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# Operational efficiency improvement in a water supply network: Machine learning-enhanced leakage identification and water resource conservation

Hongbo Liu, Junbo Zhang, Wenhui An, Yang Chen, Xiang Yuan, Guosheng

Zhang, Eric Lichtfouse, Jiale Ma, Jin Huang, Yiqian Tu

► To cite this version:

Hongbo Liu, Junbo Zhang, Wenhui An, Yang Chen, Xiang Yuan, et al. Operational effi-
ciency improvement in a water supply network: Machine learning-enhanced leakage identification
and water resource conservation. Journal of Water Process Engineering, 2025, 75, pp.107924.
10.1016/j.jwpe.2025.107924. hal-05068634

HAL Id: hal-05068634

https://hal.science/hal-05068634v1

Submitted on 15 May 2025

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# Operational efficiency improvement in a water supply network: Machine learning-enhanced leakage identification and water resource conservation

Hongbo Liu<sup>a</sup>, Junbo Zhang<sup>a</sup>, Wenhui An<sup>a,*</sup>, Yang Chen<sup>a</sup>, Xiang Yuan<sup>a</sup>, Guosheng Zhang<sup>a</sup>, Eric Lichtfouse<sup>b</sup>, Jiale Ma<sup>a</sup>, Jin Huang<sup>c</sup>, Yiqian Tu<sup>c</sup>

**Keywords:**

Point leakage model

Pipeline leakage

Pressure monitoring

Supply water

Pressure regulating valve

Pipeline ruptures in water supply networks can induce significant water loss and may pose risks of water quality deterioration, including potential contamination by pathogens and pollutants. This issue can be addressed by predicting the location of leakage points in the pipeline network and controlling the leakage. Here we designed a hydraulic model for leakage localization using a genetic algorithm-backpropagation neural network, to predict the leakage points in the water supply system of an exposure area consuming 117,211 m³ of water per day in Eastern China. Then, using the model results, pressure-regulating valves were installed in areas with lower network safety. Results show that the error in predicting the leakage points localization ranged from 14.48 m to 121.69 m. The installation of pressure-regulating valves, reduced the average water pressure from 33.54 m to 32.64 m (2.7 %) and, in turn, decreased the simulated background leakage by 9684 m³ of water per day. Compared to traditional acoustic-based methods, the proposed machine learning approach enables more accurate leak localization by leveraging pressure variation features.

## 1. Introduction

Water supply networks are essential for the life of more than four billion people globally, driving urban economic activities and allowing a modern urban civilization. Pipeline water leakage results in approximately $39 billion losses annually [1]. This leakage is caused by bacterial corrosion [2], material degradation, ground movement, and improper maintenance. By 2030, water demand is projected to increase by 40 % [3], further exacerbating the urgency to address the leakage issue. Accurate detection and localization of water pipeline leaks can enhance operational efficiency, minimize water loss and repair costs. Different sensing technologies can provide the necessary data to assist in the development of models for predicting future leaks in water supply networks [4].

Due to the complex and dynamic nature of these systems, accurately simulating leakage points in water supply networks remains a significant challenge [5]. Existing models often fail to capture the intricate interactions between network pressure, variations in pipeline materials,

and external environmental factors, leading to simulation results that may not fully reflect real-world conditions. This lack of accuracy in leakage models, coupled with low data quality, poses serious challenges for efficient leak detection and localization [4]. Inability to precisely identify leaks can result in water loss, increased operational costs, and potential contamination risks as pollutants may enter the network [6]. Moreover, unresolved leakage problems can exacerbate long-term environmental degradation and have profound economic and social impacts [7], particularly in regions facing water scarcity. Given these challenges, there is an urgent need for advanced technologies to simulate and accurately locate leaks, ensuring more efficient water resource management while minimizing associated economic and environmental costs. Leakage models are generally categorized into point leakage models and consistent leakage models, based on the spatial distribution of leakage phenomena [8]. These two types of models are used to simulate and predict the leakage behavior in pipelines, containers, or other enclosed systems under different conditions. Among them, the point leakage models have the advantages of accurately locating the

<footnote><sup>a</sup> School of Environment and Architecture, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China</footnote>

<footnote><sup>b</sup> State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China</footnote>

<footnote><sup>c</sup> Shanghai Urban Construction Design and Research Institute (Group) Co., Ltd., Shanghai 200125, China</footnote>

<footnote>Abbreviations: R², r-square; MSE, Mean Squared Error; PRVs, Pressure Relief Valves; PLC, Programmable Logic Controller; BP, Back Propagation; GA-BP, Genetic Algorithm – BackPropagation; RF, Random forest.</footnote>

<footnote>* Corresponding author at: 516, Jungong Road, 200093 Shanghai, China.</footnote>

<footnote>E-mail address: wenhuian@usst.edu.cn (W. An).</footnote>
leakage position and implementing targeted leakage control through simulation [9]. Hardware-based solutions (e.g., acoustic detection) and software-based methods (e.g., numerical modeling) are used for pipeline monitoring [10]. However, acoustic devices such as ground microphones suffer from limited detection ranges (typically 1.3–3 m), are sensitive to ambient noise, and often perform poorly on non-metallic or large-diameter pipes [11]. Their effectiveness is also highly dependent on user expertise and can be time-consuming.

Deep learning approaches, such as the ResNet-based framework developed by Li et al. [12], combine parallel classification and regression to accurately locate leakage points in water supply networks. Machine learning models analyze data to detect and predict events, aiding decision-making [13]. Bicik et al. [14] combined three models, including the Pipeline Burst Prediction Model (PBPM), the Extended Period Simulation Hydraulic Model (HM), and the Customer Contact Model (CCM), to enhance burst detection capabilities. However, challenges such as limited data availability and inaccurate calibration metrics continue to impede progress. Overcoming these challenges is crucial for improving the accuracy of pipeline monitoring. Unlike applications in image or signal processing, water leakage detection must contend with sparse, noisy, and highly non-linear hydraulic data. To address this, our approach integrates pressure sensor measurements with hydraulic simulations of the water supply network. Furthermore, the machine learning model employed in this study utilizes a genetic algorithm to optimize the initialization of thresholds and weights, thereby reducing prediction errors and enhancing model performance.

