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   "source": [
    "# Generation of Kerr sideband\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the cost of running the entire notebook is larger than 1 FlexCredits.\n",
    "\n",
    "Nonlinear materials are of great interest in many industries due to their unique capability to exhibit nontrivial phenomena, such as wave mixing and frequency generation. An interesting phenomenon that occurs in media with third-order nonlinearities is the Kerr sidebands, a form of four-wave mixing. Due to phase-matching conditions, these sidebands appear at frequencies offset by integer multiples of the frequency difference between the pump and signal.\n",
    "\n",
    "Kerr sidebands can significantly enhance sensor resolution through all-optical signal processing. By combining a tunable laser source and a pump laser with a Fiber Bragg Grating (FBG) in a nonlinear fiber, frequency sidebands are generated, with each sideband’s power dependent on input intensities. Filtering specific sidebands narrows the FBG-reflected signal, improving wavelength shift detection. This technique enhances FBG-based temperature sensors and can be applied to other optical systems for increased resolution.\n",
    "\n",
    "In this notebook, we use Tidy3D to perform a simulation of the generation of Kerr sideband in a waveguide. This example is based on the paper from `Ole Krarup, Chams Baker, Liang Chen, and Xiaoyi Bao \" Nonlinear resolution enhancement of an FBG based temperature sensor using the Kerr effect.\" Optics Express Vol. 28, Issue 26, pp. 39181-39188 (2020)` [Doi: https://doi.org/10.1364/OE.411179](https://doi.org/10.1364/OE.411179)\n",
    "\n",
    "This example was kindly created by Dr. Chenchen Wang, Postdoctoral Researcher at the University of Wisconsin–Madison.\n",
    "\n",
    "<img src=\"img/kerrSideband.png\" width=\"600\" alt=\"Schematic\">\n",
    "\n",
    "For more examples, please refer to our [learning center](https://www.flexcompute.com/tidy3d/examples/), where you can find tutorials such as [Bistability in photonic crystal microcavities](https://www.flexcompute.com/tidy3d/examples/notebooks/BistablePCCavity/).\n",
    "\n",
    "FDTD simulations can diverge due to various reasons. If you run into any simulation divergence issues, please follow the steps outlined in our [troubleshooting guide](https://www.flexcompute.com/tidy3d/examples/notebooks/DivergedFDTDSimulation/) to resolve it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Theoretical base\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In this section, we will introduce the theoretical framework of generating sidebands in a Kerr medium, as developed in the [reference paper](https://opg.optica.org/oe/fulltext.cfm?uri=oe-28-26-39181&id=444709).\n",
    "\n",
    "To generate Kerr sidebands, we inject laser light with two distinct angular frequencies into a Kerr medium. The two frequencies are a signal frequency $\\omega_s$ and a pump frequency $\\omega_p$, where $\\omega_s < \\omega_p$. The Kerr medium is a waveguide made of a $\\chi^{(3)}$ material. The total electric field amplitude at the input of the fiber is given by:\n",
    "\n",
    "$$\n",
    "A_{\\text{in}} = \\left[\\sqrt{P_s} \\exp(-0.5i\\omega_s t) + \\sqrt{P_p} \\exp(0.5i\\omega_p t)\\right]\n",
    "$$\n",
    "\n",
    "where $P_s$ and $P_p$ are the powers of the signal and pump fields, respectively. The angular frequency difference between the signal and pump is defined as $\\omega_d = \\omega_p - \\omega_s$. The input field’s power is:\n",
    "\n",
    "$$\n",
    "|A_{\\text{in}}|^2 = P_s + P_p + 2 \\sqrt{P_s P_p} \\cos(\\omega_d t)\n",
    "$$\n",
    "\n",
    "Neglecting the effects of dispersion, loss, and polarization, the evolution of this field is governed by the **Non-linear Schrödinger Equation (NLSE)**:\n",
    "\n",
    "$$\n",
    "\\frac{dA}{dz} = i\\gamma |A|^2 A\n",
    "$$\n",
    "\n",
    "where $A$ is the complex amplitude of the electric field, $z$ is the propagation distance, and $\\gamma$ is the nonlinear coefficient of the medium. The term $i\\gamma |A|^2$ represents the third order nonlinear interaction of the electric field with the medium, where the field strength $|A|^2$ is proportional to the intensity.\n",
    "\n",
    " Solving this differential equation, the field at the output of the waveguide is given by:\n",
    "\n",
    "$$\n",
    "A_{\\text{out}} = A_{\\text{in}} \\exp\\left[i\\gamma L (P_s + P_p)\\right] \\exp\\left[i \\gamma L \\cdot 2 \\sqrt{P_s P_p} \\cos(\\omega_d t)\\right]\n",
    "$$\n",
    "\n",
    "Here, $L$ is the length of the waveguide. The exponential term $\\exp[i\\gamma L (P_s + P_p)]$ can be neglected as it does not affect the overall output power.\n",
    "\n",
    "The term involving $\\cos(\\omega_d t)$ can be expanded using the **Jacobi-Anger expansion**:\n",
    "\n",
    "$$\n",
    "\\exp[i M \\cos(\\Omega t)] = \\sum_{n=-\\infty}^{\\infty} i^n J_n(M) \\exp(in\\Omega t)\n",
    "$$\n",
    "\n",
    "where $J_n(M)$ is the Bessel function of the first kind of order $n$. Applying this expansion to the second exponential term:\n",
    "\n",
    "$$\n",
    "\\exp\\left[i \\gamma L \\cdot 2 \\sqrt{P_s P_p} \\cos(\\omega_d t)\\right]\n",
    "$$\n",
    "\n",
    "The output field becomes a sum of frequency sidebands, spaced by $\\omega_d$:\n",
    "\n",
    "$$\n",
    "A_{\\text{out}} = \\sum_{n=-\\infty}^{\\infty} i^n \\exp\\left(i \\omega_d (n + \\frac{1}{2}) t \\right) \\left[i J_{n+1}\\left( 2 \\gamma L \\sqrt{P_s P_p} \\right) \\sqrt{P_s} + J_n \\left( 2 \\gamma L \\sqrt{P_s P_p} \\right) \\sqrt{P_p}\\right]\n",
    "$$\n",
    "\n",
    "This result indicates that the output field is a superposition of several frequency components, with the sidebands separated by $\\omega_d$.\n",
    "\n",
    "The power of the $n$-th sideband is given by:\n",
    "\n",
    "$$\n",
    "|A_n|^2 = J_{|n+1|}^2 \\left( 2 \\gamma L \\sqrt{P_s P_p} \\right) P_s + J_{|n|}^2 \\left( 2 \\gamma L \\sqrt{P_s P_p} \\right) P_p\n",
    "$$\n",
    "\n",
    "Introducing the normalization $x = \\gamma L P_p$, $y = \\gamma L P_s$, we have:\n",
    "\n",
    "$$\n",
    "z_n = x J_{n+1}^2 \\left( 2 \\sqrt{x y} \\right) + y J_n^2 \\left( 2 \\sqrt{x y} \\right)\n",
    "$$\n",
    "\n",
    "Here we agree that $z_0$ refers to $y$, $z_{-1}$ refers to $x$ and the Indexes grow from low frequency to high frequency. \n",
    "\n",
    "Under the condition $0 < M < \\sqrt{1 + n}$, the power in the $n$-th order sideband can be approximated as:\n",
    "\n",
    "$$\n",
    "z_n \\approx x^{|n+1| + 1} y^{|n+1|} \\frac{1}{\\left[ (|n+1|)! \\right]^2} + x^{|n|} y^{|n|+1}\\frac{1}{\\left[ (|n|)! \\right]^2}\n",
    "$$\n",
    "\n",
    "This equation shows that the normalized output power is proportional to the normalized input power, raised to an integer exponent.\n",
    "\n",
    "\n",
    "As an example, filtering out the $n = 1$ sideband yields:\n",
    "\n",
    "$$\n",
    "z_{1} \\approx x^3y^2\\frac{1}{4} + xy^2\n",
    "$$\n",
    "\n",
    "\n",
    "Similarly for $n = -2$ sideband:\n",
    "\n",
    "$$\n",
    "z_{-2} \\approx y^3x^2\\frac{1}{4} + yx^2\n",
    "$$\n",
    "\n",
    "This implies an symmetry between $z_{1}$,$z_{-2}$ and $x$,$y$ which can be verified later in simulation.\n"
   ]
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   "source": [
    "\n",
    "### Initial setup\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we start defining the parameters for the simulation:"
   ]
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   "execution_count": 1,
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   "source": [
    "# standard python imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "\n",
    "# tidy3D import\n",
    "import tidy3d.web as web\n",
    "from numpy import random\n",
    "\n",
    "# define geometry\n",
    "wg_width = 0.25\n",
    "wg_length = 2.5\n",
    "wg_spacing = 0.5\n",
    "buffer = 1.0\n",
    "\n",
    "# compute quantities based on geometry parameters\n",
    "x_span = 2 * wg_spacing + 2 * wg_length + 2 * buffer\n",
    "y_span = wg_width + 2 * buffer\n",
    "wg_insert_x = wg_length + wg_spacing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define frequency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wavelength range of interest\n",
    "lambda_beg = 0.5\n",
    "lambda_end = 0.6\n",
    "\n",
    "# define pulse parameters\n",
    "freq_beg = td.C_0 / lambda_end\n",
    "freq_end = td.C_0 / lambda_beg\n",
    "freq0 = (freq_beg + freq_end) / 2\n",
    "fwidth = (freq_end - freq0) / 1.5\n",
    "\n",
    "freqd = 1e13\n",
    "\n",
    "freqp = freq0 + 0.5 * freqd\n",
    "freqs = freq0 - 0.5 * freqd\n",
    "\n",
    "# frequency for the first sideband\n",
    "freq1 = freqs + (freqp - freqs)\n",
    "\n",
    "min_steps_per_wvl = 30\n",
    "run_time = 5e-12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define Materials:\n",
    "\n",
    "To define the $\\chi^{(3)}$ material, we will create a `NonlinearSpec` object with a `NonlinearSusceptibility` model. Since the underlying mechanism for Kerr sidebands is four-wave mixing, `NonlinearSusceptibility` is a suitable choice as it utilizes real electric fields.\n",
    "\n",
    "The `num_iters` parameter can be used if the convergence is poor, although it can't prevent a simulation with high nonlinearities from diverging, as we will discuss below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_bg = 1.0\n",
    "n_solid = 1.5\n",
    "background = td.Medium(permittivity=n_bg**2)\n",
    "solid = td.Medium(permittivity=n_solid**2)\n",
    "\n",
    "# define the nonlinear parameters\n",
    "n_kerr_2 = 2e-8\n",
    "kerr_chi3 = 4 * (n_solid**2) * td.constants.EPSILON_0 * td.constants.C_0 * n_kerr_2 / 3\n",
    "amp = 400\n",
    "chi3_model = td.NonlinearSpec(models=[td.NonlinearSusceptibility(chi3=kerr_chi3)], num_iters=10)\n",
    "kerr_solid = td.Medium(permittivity=n_solid**2, nonlinear_spec=chi3_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define structures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "waveguide = td.Structure(\n",
    "    geometry=td.Box(\n",
    "        center=[0, 0, 0],\n",
    "        size=[td.inf, wg_width, 0.22],\n",
    "    ),\n",
    "    medium=kerr_solid,\n",
    "    name=\"waveguide\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute and visualize the waveguide modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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   "source": [
    "from tidy3d.plugins.mode import ModeSolver\n",
    "from tidy3d.plugins.mode.web import run as run_ms\n",
    "\n",
    "mode_plane = td.Box(\n",
    "    center=[-wg_insert_x, 0, 0],\n",
    "    size=[0, 1, td.inf],\n",
    ")\n",
    "\n",
    "sim_modesolver = td.Simulation(\n",
    "    center=[0, 0, 0],\n",
    "    size=[x_span, y_span, 3],\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0),\n",
    "    structures=[waveguide],\n",
    "    run_time=1e-12,\n",
    "    boundary_spec=td.BoundarySpec.all_sides(boundary=td.Periodic()),\n",
    "    medium=background,\n",
    ")\n",
    "\n",
    "mode_spec = td.ModeSpec(num_modes=2)\n",
    "mode_solver = ModeSolver(\n",
    "    simulation=sim_modesolver, plane=mode_plane, mode_spec=mode_spec, freqs=[freq0]\n",
    ")\n",
    "mode_data = run_ms(mode_solver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>wavelength</th>\n",
       "      <th>n eff</th>\n",
       "      <th>k eff</th>\n",
       "      <th>TE (Ey) fraction</th>\n",
       "      <th>wg TE fraction</th>\n",
       "      <th>wg TM fraction</th>\n",
       "      <th>mode area</th>\n",
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       "      <th>f</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">5.496195e+14</th>\n",
       "      <th>0</th>\n",
       "      <td>0.545455</td>\n",
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       "      <td>0.017490</td>\n",
       "      <td>0.846849</td>\n",
       "      <td>0.898209</td>\n",
       "      <td>0.205158</td>\n",
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       "                         wavelength     n eff  k eff  TE (Ey) fraction  \\\n",
       "f            mode_index                                                  \n",
       "5.496195e+14 0             0.545455  1.108526    0.0          0.984406   \n",
       "             1             0.545455  1.094335    0.0          0.017490   \n",
       "\n",
       "                         wg TE fraction  wg TM fraction  mode area  \n",
       "f            mode_index                                             \n",
       "5.496195e+14 0                 0.858946        0.895359   0.201495  \n",
       "             1                 0.846849        0.898209   0.205158  "
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",
      "text/plain": [
       "<Figure size 1000x600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, tight_layout=True, figsize=(10, 6))\n",
    "\n",
    "mode_solver.plot_field(\"Ex\", \"abs\", mode_index=0, ax=ax1)\n",
    "mode_solver.plot_field(\"Ey\", \"abs\", mode_index=0, ax=ax2)\n",
    "mode_solver.plot_field(\"Ez\", \"abs\", mode_index=0, ax=ax3)\n",
    "mode_solver.plot_field(\"Ex\", \"abs\", mode_index=1, ax=ax4)\n",
    "mode_solver.plot_field(\"Ey\", \"abs\", mode_index=1, ax=ax5)\n",
    "mode_solver.plot_field(\"Ez\", \"abs\", mode_index=1, ax=ax6)\n",
    "\n",
    "ax1.set_title(\"|Ex|: mode_index=0\")\n",
    "ax2.set_title(\"|Ey|: mode_index=0\")\n",
    "ax3.set_title(\"|Ez|: mode_index=0\")\n",
    "ax4.set_title(\"|Ex|: mode_index=1\")\n",
    "ax5.set_title(\"|Ey|: mode_index=1\")\n",
    "ax6.set_title(\"|Ez|: mode_index=1\")\n",
    "\n",
    "mode_data.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we choose TM mode, then we create two mode sources $P_s$,$P_p$.\n",
    "\n",
    "To ensure spectral overlap between the signal and pump sources, we will define the time profile of the sources as `ContinuousSource`, which simulates a continuous wave (CW) source. \n",
    "\n",
    "Note that a `GaussianPulse` time profile is preferred in most cases, as the fields decay at the end of the simulation, making normalization in frequency domain meaningful. In contrast, for a `ContinuousSource`, the fields do not decay, so **frequency domain normalization is not meaningful**. Nevertheless, the temporal mean power of the source is equal to the square of the `amplitude` parameter.\n",
    "\n",
    "Also, note that the `fwidth` argument controls the ramping of the CW amplitude rather than the spectral width, since a CW source is nearly monochromatic.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_source_p = td.ModeSource(\n",
    "    size=mode_plane.size,\n",
    "    center=mode_plane.center,\n",
    "    source_time=td.ContinuousWave(freq0=freqp, fwidth=freq0 / 10, amplitude=amp),\n",
    "    mode_spec=td.ModeSpec(num_modes=2),\n",
    "    mode_index=1,\n",
    "    direction=\"+\",\n",
    "    num_freqs=1,\n",
    ")\n",
    "\n",
    "mode_source_s = td.ModeSource(\n",
    "    size=mode_plane.size,\n",
    "    center=mode_plane.center,\n",
    "    source_time=td.ContinuousWave(freq0=freqs, fwidth=freq0 / 10, amplitude=amp),\n",
    "    mode_spec=td.ModeSpec(num_modes=2),\n",
    "    mode_index=1,\n",
    "    direction=\"+\",\n",
    "    num_freqs=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define monitors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# field monitor for the first sideband\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=[0, 0, 0], size=[td.inf, td.inf, 0], freqs=[freq1], name=\"field\"\n",
    ")\n",
    "# monitor the mode amps on the output waveguide\n",
    "lambdas_measure = np.linspace(lambda_beg, lambda_end, 1001)\n",
    "freqs_measure = td.C_0 / lambdas_measure[::-1]\n",
    "\n",
    "\n",
    "mode_monitor = td.ModeMonitor(\n",
    "    size=mode_plane.size,\n",
    "    center=mode_plane.center,\n",
    "    freqs=freqs_measure,\n",
