+jizs2NirSkuOBvEVSfvllIs8/3/mh47yXlSv/ZtKkaVy4cBmDQU/NRu3wD6qAxs4etdoWtcaWG5FhnD22C6MxF71Oi06bjV6nvWN5NioVGo0GGxsbcnNz0WZn5/tjxsOnBCO/X467l3+x3ldBSQK3QpLAhbAuJpOJA1v+4q8Z48jJzrzreaUr1OSloeMpXbEWANHXLrLgx9FEnDuKxtaWpk3q0759a/r06Y6Tk1OB47h27QZ167bFYDAAMPCDKTRo1T3fObm5BlbN/poda+bk2+VMY+eARmOLi7sP/oHlKVO5LhVqNubw9pVsWT4DyNtQxdZWg06X1xoGKFOmNMeObSlwrHfyww8z+PHH30hJScXZ1ZMGrbpRr2VXSle4fYW791+qSVZ6ivm1SmVDjx7P4enpjqenJz4+npQpU5rKlSvc1sOh0+kwGAxoNBqOHj1Fly79cPPy5+Of/sbOoeCfe3GRBG6FJIELYZ2yM9MIDz1MTnYmGWlJXD5/nHPHdplX9LqlVdeBvDh4jLlbO/LCCXaunsOJ/Rsw6HWo1RqaNWtAz55daN++DXq9Dq1Wh8GgJydHR0JC3nPfqKhoMjOzSElJZc+eQ0TdiMZkNOLo4g4mE0M++43y1RrcMVaj0Ygx18CFU/v5e9FUbkSGkaPNKvA9N2xYh40bH2wZ17sxGo20aNGVs2fDKF2hJm2fH0ytJs9io1Lf9RqdNpuD25ZzNfwU+zf/BUD37h0ZNuzVAi9pe2sk+3OvvE+HXm+Zj+t1WrKzMsjJzgJMuLr7oLGzL9Q9FoYkcCskCVyIJ4cxN5eLZw6wbeXvhB7ZYT7+xme/UbNxu3zn5uYaiIo4x6alvxJ6ZBv6B1h+85aAoArUa9mVRm1eyNcVbDKZyM5MIzE2iusRoVwNP8PVi6dIirtBdlYGBn1eHTVrVqVevVp4eLjh4eGGg4M9RiPmJWBv/Vej0RAcXIoKFcri7180z42HD/+IBQuW89LQ8YR07pdv69QHsXfjIg5tW8Hl88cAWLFiFiEhTQpURvPmz3HuXDhefqUwGPRkpCahy8m+7TzfEsGUrdqAKnVD8v7IsCm+jTclgVshSeBCPJmuXjzN+RN7UGtsadKup3ne+J0Y9DoiL5wkMfY6NjYqlDYqbGxsUNqoSIqL4lp4KDZqNR4+JQiuWAtv/yBOHdhCWnI8KYkxJMXfJDn+BimJceYkDWBnb4+vjyclSwbg5uZCZmY2jo72zJo1tdAbmBRUUlIKly9fwdvbk+xsLSEhXdHp9Hyz4Cgu7oUfoZ+RlszEtzuRkhBD8+YN+fHHCXfc1vVOoqNjeffdT4mOjkWtVuHj44Wvrw+enh64u7tiNBr59dc5xMTEma95cfAYWncr2Jr2BSEJ3ApJAhdC3M31iFAmvNUJyNvpy6DX8/+/al1cXHB2dsTLy4OAAD8CA0tQsWI52rdvXWSt5sKqVj2EG1E3bzvu7R9UJM+gM9NT2LDoR3asmYPRZMTN1YWyZYOoXbsGbdo0p2XLJrft9vYgYmPjqVQpr1Xv4V2CBq270/6lYbftq16UJIFbIUngQoi7iY++xueDmtOyZVOWLv0dnU7H2rVbWLhwGaGhYSxbNovate++NafBYGDXrgOEhYWTlJRCTo6WkiUDKFOmNM7OeckzPT2DGzeiiYmJM49Oj41NIDExkZTUdGw1Gn76aSJt2jQvcPxe3pUpVaYanfq+S442C6VSSUBQRXxKBBe46/xeUhJjOXVwM+eO7eLqxdOkJsWa3+vXrydTp35VoPKuX79J48YdyTQvM6vAP7CcebMSB0cXTCYTacnxpCXHk2s0YGvrQKmyVSlVtiru3gEFvj9J4FZIErgQ4l4W/DSavRsWolZrKF++NNWrV8bBwQGl8p/dsVQqFApIT88kNTWN2Nh4oqPjSE5OJSMj07wRyMP69ddv6dWr+/1P/EdYWDiNG3fk1Q9/on5IlyKJ4X5MJhOnDmxixR8TiI++CsD8+b/SqVPbQpWn1WrZvfsga9Zs5ODBY9y4EYtW+++zcoVCiVqjQakAvcFA7j8zAhxd3ClbpR6evqVo1Ob5e+5/foslE3jxPdkXQoinWJ/hE2jQqjvHdq/l7NGdLFmyBrhLe0mheKBFT+52rUajQa1Wo7KxyRs1jwmtNoccrfafHcwe3KRJv6JQKKhQvVHh4imEv6aPYefaubi6uTJhwscMHTrwocqzs7OjXbuWtGvX0nzMYDAQH5+IwZBLiRJ+LF68iiNHThAREUlo6AWSk1PITEvm9MG86XY7Vs/i17+vPlQcxU0SuBBCFAOFQkH5ag0oX60BGWnJXDy9n8z0VLIz0zDocnB286J8jUaYjEbmfP8u1yPO4ujkhK1GhYODAy4uTri754029/b2wt/fF39/XwIDAwgKCqRECb8iX2nt+vWbrF69iRoN2+Lq4VOkZf9XVkYqJ/ZtIPpaODevXOD8iT107NiWBQt+ved1RqOR7dv38Pff2/Dy8qBSpXIEBPjTqFHd+9apUqnw9/dl3brN+brZHZxc8fIrRZnqzfH0LYmXbylcPX0pGVy5SO61OEkCF0KIYqTNzmTckNZkpCYBeYldoVCYF1lRKBSoNRp+/vlr+vR5/pHFFRoaxscff0lk5HX0ej06vZ70tExsVGpeeP2zYq17z98LWDXnGwDs7GwpVSqAxo3rMXbst8THJ5KUlExKShppaelkZ2vJydGRkZFFVlY2BoP+tvKmT/+el17qetf6kpJSmDNnEfPmLePKlWv4lAhm0MdfElypNnb2jsV2n8VNErgQQhSjnOwMMlKT8PR0Z8+eteYR5mFh4cyevYj4+ES++urjRzbyPCkphWfbv0TEpUhUag2lK9bGzc4Bja0d7t4BtOj4Mt7+QcUaQ4PW3dmw+CdytFlotTlcv36Tzz772vy+xtYeewdnHJxdsXPwxNnFgRIVvPAOKE3p8jX5e/GPXLlwEgAfHy+efbYV8fEJTJ48g9Onz6JQKFCpVKSlpRN55TqpKWmYTEbcvQN4cfAYmnfqi1ptW6z3+CjIILZCkkFsQogHtWv9PP76dQxqjZrevboxZswHRbo5yoOKjY2nUaOOZGRm0aXfSJo+2wsHJ9dHHgdARmoSUZHnMBqNODi64ODkioOTK/ZOLvdceMVoNDKsc97ubC+/3IOcHB2XL1/l5Klz5Br0OLl6oFAoMeYa0NjaU7JsVSrWbEKlmk0JKF2xSEfRg4xCt0qSwIUQBREVeZ6VsyZy7tgulDY2+Hh7Urp0KSpWLEfTpg3o0KF1odZWf1B79x6kV+830OlzeWv8n5Sv3rDY6ipOJpOJRb98yp6/55uPOTi5Ui/kOdo+PwRv/0e7n7gkcCskCVwIURgxUREc272OCyf3EXP9EumpiUDes3AHR0dKBPjyyisv8uabA4tkkNr16zcZOPAtjh8Pxd3Lj8GfziSo/P2nRz3ucnMNGHMNqNS2Rd6qLghJ4FZIErgQoihoszO5eSWMq+GnuXYplEuhh0iIuY6TkxODBvXm00/fe6gtTJs3f47Q0DDqhXSh79tfW/WgrceRzAMXQoinlJ29I2Uq16VM5bypUCaTifPHd7Nh8c9Mnfob02fM45m2zenf/yVat25eoFb51avXSUpKAcDLL1CS9xNGErgQQjxGFAoFVeqGUKVuCJfPH2Pz0l/5e8MO1q3bglqjISiwBO3ataRz53Y0bFjnrgn999/nM2rUeGxUajr1HUGHl4Y94jsRxU0SuBBCPKbKVK7LG5//To42i/AzBzl3fA9nj2xn2rTZTJs2G42tLc2bNeCXX77B1zf/DmGpqemYTCba9XiDzn3ftcwNiGIlCVwIIR5ztnYOVKvfmmr1W8OQMaQkxnI1/DSn9m9ix/blVK7cDBdXZ7w83fHy8iA5OYX4hLyFY65dCrVw9KK4SAIXQggr4+bpi5vnM9Rs9Ayd+r7LsT3ruHrxNPHRVzh/8TrObl6UqlCfZ2o1pVGbFywdrigmksCFEMKKefqWpF2PNywdhrCAol0JXwghhBCPhCRwIYQQwgpJAhdCCCGskCRwIYQQwgpJAhdCCCGskCRwIYQQwgpJAhdCCCGskCRwIYQQwgpJAhdCCCGskCRwIYQQwgpZNIGXLl0ahUJx279hw/K2vWvZsuVt773xxr2XDDSZTHz++ef4+/tjb29P27ZtCQ8Pz3dOUlISffv2xcXFBTc3NwYNGkRGRkax3acQQghR1CyawI8cOUJ0dLT535YtWwB48cUXzee8/vrr+c759ttv71nmt99+y48//sj06dM5dOgQjo6OPPvss2i1WvM5ffv25ezZs2zZsoV169axe/duBg8eXDw3KR6JK66n2Fr6N664nrJ0KI/U/oClTKs9iP0BSy0dSqEk20VzwWM/yXbRD13W4/wzUJj7vNc1j/O9iken0JuZREREMHv2bCIiIpg6dSo+Pj5s2LCBwMBAqlat+kBleHvn37/266+/pmzZsoSEhJiPOTg44Ofn90DlmUwmpkyZwqeffkrXrl0B+PPPP/H19WXVqlX06tWL8+fPs3HjRo4cOUK9evUA+Omnn+jYsSPff/89AQEBD1SXeHzMqf4eh0osBwVggvo3u9H37ERLh1XsvmjajkSH66CAM75b+bvcVD7bt9nSYT2wgyWWsaTKGEwKIwqTkpfOjaPRjR6FKmtB1dEcCVj1WP4MFOY+73XN/99roxsv0P/M5OK/EfHYUZhMJlNBL9q1axcdOnSgadOm7N69m/Pnz1OmTBm+/vprjh49yrJlywociE6nIyAggPfee4+PP/4YyOtCP3v2LCaTCT8/P5577jk+++wzHBwc7ljG5cuXKVu2LCdOnKBWrVrm4yEhIdSqVYupU6cya9Ys3n//fZKTk83vGwwG7OzsWLp0Kd27d79j2Tk5OeTk5Jhfp6WlUapUKSYvC8XewbnA9yuKxhXXU3zTuEveLzMhnkYm+PDAGkqn1rR0JE+l7Kx03utRjdTUVFxcXB5p3YXqQv/oo4/48ssv2bJlCxqNxny8devWHDx4sFCBrFq1ipSUFAYMGGA+1qdPH+bPn8+OHTsYPXo08+bN4+WXX75rGTExMQD4+vrmO+7r62t+LyYmBh8fn3zvq1QqPDw8zOfcycSJE3F1dTX/K1WqVEFvURSDS+6HJXmLp5sCItyOWDoKYQGF6kI/c+YMCxcuvO24j48PCQkJhQrkjz/+oEOHDvm6sP/7XLp69er4+/vTpk0bIiIiKFu2bKHqKazRo0fz3nvvmV/faoELyyqX3ABM5E/iJhhx8C+C0qtbKqxid8hvJYuqf3zbffc+M4GGMXfuRXqcpNjGMK5FG0wKo/mYwqRkzO5tuOU82COzW646n+GHRj0fy5+Bwtznva5J1cTf8V7LptQvrlsQj7FCJXA3Nzeio6MJDg7Od/zEiROUKFGiwOVdvXqVrVu3smLFinue17BhQwAuXbp0xwR+61l5bGws/v7+5uOxsbHmLnU/Pz/i4uLyXWcwGEhKSrrns3ZbW1tsbW0f6H7Eo1M6tSaNbrzAwf88A2904wUqpDS0dGjFqsWNvmwpM50Ex2vm+/bKDKTFjb6WDu2B+GaVoW/oRBZW+xijIhelyYY+oRPwzSpT4LIqpDR8bH8GCnOf97rGN6vMHe9Vus+fToVK4L169eLDDz9k6dKlKBQKjEYj+/btY+TIkfTr16/A5c2ePRsfHx86dep0z/NOnjwJkC85/1dwcDB+fn5s27bNnLDT0tI4dOgQQ4cOBaBx48akpKRw7Ngx6tatC8D27dsxGo3mPxCEdel/ZjIh1/oT4XaEsin1n5pfZl/s2cP+gKWc8t1EzdhnaXLzxftf9BhpGtWLKgkhxDtcwTurNO7aO/9//SAe55+Bwtznva55nO9VPFqFGsSm0+kYNmwYc+bMITc3F5VKRW5uLn369GHOnDnY2Ng8cFlGo5Hg4GB69+7N119/bT4eERHBwoUL6dixI56enpw+fZoRI0ZQsmRJdu3aZT6vUqVKTJw40Tz47JtvvuHrr79m7ty5BAcH89lnn3H69GnOnTuHnZ0dAB06dCA2Npbp06ej1+sZOHAg9erVu+NjgbtJS0vD1dVVBrEJIZ5oOdosZn45hPDQQ9jZO2E05uLk6skLgz6hesM2lg7P4iw5iK1QLXCNRsNvv/3GZ599RmhoKBkZGdSuXZvy5csXuKytW7dy7do1Xn311dvq2Lp1K1OmTCEzM5NSpUrxwgsv8Omnn+Y778KFC6Smpppfjxo1iszMTAYPHkxKSgrNmjVj48aN5uQNsGDBAoYPH06bNm1QKpW88MIL/PjjjwWOXQghnnQ52ZmcO74bAL0uB5VKRWZ6CusXTZUEbmGFaoELaYELIZ4emempbFk2ncM7V5Ecf5OAoAoMGPkDpcpWs3RoFmcVLfD/jsC+n8mTZVEBIYR4Ujg6u9Jt4Id0HTCKrIxUHJ3dLB2SoAAJ/MSJE/leHz9+HIPBQMWKFQG4ePEiNjY25oFhQgghniwKhUKS92PkgRP4jh07zF9PnjwZZ2dn5s6di7u7OwDJyckMHDiQ5s2bF32UQgghhMinUCuxTZo0iYkTJ5qTN4C7uztffvklkyZNKrLghBBCCHFnhUrgaWlpxMfH33Y8Pj6e9PT0hw5KCCGEEPdWqATevXt3Bg4cyIoVK4iKiiIqKorly5czaNAgnn/++aKOUQghhBD/p1DzwKdPn87IkSPp06cPer0+ryCVikGDBvHdd98VaYBCCCGEuF2hEriDgwPTpk3ju+++IyIiAoCyZcvi6OhYpMEJIYQQ4s4KlcBvcXR0pEaNGkUVixBCCCEeUKESeKtWrVAo7r4J8/bt2wsdkBBCCCHur1AJ/NZOX7fo9XpOnjxJaGgo/fv3L4q4hBBCCHEPhUrgP/zwwx2Pjx07loyMjIcKSAghhBD3V6hpZHfz8ssvM2vWrKIsUgghhBB3UKQJ/MCBA/m27RRCCCFE8ShUF/r/L9ZiMpmIjo7m6NGjfPbZZ0USmBBCCCHurlAJ3MXFJd8odKVSScWKFRk/fjzt2rUrsuCEEEIIcWeFSuBz5swp4jCEEEIIURCFegZepkwZEhMTbzuekpJCmTJlHjooIYQQQtxboRL4lStXyM3Nve14Tk4ON27ceOighBBCCHFvBepCX7NmjfnrTZs24erqan6dm5vLtm3bKF26dJEFJ4QQQog7K1AC79atGwAKheK2FdfUajWlS5dm0qRJRRacEEIIIe6sQAncaDQCEBwczJEjR/Dy8iqWoIQQQghxb4UahR4ZGVnUcQghhBCiAB44gf/4448MHjwYOzs7fvzxx3ue+/bbbz90YEIIIYS4O4XJZDI9yInBwcEcPXoUT09PgoOD716gQsHly5eLLMDHVVpaGq6urkxeFoq9g7OlwxFCCGEB2VnpvNejGqmpqbi4uDzSuh+4Bf7fbnPpQhdCCCEsq1DzwMePH09WVtZtx7Ozsxk/fvxDByWEEEKIeytUAh83btwd9/3Oyspi3LhxDx2UEEIIIe6tUAncZDLl28zkllOnTuHh4fHQQQkhhBDi3go0jczd3R2FQoFCoaBChQr5knhubi4ZGRm88cYbRR6kEEIIIfIrUAKfMmUKJpOJV199lXHjxuVbSlWj0VC6dGkaN25c5EGKopGcEM3Vi6dJSYwmOSGGwHLVqdu8k6XDEkIIq5GaFEdachxe/kEWn4FUoAR+a/nU4OBgmjRpglqtLpagRNFKS0lg5R8TOLhtOZA31c9kMmHv6CwJXAgh7iM318Cmv6axZ8MCUhJizMcdnd3Q2DlYLK5CrcQWEhJi/lqr1aLT6fK9/6jnwon8dNpsLp07woWT+9ixdg76HK35PTs7O/R6A7m5BkqWqWrBKIUQ4vFxOew4Gxb9SNTlc/gHVqBm43aUrVqfEqUrEXX5PGvn/bvPR4kS/mRmZpKSkkJmeorFYi5UAs/KymLUqFH89ddfd9wX/E5bjYril5IYy+zv3ubi6YN3fN+nRDB+pcoRVL4GgeWqU65ag0ccoRBCPBoZqUlkZaTiU+LuC4/pdVpOH9zK3k2LCDuxFzt7e6pVrcjF8FOcP7kXTCb8SpWjece+NH6mJyf3b0Sv03LjRjQAGls7HJzcSEmMuWsdxalQCfyDDz5gx44d/Prrr7zyyiv88ssv3LhxgxkzZvD1118XdYziAR3f+3e+5N2sQx9qNX6WoPI1cHRxv+PMASGEsGYmk4msjFTUaluuR4Syfc1sTh/cikGfA8Dw8XOpUjeE2KgIdDnZlCpbzfy7cM6k9zi+Zz0Agwe/wldffYxKlZcWExOTmDNnCb/9Np9lM8djMpmoUKEstrYazpw5D0DFmk0ZOGoq7/WoZoE7L2QCX7t2LX/++SctW7Zk4MCBNG/enHLlyhEUFMSCBQvo27dvUccpHkCrLgOoWKMxKBT4lyqH0sbG0iEJIcRD0WZlsG/zEjQaO7Iz0zEaczm2Zx0GXQ56fQ6pSXEY9Lo7XqtQKAkoXZEty2ewctbEvGNKJfVbdqV9zzdRq23N5/bv38ucvAE8PT14//2hvP/+UKKjY8nKymLKlJksXrIahVJJvRbP0abbaxgt2ONcqASelJREmTJlgLzn3UlJSQA0a9aMoUOHFl10okAUCgUlgitZOgwhhCgy1y+fY9nM/Ct8enp64OvrTUREFCobJaVKBnL16nWMxrytPVzdfWjSvhdZ6SlMH/8aGWnJADzzTAg7d+7n8PaV3Lh8npioCNQaDbVrVaNkSf+7xnDq1FmGDPmAtLQ06rV4ji79P8DbP4hzx3Yxe9K7xXbv91OoBF6mTBkiIyMJDAykUqVK/PXXXzRo0IC1a9fmm1omhBBCPIzy1Rrw9lcLWL/gByLOHQUgOTmVpKRkbu3FFRl5DY2dA7Z2DmizMsjOSmfDoh9RqdSoVEq02hxUag1btuwCwEal5saVMKpUqcjatfPx8HC7a/07duyl78tv4ulbihEfz6RCjcZEXT7H1I/7EHZyH87OlptK9sC7kf3XDz/8gI2NDW+//TZbt27lueeew2QyodfrmTx5Mu+8805xxPpYkd3IhBDi0cpITSI89BCRYSdIS4lHpbLF3skFJ2c3Vs35xnxemzbN0Wg0xMUlcOrUOQwGvXn6rIOjIw4Odrw6sDejR98/VzVv/hyR12L54o/d5GizWT3nGw5uW4GtrS1Dh/bjnXeGEBxc9/Hejey/RowYYf66bdu2hIWFcezYMby8vJg/f36RBSeEEOLxEn0tnOT4m1Sq3RylUkmONovtq/7AxkZNyy4D0Nja3fXa9NREcvV6nN08sVHlX0dEm53J1YunSIyNwtbOAXdvf3K02YQe2U7M9Uvk6vXE3rhMyTKVCT2yAwCVSo1CoUCv14FCAf+0R/fvP0p2dra5bJVazXOdn2HUqOFUqlT+ge7TaDQycuRYzp0Lp03317BzcGbSqJ7EXA+ne/cO/PzzRBwcHEhLSy/oR1hkCtUCv5tTp05Rp06dp2IambTAhRBPm3PHdvHzmAGYjEb6DJ9A8459iYo8z1fD2pvPKRFciZDO/andpD1Orh6YTCY2LvmZbav+IPOfZ9GOzm58u+gESuW/23FM/aQvYSf23lanWq3ByckBtVqNl5cHEZev4u/nw5o18yhVKgCAEyfO0Lv3ENIzdVSpE4Jep+X8yT3k6vV07Nian3/+GheXgv2eHjHiM+bMWUzVui2p07wj21fP5kbkeb7++lOGDOlvPi8tLZ2goDoWaYEXajMTIYQQT5fUpFjmTnoPG6USO3t7zh7bRUpiLAa9ju6DPjafdyMyjIU/jeaLoc/kvb4Sxpo/vyczLRmVKm9mTGZ6CukpCfnKb96hj/lrpdIGjUZNUFApzp7dxeXLR7hwYT/79q0jJvoMJ05sMydvgNq1qzNz5iSyMlKxUakoU6UuOm02P/74FX/++UuBkzdAREQkAGeP7WTelFFkpcbw5Zej8yVvS5MWeCFJC1wI8TTQ5WjZsPhHNv01DZPJhJ+/L6WDSnLw4DHzOQqlkj7Dv2L3+vlkZ6ZhMkHrrq9SL+Q5Yq5fIvTITi6dPUxCzNW897q9Svuew4C8edzzfhjJmcPbyNFmobG1x9s/CJXGlisXTqJRqwgL2/dASfj1199jxcq/zVO71Go1MTGh+Vr6DyorK4vPPvsGtVpF69bNadeu5R3Ps2QLXBJ4IUkCF0I86a5dOsNPn75CRloKzZo14K23XqNevVq4ubnw5pujWLJkNQDe/kF8Pn0rKrUGvU7LrnV/cmzPeq5cOAWYqN20A8079GXWt2+jzc7ARqXGoNfh7OqBi4cP18LPUK9eLZycHNl/4Ci6nBwcnFzw8C5BVOR5XnyxCzNnTrp3sP8ID7/MBx+M4+TJULp2bc/UqV8V4ydkRQn8+eefv+f7KSkp7Nq1SxK4EEJYqbNHd3Jk1xpyDXqO7loDwJIlv1GxYlmGDx/NoUMnyM3NxWjMRWPrQIngSmizM1Cp1PR//wf+/OF9roWH4u/vQ8uWTdm8eQcGkwZbOwey0xMICWmEVpuDvb0dFy5EkJmZxbPPtmLSpHFA3v4as2cvYvXqjRw/Hoper8PT04NLlw5Z8mO5K6tJ4AMHDnyg82bPnl3ogKyFJHAhxJMkMTaKvxf9yP7NS1CrNag1akoE+OLp6U65csEsWrQSEwpsbGzQ63JQKJTY2tnh7GRPbm4uSUkp2Du6kKPN5JefJ9KrV3cAAgJqUqtpJ47v+5sG9aqxZs2Dz1TKyMjgxx9/p1OnZ6hZ8/HcfMmSCbxA08iehsQshBBPohxtFpfPH2fr8hlUqNEYlcYWBQqq1Avh7NGd5tXOlEolXbs+S1paOsnJKVy+fJWDB49Rtko9Is4dw9PDjd9+m0lISBNz2aNHf8X06XPIzkxj4MDe5uQdERFJdnYWnn6l0Odo2bPnEA0btmfDhsX3XDzlFicnJz7++N3i+DiKTHa29v4nFZMifQb+NJEWuBDCWiydMY7tq2flO6ZQ3BrYZaJU2apcuxSa7321rT1Gg57cXIP5mEZjy5EjmwgMLJHvXG/vyhgMBlxcXIiMPIJSqWTz5p307/82Wm02/69Tp7bMn/9r0dycBZ07d5H27V8iPT3j8W+BCyGEsA5Go5E1c7/j1MHNxFy/ZD6+a9dq3Nxc8fPzRqvNYdiwD1m3bgv2ji5kZ6ahVNpgNBpp8syL7Fr3J5DXKq9Tpwbr1y9Ao9HcVtetcU+zZk1BqVQyduy3TJ36m/l9Pz9fxo79gJwcLdeu3aR//5eK+e4fDV9fLwYPfplJk6ZbpH5pgReStMCFEI/azasXWTXnG66Fn8Y7oDS9hn5x1w2MTh3cwvTxrxEQ4IednS1paelUrVqJVavm5juvRMlaZGVmAjBkSD/Gj/+Qnj1fZ8+eQxiN+Qckz5o1le7dO95W165d+wkODjK3zIOD65OSkoLG1pZRHwzj/ffvvsnVkSMniIqKNpf7yScTaNKkPp06PfPgH4wFmEwmFAqF9TwDF0II8ehlpqey5+/5rFvwAyobGxQKuBR6mMthx++YwE0mEzci8/asXrduPsHBQezefYBx474jOLg+lSqVRavNITExiazMTFzcvUlLjqdr1w5oNBpWrZpLVlYWS5as5tKlSDIzsyhRwv+OyRvI9zwcYNeuVcTFxVOvXq373tv69VvZunW3uezw8MvUqJE3YC0mJg5HRwecnZ0K8nE9Em3b9uDtt1+jVatmFotBErgQQjymDHodW1bMZNOSaehysvD09MDOzpaoqGiq1mtFw1bdb7sm16DnkwFNSE2KA2DevGXs23eYw0dOYGfvSKmyVTlx8gx6XY65hZ2WHI9CqUSr/XdAloODAwMH9i5U3IGBJfD0dCcmJg4/Px8AwsLCOXjwOJcuXebLL0cDeTt9vfXWIMaMGWm+9q+/fjd//fHHE4iKusGmTX+hUCgKFUtxadmyKba2tvc/sRhJF3ohSRe6EKIomUwmlv/2BUd2raFKnRb0fftr1vz5PVuWz6BatUr88ss3tGzZHZPJSI/XP6NVl4EobWxuKyc318BPn77ChVP7zceUNio69X