Leakage control in water supply networks typically involves controlling water pressure, which is a direct and effective method for reducing leakage, and one of the earliest techniques applied to mitigate leakage [15]. By reducing unnecessary or excessive pressure while ensuring adequate supply to users, this approach minimizes transients and level control that can induce unnecessary leakage. Goodwin et al. [16] first demonstrated the significant influence of water pressure on leakage volume, and subsequently developed a pressure-based leakage control model. Jowitt et al. [17] was the first to use flow valve switching to control water pressure, and obtained an optimal solution to effectively reduce leakage volume through linear programming. Rahmani et al. [18] combined multiple water system facilities and used the least absolute error estimation method to linearize the optimization problem, and found the optimal operation plan that significantly reduced both leakage and energy consumption. Ulanicki et al. [19] optimized valve positions and openings, enabling more precise pressure control schemes that significantly reduced leakage in water supply networks.

The introduction of intelligent algorithms has further enhanced leakage control effectiveness. Reis et al. [20] used genetic algorithms to optimize valve switching parameters and calculated the optimal solution. There are two methods of pressure control using PRVs (Pressure Relief Valves): fixed parameter control and real-time control that relies on pressure monitoring [21,22]. Fixed-parameter control management reduces water pressure by maintaining the pressure downstream of the pressure-reducing valves in a low range while ensuring the quality of the water supply, while real-time control relies on a PLC (Programmable Logic Controller) system [23]. Giustolisi et al. [24] that adjusted the valve settings based on the data from the pressure-monitoring points. Although real-time control can reduce the number of pressures reducing valves and cope with changes in water consumption, it relies on the number of monitoring points and the accuracy of the data, which makes it more difficult to apply in practice. In contrast, fixed outlet pressure management offers a simpler and more robust alternative; however, its optimization strategies, adaptability to varying network conditions, and long-term effects on leakage control have not been sufficiently investigated.

Effective leakage localization is necessary to repair the pipes and improve the system efficiency. However, research on selecting leakage points and identifying exact locations remains limited. To address this gap, we developed a high-precision leakage localization model for water

supply networks based on a point-based leakage prediction framework. The model integrates hydraulic principles with data-driven machine learning techniques to enhance leakage identification accuracy and operational efficiency. Specifically, we employed a hybrid backpropagation neural network (BPNN) optimized by a genetic algorithm (GA) to improve model stability and prediction reliability under varying network conditions. Furthermore, we conducted comprehensive hydraulic simulations to evaluate the effectiveness of pressure management strategies in leakage mitigation. By analyzing the impact of pressure-regulating valves (PRVs) on system dynamics, we quantified the correlation between pressure reduction and leakage incidence rates. Our findings provide actionable insights for real-world leakage control and support the development of adaptive pressure management schemes in water distribution systems.

## 2. Materials and methods

### 2.1. Data sources for water supply networks

This study focused on the water supply system of a large Floral Expo area in East China, which adopts a direct supply method from the waterworks to customers via two water supply stations. In the hydraulic modeling, each water supply station was represented by a water tank. The hydraulic model was constructed using comprehensive pipeline topology data—such as pipe lengths, diameters, node coordinates, and elevations—obtained from as-built construction drawings and GIS data of the regional supply system. Flow and pressure data collected via the SCADA system in 2023, along with on-site investigations, were used as inputs to the MIKE URBAN software to simulate hydraulic performance. The model was then rigorously calibrated using a full cycle of flow and pressure variations. The regional water supply network is shown in Fig. 1. The total water consumption of the system is 117,211 m³/d, the daily water consumption in the exhibition area is approximately 5000 m³.

As shown in Fig. 2, the water consumption patterns of the exhibition area, Water Use Area I and Water Use Area II differed significantly from typical residential use. Peak consumption occurred between 6 am and 8 pm, coinciding with exhibition hours, while minimal usage was observed during breaks. The exhibition area's pipelines, constructed in 2019, were ductile iron with an outer diameter of 300 mm and an inner diameter of 200 mm. In contrast, the existing infrastructure included ductile iron, steel, and PE pipes with diameters exceeding 300 mm, up to a maximum of 800 mm.

### 2.2. Selection of pressure monitoring points for point leakage model

Notably, before conducting localization, selecting the locations of pressure monitoring points is crucial. This is because pressure-based leakage detection enables a rapid and reliable response, which is essential to minimizing damage [25]. Distinguishing from other water supply networks, there are pressure monitoring points in the study area. In the case of existing monitoring points in the network, the selection of pressure monitoring points will be carried out in this study for better monitoring of leakage incidents in the network.

The simulation of leakage in water supply networks essentially involves adding a consumption rate to the pipeline. However, relying solely on hydraulic models is insufficient for incorporating leakage volumes, as they can only simulate catastrophic failures, such as pipe ruptures, which differ significantly from leakage scenarios. In this study, different degrees of leakage within the water supply network were simulated to obtain the data samples required for establishing localization models.