    "    mode_spec=td.ModeSpec(num_modes=2),\n",
    "    name=\"mode\",\n",
    ")\n",
    "\n",
    "mode_monitor = mode_monitor.copy(update=dict(center=[wg_insert_x, 0, 0]))\n",
    "\n",
    "# flux monitor\n",
    "flux_monitor = td.FluxMonitor(\n",
    "    center=(3.5, 0, 0), size=mode_plane.size, name=\"fluxMon\", freqs=freqs_measure\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define simulation. \n",
    "\n",
    "Note that we will set the `shutoff` argument as 0, as we are using CW sources so the fields will not decay. For the same reason, we can also disregard the warning messages in the log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create simulation\n",
    "sim = td.Simulation(\n",
    "    normalize_index=None,\n",
    "    center=[0, 0, 0],\n",
    "    size=[x_span, y_span, 0],\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0),\n",
    "    structures=[waveguide],\n",
    "    sources=[mode_source_p, mode_source_s],\n",
    "    monitors=[field_monitor, mode_monitor, flux_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.periodic()\n",
    "    ),\n",
    "    medium=background,\n",
    "    shutoff=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize structure and sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the two simulations\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n",
    "sim.plot_eps(z=0.01, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:02:36 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'kerr_sideband'</span> with task_id                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-31d13b1e-dfc8-4937-8018-12bc44deffb0'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:02:36 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'kerr_sideband'\u001b[0m with task_id                          \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-31d13b1e-dfc8-4937-8018-12bc44deffb0'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-31d13b1e-dfc8-4937-8018-12bc44deffb0\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-31d13b1e-dfc</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-31d13b1e-dfc8-4937-8018-12bc44deffb0\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">8-4937-8018-12bc44deffb0'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=32232;https://tidy3d.simulation.cloud/workbench?taskId=fdve-31d13b1e-dfc8-4937-8018-12bc44deffb0\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=308765;https://tidy3d.simulation.cloud/workbench?taskId=fdve-31d13b1e-dfc8-4937-8018-12bc44deffb0\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=32232;https://tidy3d.simulation.cloud/workbench?taskId=fdve-31d13b1e-dfc8-4937-8018-12bc44deffb0\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=23364;https://tidy3d.simulation.cloud/workbench?taskId=fdve-31d13b1e-dfc8-4937-8018-12bc44deffb0\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=32232;https://tidy3d.simulation.cloud/workbench?taskId=fdve-31d13b1e-dfc8-4937-8018-12bc44deffb0\u001b\\\u001b[32m-31d13b1e-dfc\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=32232;https://tidy3d.simulation.cloud/workbench?taskId=fdve-31d13b1e-dfc8-4937-8018-12bc44deffb0\u001b\\\u001b[32m8-4937-8018-12bc44deffb0'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "769f998b189b4966b8ef0a661da1930c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:02:38 -03 </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:02:38 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e088b96344d040c593db5ab0ad5add75",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:02:43 -03 </span>loading simulation from data/simulation_data.hdf5                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:02:43 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from data/simulation_data.hdf5                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(sim, task_name=\"kerr_sideband\", path=\"data/simulation_data.hdf5\", verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we can plot the fields at the frequency of the first sideband. As there is no source injecting power in this frequency, the fields are generated due to the nonlinear process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RERHV1ambUYIZ3URERERE5Viju2HM6CYiIiIiIiIiIiKinsaMbiIiIiKqS3ToZpSCN6MkIiIiIqIFYqCbiIiIiIiIiIiIKIAEJIRsf+mSTrTRbgx0ExEREVF9rNFNREREREQBx0A3ERERERERERERURB16maUVu/fjJKBbiIiIiKqSygKhNqBGt0Ka3QTEREREdHCMNBNREREREREREREFEQSncm2lszoJiIiIqIlTiis0U1ERERERMHGQDcRERERERERERFRELFGd8MY6CYiIiKi+oTakYxuIVijm4iIiIiIFoaBbiIiIiIiIiIiIqIg6lRGt+xAG23GQDcRERER1aUoCpSOZHSzRjcRERERES0MA91EREREREREREREQSRlZ7KtJWt0ExEREdESJ4TSkRrdYI1uIiIiIiJaIAa6iYiIiIiIiIiIiIKINbobxkA3EREREdWndCajmzW6iYiIiIhooRjoJiIiIiIiIiIiIgoiCcDqQP3s3k/oZqCbiIiIiOpjjW4iIiIiIgo6BrqJiIiIiIiIiIiIAok1uhvFQDcRERER1dWpjG7W6CYiIiIiooVioJuIiIiIiIiIiIgoiGSHMro7UQe8zRjoJiIiIqL6FNboJiIiIiKiYGOgm4iIiIiIiIiIiCiIpIToQEa3YI1uIiIiIlrqhKJCqKzRTUREREREwcVANxEREREREREREVEQSWk/OtFOj2Ogm4iIiIjqEoI1uomIiIiIKNj41wQRERER9bzPfOYzEELg8ssv73ZXiIiIiIhaRwKwrPY/mNFNREREREudUDqT0b3QGt0PPfQQvvrVr+Lwww9vcY+IiIiIiKhXMKObiIiIiHpWJpPBueeei69//esYGxvrdneIiIiIiFpLys5kdFtW013bsWMH3v72t2P58uXo6+vDYYcdhj/96U9tmITGMNBNRERERPU5Nbrb/cACMrovueQSnHHGGTj11FPbMHAiIiIiIqomHo/j1a9+NcLhMH7605/iySefxA033NDV5BOWLiEiIiKiuhRFQFEWVlakGUIImKaJVCpV9nw0GkU0Gp3z+u9+97t45JFH8NBDD7W9b0REREREXSElYHWgfnaTTVx//fXYc889cdttt3nP7bPPPi3uVHOY0U1EREREgbFp0yaMjIyUPa677ro5r9u2bRsuu+wyfPvb30YsFutCT4mIiIiIlhYpJVKpVNmjWCxWfe2PfvQjHHvssXjLW96CVatW4aijjsLXv/71Dve4HDO6iYiIiKg+AYiOZHQDRx5xJO65556y56tlcz/88MOYnJzE0Ucf7T1nmibuu+8+fPnLX0axWISqtv8GmkRERERE7SUXVD+7aZaF2dlZjIyMlD191VVX4eqrr57z8hdffBG33HILrrjiCnzkIx/BQw89hPe///2IRCI477zz2t/fKhjoJiIiIqLAUFUVw8PD877ulFNOwWOPPVb23AUXXICNGzfiQx/6EIPcRERERERNWrZsGTZv3lz2XLWkEwCwLAvHHnssPv3pTwMAjjrqKDz++OO49dZbGegmIiIiomASQkAs4EaRC2io4ZcODQ3h0EMPLXtuYGAAy5cvn/M8EREREVHPkh3K6JYSQoiGkk4AYO3atTj44IPLnjvooIPwX//1X+3oXUNYo5uIiIiIiIiIiIiIGvbqV78azzzzTNlzzz77LDZs2NClHjGjm4iIiIjmIYSA0qEa3YtRWdubiIiIiKjnSdhZ3W1vp7k2PvCBD+D444/Hpz/9aZxzzjn4v//7P3zta1/D1772tTZ1cH7M6CYiIiIiIiIiIiKihh133HH47//+b3znO9/BoYceik984hO46aabcO6553atT8zoJiIiIqK6hAKIjmR0d6AOOBERERFRL+lUje4FtHHmmWfizDPPbENnFoYZ3URERERERERERETU05jRTURERER1CSE6ktENJnQTEREREZWTErA6UaO7/U20GzO6iYiIiIiIiIiIiKinMaObiIiIiOoSQkDpQP1swZRuIiIiIqJyEp2p0S070EabMaObiIiIiIiIiIiIiHoaM7qJiIiIqK5O1ejuQNI4EREREVFvkbIzGd2dqAPeZszoJiIiIiIiIiIiIqKexoxuIiIiIqpLKOhIRjdTuomIiIiIKkjZmWxryYxuIiIiIiIiIiIiIqKuYkY3EREREdUlhIDCGt1ERERERF0gAdmBGt3M6CYiIiIiIiIiIiIi6i5mdBMRERFRXULYdbo70Q4REREREflIdKZGdyfaaDNmdBMRERERERERERFRT2NGNxERERHVJYSA6Ei6NVO6iYiIiIjKSAlYnajR3YE22owZ3URERERERERERETU05jRTURERER1CQVQlPZnW7NGNxERERFRBSk7VKO7/U20GzO6iYiIiIiIiIiIiKinMaObiIiIiOoSQkB0JKObKd1ERERERGVYo7thzOgmIiIiIiIiIiIiop7GjG4iIiIiqq9DGd1gQjcRERERUTmJDtXo7kAbbcaMbiIiIiIiIiIiIiLqaczoJiIiIqK6FCGgdKB+Nmt0ExERERFVkh2qn937Gd0MdBMRERFRXUJBh25G2fYmiIiIiIhoiWKgm4iIiIiIiIiIiCiIWKO7YQx0ExEREVF9HbsZJVO6iYiIiIhoYXgzSiIiIiIiIiIiIiLqaczoJiIiIqK6FCGgsEY3EREREVHnSdmZsiKy90uXMKObiIiIiIiIiIiIiHoaM7qJiIiIqD4BiI6kWzOlm4iIiIiojJSAZbW/nSVwM0pmdBMRERERERERERFRT2NGNxERERHVJQQgOpAewRrdREREREQVJFiju0HM6CYiIiIiIiIiIiKinsaMbiIiIiKqS1EEFKX96dbM6CYiIiIiqiBlZzK6O1AGvN2Y0U1EREREREREREREPY0Z3URERERUnxAQHcjoZko3EREREVEFKQGrA+nWsvdTupnRTUREREREREREREQ9jRndRERERFSXEAKiA9nWTOgmIiIiIqpCdqBGdyfaaDNmdBMRERERERERERFRT2NGNxERERHVJQSgdKBGdyeyxomIiIiIeoqUgNWJjO72N9FuzOgmIiIiIiIiIiIiop7GjG4iIiIiqksoAqIDGd1gQjcRERERUTmJzmR0d6KNNmNGNxERERERERERERH1NGZ0ExEREVFdihBQO5DRrbBGNxERERFROSkBy+pAOx1oo82Y0U1EREREREREREREPY0Z3URERERUl6KgIxndTOgmIiIiIqrQsRrd7W+i3ZjRTUREREREREREREQ9jRndRERERFQXa3QTEREREXWJlJ3J6JYdaKPNmNFNRERERERERERERD2NGd1EREREVJeqdCajmwndREREREQVOlajmxndRERERERERERERERdxYxuIiIiIqqLNbqJiIiIiLpESsgOZFtL1ugmIiIiIiIiIiIiIuouZnQTERERUV2qAEIdyehuexNERERERL2nE9nWvZ/QzYxuIiIiIiIiIiIiIuptzOgmIiIioroUpTM1ugVrdBMRERERlZMS6ECN7o5kjbcZA91EREREVFfnbkbZ9iaIiIiIiGiJYqCbiIiIiIiIiIiIKIgkOpPR3Yk22oyBbiIiIiKqyy5d0v5bu7B0CRERERERLRQD3URERERERERERERBxBrdDWOgm4iIiIjq6lyNbgH72kwiIiIiIqLmMNBNREREREREREREFEQdq9Hd/ibajYFuIiIiIqpLUdCRjG6W6CYiIiIiooVioJuIiIiIiIiIiIgogKSUkB3I6Jas0U1ERERES13HanSDNbqJiIiIiGhhGOgmIiIiIiIiIiIiCqKO1eju/YQTBrqJiIiIqC5FCKgdKKDNGt1ERERERLRQDHQTERERERERERERBZGUncm27v2Ebijd7gARERERBZvq1Ohu90NhSjcRERERUU+4+uqrIYQoe2zcuLGrfWJGNxEREREREREREVFQBbRG9yGHHIJf/epX3r9Doe6GmhnoJiIiIqK6FAGoCmt0ExERERFRSSgUwpo1a7rdDQ9LlxAREREREREREREFkZSdeUBCSolUKlX2KBaLNbv23HPPYd26ddh3331x7rnnYuvWrZ2blyoY6CYiIiKiulRFINSBB2t0ExERERF1z+zsLEZGRsoe1113XdXXvuIVr8Dtt9+On/3sZ7jllluwefNmnHjiiUin0x3udQlLlxAREREREREREREFkQSk1YF2LGDZsmXYvHlz2dPRaLTqy08//XTv/w8//HC84hWvwIYNG/D9738f73rXu9ra1VoY6CYiIiKiuhQhWKObiIiIiGiJE0JgeHh4Qe8dHR3FgQceiOeff77FvWocS5cQERERERERERERBZEEYMn2P6RcVDczmQxeeOEFrF27tjXjXgBmdBMRERFRXYqCjmR0s0Y3EREREVFv+OAHP4izzjoLGzZswM6dO3HVVVdBVVW87W1v61qfGOgmIiIiIiIiIiIiCiI3o7vdmqwDvn37drztbW/DzMwMVq5ciRNOOAF//OMfsXLlyvb0rwEMdBMRERFRXazRTUREREREft/97ne73YU5GOgmIiIiIiIiIiIiCiApAdlktvVC2+l1vBklEVGD7rnnHgghcM8993S7K0REHaUodkZ3ux+s0U1ERLXwXJyIiObDQDcRUYWbb74Zt99+e7e7sSB33HEHbrrppm53AwBgWRY++9nPYp999kEsFsPhhx+O73znOw2/P5FI4N3vfjdWrlyJgYEBvPa1r8UjjzxS9bU/+tGPcPTRRyMWi2GvvfbCVVddBcMwWjUUIiIiIuoQnou3Bs/FiZYQKe0a3Z149DgGuomIKtQ6uX7Na16DfD6P17zmNZ3vVIOCdHL90Y9+FB/60IfwV3/1V/jSl76EvfbaC//wD//QUB0vy7Jwxhln4I477sCll16Kz372s5icnMTJJ5+M5557ruy1P/3pT3H22WdjdHQUX/rSl3D22Wfjk5/8JN73vve1a2hEux23Rne7H0zoJiIinou3Bs/FiWh3xBrdRFRVNpvFwMBAt7sRKIqiIBaLdbsbPWHHjh244YYbcMkll+DLX/4yAODCCy/ESSedhH/5l3/BW97yFqiqWvP9P/jBD/CHP/wBd955J/7u7/4OAHDOOefgwAMPxFVXXYU77rjDe+0HP/hBHH744fjFL36BUMj+tTY8PIxPf/rTuOyyy7Bx48Y2jpSIiIio9XguPhfPxRvHc3GiJUYC6ECNbvR+Qjczuol2Bzt27MC73vUurFu3DtFoFPvssw/e8573QNM0AMDtt98OIQTuvfdevPe978WqVauwxx57eO+/+eabccghhyAajWLdunW45JJLkEgkytp47rnn8Ld/+7dYs2YNYrEY9thjD/z93/89ksmk95pf/vKXOOGEEzA6OorBwUG87GUvw0c+8pF5+9/I+4rFIq666irsv//+iEaj2HPPPfGv//qvKBaLcz7vW9/6Fl7+8pejv78fY2NjeM1rXoNf/OIXAIC9994bTzzxBO69914IISCEwMknnwygdl3AO++8E8cccwz6+vqwYsUKvP3tb8eOHTvKXnP++edjcHAQO3bswNlnn43BwUGsXLkSH/zgB2Ga5rxzcPfdd+OMM87wtuF+++2HT3ziE2XvPfnkk/HjH/8YW7Zs8fq+99571/zM888/33td5ePqq6+et0/z9VfXdbz3ve/1nhNC4D3veQ+2b9+OBx54oO77f/CDH2D16tV485vf7D23cuVKnHPOObj77ru97frkk0/iySefxLvf/W7vxBoA3vve90JKiR/84AeLGgcR2RQB1ugmIlognouX47l4qU88Fyciai1mdBMtcTt37sTLX/5yr8baxo0bsWPHDvzgBz9ALpdDJBLxXvve974XK1euxJVXXolsNgsAuPrqq3HNNdfg1FNPxXve8x4888wzuOWWW/DQQw/h97//PcLhMDRNw2mnnYZisYj3ve99WLNmDXbs2IH//d//RSKRwMjICJ544gmceeaZOPzww3HttdciGo3i+eefx+9///u6/W/kfZZl4Y1vfCPuv/9+vPvd78ZBBx2Exx57DDfeeCOeffZZ3HXXXd5rr7nmGlx99dU4/vjjce211yISieDBBx/Eb37zG7z+9a/HTTfdhPe9730YHBzERz/6UQDA6tWra/bv9ttvxwUXXIDjjjsO1113HSYmJvCFL3wBv//97/Hoo49idHTUe61pmjjttNPwile8Av/f//f/4Ve/+hVuuOEG7LfffnjPe95Tdx5uv/12DA4O4oorrsDg4CB+85vf4Morr0QqlcLnPvc5APbliclkEtu3b8eNN94IABgcHKz5mf/0T/+EU089tey5n/3sZ/j2t7+NVatWec9NT0/X7ZtraGgI0WgUAPDoo49iYGAABx10UNlrXv7yl3s/P+GEE2p+1qOPPoqjjz4ailK+Hvvyl78cX/va1/Dss8/isMMOw6OPPgoAOPbYY8tet27dOuyxxx7ez4mIiIi6gefiPBevhefiRNQM2YH62Z1oo90Y6CZa4j784Q9j165dePDBB8tOQK699lpIWX4QW7ZsGX796197l7FNTU3huuuuw+tf/3r89Kc/9U50Nm7ciEsvvRTf+ta3cMEFF+DJJ5/E5s2byy5tA4Arr7zS+/9f/vKX0DQNP/3pT7FixYqG+9/I++644w786le/wr333lt2wnbooYfi4osvxh/+8Accf/zxeP7553HttdfiTW96E37wgx+Unbi5c3H22WfjYx/7mJcNUo+u6/jQhz6EQw89FPfdd593KeUJJ5yAM888EzfeeCOuueYa7/WFQgFvfetb8fGPfxwAcPHFF+Poo4/Gf/zHf8x7cn3HHXegr6/P+/fFF1+Miy++GDfffDM++clPIhqN4q/+6q+wfv16xOPxefsOAK961avwqle9yvv3888/j0svvRR/9Vd/hX/6p3/ynl+5cuW8nwUAt912G84//3wAwPj4OFavXg1RkZ25du1aAPYfffWMj49Xrb/of/9hhx2G8fHxsucrXztfO0TUGMXJuG43JnQT0VLDc3Gei9fCc3EiotZj6RKiJcyyLNx1110466yz5qyyA5hz4nPRRReV1Wr71a9+BU3TcPnll5ediF500UUYHh7Gj3/8YwDAyMgIAODnP/85crlc1b642RR33303LKvx4lKNvO/OO+/EQQcdhI0bN2J6etp7vO51rwMA/Pa3vwUA3HXXXbAsC1deeeWc7ITKuWjEn/70J0xOTuK9731vWb3AM844Axs3bvTmx+/iiy8u+/eJJ56IF198cd62/CfW6XQa09PTOPHEE5HL5fD000833fdK2WwWb3rTmzA2NobvfOc7ZfvBL3/5y4Yep512mveefD7vZZT4ufOUz+fr9qfR97v/rfXa+dohot523XXX4bjjjsPQ0BBWrVqFs88+G88880y3u0VEBIDn4jwXbxzPxYmoLrdGd7sfvZ/QzYxuoqVsamoKqVQKhx56aEOv32effcr+vWXLFgDAy172srLnI5EI9t13X+/n++yzD6644gp8/vOfx7e//W2ceOKJeOMb34i3v/3t3on3W9/6Vvz7v/87LrzwQvzbv/0bTjnlFLz5zW/G3/3d38050fVr5H3PPfccnnrqqZrZDpOTkwCAF154AYqi4OCDD25oPuZTa34AO9Pm/vvvL3suFovN6ePY2Bji8fi8bT3xxBP42Mc+ht/85jdIpVJlP/PXXlyoiy66CC+88AL+8Ic/YPny5WU/q7ykshF9fX1VazIWCgXv5614v/vfWq+drx0iaowqOpPR3WyN7nvvvReXXHIJjjvuOBiGgY985CN4/etfjyeffJI3cSOiruO5uI3n4vPjuTgRUWsw0E1EnsWciNxwww04//zzcffdd+MXv/gF3v/+9+O6667DH//4R+yxxx7o6+vDfffdh9/+9rf48Y9/jJ/97Gf43ve+h9e97nX4xS9+UfOu3428z7IsHHbYYfj85z9f9TP23HPPBY+rlerd2byeRCKBk046CcPDw7j22mux3377IRaL4ZFHHsGHPvShprJyqvnCF76A73znO/jWt76FI488cs7Pd+3a1dDnjIyMePvQ2rVr8dvf/hZSyrIMHffyxnXr1tX9rLVr13qv9at8v3uZ5Pj4+JztPD4+7tUhJKLFURQEsnTJz372s7J/33777Vi1ahUefvjhqpdcExEFGc/F24vn4jwXJ6Klj6VLiJawlStXYnh4GI8//viC3r9hwwYAmHMZuKZp2Lx5s/dz12GHHYaPfexjuO+++/C73/0OO3bswK233ur9XFEUnHLKKfj85z+PJ598Ep/61Kfwm9/8xrucsZb53rfffvthdnYWp5xyCk499dQ5DzfLY7/99oNlWXjyySfrttfopZO15sd9rnJ+Fuqee+7BzMwMbr/9dlx22WU488wzceqpp2JsbGzOa5u97PN3v/sdPvjBD+Lyyy/HueeeW/U1a9eubejxve99z3vPkUceiVwuh6eeeqrssx588EHv5/UceeSReOSRR+b84fDggw+iv78fBx54YNnn/OlPfyp73c6dO7F9+/Z52yGipcXNqlu2bFmXe0JExHNxnovPj+fiRNQQ2cFHj2Ogm2gJUxQFZ599Nv7nf/5nzskHgDk3wKl06qmnIhKJ4Itf/GLZa//jP/4DyWQSZ5xxBgAglUrBMIyy9x522GFQFMW7jG12dnbO57snPtUudXM18r5zzjkHO3bswNe//vU5r83n895d688++2woioJrr712zkmbf3wDAwNIJBI1++Q69thjsWrVKtx6661lY/jpT3+Kp556ypufxXKzT/x91DQNN99885zXDgwMNHz55Pj4OM455xyccMIJ3t3iq1lIXcC/+Zu/QTgcLuujlBK33nor1q9fj+OPP76sH08//TR0Xfee+7u/+ztMTEzghz/8offc9PQ07rzzTpx11lleHcBDDjkEGzduxNe+9jWYpum99pZbboEQouyGTES0cIpTuqTdD0UImKaJVCpV9qj3e8JlWRYuv/xyvPrVr264TAARUTvxXJzn4vXwXJyIqPVYuoRoifv0pz+NX/ziFzjppJPw7ne/GwcddBDGx8dx55134v777/duMFPNypUr8eEPfxjXXHMN3vCGN+CNb3wjnnnmGdx888047rjjvLuJ/+Y3v8Gll16Kt7zlLTjwwANhGAb+8z//E6qq4m//9m8B2HeWv++++3DGGWdgw4YNmJycxM0334w99tij7O7slRp53zve8Q58//vfx8UXX4zf/va3ePWrXw3TNPH000/j+9//Pn7+85/j2GOPxf7774+PfvSj+MQnPoETTzwRb37zmxGNRvHQQw9h3bp1uO666wAAxxxzDG655RZ88pOfxP77749Vq1Z5N9PxC4fDuP7663HBBRfgpJNOwtve9jZMTEzgC1/4Avbee2984AMfWOhmK3P88cdjbGwM5513Ht7//vdDCIH//M//rPrH0THHHIPvfe97uOKKK3DcccdhcHAQZ511VtXPff/734+pqSn867/+K7773e+W/ezwww/H4YcfDmBhdQH32GMPXH755fjc5z4HXddx3HHH4a677sLvfvc7fPvb3y67dPTDH/4w/t//+3/YvHkz9t57bwD2yfUrX/lKXHDBBXjyySexYsUK3HzzzTBNE9dcc01ZW5/73Ofwxje+Ea9//evx93//93j88cfx5S9/GRdeeCEOOuigpvtORN21adMmr6as66qrrsLVV19d932XXHIJHn/88Tk1WYmIuonn4jwX57k4ES2alJBW+9Ot51uA7QUMdBMtcevXr8eDDz6Ij3/84/j2t7+NVCqF9evX4/TTT0d/f/+877/66quxcuVKfPnLX8YHPvABLFu2DO9+97vx6U9/GuFwGABwxBFH4LTTTsP//M//YMeOHejv78cRRxyBn/70p3jlK18JAHjjG9+Il156Cd/4xjcwPT2NFStW4KSTTsI111wzJ6Dh18j7FEXBXXfdhRtvvBHf/OY38d///d/o7+/Hvvvui8suu8y7tA6wT9b32WcffOlLX8JHP/pR9Pf34/DDD8c73vEO7zVXXnkltmzZgs9+9rNIp9M46aSTqp5cA8D555+P/v5+fOYzn8GHPvQhDAwM4E1vehOuv/76un+4NGP58uX43//9X/zzP/8zPvaxj2FsbAxvf/vbccopp5RlbgDAe9/7XmzatAm33XYbbrzxRmzYsKHmyfXU1BRM08QVV1wx52dXXXWVd3K9UJ/5zGcwNjaGr371q7j99ttxwAEH4Fvf+hb+4R/+Yd73qqqKn/zkJ/iXf/kXfPGLX0Q+n8dxxx2H22+/fc4Nh84880z88Ic/xDXXXIP3ve99WLlyJT7ykY/gyiuvXFT/iahEEQJqswW0F0DAzhS85557yp53M8dqufTSS/G///u/uO+++7DHHnu0r4NERE3iuTjPxXkuTkTUOUIuhXA9EREREbXFYYcdhtdccAUOPf61bW/rf772eaxX81Uvf69GSon3ve99+O///m/cc889OOCAA9rcQyIiIiKizrn00ksR+7/f4lMnHd32tn6zZRyXbdqKZ599tu1ttQszuomIiIioLiHs+tmdaKcZl1xyCe644w7cfffdGBoawq5duwAAIyMj6Ovra0cXiYiIiIgooBjoJiIiIqKedMsttwAATj755LLnb7vtNpx//vmd7xARERERUYtJCUhr/tctvqEOtNFmDHQTERERUV0qALX9Cd1Qmnw9K/AREREREZGLgW4iIiIiIiIiIiKiIJIAOpHR3Yk22oyBbiIiIiKqSyiAonSiRnfbmyAiIiIioiWKgW4iIiIiIiIiIiKigOpEje6lUBWQge55WJaFnTt3YmhoCIJpRkRERNQhUkqk02msW7cOitJs9erWUiCgduA8iOdaVInn4kRERNQNQToXp8Yx0D2PnTt3Ys899+x2N4iIiGg3tW3bNuyxxx7d7gZRV/BcnIiIiLopEOfirNHdMAa65zE0NAQAeO755+1Mkhbl8csWZKQEqS8A+1NPkPri16p+uYLcv1b2LYjbM2h9CuK2a0Wfluo2W6rbC+jt/qTTaRyw//7euUg3KUJA6URGd9tboF5T2v8VcA8hIiKizrGjy0E4F6fGMdA9D/cSyaGhIQwPD5ee5x/gdS3F/iymL0GbDyCYgS3XUp6vIPYJCNZ3DQjmPHGO5rdU56jb/WG5BtqdlfZ/AQa6iYiIqNOCcC4uZWfqZ7NG926q1VmwrSCFaEm/3M9odeByoVrVn6AGYusJep+D3L+g9i0o36teIKQM3Hy1qk+tPF5zjjrXH/fzgtCfblAEoHag64qAnTxDRERERETUJAa6mxTEIHc7+MfZyB/l7Z6XIAQHejG47dpd9ltXEPaXaoLar1ZqVUDQ/YwgBU2B4PWp1YuBS3G7Be17F7QFZSIiIiIiCjgJyA7Uz14KoSMGuhskpAxssLATQeYg6GawIihzQI1r5f4SxEApENx+tVLQMnLdzwhSIBcI3jwFbY6C1h+gfp+C+H0UAlCUDtToZkY3EREREREtEAPdREREREREREREREFk3xez/TrRRpsx0N3jdrdM425lubUyi4+qa8ccBzl72v3MxQpipjIQ3O9MEDOE3c9brKBlAbeyrEqQ5qfd+3bQtqNLEQJKB/oleLNBIiIiIiJaIAa6u2wxf9AGMYjUCUENAgRZu4LI7me3SiuDfu0Q1OAtELxgIBDMPrVSEPsVxGBuK/oUtH2pnceCoO1TRERERETUXbJDNbqXQglBBrp7VFCDbZ3SjQBTJ4Kw7RxXuwIzQQz2+QW5f0HtG4PdnRfERZSg9Wl37E+QxqsIQO3AV3AJf82JiIiIiKjNGOjusqUcuGm3dmQUB0EzAT1/DKSRt/RKsLvV/Qxi4Ja6h9uw