6bti8MYcmvn7F/81/4B/gR0qIRNWpUoVGjelSvXhmVqmjadFptDi1bdiU6Oo6rV48D8OyzL5GQkMjly1e5ceM0Dg72NGrUnpIlA1i69I87JuirV69z82YsjRvXIzk55Z/u6lJFEuPDMBgM5s9KutCFEOIpYzKZyNFmodbYsmHRT2xY/BMmTJiMRg5tX0HsjcsolXkJ2sXFmRo1quDh6U5iQiKV67QgPTWR0CPbuXbpDP6B5WnUpgd2Dk7Y2Kh4d+IiALRZGZw9upOgCjXw8gvkcthx9m9eWuyjwO3sbJk3bxrXr99g795DZGRkMn36t6SkpBETE4eDgz2HD5+gVKmSTJ361V1b10FBpcwJe/Lk6SxbtpZTp3bccSBdcUpNTUOv1+Pl5cnRoycZMOBtNm/+i4AAv0cax/+TBC6EEI9YVOR5Fv/yKRHnjmJjoyI314CNjZI6tWswcGBvDAY97703FjvHvN696Ji87vCjRzZTpkw9ws8cZNPSX0mOv4mtnR26nBy2r5rFqB9W4+Tibq7HzsGJui06E30tnL9mjGP3+nk4OTkybdq3xX6P5cuXoXz5MoSEdKVNmxa0b9863/t6vR6NRo2fnzcAs2cvol69WlSvXvmO5X344Vt07twOjUZDVlY2ubm5BX42/uOPvzFt2mzOnt2DjY0NZ89eoEQJP9zcXO96jU6no1mz5+jatT1ffjkaFxdnXnnlRRwc7AtUd3GQLvRCki50IURhhJ3cx9SP83be8vX1JjY2HoBly2bRpk1zjEYjUVHRtG37AgkJyahUKq5cOYKDgwMffDCO33//dyWz55/vxB9/TGHbtj307PkaIZ37U6VuCBdPH+DapVB02kyys9KJuR6BjUpF3To1WLRoxgMtolJUcnNzyczMuudmJHq9noYN2/P66y8zdOhAEhISAfDy8rzt3PT0DN5/fwxpaeksXjwTg8FAixZdGTNmJM8+24qkpGQOHDhqHsU+dOgoypUL5v33h5KYmMSsWQv54IPhADRt2pmaNaswbdq3JCen8P330xg8+BVzq1+v16NWq9m37xBlywabn+f/l3ShCyHEU8I/sByOzm5kpqcQGxuPrb0jOdmZ9OjxKkqlEqPJBCYTKBQogBYtGuLg4MC33/5sTt5qjYamTepTsWI5Onfuy/HjZzAajezduIgda2ajUmvw8nTDwcEON0dbOr/2Mp999t49k6hWm4NWq8XNzZW4uASOHDlBq1bNCtXSPHPmHMOHj2bevF8IDCx5353E1Go1R45sxmDIWzTm559n8ddfqzl7dg8KhYLMzCwcHR0AcHJypF+/nua49HoDLVo0wsfHC4Dff5/PDz/MIDo6b2Ga8uXLUKKEPwCenh7m5A388weAHoCYmHg2btzOK6+8CMBXX/3AmTPnWbx4Jk2bNizwZ/AoSAu8kKQFLoQoLJPJRFLcDYzGXDx8ShJ9NYwzh3eQnppAws2rXDxzkBxtNnXrVmfZstm4ubkwbNiHLFy4gokTPyEhIZmff/6DnJwc7B2cqVynBacPbcGg1+Ht7UXp0qWwsVFiY2ODjY0Sk8lEQkIS0dGx2NraMmHCxzz/fOd8Mb3zzieULOnPBx8MZ82ajfTv/xaXLh3C09ODkSPHkpSUzKxZUwH46KMv6NKlPU2a1MdgMKBUKvNt2ZmWlk6FCo1wcXFm6dI/CryOeVJSMqdOnaVVq2ZkZWVTqVITvvxyNP369SQ0NIxdu/bz6qt9sLe3u+3a7GwtOp0OV9eHaw0vXrwSjUbD8893uud50gIXQoinyM2rF9m9fh4n928kIy3JvH81gNLGBhulEjc3Z5o0aUBo6Dm2b9/Hjh37UCiUjB07iZwcLZVrN6d110FcizjD6UNbMeh1AMTHJxAfn3DP+t97b8xtCbxatco4OeW1cp977lnCwvbj6ekBQPPmDc1rfut0Og4fPkGzZnmt0rVrN/PWW6M5e3Yvrq7OrFq1ARcXJxYunM6hQ8fJzMwq8Ofj4eGeb37199+PpX79WkDePPHvv5/GkCH9AHjzzVG4u7vx1VcfA2Bvb3fHxF5Qt9Zzf5xJC7yQpAUuhLiT6xGhhB7dSUL0VYxGI86unnj7B6FQKgmqUJPVc77l7NEdqFRqqlatgF6v5+LFy+buY7VajV5vAPL/anZx96ZllwEkx0fj5VeKclXr8937z6NQKikR4IevrzfOzk5cuXKNmNgEdDk5KJU2aGw1eHq4UbZsaSpVKk/Jkv507NiG4OCgIrnfS5ci2b59D4MH5yXU/v2HY2OjYtasKUVS/p0YjUZzi//33xfg7u7KCy90JiYmjjVrNjF48CvFVvf/s5rtRMW/JIELIW7R63PYvW4eN66EcXDbchQKBQ72DmRrs8k1/LsZSPUGbThzeBsADo6OGAwGdDk52Dk4Ua/Fc6BQYMzNpVzV+lSs1ZQrF06gUNiQnHCTuBtXSE+JJ+LcUVKT4szP0Tt0aMPChQ+/Fvf69VuoX7+2+VmyNVq6dA0ff/wVu3evwd/f95HUKQncCkkCF+LpZjKZOH1oK1cunODkgU3EXPt3wxCNxhZDbi7G3FycXNwoW6U+1Rq0JiCoIttXz0Kv0+Lo7IajiztlKtWhWoPWqNX/ruplNBpZv3AKYSf2cu3SGQx6Hfb2Dtjb2+Lv74NCoSA0NIxKtZoSdnIfHh7unDmzEwcHh9vizMzMQqlUYm9vx/bte9i16wBjxozM98w6JyeHBg3a07NnFz75ZESxfm7FLTU1HVdXZ3bvPsD27Xv55JN3UavVxVafPAMXQggrYjKZWD33Wzb9NQ3IW+nsFoVSSd2QLviVKoe9owt1m3fCwenfecZlKte5a7kXTx/g9MEtxN28Ym6p37Js2W80adIAgBs3oqlevSVhJ/cBeVOr7Oz+fe57a6ONnJwcatRoydtvv8Y77wwmKiqaU6fOmpP3wIFv8+yzrejVqzvr1y/E3//2aVLWxtU1r0F17twFLly4hEqlMk8He9JIAhdCiALa/ff8/yRvG3oOGUNWZhpB5apTpW5IgctLjL3Ost++4OT+TWg0ttiobl8idd++I+YEXqKEP8OHv8pPP+WtG75w4a/mpLxjx14+++wbdu1aha2tLZMmjaNOneoA9OvXk379egJ5Sd7d3c28GErJkv4Fjvtx9sYbAxgypD/vvPMJmZlZTJjwCb6+3pYOq0hJAhdCiALasWoWAFXrhdB94Md33dLzQWSkJTNuSBv0uhwGDerL+PGjmDt3CUuXrqVmzapUrVqRVq2aUrZscL7rxo//ELVazbJla/Dz+/d5r5eXJ7VqVUWrzcHR0YFu3TrcsV6FQsHkyeMLHbc1UCgUPP98J7TaHKt+tn838gy8kOQZuBBPr4SYa6g1trh6PPxAqZjrlxg3pA0APj5eJCalmAe+1a1bk61blz1QObm5uSgUinzPtkVeT0Nubm6RbdTy/yz5DFy+00IIUUBefoFFkrwB1BpbnN3yWodxcQn5Rq03blyPo0dPMmPGXNat23zbtUajEaPRCMBrr42gZ8/Xza8FxMcn0rBhe5YvX2/pUIqFdKELIYSFpKUksHrud2SkJqJQKLF3sCdHm0Nu7q0lRf/g55//MJ8fEXHEvI754cMn6NHjVdavX0D16lXo0+cFHBzspAX+H56e7rzwQme0Wi3h4ZcpX76MpUMqUvKdFkIICzh/fA9jX2/J8T3r6datAydPbudG1Emiok7w1luvmdfvvqVHj+fybUJSu3Y13n9/KN7eea33Z54JeWzX7LYUpVLJhx++xeTJ01m0aKWlwyly8gy8kOQZuBCisDYu+YXVc/O29PT398XFxRkbGxvUahVqtRqNRo1Go8Hd3ZWNG3egzcnh5IntBAaWuK2szz77miZN6tOhQ5tHfRtW47ff5vP88x3NS8MWJZkHLoQQT7jsrHT0OVrOHt3Jnr8XmI9HR8cSHR171+vatGnOsmWz7viewWAgKuomN2/GFHm8T4q4uARGjRpHuXKl862v/iSQBC6EEMVIl6Nl5eyJ7Fr3J6Z/BpgplEoUCgWOTk54erjh6emGSqXGxkaJSmWDUmmDjY0NtrYavvpq9F3LVqlU/PDDF7i5ud71nKeds7MTe/euo0qVCpYOpchJAhdCiCJmMpm4evEU547t4vDO1cTdiKRlyyZUrFiOXr26Ub16ZbRa7R2XPi2Is2cv0LbtC6xcOZdGjeoWUfRPFnt7O6pWrYjJZGLChCm8/PKLd3wUYY0kgQshRBHbvnoWy2bmLZKiVCqZNWvKbQuqFDZ55+bmcvToKRo2rEPlyuUZO/YDKlYs+9AxP+lu3oxh4cIV1KlT44lJ4DIKXQghiohep+XUwS2smzfZfMxoNPL5598UWR0LF66gc+e+3LwZg1KpZMiQ/ri7uxVZ+U+qEiX8OXp0Cy1bNrV0KEVGWuBCCPGQTu7fxF8zxpIcfxMAFxcXygRXICYmjqwsLS+80Omhys/JyeHkybM0bFiHnj27ULlyeQIC/Ioi9KfK2rWb+O67X9i1azX29nb3v+AxJwlcCCEeQuiRHcz4cjCenh7069eTZs0a8sILnYt0QZUff/yNX36ZzZkzu3B2dqJevVpFVvbTpEyZIF555cUnInmDJHAhhCi02KjL/DJmAAC//TapyKcp5ebmkpycwtChA+nUqZ155zBRMOfOXeTq1et06NCGunVrWjqcIiMJXAghCini3FEAtm9fQe3a1e94TlpaOsnJKQQFlSpw+X37DkWhULBo0YwnchrUozJ37mLCwi49cYvdSAIXQohCKlmmMgBTp86kSpWKXLwYQXJyKrGxccTExJGYmGw+98UXuzBz5qQHKlerzSElJZW33noNOzvbYon9aTJs2Kv4+DxZe4GDJHAhhLiv0CM7mPXt2zg4utB/5A+Uq1qftOQ4crKzAFi9eiOrV2+8ewEKBV275p9Glpqahk6nx9vbk4sXI9i4cTtvvfXaP3tYD8DPz5dZs6YU4109PQIDSwLw1Vc/UKZMEL17P2/hiIqGJHAhhLiHnWvnsuTXzwHIzkzjesRZJn/YE/5vGwm1Wo2npzu2thqMRhNOTg4MHtyf55575o5rcH/00ZdkZ2czZ85PhIWFM3nydPr3fwlXVxfGjPkAV1fZY6EomUwmjh8/TdWqlSwdSpGRzUwKSTYzEeLp8Ou41zh9aAsKpdK8FOp/ff75SEaMGFLgcrdv30NsbPwT0xq0NgkJiXh5eT50OZbczEQWchFCiHsY/Ol0girUuGPyBrh8+Uqhym3durkk70LYuHE77du/xPTpczhx4gzt27/EG2+MJCMj467XXLhwiXXrNqPT6QD4/fcFNGrUgcuXr2D85/v6559L6Nr1FWJj4x/JfRQFaYEXkrTAhXgymUwmFApFvmOXzh7h8PaVnNi3AaNBy6ZNS6hSpQIGgwGNRlOoenJzc5k/fxmVK1egQYPaRRH6E+/o0ZN07NgXvV5nPqaxtUeXo0WlVlGxQhmCgwPZtesg2docDAZ9vkcdSqUNdvZ26HR6DHo9YMLGRoWNygZdTg4Af/zxA88/3/mBY5LtRIUQwsKyMlKZPv51wkMP4VeqLG+OnY23fxCpSXHsWDOb43vW4+7hzsIls6hWLe85amGTN8Dly1f59tufmDHjwUamP+0yMjIYMeIz1Lb2fDZ9G5dCD2EyGandtAMpiTHsWDOXM4e2cvbsFqrUaUFw5bo4ubijUmuws3fC2c2TyLATJMZF4eTigbObF47OriTEXCctOZ7k+JucObyNsWO/Z9q02dSqVZ0hQ/pRvnwZS9/6XUkLvJCkBS7Ek+X43r/5bcJQ8+svZ+9Fl6Plh496kZWRQv9+PfnuuzFFusKawWBApZJ21L0sW7aWSZN+JSwsHICq9VoyfPzcO55rMpnQ52jR2Nnft9wNi38iIeYar7z7nfna7atnsW/jIrIz00hJzNuj3cnJiZo1q/DVVx9Ts2bV28qRFrgQQlhYzUbP0O+9SWSkJlK9YVscnd354aNn0edksn3bcmrUqFJkdZlMJrKysnF0fLjtRJ9ERqMRrVaLTmdgypTpTJ36m/m98tUa8sJrn9z1WoVC8UDJG8DR2R2N3b+fv0KhoE23QbTpNgiAlMRYzh/fTejRHRw9soPWbV7g++/GMHBg70LeWdGTBC6EEICNSk3jtj3Mrxf98inJ8TdZsWL2bck7IiKSuXP/YtiwV/H1LfgCIVFR0dSq1YqVK+fQokXjh479SXHq1Fk6dX6ZzP8bkNbzjbHUb9kNJxf3IqurRaeX7/m+m6cvjZ95kcbPvMjV8DN8/U5nrl69XmT1FwVJ4EIIcQfhZw6iVCr46affsbe3p06d6syd+xdTpswgKipv1zF7eztGj36nwGU7Ozvyww9fmJ+lCwgLC6dly27m190HjqZU2ar4BZbD3cvfcoEBMdfzuu+HDOlv0Tj+nyRwIYT4D4NeR3joYVKSYjEYctm2bQ/bt+8FhSLfVLKWLZvy4YdvFbj8c+cuUqVKBfr161mUYVu9ChXKUqKEPzduRAPQrENvHJxcLRxVnisXTqJSq/H397V0KPlIAhdCPPUy01MIPbKd04e2EnpkBzptFmq1BhuVilyDAZPJZJ6OVLZsaaZN+7ZQU78OHDhKx4692bBhMY0a1S3q27BqSqWSTz8dwdChoyhdoeYjT95xNyLZtPRXoq9ewEalJik+mucHfUzd5p2Ij76KQa/n008n8OWXHz/SuO5FErgQ4omXEHON0CPbuXDqADnaTGo1bk/D1s9z8fQBNi+fTsTZo5hMRhydnGjUoAYDBvSma9f2JCYmMXjwSLKzs/H09GD48EE0blyv0HE0alSXBQum07BhnSK8O+sXGXmVV14ZxrlzFwksV43XRv/yyGPYvGw6+zcvyXds9dxvqdOsI26eeV34a9ZseqwSuEwjKySZRiaEdTi5fxMzvhx823Fv/yDio6/i6ORI5UrlmTjxE+rVq1Xk9WdlZfPWW6MZOLA3zZo1LPLyrd2CBct4993PsVGp6dDrLVp3H4Ra/eh3YAs7uZeNS34h9kYktrb2KG1s0Njao7a151LoIQDWr19AkyYN8l0nS6kKIUSx+beN4urhg4t73qjx+OirAGRmZHL06Ek+/XRisdSu0ajJydGRkZFZLOVbuw8+GI/BoGfIZ7/xbM83LZK8jUYj21b8zoVT+0lJjCEzNQ6NQotNbippcRGo1RqqVat8W/K2NGmBF5K0wIWwPiaTiajLZ9mw+BdO7PvbfFyhULBz56oineu9du0mypYNpkqVCkVW5pNo+vQ5jB03CRuVLV/M2vPIn31nZaTy67hBXDp7hJo1qzJu3ChCQpo88PXSAhdCiGKUm2tg5ayJjOpdhwlvdeLskW1UrFiObt06sGnTXyQlXSzS5G0wGPjmm59ZtGgFqalpRVbuk+iNNwawaeNitFnp/DVjLMbc3EdWd3ZWOj9+8jKRYSeYMOFjdu5cVaDkbWkyiE0I8UQyGo1cOnuYs0d3cnL/RuJvXqFGjSo8+2wfRowYgp2dXbHVrVKpWLt2HkqlkvLlG/Hrr9/ywgsPvkHG06Zmzaq88sqLzJ27hJhrl3jry/k4OhdfS1yvz2H/5r/YsPBH0tMSmfbLN7z0Utdiq6+4SAIXQjxxUhJjmf3d21w8fRCVSk3Jkv58991YBg3q88hicHd3IzMzi2nTvnmoketPA6PRSJUqFVEoFFwNP402K73YEnhs1GWmftyH5IRoAgNLMvuP32nVqlmx1FXcJIELIZ4opw9tZc73I9Drsvn00/d4553XLbZhiKOjAz16PAfAyJFj0WjUTJhw97W8nzYGg4Fx477njz8Wkp2djYd3AF36f4Cnb8kirys318D+zX+xbv5kdNnpLFo0g/btWxd5PY+SJHAhxBPhavgZtq38jSM7V+Pr683atUsfq60g69SpTnp6xv1PfEqcO3eRZ9r1JCszk0q1mvJsz2FUqNG4SHd7uyUy7ATzp37IzasX8Pb2YsnKOU/EQjqSwIUQVs2g1zFt7EDOn9iLWqPhxRe7MH36d3dNBFqtlj//XMrevQcpVaoEQ4b0JzCwRIHqzMrKJiEhkcDAkmRkZDJgwFsMHz6Ili2bcu7cRTZu3M6bbw7Ezs6Wa9du4OrqTJ8+LxTF7T4R0tLSad++F0qVLe99M5vy1YtnfnxGWjLnju1i4U+jsbFR8Msv39Cnz/PFUpclSAIXQlitXIOeJb9+TtjJfbzxxgDGjfsAjUZzx3N37z7ABx+MJSLiGrm5BvPxRYtWcvnykQLVO2DAW2Rna1m7dj6Ojg44OzuZ6714MYJffpnF22+/BsCHH44nNTWNdesWFEvr0toYjUZatuxGVraW97+dR3Clgi9Jez8GvY5d6/5k3fzJaLMz0Whs2b13LWXLBhd5XZYk88ALSeaBC1E8TCYTJpPpnslOm53Jmj+/5+DWZWRnptG79/NMm/bNPcv196+OVqtFoVTm25Rk6tQv6dfvpfvGtWnTDkqXLkXFiuW4eDECo9FIpUrl73vdhQuXsLXVULp04H3PfdIdPnyCDz4Yy+nT5xgw8gcati661rDJZOLCqf0c2bmK0we3kpGWRIUK5Zg583uqV69cbH88WXIeuLTAhRAWZTKZiLsRyeXzx4i+Fs75E3uIv3mFtycsoEyl29cMN+bmMnnUi9yIPE/t2tV5993BdO7c7q7l63Q6Zs9ehKOjA1qtloDACty4EgaAQql8oORtMBgYO/ZbWrduzldffUyFCmUf+P4qViz3wOc+iSIiInn11Xc5H3YJvU6HjUpN91dHF2nyDj9ziBV/fMWVi6fQ2NpRuVJZRo784p4/F08CSeBCCIvR5Wh5p3vFO763fdUs6AYBQRWxs3c0H79waj/XI84CcOr0OTw83G671mAwMGfOYubMWczZsxfyvXfjShg2NipKly553y09z569gLOzI4GBJVmzZj5eXh4Fu8GnUGhoGH//vZUjR05w5kwYcXHxqDS2tOj4CuWrN6JctQY4Ors9dD0HtiwlPPQQUZfPcT3iLI6Ojnz++Ujeeef1p+ZRhXShF5J0oQvx8Iy5uSz46SP2b/4LR2c3MtNT7nhe7Sbt8Q4ozeXzx0iMjSI5ITrf+++//ybBwaXYvn0vJ0+Gcu36TQx6PUobFcZ/nneXKRNIo0b1qFatEj17dsXT897J+MSJM7z55iiaNGnApEnjiuR+n0Rpaem4uDhz4cIlevceQmTkNQDUtnaUKlOVus07U79VV5xdPYukPqPRyJWLJ/nuve4ABAWVolOntowbN8oi0wUt2YUuCbyQJIELUXBpKQmcPbqTa+GnycpIpf1Lw0hOiOGnT18BoEQJf8aO/YC2bUMAmDp1BlOmzATyurtLlvDD1dWFGjWq4OPjZX7vTgLLVcenRDBHd63B0dGBqKhTBYo1NzeXzMws7Oxs7zow7mmVkpLGCy8M4Ny5i2i1OWhsbTEZjag09nTp9z7V6rfGw6dkkbeED21bwZxJIwBwcHBg9+5VFh+YJs/AhRBPhUkjexB3M9L8OiUhhgEfTMHJxYOMtCRu3Ijmm29/Ni9+MnBgH/78cylqtZoZM767bZ3qgQP70K7di8TGxgOgUqlxdHJAoVAQf/My0VcvoFAoHmiw2X+FhYWzePEqxowZiUKheMi7frJkZWXRtu0LXLkSZR7NX6VuSzJSk+gxeAxB5asXW92bl/0KgJubC2fO7MLJyanY6rIGksCFEI9Mp77vsnTmOLLSU7G1d0Rj58B37z9PVmaq+RyVzb+/lgIDSxARcfiu5QUGluDo0c28994Y2rVraU78D+vMmfNs27abESPewNX16e5hS0lJo2XLrri4OOPn58Pu3QfJydHRZ/hXNOvQB11ONrZ2DoUqOzz0ML+MGYBBl0PzTi/j7RdEo7YvkJGaRELMNbIz0/EtVZbkuJtERZ7j5tWLAHz99edPffIG6UIvNOlCF+LB6fU5ZGemA2DQ5eDg7M7ViyeZMro3gYElqFWrGv7+vpQvX5b+/XtabOnT/9Lr9ajVakuHYTGnT5+jR49BxMcnmI/Z2KioWKsp3V8dTcngyg9dx/WIs0x4q2O+YwGlK3LzyoXbzlUqbfDz8+b55zsxbtyox2agmnShCyGeOJnpqVy9eJI9GxZw6uCWfHOv/yslJZ2qVSsxatTwRxzhvW3ZsovNm3cyefL4xyZZPAqbN+/kr79Ws3nzTtIzMmnT/TW8/EpRo+EzuHsHFOkjhVJlq/Let0v5a/pYTMZc6oY8x5q53wHw+ecjeeaZEPbtO0S9erWoXbv6U/V9eBCSwIUQRSIjNYkda+cQef44UZHnSU/Ja7nZ2dvTsUNr87rkDg4O3LwZw6FDxzh/Ppy0tDQmTpzK1Km/sWjRdFq0aGzJ2zAzGo0kJiah0+mxs7O1dDiPREpKGi+99Lr59Ruf/07NRs8Ua53lqzXgk5//xmQy8cc3b5mPnzlzjhEjhlCtWqVird+aSQIXQjy0+OhrTBs7kJioCHy8PalcIZAaNdoTEtKYjh3b3rXlNGHCFBYsWM7NmzFkZWXRtWs/3nnndcaOHfWI7+B2nTu3o1OnZ57oQWyLF6/k3Xc/o3nzhjRr1pCcHF2+96vVa/lI4oi+dpFFP39KeOghQkIas2LFHGltPwBJ4EKIhxJ3I5KxQ1pjMhrp2LEtOTk5JCencPnyVSpWLMvixau4fPkq4eERHD58gqSkZHQ6PS4uzpw6tZOPP36XyMirjBo1nq1bd3Pt2g1L3xIAQ4aMpGbNqrz55kBLh1JskpNTyMnJYevW3WzduhsA/8DyVK7Tgvotu2KjKv4xAAe2LGX+1A9Rq1V8/vlIRowYUux1PilkEFshySA28bQy5uaSnHCTXIOBrIxU5v/4ETciz+c7R2mjwmjMhfv8epk8eTwDB/YuznALbd68pSQkJD3RCeXWACyArv1H4eETQL0WXVDa2DyS+jPSkvno5foEBQawY8dKXFys73epDGITQliFuBuRTP6wJ6lJcfc879bqZwqFgv+2EV5+uQcvvdQVNzc3vL098fX1LtZ4H8Yrr7xo6RCKXHR0LL6+3iiVSrRaLUeOnDS/5+rhQ4NW3R9pPPE3r5Br0DNkSH+rTN6WJglcCHFPyQnRKJVKrlw4xfQv8gY4+fn5kJ6eSWZmJgCrV/9JcHAQsbFxXLsWxfXrN7l0KZLduw9y7VoUAGq1io8/fhd/f1+L3UtBGI1G5s1bSvv2rR/rPzQe1OLFKxn65oeoVWpKly5JRMQVjEYj9o7OhHTuR60mzz7ymALLVcPewZlVq/5m8OBXHnn91k4SuLinrIxU0lMS8Q4ojVKpRK/PYcvS6TRt3wtXD+v4RSwKxqDXcerAZg5uX871S2dJTYq97ZyYmPwtcI1GQ6lSAZQqFUC9erUeUaTFKy0tnS+/nIxCobjvpifWYMuWXWAyUbpSHZLibtCk3UtUrNmUCjUb4+LmZZGYbFRqqtZryaG962natDNr186/4+Y04s7kGXghPenPwGOuX2Lyhz1JT0kEwMXdi+oN2uLuHcC6+ZOBRzPFRDxaOm02M74czLnju3F2dqZChWAaNarL9es3+fvv7RgMehQKJYGBJahcuRw1alRl6NCBuLk92md/j0pycgru7m6WDqNIREfHUrNWa0BBrSbteWnoFzg6u1o6LDLTU9mxehbrF06hW7cOzJ79o6VDKhB5Bi4eG5fDjhN5/jjJCdEY9DoUCgVvv/0aM2fOY9+mxfnOjbp8ThL4EyAq8jxLpn2OXqclLSWB5IRo3ntvKJ999p6lQ7M4Ozs7srKycXCwt3QoD83f35cNfy+kbdseHNm5mqr1WhbpntyF5ejsSofeb7F99SzzTmbiwUgCF/msnTeJsBN7/3NEwfTpf5KTk2M+8vygTyhfrQElyjz8UorC8tb+OYkrF47j4OiAUqFkyeKZtGvX0tJhPRZWr97I++9/zrlz+56INdF//XUOCoWC979dStmq9S0djtmpA5vIzkzj7bdfs3QoVkUSuMhn4Mgp/L3oR07u30hqUhzV6rci/uYVYm9cBqB0hVq06f6aLLLwhMjNNRBzPRy1Wk3LkMbodDqmTZvN6tUbqFixHE2a1KdWrWqPxdrkltCkSX2+/37sE5G8U1LSWLNmM7WatH+skjfA8T1/o1AocXAo3KYoT6un8/9KcVcu7t70evMLer35hfnYsT3r+X3imwC4efmRlZGKk4u7pUIURWTfpsUs+vkT85aQa9Zsuuu5dvb2eHq44e7uil5vQKvVEhMTZ1656/nnO/HHH1MeRdiPVGBgCQIDLd/NXBQmTZqGXq/juVcev0cjzTv2JfLCCfr2Hcoff+Q9Cxf3Jwlc3Nd/9/c9uX8jwZVq067HGxaMSBSFfZuWoFIpadOmJTqdHnt7O+zt7dDr9Rw/foYbN6LN52qzs7lxIzvfsf/y8vJ4VGE/MkeOnGDXrv28885gq96VzGAwsHz5eqKibgJga//4bcNZsWYTPvt1C9+//zxD3viAxo3rPRFT94qbJHBxX0nxN/ls+lZiroVz7tgu6oV0sXRIogh4+wdy/dIZypQpzYcfvnXHkeTXr99k06btHDhwlOxsLY6ODjg7O+Hq6oyPjxfdunW0mnndBXXo0HG2bdvL+++/idFoRKFQWOW66CtX/s0bb4w0v178y6e8OXaWBSO6Mzt7RwZ/Mp3xQ59h4MC3+fvvRZYO6bEn08gK6UmfRvZfQzsGAeDlH8ibY/7AP7CChSMSRSElIYZfxw3iWkQoHh7uzJ499bHZCexxoNfryczMws3NleXL1zF58nS2bVtudTuTVarcjNiYWJ57+T0MBj1lq9Sj6iPapKSgMtNTGPlSTQBCQ3dTooS/hSO6P0tOI5ORSOK+bnWXJ0RfY9e6eRaORhQVNy8/egz+nPLVGpKUlEzXrv2oU6dNkdaxbdseFi5cUaRlPipqtRo3t7x50qVLl6Jdu5bm5L15806spe2TkZFJlTot6NjnHbr0G/nYJm+Ay+ePmb+WLvT7kwQu7qvbwI/o994kAI7vXW/haERR+nnMAMJDD2FnlzfPuXv3TkVW9osvDqJHj1d5663RRVampdStW5MxY/K6oc+evcBLL73Orl37LRzVg1HZKAkPPWzpMB6Ii7sPAO3atXxqZz4UhHxC4r4UCgWN2/agdtMO5GRnWjocUURycw3Y2TsSHBTAwYMbCnz9+vVbeXXQu1StUoGNGxej0WjM7507d9G8PWWdOjVYs2YTnTs/80RMP6xatSLbt6+gdu3q9z/5MWBjo0Sv05KaFIerh4+lw7knm3+Sdvv2rS0ciXWQBC4emJ29I3b2jpYOQxSBrSt+Y92CH3B0duPChUsEB9enc+e29OnzAqmpaaxZs4nz5y+SlZVFXFwiaWnpqFQ2+Ph4ExDgh5eXBxs2bsdkNHLixBl8fasCeZucZGdnk5qabq7r6NGT9O8/nCFD+vH1159Z6paLzKFDx0lLS7//iY+JZs0asmbNJpwttN55QYSd2INCoaBt2xBLh2IVJIEL8RTavX4eOdmZ5h6VlJQU5s9fxvz5y+56jU5nJCrqpnk60p38/yYnSqUNKpWSsmVL8+mnI4omeAtYufJvPD3dadGiMTNmzCU1NY1nnrGOJFO2bDAAacnxuHk+vjMGLp4+wKo531KmTGlKlQqwdDhWQRK4EE+h5/qN5FLoYUKPbCc1MQZHR0fS09PNA7NcXV0IDCxBUFBJKlQoR4UKZbl5M4bw8Mtcv34Dk8lExYrlGDSoL1WqVECr1TJ8+GiWL18HwIQJH9OzZ1c8PZ+M+eFTpsygTZsWtGjRmD/+mEJWVralQ3pg9evXAiD66sXHOoHv3/wXapUN27cvt3QoVkMSuBD/JzUplgunDhBcqQ7e/oGWDqdY1A/pQv2QLvzwUS9sbQycO7f3/hfdg52dHTNnTiIi4grxCUn06PHcE5O8Af7663fzqGiFQoGjo/Us+VmlSgUUCiUXTu2ncp3mlg7nntQaDS4uT/a03KJk/SNKhChC547t4vPXQpj93Tv88c1wS4dT7FQqNTqd4bbjRqOxwGUplUp27FhJ6JldeHs//s9bH9T69VuoX78d167dsHQohRIUVIqSJf05uO3uj0ceB6XKViUjI4PExCRLh2I1JIGLp5pepyUmKgJtdgZfv/McP33WD502r3u0UZsXLBxd8atYqylJScn8/PMfAISGhtG4cUc8PStSunRdC0f3eGjRojGffPIu/v6P9wjuezEajaQmxbFu/g+sXziVY7vXPVbz2C+eOYhaY4vJaGTfviPm44X5Q/JpIiuxFdLTtBLbk8hoNHL64GYW/PgRGWnJ1Gj0DKcPbgGgTrNONGjd/anY61ynzeanz/tx6Q7zhAuyQUl8fAKXLl0hNTWNdu1aPhHTxe4kOTkFd3c3S4dRYLt3H2DIkA9ISMhr3RoMemo1aU+uQU/EuaNUq9+KASOnWGSpWINex1tdy5tfq1QqKlUqR3j4FfR6Hb/9Nolu3To+tj9TshKbEI/Y0V2rmfHlEDLSkgE4c3gbrh55A3ye7Tn0qUjeqUmxbFjyEy079893vGXLppw+veuuyTsiIpJPP51ASEhXKlZsjLt7eSpUaEzHjr3p3XsIP/30+yOI/tHbt+8QDRo8S2ZmlqVDKbAWLRpz/vxe4uPPER9/jgYN6nD64BauhB0mOMiPwztWMW3sQFKTYh+qntxcAznagn0+Nio1Hj4lza99S5YjIvIGVRu0wWQyMWjQCPz9axAS0pVffpn12HWxd+zYx2J1Swu8kKQFbt0y0pJZv3AKe9bPR6VSotPpzV2KP60OR6XW3KcE67dy9tdsXvordg7OaLPSmTfvFzp3bnfX83fvPsAbQ0cRfTPmju8rFAqqVKnA9u0r8i3q8qSIjY1n9uxFDBnSzypb4ffy5Zc/MHXqbyhsbKjX/DlKlq1K47YvYO/44C3K+OirjHmtJSaTkXohXXD38qdllwF4eN9/SlhKYiyHtq9AoVDQoGU33Lz8iL4Wzvg32prPUWvs0Ou0ACiUSkxGI0GlAzl5YlvBb/ghXL16nblz/6Jbtw5cv36Tl18eCmCRFrgk8EKSBG69Is4d5fuR/z7f9vLy5MKF/fj5VSOoQm3e/26pBaN7dDYvnc7K2RPNr6tXr8zOnavu2FU5fvwkfvhhuvm1Wq0mNzcXo9GIWq3mxIltVrHxhLi7iIhIhgz5gLALEWRlZmLn4ETft76mbovO97wuMz2VlbMmsG/TYvMxR0fHf3oqTPR5ayLNOxS8lWrQ6/jzhw84snMVLu7epCXH3/G85ORw4uMTOH8+vFg240lJSeODD8Zy6tRZUlPTiI9PwmT699m8Sm2LQZ9jkQQu08jEU8fByTXf68TEZJo164xer6dFp5ctFNWjV7lOM1bOzvu6SbuX2L95CQEBNfH19SQrKxtnZyd27lxFjx6DOHLkhPm6ESPe4NNPR+DpWRGAH3/86olP3hkZmcyY8Se9e3cnIMDP0uEUi7Jlg9m6NW+k+qlTZ2nTtger//yOirWa4uTifsdrjEYjI1+qke9YjRpVSEpKJTMzb5GgXIPugWPISEtm7qT3MOhzuHrpDNkZabTqMpC2Lwxm3g8jCTu5DwBbe0dysjPRaGwxGo3UrNWG7Kws3nnndcaOHVWY2ycrK4vffpvPwYNHuXjxMlptDmlpGWRmZYHJRIngyngEBNC4QxMq1W7G/k1LKF2xFtUbtmFU7zqFqvNhSQu8kKQFbt2MRiOXzx8j7MRejuxaQ9yNy9g5OPP9kpPY2Dw9f9dGhp3gRmQY+zcvJvLCydver1WrGidPhuLk6klGaiLt27di0aKZALz55ockJCTy119P5jPv/4qKiqZZs85s3LiYSpXK3/+CJ0CDBu0JD48AoERwJQZ9+DP+gfnv3WQyMfPLIVy9dIaM1CT0Oq25e7tK3RBadRlIuWoNmPHF64Sd3MczLwyhS7+Rd31EderAZqZ/8Xq+Y8PGzaFa/VYYc3PZsnwGkWEnOHVwMwBt27bg6tUowsMvo7RRgclIlSoV6Nv3Bd54Y8Ad60hMTOLAgaOcPx9OZOQ1bt6Mwd/fhw0bdpCamopKbYt/YDkcnNxw8/KjZHBlajRsi0+J4DuWl52Vzns9qkkXujWRBP5k0OVoead7XkuyUdse9P9n17WnSUZaMh/0qgVA2bKlUSgUXLlyHYMh//zwihXLsX37chwcrGcRk4eh0+lYvnwdHTu2xdXVhRs3op/4nob/t3fvQVat2siCBcuxdXDlvW//wieg9B3Pjb52kdOHtqFW22Ln4EStJs/i4ORKTFQE4wb/uznJV3MP3PW5uMlkIvLCCXINeRvtePsHYefgdNt5f3zzFkd3rQHAxd2bls/1p1WXgayZN4kdq2cBCgYO7MVfS9ei0+mx/2cb2OxsLXp9/h6BW2NAPLwDeGXEd1So0aRAI94lgVshSeBPhvDQw0we9SIA3yw4iov707cH8dmjO/n58/50796R6dO/o0OHXhw/fsbc6lap1LRv35I6dWpy9OhJRowYQr16tSwddrFJSUnFzc2V9PQMgoLqMHv2VLp27WDpsCzq8OETdO78MhVqNmX4+DkFvt5kMpEUF4VKrTHP9ngYJpMJY64Bk8mEjUqNQqHgavgZvn4n//P6KnVDKFWmKqnJcZiMRty9A/ApEYy3fxAe3gG4uHujUmvI0WahsbUv1DQ6Sybwp6evUIg7KFe1Pq9/PA0Xd5+nMnkDrF8wBRcXZ9zd3QgMrEtOjpY23V/DJyCYRb98gsGgZ926LaxblzdPPiUllfXrF1o46vu7cOESvXoN5vfff6Bu3ZoPdM2RIyd4/vmB/P33QqpXr8KZM7ueulb33Wg0Ki6c3IvJZCpwolMoFHj6liqyWBQKBTYqdb5jJ/b+bf66Ys0mdBv4EaUrPNj33dbOOnuVJIGLp5pCoaBOs06WDsOi0lISSEtLZ9asf5Py9lWz8PIrme88NzcX+vd/iVGjrGOJ2V69BnPlynVmzVr4wAm8QoWyfPjhW1SpkvdY5WlP3qdPn6NP36HciLqJ2taODr3ftshiLw+iS/8PaPlcf5zdvYptHIs2K4P9m/9CY2dPrkGPQqHE0dmtWOp6EJLAhXjKteo6kGUzxwPg4ODIs8+GoFQquXgxAmf7IL7/fiytWjUD8vb2fu65lzlzJoxJk8bxyisvWjL0u/rjj4VcvRoFwL59t68y9/8yMjI5dSqUGjWqMnz4oOIO77F3+vQ5Ro4cy9Gjp9DY2dPrzS9o2OYF7OwdLR3aXSmVSty8Hn6GQK5Bz/XL50hJiP7P0bw/Wo7sXM3xvevzjigU5D2AttxTaEngQjzlWj03gB2rZ6MwZnEhbP8dB/Bs27aHYcM+JDb237m4585dfJRhPrCMjAxGjhxjfm0w5N73mvT0DLp1G8BPP02kV69uxRjd48lgMDB37l8cP36KvzdsJyU5xfxeTnbWY5+8Cys26jJbls/gWvhpnN29ycnO5Gr4aQz6u099c3FxYf/+dZQo4Y9Op+PAgaN069b/rucXJ0ngwmoZc3PZue5PDLocmnXojYOTKyaTifTUREzGXFzcfR7b7r7HSVTkORJjrzNx4ifm5H3jRjTz5y9jx469nD17kYyMDPP55coFs3z5bAIDS1gq5HtycnJCrVbj6ulPidKVOH98J+Hhlylfvsxdr/H392X//vWUK3fnqUJPssmTf+WLLybf8T0blZoxM7Y9ccl75eyv2bthIVkZaahUKgIDA0iMSsDGRskzbZvTvn0rGjeuj1KpwGj8t4WtVCoIDg4y/3+i0WioXbu6pW5DEriwXknxN1g6YywAK2dPpPurH7Ny1gTz+72HffVULcxSWJfOHkWhUNKnzwsYjUb69RvG+vXbABNOLh7m5B0YWJLffptMgwa1LRvwfeh0Ouzs7HD3DqBL/w+4dPYILVt25+TJbffc5rRcueCn8g++uLiEfK89Pd1JTMzbIyDXoMegz7FEWEXm5tWLnDqwiYtnDhJzLZzsrAxysvMWmend+3kmTPgEN7dHO3q8qMg0skKSaWSWc/n8Mc4c3oaTqycu7l6smz+FuBuX853j5unHyO+X4+lb8i6liFvWzp/M3wun