Leakage volume is a specific type of water consumption, primarily influenced by supply pressure and the extent of damage. Therefore, to simulate leakage effectively, a model that connects leakage volume, supply pressure, and damage extent is essential. This research employed
<figure><img src="image_2.png" /><figcaption>Fig. 1. Water supply pipe network diagram. The map includes six pressure monitoring points, two pressure reducing valves, the water supply stations and the user.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2. Time multipliers. Floral Expo area is a separate water system. Area I and Area II correspond to the time-varying coefficients for the areas served by their water plants, respectively.</figcaption></figure>

the built-in jet nozzle model from MIKE URBAN for leakage simulation. This model is commonly used in practical applications, such as fire hydrant systems, and can also simulate leakage at individual nodes. It represents leakage flow as a function of pressure changes, described by the following equation:

$$q_i = C p_i' \quad (1)$$

In the formula, $q_i$ represents the point leakage flow rate, $C$ is the jet nozzle flow coefficient $p_i$ denotes the supply pressure at the node $\gamma$ is the pressure exponent. Experimental research by Greyvenstein and van Zyl [26] found that for most leakage scenarios, especially in older or medium-pressure systems, a value of $\gamma$ around 0.5 is commonly used to reflect moderate pressure sensitivity.

To effectively monitor the operational state of the pipeline during leakage events, it is essential to select appropriate pressure monitoring

points. The principle for arranging these monitoring points is that pressure fluctuations should be significant when a leak occurs in the pipe section. Currently, there are 18 pressure monitoring points in the study area, and six monitoring points were selected based on notable pressure variations. The identification numbers and coordinates of each monitoring point are provided in Table 1, and the distribution is illustrated in Fig. 1.

## 2.3. Modeling algorithm

On the basis of water supply pipe network leakage simulation, machine learning methods were used to construct a nonlinear model between the pressure change (pressure residual), leakage flow and leakage location data at the monitoring point, and a leakage localization model was constructed for efficient and accurate leakage localization in the water supply pipe network. This study used three models for comparison.

### 2.3.1. Random forest, BP and GA-BP model

In this study, the Random Forest (RF) model was employed as a comparative prediction method, implemented using the TreeBagger function in MATLAB. The model parameters were configured as follows: the number of decision trees was set to 100 to ensure model stability, and the minimum number of samples per leaf node was set to 5 to prevent overfitting.

Among the artificial neural networks that are more suitable for the resultant water supply network leakage localization problem are the

<table><caption>**Table 1**
Pressure monitoring point identification numbers and coordinates.</caption><thead><tr><th>Pressure Monitoring Point ID</th><th>Network Node ID</th><th>X</th><th>Y</th></tr></thead><tbody><tr><td>1</td><td>116</td><td>1924.32</td><td>51,810.65</td></tr><tr><td>2</td><td>587</td><td>1539.53</td><td>48,866.42</td></tr><tr><td>3</td><td>781</td><td>-0.17</td><td>43,953.22</td></tr><tr><td>4</td><td>852</td><td>4916.24</td><td>39,924.67</td></tr><tr><td>5</td><td>616</td><td>11,560.01</td><td>42,215.78</td></tr><tr><td>6</td><td>392</td><td>7296.17</td><td>47,485.46</td></tr></tbody></table>
forward feedback (BP) neural network and radial basis function (RBF) neural network. Compared to RBF neural network, BP neural network can handle more data inputs and outputs, while its learning ability is stronger.

Genetic algorithm optimization of BP neural network refers to the use of genetic algorithm to optimize the weights and thresholds, so that the BP neural network can achieve the best fitting effect. It is divided into three parts: determination of neural network structure, genetic algorithm optimization and neural network prediction. Firstly, the structure of the BP network was determined (number of hidden layers, number of nodes in hidden layers, transfer function of each layer), and then the weights and thresholds were optimized using genetic algorithms (coding method, fitness function, selection, crossover operation and mutation operation), and the individuals in the genetic algorithms were composed of all the weights and threshold parameters. In addition, the approximation error, convergence speed and learning speed of the neural network were also factors to be considered. The algorithm flow is shown in Fig. S1.

### 2.3.2. Modeling of leakage localization in water supply network

The training samples were selected to determine the relevant parameters of the mathematical model. In this paper, 800 sets of training samples were randomly selected from 810 sets of data generated from 135 simulated leakage points and six leakage flow coefficients in the simulated water supply network leakage simulation, and the remaining 15 sets of data (five groups are historical data) were used as the test set. Taking the pressure change and leakage flow rate of the six pressure monitoring points as input data, and the horizontal and vertical coordinates of the leakage nodes as output data, BP data network and GA-BP neural network were established to construct the leakage localization model of the water supply pipeline network, and the algorithms are implemented using MATLAB2019a. In addition, ten sets of data and five sets of leakage history data were used as test samples to verify the accuracy of the model. The training and test samples are shown in Table S1 and Table S2.

By observing the data of the input layer, it can be found that the leakage flow compared to the pressure residual value is large, if they are directly input into the neural network calculation, it will lead to the leakage amount of the output of the impact is very large, that is, the error of leakage localization will become larger, while the pressure residual has almost no influence on the leakage coordinates. Therefore, it is necessary to normalize, all the data into the number between [0,1]. In this paper, mapminmax function was used, and the specific formula is as follows:

$$x'_{k} = \frac{x_{k} - x_{min}}{x_{Max} - x_{min}} \qquad (2)$$

In the formula, $x_k$ is the original number, $x_{Max}$ is the maximum number in the data series, $x_{min}$ is the minimum number in the data series, $x'_{k}$ is the normalized number.

In general, the number of neurons in the hidden layer is determined by the number of neurons in the input layer and the number of neurons in the output layer with the following relationship:

$$\begin{cases} b < a - 1 \\ b < \sqrt{a + c} + n \\ b = \log_{2} a \end{cases} \qquad (3)$$

In the formula, a is the number of neurons in the input layer; b is the number of neurons in the hidden layer; c is the number of neurons in the output layer; n is a constant from 1 to 10.