83ohk7rTltrvLiIiIiIiap9O/Bm0FP7UYqC7Ry2lP/YXq9OZnk1ldte6tkQodd+2kDG53Znvbdx3Fi9oZR1cQd22zOrurKDOU1D7tRgL3bd7cR4EOlWjm4iIiIiIaGEY6CYiIiIiIiIiIiIKIikAqwMpIbL3004Y6O6SoGdzLaZ/3cgo7UYZk4Yy+YRSPavbfa5OZvdCsx+l7E5W9+5UvgRY+lndSzH7th04T50X1CsXljpFEVCVDmR08/tEREREREQLxEB3FwQxKNLqAKBfJwMS3ShjsuBgN2A/v4hgtxDVayg1EuzuBUEPdlPnsdYzAYvbfjwOEBERERFRL5Gydlippe10oI12Y6C7w4KUUdqpP/SbqmndAp3O7g5qsHs+vZDVDQQ7IMmsbqLu6eVgdyPtB+24pwCdqdHNQw4RERERES0QA90d1I4/qpv9Q7+bf9j72+7EH/CdDHgHMdi9VEqYELULFwSoFYK8GEdEREREREuD7Ej97N7/u5aB7g7pdhCk2+1X6mRgoFNBqEWPaQkFu4NsdwlKBi2Iu7vtZxR83T4WdLv9ZikCUDvQ3dq/BYmIiIiIiOrrqb8n7rvvPpx11llYt24dhBC466675n3PPffcg6OPPhrRaBT7778/br/99rb3k4iIiGgpEUJA6cCDN6MMNp6LExEREXWeW6O7E49e11OB7mw2iyOOOAJf+cpXGnr95s2bccYZZ+C1r30tNm3ahMsvvxwXXnghfv7zn7e5p+XanbFV7/OlEIHNGOtk34SUC8omlbL8Me/r5xtPnYxt+wMWd1Sp1nxL+t1sP1qcuRvk/rWyb8x4JmrOYr9/rbrqgWh30avn4kRERES0e+ip0iWnn346Tj/99IZff+utt2KfffbBDTfcAAA46KCDcP/99+PGG2/Eaaed1q5ulunmDRGDGuCu1EtlTNxu1vuIecdTr1Y3ULeESUM3MFvgzSlbrdWX5Qe59EWQ+9YKQStfEsSSD0HsEwVfL+03ihBQFd6McnfXi+fiRERERD1PdibbeimENXoqo7tZDzzwAE499dSy50477TQ88MADXepR+/RqkNvV6ezuxZovw3ve8Swis7uR/lc23Y2s7qALamA6qP0KmlbNU5D2+yD1hYioFXanc3EiIiIi6r6eyuhu1q5du7B69eqy51avXo1UKoV8Po++vr457ykWiygWi96/U6nUgtvvpcBtULhz1u4xuZ+/2OzoRm72WPvDF5fZDdTvv/ujZqay1ZnJQc/qbmX/Wtm3oGVQt9JS7lMvZedScPTKfqMI+9FuwZ8Jaka3z8WJiIiIlgIpBaRs/5lyJ9potyWd0b0Q1113HUZGRrzHnnvuCaD5oDWD3IvTS/M3X2Z3/Q60/ysoRHcvBQ96ve5WCnLflqKlevxbLO6HzQnCfrTQ+0gQLUW1zsWJiIiIiOazpAPda9aswcTERNlzExMTGB4erppBAgAf/vCHkUwmvce2bds60VUiIiKiwFIEoArR9ofChZolhefiRERERC0gAWmJtj/QgTrg7bakS5e86lWvwk9+8pOy5375y1/iVa96Vc33RKNRRKPRRbUbhGzkXrkUup5OljGZrwTIfF2oV8JkUTenrFO+BGjPdg5iaYnd0VL4DnfCUi7z0m2cj+7h3NNS0a1zcSIiIiLaPfVURncmk8GmTZuwadMmAMDmzZuxadMmbN26FYCdAfLOd77Te/3FF1+MF198Ef/6r/+Kp59+GjfffDO+//3v4wMf+MCC2m8kmNKJwNTudolzp+a07s8b6EK3Spi0Y1/YnQKsS/27tDtty8XiXO2+gnIcCPKNpQXsbOt2P4IzYqqm2+fiRERERLsrKTvw6PYgW6CnAt1/+tOfcNRRR+Goo44CAFxxxRU46qijcOWVVwIAxsfHvRNtANhnn33w4x//GL/85S9xxBFH4IYbbsC///u/47TTTutK/1uhmT/Gg/KHeysEYQFhsV1YcLC73g0r3bcGfFsHvU53K/sXpMBUKwV5joK+//eids0pt9X8OEcUZDwXJyIiIqIg66nSJSeffDJknT8Ab7/99qrvefTRR9vYq85ZyB+/S6n8wYJLmfgDxYu8+eN8ZUzcn7W8jMk8JUzaodWlHJbSvkjBE5T9Kyj9IGo1RQHUDvwa4tcn2Hb3c3EiIiKibrAzrtt/otyJNtqtpzK6d2eLCTgutVIniwoiSWveDOlWzNWiy5hUC2p3oN9zmgxwxCHIWcGt6ttS+t5Wase+1ZLvboD3+Ua0ov+duC8CERERERERUav1VEb37ipoQYF6/elUkGjR2cZu0LhGlnS9rMxGbk45b/ON9L/eTSprvSXg2aSt7l+Qs86DdHPDIPXFrx03nA36dyDogrifUDC4NbTbTfD7S0RERERUTgpIqwMZ3R1oo92Y0U1EREREREREREREPY0Z3QHXzUzHhdYE92tnZmVLslTr1L52P7vaGBrJ6payfq3RhrO63X42KOhZ00SVgraPBa0/jVrM936x461se77P8/+cGfjVBW1eFAioHegTMzCIiIiIiMpJLL6ywO6Cf08EWDsCLfPV63Z/3qq2210fvCWBgCbLg7iEWPxNsxruf6263bVe3uI5b2XAJch9A4JXuiEo/Wl30E0KsWRqm3e7/U5wt1et7VbvZ5V2h/kiIiIiIiKi9mNGd0AttZuB1cuOXqyWZXYDVYPJi8mQni+rG+jd7NGlrFVZ8UHZtkHoQ6NaVbd7sdswKNuu3ToxxkbmcjG/I3aXbdVtQoA1uomIiIiIukTKDtTo7kAb7caM7gBayn+wtyvDu27mYBOZ0LWyu+tmwXcys7sJS3k/qsT5a69ulFBoRZvchu3Rzv2B24yIiIiIiIgWihndAcM/8hennZl982WIuj9aaPNN971OfXGvTy2u1x1krcoE9mvF/PV6tulCxl9tvAvN1K31eVSuVdupHe10wmK/Z90al7/doO/nigDUDqRHCNg1CImIiIiIyCalgGW1/28WawmciDOjm4iIiIiIiIiIiIh6GjO6qSvaWbO7eoNK4zedrJMp3UiG72KGFOSs7l7JTO6VfvaCZvabRmowdyNrdne6qmGp2F23WdCPXYoQHarRzYxuIiIiIiI/KRdePaC5hjrQRpsxo7sJjfwBGuQ/UoOo0flyv9T+R83XtqJWdxc1HeCRVuNB/N1E3ZrtTer17/RC+8+658Et00HVcXsRERERERHt3pjRvRsKWk3QhWbvuV2v9taamXGNZnYvMqt7sVpdm5gZkouz2PkLeqZmJy1kLjl/9S3F7/bucMzqtfG14sbLDbXT/iaIiIiIiHqOlO0/U+5EG+3WGymuAbAUgizVslxbmfm6GIu6iViNt7ZzXELK7u8TlYF4ZnZXFZR9vBs6lc3d9e/CEsF5JPe7t7ses4iIiIiIiBaDGd1tEMRMtPn6E4SMycXMW63s7naPq93bekH9b6Budzd167ux2H0hiN/r3UkQjlEUfL2yn/TisURAQOlAvrVgTjcRERERURkpBTO6GxTcaBgRERERERERERERUQMY6F7iminbEIQMs8Vm4jX09hZnOy+mjMmibq5Z94ObL2HSiSTIRsfizmmtR7vb351xjoi6K6jfQbdGd7sfTOgmIiIiIionAVhStP2x2LDQZz7zGQghcPnll7dg1AvDQPcStqAbPAagnnFHLjufL9i9gGD4YgOx9QLerdgm9frl/qhdU9/MftXI/PVCaYJW6fb3kYi6Z3c61hERERERUe966KGH8NWvfhWHH354V/vBQDdV1cvBtcq4QM2x1ApmtyDjux0B73ZtkznzVaPbnQi4BD2oE/T+LXW9clzqlX7S4u1OxwQBQBHtf/DbQ0RERERUQQpIqzOPhchkMjj33HPx9a9/HWNjYy0efHMY6F6iWhFoaWt2t7TKHxU6FjwQytxHKz9+kQHvRVlA+ZJ2aNc+tOB5ZRCyJs4NERERERERETXjkksuwRlnnIFTTz21211BqNsdWIqWWrDIHU+1wGKtsdYNQlYLwLrPNRlotqSEUqUPUjq1PgPCnY9m941Fj0Nadee0ZqmUFs1fT34XFrAvVs5jU8Ne4L4/nyBnmgope3PfINqNeTW0O9AOERERERGVzHdvt9a2I5FKpcqej0ajiEajVd/z3e9+F4888ggeeuih9newAczoXoLaVuLCyfD2P5ruw3xZxr6f+wN11T7Ocn5uSen9/1LUtrrZS2DKagVzG7nJ59w3VVxdUONqg2ptNfJczTar/X8jb+3QL7qeUOfqkLpvW8h+soj2KLiCuDAUxD4REREREdHSNjs7i5GRkbLHddddV/W127Ztw2WXXYZvf/vbiMViHe5pdczoJiIiIqK6BAClAxW0mdBNRERERNQ9y5Ytw+bNm8ueq5XN/fDDD2NychJHH32095xpmrjvvvvw5S9/GcViEaqqtrW/lXouo/srX/kK9t57b8RiMbziFa/A//3f/9V87e233w4hRNkjKCsMtSw2GztI5QAWXOO7Rla3X7UM7nZkdTeTxT6n7niVjM/5MvRqZae7TwVp+86X1drSvjaROVvt5prz3qC0DZm58+6O9Ur4zPO5/s9uOCN5nrr487W34Az5JjLjqz0aaqfev+u0V639dra3oLnsZLZ6J/eRBbZV2V7jb1p8W51oL8jc0iXtflDwLfVzcSIiIqIgsqRo+0PCPl8bHh4ue9QKdJ9yyil47LHHsGnTJu9x7LHH4txzz8WmTZs6HuQGeiyj+3vf+x6uuOIK3HrrrXjFK16Bm266CaeddhqeeeYZrFq1qup7hoeH8cwzz3j/FvwrquOkEDUDvMIXBJAtrlG8GPOVZZkznnrBjAZrMPsD3O7/V6s/Xouo6IM3n/PU6W7ss+cZbwNtNF3Hul5AsUZbdUvDL7TueJ2xLXhtZb79pdXjqxVUb0f99gUE9lo6Lvf5BYxtXt1ur4FjSb1SOk1vt07W+F/E2BZ8LOnUPtKCYzBRUPBcnIiIiIhcQ0NDOPTQQ8ueGxgYwPLly+c83yk99ZfX5z//eVx00UW44IILcPDBB+PWW29Ff38/vvGNb9R8jxACa9as8R6rV69uax8DlXHbBc1kvlUGZoW0Ss81kNVdTbVs6GbrnDayDReTqb7Q8dR7W+VcztuVxSS/1wv2uf9bMT+1AlQ1+9GmLMiq7TXSViv70+kMz0YWYZr9yIVst1aPu4l6/91sb74g/oLaq/Fda9vCS7Wn23EMWWB7rZjHRrVjjnul3r4AoIj2P3bz06jA64VzcSIiIqKlRkrRsUev65lAt6ZpePjhh3Hqqad6zymKglNPPRUPPPBAzfdlMhls2LABe+65J/7mb/4GTzzxRN12isUiUqlU2aPTgh4sF1JWfUhZXnqj3h/v9QKz1YLdLVXnkvlqAdpawftq28kN1lc+5lOv7ErNn3mB8+qf32i7/se82hAYWlCAp8pCiP9zmhqTTzPbrlZ7jS5OVGuv9Ma57VZ+VstK9XQy8N7GtqpurwXMY6PT2uziUlOCVu6ild/7TgaXF2jBx6wFLIb0QoCbyLU7nYsTERER0cLcc889uOmmm7rWfs8Euqenp2Ga5pwskNWrV2PXrl1V3/Oyl70M3/jGN3D33XfjW9/6FizLwvHHH4/t27fXbOe6664ru7Ponnvu2dJx9Lr5spHdnzYc7FtAndYFB/iaaKNWkH6hTVcG72vNo6x4uGqNeU5wtM4Y3cC8+1G16pzPF1h32200QFv52c0EoBeyaFCr3UbaqqvGz5uqF18xh/OptpjiH8+8Y2tkm1V7m28faXi71Wmr3j5Sry3/olk9osl59bdX+f9VVRlb5f9Xvq5We82Mre42a1VbC9xH6rXXiFa31czYar2m2uc0PK4WzGPQg96igw8KJp6LExEREXWHRIdqdAf8b5JG9EygeyFe9apX4Z3vfCeOPPJInHTSSfjhD3+IlStX4qtf/WrN93z4wx9GMpn0Htu2betgj4mIiIiIlgaeixMRERFRJ/XMzShXrFgBVVUxMTFR9vzExATWrFnT0GeEw2EcddRReP7552u+JhqN1rybKDWmMqtbEaLxGzi6mZKAfTNFWefGfL7/nzcDbIFZddWy+BSnNSHQ+LiEAiGtqjfc9LItq/UFDWa3VctS9LcpLUDUvtttvXYqx1ctQ7HW2ObLHq93s816JVnq7RtlWZ4ojateezWzcutst0puqwtqz/n8srZ826zePPrbkjVuzDfvNqsyl61sa057VT6zlqrz2EDZnk7N45z2/N2UC7gCpc6xym2/3v4439jm++zK5xbT1px5bMPY5huX/7Obaatae/62Fvxd62GKEE3dIHmhBHO6A4vn4kRERETd0pn62azR3UGRSATHHHMMfv3rX3vPWZaFX//613jVq17V0GeYponHHnsMa9eubVc3d2v25d2A5cQG6oZ3vFIecu7DUS1gUCsoXPlv929xIeWCLylfSH3nskCOfzz+n9UtOVD+AKqXgylTOZcNsKScUyLF//+WlFU/qqwES4MlZ/zbrLLNqmOqVg6ioq3KbVcZwGxmXHVLTzTQXmWZmTn/bqS9OmOrVDmP8+0f9bZZtbbmrNsspK0a7c2nVumeav0q/2FjY6vXVs2Pr/y8GuNqtj3At782+l2r01ZlUL2heaw3tnnGNae2eZW2allsew3NY7X9oIltVtletefranL/IOoFPBcnIiIioqDrmYxuALjiiitw3nnn4dhjj8XLX/5y3HTTTchms7jgggsAAO985zuxfv16XHfddQCAa6+9Fq985Sux//77I5FI4HOf+xy2bNmCCy+8sJvDaCshZVdvZindmrcQUAScOkISqq9PpSCHLwjs/VBxnq+dye1nSUBxPrp+VnKdTEy33bLPrZ1lPV/W59xIofNvIWpnfs75CAkhhDe+mmOrnMuy/587h9ViNG5Ave48+oPP1eZSWqUs/CptVZtHt53K/cP/mXM+TAiUZR9XyUQuBb9Kz9Wdw8r2/B2vbM/HH/CqbMvfj4W0VW0/cYPq/jbn2/erbjN3x63TVr3+NzyP/rZ8/Wl2H6llztiq7CMLmce6GcI19v1q+0ej+381Nb9rNdqq/NxazzU1NvffVeax3tgq2/J2gWpB9cr2anyvF33VS5PzWK099//dY1bZPPrHNt88Ov9frT2l7LvS6LJBB4kav/Pa0A4FF8/FiYiIiDrPiZK13VJIz+mpQPdb3/pWTE1N4corr8SuXbtw5JFH4mc/+5l3U5ytW7dCUUp/QMbjcVx00UXYtWsXxsbGcMwxx+APf/gDDj744G4NYUmzg0T2/yuQXrC7+ovLg9xl5SiAOWU2KgMr/sBiZaDW5QYK6gZoawQwqwVLXYqoEzCqFrwvvdMOFDjBDgEF0vcXvSVLCwX2R5UvGpS3Y82/YOC+DuUBaHd8lWOrNY+V7VZPP3XKuXiBHNVry//ZlRRhxzRqljiotWiA6uOqHEu1BYM546kcly8w5e9Y5diA8n3Rv73csVXuJ+XZpVXGViWwXvmyWvu+/2VlQbJaiyENBPFr7f/u0/7tVnNsFQsvlUHTWvujv73a37fmx1Zqd+7/+/f9hsYG1A2s1zuWuG2VjW1O9nHVpbaaQfz5jlvVNDq2RheV6rVVemOVsdXYZt7Xcp62qu0jNcc2zzar1V7lgpmUpf9veB4rxlZ+3Kqx6EcUIDwXJyIiIqIg66lANwBceumluPTSS6v+7J577in794033ogbb7yxA70KlnZmdVetSe3/ufNfSzYS7C4vUVFWN9XJ6vYHM/1BI1nRB+HWTa3SRs2gqf2pNYM4lQHM0jtEKfjmrybqC5rOKXXhtiHFnExFN8gnpawaxJGyNI/+wIo3piqX6Dv5gXXT7yrHBaAsKDz3DVWCwaWeolYWfr1tVrMt+w01FgwwZyHE35Z/wcDdD4Hy7aZWZmP6xuXthw2MrXJcle2pC5nHKtuscj+Z8/Nq+2Tlvl+rrSqLIe5YarUFAAI1amcvYB+p9V1z95HKeRSNjg3uAoVaFlRsaB5rja3quND02Ny2Krf23BJI81/xUjm2yvYq57HqwlLl2CoWeipVW+Txt1VzoafW2OaZx1pjqzWPc8ZW1l5FW5WLS7723LYqj1d19xF/W/55rJX97/w36CFuBZ2pdxf0eSCeixMRERF1mpToUP3s3j8b75ka3URERERERERERERE1fRcRjd1X7WsbikELKuUkWxn85WyussykZ2svvKszFKmW7XsUj//jRrdtvxZz36iXtank2k3p/4s5mZY+y9Xr5ap7i+PUq1Oq3DGJ+U82cFVxuq2WTWzUlavnS28z1K8jrtZn+74/HPojk+B9LLj5/S/Rva41++KLOt5s9VrZD5XbWtOOQ8F1cuXzN1uleV01GqZ8cCcbeeVJ6hT3qb6TUNL++ScKyvmGZu3zdyxobKEz9x5rBzbHE57VbdZRQa5fx/xZ8VXtlVtH6k6Nv+PvczWKn2sMTZ3H5FCVC/rMN/YqtWpn2dsld8zbxvVa2u+utk1xjZveaeK9uZcZSCtsu9d5dUMZeNCaR4VX7Z7rbE1e0VD2bEKqF/MuVZ7dY6L1cZWeez3rmjwf5er7JNeW1Wy413zXRlSN4Pc4ZU7sntbdU6qfJ2DS4jq330iChTRokws2VtHKE+rxl9Lr84LERH1NgkBqwMZ3Z1oo90Y6F6i2n1TymqfLaWE6QXAhRfsnhPEhD/AUj3YbT9XCtJWBk39p5jVApmlzwBgmeVtAt7l6nbwrVROxLvq3AlM+QOlQHkAH17QqGJ8tcbk/VstC677g7OVJRVU34fPKeFQbbHAr+aN3UpzWGtsVeexVntCcepslwJi5SWTJUxZatvfFmCPqVo5kTltVe4fboBPWrBK4bWGtlsZf9Ct2rZz98WKsVXui/6xmU48V0X1uru12vIWKCpr1KMUVK8MqHv/38zY/G25Y/NppC1Zsb3c/4q628xXhx/lQXV/e/5+wJlHdzilsiwVY/OpttBTa2yVPxO+sZZppC00NzbVd3zz9hH/2Cxjblt1guqVizz+tgB7bFUXQzC3rIj39ffG5rZh99d/3KrVVvVjY/Xt5p/HuXWzq4/N+57N86uudlso+65J37Gjcmw1f89UjK1y4bFyHquNzTVvbXMiog5aSMC40SBwu4PR7dTLfSeiYOHCGVF7MNBNLWNJwLScQJ97u0XhBm79QTEnw84yAKsU+JZC8eIp1W60VhkQ9qsMqnsZ59WCfM7zpWDf3MCzVRnw8HVdVUT1gJg7Lvdhue+yAOfGTEJadkDYjWn5507Ct1BQ+oEQwsu2dt9YlvVc2Z5iBxOFUKrePLFWUN2U8LL1FEhnPkvBt6qLE765tAOaipO9rpYFZ2u15e0fFTfjm9OWZXnj8m4gV5Ed7w88m84OUgpRlxZe5pxOuGNzg4qmUXMe/WNz26w3trmLExXbrHIelVD5ogFKtZf9CwZue5Vt1Rybf3yVbTlj837k+69/bHPGhcoAray7gCUsA1IJobJiljuHlWMD6sxjvbEBpbFVBNZrjc3flrs/Vo7NG0vldvPPY42xVbbnb8t/lYHSyKKSUKrOo9cGfFcZyFL2vVJxzCmNR1YfV8Xiy5wrX2osBrptVZ1H/9gqtxn8AWgV7mJWWV31Om3NyY6vNzZ3PEqotG0dlUHuagtmqnB/JqG4vx3qtDVnbF5bpe824F7HEVwKOhOMZ9I40SIt5ktUeR7abNMBP44REQUJj5nB0ROLDhIdqdG9yFOBQGCgO4Dq3ewxyCTsQK0bDHaD3dVKRLhB7rJSEdICpGIHU7wAlep9thvosyoCwv5Apj+oXhYM9gXVS2902lFCFXH4UrazOx4vcCTsH6iKgGlJKJXRN2kBplHlpmsV4/IdoNz2ZEVb7uAUJzu/WskKN6giTMMXFHPbMiCUEKS0A8MWlLLAmzs2lxB2W2bN2hIV28192lmgqLY44QWea7RlOfvH3OCbVT6XgL2K4rbn3UCuslyK80DpAG0CECjtl5aEcyNFlEocWIYXUPe3VzmP1faTemNzy5dIWQreC7ctd7v5h14tY903LnfRpaw939j8yso3WMbctoCKwHr52PzfM7c9ty0hBNQ5QfzyILe/PSkUQA2VSjlI5waRzs/NKmOrNo++DpYHuX3bzd5m/iC76sxheRayf2zCngT7/xVRnvlcud18+6S9vQw7IKyEvO3nZgZXjs3ff7etudnxFQsG/uOWuxDoLRqgfGyYuzgHKb1jZPUgvjO2ymOk4tsnqpR/cRcnKsfmtiVQJYO87tgU3/5olbXpPyb721Oc+XXnce4VDRVBdf9ioG/xxX/sqgxyu/uJ25aqiLIFCrcdoMqVGu4Cnb8sl/t6Z7/0Z6orkJBSlA2jnVdm0eL9/ve/x7HHHotoNNrtrhD5NHMLpopzYx5ziIg6q0djP0uNsP/S7XY3qEUY6KaaqtXhrsfO6JYw3fcLAUVxg7ROiQ8v8OAGjqT3nB2gkhDCgpTOrikt77NLmZ/SjXk6/bIDcHOC6pVBPi8Q7AREnICwUAx7bF4GYXk7/qC6lPDS+tygKfzBDjcIZpk1xyWcIB+kBUsqpXlz/lv2J4clyoLr/nms1p6z4ey23Axo/4KBL+PZtMqDRqqw21IrAm/egoEbUPRtNzvD1FmgUEJlQfyybWa5ixClLGtVEd52C7ubxb/tKsfmBov88yhLta3dhRB3u7ljU5x90Z3HUGVKYuVigVPqxh1b2TxK4QUVa43NnUcBCVWKsirbotp28zONUkC4Ymx2UL16JrK90ICyIL43Nnf/r2zLDdK62w0oG5v/e+bPRPb2EYkq+0n1sXnzaDmLBv7pl7JsEauyLXceqwWfq41NCCeYXjGP3hRXLGD5s56hlOa7dJWBLB+bu/87bUEqkGrIt4+UBzH9Y/OTwt5mgC/HtywIXHHcArxjVtmigU+1xTmvrarzWBpb3bYsA1INl/cf5YsT/sVAex8sX+iZb2wSIXj7YwX/AuectkTpagAh3XFapbH5F3n8i4FqyHlP+Tx6C4FW+djctUnhHKeqXWEwZ3FOWmXz6D3nlECSvvbctoNMiM7EwHolzGZZFl73utfh4YcfxqGHHtrt7hB5mssQdBNKAn4AIiJqmDX/S4KEC4zB0AMLDu7fDm1vJ/hTMS8GuomIiIiI6pidncX4+DhM00SxWMSdd94JVVXxiU98ApdddhkGBwehqirWrl2LZcuWdbu7RERERES7JQa6qUy9sinz3eBSSgnDchYlLQlVARQng9B+gVXK4LbsLEyvHACcLC738n83G9qXiezP1jV8S1mqcLNa7SxC/w0ihZup6Muw9vqrhJzsYKts2crLDrbc9kp5Lt7onfIGpiWdIrFWqVSEqZddLi8AO4NPCdkZu0Lx2vOyPWF/lm6V59RI4YxP9c2jtz188+i/PN8t7SGU8pq6KK+HbFjSqxHrb8vNWJf+lEU3C9/US/PoZZA7Y7MMp561XVfXX9qjlInpjFsIWJZdPkFRJCwpyrJMvcxZd2z+toRSqqMt3JrgSqlGd8XYTACqO+WKqMjW9WWzuvuIv3awO4+ANzb/dqs+ttI+KZWK+tL+rGB3/y9rSzg1s+fWeraz8P039rT3dwEJWPb+L+2OVh2b/7tWukGkal/V4Mtq9cZmoWxsblvuPLp1ut19sTLj2WvP2WbCNEqZz/5xuQ+roiyLlGXzWBozyrOQK+fRfbt/Xn1jsyrG5rZlH7fs/bCsnE7l2Nx90v2xr0a3V899nrEJpya3KuCVJVJ9xyxvbO6xxN+WAueYVTpGlq5oKL+awbt4RdrzaDrlYCr3EVQ5RnrHR8DORq7I+q+8CqWyLXceS9nqcs528+8Lwm7UKe0RKduO/isM/FehmNKpG63CyRyvMrbKK17sSba32Zz90XfFiy+LHBXbDhBeSak5Y/Nf8eK25Z/HCm75F2Bu+aGgEWiuIMJi2gmaT33qU7jqqqu8qzQAYHR0FN/97ndxxRVX4IQTTvBeK4TARRddhFtvvbVb3aXdnZjvz7u52Y41v3dy7ms7ps7Nl3taN+eUaLegzv+SAOEVNUFgB7GEDPj2kEBHanQH8my8OQx0k6eR2uDua6oFvE0J6E4AOmzX13DKRgDeKbQX6NDtwGhFXVipWnZpA8sCFKvss00pYZiyrB3AjvG5QXXTu1OYv9yGCWFqpbadciLezcgs+3J590aDpQCVHVjUfG0pvhCYELAvz3duFukGVNx6t/al+U7gxS1/oah2WRZ/8MoLqgOGBZRdSK/YcycsQFGkPY+VY/O3BwCKG3i2A+t2fWnLfitK49ItJ4jp3iJN2HMJ2IF1b9huEN/US235AovuXEJRnXrd9mGlFJx1A8/w2lO9oLq93cpuWOoG2yrHpjgBU6HYbSmlAK0/qKj7xub1EU5NYkvCrCgnArNiHivGZtdwV+z90S1RIkvbzT82e7+Q3j5p+cYmfPuf254wtfK23Jv/WXa7btkE/9ik177dliLglFUQsBSU/2r2j83UympLe3uyu/gC+D5bwrDswGllW+48Smcf8ebZqgjgW0bpuyZKt1KtdkND/yKPV69YOIsEih2YVgXKg5gVYyvjjs/ZRyrH5n4HTN9kufOoKnAWzJyPcstfuNvNPZa4Tbnz6O4jVcbmHk/8Y1OFsOtLy9JrVTdAWrk44VsMlNKaW9cebkC9fHHOXYcTKC32WFKUFu58Y/PqdPt+5g/iI1RqsbQY4gaEnVNCWWrLlKV5rFzoqTaPkKFSeyH7e2JJtSzIXW3hUfrm0d+Ov2xJ5TzCKZ/jja3a4kvF2Ow1wNKx2HIWcQVK5Vi8sVVZnKs2j6X90Z1z33TwMtZAueWWW3DttdfinHPOQTgcRjgcxqpVqxAKhXDWWWdhYmICxWIRpmni/vvvxz/+4z/ihhtuwMDAQLe7TrshIcLzv8hj1v2p7GAJALFUA9uVmji8SwbFiZrQm98XnvG1SVPHT9UJcNf/nUi9g4FuAtBYkHs+piWhmRYU7w90+waRqu+jhRvoMDU7AOFkCNs/dCIxqgVhRb063W6NbzcYrFsSmi9CFVZKNw4QlVnI0oIwNTuoYvoyFoUCKAYQigBmGFAMSMWXrWvZgT7dkjDMUjDYf0sxN2vRedOctvwZtHYmpgWpRkrBZ/gyPqX0ArSVNcFDznm/IkoBqrKxWQZgOEFoodjHZ8V0ArQC0jIANVxWU1p3xldW61xxMxR9NYtdbhDT1CAMX1Y3YLejOmNz+iUB3/iccfkS501hZ/y7f4pFndrXqj8r3tQgDK0UODKdfURRIdWInSHs1JeWkN52MyvG5tbwDSnwMpG92upO5rF/bG6guxRU15z5UbwAmTs2dx/xj01V4M2jKe26yB43S9e/3fzzKBRIRFB2c0UpffskKoLPdnvOHQDLV18rx6YXvR95GcjOBHk18atsNzfY7o7NchZEIpZ09n+nTasUCHbH5v3Uf4WBU8PYvTmqdNrxj83e351655CwVDuYqbr7vjOuyn0S/rbg20dQvmDg7h9ue4pTe9gSQMgSFTdQtK9m8LabO49utrp/Hi3Faat0w0vdF+j2ZwVbXuY/SvtIxdiEXvCtJDg/VyMVN+z1X6lR2vct78ah9gKdhISp2Is8ZYtYzj7pHpNLO7LqfLciZZnq3lxac8fmb0sV9g0ivX2kcmymZs+r80aplgL4lVcYyCpjA+y2hJDePCruIpZ3PPaNzd2OcBZUpVPr32mjtO/LssUJd1HVbcttR0jMuaLBG5cXaLcg1ZB3vBdCKQta+BcMvP1C+m4UGjDCucKiIw0FzK5du3Deeedh/fr1c34mhMCaNWu8f++77774p3/6J0xPTzPQTV2hKpH5X+SYL5AqRf2ft5LoyDUj3dfM4kEAD4fUI7hI0ksYXG2LGsfP6t8NNxlSlP/tFTASAlYHlkaY0U1zzFfeI4gWEuSuNk5TAumiCVUB+sMqAMUO4lRekm8ZdkDR1CD1on1ZuWUCoQhEKAwpI0DYudTc6Zsb5NBM6QS6rdKN8kKKtyOrojxbUZh2sMjNIJeGbgccFAVQw4CUkKFY2YqfPyvYMIGcbnrBZzsAocIO4tvB2tIbnSCHOzZDBwwNUFSIUBhQwxChiJPVXcpYdAOzhiWR1y07qO8GO8J2A4pAWVDaG5uh2WMzinZ7gNeeDIVLwTAp7UCXdNuCPZdmqcRHNKQ4QXV7bF6WtZdlagf53LFJyyyNzQpBqPbYoJaCb+7YNNMNQtuBnLAqEJYK3MNo2R7o7SMVY1NUb7uJUMQrceO15QUxy8cWVkVZhm5l9jgsc87Y4B+bqtoBTSvsbDN/GZHysQFAWLUDpRaAmHQzie2iJ16Qzxmb1PKl/TUULv1Fo6j2AkXF2NwFCnfhR1XsIKmUQER1yki4KbyVY9Pz9pUS7v7vBi99pWD8Y3O/a+7Y3LZUYS8uld1g0RfkK9tulmW3paj2PLpjc9+G0jZzF3ncoJ/iLCSF4MvodrlldNzFCS1b+plSyteXQvGylN0FA93bH91At3TaEpCK0wfpHnoE3CtQSttNs4OnvrZKwe7yUkGyYmz+QLc7j6pE2TGrbGzOccv7TBkpnXb4jnXu2CoX57zFEGd/jFgSpkDphqz+qwv0Ynl5GyvkfZe9rGdpQULx7Yvl2dbOfVGhSiCkSKhwF5YkQm52tTs2vVjKsnYWOUvrT76MaJT2e3dRyT0e2ze1BXQnAFu+jxjlY/N/pmUCodLVDX7+BVW3Tbct+yoep13vPNhddLTHVrY457blLKq6QXz4Fnrc75ibLU7B9MILL2DdunUNv/7ZZ59t6vVErRRS+2v+rFbwq17wtV0Bs90mg7tFGLhcOjqyLXsrHLFA/E7UsrsfL+r9TqsWqpPSgoTunNzv3nO3VDDQvUQ1GmxfTCZ3ZbBbtyQymuHL6AYUocBURClQYrnBgAJkIQtZLHgBWhEKA9EYRMSEiA54QTkpAcMsBagKhoWMVgr+WFJFzMkCUZ1rv72a1ZZZCigW84AToBWhCEQ4DBGJQUT6IM2wd0m5m83nBrnTmlGWZWqPzw7ih6xSwEhIy85iLWZhFfOQWsHOIFQUu71oDCIULWU+o5TRp5n22DKaUZZlakrVWTSwz1cs6VwqbzmZwUYRspiz51Er2G0pqj2PasQJYoYhQ5ZTB7yUEZ/RDOhmKYNctxREVRUIK05mIUpjM41SsFQr2O1ZJoSiQobCENE+iHBpbP5sVs2UyGomdFNCd7ZpWFEQC0lvu5lOKQxvH9EL5WMzNGcfcbabMzZ3u0mUMkwrx6YKgWhIQcxO6fbVxBXOgoFWNjYrbwdN3bEpTt14oYa9kwYLmDM2d4EiFrLnMQYFuiXLgorC1MvHls+WzaMy6KbvhyBDEeczS2PL6xaKpuktGChCIBZSIJ20fytce2xWNm0H3QBIRbVnXvbZ+4gaLtsf3bEVDQu6ZUE37bbCqijNo7vNnC+pG+QTRhFSL0Lms4BlQiqqvc2czGo3wxrO/uz/XrttAXYAM6qqiEDAUCXCTikYCJRKo7hjy2e9bF0oij2PQnhXALhtuXPpH5sbxA8rCqIhBRHV3mcs9+9vs5RdLYwiZD4NqeultvpMQPbZxzinBJIUStn+qFsWCoblLSqFFQVhVSCiCvs44jtGemWd9CJkIQOpl8qyKH0D5ZnrspRx7x5HdEtCM+w23X0krCiIhARiKpx62k5I2T82PW8vYDnt2d9pN+O6vEyQez8Gd9HR/W67bdljk87iQek44l94kVreXgh0iNiAdzVI5cKjf+HFHRsA77utCgFVcedClI5ZFWNz938RiVXMo3/BrLTdDMveT1xhVUBVFKc8i/BdGeKMzTIgjCJg6uW/10JRr62yjG64i4HwssWDm0Pilm/pTDtBs2HDhqZev+eee7apJ0Tzi4aG6v68WgDEqvGHfbuDJbtjsHt3D0B1k5S7R+ZsJ0sOdcNS+w4thfH08j4npQUpDTtpTRoAStUCgsZNpGp7O8EcflMY6G6DXszqXih/zW7DkojndSi+v4RVRSBUSqV1LiXXYGVTsHJpyGKhFOwIRSC0GJT+IYi+Yad8iR2scQMPOd1ETjeRLBowLQlVEU62pApEVKhCVgRoNchCBlY25QVoAdjZutEYlNgARHTQDpw6mZNuwMgNcicLhhegcjM/LalCCBVhxR9YN72gm5VLeUF8oSh20EiLQY0N2n0yo4C0YErFyx7PaAaSRQO6L3XbzYYcjIRKAVN3bJYBWczZYyvk7EC+bx5DkZidPRuKQUgLJuBljhcMC8mCYbfttNcfVtEflgBCUHylCsrGlk3DchYovGz1aAxCK0CJDkCoGqQV9dWvhhN4Nu1Apmlvz2hIhW4pMKUKJRIq/9XoZLTKfBpWPlsam6JChCMQkRjU2ACgqt4+YlbsI+7Y3H0kaijQwyqGooBpKXYwzJ1HQysfmxPo9sYW7bODtKru/CKsPTYAKIYU9IclLKmiPyychQQn/dNZ6HHH5gWf3WCwqkL0wS53YEadr015AL9gWCia7oKBQNFU0G8pGIyE7AC4At+CQSnIbWUSkKYT6FNVO0jrjFOqEW9/cwNvGc1ATrfHpVtuwFRAD6voDwMDYaXsu2ZnVxfLt5tp2m2FwlAVOzNeqHbWsBSlMhHu4pUd7Jbe2PSQRL9U0RdSEPGVgLEXDHxjy6XKAt3uPEJRvLH5g4rucaRoWHZJFGFf0aBbCqKhMEKKhISdKV8qj2IvTpjphBPUtBexALeUvgKopcx4y/m+FQzLbsss7SNhVUFUVRBRFagCpX3EK1lij83MpsoCtLAsiKju1B8v1fqXQNn+XzRKgfWwqiCsWN48qibsrH/4xmbqsLJp+xjptKUYur1g5nzXKhfn3IWXnG56CxSqIhBWLERDCqIhAVU4Vzo4i1je1Tx63j5uuQuPigphmnYg373CICS9EjBalbGpil0aS7cUhJQwVAv2oqpTsscLqrtjMzRvHxG6DtFneTXj3SC+mxVvLzxazn5iv0cR9gKFqggoEL7FOeldzSP00kJI2eJcVIeI9pe+K779UXMy8N1scQq+a6+9FieccAJe97rXlT2fzWZxww034Morr+xSz4hsA6GVVZ+vFsyWFZfM1wu41AqGN0JpQVmSVgfFezG4tJhtsJT1UgC7lwOCQHC+N5XHriAIzNwEpB9A945Zzc6BaRUBKDCk7gS7qdftfsvoRERERNQU0cFHkF199dU4/fTT8fnPf77s+Uwmg2uuuaZLvSIiIiKipcy9erUTj17HjO4lqBvZ5MKpVTxb0O2bkDk3kHSzQO2OWXa5Db0IM5Ows+zc7GDAzp6NDQCWCXV4hV2nG3bmm+ZkDyaLBtJFA/GCDsuSUBSBkWjIq+urCjujDwJOzVQNVjrhZZDD0OwsUyczGIaO0PAyO8vaWflzL8d3s7lnchrce1+6lRpMp/ZzRLWzPt3scTdz1s6yztolDpyMbiXWD2VoFEINQ4SNUkafU/4iWTAQz+vQzVKWqSkB3VShCGGXN3DqRrm1x610AlY6Dqtgl8GAonhjs2IDUEJRO/NbWjBhZwa72bPxvI6CacGypDduL5tWDXtzCqf2ctnYtAKkrtmZsxF7u6kjy4FQGMI0ykoA5HQTs3kdRcNC0bC3aTRkoWjYGcGqEBi1VDs72MnGtLPw06WxFfNOZrA9NmVgCFDtWu72jQ3dDGt7u8XzupetHlYV9IVVb2wjUdXbnnZGd3HudvPtI0okBsWteW4asELwsj4zmuGNzS2nEFMVFAw7y3w0FrJLnLttOeUorGza3iczibJ64CIUgaKoEGoEMmyW1bDWTHv/L2UiS8RUu9yGaYWgCIGRqFq6eYS0IIyCNzYzOVPKDFbsjG5YFhQ1DBGOed8194qGdNFERjOcsdnfZTvr2c60HYmpXo1rFYAwdOcKg3Tp+1Y2trA9NkUF3Js1OpnxyaLuZca7pSLCqkBUtdsbitq17KW0b8FhZ1gXvGxuKzlTqj8eCgOKat8oUAlBhEpZ1m7Wc7Jg+LKspZfR3R9WMRQNIaxI3/5vlsaWTsBy5tGrPw7Y8+iUn3FvjuqWCcpoBtKanUFuOBnddsaziqGoirCiOqVo4F3x4o0tOTM3o9vod+Yx5NWBduuqu1cz2FnWpfrjMWe7DURU5+a9znHbOR7LQsbePwzNK7khYwNQjAG7NrUS8q54cbP+87rlZP2bXnvu8b4/rGLAucImCrs9+54CBW9spn9sigrFybhWFNU+BqB0rHWzuTPOPBbcfUQRzhUUKlRFQcSSpdIlvvIvViYBqZfGpgwMQXHLmPgy40v7iOXNY053MtyFQFi1SyCpQiBk2TXPIfz16Qsws2mnlI597wkR7bO3mWVC9DvZ6tKyK8c4JbLsDHW71Jfl1IffTS4K61nf/OY3cckll+Cxxx7DV7/6VUQijd/8j6jdxkTt0jmV2aRWZUZ3jZsFWC24iYAiW5dfVevGlU1ly3bpONuKuQyqyv0pKLqVRW11IdO8W2PtVIZ1pzKEOzmP7b4iod1z1q5t36nMdAsWDCuHolBgWgVIUVwatTt2cwx0t0m3ypd0s2RKRjOwYzYP1QlsWzKMsKpgIOLUmPZqBudhJWdgpRPQM1kY+SIU1S5hER60A90hPQ8RHfBu/qeZEsmijtm8jlRBx0S6WCpdMhj1gpixkOIEMd1yG0VY6TjM5AzMbAZmQYM0LSiREEL9MUhDh7p8DUSkzwvkGJYd5EsW7ID6RLoIzQmsREKKV5olrNilB+zB2oEO0wkoWuk4jFzBG5saiyA0OAhlZDnUaL8dBJGyLKiSLBqYzBSR18yysiyWDHvBKq8mrFOf223LzGagpbLePIb6Y3ZQPRoDIn1e7Vt3bLN5HVM5DZmC4QX0lg1GMBSxDwlhVcAacA4PznarHJulGRCqgtBADOpAAVZ6OUS4zw4COm3ldctbLMgUDeQ1+xfhYCyE4WgIplOL3LDCdhDfqVFsZVNlYzOyBQhVgRIOITQ4CHNoFCG3vrq0vFIK7nabymnIayY0w0JfRMVgNISiM7Y1VqSsRrHMlwLBVjoOPZ2z91dVQajfWXwJhSHcEjBOWwXDQrpoIp7XkdXKxzYUDcGSEqsHos42dPZ/owDTNzY9kYA0LQhVgRqLAKEIoChQIzEIsw9AaTHEbSvttOWObSAS8vb/Ff1hr5yIMA3IQtYbmzE7BUu393ElHPKCtG7teLctd2zxgo5kwUBWM6AZFiIhe8GgGHHGZkW8G3DaJSkKMNN2WzKXgh6Pe8FgNWYvGghFhRLp844ZbhAzWTCQcoL4ed3e//sidn16S0qsGohAV6W3/wtD9xYKzOSMPZ9O3X41FvHqcquhCBCOem25ZUviBR2ZooGsXvquDTiLISt0CxFVeIshwtQgtCxMd1EpOQOzWCztI0CpFr+vBIy7GDKb130BWtM5TqnoD1sYiYac2uDOzV/dY5ZvbHY5EacmtaFDGRy159EJqttjsxcD3eOIu0ChmRYiqh0I1k0LK/rD9tjcncQ5HpvZFKzUTFk9fKVvAFIbhrRMqIpqB+FRKpPillpKFgwUDRMF0/JqZhdNCyOxkH3TUvcc1dQB33bz2nNKl5SVgxlc6R2zTAso6KVjZN4XfHaD6oOREFRFIKI4FfXcBQNnbGZyxi5/5Ja3MTTIYgGKZUINheeUrfLmsWgvUgBueRsVsZDiBPTtBTPh/13jtlXI2rXOLQuibwCK07aiqBCDhlfr3F2cyOkmFGeBuOxmuQHTqfIqvRDkf+1rX4sHH3wQZ511Fk4++WTcdddd3e4SkWdPuabmzyrrjlaGEawqdUlli//gF73wJW9Aq+elEdW2T5B0un/tDka2ejydmJ92BjVrLYQ1oxVz0I4xLnZfWtQClnNIbMW4Wj03i56XRQTBF7O/Nbs98koSAip0IwfLymHub8dgsFO+2v87NNi/aRrDQDe1TLpoYstMFpGQ6gW7o6qCkWjIDlBZBoRhZwYb8SkUZpLQUjkviKnGIoiOFhGzTFj5vSH6hgHYJ5L+jOeJVAHb43kvWKMZFvThKBQhnMAYnIxuDVLLw0zOQJuZRTGRhp61A7RqLILIcD9iuoHQygSUvmEvCOJm6sYLOibTRWyZyZUFut2xxUIKRmLOV8iy7CBOOg5jtjQ2s6B5weDYqAZ1eQLKwDBEzL5RkBt4SxYNTOc0bJnOeYE+VRHOjTGlNzY7M1iW6nMnZ1CcnoWWzqIYz3jzGBnuhzoyY2ck9w0DpgFdkWVj2zGbR7poeGPLayaWDUa87eYFMZ0bUbrB0sJMEnq2ALOgQQmHEB6IITqmQV2eRmhoFMIa8jIj3bHtShaQyOne2Eb6whjtD8OUTi1mXzAYluFlzrpj01K50tiG8hgYWQ7Lqa8OaTlBRTvjuXJsfWEVo/1hrByOQhX29vWCmJbhZZcas1MoJtIozKQAwBvb4MAwRDiM0MCwfZM5p46vO487E3kkcjo004JpSYz2hbFsMALLCRj3h0sZ1rKQ9camxRPIjs94weDwQAyDkZid+dw3AMRK81gwLG8hZDajIV2wb1rqjs2S9v5vOFnP9j5peoFnY3YKmR1TXjA41BfBgBOkFaEI1NgggFKgL6PZ++NsRrPH5tREHoqFvH1Ec2726WarW9mUFwjW4gnkZ5JeED88EMNA34CdJT8wbM+FLNWLjxd0zGQ1JHM60gW7j/0RFSP9YVjOftTnLmK5Cwa5tBfkzu6YguXssKFYBANOoFuJDXjfNdOpqe5m/M9m7YWenGYiGlIw6Ixt1UCkdBwB7GNWPustmOUmpmEWnPrLioJ+AKplASFnH3HmRLckCro9tkReRzKne4shfc7YxvrC6A+r6AvbNza0s5Dz3tiKk9MwCkVIZ2x9poWQmyUftW+oaDl1zg0TiBd0xPM6knndW8SKOGPT+8JIF017wQzu2HTnyokE9Jlp6Nm8txgSGS4grPtuqLjcuXpCqjAsIK3ZCy+JQmlskZBij60vjBX99jwa7nfbNzYrHYc2MwMjX/T2/5iTAQ1FgbJCK2WrS3ubJYv2dks57amKQCSkYKQ/jOFYGLGQgpiq+NoqemMzZqdgFjSYmj2e6FgB6pBuz2MkBoyW6o9nNRNp5/dNsqhjNmNva1URGIyFMBhREVUV9IXtG45CsbzfNVbOzsI3MhlYugFpWggN5KAO2N8vKAqwzHT2Efs4lNYMZDTTu9mrJcOgYHODdPvttx/++Mc/4pxzzsExxxyDW2+9tcs9I7LtNxSr+bPKP90rY7XV/rRvZUA3qEHuTgatrS5EEDrV5GLH1s65WczN5Rbbr8XsXosJDLdjt15I+K/Z79ei96MFzNl8XWzHZ7ayLavJxhbyfVjItl/YvDW5vyxoLOXvSYsRIAQUQgkYZhKSdbp7HgPd1DI53cT4bB6RiIo+5zEYCXmXmbvZumYuhWLcDii6wWc30CdNC4qqIlLIQZimE8gBioaFjGYintMwmSpiPJH3gm9e4NkJqtuZwXZ2sMxnYaRSyM8kUZhJwcjmYeoGQrFIKaCTz0I1NfsGZrDbyukm0kWjrC03y3QwGrKDR5EQcroJS0ovW91KJ7xgaTGRhlHQIBQFkeEBSNNCLJeGzGchhgzvZpQ5pwTAbEbDVLpgBzGdQLcbWO8PqyiabrauHQyWxQL0VAoFX3tCURDqi8AsaOhfm4YylLODPtKClHBusOmMLV1Ewsl8doPqgB2EG9ZDzg3y7O0GowiZTXlj09JZGHkNaiQEPdcHaVmIZlP2JfsDhh3ENO32Us6CwWymiJyTrZ73ZQn3h9VSW3ACi7l02djsbHU70G1pBvpzKViFUYRM3ZnH0k3/EnndG5tmWBiKhaA5gcJYyC234TRmmXa5Et/Y8pNxAIASCcEsDKB/TQpK/xCkVijL6M7pJjLOPCZzmjc2zbAD75GQgpxuYiSmetnq9o1K7bHlZ5IozCZhaQaUSAh6roDo2BBE3wCsfBbKoO7d+K9o2mVEplJFzGQ1ZAp2uZShWMhbqLBLmNjBauGUbrDy2bKxmYWikxkftrP+IzGI/mGohp2h7GYi53QTsxn7u5bMa14wOK/bQbhISLHLp1iKt4/IQs4bWzGRRn7SzuhWwmEY2QKio0MQsQE7I9n5XpvOQk8ibwcU3bGZlsRgLIyi8x3P6aZTBsbecFIrQDpjK8wkkZ9JwdJ0CFWFGg4hPNCHaDTm7CN2oNKeS4mcbmEqXcRMRkMyr0MzTERCqlcyJTdql6ExnUushanDcsZmpFL2ccS5MkQNh6DGIugLhSGdfcSdR7st+7s2lSoikS8PdJuWRG7EzuYdjjqLWJZhj83ZJ/MzSTtAqxtQFAVCVRADEI7EYOWHSiU3AOiWhUzRwGxWw0zGvoLCzfp327VLjNg3LRVS2ldqFOx9sphIQ0vlvOOiG1yPhMOQfQNe4Nmdx4xmIFHQ7bE5i1hu1r9pSeSGYygaFqyI4mSrl/ZHK51AYSYJI695gW5FVRF1As/CMrxjv5317JRbchdf8naguy+sQjMsrOiPYChil4Jxj//CMsrG5i5yuqKwA/iyb8A79pvOMdLOVte97wBgB7rzmollfRH0h02MWM5NdL0bA+chsylo8QT0bN7bRyK6johp2Ys80T4o7tgAb3/MFA0oikB/WAl0BoUQIrBBqk7y/wE2PDyMn/zkJ7j88stx9tlnd69TRD77DFV/3h88kjWer2Tv7nO/927AYzHFSNpxOFl4YK/UGX8wp974Fpvv18mAd6uasmT5lT2NjMEtqScqeiEXmZW40PlbyPsWOn8L7WMzQcjGtsHiNdROEw01+/1pdi7nD1wv/jOa/bzyz67/4Y2Ot9lt28jnNrZPtXYfBVqz2LCQ3wEWgKgeQhEFpJTtyAf8NoadWJddCpVbGOhu0J///GcMDAw09Z6FlhERC9yzFtLeQtuq5i+bE9j5lxeghlVklvVhZqwfsyv7ER+OwVo9gFghAZEch7FzM+KPPI38dBKFRBF6wc6yiwxEEBuNoW/lGMZEH0JrkzCHJ5BVB/HniQy2JQvYNpPDeCKPZDwP05RQVYHp4RgmRmOYGuvD9Fg/sGYQw4oBNTUOa2ILko8+hfxkAvl4FlpKg2VZCEVURIaj6FsxjOUigtB4Ali2B4zBnXhsKocdyQK2zOawPZ7H1FQWhhMEC4UUZFcOYHqsD9PL+jE7EoO+sg+R7AxEfAfSDz+J3EQchekkiikNWlaDGlYRHgijb1k/VkAivCsJZVUC1sgkXsqHsDWZx7ZEHtvjeWzekYahG97Y4qMx7Brts8c22ge5ZhBDKEJJ7oI1uQWJh59GfiqOQjKPQsIOsoVjYcRGo1humoiuiSO0Kwk5shbjyih2ZTRsTRawfTaHJ7YloBVNmM7YJgcj2DUcxeRYPybH+qBODGF5Xwih9AQwuxOZR8vHphd0ex4HI4gtG8AKU0IdT0BZOYP44Axm8ga2JgvYkcxj0+ZZ5LMGDN2u3x3rD2GgP4LJkT5ML+9DdGoUy/tCGLByUJO7UHjsMWR3THtj01IaoAiE+0KIDkexwjAQXjOL8NoErOEJbJeDmMjo2J6yx/bk9iR0zYSpmwiFQ4j0hbB6JIbp0RjCk6NYPRDBWFSBmt4F48nHUBgfR24ygeJsCtnpPABADSmIDkcwlsujb800QjvjwIpJzPZNYCrntJXI4y9bE8jldHsfsSTG+8MYHohgYiSG0N5jmByKYiwWQr+Rhnz2UegTO5DbNYvCTBKZ8RRMw4IaUhDuD2EklUX/mkmE18QRWhNHeiyLeMHAeFrDjnQBj740i0RWh14wYVoWwhEFA/0RrBiKYmqsD5GJMSzvD2E0bEFJT8J4chMKuyaRm0wgvW0GZtFwSomoGI6n0L9qCpHVMwiNJ2BOAfGihamcjh3JAh7dlsBksoB8QYdp2PXww9EQVgxFsHakD6EJex6X9amIFRKwnv9z2diyk2lIU0IJK4gMhDGSyaF/9TTUVbNAUkVW6cds3sB4pohNWxOYTBUwm9WhF52s84iKob4wxodjELtGMTkSw1ifimFhQHnpERhT21HYNYnCdBKZnbMwiiaEIqBGVQzPJtG/ZgrhVbMIrZqGvsvATMHAdE7HeKqIR7clMJ0uIl8wvPnvi4WwYigKTI5icjiKFf1hjMVURHc9BXNiG/SpceSn4khvmYDuZJ0rIQWDk7OILR9GbPUUQiunYU1ZyCCCeMHEznQRj2xLYDpTRDyrQdNMCCEQdbPVJ8ewazCCtYP2PjKc3go5sxPG9A4UJqaQenEnjLwGy7AARaBvxTD6lo+gb+UE1BUzUJIqin2jiBdMTGV0PLIziclU0ctWt0wL4bCCkb4wxgaikHuOYGrQnsexqILQzsdhTu2EPjWOxPPboadzsHQTliURGxtEdGQQfStGEF4xCTUbhjW4EilDQbxg4s+7UtiVLGA6oyGR01DQTIRUgb5ICMsGIjDWD2PXUAwrnXnsj78Ea2YHzNldKExMIbNtwg50O1n/fSvHEB0bRGzFBMK5CKyhFSiEBhEvmHhsMoOpTBETySLiuSKSOQOqAkTDdva4vn4EuwYiWDsUxWhUxWhhAiI5CWN6J4zpCSRf3AkzX/QyumNjQ4iODSG2fBzq8kkoCQFzcAUSRQtPz+QwlbUD3NOZIqbTdqA7HFIwFAsju3oQqwejGB+MYFlfCKuQgZqZgTk7DnN2F1LPvgQ9V4CZt98XGuhDdGQA0bFxhJetQCglYA6uRE5EkSgaeHoyi6RmIKQoWNEfhrlqALGQXcYEALLZbMt+R1Nr3HbbbRgZGfH+rSgKvvjFL+Koo47Cfffd18WeLX1HHHEkVFXtdjcCb/3+tUuXuJo9/W/0Uulqy3W13tvI0t6iLtGu9vHVPq7Df9AvJkA+X8Co2SB0qy2mzcV0dyHvbTpwuoA2vPd2ILheazxu29XCBPX65f6o8m213tLMft3s2Gr10/+0qPG8q+l9c5FB7nZ//2rNdyP7WvPzv/igdjNtLmaRYiGZ6lICKd3AGPqxwigiUxiFZRW9n5umiT//+eGmP5e6i4HuNupWne5mSCEWFez2j++xHSm8+PtfQQlHMbbPYZhdPYbpzDCsDaM4fNUAoOdhTmxD5rnnsfnXz2E8XsBE0UBSN+0bEoZVrOsLYfXqafSvGoXSPwQxsAIaJJ6dzOCRLQnM7sogNTWNxJYnIU370vOR9QdgatUabF8ziMyeozh4ZT+EMIFsAvr4S9h277OY2p7CzoKBGc2+aVp/SMHqaAjrByIIxaIYARAeHAOwEjuSBWzansTmbQnMTmQQ3/xnGPkMAECN9GNm/6Owa9UgJtcNAXsvw8Er+yD0PIzJ7dj1f89h9vk4diaLmHLGFlMVjIYVrIuFsBHAskNMxPqGgKEVmMyaeGYig79ss8c2+cxj0HIpWKYBRQ1heP2BmFy1EtvXDCK35ygOXTkAQIdMz6K4bTO2/e5ZTO9IY2fBwK6CAVUIDIdVrIyoOCSrY/nBWQyFQlAHxpCRFrYk8nh0Sxxbd6ax88mnUUzPwNTsA/nA6r0wsWIddqwaxL7rh3HoqkFAAkKzxzb+wLOIb054Y8sYFqKqwLKwivV9IUAoWG6ZiAyOoNhnYVemiGcm0nhsawJbHn8Jhfgu6PksLFNHbGQl+lesw8SKEexYM4T9lw9gJKra2dyJSUw+/Axmn5vxxjZVNBFWBIZCCtbEQnhZ0cDyg/IYCEWh9I8ia/bjpUQOj26JY8d4BuNPPwstk4BRzCHSP4zYyCpMrliB7asGsedoH0aiISAqoBRzSD71PGaf3on45gR2JYvYmreDYX2qguURFRtnC1ixMY1RwB5bxB7bEztTeHpHEluf2IJCchJGIQtpWuhbthp9y9ZhfMUA1o7GMBhRMRINQRga8ls2Y/aplzD7/Cxmd2bwXEaDLiXCwh7bwTvSWHbAciw/WIeIRGCN7o9k0cBLiRz+vC2Bpx6bQHZ2HHomAcswEO4fQmxkFcZXjGHbyn5sGO3HYFQBQhJKMYPEky8g/uwuxDcn8NR0Dnnnxot9qsDGNXGM7TOKZS/LYFgowF5HomBY2Jku4vHxNP782ASy8SQKyUmYWh5KKIro4Bgmlq3AthX9WD0Sw2A4hLGYChgF5F58Holnt2H2+VkkxzN4PqPDAhASwFhYxf67Mlh2wCqMvUxDZO/DYUAiXjDwwkwOf3p6CunZHPLxKWjZFKQ0EekfQXRkJcaX92OkL4RISGAo0gehmjDGX0Li6c2IPz+JxJYknpvKoWhJKAIYDCnYb88klu07hbGXZTEgFFjrDkXBkBhP2WP7yxOTyCZSKGbiMAt5qLE+RAfHsHN0GGFFATCMQaemuzm1A7nNLyDx3HbEX4hj80tJZAwLFiSiioJ9Vs1gdMMIRvdLYfRAA+peh0GzJKZzOl6YyTpjy6OQisMo2MeRcP8w+kdHYVkSh+4xglhItbO607Mwdm5G4tktiD8/iS2PTyKp2zcDVYXAXsMRDO0xhLF9lmP0gAL69j4EUgJ5XWJHuoAHn5tGajaPTCIHPROHaeoIRfoRGx7F4EgM/REVCgT6IjEACsypncg+/xySL+7E9gd2IF4wkHMyudf1hTGwuh+jG0Ywsl8GY3tvhDWwAkXTwmS2iAdfnMXkZAaZZAH5ZBpGIQMlHEW4fxgDI/Zl88YaiX53bKlpGDs3I/n8diQ2T2H8sUlkDLsmfFgRWLNqCkPrhzC6TwIr1u8H0TcMUwWymoXHd6Twwq4U0vE8cqkiiqk4oCgI9Q2ibyCGnGbg4HUjCAmBvlAUSjEHfXI7cps3I/niTuz80zgyWQ1F58x45bIYBlcNYGTDGEb2y2Ngz40AgKIp8fx0Fk+PpzE1lUU2VUQ2noSUJtRwDNGBGGYzYzhwzRAsOYj+sAoBHVZyCvqOF5F6YQe2//ElFGYLyGkGLADDAxH0r+jHyJ5DGNqQwNge+0H0j8JQIkgXTfx5RwqT6QLCqsABq4ewcXk/oqFgnjcIsEY3AJx33nlVn7/gggtwwQUXdLg3RHMdOJgv+7c/k7ZeFm1lxq0iqgSt5dz3V3tdNZXvrfU+/+uazfpdbGzJ33aj46p8X733Vpu/yjE2kvns/9NtvmPmQv/MW2ygVVYZ60LbbCTY2syiSPOBvvLPrhZoq8wBrV4GqIk2GxzPnHlvvInSe5pe+Kpss7G+NlIqqfZ7y9uo7LKo8bzX1gIypBvdhys/uuns7sp/N9jXqvt8k0Hixualue/yQvfJOfM4zxurz1vz28ySQL8WgZ4bxaw6BCFCAIq13tpVUlb/PdLydgJ6v6BmMNBNC1YZxJ9IOZft60Vkdm2GEooiNhBBPKvbdVNNHVY+jfx0EjviBTybKR1ALCkxpRnImhZUAWyYTSOaz0FxatBOpItIx/NITkwiteNZO8gNAJaJ5I7nIC0L4eiemByJ2QdFp5RCcTaFxM40XsjqiOulmyFkDAsZQ0PRlNhjOoHhbArCsj8zVTSwK5FHciaPzK7NXpAbAEwth/SOZ6GGNmJqIIxETrMPqqYBmUsjvSONbYkCXshq3ntypoWcaSFjSOwxnsLAuhSihSxgmchoAtOZIjLxPFKTUygkp0pzYplI7XgWABCJhTA9VvRKl8hCFsV4CrM7M3g2oyPl3OTOkhIzmoG4ZmCvHWkMrE5iMJuGMA0ULBPxrIapZAHp2Ryyk1vKtl9m5wuwtCLU0H4YH4hAM+xfAcLUYGUSSO1IY0u8gM250th0QyJjWMibEut2JTG4Po1wPgvTAjKaiYlUAel4Hqkdz8LSS9s7P7sTRiEL4ABEY2GnBIy9Pa18BpldGUzvSJeNTTclcqaFtGFh/XgWg2vS6HfmMaebiGd1zDhjy+za7LVVSE6hmEnAMnWEwnsio5twhgZpFJGfiiO5PTVnbDnTwoxmYP2ONPqXxTC4RxphXYNpSWQ0E5Pp6mPL7NoMo5iDohyAmYyGwjK7Hi8sHfnpJDLj9tgq98m4bmLNzgwiQ1EMrEkjms/ChEROszCT0TAdLyC180XkZ3eW5j+XhJZNQMr9EI6sQla3a3dDSshiHvnJ0tgmioavLWB4Oo9QREXfsjgG90hAAWBI33abnERmakvZ/l9ITsEwClDUPe0sXsO0awpbJgozKaR22PvkC1kdM74yEVNFA6u3pxEdimJgTQZRp660vd00pGdzSO3cjNz09lJb8QnEckkA+2M6oyGnW87YLOipNHKTCSS3p7B1No/tzlUhADBRBMZ2ZRDuCyG2IoW+Peybi2qGRFIzMJ60v2u56e3QMnHvfdrgGExtD0ymR5HR+r3938plUJhOIrUjhZkdaTydKT/xiU4LO/t/KIKBNcugOqV0cpqJ6bQ9tvSurcjHd3n7iRrph15Yi6m1Q5gZ7UNh1C57A62AQjyJ3KT9fXvet/+79tyatq/aWJ5Gn6lDSqBgmEgUDHt/nIwjN70NhfQsYJlQwlFomZUwjQ2YTBex17I+756MVsYu+5LakcKLqSKmNANF56wyb1rYw7SghFRERpIYNXRASpiWQEYzMRsv2MfI6R0oJqah55IQahjR4WUwi+sxvnYI68f6UDBMSBm2j1mJNDK74kg7Y0voJnQpEfVFTyMD9muFlLCkRMEwMZ7MI5MoIDWdRG56JwrJKbvcVd8gjNE1mFnVj+mhGNY7x39pFCFzaeRnUsiMZ7AlXkBCN70gvm5JrNEtqBEVkZFBDJilGxFPpoqYjReQTuSRnZ5GZmqLXWs71gdtaDlSo32Y6A9j7WifvY8oul0eZTaFzK44pndlMFkwkTUt6FJitW5hTcGAUIDwYAyykAekhGUBOd3CZLqARKIIJQSM9Ue7koFHjfniF78472uEEHjf+97Xgd4Q1bZhZQJoMEhbGSQVFe/z/7xa8KWZhalmg2lzAluNvH+RAQB/m5Vz0Yr3LmhM83yGn9vuQoLMgFcRrfTvZt47Z2xNLlIstM9NvM8/3039vq0MdM+z4NPIgkbd5vz/38D4FrPIUG0fbDjIXvm6BubUPzcNB4QrA+TNjK+ij41s9mYW2yrnr7HPr/Y5849pbkC9ubHJJud+7tgWsC82+Nne+5tob6ELJ9UWTUKKgrQWRtQchBC8cmwpYKC7QUHPzA6CdKoUJCymZpCb3o7cWB+SeTcobcHK51CYSWOzLxDslzMtbMsZODSZgdTsgIApJabTReRSReSmt5cFFe3PNZGZ3ILoyErMZOxAJKQFq5BHMZnFtpxRFlD0217QkZ/OQc/kEXHq68azGvLpIvLxybKgoquQnEJmehiDo32IZ52btVk6jFwe8ekctub0Oe8BgJRhIjdt90kW81CkRF6zMJ0uIpMsIju5dc57LN0ec2Z4DNPpIkxICClhFrIoJrPYnp8bCAPsA316OofR2SzMfA4Ry4RmSSTzBnKpIjLTc8cFALnp7YgMjiE/2gfNsj9X6kXomTzi8Ty25KpvtynNQG4qBy2Vg9QKsKREXrcDfdlkYe42gx2kzU5tQ//wEPKaXevcDtAWkJ3IYlveqDq2nGkhM55BYe+0HRCzTPuGhlkN2WT1sUlTR2ZyCyKDY0jmdehOEFNqReSmspiJF2qObSJRwNB0Hlo6h1gxD0MC6aKBeFpDJlF9bIX4BEJ9g5jNrreDwQBgmdCSGWQm7bFV2ye353UMTWRRTKRgaQVAAgXDwmxWQzZVqLo/GvkMspNbER0cRlY37ZuISguymEd2Ol9zbFvzOoanchiatff/KOyFi3RBRzJTRHZ6e1mQ25vHXS8hHBtEMrcemuUuhhgozKaQnchhS648yA3Y++N4Rkf/VA7FZBZDlgmp2PWQpzP2NvMHuf3zqIZjmM2uKS2GSAvFZAY5Z2zVvm/b8gYGJ3MYnk3ByqUBAJplIpXTkcoU5wS5AXj/TmX2QiqnQ7NMSIQh8xkU4mlkJ3LYnp/b1ta8jsHpHPqm+zGcziEG+yQvZ5iIZ4vIzUzMWVQytRyyE1uRS++HRF5HwbCrzlmFPLRkFpnJLKZn83P2/805DWFFIDadw1Ay4wSD7X0kldORSRSQndqCYmqmNPfOMUQoKmYzK5EuGt7NWM28vRiYnciVLRYAwE6nPEvfRAaDawcgNbeOu0ReM5FNF5CPTyI7sdVbeJSmjkJ8ArAsZDNrEc9qztgkrGIexUQWuekcZqZzmPLtI0VLYnteR3Q6h75lfZDFPIS0vzcFw0I6ZR9HslPb7M8HIE2gqBdh6kXk0+sQz2n2zW6lhNSKMDJZu6zNZBZbclrZya971UZkOofBZMZb5DQl7O9auuAFud3vgKnloOczyK5dj+mhKNIFex8RMCGLBRST9ti25Iyy/b9o6nbW+mQOAyuzzu81+4qAgmEilSkimy5AUQTiI0VYkPaJewBPOQQ6060ADh0AcOONN5b9e9u2bVi7di1CodKpNAPdFARDLwMqv0livpKj7t85lX/vNFuqtPKyj2oRnWoFvudLf20gIlwZpK37HiFq/LzGexqq+9BQJL6pCGvVLtaK5sw3n41oZg5rtdNo5mwjkS7XPGNraMGgMohfv/5B9aebTnNv/LPLXuJ737xtVgbtKsfZaFC4yX7Zn91cW2Wvr7YYUOf9zbblva6JxZt6izX1+9bcAlbVQ0CNz68V1PcHgWsHk6vcX0HW/3npZ/XbBBq4iqJsYan2vR6aeU+1flS8re57q32GPdYIZqIqorkBCAQ30C0hOpJtzYzuLvjKV76Cz33uc9i1axeOOOIIfOlLX8LLX/7ymq+/88478fGPfxwvvfQSDjjgAFx//fX467/+6xb2KKB/kbr83/oWdrNa4D81myv7d3ZyC5KjK7Fj1YDdDd3OME28lIRe5zfAlGYgszOBFZkkIO3AXXwmh/jW5+YEp1yWXkRy65MYHD0augU7Yy6fRm7X7JwATqX45gTGDoyjP2/XQh1P5pGYyiK57ema78nsfAGJkVXYvmrAvu9lMY/CTAJPO6Uoatk+nsHorjisTBKKZWImZyA+m0dy+/PQc8mq79EycSS3P4/k8sNgWBKwDMiMXZfYDUZV80RKw9BLSYy9LImYUUTGMLA9nkNiMoXMzhdqvi/x0mOIDY8hpzk3nysWkJuM44lUse5588T2FMYmZjGSTcOQsG9kOJ1FctszNd9TiE8gOT6CpLafna1r6TDTcbw0mS3LQK70TLKAoa1JjB4YR8TUkC0a2BHPITmVqDk2Sy8isfUJTKb2RcEwIaQCWcgisSWFJ+uM7cl0EctfTGB03xkM5nMoGhYmkwUkZ3JIba+/j+yMH4J00YBl2cHg5LYENk/mao5tZ8HA4FQOw9uTGNsYh2lJpDQDO+N5JCeq7/uAvY8ktj2LqdTeMNbYvwKtXBqJFxN4LFmoOracaeHptL2PjO6XRBR2cHY8UUByOl9zf4RlIv7S4xhP7o+MZthHQEtHZmcaL83ka47t2UwRw9tTGNlrFpAmDCmRLhqYiOfr7o/ZyS2YiR+CZN6+SSWEidyuWcTrjG2iaCA8k8fAjjRGZ1OIAMhpFsaT9thqHUe0TBzJ6TwmVhe9/b8wk0RyaxIvTOeqft9ypoUn0xoGXkpgdO84hi07Ozue1zExm/euyqgkTR2JiSS2j8aQXjts3yQ1m0RuYhbxzUk8map+ydyzmSKiOwUGVycgDA0Szo2Ak3mktj9dFuSunMf47Eb7KgPDBKDaY9uWxgvTuarv2VkwgEQB0ZeSkIWsnU1vqZjJaUiMT9Y8RhaSU0jOZDG+PIaMPmxnWWeSyO2aQXxzEk9UGVtcN/FkWqL/paR3fLQsIKUZSM7YxxH/FS8uI59BfCKOvqEIEmuHYVqALGSRm4wjuTWFzZO5OftIzrTwdKaI8E6B/mUJ+0aSQqBgmJiaySExPonE1ifmtGXpRSTHdyDaF8LEigHkVlgQIQ1mOo7MTntslYs8upTYmtdhzQCRl5LYK5+FYpkwFXv/T07bVxiokSimBqJ2/wOa1S1EqXZ4W9tZ4MlKs+eHzdq8eXPZv4eGhnDvvfdi3333bVkbvSJ45+Lkp5x+XO0fzglk+yKIQpkb2K4RIZfzRs4rPqZuVNH9UN9rrBrP+1nlz88pxdhIm42qNt6Ffn5llKuhO78trC2x2Dv4VbY7/51Lndc10N+an9VEn6u1M1+x6lrvrVuwegHbzHtvtT7Web2/X3Oju1VeP89r5tsWcyOEjfWt2pvnq4sx3/5Uq2nf62TduW9gfhopbu1vz3u+ziJV2fGqgW2GBhbmarRV9jKrxv/X/Ez3+cqXNbboVLNt/2vq7CJzmplnd6