4uLqilarRZeTQ5tug2jWsS++JcqwdOZ4dqyeRUTEETw83CwdLllZWcyatYh69WrRqFH+LU+NRiPNm3fh/Plwhn8xlyp1WnAjMoxvRnTFZMzFwcGekiX98fX1pmLFsvTr15PKlSsAMHPmnyxfvp6//16IjY2NJW7NYtLS0omMvIabmws9e77OpYirvDR0PLUat7Pq2Rm3FoZRKJR4eXlQunQpXF2dcXNzpUOH1jz//L2XiX0QltyNTFrgwqpkpCUzZXQf86YGAP3fm0TM9Qg0tnY802MIao2dBSO0Pq27vkpGaiLarAzsHV2o3bQDfqXKkmswkGvQc2BL3trwTk4OGI1GNm3agbu7223J81EZN+57Zs6cB0CVKhVZsWI2Op2eX375g23b9nDpUiR9hk+gSp0WQN4qYu99+xenD24hPvoqJ/Zt4Ny5C+zYsZfp0+dy/PhWgoODqFChLB06tH7qkndWVhbjxn3PsmXrSEtP55+RWWxc8jMtOva1cHR3ptdpWfHHBPZvWYp/qXK8/O63+JYsg1ptm++8axGhAKxfv4DGjetZItRiJQlcWJUbV8LMyVulUuUNvpn8PkqlDY3avoDq//4HFvfn6OxG72FfmV9fDjvOJwOakmvQ4+TqiTYrHYDAwDrk5PzbnRoffx6V6tH+CsnKysLH598W4blzF6hcuSkKpRJjbt5gtS79P6D5/yWe0hVq4uzqwZblM2nTbRCbl03Hw8ONrCwtmZmZXLsWRcuWTWnZsukjvR9LOXjwGJ9//g2XLkWSlpZBbq6ByrWb07FxOyLOHeXIztU8++JQS4dpdvboThb98gnuXgHocrJJTogmIzWJoKASXAk/zVfD2qPW2DH4k+lUq9/KfN3+TUsAHoueo+IgCVxYlcPbV5i//u9Sn0ZjLpfPH38qn2EWtZP7NmIyGhk+fBAHDx7j3HktWZmZ+ZL3Z5+998iTd/v2L3Ho0HEAHF3cCencj9Lla3Jox0q8/YNo+uxLaGzt79Hlq+BGZBgly1YB4NNP32PgwN5MmvQrs2cv4vTpnQVaQtPa6HQ6XnllGLt2HyRHq0WtsaNctQY0KFedeiHPUTK4MgDNO/Tl5Xe+RWP7+PRkRZw7SmJsFImxUeZjw4YN5MsvP2bJktXs2LGHJUtW88uYAfyy9jJKGxtMJhNZmakA9xzAaM0kgQurUr1BG/Zv/gsApY0NTq6epCXFUal2M14d9aOFo3sy3LgShpeXO1988ZH52Lp1m5k+fS6VK1fgu+/G3OPq4jF16kxz8n75nW9o0u4l8x9r1Ru2eaAyPH1L8v53SzEajezdsIiNG7czcGBv2rdvTbNmDZ/IP/6MRiO//TaPpUvXcPbsRbTaHBq37UG1Bq2pVKvpbTvzQd7/V5rH7DFCl34jadj6eWKiIkhJjGHxL5+yctUGqlSpxPDhH3FrKJe7dwC5uXqUNjYoFAo6vPQWq+d+i6dnRUqUDCD0zC4L30nRkgQuHntZGakkxkahzcobDd2q60AuhR7G06ckLh4+RJw7SrcBH+Hs6mnhSJ8MGlt7UlLTSUxMwtPTA4DOndvRuXO7Rx7L4cMn+PDD8Zw8GYqrhw9Nn+1F42d6PlSyVSqVuLh7mUdfV61asajCfeTatu3BsWOnzJ+HRqPBwcEOJydHTCZISEhGq83G0dmNmk070ax9b8pVrW/hqAvHt2QZfEvmtaSvh59h3+YlDBv2IS7u3lSp04LyNRpTqkwVfv58ANkZqZQoU4XmHf59lNK5U1tLhV5sJIGLh5KZnoqDk8sD/ULV67TMn/oRx/euR2NrT5W6LShbpT5nj+7EwyeAgKCK3IgMIzE2isz0ZLIyUslITyY7Iy1fOTY2Kuzs7LgecRaAL2btwcsvsFju72nUtf8HhB7ZTq1abfjll6/p0uXZRx5DePhlXnzxNa5evY6tnQNtXxhMt/6jblv/urBKlanKlQv3X6HtcXfs2CkAcws0JyeHnJwckpPzuo7rtXiO5p1eplzVBlb5eMCg13Ht0hk0dg44OLpw9tguNi75maS4G+Zz0pLjObhtOQe3LUdpo8KYm/do7frlczi5eKBSa3BytOeZZ1pa6C7+1959R0VxtQEc/u3Sq4B0BTSogA0riqLYS0RBQ2I0KrHGGkuMJZoYe4vGFo3GGjWJUVTUGI2iKBoVLFjAAggIClhp0tn5/ti4n0RQQBAx9zlnz3Fn7ty5s+7yzsy9c9+yIwK4UGzZmRkEBewlYP8W7kZd5/2+Y+neb8IrtwsO8CPo+B7c3V3Jzs7h3KmDnD+xH11dXa4FZ4AMdHR0qGSoj56eLtbm+hjVssbBoQZ16zphZWWOtbUVjo41kMvl2No1wrp6vVJNkiCApU0NJi725cfZQ/HxGY22jg7NXBqwdu0SLCzK/pGi6dPn8eParWhqld0Unna16nMl6CgKhaJCBrZnnjwJJykphf37D2NiYsyePX/g53dINT6kZZePqVWveTm3suT8fl7MUd91+ZZpaWkxcuRAunXrgKamJqdPB+Hi0oiEhPusX7+dv//+/4lZrfqu1KrfnC1Lv+Djjz8jPv7KGx+7UZbenSMRypRCoeDK2b+4eOogl88eITszHTMzU+RyOWEXThQpgD+Iv4NcLmfXro2oq6sTG3uP7Ows7O2rEx+fiIGBHvr6L+b+Lagt8+cv52laOlWrO72TfZflza5WfWZtPMn1i4GcP7GP06f/pF69Njg712bgwD58/LFXqQe+GzfC8fYezN278Ti7dqLPqLlUMjEv1X088zQ1CUmhwN7ehaVLZ9Gz5/tlsp83wcjIUDUn/aJFK8nNzaVWfVdq1muOfQW9Xf5MU/ceqgDu49Ob3r29qFPHAUPD/8+90aRJA2Jj7zFnzlKCz19WLbeyrYVtjTpUMrFg5LcbWTyhJ05OLXFza0aPHl3w9OzCiRN/06fvCHJzcjE1NWbXro3Urev4xo+zpMRELiX0X5nIJSI0mLTkRzyIj2H3hnloaWnRpIkzX301luvXI5j45bd0/2Q87/cdW+D2kiRx3G8TdyKuciXoKBlpKXh5dWXTpuIPOEtJSWXcuOkc/PMYWZmZODZoydCv1hQ4EEcoXYlxt/lr1xounT5ExtMUNDQ00dPTITdPgba2JsFBf5VoNiuFQsH3369l06ZfuXcvER09A/qMmkvj1h5lemJ2J+IqFwMPcvaYL/racq5dO1lm+3qT/P0D8fYexAdDptGh17Dybs5r27joc4ID/FBXV+PmzbOYmBhx4cJlfv75d2Ji4lBXVyMiIpo7sXeRy9Vo1q4X7h79sa3x4vSmZ47sJGD/FuKiwlDk5aGhoUlOTg6mllUxMa/CrStn8fbuzk8/FTytbGHKcyIXEcBL6L8QwINP7GPjwjH5lvXq1Q0fn95MnTqHsLBbvOfYiAmLdxaavi/udhhzR3dFTV0dfT09cnNzmTJlTLEyPqWnp/PFFzPw9T1Ibm4ODVp0oU13H2rWay6uvt+wvLxcIq4FcS34GPF3wgk9HwCAoaEB+vq6fPfdTLp2ffmo8Lt345k+fT5hYTdJSHhISkoKVna1aNHxQ5q390a/kskbOBKl9QtGE3k1kKjbwW9sn2UpPj6R2rXdsKtZnynL95d3c15bZNh51swczNPUJAA0NNTJzVUgSQp09AyRJAUymQx3Dx/adB9AJROLV9aZnZVJ+NWzXL8UiJl1NVzaeOK7fi5njvzOpUvHij3Hv5iJTXgr3Y26AYCdnQ2tWjVDX1+fDh1a4+k5QFVm5MxNL8296793AwBH/vq92JP+P3r0mKlT5+K37zDZWVk0buWBR7/xWNrUKMHRCKVBTU0dB+cWODi34PrFQFUAT0lJJSUllaFDJ9CsWWPi4xPQ1dXB0tIcQ0NDbt+OJi4unqSkVNIz0kECa7taWNg50d97BHWatCmXkzEzKzsunTrIjh1+9O7t+cb3X9qOHlXeSfhgyPRybknpsK/dhEW/XuLXH6Zx6s9fyMnJxdm1MwPGLy7xnTdNLW3qNGlDnSZtAOWYnrCLJ7CxqaIK3tnZ2Vy4cAUXl4aoqalx/PgprK0tcXB4u/72iAAuFKqRW1cO//4DMTGxVKliycaNy1m4cKVqfZfeo1+ZzL7qe8pJMzp08Gbt2u/w9u5epH1PnPgtmzfvIC8vD2fXjnj0m6CaaEJ4Ozg1asWERTuJuHYOHT1DFHl5/PHLMv4+ewmjypYkPHhAyOXr5OXmYGBkikWV96hW9z0sbWrQpHX3Usnd/Lo6fzSSq0H+DB8+kdFjprL6hwV8+GGP8m5WiSgUCk6ePAOAgVHpPlKZmZ5G7O0wZDIZv6ycSvydcACmrjhQ4O3q0hT453ZO/fkLAHK5Gp4+X5Zqt9neLYtIephI/zGDOHYskHbtWnH16nXef78Phw7toEkTZ6ZMmc0XX4zEwaEG27btRKGQGDDgo1JrQ0mJAC4UysquFva1mxAZdp6//w6mTp1W5OXlYVfLGY9PxqvOYP8tOzODdfOGE371HPJ/JoRQKBQMHTqBli1dsLJ6+W2u3NxcNmzYDoD30K9p33NIqR6XUHpq1nWhZl0X1fu2ngPzXUlLkkRebg7qGprl0bxX0tbR44vFO7l+8SQbF41l//7DFTaAz5nzPbt27UdNXQM9Q+PXqkuhUHDp9J9EXDvHvZhbxEZeI+Np6gvltHVePej0dZlbV0ff0BgT86r0GvwVVrY1S63u5Mf3Oe63iQ4dWhESEkpubh7t2rWiXj0njh3bjbNzHWQyGcuWzcXRUXn1fflyGAqFAoCEhPtMmzbvZbsoUyKAC4W6FnSMyLDzqvdWdg4MmrQcK9taL93uj1+WEXbxJO6tXVFXV0NbWws7u6pkZGQV6TEkuVyOlpYWWVlZ7PppNu4eA1DX0EShUPAwPoaUpIfo6BliUaU66hqa5ORkkZudhUyuhpq6OmpqGqoR0pIkkZbymLzcXIwqv7p/THg9/74NLpPJyiV45+Zkc/HUH9y6cpbu/Se8tG9UR9eARm7d2LNxPufPXyY3N7dCPmp07FggyGQs/vUiOnol74tNiItkw4LRxN0OQ1tbB3193XzB28S8KkOmrKK645tJK+vU0I3Fv4Wo3ivy8niYcId7Mbeo5tDgtX7X0bcuAxJjxgyhRYumqv/3f+f5fj4RyvMzESYlJfP4cVKJ9/+6Kt63VCgzt29c5MiuH8nOyiQt5TH6Bsa06voJj+7H0fGDYTg2cFOVjb9zi42LxmJoZIr3sG9UZ8XZmRkc37+ZRg3rsmfP5hK1Y/ToqWRlZVHZvCqtuvXjyO513Aw5TfTNELIy01Xl5Gpq6BkYk5r08IU6ZDIZcrkayGTk5eYAYGppyyefL8CxwX8jYcV/iSIvj+Qn93mcGMfNy3/zx68rVBN6uLT1KtLgJtua9bkY+AdW1vUJCz350tzhb6MGDepy+XIo8peMSXkZSZIIDvDj11VfIUl5fPfdTAYP7ktY2C3c3DxAJuOjz76lTXefUm75y9uU8uQ+UTdCiAgNIuJaEHejb5Cbkw1AJRMLpq8+jH4J7zjY1qiLTC5n/fpttG7tWuztHR1rsmXLSuzsGpVo/69LBHBB5eAvKwg9fzzfsvrNOjBm9s+A8rbavp+/47jfRrKzMgDQ0NRk0Xgv5m75G139SsjU5ORkZXLhwhW8vQexa9fGYrfj11+VCUse3Y9j76YFyOVqVK5sjGvzhrRq1YzatR24c+cuZ8+e5969BN57rzWVKhmSm5tLTk4O2dk55OQoX5IkUb26cpa2LVt+Z+X0/kz8zveNXT0IZSs7K5O9mxdy8sDP5P0TsGVyOdI/tzgnf+9HNYcGRapr0JfLsXdqws51M/HzO8SQIf3KqtllokoVKwASYiOxq1n8funf135LwL7NWFtbcejQb9jYWAPK6WwlSeKrFX9gY1+nVNtcmAfxMWxYOIa422GqE3ANTS0sLUzp2KEVzZo1wta2KkOGTmDRBC96DZqKs2vnYg+ENDa1wqF+C9U8+xWNCOCCSv9xCzl71JeggL0kP0rEzMqOTz5fAEDG0xQWjOvB/btRAAwe/AkffeTJo0eP6dt3ODHhV3Fq6MZfO39U1Rcbe69E7fjsswHcvh2DtbUFLi6N6dXrfbS1X8yMNGxY/2LVO2HCCOzsGrFz3UwmLd1borYJb4/kx4ksn9aPhDsRtGzZlBYtmlKvnhPNmjXixx9/ZunSNdyJuFbkAK6mrkGbHp/y1641fPvtd1y8eJXVqxeW7UGUos8+G8D8BSsI2L8ZnwlLir198PG9ODvXISBgb77l8fEJAMRGhr6RAB4XdZ3lX/UlJ/MpXbu0oWZNezp1alNg/vm8vDwmT5nN2jmfYWNfB6+BU3Bq2KpYgdzMuhp3wi+V5iG8MSKACyqVTCxITX7Eveib1Kzrwpg5W9HQVAbOnOwsHiXEAspnfhMSErGxsebGjVuAMuEIQMS1cwA0b94YX9/iX30DLFjw9eseygtiY+/R2l35mFDz9t6lXr/wZj1KjOX7yR+T/CSRzZtXvDBf+9dfT2D5ip+4E3FVtSw7K5OwCwHERoZS9b3aNGzZ9YV65XI53T4Zz651s/j1tz0MHtyXxo2dy/x4SsOkSTORFArec1IGOkVeHkd3/0ROThZdeo966eOeoExiU5CnT58CsHXZlzg2dMPEzLp0G/6cyLDzrPx6AGoyiYCAPTg6vnzAmrd3d7y9u/PDDxtZuHAVK6f3x9m1E/3HfYeeQdFGqle2qEpmZmaFnFa3YrVWKHN3o64DEH4tiEfPJQwwNDZj4pLdNHHvgYFJFf48FICr6/u0bOmCTCZn/fyRTO3vwpOHCWjp6HH27AVaunmU12Hko1AocHf3JDMzh7HzfqF1t4p1a1TI7/b1Cyya0JPU5Afs8/u5wGQrCoUCNTU1tHWVo6QfxMcwtqcDa+d8xsFfV7Bu7nDVSOJ/a9W1Lwu3n0dLS5dJk2aW6bGUlnXrtrJjhx/uHgNw69IHgCO717Fn03wObFuq+l0XJi35ManJj7h7N1617M6du0yZMps1a37GqWErxs77BWNTqzI7BoVCwaZFY9FQlxEUdOiVwft5o0YNIjr6PCNHDuRqkD+/rvqqyNtmpqciKSTV/PEVibgCF/J5mpqETCZDkiSeJj+GqvaqddVqOTN4svI58IjQYFZ9PYAmTf6fYjI16SFJjxIB5dl8dFQsSUkpJZpiszStWbOZJ0+SGD1rixjA9pZ7EH+Hhwl3kCQF0TcuEXs7jLy8XDQ1tZGrqRF3+zrxd26hq6fHkb9+p3792gXW07q1J9lZWdjVrA+AlrYu9nWaEBl6HreuffH0mfTSqy1tXX3aeg7k8O8/8OOPmxk+/NOyONxSc/58CADdPhmHTCbjQfwd9m5Sdn/Z127yyme1dfQMsLWvS0z4FW7cCGfFivXs2OGHQpGHjX1dBk5aXubpehPjInl0P45vvpmo6s8vDrlczty5X5GZmcXGjb/g0q4n9ZsVnkI0NvIaf+74gUunDlKvXm00Nd/ORx1fRgRwQSU9LRkH5xaqNJ3qGlqFltXVr0T9Zh1R09DAvnYT/DYvIi3lMQD1m3Uk8e5tUh7Hl3vwTk9PZ8HClZhXqY5To9bl2hahcLk52Rza8QN//rYShSIPUE7a8ezfyvdyqle3ZfTowXz99YRC/+DOm7eM0NAb9B4xk6ZtlN0mhsZmTFzsW6w2eXwyjtvXzzN9+kKcnGrh7t6ihEdX9j7/fCg7d+7j6jl/WnT6iP1bvwOUT2P0G/vqfnw1dQ0GTV7J9IEtcXVVJnZp0ak3Hv3Gl+lV9/OunD2CTCbDy6vLa9WzePEM9u07zE/zRlC/WQcauXWjTtO2aGnrkvLkPrGRYfjvXc+NS6fQ1NRiwICP+P772aV0FG+WCOCCypIvP+RezE3qNetAv7ELMTQq/DGaQztWEXzCD4Beg78iPTWZK+f+wtrOgVtXz/EwIYZ1axe/qaYXytt7ME/T0hkxc2mF69/6r0hLecIP3/gQfesKrq6NmT59PAqFgmXL1uHvH4iBUWXS01JQU5Nz/vyRl9Z192483y1ZQzWHhrh7vN7jTmrqGgybto6F47rT++PPOHf2IHZ2b2fq2t9/V/4WzatU56y/L8EByvcDJ60o8tTDt66cUf3bsUFL+o9bVPoNLYAkSQQd28O+rUtwcqpF9ep2r1WfXC7H39+XceOmcy7oBBdPHVTlkX82ol1HV5chQ/oxe/bkAgfIVhQigAsq9Zt3wMDYlA49h7w0eAN0/mgUkiRhaVMDfUMTOn04nHrN2jNruPKW1ciRA+nVq3z7wL/+egFnzpyne78JvOdYPs9pCi+XkZ7KdxN78TDhDitWzFWlxVyyZA3+/oF0+nAE3fqOY/W3A3mSEP7K+iIiopAUCnr0/6JU5lbXM6jE6FlbWDCuO02adKZ37x6sWrXgtestbZs378C+dhN2r59D1M0QAKpWd6Kpe9Fnlbv5TwCv3did3iNmlUUzX/Aw4Q4/f/8l4VfPUq2aLYcP/1Yq9draVmH37k0AnDp1lh9/3IJMJsPBoSaNG9enc+e278QJvQjggoqnz6Qil61SzUHVH34t+Dg7135LWsoT1frnZy66du0GXl4DcHauW+KR6cWhUCjw8PiEM2fO06hVN7r0Hl3m+xRK5mbI3yTG3aZXr2706dOT7dt3cfXqdX76STmV7pkjOzm6ex2KvDxat27+yvoKG5j2OsyrVGfaqj/Zu3kR27f7kpycwtatq0t9PyW1fPk6UlNTsdM3JDQ4AE1NTbKzs4udTtRnwhJ8Jix5o0ll/LYsJur6eaZOHcukSWXzO3Vza46b26u/OxWRCOBCsQQd38umxWNx69IHr4FTOX9yH74/zSEnOxMAT88urFo1H319fWJj7zF06HjVJAkREVFvpI0DBozizJnzePpMovNHI0XK0beUIi9P9b3ZvfsP9u49lK/PG8DG2gSoTFjYTVq0cCmglvzy8pTbq2lolGpbK1vYMHjySoxNrTjgu5batd3w9u7OrFmTS3U/xfXLL7uZPed7rGxrci3oGO7uLQg8dY72XoNp1r5Xseoqj99JZGgwdeo4lFnwfteJAC4UWWRoMJsWjwXAxLwKm78by7XgZzO3yUAGBw4cISTkGnp6ety8GamaHUtHR5vAwH1l2r6TJ88wYcI3REZG0+GDYXTpPapM9ye8ngun/mDjos9V7xWKPIzNrMnNySY7I5XduzeqgvaNG+HUqmVfWFUqZmamIJPx66ppuHXp88+c4BKSJFGluhPVar3eM91eA6egZ2DEpdMHWblyPQ4O9nzySfnMK+DvH8iYMV9hY1+Hh/ExmJqZAhIymZyO3p+VS5uKQ5GXx5OH8XTu0Ky8m1JhVfxOAOGNsa1Zj75j5jNt1Z90/XgMdZq0VWUj8vDogJamJnl5ecTExHEvMZnW3fpTpboj6urqrFu3BENDg1JrS1JSCmPGTKV69SZUruyIsXEtPD0HcC/hEX1Hz6PXoKI/ByqUjwd3o5DJ5QQE7GXYsP7Uru1A8uNEUpMe0rdvr3xX3I6ONYvUZ+nsXIef1i0hN+MJvuvnsHXZl2xdNoltyyezcFwPrl869VptToiNIDYyFG1d5Xc5JCT0teorKYVCwcBBY6lsUZWeg6byNC2ZCeOHcepUMG5d+hRp7vfy9mw65kqVivakSmRkNFFRMar3kiSp/v30aTqPHz8paLNiy8nJKZV63gQRwIUi09DUplXXvqoc36aWNphaKecZr1q1Ct7eHlSurEwqMGvjSWrVd+Vu1A0+/fRjPDw6FVpvcbVs2Y3q1RuzbdsukpKS/7ntqvwxew/9GreufcVt8wrg8YN76Orq4uxch4ULv+H06QNcu3qC3bs3sXRpyQdReXt3Jzz8LPfvh5GYGMqDB9cJCjoMwN2osBLX+zQ1idiIa1wIPMDNy3/TvHlj5s6dWuL6XseMGYtITUml75h5aGgqH/dcv34bkqSgk/fwcmlTcWVlKmd4K+zE/urVMD77bKIqoH7zzUKmTJkDKLtKLC3rsGvXfgD8/P7E3t6F5ORUnjxJonXrHqp1kZHRzJq1hNTUNACiomKIi/v/hDWSJKlOBhYvXkWHDv+/o5KVlVWah1zqRAAXSmzv5kXE3Vb+Qfzxx81s3+5LrkKD5h28UVfXZM/GeZiaVmbhQuXUqGlpafTo0Y+ff97xWvtt00Y5Gct779kxevRgvL274+CgfFRm67JJZGakvVb9wpuR/Pg+err5H+GxsrKgbVu3QrYomk2bfsXWthENGrbnl1/2MHjwOFq18kRTS0f1XHhxXb8YyMTezmxeMl61bNq0cW9k8o/c3FzS0tJUA/QeP05i3U/bqN3YHccGbuj/M8HK7dt3aN7hQ0zMq5R5m0pDVmb+K3BJklix4icOHPgLgJycXKKiYrh/X5ltcP78acyfPx1Q3oGYN28aTk7K1MadO7dl27bVVKpkgLGxER984IGVlTkAcXH38PU9QGamMhjPnbuMQYOUXTfZ2dlYWtZRJVByd2+Jj09vAL76ai6tWvV4q4O4THr+PoRQZCkpKVSqVImlu66ho1t6t4YrkpQnD4i+dRkZMkytbKhsYcvT1CdEXAuiqn0dZn3WnvHjh/PVV2MZPvxLfH0PAFC1qjUymQw3t2avTBaRm5tLcnIKlSubvLDu++/XsmjxD2RmZPyzREb7noPxHlr6c6kLpW/Jl95kp97j8uWAUqszMfEBteu0wsSsCk9Tn5DxNBW5XI06TdrQY8BE1d2josjLzeFGyGmSHiWgpavPlu8mkJuThbq6OiaVjTny105sbV8/WPbtO5yTgefIy1OgyMsDmQxNTQ20NNVJz8giMyMDSZLQ0NDEzq4KsbHx5CkUTF3xB9Z2ygC2a90sHibcof/479AzMHrtNr0JT1OTmNjbmQ8+8OCnn5YqJ53pN5IGDeoyceLIMtvvvXsJpKamqU76N2zYjrt7C2rUqJ6vXHT0HYKDQ/jwwx7cvh1D//6jWLduCXXqOBAbew8DAz2MjCqRkpKKnV0jkpOTMTR8sxNXiUFsQokZGpuppirMycni4C/LObTjBwBqN26DTC6nbl0HatZyJelJEgCWlubIZBAbexdf3/2sWDEXdfXCv4a9ew/j2LFAQDlBg5WVBaamJkRFxZKSksJ7To1x7fght8MuoK2rTyUTcy4E/oFjg5YV5g/Zf5WxqRUh4ZdJS0tDX1+/VOqcPHkWkkJi/ILf0NLRIzHuNlZ2NYt1kp3y5AH+ezdw6s9fVEl6npHJ5Ghpa3HyhB8WFmYlbmdUVAxffjmThIT7hIbeBKB9zyFoaumQl5fL05QnPE19gkElU6zsaqKto8/d6BtcOnWQGvWa02vwNFXwBvAe9k2J21Je9AyM0NbRx9f3AMOH+9CkSQO2bv2hzLu/rK0t870fPPiTAstVq2ZLtWrKLkJ1dTVatGiq6iJcvHgV589f5vTpA2Xa1lcRV+AlJK7AlR4lxvEoMY6je37i6rmjAFQyMcegkgla8kwePHxMSnLKS+vo1q0D27atKXBdWNgtWrbsVui21R0bEn3zMpIkIVdT9ggp8vKQyWTY1qhHnSZtqNO0rZjI5S0UF3WduaO6MHr0YGbPnlIqdVpb18excVuGfbWGdXOHK299f+dLleqOBZZPefKA/VuX8PjBPRycW1CzbjMWTfAClAPixo37DA+Pjly9ep333+9LZmYm6hqaGBjocfHC0WJPFZybm8uIEZPYvecgMpkcg0qVSXqUQKcPR9BzYOl8BhXJwnE9yEi+R1jYqQo1sUpi4gOio2Np1qwRkZHRNGnSsVyuwMv1E/v222+RyWT5Xo6O//+htWnT5oX1w4e/fIDGp59++sI2Xbrkn1v31q1beHp6YmpqiqGhIW5ubhw/fryQGoV/U+Tl8fvamXzxUX2mD2zJ91N6q4I3wISFv5OZ8ZSUlFTlYJPnEqJ4+kxiwPjv8tX3srzhtWvX4smTcOLjr/Lnn7+xePEMhgzpR/v2rahduxbZqQl4eXUhJMSfRw9v8OjhDY4c2UmfPj3JzXjI4d9Xs3hCTzYtHkdmuugbf5tI//Tp2tlVfa16unTpjbFxTUxMapGRkaGafSz6ZgiZGWn8NH9EgdspFAp+/WEapw79SuS1s+zZOJ+UJw8AsLGxJiBgL15eXVFXV6dhw3poaGjg2MCNMXO2kpKShnODtqokIkXl2qIbu3btp1m7D5i35Qzzt55j9R/R/8ngDaCtZ4CamrxCBW8ACwszmjVTXhS8qfktClLut9Dr1KnD0aP//+P/79upQ4cOZdas/49I1dXVfWWdXbp0YdOmTar3Wlr5k3J4eHhQs2ZNjh07ho6ODsuWLcPDw4PIyEgsLS3/XZ3wL6HnAzju9/8Z1UxMjHj8OAlNLR0mLPwd8yrVefhP7nCAnNx4eg6cQuPWHlS2sMF3w1w0tbSIv3elyD9cbW1tmjdvTPPmjV9ZtkmTBjRp0gBQDlKZNGkWW7fuIjLsPEO/WoNdzZdnZhLejPg7ylzyXbu2f616nk0UZGZdHefmHannoqzPvnYTzp/cj2MD5aC43JxsTv6xjQcJMWhp6xEXGUrohQB69epGixYuTJw4g5MHtwHQrl2rF/ZTp04tQq6EUKtec8bO+4X180fSw9OHuNhLRfoeP36cRET4bTx9JuWbo+C//MREJRMLbocGl2o3ypv2/KyTb1q5n/aoq6tjaWmpepma5p+DW1dXN9/6otyi0NLSyreNsbGxat3Dhw8JDw9nypQp1K9fn5o1a7JgwQLS09O5du1aqR/fuyjq5qV873NzlbNfTfzOF7ta9UmIi8y3PjsznT2bFrBwnCcpTx6gJlcnJyeHPn0+Iz09vUzbqqmpybJlc9i1az0ZaY9ZNN4T3w1zX+jbFN68+DvhaGholih1JCgDYoMG7VTvPxkzj84fjSQiNJjkx4l4D/uG9l6DcevyMaB8hnvnupkE7NuM/+51RN0IZvTowWzYsIyWLZuipq5O2IUT1KhRnWXL5qjqTUx8wDffLCQk5BqZ6Wkc2vEDNeu60GPARDLS00lKenkX0fPkcjWiblxE9Fwqdeg1lOzsLIYP/7K8m1IhlfsVeHh4ONbW1mhra+Pq6sr8+fOxtbVVrd++fTvbtm3D0tKS7t278/XXX7/yKjwgIABzc3OMjY1p164dc+bMoXJl5aMWlStXxsHBgZ9//plGjRqhpaXF2rVrMTc3p3Hjwq/usrKy8j1OkJKi/NH+F2/LunsMwNm1E0f3rOd8gB9qmgZ4fDKUypY2ZKSncvCXFahraNCje0d27z4IwIoV8/j88684cWAr7t0HkJ6WzF9/bqdFSw9O/pPVrCw1buxM0Lk/GTJkPP671xN0bDdfLPbFwKhscxwLhXv8IJ6cnGycndsyYsSnfPxxT+Tyol2NLliwgnXrtqFQKHBp1xNtbT1+W/M18THKhCcymRxTS1sexEdz6vBvTF2+HxOLqjRx78H5E/to3rwR27crx12kpKRibW3JzRunOXr0JM2bNyElJRWAVas2sGTJjygUeWhoKh95O/mH8jscGxmKuroG6upqqvIvo66uxvDhPqxevZEzR3bS0K1rST62d8qlU38C8McfR0lMvI+Ojk45t6j4nj1fXi6kcnTw4EHp999/ly5fviwdOnRIcnV1lWxtbaWUlBRJkiRp7dq10qFDh6QrV65I27Ztk6pUqSL17NnzpXX++uuvkp+fn3TlyhVpz549kpOTk9S0aVMpNzdXVSY2NlZq3LixJJPJJDU1NcnKykq6ePHiS+udMWOGhHK2ENVLU1PzhWXiJV7iJV7i9d966evrS0lJSa8fFIvprRqFnpSUhJ2dHUuXLmXw4MEvrD927Bjt27cnIiICe/tXz4sMcPv2bezt7Tl69Cjt27dHkiS8vLzIyclh2rRp6OjosH79evbt20dwcDBWVgXfzivoCtzGxobY2Ng3PvJQECoS8VsR3mXPvt//+efAjYyMqFWrFhEREQWub9ZMOel9cQL4e++9h6mpKREREbRv355jx45x4MABnjx5ovqwV69ezZEjR9iyZQtTphQ8GlRLS+uFwXAAhoaG4o+SIBSB+K0IQukq90Fsz0tLSyMyMrLQq+CQkBCAQtcXJC4ujkePHqm2eTZo6t+jRuVyeZnkEhYEQRCEslCuAXzixImcOHGC6Oho/v77b3r27Imamhp9+vQhMjKS2bNnc+HCBaKjo9m3bx8DBgygdevW1K9fX1WHo6Mje/bsAZQnAF9++SVnz54lOjoaf39/PD09qVGjBp07dwbA1dUVY2NjfHx8uHz5Mrdu3eLLL78kKiqKbt0KnzBEEARBEN4m5XoLPS4ujj59+vDo0SPMzMxwc3Pj7NmzmJmZkZmZydGjR1m2bBlPnz7FxsaGDz74gOnTp+er4+bNmyQnKx8JUlNT48qVK2zZsoWkpCSsra3p1KkTs2fPVt3+NjU15dChQ0ybNo127dqRk5NDnTp18PPzw9nZucht19LSYsaMGQXeVhcE4f/Eb0V4l5Xn9/utGsQmCIIgCELRvFV94IIgCIIgFI0I4IIgCIJQAYkALgiCIAgVkAjggiAIglABiQD+j7lz59KiRQt0dXUxMjJ6Yf3ly5fp06cPNjY26Ojo4OTkxPLly19Zb3FSlz569IiqVasik8lISkp6zSMShNJXrVq1F9L1ymQyRo0aVWD5zZs3v1BWW1tbtT4nJ4fJkydTr1499PT0sLa2ZsCAAdy7V3iKWUEoLfPnz6dp06YYGBhgbm6Ol5cXN2/efKHcmTNnaNeuHXp6ehgaGtK6dWsyMjJeWvcPP/xAtWrV0NbWplmzZgQFBeVbn5mZyahRo6hcuTL6+vp88MEHJCYmFqv9IoD/Izs7mw8//JARIwrOHXzhwgXMzc3Ztm0boaGhTJs2jalTp7Jq1aqX1uvh4UFubi7Hjh3jwoULODs74+HhQUJCwgtlBw8enO8Zd0F42wQHBxMfH696HTlyBIAPP/yw0G0MDQ3zbRMTE6Nal56ezsWLF/n666+5ePEiu3fv5ubNm/To0aPMj0UQTpw4wahRozh79ixHjhwhJyeHTp068fTpU1WZM2fO0KVLFzp16kRQUBDBwcGMHj36pSlkd+zYwYQJE5gxYwYXL17E2dmZzp07c//+fVWZ8ePHs3//fnbu3MmJEye4d+8evXr1Kt4BvPHZ199ymzZtkipVqlSksiNHjpTatm1b6PoHDx5IgHTy5EnVspSUFAmQjhw5kq/s6tWrJXd3d8nf318CpCdPnpSk+YLwRo0dO1ayt7eXFApFgeuL83t6JigoSAKkmJiYUmihIBTd/fv3JUA6ceKEalmzZs2k6dOnF6seFxcXadSoUar3eXl5krW1tTR//nxJkiQpKSlJ0tDQkHbu3Kkqc/36dQmQzpw5U+T9iCvw15CcnIyJiUmh659PXfr06VNyc3MLTF0aFhbGrFmz+Pnnn196VicIb5Ps7Gy2bdvGoEGDkMkKTwOalpaGnZ0dNjY2eHp6Ehoa+tJ6k5OTkclkBXZlCUJZejYp2LO/6/fv3+fcuXOYm5vTokULLCwscHd359SpU4XWkZ2dzYULF+jQoYNqmVwup0OHDpw5cwZQ3tHNycnJV8bR0RFbW1tVmaIQ0aKE/v77b3bs2MGwYcMKLSOTyTh69CiXLl3CwMAAbW1tli5dyqFDhzA2NgaUWc769OnD4sWL8+VBF4S33d69e0lKSuLTTz8ttIyDgwMbN27Ez8+PbduU+btbtGhBXFxcgeUzMzOZPHkyffr0EYlPhDdKoVAwbtw4WrZsSd26dQFlNkuAb7/9lqFDh3Lo0CEaNWpE+/btCQ8PL7Cehw8fkpeXh4WFRb7lFhYWqq7ThIQENDU1XzhJfb5MUbzTAXzKlCkFDrh5/nXjxo1i13vt2jU8PT2ZMWMGnTp1KrScJEmMGjUKc3NzAgMDCQoKwsvLi+7duxMfHw/A1KlTcXJyol+/fiU+TkEoDxs2bKBr165YW1sXWsbV1ZUBAwbQoEED3N3d2b17N2ZmZqxdu/aFsjk5OXz00UdIksSaNWvKsumC8IJRo0Zx7do1fvvtN9WyZwmuPvvsMwYOHEjDhg35/vvvVSem5e2tSida2r744ouXXh2AMt1ocYSFhdG+fXuGDRv2wrzs/1aU1KXHjh3j6tWr7Nq1C1AGfVDO2T5t2jRmzpxZrPYJwpsQExPD0aNH2b17d7G209DQoGHDhi+kDH4WvGNiYjh27Ji4+hbeqNGjR3PgwAFOnjxJ1apVVcufZbGsXbt2vvJOTk7cuXOnwLpMTU1RU1N7YUR5YmIilpaWAFhaWpKdnU1SUlK+q/DnyxTFOx3AzczMMDMzK7X6QkNDadeuHT4+PsydO/eV5YuSutTX1zff4wjBwcEMGjSIwMDAIuc8F4Q3bdOmTZibmxc7g19eXh5Xr17l/fffVy17FrzDw8M5fvw4lStXLu3mCkKBJElizJgx7Nmzh4CAAKpXr55vfbVq1bC2tn7h0bJbt27RtWvXAuvU1NSkcePG+Pv74+XlBSiv5P39/Rk9ejQAjRs3RkNDA39/fz744ANAmZjrzp07uLq6FusABEmSYmJipEuXLkkzZ86U9PX1pUuXLkmXLl2SUlNTJUmSpKtXr0pmZmZSv379pPj4eNXr/v37qjrOnTsnOTg4SHFxcZIkKUehV65cWerVq5cUEhIi3bx5U5o4caKkoaEhhYSEFNiO48ePi1HowlstLy9PsrW1lSZPnvzCuv79+0tTpkxRvZ85c6Z0+PBhKTIyUrpw4YL08ccfS9ra2lJoaKgkSZKUnZ0t9ejRQ6pataoUEhKS77eVlZX1xo5J+G8aMWKEVKlSJSkgICDfdy89PV1V5vvvv5cMDQ2lnTt3SuHh4dL06dMlbW1tKSIiQlWmXbt20sqVK1Xvf/vtN0lLS0vavHmzFBYWJg0bNkwyMjKSEhISVGWGDx8u2draSseOHZPOnz8vubq6Sq6ursVqvwjg//Dx8ZGAF17Hjx+XJEmSZsyYUeB6Ozs7VR3Pgm9UVJRqWXBwsNSpUyfJxMREMjAwkJo3by4dPHiw0HaIAC687Q4fPiwB0s2bN19Y5+7uLvn4+Kjejxs3TrK1tZU0NTUlCwsL6f3335cuXryoWh8VFVXg7+r5354glJXCvnubNm3KV27+/PlS1apVJV1dXcnV1VUKDAzMt97Ozk6aMWNGvmUrV65UffddXFyks2fP5lufkZEhjRw5UjI2NpZ0dXWlnj17SvHx8cVqv0gnKgiCIAgV0Ds9Cl0QBEEQ3lUigAuCIAhCBSQCuCAIgiBUQCKAC4IgCEIFJAK4IAiCIFRAIoALgiAIQgUkArggCIIgVEAigAuCIAhCBSQCuCAIJRIdHY1MJiMkJKRM6pfJZOzdu7dM6haEd4EI4IJQQX366aeqZAnlwcbGhvj4eFXu5ICAAGQyGUlJSeXWJkH4L3mns5EJglB21NTUipX6UBCE0iWuwAXhHXTixAlcXFzQ0tLCysqKKVOmkJubq1rfpk0bPv/8cyZNmoSJiQmWlpZ8++23+eq4ceMGbm5uaGtrU7t2bY4ePZrvtvbzt9Cjo6Np27YtAMbGxshkMj799FNAmZJx2bJl+epu0KBBvv2Fh4fTunVr1b6OHDnywjHFxsby0UcfYWRkhImJCZ6enkRHR7/uRyUIFZYI4ILwjrl79y7vv/8+TZs25fLly6xZs4YNGzYwZ86cfOW2bNmCnp4e586dY9GiRcyaNUsVOPPy8vDy8kJXV5dz586xbt06pk2bVug+bWxs8PX1BZR5jePj41m+fHmR2qtQKOjVqxeampqcO3eOH3/8kcmTJ+crk5OTQ+fOnTEwMCAwMJDTp0+jr69Ply5dyM7OLs7HIwjvDHELXRDeMatXr8bGxoZVq1Yhk8lwdHTk3r17TJ48mW+++Qa5XHneXr9+fWbMmAFAzZo1WbVqFf7+/nTs2JEjR44QGRlJQECA6jb53Llz6dixY4H7VFNTw8TEBABzc3OMjIyK3N6jR49y48YNDh8+jLW1NQDz5s2ja9euqjI7duxAoVC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