In the study area, the number of input neurons was 7 and the number of output neurons was 2. According to the empirical formula (3), the number of hidden layer neurons was preliminarily determined to lie between 3 and 6. To select the optimal configuration, a comparative experiment was conducted by training models with 3, 4, 5, and 6 neurons in the hidden layer. The corresponding mean squared errors (MSE)

on the validation set were 0.0068161, 0.0064019, 0.0048257, and 0.0027556, respectively, showing a consistent decrease in error as the number of hidden neurons increased. Based on this result, the network structure was finalized as 7-6-2, which achieved the lowest validation error and demonstrated stable convergence. This configuration includes 54 connection weights and 8 thresholds, offering a broader parameter space that facilitates more precise optimization. In particular, it enhances the performance of subsequent genetic algorithm-based optimization for initializing weights and thresholds, thereby improving model accuracy and robustness.

The other training parameters are the type of excitation function, the type of training function, the number of training sessions, the target error value, and the learning efficiency [27]. Among them, the excitation functions are Tansig function and Purelin function, which were used for input and output, and one for hidden layers; the training function is Trainlm function, which is mainly used to train the network with Levenberg-Marquardt algorithm [28].

The training number refers to the maximum number of training times, which is set to prevent the training from not converging. In this paper, 50,000 training times were used. The target error value refers to how much the error calculated by training to meet the requirements, this paper used 10-9. Learning efficiency is the minimum error gradient, when the degree of error reduction is less than the learning efficiency, indicating that the error has been unable to learn further, the training will stop in this paper for 0.05. The training of neural networks in general did not reach the target error value, and when the learning efficiency was reached, the training was considered to be completed.

### 2.3.3. Implementation of GA-BP neural network model

As shown in Fig. 3, the optimization of the genetic algorithm of BP neural network is to make the initial threshold and weight training to get a smaller error, so that the BP neural network training results are better and the prediction results are more accurate. The specific genetic algorithm optimization parameters are shown in Table S3.

First of all, the initial individual should be constructed, because the threshold and weight are not binary numbers, the real number coding is used for the initial determination of the individual, the length of the chromosome is the sum of all the thresholds and weights, there are 8 thresholds and 54 weights, a total of 62 genes to form an individual. In a chromosome, the weights between the input layer and the cryptic layer are in positions 1 to 42; positions 43 to 48 are the thresholds of the cryptic layer; positions 49 to 60 are the weights between the cryptic layer and the output layer; and positions 61 and 62 are the thresholds coded for the output layer.

After determining the chromosome length, the initial population can be constructed, i.e., the population is formed by selecting the number of chromosome length digits from a group of random numbers to form an individual. Therefore, 62 genes were first taken to form the chromosome by linear random sampling, and since the training samples were normalized to be between -1 and 1 again, these random numbers should also be in this range. And the size of the population (i.e., the number of chromosomes) is generally not <10, and 20 was used in this paper. Unlike the gradient descent method of BP that starts from a single initial point, genetic algorithm has a population size of 20 and explores different regions of the parameter space simultaneously.

For the BP neural network computational characteristics, the fitness function used in this study was the sum of the absolute error between the trained prediction results and the actual output, because there were two outputs, which need to be summed up in further. The calculation formula is:

$$F = k \left( \sum_{i=1}^{n} abs(y_i - o_i) \right) \qquad (4)$$

In the formula, F is the fitness, n =15, $y_i$ is the actual output of the BP neural network for each test, $o_i$ is the predicted output for each test, k is
<figure><img src="image_4.png" /><figcaption>Fig. 3. GA-BP neural network diagram.</figcaption></figure>

the coefficient.

The crossover probability was set to 0.5, and a number from 0 to 1 was randomly selected to be crossover when it was less than the crossover probability, specifically, the crossover operation method is as follows:

$$a_{ij} = \begin{cases} a_{ij} + (a_{ij} - a_{Max}) * f(g) & r > 0.5 \\ a_{ij} + (a_{min} - a_{ij}) * f(g) & r \le 0.5 \end{cases} \quad (5)$$

In the formula, $a_{Max}$ is the upper bound of gene $a_{ij}$, $a_{min}$ is the lower bound of gene $a_{ij}$, $f(g) = r_2(1 - g/G_{Max})^2$, $r_2$ is a random number, g is the number of iterations, $G_{Max}$ is the maximum number of evolutions, which is 100 in this paper; r is a random number of [0,1].

During the training process of the model, the curves of the MSE and the $R^2$ are calculated using the following formulas:

$$MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 \quad (6)$$

$$R^2 = 1 - \frac{\sum_{i=0}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=0}^{n} (y_i - \bar{y}_i)^2} \quad (7)$$

## 2.4. Leakage pressure control in the water supply network

A safety evaluation was carried out for 862 pipelines in the study area, classifying them into four levels: excellent, good, moderate, and poor (the evaluation methodology for the area of lower security of the pipeline network is specified in Table S4). The results showed that 445 pipelines were rated as excellent, accounting for 51.62 % of the total, 412 were rated as good (47.80 %), and 5 were rated as moderate (0.58 %). As illustrated in Fig. 1, the figure under consideration contains five pipelines with medium ratings, and the area is denoted as the “Area of Low Network Safety”.

Using the installation of pressure reducing valves and other methods, pressure was controlled in these lower security areas of the pipe network. A hydraulic model simulation of the water supply network was then conducted to analyze the leakage situation after pressure control, thereby supporting research on leakage control in water supply systems.

In this paper, the pressure management method of fixed outlet pressure was adopted, specifically, two pressure reducing valves were installed in the area of lower security of the pipeline network, and the pressure reducing valves were installed in the pipelines numbered 84 and 214, and the specific location is shown in Fig. 1.

The relationship between background leakage and water supply pressure was expressed by the following equation [29]:

$$Q = CH^{N_1} \quad (8)$$

In the formula, Q is the background leakage flow rate, C is the leakage coefficient, H is the water supply pressure, $N_1$ is the leakage index, which was shown to be between 0.5 and 1.5.