q74FNngadmAL/G50sJDE6PIKkPYSQzhp1KGPWjR9QLmg50n3feeXjXu96F17zmNe3oT13f+973cMUVV+DWW2/FK17xCtx000047bTT8Mwzz2DVqlVzXv+HP/wBb3vb23DdddfhzDPPxB133IGzzz4bjzzyCA499NAW9iygwe5qiQZNddP9gAbeJAGtSkBZz6ag62vtfxg6zKIOLTv/ocPIGzA1DSEAFiS0ogE9n6r7Hi0Th150M4MtwGysrWJeh5EvekfQgm5Bz1cP/JT1MZtCQVsJAJCWBSOvIWPUP5lI6Ra0jAY4l8oXdQt60YCWSdR9n55PQSsa3iaRhg4tU39sKcOElrXnHIYO3bBQ0MzaAUz/2IpFGO5vFNOAqeleSYNaMoaEltUgTQOmJVHQTRi6heI8Y9MycRR15z7K0oJV1JHS689j2rCgZ3VYmgFIiaIlUSwa0LP19xEjn0HRsMrGlsvpdRcnACBrmDAKOmDqMCwLRcPebvONzdBMGG76rJTQMxpS8+wjSd2EXtBhaTokAN20oBdMaPn6203PplHQLXsxBIA0Da9OcC0pw4Se1WEU7MCjYUlvbHVZJvKaAcOSXukeLashUePKiVJ70v5OSsspuWFBK9Z/DwBoBQNF3YQp7T9kjbyGlG7OMzZ7H3FvQGhIiZxmVD1OVbaVLxrePmJqOvSsjnSd7ZYzLeSLJoy8nTlvSaCom/POo5HPwNBWoGg5+79ehJHXkTGsuvtkUjdh5A0A9jxqpl0qZb79US8a9j7i7JNGXoOe1eqOzZ1H95hlSWkft3L1v2t60UCuaMI0LVgSsHQNWtYeW61jSc60kNEMuy1pQUoJw5LzfteMfAa6thJFN+vfNGBp9vG/3j6Z1E1oWQPS+cPNMCWMolF3bFomDl3bE0XDdPYR+5hl5I26x/+UYUHL6pC6vY9Ipz2toEPPpWDpUWja8obuKE9zNXt+uBR063w8uOfi5JIB3Od5ZOsOb94bLlBcGYisFc1a4Pvq9aWZYH6Vz6i5mDKnbw20Wa3/Ne+IOE+/q17V0NhnlS1UVE2xrRM4rhm8bHRxoMH3z9dmo4H7qvU85m67OVGJmvvofAsAjS0QzKlCNCdwXl5so/xn1bu26AWAuoH/OuOsFZCvMvdzgt+1Pqfa51V5TtZqu+Kz6wbT635G/b7MHU/5y0cyRQyGBhBBCEIJckZ3k2WXFtrOEvil3XSgO5lM4tRTT8WGDRtwwQUX4LzzzsP69evb0bc5Pv/5z+Oiiy7ybvZz66234sc//jG+8Y1v4N/+7d/mvP4LX/gC3vCGN+Bf/uX/b+/eo2wpyzvxf9+37vva9+5zP4AoQYSjoAyaKEYSvCyjGYeYiQqowZhIIsFllMQBoyZkRTNhNBkkmRGUkZ/mImhcEUNQNFHwgh5HTUAhOCBwLnA4p0/36e69d9X7++Otql37ful97+/H1UtO9971Xurd1dVPPfXUOwEA73//+3HHHXfgL/7iL/DRj360x70b0WD3AJXC0h8V39s4AT/8418FAYJCEcUmWciR4okiVMnXR0UFlApBTRmFun0o+uXfN6USSm0E0VZKSgcVSzoAtlEM4ofGNW2rcAJ+VFogDAa3suoHKBV8BL4O0Bb9AH4xgF9oHljXAbEgrtEdlHz4rYKRAIrrOuCjggA+wsDrxlrL95XWVuJgmCoVEWy0HttKyYe/7gOlIoIAOqBc8Mv11Bu1VVhD0dcBO6EUgpKub9vMCT9Aca2EoFQCAh8qUPBLCqWN1hco1oul+BeEKhWx5rc+kp8oKQSFEpRfqhgbgubrqxQG1ZVSECqAX/CxUqccS9KqH+gLPcUSlNKBbr/kt1z/fuFEWDNbRY1jpdR6bOtFH6oUBjEDhfViCcVCG2srGVRXCsUT5QcZNrJS8uPPpFJAyQ/i40MzfslH0S8/R7tU8Fvut5WSj+JaCf5GKWxLoegrlDbql6iJlIq6lEg0j/5GCaWC3/Ii1kopQGmjpB9CDF1TvtXYiusrKJUCPTal12Npo3Vba75C8UQxPgvRwec21mPBL5fSAaBKJRRbBGhXSno9RsfHUqCPW62OkaXCup7vqI9FfcxqNbYTJYXAL8WZeaVAoVRYb3ocKa6toFTQa0SPq4jSRhGltWLTNbkaBrbjID70Z9ZvchwpFdZQKpbCoLrSdw+F67r5PPoorpeglE7ZVkpffCltFFBaX4EKfPhFf6SjQUIM6EGRXbTR6fnhJBjW+fhon4sTUV1tP0m4KsBjjG7Ap9pAfn02zWhvFtyvFzhvvQ2VfF8HwdG6Qf+GQf12X9eqbkmTvlYEw8uvq7njoJs7CtoJ6LcMBjd4baO7FDoOoje52FK97Q63X3MnS0U/G/SpjeB8fMRosO9qftZgc42D8m0E0psGxDsIxDeZz2hInleAZwSwhITouGYXjaKOA9233XYbDh8+jJtvvhkf//jHcc011+CCCy7Am9/8ZrzqVa+CZVn96CcKhQLuvfdeXHXVVfH3pJS44IILcPfdd9d9z913340rr7yy4nsXXnghbrvttobtbGxsYGOjfFv18nLzrLWuDSsu3sd2g1JtICIobiCIDjyBDthttAiGAYBfCuJf7oECgnZugQNQKpbijDhVKiJoEVQE9IPJgiioDiDwAwQtgrMA4Bc2EJTKx1XVxrg2AoWgqBAUyw9YbCfQB+g5iA7LquTDL7Q3Nr/kA4EOSgaBgl+svSBR01Zy/CqAX2wd+NwI9H6L6s/6SpUvBDThFxJrBEDg+1hvYy6Doh/PY0kpBKWgbr3sakVfVazJYhuXRTcCvX0EAQIAxZIP3289NuUHKMZj0cHxVpnxG75CUAziX8K+AvxAISg2D9ACOlAd3dGgVPOs4Li9oLweA+hgcFBqPY9BEJRPMgIfQZNM3XJbCD9r4dgC1dbnxi+pxDwCQTFouUYC6DUSB/GhUCwFdY9TScrXJVKiQ44KAvjrpZZzWVLQawRRtm6AUovPdlAqQvlBvB6V0vtio0UQfyMoHzcUFFSgyncONGvP16+Lg/glH0Gx9TopBUHFSXHRV/BbfNb8wgYCP4jrgSMIEJRUW2MLElnYKlDwC83bCoq6rZIfXqAIAqiSr9trsiY3ggB+4mJooJS+8Njks+YXNuCX9ANHgwDh2tcXVJqNLQD0777EsdVXCkGpCL+4ASEk/JLiwyhDvu/XnH85jgPHcWpe28354SQYxvn4xJ2LExF1oukFgyYXBQZ8wSA+lWgnSxroOhDfPLu7/s+SQXjVKgDcqr12s+wrgqCNg9ANM+ibBfEbBcPbyZJvNn/V265+vWrQ16b7oMV26rbZoJ3q7QBNg9wAIBoF2JPfbtT/iu/XD8Q3zW5vUMJGAIBSsNx12BIwOizLNXAKHT2MtVuDaKPfuqrRPT8/jyuvvBJXXnklvvOd7+DGG2/EG97wBmQyGbz+9a/Hb/3Wb+HUU0/taUefeOIJ+L6PxcXFiu8vLi7ivvvq1wk9cOBA3dcfOHCgYTvXXnst/vAP/7DNXnX5F6lK/P/IrqHqK25tPAm4zi9JpYKKIKbyg/aejxKoioOV30Zmqn5fdJtRGDhqI/hTVJXBNhUGH1pRfhFxjmng1/4yqcOvassPVMX8NG0vKP+HCgIEbYytVNVeoK8atHxf4Bcrb6FvI2CqA0zl7Qdtjk35RfgqbCLMdmxnjahA/0KLgq1K6UzQVvyqPrUTDC6GY0mekLXKVAfqnK+0ux4TAeAg/CzU+3y1aq+dIH6g9MWdSLvB55qHu7TVlqqYgyBQ8NtqS5X3mwqg/PYCgkHVcSNQquVFrKBU1Gs3/mwHbd1NWwxUzf5t9T7lF/X8x8erAEGAluUrSvFnTQdpfbQ5/1WvUX7Q1vuS6yhQqr01El4IibavggBBELQcm68S+xl6bK2OWSosc5L8bKsg0AH6Zl1UYUZIIhO+1fFf+cVwPVZ9v439Vn1XZRDo9aj8AIFfalwjcUQIpWozh/rUzv79+5HP5yu+f8011+C9731vzeu7OT/sRnWwVQiBlZWVmu/ncrmetdnKoM/HR/NcnIiI6qobmK8TdO8mEN8sa7lRVrGqOlPqMmMdqJO13oOguap+baMAeSclZjrNSm8V/Ead17YbjK4XHO8k+3wTmeeVbbaRdV7xuvaC4hWrvabtyn9WB8ilqWAKpe9eZEb3RNjUwygff/xx3HHHHbjjjjtgGAZe/vKX4/vf/z5OP/10/Omf/il+93d/t1f9HJirrrqqIvNkeXkZu3btavBqga6C3dHbRjbIDVSOrb2OCll7UBBCQiZ+yQpDtnfokAIi8T7DbHOywj5ED8sURuv3WUJAGOVeCSEgzdaZUMKwyg/NkkYbj7QHjKq2DCkq5qdpe7L8H0JKyDbGZla1J6WofPBQA9KwIJP7vY371aUQ+oQq3L5sc2zCsGBEt8QLCQj966VVbFFIQEgBEe1rISCN1oc0o6pPVhtjs8KxiMQ+FkbrNVI9/LbXoyzvNxl+Fup9vlq1Z7Ux/1IAsmpNJtdMI9XLXbTVlqiYAykFjLbaEuX9JiSEIdq6C1dWHTekEJAt9ps0Lb1248+2bOejDUuKmv3b6n3CsPT8x8crCSlR+dmrw4w/a/oPEwNtzn/Va4Qh23pfch1JIdpbI1JCiPJxXEgJKWXLsRkisZ8R/jnWYu0LodtKfraFlDBbvE8KfdxAohZfdPz3GyR1C8MK12PV99vYb8m3CBEeIw1L73fDjI9lBOzbtw933XVXxffqZXMP0tTUVMU+Ukrh2c9+dsW/hRBt3e3Ta5N2Pt7ZuTgREdU14hndvQhO18WM7vD7o5XR3bBkShcZ3fW+301Gt36ZQlASKCmhk3TaS8scCgUBNYAg4min3rSn40B3sVjE5z73Odx44434p3/6J5x55pm44oor8Gu/9mtxFsutt96KN73pTT09sZ6bm4NhGDh48GDF9w8ePIilpaW671laWuro9UDjW2N7blh/z/ax3XrBYWk55eCKlDBMA04bgS3DlHFgT8dO27uyZlpmHGwQpgVptr5CbUmhXxdGpaQhWwbDAMCwHUizHFxsJzjoSAFpCUhL98syBAyzvbFJWQ6jCNOAYbc3NsM0AClhQgdrDctt3VZy/ELCsFofKhyp9xuEDmYZImy7BcN2KgJw0jDgGrJlvWdpGfE8mkJAmhLSav3ZtQxRsSbbCQY7Um8fUl+osUwDRhvZD8KQsOJ1IWHaBhwpmpZTcAwBacn44oIhwgsilt2yPSmFDpoKCSFkW0F8vSaNsIdCz4/Zeh5lGMjU/zAgTdl6bBLhZy0cW5tBdcMUiXkEpCXhtnifhF4jwjTDfwtYpmx5EUsYVngBKvy3lDBcE5YQTbP/TQG9RqAPs6YhYbb4bEszDHJGwWCh94XT4oKII8vHDQEdTDbbuIgiDf26KLBumAakJVuPTcqKYLNlCBiW07RuvGE7kIZE3C0pIU3R1tii9QiEFzns5utRWrot05DhBTcJYRq6vSZr0pEShlNuSwoBw5IwLLvh09YN24FhShgiXCPSgDRNGI7ZdGwS0L/7EsdWIwyqG5YDaTkwzPYu4AxNWBZpAA3BMIy2M6O7OT/sxpe//OWebasXhnE+PnHn4kREnWh2F1wfanR3m/HMGt1oEXRuEoBtFNgd0RrdzYPhDd7TyQM027h4UdnXxu8f5RrdxXUDhQDwB3KeS4PQcaB727ZtCIIA//W//ld885vfxL59+2pe8+IXvxhTU1M96F6Zbds4++yzceedd+LVr341ACAIAtx55524/PLL677nvPPOw5133okrrrgi/t4dd9yB8847r6d9G9nU7OqE84672cEbBJDKZfFk1be9mSW4nl5mwnZgZVNIz3rAk80fGujNejBTng6aCoFU1oY3sx1rRx5r+B47Mw0vY+uAj5CA4yE10zqoO5u1YeczEGEgMZ8y4U0vYPnRHzV9nzu9hHRKv0dYNpx8GhlTNn0g2TbXRGrGg7RdQAhkXBNupvXYvJntSGVtHVgJJAzXhTfrNe1fzjSQnvVgZVMQtgO3JJHJ2PCml7B66P81H9vULNwokObobbQKYi65JrxZT7/eFMh7Fty0BTc/j/Vjhxu+LzWzHRnXhBSAkgbMtIttrokHVxvXyV10TKTmPJgpFzBMuKZEKm3Bm1nCiSd+2vB9ppdB3rNhR4FFx8Ns1oZ1fKNpoG/eNWHnPAjbg21K5FwTXtaBk5vFxnL1qi9zUxZcUweElTTgznjY7lp46ETjsW13LXjTHqy0hw0BpGy9RlIzO7D+1MGG73OnFzHlWbANCcCHcFwspCz8aGWj4XXpnGkgO+XCyqQBALYpkfdspLItPjfSQC5th2PTF5XS8ylsP7jaYmym/kwKnbmccUx46dYXlbyMg4xrwhT6bg0372HRNfFAk7Ftdy2k5jzYWQ9CAK4pkfcspPLppm2lsh6m0zbsMLBrZz140y62HVzFw2v1w58pQ2I+bcGdSkEJAUNAf7bTzS9OeNNLcFMWMpahs4u9DNwpF0uuiYdPFBuuyeizBmHEY5tO2y3Xo5d1wjWi17+VTSE1l8K2I2sNx7bdNeHN6c+1AmAbEhnXhDezvUVbGeRTJhxTj81MefBmPCy5Jn66Vqx7LEkZEgspC9J2oaQBIQRcU8LLZpoeR7yZJXgZKz6OCMeDHY5t+3Kh4ZqM1mN07LcNATdtw51eathWamY7vLSNnBeuEaGPWe6Ug6Umx63troX0bPnYL8P9lsqnUdrYDsN24KWd+CIEta+b88NuvOhFL+rZtnphGOfjo30uThFx6FCTH1YdY5KJJPrWlKrX179gqzqsY1o38Faz0WSwqcH3kzoJvG1WvfF2u/1mQcBGumyrJpDYsp0uAo313tvOs5UabquDPtdrp9F2W2ajtjm2Zm3UfW+9PjZ5fScB1Xp9aTXOaq2ycJtuq4MgKdB6PTVqumnWbosNNMvkrXhr/exf1eD7DbfZzj5DnWXR5hqreFnQ4L8bbjP6fvXL2hhXs7aTr2myRGqaabGcVJOPAqpqSFe+tsG5dIPtKwUceCqPwxsmVtUagqB1edJhUejs8LOZdsZdx4HuP//zP8dFF10E120cCJmamsJDDz20qY7Vc+WVV+KSSy7BOeecg+c973m47rrrsLq6Gj/5/eKLL8aOHTtw7bXXAgDe/va340UvehH+7M/+DK94xSvwqU99Ct/+9rfxV3/1Vz3s1Yj/UTrA7jle5XKSlgMnlUbGDQPdhgkr7cGdah18ttMWYNsAdCDN9kw4udnmge7cDBzXKmdYWzastNuyDIY75cD0bCDMBs64FpyUDTszjcLKU3XfY3oZuCkH2XBsMC2YnoMZy2ga6J62DZgpG7AdQAikbAOO23psTm4WthdmqwsBOB7sFkG0WduAO+XCcG0IIWBKgaxrwkm7ML1Mw2xMNz8PN22VM0QNE1baxaxt4LH1xjWwc2kbZkoHjQSAlGPADYNvzQLddmYaTlTSQAhIW7f14GrzsTk5B6bnQgkTlqmDb04q3XRsTnYWKduIxyYME+6Ug5knTuDgRv2xzdr6NWbKgzBNmFIg5ZhwPQtOtnFg0bBTSLsmLEPqNSkNuDkbs7bEQ02u80xZEk7OhuG6EEJnpKZdE3Y60/hNANzsHDzbgBT6liZhOXCnHMwfW28yNqO8/qEDfVnXhO01/9Xg5uf06xLZ6nbOxnSTseVMA5m0/kxGRR4sQ8J1zKb7TBgWLNuAY0l9PBMSZtrTYzu+0XhsjgE7bcP09OfaNAQyjgnbbT422zWRsgzY4YUe03Pg5BzMOUbDYPCMZcCZcmG6TnxxzrMMWC3G5qRdZD1LZ5pDQFj6M+TmHMysFOqOLWcamLINOFkbKiy5YUqBjNN8PQrDguOa8GwDZvgHu+HacLJ207HN2ibcnANhmFBCr+WUbcDJ5GGl8iieONZwHjNuGFQXACwHVkaPbX61iJ+u17Y3ZxtwcuXjoxSALSXclNX0OOKk0vDc8KISot81Lpwma3La0vNoZ2zAtCCUgikkHNeEm5tqeNHAzkzD9iykHb1GlDQgbRdO1oObc+pexHWk0OsxZ+uM7vCuF8uQsF0TTiYPwzRgufrCxajGugWC9oJVm9VFHfBW54eTaFjn46N5Lk5J4sEwmaHewaTe95LB7kSZvKZtAC0ezFdHpwHdVlmPNe9tFlHpkWj+ut1+JxmpNe/t8vjbaVeblhtosfGmAdwWHenm90s7GaUNXt/6GSVtBE2baSfwWPd9bWatNmqrYTmPxm03nPpW/a3oa4t+1ft33cB9o75Uv7VV31o31ai9ivloJ27dQcC2nlZB3EbbqQjo1vsoBFXB4IpDZOOfNetLwyBynW3UtNGi0kltnzr9d22fGl9PE4nXCDy1bmOlCGyIjZEuXULt6zjQ/YY3vKEf/WjLa1/7Whw+fBhXX301Dhw4gH379uH222+PH3Lz8MMPV5S4eP7zn49bbrkF73nPe/D7v//7OPXUU3HbbbfhjDPOGNYQJoZQKq6DHXG8ysCrN70EN20h7+nAp5JWnPnW7Fb5eduEnU9D2F4c7EinbKSmZnCiQfBZWg68qSW4GVvfKi8EhKkzyLe7Vt2gSrmfHuxsOi5xkA2zTL3pxYaBbnd6EW7a1mMTOrBipjwsOAYeXy81HFt61oOdS4dZfRKeKZBKWXBz0w2DRnZmGm5uGp5r6b9FlAFh6Sz07a7ZMPi85OogppX2AMOGKwzkPRte2oY7vYiVRoHu6SW4iaxPYeoLFEuuiQPrpYaHfm/ahR1mjxtCZ+s6ngU3P4djj9R/jzu9CC+bijODIUwIx0N2ysXiiWLDIOacZ8GdcnXWv5SwDZ2J7KathmOTlgNveinMDJY6G8ly4OZ1JubhjfpjW3QMeDM6Q1RYNgwpwqC6BSc/DzTI/PfmtumsdlMHaJUQcPIp5KZcLK6V6o5tu2siO+XCznmA40EKwDEN5D0LXqZx5rOdmYY7NRNntAL6Qo8742Hp4GrdsaUMiXlHXwyx0vruAFOE2bppq3EQUxp6Hh0d6NafbQN2xkE+6zQc25Jrwpt2YWVTgNClbVxTIp9qvM8AIDW7PcweN3T9ZiF0lvWMh6XDJ3Ck4Nd83hYdE9MZG+6UAyMMdEdBfC9jNbyIpe8KsZB1TURVR8yUC3fKRT7rYPt6qebzljJk+Fkrjy3Ksp5JW0jNbK97d4gwLHgZR48/yoy39TrzZl0sHVlrPI9z+i4LSAMCOss665pw8vNwjj9ZN0Cbmt2OVMpKXOgR4YVHvd9wdL3mPdtdEzMpE+6MG18INATgmQa8rIPU7DYsb5yoeTCrm5+Hl3aQdUzYYbkf4eg7B7xZFwtPrdUck6ctAwvhZy06PiKcR8ez4ebnsLGyWHNXg+llkMra8TxKCR1UDy+qTk25kEfXK9Z/ypBYdPQ8OvkURFjb3zQEcmkbx9I2vKkl+MWNiosU0nLgZVNwPJ09boalS6K7XrxZF7OPmniyUN5vlhDxekzNeBC2DZVYI17GQqmoyzdNpa3RLl0ywlqdH27Wm9/8Zlx33XXIZrNtvf63fuu38L73vQ9zc3M9ab+eYZ2P81x89Pk/qs3obpmAHT8foTrju8PGqw9i9aIM0QFZ1vleUoeB65YZkklCdBasrneBoJfB7gbaCqpFWs1nOzqZw0bttDm8drNIAbQcW1u7ojrI1kH8vqN2mrTZbNsVL2mWyVrz4hbBxCaByc30S2+7s7YqXl/ntZsJojZ8X4vgarNtJv/dvG9tBo1D9ct3199+0CjonchmbBjLrfPeyqon7c93dZsAELTKqKxIhq/Tly7eU68fVW9r+t5621AKeKJgYbkErIsTCILBP2ulE4Op0T3+f5Bs6mGUw3D55Zc3vD2y+sFFAHDRRRfhoosu2nS7ol+ZARMkmysHup3cLFJzO5HKOsh7YYBOSkgvBXc2i5PSNn60slGzjZQhsStlwsln4kC3IQTmsg6eyDlIze1EaeMEgmLivdJAZmEPMlMuZjM6EAkhIV0PTj6NXSkTq36Ap4q1B62drgVvLgUr40HYOitqOm3DyzrwphdQWF2uybR28/PIzOlSItNh2QUlLZgpD9NzKewuBnVvX8+ZBlJzuk/C8aCEgGdLzGUdHMk7WF/YjaM/+X7Fe6Slx5yZcjGXdWBAQAkB6abh5NPY6VlYKSkslyrHJgFk51JwZ9IwvJQOREIg75lI5Rxk5rZj5bEHa+d/bifSMzPwsg7sMFtUWA6sjIfpaQ97CkHdMgDztonUfAp2LgVhu3FG61zWxrG8C2k5lfsMgJXKIz2/C+mcE2ciQwgIx0V6MY1dT61hza8dW8qQyGzLwJ3OQrhpXRIkLN2QztcfmzAsZBb2ID2VQd6zYIUPKRS2g9R8GrPTK9hTrD+2xSk3LIGRgnA8mEJfDJnO2liecvFknbG504tIz2zDTBiglQAgDdj5DDILaew6uo5CoGrW5E7PQnoxDWcqFwb7ooCpjXTOrVvixvQySC/sRibvIm0Z+iF5QkI4HtJzHman3bpj2+1ZmJpPwZvR6x8ALFMg61rIZxyk53Zi5bBfEegThoX04m6kp6eRT1lxCRhlmHBnckgvHsOelQJKChXBPglgW8ZCaj4FJ5+OA7Qpy8BcRu+zoLBRU3bGnV5Eam4nZtI2UmF5DwgJJ59Bas7D7CEXu0u1n7ddnon0QgrOTA4ylUUAwJYGcikLuYz+TAGoCHbbmWmk5nYil3GQS1mwwz4KT6+19OIydq4UagLduz0L03MppMM1Aui/81Omgem0g9TsIvziBtaeOhCvE8NOwZvbhlTWxpRXzkSWrgc7n0ZmIY25J9eRWy1WrP+TUjaWUhbScyk4+Yw+FoRrJJeykJly4W/sgRAS68ePAIEPaTlw8/N6HjM6+BzfrOHpOUovrmDn4RM4XCjFJUW2u2a4HjNwpzMQYZ1sKQQ820A662J9egG+X8TG0SdQPHFMZ43nZpCa2YF0xsJ0VN4GAtLx4EylkZpLYfboBuZPlHC0qC9SOFJgZziPqbBMCoT+3LimRDZnY23VRamwC0IYWD92GEJKWF4G3tQSvKyN6ZQNz9IXQ4TtwMyk4c7lkVlYw55DJ3C06Md1/3d7FpbyDtJzKdj5DJTUx3FDADNpG0eyLophgHLl8P+D8gOYrgcnO4t0zsFc1kbW1WtESQPCceHk9dj2pEx4UmDVD1BUCouOiSVXr0d3Rl/AVWHWv2sayGUcBCVAmsB0ytGZ/aN6bjnAGt3daHZ+uFmf/vSn8fa3vx1nnnlmy9ceP34cf/VXf4Wrr766L30ZBcM6F6f2HL8fgGjvc1Q+3qjw36rBz+sHPjo5XnX6p1SnwSP9ouR7WnWueaCtei5avb+d93Y1phbbqOhR2G7bwc3qbXcQFKx5bweZn+28vx/va6cKRf03VgX46rQpE/u83s87CRxVxIPbGF9t9ZLu5qTcfnvvrwl0tpWwnsikbauV2u22CmJWvrVqXbbxnuT2W81FTQC+re3X207rMdVW/uhsbKrDua8dWxdrsc1tx+/voL1GlVlaqTlWATi0IbG84WMDq1AY7UA3tWfsAt3jpDrjedJUZ3Xv2z2F+/Y+C4btYHppBtlpD6dty+Lk2ZSuv2ylYM7vxMzpJ/CsA8ew99HjWDuyjtWSDwkg7ZpIzaWQ3ZlF9qSdMGbmERg6s/iMbTlYUuI/cg6O71jE8hPHEZSKEIZEdjaP7LSLXfMZPGNbFrYUUMKAkZ9Fas8enPRzj2Hm4WM48cQaVlcKKCkFz5BITbtIL6Qwd8bJMJf2ILB17d5deQ/79kzjx66JJ+dSeOLxPQgKOkAlbQezSzlMz6bw9MUs9s6EY7NdGAs7sf0/7UH2kSM45fGVeGym0Fnb6YUUFs8+GemT98KYnodvWFjKSDxzRx5SCjycc+DmplHa2EBQKkKaFrKzGWSnPexeyOBntuVgGQKABWN6Hum9u3HKCx7H/KPHceKJNRw7vgGJsKbzjIvtz9uD/MnbYS7ugrI95GFi72waRV/hR54Fy34x1k8UEJT02NJTOaRyDuZnUzhtWxaeLQABBHYK5tIe7HzBXuR+8iROPbSK9aMbOFEowTEknKyNzEIaC/tOgrd7D2R+Fo4hsJRxcMbOPCxDQqmfxdpKEaWNDSjlw82kdd3lKRdPW8xiLmXDlIAybRizS1g6ey9SMy4WH1/BiSfWcPz4Biwp4HiWrpf83L3In7wDxuwSYLqYNiycNJtCyZ/BAykblv1iFNaL8ItFmI4DN20hk3exbS6FbVkHbhjohpvF7DNPgplykX/kKZx6aBXHn9ClByxTZ8QvPWc7cnu3w962E0jl4RgSSxkHZ+2aQso2IcTPYX21iGKhBKUCeGlXZ5hOe3jaXBoznqWD+KaL/Kl7IMMLHouPr2D5keMoBQFMKWFlLCyeMY/snkVkTtoFY3YJphCYS9t42pxem6XCmTixchoK6yX4JR+2a8JL20jnXeyaT2Mx48CREhABkMpj7sxTYGVTmHrsKPY88BT8Df2L2/RMzDxtGtlds8jt3abXPwDXFNiecXDW7ikcWzkJJ5a3Y/1EEaWiDyklbM9EOutiftbF3pkU8q6pA+umi6mn74FhW8guHcLCgRUc/+lxqEA/ONJOW1h41iKyuxeR2rMHyjBhSYE5z8bTFjJ46OlzOLqQwfrqM1BY04FkyzHhpnSG+88sZbGQtmBJCSUMODt2Yy5QsPNpPbb/dwz+ekk//NMzMfv0GWR2ziO7exHm3DaUIJC2JXbmXBT3TOP46h6cOL4NhfUSSgUfpm3okiVZG2ftmsL2rKvXPwBzYSemnr4Gw3WQXTqEqR8dQTHsozQlpk+ZQnppCrndi3B27AakCUdIzKUtnLaUwU+fPoflpSzWVp6GUiEIx2bASzvYt3cGJ8+mMOPa+iGg+Vl4e/ZizjBg59NwcgdRXC3ALwUQQiC3I4v0YgaZnQtI7doGmC6EALK2gT1THn7m1Dk8OpPC+uo8NtZLCHwFwxRwUjYyWRtn7MjpNRKuf3NxF2b8EqyUC9M2sLFcQCkM5Hth8D61NIvsnkXASQNChJ9tG2eePI2HpxwcPZrFxloBxUIAaQC2o2uTP2fvNHblPeTCuwyMmUV4u1exYJrw5g7BmzmEwmoBQTGAtCRyO7LwFvLI7lwIj48mDCGQc008Z880fpK28fhcGmur81hfLUAIwLT1Gjlr7wz2zqawkA7r77tZGAs7MV0q6rF5JjaWN+AXfAghkJpPITWXQWppBumd26DCi5yOKXD69hxStoGfTrs4upTB2vHtCAIFw9Jlpk7bNYVdsx62Z5xwjTgwZpeQ2rMOw7EhpcDuo+sorpWgAl0ay532kFqYRnr7HGRmCr40YApgxrPw7D3TeHJmA4YU2DWTgmWKCcihmDzPec5z8PKXvxzPec5zYJomLMvCz/zMz+Bd73oXPvaxj+FrX/sa1tfXEQQB/u3f/g1LS0s9fRAmUSf++aFtbSdiJwMDnSZv11N9/NpsmlC3l9aatZvsYyf96/Z99Wwmf6pRFqWs06uWGZetdNjPzVwKHUTd2c20t5nudfPejsqA1/telx3ebKWbptvusNJM3fY6fH03bXQTvI50vI5bBHyrj8utyqB3qpP57PhiZcPttN7QZtZ/p/0MABwvFnFAHcGKfxBKNX7e1LApNaAa3ROQ48tA9xa32Uz15Pvn0jZmdizCsAxMz3jYMZ3C0pSLrGvqjA/DhEjnYORnkd8zBzttYf3oBvLhLex22oY75cKbn4bMTesMayEghcB0ysKOGQ+BUnjcNmDZBnxfwTAEUjkX26Zc7Jj2MOOVM7qV6UBm8sjsnIdhW/BmV5FdLiAIAh3Yyjnw5nIwZ+cgUlkgzOrLOAaWsg42in78YLBSWHfbNCXm59PYOe1hMe8i54QfIWlBprJIb5uFkDqwt7FcQG61AMMyYKUteDMpeAvTMDJ5KFPfmp+yTEy5JnZO64xav6hQKnrx2LJTLpamPOyc9jAVl6WQUKYLIzeNzM4FGI4Fb3YN6bD0gOVacKccZLbPwZyahUhloIQBSwpMuSYW8y6KfoATa0UUNlz44dhSGRtzOQc7plOYyziwottPDQsiMTYnZ+uxrRf1PGZsuDNpePMzkJk8lBGW97ANzHg2ds+mcGg5g7VMCaWih0ABbspEOmVjW97DjmlXlxwQQmf62imklmahfAXTs+HNriG7XACkgOWZcHIO0kszELlpyFQGgTRgKmDKtbBtyoMfKKytl1As+PCLPkxL15xezLvYMeUiYxswdZ0UHXCdnUOmUIS0dC1ud0bvC8PUY01vm4M9PRWOzSyPLWVjx7SHJxYzOHGiqNdIoOCkLOTSNrbl9dqPSksraUDmppFaCgPpaRd22oZfCmCYElbKRGbngl7/mRkIN6VLG0sg71pYyrtYWkzjaMZGcd2HHwSwbIl0ysZc1sHOaQ8ZywzXv4AybVgz0/HYhBTwN0oQUsJwDeR2zyO1MA1zekavfwCmEMg4BuY8C7sXMjjkmFhbL8IvKUgpYDkm5rJ6v+UcXa9cr38DMj+L1JJeg6bnwE7bUL6KA916bHqNRHWsHVOvyZPm0jhkGziyaqMYluswbQNZz8JizkXetZCyTF2WQkjITB723AwypRJMx4LpGCht+BBSwHAMZHbOI7U0Czk1B+HpiwSGBDJ2eWxPeBtYWy/F8++5JuayDmbTNnKOEa9/4aXDsRUgwlTo4no50J3ZPgt3NgdrZhoyk0cAnWWdsgzkXQsnzaXxhGviqbSFQhhodWwD+ZSFxXCNOKaAgIAyHRiZKdhzq8j4PoJCEaW1AoJSAEgBby4HbzYPb34KMjOjPy8irD9uGdgx7cEyJI6sFrCyXkLgB7As/RDO6bSDac/SayQ8rolUJh5bfuUEisdPICj6CAIFdzoDJ5+BN5eHkZ2GMm19h40USFkmdkx7MKTAE66FoycKWC/4MA0BzzYxk7Yxl7aRc/UFDSkAZbrhfltD2vehSiWU1gpQfgBhSHjz03CmM7Cnp/TxUeoMfksKzKZtBErBMQ08lTJxLG3DkIBj6bI+26Y85F1LZ3RL/bmWqSzk1BxSJR/FE+vw1zbgF/TvGnc6C2c6C3c2D5mbBoQZr/8p14zn8QnXxBOuvhBomRJZ18LSlIsZz0YmXCMKBqSdgsxNwy5tILNzGc6Mbg8AzLS+i8eZzsGcmoUybSih72CxDF2GyTIETCn1Mb7t375DoNRgznxH8OT6k5/8JP7qr/4Kjz32GIIgwPr6Ov7X//pfuP3223HffffhggsuQD6fh2EY+JVf+RVcfPHFw+4ybWEKaJmP1klpXP2z9gKmnQRb672223brqrf5epsbdIB1M+9t2ldRUTlm0IHjzbY5yoHkbtuI39tN5n6Hr2/1DM5Oq+9EP2r34lVHAdMOXgs07mfy260uRPUqAF1PvbH36vPXaF67LasOdDP/7b+j4TrsqL0WbTR7bxefVKUAXykEUFAq6Gi8tHlf/epXu3rf3r17sXv37oY/Z6C7TWeddRZyuVxH7+k2o3szwedO2+xlSZaD+aO464ksbNvAyQsZ7JlNYUfew94pD2cupuBtPAXz6CJKSzkcFwWsHT6KjaPHUVxd17ehp104U1mkFqaRefZZkIt74U9tx4qRgXnoBOaPrWPx2Bp+emQN/+/JVRRKAQwpsHMmhW15F7umPezKe3j2Uhp5WYSxvAgcmsYJeQKrB57E+pPLKK2uwS+WYLo2nKks3NkccvvOhLXzFPi5JRSzS7APr2H+2Brmltcx98QJ/PjgcRRKAfxAwbMNnLqYxc4ZDzvzHnbnXZwx78FZPQzj2ALWcRQnDj6BtUN6bKX1gg5859JwpjKY3ncGrG17IRZ2o5Tfgak1E3PLG5g7tob5p9ZgLx3D8fUS/EDBkAJ75tLYlnexc9rD7ryHsxbTyGEdxvLjwOFZrIoTOHHoKaw/uYyNo8chpITp2bCzacye9QyYS7thbj8Zfm4Jj8kZPL5SwMyxdSweX4dcPIqjJwpYK/gwpMBCzsW2vIsdMx52ZF3s257FfMqEuXwAxtEFrMvleGyF46sorRVg2CbMtAdvNof8mc+CuW0vMLcLRzK7cfiEj9nldcwdW0Np9iiOrGzgRMGHHyhMpSzMpB3snPGwI+/hrJ05zHsmMsEJGEcfRcFYRmHb4/HYCsurkIbUD8+bymLmrNNgLO6GubgHpantmA+yOLRaxNwx3Z5cPIajJwoolAJkXRP5lI2FrINdMymcs2sKs56JWVfCOPYYAnsV/o4ZnDj4BNafXMbaIV3OQtom7GwaM888Ceb8DpjbT4Ka3oHD3nY8sVbC/LF1LCyvQ80fxbEThXhssxkHM2kbO2c8PHvHFLZlbcx5BlLFZYhsAaWDcygc1GM7se1JBIUSpG3CcB1MP30X7MVtMBd3Qc7vxvG5p+OJNR/bVguYO7YOf/YonlwtYGW9iI1obJ6NhZwOdD97xxRmPAMzVgBj+QB85wT8w4/ixMEncHz+EPz1DQhDQloWcnuX4C3Ow5jfAWv7XhR2PQdPrgd4cq2ExeV1+AtHcWh5A8fW9NgcUyLjWljIOliacnH2jinMpUzMpUw4609BZIsoHZxG4fAhrD95DKuPPwkVBJCWBdO1MfX0XbAWtsNc3I3g5GdjRabw5JqPpZUCSgtHcXh5Ix6bHyhkXCsMYrp4zo48tmcdzLgG8kYJ5hxQOjAXj21l4QkEhSKEYcCwTORO2gZnYV6vkYVd2NhxJp48UcKRdR8PH9Nje3KlgGNrRRRKPuywDvpsxsZzdk5hZ87BjGdizjPhPu7APzyH0sFZFJ98AsuzUyit6wCtYZlI75iDNz8DY34HzKXdKO3ah2Vl48h6CYvHCxDblnHg6DqOrhWxVtChB882MJu2ceaOPLZlHCxlbMx4BvLLMxBPLaB0YAb+kwdwNJeBv16AXyxBSglvYQrubB7W3CKM+R1QpzwbBXcaR9Z9HFkrAY8ew8HjG3hypYCVjRIKpQCebSDrmJjPOThzKYftOQczrl7/9pKF0qE5+E8uYCWXRmH5BIKiDuI7Uxk401nYs7MwZrdBnLoPQXYBx0oSR9Z94MBxLK1s4PDyBo6eKGKt6MM2JTzLwGzGxrO25bAt42AuZWLaNZA5OgU8OQ//8KPwnzyA43NTOtAdBBBSIrUwDWcmr+9keMaZ8LOLWLOyOLJWgnX4BA6tbuDQ8Q0cWSng6FoRhtTlkbKuiZ9ZymJbxsFixsaMa2B6/RCMlcMoHVqA/+QBrE5nUVxdRxCW03Fnc3CmszCn52HMbgNOOgt+dhFPbQRwnziBQ6sFPHGigCMrBRxaDsvNhA/zfcZiFgtpG9uyDqZcA0tiBcbKYagji/APP4q1rIvi6lq8RuxcCnY2DWt2Dsb0AuQp++Bn5rEqXBzd8OEcPoHl9SKkFFhI2zhrMQ0vemYBgOXl5Z79jqbu7dq1C+9///srvvfwww9j7969+PznP4+Xv/zlQ+rZ1vK97+3HyD8EfgQ8uv6Kut+vqM3a4PvVWt1evpmLc/248bUXf9a0m+W+2UJOgwxE96qpQKHjYHpUfkBU9WKzNWC7nb9u3tft/HXbx06Cde3tg81rq51NBoc3237SZoKl7W6j0+1Vbrv5xtsdbz8y8NtbU71do0A7+6z1hrr5HRAAOFos4FHxOH5a+CGW136CIFhLbrXzjfaJguiodE+3BtFG5JJLLun4PUIIXHHFFfid3/mdhq9hoJt6JmUZ2DbjwTYNLIS1uTO2rp8MQGdZGxZkSgcZdBDMhJVah4iDmBnYUxkINwUYRpj5CR1ksw1Mp2wUSgEKvg48A8C2vIv5nIOcayFlGRBhrWcICeGlYeZy8IolSEOiuOoiKJRguDbsXEpn9HlpKMOGkvrj4JgSKUsHhxZyThwwAgDblFjIOZjyLGRtI6wbLKCEhDJsyOwUnI0NKF+PzV8vQBgyfAhnFjKVhfDSui0hYQiBlCWRsU3MZGzMZ11kXD8OdC9kHcxkbGRsE44h44xuSFM/3C2Xg1vQYzMsM55HO5eCTGch3BSUNKGEDpykLANZ28CKY2Ihq2tjR2OL2sraJlJhrdtov8F0INI5OFN6bIZrw18v6P2XduFMZyHTOUgvjUCaEELANHR7uTA4ahsSa0U9trxnYSplYTplI2Mn2oIuXyJTWVi51XhspmuXx5ZNQaRykG4ayrDieXTD/TblWRVj8ywDUykL8zkH2bCt+CRdGpDpHIITx+FMVdbZjsYm0zkILw1huwiEhBFmmaYsA5lwHm1DxmtyyrMwk7GRc6J5RLgmJaSXjsfm+QGCQikO9FlpvT9lKqvXpGlBCAFDAI5hIGPrYGUU4POVKo8t6yBj6zIihoBej9LU/Q73WzLQZ3p2uM90WzCj+suAGY5tJmPHaz66qJR19TrNOxYcU+rs8XCNCDcVjm0dyg8Q+EGcrWulXZi5fDyXKvxcGxLxPovWvGfp2vCpMOt52rPi9SiiTGTbjcfmzhbhrxcQhPWXTdeGnc8m1ogehwjr/acsifmsnseUbSSC+HpsKUvCkjJek8qw4rGZxQLc2VX464Vw2BLOdBYisUaiedRt6eOIn3Ngm7Ii0J1P6XGlwrrq+qNt6rGlcxDrJ+DN5lFa1585AHBn8zBzeT02Lw0/fLqYBGBJiYxjohjOY2rdgB8o2OHYonm0pIAh9UVRZdh6jlJZOFNZSMuMA912Lg0rl4NI5fTxOGwrmseMbWLDLR8X1wphoNvWFw1SlgEnvFNDCAFIUx8f0jnIwjrc2VWU1jbi9W9PZSCzUxXHR71GysfIYkqPzQ7Xnh0+zDSTOGZFx38lzYqxma4TZ3Q701kY2al4bEF47DfCY2TGNlH0K09qo4fQZsLjvimFDoBEnzXHg0jn9N0flgmrWNK1vdMujHRGH/vD43E8tnA9BsqEDNfLaIfwVG0B1361MwZ2796NCy+8EPv27Rt2V4gqPHS88c9aPWuw/jMhe/eZFP2IbvfAIDP4hpFpPagm64+tYQGDNt/fG91kekY226/NlarZRAJcH+azq9IhHXZk8+U4usvqHfQ2e9lW0GFj3Xweutn33c1bh+ulq7FUvue4OI5lHEIxWMPmL2NSJx566KG+bJeBbuqZrGNgz2wahhRYyruY9ixkHROuFQZopamDmGmdSZcyLVhpHeyQhgFpm7AyukyA9NIIEgGqtG0g74ZBnDD4EQXG5jIO8q6JvGMi6xhxEFMZNqTtwcjPwgbi4KzyA0jbhJlyIbPTkNkpwLSg6yIAtqGDONNhEMcPVGWgO+tg2tXBlXLpBgmYFmR2GmYQIGWZsHPpeGyGa8PMhIEcJxWPzQwDfXnHRKAU9sylsFYoB7rns7rcQDQ2oSdEB3GclK6HLXUg3Uy58TyaKVfPYzoHZTmAYcISomJsO2Y8rITZ4wDiILfeb+FDDQEow4SyHBj5WQBAyjJROqGDplEQ30jrIL6yPN23RDA475hYyrvIuGYc6Mu4JnKOiZxrIZ8sbyB0EF9mp6BKxXhsdjYdZiKb8TzKdBaBaQNCwpQCtiGRd0wUfT22tYIfZ7RmHBMZW7dnGyJ+GJ+SJox0FkZBj01aJkxXB32FIfU85mYhM2E5BUMHn01ZnsftUx7yKatibFlHr0e3JhicjsdmSwlhyDgYbLi23mfZqfAhm+V5dE09toWMDuAnx5a2zXj9mzIKBitAGjCyU0BJB/cyQBzElJYJc3o+bk8Z5YfxRWtkLmXr4KlrolAK4mzdTNiensewnrCQeq2tn4AKAjiWBWmZcRDTcG09j6ls+JBZGc+ja0pMu5YOnjpmfDHECwOK+fCCgRkG8XVZIhcylY3HlgbiIL7h2pDTC+WgqRmOTeqxRWvcCgOX0WctHV64yNomTKOcsaRMW194yE7r9V8qwt/YKK+R6Xl9HElldVBdCAgV7jdLj82SEmnbxHr4YEnXLI/NNfX6lUKvR2V58dicjTXYhXWoQB9/jOwUZGZKB2nddFwCxpD6wtK0a4UlYXS5koIfwDZkGLw1kHUSAVoAyrQg01moUgHW7BzMzDpUSQfxpZfWAfXslL4YJI34AoUpdV3wQOn20paBdT+AEbYdtWcbouHY7MI6rI11qMCHCEvfyHROf9YMWx8LoIPB2UTg2TV1QB+oPMa4loQpkWjLicdmzszD2FgDwnnUFwHT+kJPOodAloP4advARilAoFR4USvqB+CY+sJF+YJBWCYr/F0jU1nI/Cwsx4UqFoAg0KVv3JReI+E8IgzIGwLI2mZ48U2UH7hKY+MLX/jCsLtAVOPB4+sNf1Yd3Kip91onYNDrIPCoBrs7NYzb2zcT9ByEQfdP9Tkg1evxDGJ+gj7OiRKb33Yv5qAfY9zsWgp6Mjeb2IbowTbq2PS8dPNgx+hvsE203en+WFPHsFo6jGLpBJQa3YdRKgzmwuVo/6ZpDwPdfTKsB1FWPyBykDK2iR0zHgwhMJuykbV1sCOqdRtlPSvLg8zPAqYN21uFtaFPyIXjxsGHKGAaBVZsQyDvWAjCAJKUAkGg6wbnwyCmDr7JOBgGaehgR3Za17J1PJilApTvQ1g2hB1m60aB5zCrz5QCWcdAMdD/DgKFKLnPEDqYlHd14NmOIqbS1MGOzJQei2lBequwikVASgjH08GOdA7KdIAw2GFFAVpXB7o3Mg6KfgBf6bZyrs4cj8YWB0EMS9fzzeu62cLxYHgp3VY0tlQOws2EwTed0R2NLVAW1oo+so6JIAx0Z8IATjSf8e2aUSAnObb0OlSxAGEYOgM1CuCalp7rcB49S49tNqWz0jfCQJ9jGkhZEnnX0oHFKEAbZf2nczpIFI0tvQZhGICpx2Zkp/QaCccWZVlH+22+aKPoKhT9AJahA7TR2EyZ+CPLMCG8LGSxWLHfkmvEiAJ9pgWVaMs1JbKOEWfKFr0w6zZsLwoGJ4P4ynTLYzMtCMsGAl/PmWnBiC5OmG6ciWxIfYEiassxJTbsAL5ScA0Z3u1gVlwwAPQFCuGmITPF8oc0CH9xR4HFVLj+LTduKxqbDtAKZG0DxUDFAdTorgAzzAwGwsCi6eqLRgAC04JlVo5NZqcg0rk4qK4/T3ps+qGWOpt7I7yoZBkCjiGRdSrnEYgCtHoeISWElHEwOGrLyE7pByhG8yhEfGFp2rXgGBIZP0DRV2EQU4/NtaQOYiYumCk7DZnV86hKRcjAj4P40TzKdA6BVZkZbxsCM54FK9xPJd8I178Mg6ZG+YJBWKNeB2j12IyNNahSMd5vMjuta7eHawSiHIS1pZ5HQK/PYqAQKL3forsdbEOWA7QAEB2P0znI3CxQKuj2AL12vHQYFM5AGXrbItx+xi63daIo4zUSBZ/TVWsEhqUvBoZjU8VCeWzS0EHncB59qe/mEdAXKFxLIq/0GjkRXnCJ2o4+h9EaEeHnOjk2Iz9bbg860C3ddHkeExnd0XqMtu+EdyTpmtr6gpMXBtWjuydEYr8Z+VkEtqvHFfg609tN6QsHbia+c6icrW6EFyt02yMdAFIBxAAyuntZUo1oK3pEHGj4s+rAQXUQom5gQfQmiCNV755CIBoUFel34LOqE13pxVyOqq6CWgMw0HWREAwhYDassaoB7ft+BvKTBjmP/Q6s9nvO+rXv1UDuItTzUwpOYKN0HH6wDmZ0D16pVILv+3AcJ/7ewYMH8dGPfhSrq6t45StfiZ/7uZ/raJsMdE+gYQS7VRiwmnGtOPicdcrBDt0xqQO0lqODptKAsl0gzCCEaUM4YaZmGDAF9HmkHQZNA2XGgZooEzPKHkyHGYRRoEAZZlxORAebXagwqK4D366+td104oApgDj4nLXLH4/odiApdOAqGTQSUZapYcNIZ8PtSyjbhSoV42CtDghnEJjlMilREDNtGwiUQjHQwdmIfghfFKSKxibDLGs3MTYPyk1VzmM6C2VYFWVSosCQH2a1JtuLyihk7DBbN85WNyvHZlp6HkuFeB6F7caBZ2WY8W35UdBrxrOwXgpQ9I34oXLRuKLSDVIgzlYXbgYy8Mtj21jTbYXBZ+Gm46C6EhJS6SzTaL9teAGKgYrXiGNE5WiMiiBm/NDS5NjC8hPx2KJgmGED4diiIGbGNjHjBeHYwkC3WR6XDqojznpWlgOZGBukUQ4GWzq7VjgpBIkLBoYojy3v6BI2G2FbltTBt2guzSjQF4/NLY8NgPL1iYgwDMjMVFgixYnXY9ReFFg3pEDRD2qCmHFGa5iJCoTBZydV3m+WrS8YGEY5wOmkoazoIbM6iGlKIO/ojG7HCFC0VDy2aC7N8DMvwjJBMOyasUXZupBSZzx7WSjTqggs6s829EUBQ2CjVL6o5JgSrinjLOTy+jfKYwOgwgBmFGQvz2O4RoSEgIAhVRwQjoLN0RqxDAnHkLCNMGAaZSIbJkRibKpUqAx0p3Lh581DYDnlrGcp4mNhtAbXw6xkKzxORccQQ5T/NleGBVgOpMroYHBBZ1gDgLTd+OKjspw4GBwFaD1LAjDjCyBFX8UXSpxwHq0we1wgPB6bLuD7er8FPlSpGGd0R8FgZbrxPgPKF0NUIrjtGLqUjhQI91k5gC+j47E047HJzJTOVI8uhkSlb2xPf9aiEjDhGvHDi4+WoT9f5Z/J+EJI+YKBbksZNmDqsQnbjTPjRXg8Fk5Kz7dhVlygMA3Ahb4QaRu6/6Mc6ybtX/7lX3DDDTfgwQcfxN/93d9hx44duPnmm3HSSSfhZ3/2Z4fdPdrinlKP1P1+vWBHdYCiWWBhM8ES2YNH7QrR28f1DiqI0kuDCvKNm1HOwKw2rEB0r4zK52ZQgfVOjMzcjEg/gOEdszqdAz/YQMlfh1LF/tT86RGFwdTPHvQUXHbZZbBtGzfccAMA4Pjx43juc5+L9fV1bNu2DX/+53+Oz372sx09E4eB7j4YVkb1MCTHakqBaU/fyp51ysGwKKMbQsSBHJnOQRgGlL1eziA0rThoGkQ1s8Nb83XwAVBh9ltSlIVZkfWpUM5EdjOQ0tDBUjcMrJg2hGXptiy3HBBGOdAXBVaAcq0wKXTmejIgpuchyurzILwAUkooe72ccRoGO5Rh6YCIEQWfdWClFAbEir7ST/0NH/YSBRWT5Tb0ZMly+RLo8grKdsPsVh2gVZYHZdpxMNgIA7RRuYi8W24P0AEjxzDCbMVywDQ5NpkGhGXp/RYGqGBaEI6ng8RhZrwQOtBnyqgUhgHHkCgGUYBWBxVdS+oyEckdKnVWt/Cyer+ZdhzEj/ZblPFcHcSP1khybFE5hSggFgfDonk0bSAojy0IS13EYwuDYVE98EZjC5RuL5rHqGxJuda5CC8alMcmpFE5j15WZ/yHFwz028pjy9gmLMNHKjAqsnWjwGL5goEuAaMsB1ABZDpayImMbi9dXiNhlnUUxDSl0m3JAMVAZz3rjFZRnkeBinnUwecShAdI0w7ntRzEF26mHHiOsnXDshu6jr8JxwziEhWG1LXJbVNUXpxAOUAbj03KikB3NI96PZYvmEVji+o5u2YAP9BtWVLPYxTAD6+76Asv0dgAGFkfKrwDAFIm5rEyqC6B+LNmSMAqCQQqupgmYRkivqAUrxFpxgFaPbacLoERfTTCeupx4Dmqm40oiC8ghBGOJYjXiCVlPI+mkciMj8ZmlHRgPSq5AeiLc5YTXrwqXwjUJTd0EB+WHlvR15/tqC0rPBZXXDCIgs+mBSgPIh1AlMpjE2E9dWVGwWB9EUUKBVsKKAMVY0O4fqJ9ZoZrREQXy6QRfo7CsSUuGAjbLc9jokxKVE7HNgAg+t1TPlGO9pm+OCHiUlIIL6rCCiC9LIRdrPi9BtPRx37LrdhnhhSwo4sB4fF5pCnW6AaAv//7v8cb3vAGvO51r8N3v/tdbISljI4dO4Y//uM/xj/+4z8OuYe01a2WDjf9eb0AQKNgRL8DJr0OXo+DUQpCbTXjFBDfjHEPprcyaZ+hSRjPOK85pQIoVYIfFABVGnZ3tqSvfe1r+Iu/+Iv435/4xCfg+z5+/OMfI5/P413vehc++MEPMtBN7Wd1KyG6vk24evtR9mL0gDmdrZjIIIyCb4YOVApP6uzjKEPStCHCYKmSZlwzWwjANAQshTDYIWFIE1His2vqYGkU7NB9kxDS1EFew4KwdcBBlYo6gCXD7PIoiBMGVoAwOzJQ+mGK0EGyKBgcPfQwCqrHt+ULqYMlpgUgDWlaUI5Xzno2LcCwdE3p8LZ8PTYdNIpKoGRsMw6YGkLE2YMVJTAQji0sXyKE1GNzEpnIpqUzPqOAaeLhf1YYNIoC61G2uhPWCo4uTlQEg8M63fqfRnm/RWOTZlj+pfwA0Sj4rACkbf1gPD8M0OpAmN5vdpStm1wjYSmNeGylom4r3G/R2KL9JlRYdiNcI8mxWYYOmEVZ2PHDIVEupwMriMcmq0puxMGwRGBRhuu9cmzh5yAsRRGtycp5tIDE2KKsbSBcn6ajA/thUF2G6yMam2fpIGKgVBygjbJobSMKmNUfm0yjXN5DSihbZ8WrqkBfFMTUQVMJK9BB9qitaL/G+yz8kCrDAkw7HltUTkTIchkhvc/KpUvKWda6LVuJuGa8FOV5jErbVARoo0C+qMoTk0YYMLXL6x/RAyLLd1EYwoCvZBwMNsLyEVG5jXi/GSaUb5b3mwcIu1huy3TD4Gz5olIUxLQU4rEZUsRXx0Xi8xGtkTjr37AggpLO/lcZCLt8wqWzhqPjlhXWei6PTbcFSAjY0O1FbUUPKk2ukYqxBT6EYcUP1Cwfq+24jFQ0jyIsAwMISCFhScBXMm47OY/JOwwQBZ8BSBXEAf3oomQ8j4nAR7zPEvNmh5kM5bYQl3+JM7oNU3+Oo7GZThykjQLuqvpzHe8XJMYq47ZEuF6jCy8VF3qkoefM1BchRPiAVxWV0TIr29Kzh/BCQHkNjniomwB84AMfwEc/+lFcfPHF+NSnPhV//wUveAE+8IEPDLFnRNpGqfHTKBsFVJoFKfoVhNmKQe7NmIRgGGncl73CeWxkq6+xTgPvOthdhAr/N8omMeXk0Ucfxamnnhr/+84778RrXvMa5PN5AMAll1yCG2+8saNt8gyDiIiIiFoIM7r7/jXaf2Dcf//9eOELX1jz/Xw+j6NHjw6+Q0REREREY8p1XaytrcX/vueee3DuuedW/HxlZaWjbTKju8fGsWxJN1nd9cZpCCDrGHH2bGUd0zDLOrx9XZlRNqIs3wod3dpuGOWs5yjLOs4gjL4lESZbx5nV1RmEupZvmB0sJeDrB5QJFVRk2SUzrKNxRNm6gEIKRvyEaInyretmsuQAEN++LqKxRiUIEGYQRpmmYVZf9KBNQwqY0OVRPEvG2ePR2KJ24hIAybGZth6bkBBGudasnke7Yh51tq7OfgyUih+CF3dfluc5fjhkNOFRaYpwP0XzWG7LiLNnk5mf0dhsQ8AXAiqxD6Ps8orM4OQ6CLODIWRtdqRRud/ibF2BuKRINLYo4zVakxUPe6vKMkW038K24geohncYJPeZoQBD1Y4tOY9RfW4phI4RSROQ+u6FeL8l5jHOQpZmZU1kUV6XUujaXEpWzaNI1CiuN7YEBVRkISOZ9RyOLcoOVlLEpXuisUX9qchWj9YcEI8taivO2E1kjwOoqEEOCEiFeL9F2bVGooZ1LCpvE20f6crjiJUoWxLVHxdRRm60zwBf6bnUmbvR2BIPh4z7buntm7YOwhl2ORM5nEddlqj8KzUqfRI9/NRXqMnoNkR5jURrTiTHZpTi/usdYJTnMfH9aGzRHS0S+mp/lNEdZT5HtbzLbww/Tyo8VvlGuZOGUa49HR1bhNR3T4RrEVAQSkAIBUOJyraEvsNACr3+9fowARXosVmBLjcTdlJFD5CMsp4T68RK9FkiPvRXZFnXrhETkKXy2FQQl7dRhlkuEyMNJBkynAJZWS87aitZLz56ZkKcQR6vkSBur6Itw4qPK1FZlqiOedQ2jb6lpSU88MAD2Lt3b8X3//Vf/xUnn3zycDpFlFDyT7T92lZZf4O8Hb3RAyYnzTjf4k/jY6tn9I6XrVFSZ1TU/2yE5+4jfnxWSkANokb3gO8x3bdvH26++WZce+21+Jd/+RccPHgQP//zPx///MEHH8T27ds72iYD3QRgcyVMIrokQFSDNqxjmggU6HYkRBTQELKyti6qAhBROQWl4rIbUQCiol1RLt9QcYosZBxgAwDIcsaYikpkSFMHZ5PtRSUMEAWgVPxfEnpcNYGVMBgctyVKgC8SkSZZDqokgsFRwDAIb5dXqhzEiebREKKyBEz12KKgkB++M2xLxYGcRN3gKDCqRPwQviiIHwXeKkqyRMJtxUH8oBQHT6O5LAfVdemGKIgZiPKt/krp9uJgaaJ0gxQCCuWgfRxUD0qAX4oDzXFwzjAT7Yk46BsAMMOxlddmeXzRPET7TUkTUCoeG4LyHogD3YYV10ROjs2UApYqjy1aCtHYjES5Dd0RHQyLg+pAxTyWg7OyopxINDZTKhjQged4aQnEa6TygkHV2IByUD26cFH1WUNibIaOWtZtKxpfxUWDqEQJymsyvqgkatd/FOiLapAD5bFF61VAtyPDPoloTUgTSgYVY0uK5lElHvwXXTBQ4X4TYZA27n4isF6xz6LjVLQmgXJQHaicx/izpscWlSeRCpCqstZ/dGGiYo1UjQ2WWw6aRm3FZTJkxdhkYp8JKKhwLpPzGM1hZTkd/bmOj4uJcccX6KpuL48uiAB6XIYq74O4LVm1RqrHZtj6mBxv1Kzbnl7/qmZsUVsyMY/lNSJqx6aCcnvJthJlq/TaDy9aBfq4L2T5N0Bc4kZUHl+SF3Hji6kq0AfzMLAdj6tqbIZQ8Q5JPtx1JCXXYn8bGkAb3bvsssvw9re/HR/72McghMBjjz2Gu+++G+94xztw9dVXD7t7RLrOaPuvbvrTQQbLWMqkFoOVRJ3g54USOjx+KiSykmigrr76arzsZS/D3/zN3+Dxxx/HpZdeim3btsU/v/XWW/GCF7ygo20y0E2xdoLdzTLWoyBpRaCvOus5DtCWMwRrgh1RYKkqyzoQAjAAUXXMimoui0TwJc4Gj4IZ0Fl2IgoyIsyujV4jyoHFKCAWhIEVG5VBHCsxLgGU5yyRHSkS24t+VhFUrA52SAWldPZn8gpaOaiIyoBRxdhQ2V4iGBwH1aOgKcqBHEshrM8dBqwSAcwoEzPuu9CBvvI4zZpM/OgiRRQ0ioJhARRMpesFR+3JaH/FWa2VayR+AGY0tih7NcqybrDPVBjFK7cVzmMULK2+YADUBJ9Rtd8qgoqJscUPpVSV7cnEmGRibHEwLDG2CtH6iQKmQlbUX1bh5yu+QBHWlo7nUZaDmvXGJpD4g6kq8JxcH8kazCL8fZ9sK5rH8mvDtsIgZnTBQAip20usn2TgGYjmSMWf2ygLOe5+PM91MvGrxlYhmsfEGkmOzRAApA7SlvtSzuhOBoOjuzN0vXF9LBGJE6d4HmXt5zoeW6AD+smLIVHwXsZrRcQB2nhsyqzbVvxZS+7q8DiBQOjrh0hmdEfHSFGxRpJji9dc8k6NZCZ2Yv4rArSJsSXbqp7H2v1mV56AhhfKojWiKta/CK8UlMcWvy0xj8l24rEBulZ3sq3kxbKqCz3R5w1S1/73VbkdoHwsji4uKMiw3n64P4ISlJIVx8dG8xivx+iOoeQS54n2yHr3u9+NIAjwkpe8BCdOnMALX/hCOI6Dd77znfj1X//1YXePCEoVW7yigz/+BxhorTnsTWrgm8FrIkoY9ZrQW8N4HJcVBtPTQc/Gi170Inz729/GHXfcgaWlJVx00UUVP9+3bx+e97zndbRNBrqpQjKQXf2HdquyLOUHlCEuEVHzwK4o0y6RHRwHoKMgY/SaeplvCAMeqA4WIBFYEYhC01GJD53VJ6BglKM/Qla2GbUVtRcGVpJp4slgaTKrMbktYVhQgR8HjSraiLOQRcW4lEJFsC8SZXtWBJ7j/aGDK0LIuL3kPNcEFREFZsrZyDrDurKtOFu3Yufqiw8K4YPyokBOYn/FFynCfSCEXkNRMFuFmdxBYh5FIiCc2Nk6QA+Uxxa1lfh5vQsUQuhM3eTYosBbNLaagGlF5qcon2ok5zExtuR+A2rHFs2jEGgY6Ku5OBHt0zgYVj+D1lBR8FklSrOUS0U0G1tt1rMVB2frjS0KmkIks9XL85gcVxTYjseWuKAU7bNkBn7FuKoCi9HJnqiaR/16PZI4kF5vbMmgYnW2ehx8BqSKMuNVPG/RxYKKCwZRn6OxAXr9h23FwdmqNVJvbMkPVXm91p/LeGxVAdry8aQyiF8O4CsoVdle+RhZf/1Hwef4GJlsq2q/6TsvEgF8I7rQU9lWxZ0T0X6pGhsSGfXxA4hlVVA9nh9RObZEW9E81h1bdLEKRuXFubrrUR8/yhcnyuskGkvy4lx5jSTGJqOLE0Z83KqYxyr6IpaIxzrSAlVxB1bfjHiQXwiBP/iDP8A73/lOPPDAA1hZWcHpp5+OG264ASeddBIOHDgw7C7SVqdKrV9T/ZZRDLQo3s5PRN0Yj6AljRg1+g+inGSnn346Tj/99Lo/e8tb3tLx9hjopoY6rTceBeOqg5gVwec4005CIQwI1AuuJIO24baViAJaUbA7bBeVGXYxEaakxll9Vb/0oiCfrA7kVLYTBTriMSYCwXWDmH5Jb6rBuJKZ1+HL4sBKHBALGbI6g7Y8j7pYrgkV1ryNg0ZVwbDqQJ9QKs60VkrEpVIqMj8Tw9IBnDDjE6XyfgvfWZGBXz2PUWBdIq4nZVS1FZUASFyfqJ1LVJYTKY+xHCCqvhhSb2wV9YmT+y0KfAVBxa2zNfMoZBzoa2ds9S5OxAHhaL8lJbJZq8emLxrobydrc0XtRes/eaEnvgCBOkG0OKhYG8RUIpFFi3J71dnBteuk/tjKJWcqg8GItwUYUBVjq57H2s92eWwVmciJoGJyHuMpFvpuDR8qLDkj4pfIxHwnJ7heID9uq3rtV3/eEmOrmH7Uz0SGUJVjSx63Em1VzyNQDgirqvYaz2Nl8LlhW7L2VCGaJ6kUgiigj8pjSPn/2xybbBwMjlpNjk0mfp5c/zrLOihndRthO1F7TeYxOhQbUUZ3uE5k4uf1DiMVF0RUor0G60NfCCy3F22bRtfGxgbe+9734o477ogzuF/96lfjxhtvxC//8i/DMAz87u/+7rC7SdThH+oMCBERDdWIX+Cn0aFQ/ruhr+0McEmedNJJlX97t+mKK67A7/zO7zT8OQPdRERERNSUQFBRSqdvRvQPvquvvho33HADLrjgAnz961/HRRddhDe+8Y2455578Gd/9me46KKLYBhG6w0RERERERFuuummrt5X/VD4agx0j6BePBhyGAQqyzYY9TIIgTijDyjpO8lFosRHnczgaNtRLdN6N3lHpQaSV4PiTOSoveri3nE7lduKsmfrZUcms6sbZgdHGX0NsiMbZVlHZTeSDzlLZkc2zCIEKrMxZbjtKHs2zNpMZusGKqzVXZX1U1NuprqtcB5rHo7TIMO0nIlcv62aMhFRO9G2knMZjavOHEaibPyo9Hh16ZJoHpMlMBBmfSIoAYaE8ksN57HTsVVktKKcHSsC3YYyqg7BDcYWZcfrz0BltlZ13eVIPLZoDDWT1TjrOSrxAQgopeKNV89j3b5H7SfHVpGFH/YrzGgFdEaujyZjq8oejzORo3GgpB9k2mQeo/UfoPnYGt0ZEu+3ZMZu9TyWu1cxNn1M12VSqttqOjYhw1sGkvWeE3ehVI0t2i9BNJeJsizJevEVDzyM1iTQ+BhZJbn/o7El1Z3HRmOraqv6gY3RfEXH5Ghs1W3VzQQQUmfgR88UiNqLjpt1952AhEqUKylvN/l7pmaNKL+c+R8xEmMRlXfzVLcXj7NqGON4LjCp/vZv/xaf+MQn8Eu/9Ev4wQ9+gDPPPBOlUgnf+973uspEIeqfTVyQ4jGHiIi2IJYtGY4XvehFfdkuA93UM7p0if5vIxEQq/kDUIjK4LMKykGqFrflB9BBgeRhSCR+HomDL1FQKKwDXhEwAuJ2qmvrRsHg6sBKZbC0OkArKgM0UYCqOtBRVX+2su8iDobpTdQGqaL+xkH8eN4a1PKtmqsosKIfZlAbxInaqgmGRQGqqL2q4Ft00aD64X9xyRlRWQIm2lZNXd3wokdFW6LOfqua72hfxaVZFOrut5pwRE0wrDJAm5zH5Nii7dYbW3L8lW0JRAHvGhWBvvLYpBDww4ClIVRFe9VtNRwbgOoHiDZbIxX9b9BW1Lfk2CqCmNHnrSqoWB3Ji9YIUJ7L6r40uvBSMbbqn9ULKtYZW/XY6weDwx1dZ5uVAfz6Y4vnr2psyYCpEOHnOqrrDJSDpjXHkDoXJ5AouyEq51JUvS7Z9/hyUPWarPis1R6TawPClduvO4/RGJD4vCW/n1yPVeu/ZmyJ9upe7GkxtvL6qN1n0e+Z6nUf/Tz6t4w+zyqo/GxXHx8T81jZVvmzndz2yFIKg3mI2mj+ofHTn/4UZ599NgDgjDPOgOM4+N3f/V0GuWn0DDFY3W6gQNSesRARbTkMrlKn1AB+fw6ijX5joHtCdVpfu1P1HlSZrLfcKOszCuTUDZpWBG0rAzlSCASJjL5k++VAUb0su3IAIqrlW/Hzqu+Fib4VQZwosJIMdFQGccp9qRhXvfYaBNYrAvh1An3JcSa3V9Neo3arthMHhBMBxsogd9W2EsE3JIJxyXEng7PJaYky8aO5rDeeRsGwuK1mYxISEnp9xONrsd8q2yqvj7r7rsHYqtdiowB3zdiQyLauDgbXCYYltxkFTYHauWxrbDX7rPaCQbS9KPhcb7/VZiEnArGJPrf6vEWf6+TnrbofNcOJA84B6u636rFVqR5b9c/qXjBIjK1pW12OrfqiUjy2+MGGibaaSGYHJ9srX5ho/N7qY4mqXv9xG4BEIju+qr1kW9Xb0/+uv9+S81i3bnaTtlqp+LyhyedMoFw3u6q9hr9nqsaWnLf4TqXw343GFrff3nBoSHzfh23b8b9N00Qmkxlij4j6q58BmH5uu99BdAamiIiIRhsD3dSxerdSR7eV12SYxqUHUPmgwTAztSLYHWoVzEkGquK31wk+RNsSyWy7mo7XBh6ivgcQ4cPqGpSlqNtOk2BY2F7dbN5E20nJ4Er9N1QFFpPtNQhQRRcMquewWTtxBnk4hzUroM6Yqi9OSNTOY3KMdduKAkU1GZkiDvRXttlegKrq+kQ8huS+a7YOk2NLzmN1ezWzmcwyjbpQczGk/tjiMdT5A6vR+g9/CNR7V72s4MQaEULfXVAbbG68FmvGVrcvjbrZeI3oz2TzQH5NXxp81kS43XbHVi9AW9NWC43G1jCorl9Q2171caTB+kfVOxvNY9OxtXvMqprLtrJbm42t3svRfGzlC6tR6Y/kZ7nOmmxjbFF70dgqSsA0HVu4vepjV4N5qXcoGllKDSZTdEQnRCmFSy+9FI7jAADW19fx1re+Fel0uuJ1n/nMZ4bRPaLYVg/EbvXxExHRZFJq8h5G2S8MdE+gfmZzt6oXGrVcEeRu+OJEkDaZ+QbUDfZFwUV9S3/joEF1G+VARp1brutkmZbb0weS6rZqgirJAL7yK8bVuC+VY2oUDAYq57EmQJsI4NcEjVqsg0Zz2CyjFQj0ka9B4Lnu25q0106grxP1AmLVAfV62ePJcXUSDNM/1turFxCr/4YW81hH9Tqp/Xn5dRVZz3E2fp25jNpqEFRvp62Ox9YsqFjns5ZsLykO0LYaGxLlZqou9DQaW+OgemUgv6atJmMDattrtPYrxlZvu3XmMRpbssRHveNWdZfL32gxtjqizxrQeGzV89h0bC3mMdpu9djaCzxXrcnqtupc7FSo/Qy0ulOj4fpvMbb+/cbuta1duuSSSy6p+PfrX//6IfWEiIiIiIgaYaCbeiYZ+GiaYapfUA4GVN/e3eTl1e1U14dNih7qmcy2bkcUVATqb7thIKx6XPV+hmTNW1Hx93wyqFL9ELnabYUZi1Ggr2Gb9bKey8E+oMMyAHFArL7q7Ovqtmo2F/1/vcBbHMREYuc3Ds5W77dkQKy6zm5tO8lx1QlQ1RlbtL1GbUV9SorXYrTt5IWjBsGw6pc1WvvJlioeaFtvLlsGZ8tB03rq7beasTVoq9V6bNSf+h1JjK3OGmmm1TGk5diq+4HGY4uCpvX7kdyJsioI3PjiXN2Mf6BpW/W0O7ZG2fGdtlfeVpMLj8lvhY20aqtRiaCGY2uxzxq1l/xn8sJj2/NY9