Set the average value of the initial pressure of the pipe network as $H_0$, and the background leakage volume $Q_0$ is estimated to be about 10,000 m³/d (obtained from the water supply sales data of the water supply company in previous years, which is about 10 % of the total water consumption). From Eq. (8), the following formula was introduced to calculate the amount of leakage loss after pressure control:

$$\frac{Q_1}{Q_0} = \left(\frac{H_1}{H_0}\right)^{N_1} \quad (9)$$

In the formula, $H_1$ is the average pressure after control, $Q_1$ is the new leakage water volume. $N_1$ was taken as 1.18, experimental experiences have shown [16]. The water supply network in this study is located in East China, with the pipelines composed of approximately 1.7 % PE pipes, 29.9 % steel pipes, and 68.3 % ductile iron pipes. According to established studies, for non-metallic piping systems, the $N_1$ value generally ranges from 1.25 to 1.75, while for metallic piping systems, it typically falls between 1.0 and 1.5. Field data from the study area indicated that the $N_1$ value ranged from 0.5 to 2.5, with an average value close to 1.15. Considering that the majority of the pipelines are metallic while a small proportion are non-metallic, selecting an $N_1$ value slightly higher than the average, specifically 1.18, is reasonable and consistent with the material characteristics and leakage behavior observed in the study area.

## 3. Results and discussion

We designed a hydraulic model for leakage localization using a genetic algorithm-backpropagation neural network, to predict the leakage points in a water supply system. The following sections will be presented.

### 3.1. Network model calibration and pressure monitoring point selection

The simplification of the network topology was based on the principle of equivalence and the principle of minimal error. The nearest node allocation method was employed to distribute the water consumption across the supply system. Finally, the model was validated by adjusting
and verifying the pipeline resistance coefficients (Hazen-Williams coefficients: new ductile iron pipe 120, old ductile iron pipe 100, steel pipe 100, PE pipe 120), as well as considering ground elevation, valve operations, and the characteristic curves of pumps. The data from six pressure monitoring points are shown in Fig. 4, with pressure errors remaining below 2.5 m.

Leakage simulation was carried out using the established hydraulic model of the pipe network, and the pipes in the study area are 200 mm, 300 mm, 500 mm, 600 mm and 800 mm, of which 800 mm is the water factory pipeline, which has a low risk of leakage and loss, and was not taken into account in the study. Simulated leakage points were set on 200 mm, 300 mm, 500 mm and 600 mm pipes with leakage point numbers 187, 60, 494 and 696, and the flow coefficients were set to 0.5 and 1.0, respectively, for steady state simulation. The pressure change rate of each pressure monitoring point is shown in Fig. 5.

Through the trial simulation, it can be found that the pressure change rate of the monitoring point was greatly affected by the pipe diameter, the leakage coefficient and the location of the leakage. To comprehensively analyze the relationship between the three and ensure the accuracy of the positioning model, 135 nodes were selected as simulated leakage points, which were uniformly distributed in the entire water supply area and in the main pipe, trunk pipe and branch pipe of the water supply network. The leakage coefficients were taken as 0.5, 0.7,

0.8, 1.0, 1.2, 1.5 to simulate the different leakage sizes. A total of 810 groups of samples were generated by the 135 simulated leakage points, together with the five groups of real historical leakage data recorded by the actual data. The model inputs were six pressure monitoring points. The input of the model was the change values of the six pressure monitoring points and the leakage flow rate, and the output of the model was the horizontal and vertical coordinates of the leakage points, thus, the training samples and test samples required for modeling are obtained through leakage simulation.

## 3.2. Results of predicted leak locations

### 3.2.1. Training results analysis: RF, BP neural network, and GA-BP

To further evaluate the performance of different prediction methods, the Random Forest (RF) model was trained and tested using the same dataset. The RF model parameters were optimized to ensure a balance between model accuracy and generalization ability. The training process demonstrated stable convergence, and the model exhibited good predictive performance on the validation set. The R² for the test set reached 0.98434. The training and validation results of the Random Forest model are presented in Fig. S2 and Fig. S3.

The performance comparison of the BP neural network and the GA-BP neural network reveals significant differences in efficiency and

<figure><img src="image_5.png" /><figcaption>Fig. 4. Pressure calibration at six monitoring points.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** The pressure change rates of six pressure monitoring points of the pipeline under different leakage levels (leakage point numbers are 187, 60, 494, 696): (a) DN200; (b) DN300; (c) DN500; (d) DN600. DN in the diagram indicates the diameter of the pipe. The flow coefficients were set to 0.5 and 1.0 respectively for steady-state simulation.</figcaption></figure>

accuracy. The single BP neural network achieved a minimum mean square error (MSE) of 0.0028416 after 371 iterations, as shown in Fig. 6 (a) and Fig. 7(a). Its training process exhibited two distinct stages: a rapid decrease in MSE before 150 iterations, indicating strong learning ability and quick parameter adjustments, followed by a slower decline in MSE as the error gradient approached the learning efficiency. The training achieved an R² of 0.99676, meeting the requirements for general predictions.

In comparison, the GA-BP neural network demonstrated superior performance, achieving a lower minimum MSE of 0.0027556 within only 95 iterations, as shown in Fig. 6(b) and Fig. 7(b). The training

process of the GA-BP model showed faster error convergence and higher learning efficiency. The R² for the test set reached 0.99706, indicating a higher degree of fit compared to the single BP model.

Overall, the GA-BP neural network demonstrated superior learning efficiency, faster convergence, and enhanced prediction accuracy, evidenced by a higher R² than the Random Forest model and both a higher R² and lower MSE than the BP neural network. These findings underscore the effectiveness of genetic algorithm optimization in improving neural network performance for leakage localization.

Through the comparison of the training results, the GA-BP neural network exhibited smaller errors and shorter training times. Specifically, the GA-BP neural network demonstrated a larger gradient in same mean squared error reduction, indicating its superior learning ability. When comparing the regression curves of the two sets of prediction data, the GA-BP neural network showed a higher degree of fit, directly reflecting improved accuracy in the localization model under the optimization of the genetic algorithm. This enhancement in accuracy also signified a significant reduction in error values. In fact, the method of combining genetic algorithm and neural network has been successfully applied in the fields of image processing [30], spectral analysis [31], etc., and with the continuous deepening of the research, it will be more widely used.

### 3.2.2. Testing results analysis: RF, BP neural network, and GA-BP

The results of the random forest-based regression prediction, as shown in Fig. 8 and Table S5, indicated that two prediction errors exceed 1000 m, while eight data points exhibit errors between 200 and 800 m. Errors of >200 m are unusable in practical engineering. These findings demonstrated that the random forest algorithm struggles to achieve precise localization accuracy in this application.

After 10 training iterations on 800 sample sets, both the BP neural network and the GA-BP neural network established a nonlinear relationship between pressure residual, leakage flow, and leakage location for the localization of leakage points in 15 test samples. The predicted results are summarized in Tables S6 and S7, while the actual and predicted horizontal and vertical coordinates are shown in Fig. 8.

The BP neural network exhibited a maximum prediction error of 379.85 m and a minimum error of 28.28 m. Out of 15 test samples, three had errors below 50 m, one fell within 50–100 m, seven were in the 100–200 m range, and four exceeded 200 m. Such large errors, particularly those exceeding 200 m, indicated that the BP neural network struggled to meet the precision requirements for practical engineering applications. Furthermore, the prediction errors for actual leakage data were notably larger than those for simulated data, highlighting overfitting issues in the model.

In contrast, the GA-BP neural network achieved significantly better performance. Its maximum prediction error was 121.69 m, while the minimum was 14.48 m. Among the 15 samples, two had errors below 50 m, eight were within 50–100 m, and five fell in the 100–200 m range, with none exceeding 200 m. This result indicated that the GA-BP neural network satisfied the accuracy requirements for leakage localization. Moreover, the optimization by the genetic algorithm effectively reduced the error gap between the simulated and actual leakage data, achieved more accurate localization than the Random Forest model, and addressed the overfitting issues observed in the BP neural network.

Comparatively, the GA-BP neural network demonstrated superior accuracy and reliability. It reduced the overall prediction error, improved consistency between simulated and actual data, and ensured compliance with practical engineering standards. These advantages emphasized the effectiveness of combining genetic algorithms with neural networks for optimizing leakage localization models.

Comparing the prediction results of single BP neural network and GA-BP neural network, it can be found that the model optimized by genetic algorithm not only reduced the overall prediction error, but also the prediction error gap between the first five actual leakage data and the last ten groups of simulated data became smaller, which effectively improved the problem of overfitting of single BP neural network.

<figure><img src="image_7.png" /><figcaption>**Fig. 6.** The relationship between the mean square error of (a) single BP neural network or (b) GA-BP neural network and the number of iterations.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>(b) GA-BP neural network</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 7. Regression plots of (a) single BP neural network and (b) GA-BP neural network.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 8. The coordinate values predicted by RF, BP neural network and GA-BP neural network. In the test set data, the first five sets are historical leakage data.</figcaption></figure>

### 3.3. Leakage pressure control results for water supply networks

Pressure control is most effective for reducing background leakage. The pressure variation from 2:00 to 4:00 am, during minimal residential water use, was analyzed. The background leakage proportion is highest and pressure changes have the greatest impact on leakage. The network model at 2:00 am was used to set pressure management strategies. The most critical node, identified for pressure monitoring, ensures reliable

data. To meet the local water supply company's requirement of maintaining a minimum pressure of 24 m (0.24 MPa), the pressure at the pressure-reducing valve outlet was set to 33 m, ensuring the critical node pressure remains above 0.24 MPa at 2:00 am.

After adopting the pressure management method of fixed outlet pressure, the pressure distribution of the water supply pipe network in the study area is shown in Fig. 9, and its specific average pressure change, pressure change at the most unfavorable point, maximum
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** (a) The pressure distribution in the water supply network at 2 am; (b) The pressure distribution of the water supply pipe network after pressure control; (c) Daily nodal pressure variation curves before and after pressure control at the most unfavorable point. Different colors on the graph represent different pipe network pressures.</figcaption></figure>

pressure change, etc. are shown in Table 2. After the water supply pipe network was subjected to pressure management, the pressure of the network was significantly reduced due to the action of the pressure-reducing valve, and the head of the water supply pressure was concentrated in about 30 m. The node pressure in the area of low security of the water supply pipeline network with the largest drop in node pressure was the most unfavorable point of No. 852, whose water supply pressure dropped from 29.67 m to 24.55 m, a decrease of 17.24 %. The mean value of water supply pressure in the study area decreased from 33.54 m to 32.64 m, which was a reduction of 2.68 %, indicating a good pressure control effect.

$H_0$ and $H_1$ were calculated based on the hydraulic simulation obtained, which were 33.54 m and 32.64 m. From this, the leakage reduction can be calculated: $Q_1$ was calculated to be 9684 m³/d, and when converted to each year, it is concluded that it can be reduced by 115,200 m³/a. This showed that leakage loss control can be effectively

carried out by pressure control. In terms of practical application, the installation of two pressure-reducing valves was considered for the targeted low-security areas of the pipe network. The cost of each valve, including equipment and basic installation, is approximately 700 CNY (about 96 USD), the lifetime is usually more than five years. Even when accounting for routine maintenance costs, the total annual expenditure remains significantly lower than the economic and environmental benefits associated with saving over 100,000 m³ of water per year. These findings support the feasibility and cost-effectiveness of implementing pressure-reducing measures in real-world pipe network leakage control.