f7KskvjE/Leim688cZhd4GIiIiItigFsEZ3mzpLlxyiI0eO4HWvex1yuRympqbw5je/GSsrK03fc/755+vyAYmvt771rQPqMREREdFkEOFDnfv/NZoZ3cRzcSIiIiIafWOT0f26170Ojz/+OO644w4Ui0W88Y1vxFve8hbccsstTd932WWX4X3ve1/871Qq1e+uDlW/H0LZSr0M2oZZrY0ykVuUikhq90FecfmCqtuuGz2ULJnVV+9nUTfrapSFjPpZmEA5W7G8iRaZyPELO8torc4OTraR7Eu9dsoZ5Ims0+TPm2QiV48v2U7TbF2gMhs5+f3q/05st1UmZsP2qttq0AbQXuZ/07YA1GS1NsnWTWYHV7fZqK2Kuxmqs02btJXcZqP91lCj/VanrXbXSD01Y6uzRrqZx6ZZtQ3Wft2X9nJsLdqqt11V53sdjS3RVr39hsTYku1VtxU3mbh7p+XYmmRZNxpbU8k1WWf71eq1J6p+XrP96ocs12mr2e+bahV3ZRCNCJ6LExEREQ2HwmBqdAf1Hjo3ZsYi0P3v//7vuP322/Gtb30L55xzDgDgIx/5CF7+8pfjQx/6ELZv397wvalUCktLS4Pq6pYmhah4AEzLQF+9oGJCsyBVTWC4+r1hxQQlBATKAZVWD7qs11a7auvPth+cLbdZ+722AsIt5rK2ncqx1Qvk1Ntcp2Vgkm01u2hQIRlUD/9drx8VbwmDiu0EqKqbrCw5UD/41qq9qC2gdi221V6ToGK1Rnu50fpots/qP/CyqqJKN20BHa2RZm0l+9X4h+2Nrd22arZdHaCto5v2ml0062a/JctgtLoOp//RemyNxlV9DOnkXGiz7VW3VXceq8cWfa+Ntpq113Z5kQ7aGhtqa9fo3up4Lk5ERERE42As/vK6++67MTU1FZ9YA8AFF1wAKSW+8Y1vNH3vJz/5SczNzeGMM87AVVddhRMnTjR9/cbGBpaXlyu+qDMC5czIOABRL2Bc7w9/EdWvbl6zO9lORxl97bw0scFoDMkvIRJJsY3GVf2F5gGcaDw1m2q709VZ8VVtbiLoV32XQL1xNMwKDt9aLzjUKmDUaJutxpTcbrsBqsoHoba336pVr8WO2mvx/WbzmPx+oyZb7rM2s0y7bavZ95u11fBniX3Tydj6MY/Nvi9E5XGwLU3mqdF4k1qNrdm22/le9TabfbZrftSHsbUzt9XbbaetetuuOLZ0+1kjGlM8FyciIiIaIqWzrfv9NQkpJ2OR0X3gwAEsLCxUfM80TczMzODAgQMN3/drv/Zr2LNnD7Zv347/+3//L971rnfh/vvvx2c+85mG77n22mvxh3/4hz3r+6RpdTt1vezj5G3utW/oPADQaaZ1TVttZKRVVx+p/n6nqoOzjUrMdJ2tm9h2I/rBnKJuNnKrduLtt8qM7zKY2UgvAkTttlsxl/W0mdnatM2qOWzaHsr7rN629QMcW4ytnX1W722JNdL+m9psq0l2fLP+NJOcy3bHudl5FMmH6CZf16K9mk03abqTeey6rS7XSDvtNdPrtroeW4P12Kq9rtpqtYlRv0VQBYPJ6GbJlpHEc3EiIiIiGgdDTTN697vfXfOAmuqv++67r+vtv+Utb8GFF16IZz3rWXjd616HT3ziE7j11lvx4IMPNnzPVVddhWPHjsVfjzzySNftd2vU63IqIep+VWcvJjOfa7fRPLNPb6BPy7NOtnX8o6q5j8ZQbyz19lOUKVj91UrHGa3RONBdBm1y28mvlrrYJy1jiN0EdyqyoGuzRzsaU0In+65Z5n+917TTXvmN9UtTJHUTXKxrkNmmfWyr7v7qYh7bnda+ZumOWgZwLz/3fTiG9FrXx6xWY2tjPRINGs/FiYiIiEafghjQV2euv/56nHnmmcjlcsjlcjjvvPPwhS98oS9z0K6hZnS/4x3vwKWXXtr0NSeffDKWlpZw6NChiu+XSiUcOXKko5p/5557LgDggQcewCmnnFL3NY7jwHGctrdJRERENPlYo3sS8VyciIiIiLq1c+dO/Mmf/AlOPfVUKKXw8Y9/HK961avw3e9+F8985jOH0qehBrrn5+cxPz/f8nXnnXcejh49invvvRdnn302AOBLX/oSgiCIT5jbsX//fgDAtm3buupvO+qVGthKOhl6demGxnWL299ovSzXVuVWqrWzD7vKum9RtqSedrOD2ymDUdEVsYm7wxO36Nd8P/rPqjms117TaWjUxia1Km/QtD8960R/xtZVe70usdCHthp3osU8jkh7zT5rXX8GGnzWuv5cd7Hf+nIM6bK9Xsxju7r+9d7m3SFEgzaJ5+JEREREk0ZB19DuezsKjWvq1vHKV76y4t9/9Ed/hOuvvx733HPP0ALdI3Z/dH0/8zM/g5e+9KW47LLL8M1vfhNf+9rXcPnll+NXf/VX46e8P/roozjttNPwzW9+EwDw4IMP4v3vfz/uvfde/OQnP8HnPvc5XHzxxXjhC1+IM888c5jD2XKaBYU7Ke8xSEKphv1u+8GayZ91WL6kF2U32upb29uurtvSfm3i+CVNysDUf0NVeZkG5Waq2+jmZy37McBt9mV89bbZqqZ7g/3Ucsxtrvd2t9nxuJp9v51tNn3jkNtrY27rlllq5zM3yDXSqP0ux9bWtnvQVlvqbXvEfr91TSkIFfT9ixndo4nn4kRERETUjO/7+NSnPoXV1VWcd955Q+vHWDyMEtBPbL/88svxkpe8BFJKvOY1r8GHP/zh+OfFYhH3339//CR327bxz//8z7juuuuwurqKXbt24TWveQ3e8573DGsIbdlsfe5RyijveixtZHN3/CDFLnU0hjaCGa32TasHrY1U/fZWgaJersUOAkXVmZ91A3E16eW9z7JuK7BY3WabFwyA8hjbnuJBPyCvh8Hulu0k57HNdttZJ71uryuD3G9j0Nag2xv0fiMaZVvlXJyIiIho1CgAg7gvPACglMLy8nLF95uVlvv+97+P8847D+vr68hkMrj11ltx+umnD6C39Y1NoHtmZga33HJLw5/v3bsXKhG12LVrF77yla8Momsjp1/B7voPX2wQmG14j3n7t/8nt11vc1Gwux8B7lHSr+FtqvTAiGi4/jYTCIvWZ5cB047aT34eBhUYnkQMYtImjcoF4qSR65MKgGAAp9fj/otpgvFcnIiIiGjyHTlyBPl8vuJ711xzDd773vfWff0znvEM7N+/H8eOHcPf/d3f4ZJLLsFXvvKVoQW7xybQPU5GKau6F5plEXddq7qLTNZ6WmVAj4pu18Omx9HG7fl1K7H0KhF7HD8Lg6zb22V77ei0Nv0gjd2aICIiIiIiIhoWBSg1gL+jlcDMzAweeuihim83e1C4bdt42tOeBgA4++yz8a1vfQv/43/8D9xwww197WojDHQTERERUXNKDSbbekQv0BERERERbQVCCORyua7fHwQBNjY2etijzjDQPaF6kUnb12zQFlmsA8v4rFdGpYcZtpsZR7+zuQelX1ndXWfJM4jS0Fhm4BMRERERERFNsEHW6O7EVVddhZe97GXYvXs3jh8/jltuuQV33XUXvvjFL/alf+1goJvqGudgYHWcruFYGtUKV8Gmg8T9CHD3a5+0+1C+QZTCGOVyGwBLbgzbKK+NJF4w2DpG/ZjVU0r1/KG9DRoaQBtERERERLRZhw4dwsUXX4zHH38c+XweZ555Jr74xS/iF37hF4bWJwa6J1g3wZZR+IN9IAGiVn+sdxHs3nQGfZO392K/NOtfFOzu24Mvw/63M0ftBI62UhCRQVOirYuffSIiIiIiUgCCEawi+L//9//uT0c2gYHuCddJgHESgtxtvb3HGWn9Lk/S9QM/+9CXzWo3aNuv4M4orPFRx8A60XCN6mdQKAUxgIxufZwevfETEREREdHoY6C7D0b1D9SmGb0jEAAcp3IfkX7v60EFuQdpWEGcza6FUfxcbyWjcIyi0Tcu62RUg9lERERERDR6FAZT4G88/ppqjoFuIiIiImohGGCNbl4EICIiIiKizjHQ3aZJyLyqV8ZkVLLfxi2beyTWQ3XAYcQzuYdlVNb4MHT7oLxOs0231AP5+ojzSNH+Z8Y3ERERERFFlBIIVP//PggmIOGEge4taNQCKd3+Md/VwxvbzUZrEjQeRPCh1/toqwZMejWPm64dP2KfuWHqZi45f81NYlB00sYzEZQaTEY3P+9ERERERNQlBro70E62Hf8470y789XJtG46yD1kHQf1mMldo5eB0XH/TA8qq7vdvowTBtjHy7jtr0m8QEFERERERP0xXn/tDA8D3TQUA//jvpMg9yazuatjLT0J0jd8Q+sgd6/melyCSOPSz3HQSSCuVUmkYQX0GEgcP1t1n438sUsFQOAPoJ0RnwciIiIiIhpZDHSPGNZo3Zxh1uXebNOjHOQeB/3Y972Yv3H/PHeTdbrVLq6Mgm73U6dzPKpZyJtdK8MaF9c4ERERERG1ogAEA/jTYRBt9BsD3SNokoPd/QokNJ2vHmRzN+t3L3bVqAZpxwXnr7+GEQTsxT7lPuyPfq4H7rPRpYIAKuh/CTA1oec/RERERETUfwx0ExEREREREREREY0gBWAQT52bhJQTBrpHVL+zuhtlzfWrzX5m6fWkz5usy91ws228dVKz98fZpJXeGKe7RHrVz83uw3GZr82K5qmf421n25s6zm6RfTV0A6vRPR4PjiYiIiIiotHDQPcI60dwqmWd6RYPlOt1e5vV7yB3MwMtWRL94d9mX3s9771ch6PcN2D0SieMSn/6Xb5klNfYuLU/CNX7q3rMnezPrTBfREREREREXVODeWb7JOQQMdA94noZ7O7VA+Wa9WeQAYthZnK303SrqWir/11kto16IJmo2qitsVHrT7s2czFis5ndnb6Pwe3WRm4dBoPK6B6xcRMRERER0dhgoHsMjFrZgVEIUGx6PlpkRo/EwydHIMjda6MehO9l/0bpMztKfUniQ0RHz6j9viEiIiIiItrqdI3u/v+tO4g2+o2B7jGxmeDDpAV+NhWEaaP0Ry/mq9kmWva/UYB7E8H5bo1ywGsrBLkn7bObNKpB7lFe8+3oRYmZYT0jgkZcEED5rNFNRERERESji4FuIiIiImpOBbp8Sd/bGe+LTUREREREvaYwoNPkCTgVZ6B7jHSTZTdJmXNdZxh2+bDJelp1YdN1ubvM5u6HUc6YJqo2KutrVPoxqjg/RERERERE1C+Dj5516Y/+6I/w/Oc/H6lUClNTU229RymFq6++Gtu2bYPnebjgggvw4x//uL8d7bNOggSTFFAYRDkBJURfa3P3M8g96vuatbnH3yjP0aiv/3HEOR2ekZ376GGU/f6a0GPoJOC5OBEREdFw6Brdg/kad2MT6C4UCrjooovwm7/5m22/50//9E/x4Q9/GB/96EfxjW98A+l0GhdeeCHW19e76kM7wZlRCMhOmkHNadOft9GFvtTlbsNWq83da5P+WdpK+3KzOFdb16gcB6r7wTVJo2QUzsWJiIiIiJoZm9Ilf/iHfwgAuOmmm9p6vVIK1113Hd7znvfgVa96FQDgE5/4BBYXF3HbbbfhV3/1V/vV1Z48DKwdzUqZjMof7ZsxqD/wRzrIzQdQblmT8BkehF7NE9d9rX4/lJIaG8W5VyqACvr/MEqlAkzAw94n0jidixMRERFNEgUgGMCfB6P1F0h3xiaju1MPPfQQDhw4gAsuuCD+Xj6fx7nnnou777674fs2NjawvLxc8QV0HgQZlSDtuBqn+RtWJne7lBruneCjXrakl0a5b5NoUo9/m8V12JlRWEdb7U4t2hp6fS5ORERERNTKxAa6Dxw4AABYXFys+P7i4mL8s3quvfZa5PP5+GvXrl197ScRERHRyFMqrNPd5y9eqJkYPBcnIiIi6h01oK9xN9RA97vf/W4IIZp+3XfffQPt01VXXYVjx47FX4888kjX2xqnrORRIZQaqTrnrbqyqalvlc3dpGxJO/3vJpN71B8SOMr962XfJrkcxyT3aZKOxTQ4XDc0TJN+Lk5EREREW8tQa3S/4x3vwKWXXtr0NSeffHJX215aWgIAHDx4ENu2bYu/f/DgQezbt6/h+xzHgeM4XbU5TNX1PAdVJ7xXBhn8/Ex7xwAAKNlJREFU6ne5EqAHJUtaBLlbqW6+nSGPYgCyn0b18zGq/Ro1k3gxYJT6QlRNBYOp0c2M7sHiuTgRERHR6FNqQDW6J+BUfKiB7vn5eczPz/dl2yeddBKWlpZw5513xifTy8vL+MY3vtHR0+I3a1AB53oBknEJdo9TkLsnAeMBB7mHZdSzuXtplPvWC6MWVB7F49oo9olGH9cNDdtWOBcnIiIioq1jbGp0P/zww9i/fz8efvhh+L6P/fv3Y//+/VhZWYlfc9ppp+HWW28FAAghcMUVV+ADH/gAPve5z+H73/8+Lr74Ymzfvh2vfvWrB9r3fgfBmm1/UKVAujHIvnX7oC8hKr9avr6PQe521Gt+GNncox7knvSSJURbxWY/f734zG2Zz60KgMDv/9eInrPQeJ+LExEREY27qDxtv7/G3VAzujtx9dVX4+Mf/3j872c/+9kAgC9/+cs4//zzAQD3338/jh07Fr/m937v97C6uoq3vOUtOHr0KH72Z38Wt99+O1zXHWjfgeFnV0fBgFH5g3ycsrjbtekxtQhyt1OTu2aTLFlSY1Q+A/02atnTW22d0egb9rFg2O0TdWrcz8WJiIiIaPKNTaD7pptuwk033dT0NaoqkCKEwPve9z68733v62PP2tePYHenwaPk6wf9R/agA12DHF9bY2uWzT1BQW4Gb2hcjNrFABpPW+YiThDor37bKvM5hibhXJyIiIhoHCkAAzgTH0gb/TY2pUuIiIiIiIiIiIiIiOoZm4zuSdHLrO7NZpENKrt7kjO5gdHM5m7HuGRzj3K25KjW52amMm0Fm1nnw17bbT1YeNQ+f0EA5ft9b0YNImuciIiIiGiMKAUEAwiNjG70pX0MdA/BsOt111MdMNhM/4YZmGSQO9zsaC2vro3yQyhpOEblQiEN1zgHuYmIiIiIiKg/GOgeklF7OGS1cQsCjWSAG+hrkLvhZiekNveoB7mZzU0A52kYxu3308RQARD0P6ObNbqJiIiIiCopDCbbehLOxBnoHlP8Q79sZIPcdd/cXln8tm5rH6EHUG41kx7k7jUG3wdrVOdpVPu1Gd2u7VG8s4uIiIiIiGjcMdA9ZPxjt3vDmLeOgxptBraTxi2TGxjtbGlgMgNs1L1RvSBArXHfDVEwqIxu1ugmIiIiIkpSGFCN7gn4c6vzKBwRERERERERERER0QhhRvcIYFZ358Yim7sLnYyr0ykYl2zuXhvlbPNRn7teYAYuAb1Z65O2ltr53T9KxwilAqhgANnWE7afiYiIiIg2T0Ch/38bTMKZODO6x9Qo/fE7SEoIBrm7ME5B7kkLZg3KKAbgR7FPo2oU65iP0mdxlPoS6VefRnGsRERERERE44AZ3SOg28CEEmJL/UE8rEDXuM/xVg5yb5Vs7lHtV69M+lyN4jFmlALvW3GfjeSdXgOr0T16nwciIiIiomFSajA1ugfRRr8xo7tN/cok3uw2h5XhPGiTPsZJH187RjnILZQayWAbMJpBQGCyg7fAaAZMR61Po3ZcG7X5ISIiIiIiot5iRveE6Gd2dzvBAf7hvzkjmb3XxChnSgOjHbAd1b5Ner9GMXjLeWptUvsDNO7TyN6tpQaV0T2AOuBERERERGNEYTDZ1qP4Z0inmNFNRERERERERERERGONGd0dGtlMqx7rNPut+vXjNEej0tdkP3qZNRptq5fjHLcM9FExqnM2ilnKo9inXhq1eWJ2+eC0059R6zMAKKWggv5nW6sR/LwSEREREdF4YKB7QvQiIDCpwYlh96eb9uu9pxf13DfTp2q9DMz3q2+9WEO9vLg1qhcIRrVfNH5G7bjdS6P0e5aIiIiIiLaWQfyFNHp/hXWOge4Wosyi48ePAxh+0LQa+9PYKPUFGL0gySjXngZGt3+jmPkKTOZ6B9ifVtif5sa9P9G5x0hkOQf+gGp0j8BYaaSU1z/XBhEREQ1SmEDH89OxwkB3C9Efmac+7WlD7gkRERFtRcePH0c+nx92N4iGIjoXB/igUiIiIhq8UTgXV2pAD6PsfxN9x0B3C9u3b8cjjzyCbDYL0SATa3l5Gbt27cIjjzyCXC434B6ODs6DxnnQOA+cgwjnQeM8cA4i7c6DUgrHjx/H9u3bB9i7Rp0JBpTRzWAmVWrnXBzg8QXgHEQ4DxrnQeM8cA4inAeN8zCm5+LUNga6W5BSYufOnW29NpfLbdkDRRLnQeM8aJwHzkGE86BxHjgHkXbmYdjZI0TD1sm5OMDjC8A5iHAeNM6DxnngHEQ4DxrnYbzOxVX4v/63M/4Y6CYiIiKiplQQQPms0U1ERERERKOLgW4iIiIiai5QQDCAsiIMdBMRERERVVAYUI3uCTgVl8PuwCRwHAfXXHMNHMcZdleGivOgcR40zgPnIMJ50DgPnIMI54Go9/i54hxEOA8a50HjPHAOIpwHjfPAOZh0QqlJiNcTERERUT8861nPwh++4my89NnP6Htb7/+bO/DEzCn467/+655u9yc/+Qne//7340tf+hIOHDiA7du34/Wvfz3+4A/+ALZt97QtIiIiIqJeufzyy/Gt/7MfL5r5xb639f/W/gPfzX4dP/rRj/reVr+wdAkRERERTbT77rsPQRDghhtuwNOe9jT84Ac/wGWXXYbV1VV86EMfGnb3iIiIiIioBxjoJiIiIqLmggAq6P/DKPt1o+FLX/pSvPSlL43/ffLJJ+P+++/H9ddfz0A3EREREY00pQZTo3sQbfQbA91ERERENDJ838fy8nLF9xzH6XkdxWPHjmFmZqan2yQiIiIiouHhwyj7aGNjA/v27YMQAvv37x92dwbql37pl7B79264rott27bhDW94Ax577LFhd2ugfvKTn+DNb34zTjrpJHieh1NOOQXXXHMNCoXCsLs2cH/0R3+E5z//+UilUpiamhp2dwbmL//yL7F37164rotzzz0X3/zmN4fdpYH66le/ile+8pXYvn07hBC47bbbht2lgbv22mvx3Oc+F9lsFgsLC3j1q1+N+++/f9jdGrjrr78eZ555JnK5HHK5HM477zx84QtfGHa3hupP/uRPIITAFVdcMeyutEWpACro/xeUwv79+5HP5yu+rr322p6O54EHHsBHPvIR/MZv/EZPt0ujZSufiwM8H+e5eBnPxXkuvlXPxQGejwM8F29knM7HFXRWd7+/MAEZ3Qx099Hv/d7vYfv27cPuxlC8+MUvxt/8zd/g/vvvx9///d/jwQcfxH/5L/9l2N0aqGQ90B/+8If48z//c3z0ox/F7//+7w+7awNXKBRw0UUX4Td/8zeH3ZWB+fSnP40rr7wS11xzDb7zne/grLPOwoUXXohDhw4Nu2sDs7q6irPOOgt/+Zd/OeyuDM1XvvIVvO1tb8M999yDO+64A8ViEb/4i7+I1dXVYXdtoHbu3Ik/+ZM/wb333otvf/vb+Pmf/3m86lWvwg9/+MNhd20ovvWtb+GGG27AmWeeOeyujKR9+/bh2LFjFV9XXXVV3de++93vhhCi6dd9991X8Z5HH30UL33pS3HRRRfhsssuG8SQaEi28rk4wPNxnouX8Vyc5+JbGc/HeS5eD8/HJ5dQ/SqGuMV94QtfwJVXXom///u/xzOf+Ux897vfxb59+4bdraH53Oc+h1e/+tXY2NiAZVnD7s7QfPCDH8T111+P//iP/xh2V4bipptuwhVXXIGjR48Ouyt9d+655+K5z30u/uIv/gIAEAQBdu3ahd/+7d/Gu9/97iH3bvCEELj11lvx6le/ethdGarDhw9jYWEBX/nKV/DCF75w2N0ZqpmZGXzwgx/Em9/85mF3ZaBWVlbwnOc8B//zf/5PfOADH8C+fftw3XXXDbtbTT3rWc/CNb94Ji4869S+t/WBz3wZR+afjr/+679u6/WHDx/Gk08+2fQ1J598MmzbBgA89thjOP/88/Gf/tN/wk033QQpmfMxqXguXovn4zwX57k4z8W3+rk4wPPxyFY9FwfG73z88ssvxz0378fPTf9i39t6eO0/8P381/GjH/2o7231C8/u++DgwYO47LLLcPPNNyOVSg27O0N35MgRfPKTn8Tzn//8LXtSHWE90K2hUCjg3nvvxQUXXBB/T0qJCy64AHffffcQe0bDduzYMQDY0scB3/fxqU99CqurqzjvvPOG3Z2Be9vb3oZXvOIVFccH6t78/DxOO+20pl9RkPvRRx/F+eefj7PPPhs33ngjg9wTjOfitXg+rvFcfGvguTg1s9XPx7f6uTjA8/FJxzP8HlNK4dJLL8Vb3/pWnHPOOcPuzlC9613vQjqdxuzsLB5++GF89rOfHXaXhor1QLeOJ554Ar7vY3FxseL7i4uLOHDgwJB6RcMWBAGuuOIKvOAFL8AZZ5wx7O4M3Pe//31kMhk4joO3vvWtuPXWW3H66acPu1sD9alPfQrf+c53el5veiCCAMrv/1e/HvUeBbl3796ND33oQzh8+DAOHDjAY/IE4rl4JZ6Pl/FcfOvguTg1spXPx3kuro3t+bjSp8n9/pqEkh8MdLep3RqQH/nIR3D8+PGGtSTHWad1MN/5znfiu9/9Lv7pn/4JhmHg4osvxiRUymE9UK2beSDayt72trfhBz/4AT71qU8NuytD8YxnPAP79+/HN77xDfzmb/4mLrnkEvzbv/3bsLs1MI888gje/va345Of/CRc1x12d7acO+64Aw888ADuvPNO7Ny5E9u2bYu/aDzwXFzj+TjPxSM8Fyfq3FY+H9/q5+IAz8e3CtboblO7NSB/5Vd+Bf/wD/8AIUT8fd/3YRgGXve61+HjH/94v7vaN53WwUz66U9/il27duHrX//62N8ew3qgWjfrYavUBSwUCkilUvi7v/u7ijp4l1xyCY4ePbols6m2el3Ayy+/HJ/97Gfx1a9+FSeddNKwuzMSLrjgApxyyim44YYbht2Vgbjtttvwy7/8yzAMI/6e7/sQQkBKiY2NjYqfjZJnPetZuPrnn4lfPPNpfW/rj277Cp5aOq3tGt20dfBcXOP5OM/FIzwXb4zn4rW2+rk4wPPxalvtXBwY3/Pxyy+/HHd/Yj9eMNX/Gt2PrP8Hfjg13jW6zWF3YFzMz89jfn6+5es+/OEP4wMf+ED878ceewwXXnghPv3pT+Pcc8/tZxf7rt05qCcIAgDAxsZGL7s0FJ3Mw6OPPooXv/jFE1kPdDPrYdLZto2zzz4bd955Z3wyGQQB7rzzTlx++eXD7RwNlFIKv/3bv41bb70Vd911F0+qE4IgmIjfCe16yUtegu9///sV33vjG9+I0047De9617tG8qSaaJTwXFzj+TjPxSM8F2+M5+KUxPPx+rbauTjA8/GtgoHuHtu9e3fFvzOZDADglFNOwc6dO4fRpYH7xje+gW9961v42Z/9WUxPT+PBBx/Ef/tv/w2nnHLKWGePdCqqB7pnz564HmhkaWlpiD0bvIcffhhHjhzBww8/DN/3sX//fgDA0572tPgzMmmuvPJKXHLJJTjnnHPwvOc9D9dddx1WV1fxxje+cdhdG5iVlRU88MAD8b8feugh7N+/HzMzMzXHykn1tre9Dbfccgs++9nPIpvNxnUh8/k8PM8bcu8G56qrrsLLXvYy7N69G8ePH8ctt9yCu+66C1/84heH3bWByWazNbUgo7q541AjUoU1uvvfEG80pM3hubjG83GeiyfxXJzn4sDWPBcHeD4O8Fw8Ms7n4wrAAM7EB9JGvzHQTT2XSqXwmc98Btdccw1WV1exbds2vPSlL8V73vMeOI4z7O4NTFQP9IEHHqj5w2qrVQy6+uqrK24Vfvaznw0A+PKXv4zzzz9/SL3qr9e+9rU4fPgwrr76ahw4cAD79u3D7bffXvNQnEn27W9/Gy9+8Yvjf1955ZUA9G2jN91005B6NVjXX389ANSs8xtvvBGXXnrp4Ds0JIcOHcLFF1+Mxx9/HPl8HmeeeSa++MUv4hd+4ReG3TUioonE83GeiyfxXJzn4sDWPBcHeD4O8FycthbW6CYiIiKihp71rGfhv73wNPzCGaf0va0//od/wdEdp7NGNxERERERdI3ur398P84bQI3un67/B/59erxrdE9OkTIiIiIiIiIiIiIi2pJYuoSIiIiImgqCAIHv970dpSahMiARERERUe+wRnf7mNFNRERERERERERERGONgW4iIiIiakoFwUC+wEfHEBERERFVUNAPUh7EVyeuvfZaPPe5z0U2m8XCwgJe/epX4/777+/PJLSJgW4iIiIiIiIiIiIiattXvvIVvO1tb8M999yDO+64A8ViEb/4i7+I1dXVofWJNbqJiIiIqLlAQfn9r9rHEt1ERERERJUUgGAANz522sTtt99e8e+bbroJCwsLuPfee/HCF76wdx3rAAPdRERERERERERERFucUgrLy8sV33McB47jtHzvsWPHAAAzMzN96Vs7WLqEiIiIiJpSYUZ3v79Yo5uIiIiIqJYa0NeRI0eQz+crvq699tqW/QuCAFdccQVe8IIX4IwzzujZuDvFjG4iIiIiIiIiIiKiLW5mZgYPPfRQxffayeZ+29vehh/84Af413/91351rS0MdBMRERFRU0oFUMEAanQzo5uIiIiIqIJSA6rRrQAhBHK5XEfvu/zyy/H5z38eX/3qV7Fz584+9a49DHQTERERERERERERUduUUvjt3/5t3Hrrrbjrrrtw0kknDbtLrNFNRNRrhw8fxtLSEv74j/84/t7Xv/512LaNO++8c4g9IyLqjgoUAj/o+5caRKoKERFNNJ6LE9GkUdAZ3YP46sTb3vY2/J//839wyy23IJvN4sCBAzhw4ADW1tb6Mg/tYKCbiKjH5ufn8bGPfQzvfe978e1vfxvHjx/HG97wBlx++eV4yUteMuzuERERERFNLJ6LExENxvXXX49jx47h/PPPx7Zt2+KvT3/600PrE0uXEBH1wctf/nJcdtlleN3rXodzzjkH6XS6rScVExGNIhUEUH7/a3SDNbqJiKgHeC5ORJNFQWEQ58mdtTGKz9dhRjcRUZ986EMfQqlUwt/+7d/ik5/8ZFtPKiYiIiIios3juTgR0dbDQDcRUZ88+OCDeOyxxxAEAX7yk58MuztERF1TvoLyg/5/sUY3ERH1CM/FiWhSqAHV556EU3GWLiEi6oNCoYDXv/71eO1rX4tnPOMZ+PVf/3V8//vfx8LCwrC7RkTUOaWgApYuISKi8cBzcSKirYmBbiKiPviDP/gDHDt2DB/+8IeRyWTwj//4j3jTm96Ez3/+88PuGhERERHRROO5OBFNEoXB5INMQsoJS5cQEfXYXXfdheuuuw4333wzcrkcpJS4+eab8S//8i+4/vrrh909IqKORQ+j7PcXM7qJiGizeC5ORLR1MaObiKjHzj//fBSLxYrv7d27F8eOHRtSj4iIiIiItgaeixPRpFEAggHkW6sJyOlmoJuIiIiImlKBfhjlINohIiIiIiLqBgPdRERERERERERERKNIDahG9wTknDDQTURERERNqSBAEAwgo3sSzq6JiIiIiGgoGOgmIiIiIiIiIiIiGkG6Rvdg2hl3DHQTERERUVODqtE9EfdLEhERERHRUDDQTURERERERERERDSCFNRASvxNQs4JA91ERERE1JQKAijf7387ahA3ZRIRERER0SRioJuIiIiIiIiIiIhoBCkFBAPItp6ElBMGuomIiIioORVABazRTUREREREo4uBbiIiIiIiIiIiIqIRFWAANboH0Ea/MdBNRERERE0pX0H5/c/oVoO4J5OIiIiIiCYSA91EREREREREREREI0hhMBX+JqGKIAPdRERERNSUCoKBZHRPxNk1ERERERENBQPdRERERERERERERCNIgTW628VANxERERE1pQKFgDW6iYiIiIhohDHQTURERERERERERDSKFGt0t4uBbiIiIiJqSikFFQwgo3sSzq6JiIiIiGgoGOgmIiIiIiIiIiIiGkEKijW628RANxERERE1pYIAagA1uififkkiIiIiIhoKOewOEBERERERERERERFtBjO6iYiIiKi5QEH5A7hdcgBJ40RERERE40QBCAZw52MwATdXMqObiIiIiIiIiIiIiMYaM7qJiIiIqKnAVwgGUKNbsUY3EREREVEFFf5vEO2MO2Z0ExEREREREREREdFYY0Y3ERERETWnFNQgivYxo5uIiIiIqIICMIhH2UzCmTgzuomIiIiIiIiIiIhorDGjm4iIiIiaCgJdp7vf1CBSVYiIiIiIxkwwgHzrQbTRbwx0ExEREVFTKgig+DBKIiIiIiIaYQx0ExEREREREREREY0gpRQTQtrEQDcRERERNRcoqAGULsEgHnhJREREREQTiYFuIiIiIiIiIiIiohGkwBrd7WKgm4iIiIiaCvwBPYxy/M+tiYiIiIhoSBjoJiIiIiIiIiIiIhpBCmog2daKGd1ERERENPGCAMoP+t6MChRE31shIiIiIqJJxEA3ERERERERERER0UjSOd2DaGXcMdBNRERERE0FCgiCQdToZkY3ERERERF1h4FuIiIiIiIiIiIiohGkgIHU6B5EG/3GQDcRERERNRcoKH8AJ77jf25NRERERERDwkA3ERERERERERER0QjSFboHUEZwArJOGOgmIiIioqaCQCHwB/AAnAHUASciIiIiosnEQDcRERERERERERHRCNI1ugeQdMKMbiIiIiKadGpANbqVGv+TayIiIiIiGg4GuomIiIiIiIiIiIhGkBIKSgwio7v/bfQbA91ERERE1Jw/mIzuCTi3JiIiIiKiIWGgm4iIiIiIiIiIiGgkKQQDqJ89iDb6jYFuIiIiImoqCBQCfwC3S7JGNxERERERdYmBbiIiIiIiIiIiIqIRpKAQDKDGn2JGNxERERFNPKWggv6f+DKjm4iIiIiIusVANxEREREREREREdEIUmFOd//bGf8nwzPQTURERERN6Rrdg8jo7nsTREREREQ0oRjoJiIiIiIiIiIiIhpBCgqBGEBGt1AQfW+lvxjoJiIiIqKmVKCg/AHcyjiAOuBERERERDSZGOgmIiIiIiIiIiIiGkG6Qnf/k04CKBh9b6W/GOgmIiIiouZ8BcUa3URERERENMIY6CYiIiIiIiIiIiIaQYPK6FYDaKPfGOgmIiIioqaCQCEYREY3a3QTEREREVGXGOgmIiIiIiIiIiIiGlGDyLZWGP+kEwa6iYiIiKg5paCCAdzKyCLdRERERETUJQa6iYiIiIiIiIiIiEaQrtDt972dQbTRbwx0ExEREVFTA6vRzYRuIiIiIiLqEgPdRERERNSUCgDFh1ESEREREQ2BGkiNbkxAjW457A4QERERERERERER0Xj56le/ile+8pXYvn07hBC47bbbhtofZnQTERERUXNBAOXzYZRERERERIOmhEIg+n8u3k0bq6urOOuss/CmN70J//k//+c+9KozDHQTERER0ZaxsbGBc889F9/73vfw3e9+F/v27Rt2l4iIiIiIxtLLXvYyvOxlLxt2N2IMdBMRERFRU4E/oIdRDiBp/Pd+7/ewfft2fO973+t/Y0REREREm6QrdPsDaCeAUgrLy8sV33ccB47j9L39XmCNbiIiIiLaEr7whS/gn/7pn/ChD31o2F0hIiIiIho5R44cQT6fr/i69tprh92ttjGjm4iIiIiaCwA1iIzuPtboPnjwIC677DLcdtttSKVSfWuHiIiIiKiXVJjTPYh2ZmZm8NBDD1V8f1yyuQEGuomIiIhohPi+3/PbJZVSuPTSS/HWt74V55xzDn7yk59sspdERERERJNHCIFcLjfsbnSNpUuIiIiIqKkACoHq/5dSwP79+9u+XfLd7343hBBNv+677z585CMfwfHjx3HVVVcNeOaIiIiIiDZHKYVA+X3/UoN4YE6fMaObiIiIiEbGvn37cNddd1V8r1E29zve8Q5ceumlTbd38skn40tf+hLuvvvumu2cc845eN3rXoePf/zjm+kyEREREdGWtLKyggceeCD+90MPPYT9+/djZmYGu3fvHnh/GOgmIiIioqYCpeD3sX52REHBMIy2b5ecn5/H/Px8y9d9+MMfxgc+8IH434899hguvPBCfPrTn8a5557bdX+JiIiIiPpvUDW6O2/j29/+Nl784hfH/77yyisBAJdccgluuummXnWtbQx0ExEREdFEq84myWQyAIBTTjkFO3fuHEaXiIiIiIjG3vnnn9/XB8p3ioFuIiIiImoqUIA/gPPXYHTOkYmIiIiIRoJCAAV/IO2MOwa6iYiIiGhL2bt370hlnhARERER0eYx0E1ERERETemM7kHU6CYiIiIioiRdoXs0a3SPGjnsDhARERERERERERERbQYzuomIiIioqQCDqdHNaiJERERERNXUQLKt1QTcX8mMbiIiIiIiIiIiIiIaa8zoJiIiIqKmAqUGUqM7mIAsEiIiIiKiXlJQUMrvfzuKNbqJiIiIiIiIiIiIiIaKGd1ERERE1JSvWKObiIiIiGg4FIKB1OhmRjcRERERERERERER0VAxo5uIiIiImgowqBrdRERERERE3WGgm4iIiIiIiIiIiGgEKSgoDOBhlBOQdsJANxERERE1FbBGNxERERERjTgGuomIiIiIiIiIiIhGkVJQig+jbAcD3URERETU1KAyusf/1JqIiIiIiIaFgW4iIiIiIiIiIiKiEaSgEAwgJURh/OsIMtBNRERERE0FSsEfQAFtxSLdRERERETUJQa6iYiIiIiIiIiIiEaQQgCl/P63M4A64P3GQDcRERERNRWANbqJiIiIiGi0MdBNRERERERERERENKLUQGp0j3/aCQPdRERERNRUoDCgGt19b4KIiIiIiCYUA91ERERE1JSvWLqEiIiIiGgYFNRg6mdPQNaJHHYHiIiIiIiIiIiIiIg2gxndRERERNRUADWg0iXjn0VCRERERNRbART8vreia3SLvrfTT8zoJiIiIiIiIiIiIqKxxoxuIiIiImoqYI1uIiIiIqKhUGowNbp1G0bf2+knZnQTERERERERERER0VhjRjcRERERNRWoQdXo7nsTRERERERjZkAZ3Rj/k3FmdBMRERERERERERHRWGNGNxERERE1pTCY+tnjn0NCRERERNRbCgrBAM7G1QQ8MYcZ3UREREREREREREQ01pjRTURERERNBQoDqdEdMKebiIiIiKiSwmBqdE/AA3OY0U1EREREREREREREY40Z3URERETUlA/AH0CCxwQkkRARERER9ZRCAKX8/rczgKzxfmNGNxERERERERERERGNNWZ0ExEREVFTgVIDqtFNRERERESVFNRAzpTH/2ycGd1ERERERERERERENNaY0U1ERERETQWKNbqJiIiIiIZBKTWQ+tlqAk7GmdFNRERERERERERERGONGd1ERERE1FSAwdToVhj/LBIiIiIiot4aUEY3a3QTEREREREREREREQ0XM7qJiIiIqKlAAf6A2iEiIiIiojIFNZBs60m4u5IZ3UREREREREREREQ01pjRTURERERN6YzuQdToJiIiIiKiCmowNboxiDb6jBndRERERERERERERDTWmNFNRERERE0FAPwBpFuPfw4JEREREVFvKWAgGd1Kjf/9lczoJiIiIqKGHvvB/dgYUAh6AwGk5OkpERERERF1jn9JEBEREVFDT0cG+3EMQZ8raK+ihPuxgksuuaSv7RARERERjRcFfe/jIL7GGwPdRERERNTQXeuPo4gAP8ZqX9u5F8ewCx6e//zn97UdIiIiIiKaTAx0ExEREVFDjuPghltuxrdwFMU+ZXk8hQJ+hFV88Uf7+7J9IiIiIqKxpRSUCgbwxRrdRERERDThXvva1yIFA9/H8b5s/x4cxWnI4NRTT+3L9omIiIiIaPIx0E1ERERETUkp8bd3/RP24xjW4Pd0249hHY9jHV8+9GBPt0tERERENAkUFBSCgXyNOwa6iYiIiKilF73oRdgOF/fiWM+2qaBwD57CPuQxPz/fs+0SEREREdHWw0A3EREREbXlH3/4bdyHFRxFsSfbexAncAI+7lp9tCfbIyIiIiKaPIOp0Q2wRjcRERERbRGnn346no40vomjm96WD4Vv4in8xY3/C6lUavOdIyIiIiKiLY2BbiIiIiJq25ce/zF+ijUcwPqmtvMDHIcFiTe84Q096hkRERER0SQKAPgD+GKNbiIiIiLaQpaWlnAmcrgHR6G6vL1xHT6+g2O45fZ/gGEYPe4hERERERFtReawO0BERERE4+Wu4z/FYnYKD+EETka64/d/F8tYgI0LL7ywD70jIiIiIpocSiGsod3vdpjRTURERERbTCaTwZ/f8D/xDRyF32FW9zKK+CGO4/P77+lT74iIiIiIaCtioJuIiIiIOvamN70JEgL/juMdve9bOIpTkMJZZ53Vp54REREREU0SBV0/u99f3ZUlHCUMdBMRERFRx0zTxM3/8Bnci2PYaPPBNYexgZ9gDf/88L/3uXdERERERLTVMNBNRERERF15xStegRlY2I9jLV+roHA3nsIZyGLXrl0D6B0RERER0SRQgAoG8zXmGOgmIiIioq4IIfC5b38NP8BxrKDU9LUPYw1PoYi7jj48oN4REREREdFWwkA3EREREXXt7LPPxl54+BaONnxNAIV7cBR/+uHrkM/nB9c5IiIiIqKxpwb2v3HHQDcRERERbcodD/0QD+IEnkSh7s/vwwoCKPzGb/zGgHtGRERERERbBQPdRERERLQpe/fuxTORxT14quZnRQS4F8fwv//2/4Nt20PoHRERERHROFMAggF8MaObiIiIiAh3HfkJDqOAR7BW8f3vYRlZmHjNa14zpJ4REREREdFWwEA3EREREW3a9PQ0noM87sFTCMJskBPw8T0s4zNf+xKEEEPuIRERERHRmFKq/19dZnT/5V/+Jfbu3QvXdXHuuefim9/8Zm/H3gEGuomIiIioJ768/jiKCPBjrAIAvo2j2AUPz3/+84fcMyIiIiIi6rVPf/rTuPLKK3HNNdfgO9/5Ds466yxceOGFOHTo0FD6w0A3EREREfWE4zi44Zab8S0cxWFs4EdYwe33f3fY3SIiIiIiGmNqIP/rJqP7v//3/47LLrsMb3zjG3H66afjox/9KFKpFD72sY/1fhrawEA3EREREfXMa1/7WqRg4PM4iNOQxdOf/vRhd4mIiIiIaMypAXx1plAo4N5778UFF1wQf09KiQsuuAB33313N4PcNAa6iYiIiKhnpJT4+6/+M2Zg40sHHxh2d4iIiIiIxtbevXuhg9D+AL4C7N69G8vLyxVfGxsbdfv2xBNPwPd9LC4uVnx/cXERBw4c6NUUdMQcSqtERERENLF+7ud+Do+r9WF3g4iIiIhorL3jHe/AW97yloG198EPfhD5fL7ie9dccw3e+973DqwPm8FANxEREREREREREdGIEUIgl8sNrL33vOc9eOc731nxPcdx6r52bm4OhmHg4MGDFd8/ePAglpaW+tbHZli6hIiIiIiIiIiIiGiLcxwHuVyu4qtRoNu2bZx99tm488474+8FQYA777wT55133qC6XIEZ3URERERERERERETUkSuvvBKXXHIJzjnnHDzvec/Dddddh9XVVbzxjW8cSn8Y6CYiIiIiIiIiIiKijrz2ta/F4cOHcfXVV+PAgQPYt28fbr/99poHVA6KUEqpobRMRERERERERERERNQDrNFNRERERERERERERGONgW4iIiIiIiIiIiIiGmsMdBMRERERERERERHRWGOgm4iIiIiIiIiIiIjGGgPdRERERERERERERDTWGOgmIiIiIiIiIiIiorHGQDcRERERERERERERjTUGuomIiIiIiIiIiIhorDHQTURERERERERERERjjYFuIiIiIiIiIiIiIhprDHQTERERERERERER0VhjoJuIiIiIiIiIiIiIxtr/D8OHAXasG4v0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the fields\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(15, 6))\n",
    "\n",
    "ax1 = sim_data.plot_field(\"field\", \"Hz\", val=\"real\", z=0, ax=ax1)\n",
    "ax2 = sim_data.plot_field(\"field\", \"S\", val=\"abs\", z=0, ax=ax2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can visualize the transmittance spectrum, where the two sidebands are visible at frequencies equally spaced from the pump and signal pulse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transmission_amps = sim_data[\"mode\"].amps.sel(mode_index=1, direction=\"+\") ** 2\n",
    "f, ax = plt.subplots(figsize=(10, 5))\n",
    "transmission_amps.abs.plot.line(x=\"f\", ax=ax, label=\"absolute value\")\n",
    "ax.legend()\n",
    "ax.set_title(\"Flux of the fundamental TM mode (forward)\")\n",
    "ax.set_ylim(0, None)\n",
    "ax.set_xlim(freqs_measure[0], freqs_measure[-1])\n",
    "ax.set_ylabel(\"Power (a.u.)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Amplitude analysis\n",
    "\n",
    "We can now vary the amplitude parameter and observe the power at the n = 1 sideband (at 565 Thz). Since the amplitudes of the pump and signal are identical, we expect the power of this band to vary approximately with $\\text{amplitude}^5$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Simulations = {}\n",
    "Amplitudes = [50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 700]\n",
    "for amplitude in Amplitudes:\n",
    "    # creating the sources with the new amplitude value\n",
    "    s1, s2 = sim.sources\n",
    "    st1 = s1.source_time.updated_copy(amplitude=amplitude)\n",
    "    st2 = s2.source_time.updated_copy(amplitude=amplitude)\n",
    "\n",
    "    sim2 = sim.updated_copy(\n",
    "        sources=[s1.updated_copy(source_time=st1), s2.updated_copy(source_time=st2)]\n",
    "    )\n",
    "\n",
    "    Simulations[str(amplitude)] = sim2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f25ef18f39e24944aca44ba8a77116af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:02:49 -03 </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13</span> tasks.                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:02:49 -03\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m13\u001b[0m tasks.                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:03:06 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.327</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:03:06 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m1.327\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ea4bdfeab3a4bb9bf70b4cf7809cf9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:03:17 -03 </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:03:17 -03\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f71daa06f73483794ac14789f035cc3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch = web.Batch(simulations=Simulations, verbose=True)\n",
    "results = batch.run(path_dir=\"batch_simulations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:03:46 -03 </span><span style=\"color: #800000; text-decoration-color: #800000\">WARNING: The simulation has diverged! For more information, check  </span>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'SimulationData.log'</span><span style=\"color: #800000; text-decoration-color: #800000\"> or use </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.download_log(task_id)'</span><span style=\"color: #800000; text-decoration-color: #800000\">.           </span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:03:46 -03\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: The simulation has diverged! For more information, check  \u001b[0m\n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'SimulationData.log'\u001b[0m\u001b[31m or use \u001b[0m\u001b[32m'web.download_log\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[31m.           \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Amps = []\n",
    "Power1 = []\n",
    "\n",
    "for amplitude in Amplitudes:\n",
    "    sim_data = results[f\"{amplitude}\"]\n",
    "\n",
    "    # recording the power for the band n = 1\n",
    "    band1 = sim.sources[0].source_time.freq0 + (\n",
    "        sim.sources[0].source_time.freq0 - sim.sources[1].source_time.freq0\n",
    "    )\n",
    "    bm = (sim_data[\"fluxMon\"].flux.f > band1 * 0.99) & (sim_data[\"fluxMon\"].flux.f < band1 * 1.01)\n",
    "    max = sim_data[\"fluxMon\"].flux[bm].max()\n",
    "\n",
    "    Amps.append(amplitude)\n",
    "    Power1.append(max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fitting the data with a polynomial function\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "func = lambda X, a: a * X**5\n",
    "Y = np.array(Power1) * 10**19\n",
    "res, err = curve_fit(func, Amps[:9], Y[:9], p0=[1e-9], bounds=([0], [1]))\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(Amps, Y, \"o\")\n",
    "ax.plot(Amps, func(np.array(Amps), *(res)))\n",
    "\n",
    "ax.set_xlabel(\"Amplitude value\")\n",
    "ax.set_ylabel(\"First sideband power (a.u.)\")\n",
    "\n",
    "ax.set_ylim(-0.1, 1.1 * Y.max())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be observed that the power in the n = 1 band follows a power law until amplitude values around 500. Beyond this point, the results deviate from the exponential trend until the simulation diverges for amplitudes greater than 700.\n",
    "\n",
    "This occurs because the simulations remain stable only for small nonlinearities. The nonlinear permittivity should be smaller than the linear value. Therefore:\n",
    "\n",
    "$ \\epsilon_0 \\chi^{(3)} E^3 \\ll \\epsilon_0 \\chi^{(1)} E$\n",
    "\n",
    "$ \\chi^{(3)} E^2 \\ll n_0^2-1$\n",
    "\n",
    "Using the Poynting theorem, and the relation $\\chi^{(3)} = (4/3)n_0^2 \\epsilon_0 c n_2$, we have:\n",
    "\n",
    "\n",
    "$\\frac{8}{3}\\frac{n_0 n_2}{n_0^2 - 1} I \\ll 1$\n",
    "\n",
    "Since the Intensity ($I$) is proportional to $\\text{amplitude}^2$, the simulation can quickly become unstable for high amplitude values.\n",
    "\n",
    "Additionally, we should mention that the $\\chi^{(3)}$ formalism is only perturbative and may not be accurate for extremely intense electric fields.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further verification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous derivation, we obtained the following conclusions about $z_{-2}$,$z_{1}$:\n",
    "\n",
    "$$\n",
    "z_{-2} \\approx x^2 \\left( y + \\frac{y^3}{4} \\right)\n",
    "$$\n",
    "\n",
    "$$\n",
    "z_{1} \\approx y^2 \\left( x + \\frac{x^3}{4} \\right)\n",
    "$$\n",
    "\n",
    "To verify this symmetry, We set the amplitude of the two input signals to half of the original amplitude and observe the change of the sideband signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_source_p = mode_source_p.copy(\n",
    "    update=dict(source_time=td.ContinuousWave(freq0=freqp, fwidth=freq0 / 10, amplitude=amp))\n",
    ")\n",
    "mode_source_s = mode_source_s.copy(\n",
    "    update=dict(source_time=td.ContinuousWave(freq0=freqs, fwidth=freq0 / 10, amplitude=amp / 2))\n",
    ")\n",
    "\n",
    "sim_1 = td.Simulation(\n",
    "    normalize_index=None,\n",
    "    center=[0, 0, 0],\n",
    "    size=[x_span, y_span, 0],\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0),\n",
    "    structures=[waveguide],\n",
    "    sources=[mode_source_p, mode_source_s],\n",
    "    monitors=[field_monitor, mode_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.periodic()\n",
    "    ),\n",
    "    medium=background,\n",
    "    shutoff=0,\n",
    ")\n",
    "\n",
    "mode_source_p = mode_source_p.copy(\n",
    "    update=dict(source_time=td.ContinuousWave(freq0=freqp, fwidth=freq0 / 10, amplitude=amp / 2))\n",
    ")\n",
    "mode_source_s = mode_source_s.copy(\n",
    "    update=dict(source_time=td.ContinuousWave(freq0=freqs, fwidth=freq0 / 10, amplitude=amp))\n",
    ")\n",
    "sim_2 = td.Simulation(\n",
    "    normalize_index=None,\n",
    "    center=[0, 0, 0],\n",
    "    size=[x_span, y_span, 0],\n",
    "    grid_spec=td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl, wavelength=td.C_0 / freq0),\n",
    "    structures=[waveguide],\n",
    "    sources=[mode_source_p, mode_source_s],\n",
    "    monitors=[field_monitor, mode_monitor],\n",
    "    run_time=run_time,\n",
    "    boundary_spec=td.BoundarySpec(\n",
    "        x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.periodic()\n",
    "    ),\n",
    "    medium=background,\n",
    "    shutoff=0,\n",
    ")\n",
    "\n",
    "Simulations2 = {\"1\": sim_1, \"2\": sim_2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "032946c5850444fbbbea6fc6b38126f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:03:48 -03 </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> tasks.                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:03:48 -03\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m2\u001b[0m tasks.                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:03:52 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.204</span> for the whole batch.                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:03:52 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.204\u001b[0m for the whole batch.                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after the\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Batch has completed.                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after the\n",
       "\u001b[2;36m             \u001b[0mBatch has completed.                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bc44c9ec0764fd9a258420adcd1cae0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">16:06:25 -03 </span>Batch complete.                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m16:06:25 -03\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7a2b3e098c64085b1344872de1f8c6c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch2 = web.Batch(simulations=Simulations2, verbose=True)\n",
    "results2 = batch2.run(path_dir=\"batch_simulations\")\n",
    "\n",
    "sim_data_1 = results2[\"1\"]\n",
    "sim_data_2 = results2[\"2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the output spectrum of two further simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transmission_amps_1 = sim_data_1[\"mode\"].amps.sel(mode_index=1, direction=\"+\") ** 2\n",
    "transmission_amps_2 = sim_data_2[\"mode\"].amps.sel(mode_index=1, direction=\"+\") ** 2\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(10, 10))\n",
    "\n",
    "\n",
    "transmission_amps_1.abs.plot.line(x=\"f\", ax=ax1, label=\"absolute value (Top)\")\n",
    "ax1.legend()\n",
    "ax1.set_title(\"Fundamental TM mode flux with half signal\")\n",
    "ax1.set_ylim(0, None)\n",
    "ax1.set_xlim(freqs_measure[0], freqs_measure[-1])\n",
    "ax1.set_ylabel(\"Power (a.u.)\")\n",
    "\n",
    "# Bottom plot1\n",
    "transmission_amps_2.abs.plot.line(x=\"f\", ax=ax2, label=\"absolute value (Bottom)\")\n",
    "ax2.legend()\n",
    "ax2.set_title(\"Fundamental TM mode flux with half pump\")\n",
    "ax2.set_ylim(0, None)\n",
    "ax2.set_xlim(freqs_measure[0], freqs_measure[-1])\n",
    "ax2.set_ylabel(\"Power (a.u.)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the figure above, we can observe the symmetry for $z_{-2}$, $z_{1}$ and $x$, $y$, which is consistent with the theoretical derivation."
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "This notebook demonstrates how to run an FDTD simulation of Kerr sidebands in a waveguide.",
  "feature_image": "./img/kerrSideband.png",
  "features": [
   "Nonlinear material",
   "Mode analysis",
   "2D simulation"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "Kerr effect, nonlinear optics, photonics, optical waveguide, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "title": "How to model Kerr sidebands in waveguides using Tidy3D | Flexcompute"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}