## 4. Conclusion

To address the problem of leakage in large-scale water supply pipe networks, this study proposed an intelligent control method based on leakage simulation and hydraulic modeling. Point leakage model simulations revealed that pipe diameter, leakage flow rate and location are the key parameters affecting the pressure residual at the monitoring point (positioning error 14.48–121.69 m). Validation of the hydraulic model demonstrated a pressure monitoring error is lower than 0.5 m, underscoring significant pressure fluctuations at monitoring points during leakage events, thus confirming the effectiveness of the leakage detection approach. Additionally, a comparative analysis was conducted among the Random Forest model, the standalone BP neural network, and the GA-BP neural network during model training. The results showed that the optimized model attained lower mean square errors and higher data fitting accuracy. After the implementation of the constant pressure water supply strategy, there was a reduction in the average

<table><caption>**Table 2**
Pressure control results.</caption><thead><tr><th>Project</th><th>Before pressure control (m)</th><th>After pressure control (m)</th><th>Pressure drop (m)</th><th>Degree of reduction (%)</th></tr></thead><tbody><tr><td>Pressure at the most unfavorable point (Maximum pressure drop)</td><td>29.67</td><td>24.55</td><td>5.12</td><td>17.24</td></tr><tr><td>Regional average pressure</td><td>33.54</td><td>32.64</td><td>0.90</td><td>2.68</td></tr></tbody></table>
network pressure, resulting in a decrease of background leakage by 9684 m³/d (equivalent to 115,200 m³/a), which verified the dual benefits of pressure management in reducing the leakage rate, prolonging the life of the pipe network, and enhancing the sustainability of the system.

This study underscored the significance of hydraulic modeling and advanced neural network optimization in addressing the complex issue of water loss in large-scale water supply networks. The proposed method offered a viable solution for balancing water conservation with system efficiency, providing a reference for future research and development in the field of water resource management. To extend these innovations, future efforts should prioritize real-time SCADA integration for adaptive leakage control, develop hybrid models embedding hydraulic physics into deep learning architectures to enhance localization precision, and evaluate long-term pressure effects on pipe degradation through life cycle analysis. Further applications in intermittent supply systems and climate-resilient network design could redefine sustainable water management strategies, particularly in water-scarce regions.

### CRediT authorship contribution statement

**Hongbo Liu:** Supervision, Project administration, Funding acquisition, Conceptualization.
**Junbo Zhang:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis.
**Wenhui An:** Writing – review & editing, Data curation.
**Yang Chen:** Formal analysis, Data curation.
**Xiang Yuan:** Formal analysis, Data curation.
**Guosheng Zhang:** Formal analysis, Data curation.
**Eric Lichtfouse:** Writing – review & editing.
**Jiale Ma:** Formal analysis, Data curation.
**Jin Huang:** Funding acquisition, Data curation.
**Yiqian Tu:** Funding acquisition, Data curation.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

This work was supported by Shanghai Municipal Commission of Housing and Urban-rural Development Research Project of China (2021-002-053) and Shanghai Water Authority (Shanghai Municipal Oceanic Bureau) Research Project of China (2022-25).

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jwpe.2025.107924.

### Data availability

The data that has been used is confidential.

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Operational efficiency improvement in a water supply network: Machine
learning-enhanced leakage identification and water resource conservation

Hongbo Liu<sup>a</sup>, Junbo Zhang<sup>a</sup>, Wenhui An<sup>a, *</sup>, Yang Chen<sup>a</sup>, Xiang Yuan<sup>a</sup>, Guosheng

Zhang<sup>a</sup>, Eric Lichtfouse<sup>b</sup>, Jiale Ma<sup>a</sup>, Jin Huang<sup>c</sup>, Yiqian Tu<sup>c</sup>

<footnote><sup>a</sup> School of Environment and Architecture, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai, 200093, China</footnote>

<footnote><sup>b</sup> State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China</footnote>

<footnote><sup>c</sup> Shanghai Urban Construction Design and Research Institute (Group) Co., Ltd., Shanghai, 200125, China</footnote>

<footnote>* Corresponding author ADD: 516, Jungong Road, 200093, Shanghai, China;</footnote>

<footnote>Email: wenhuian@usst.edu.cn</footnote>

<footnote>Tel: +86-13278885047</footnote>
**Table Captions:**

**Table S1.** Training sample data.

**Table S2.** Test sample data.

**Table S3.** Genetic algorithm configuration

**Table S4.** Evaluation criteria for pipeline security levels.

**Table S5.** RF prediction error results.

**Table S6.** Single BP neural network prediction error results.

**Table S7.** GA-BP neural network prediction error results.

**Figure Captions:**

**Fig. S1.** Flowchart of BP neural network algorithm for genetic algorithm optimization.

The structure of BP network includes the number of hidden layers, the number of nodes in the hidden layers, and the transfer function of each layer. Genetic algorithm includes coding method, fitness function, selection, crossover operation and mutation operation.

**Fig. S2.** Random forest training results (Horizontal coordinate error value).

**Fig. S3.** Random forest training results (Vertical coordinate error value).
<table><caption>Table S1. Training sample data.</caption><thead><tr><th>X</th><th>Y</th><th>Leakage volume (L/s)</th><th>Pressure residual 1 (m)</th><th>Pressure residual 2 (m)</th><th>Pressure residual 3 (m)</th><th>Pressure residual 4 (m)</th><th>Pressure residual 5 (m)</th><th>Pressure residual 6 (m)</th></tr></thead><tbody><tr><td>-245.08</td><td>52315.87</td><td>2.78</td><td>0.24</td><td>0.16</td><td>0.18</td><td>0.02</td><td>0.00</td><td>0.00</td></tr><tr><td>-245.08</td><td>52315.87</td><td>3.88</td><td>0.33</td><td>0.23</td><td>0.26</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-245.08</td><td>52315.87</td><td>4.42</td><td>0.38</td><td>0.26</td><td>0.29</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-245.08</td><td>52315.87</td><td>5.54</td><td>0.47</td><td>0.33</td><td>0.37</td><td>0.04</td><td>0.00</td><td>0.00</td></tr><tr><td>-245.08</td><td>52315.87</td><td>6.62</td><td>0.57</td><td>0.39</td><td>0.44</td><td>0.04</td><td>0.00</td><td>0.01</td></tr><tr><td>-245.08</td><td>52315.87</td><td>8.24</td><td>0.71</td><td>0.49</td><td>0.55</td><td>0.05</td><td>0.00</td><td>0.01</td></tr><tr><td>-137.26</td><td>53063.78</td><td>2.75</td><td>0.23</td><td>0.16</td><td>0.18</td><td>0.02</td><td>0.00</td><td>0.00</td></tr><tr><td>-137.26</td><td>53063.78</td><td>3.82</td><td>0.32</td><td>0.22</td><td>0.25</td><td>0.02</td><td>0.00</td><td>0.00</td></tr><tr><td>-137.26</td><td>53063.78</td><td>4.36</td><td>0.37</td><td>0.26</td><td>0.29</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-137.26</td><td>53063.78</td><td>5.46</td><td>0.47</td><td>0.32</td><td>0.36</td><td>0.04</td><td>0.00</td><td>0.00</td></tr><tr><td>-137.26</td><td>53063.78</td><td>6.52</td><td>0.56</td><td>0.39</td><td>0.43</td><td>0.04</td><td>0.00</td><td>0.01</td></tr><tr><td>-137.26</td><td>53063.78</td><td>8.12</td><td>0.70</td><td>0.49</td><td>0.54</td><td>0.05</td><td>0.00</td><td>0.01</td></tr><tr><td>-170.18</td><td>52917.12</td><td>2.79</td><td>0.24</td><td>0.16</td><td>0.18</td><td>0.02</td><td>0.00</td><td>0.00</td></tr><tr><td>-170.18</td><td>52917.12</td><td>3.89</td><td>0.33</td><td>0.23</td><td>0.26</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-170.18</td><td>52917.12</td><td>4.44</td><td>0.38</td><td>0.26</td><td>0.29</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-170.18</td><td>52917.12</td><td>5.55</td><td>0.47</td><td>0.33</td><td>0.37</td><td>0.04</td><td>0.00</td><td>0.00</td></tr><tr><td>-170.18</td><td>52917.12</td><td>6.64</td><td>0.57</td><td>0.39</td><td>0.44</td><td>0.04</td><td>0.00</td><td>0.01</td></tr><tr><td>-170.18</td><td>52917.12</td><td>8.26</td><td>0.71</td><td>0.49</td><td>0.55</td><td>0.05</td><td>0.00</td><td>0.01</td></tr><tr><td>-225.17</td><td>52621.75</td><td>2.80</td><td>0.24</td><td>0.16</td><td>0.18</td><td>0.02</td><td>0.00</td><td>0.00</td></tr><tr><td>-225.17</td><td>52621.75</td><td>3.90</td><td>0.33</td><td>0.23</td><td>0.26</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-225.17</td><td>52621.75</td><td>4.45</td><td>0.38</td><td>0.26</td><td>0.29</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-225.17</td><td>52621.75</td><td>5.57</td><td>0.48</td><td>0.33</td><td>0.37</td><td>0.04</td><td>0.00</td><td>0.00</td></tr><tr><td>-225.17</td><td>52621.75</td><td>6.65</td><td>0.57</td><td>0.39</td><td>0.44</td><td>0.04</td><td>0.00</td><td>0.01</td></tr><tr><td>-225.17</td><td>52621.75</td><td>8.29</td><td>0.72</td><td>0.50</td><td>0.55</td><td>0.05</td><td>0.00</td><td>0.01</td></tr><tr><td>-306.80</td><td>52027.14</td><td>2.80</td><td>0.23</td><td>0.16</td><td>0.18</td><td>0.02</td><td>0.00</td><td>0.00</td></tr><tr><td>-306.80</td><td>52027.14</td><td>3.90</td><td>0.33</td><td>0.23</td><td>0.26</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-306.80</td><td>52027.14</td><td>4.45</td><td>0.37</td><td>0.26</td><td>0.29</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-306.80</td><td>52027.14</td><td>5.57</td><td>0.47</td><td>0.33</td><td>0.37</td><td>0.04</td><td>0.00</td><td>0.00</td></tr><tr><td>-306.80</td><td>52027.14</td><td>6.65</td><td>0.56</td><td>0.39</td><td>0.44</td><td>0.04</td><td>0.00</td><td>0.01</td></tr><tr><td>-306.80</td><td>52027.14</td><td>8.28</td><td>0.71</td><td>0.49</td><td>0.55</td><td>0.05</td><td>0.00</td><td>0.01</td></tr><tr><td>-436.13</td><td>51290.81</td><td>2.81</td><td>0.23</td><td>0.16</td><td>0.19</td><td>0.02</td><td>0.00</td><td>0.00</td></tr><tr><td>-436.13</td><td>51290.81</td><td>3.92</td><td>0.32</td><td>0.23</td><td>0.26</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-436.13</td><td>51290.81</td><td>4.47</td><td>0.36</td><td>0.26</td><td>0.30</td><td>0.03</td><td>0.00</td><td>0.00</td></tr><tr><td>-436.13</td><td>51290.81</td><td>5.60</td><td>0.46</td><td>0.33</td><td>0.37</td><td>0.04</td><td>0.00</td><td>0.00</td></tr><tr><td>-436.13</td><td>51290.81</td><td>6.69</td><td>0.55</td><td>0.40</td><td>0.45</td><td>0.04</td><td>0.00</td><td>0.01</td></tr><tr><td>-436.13</td><td>51290.81</td><td>8.33</td><td>0.69</td><td>0.50</td><td>0.56</td><td>0.05</td><td>0.00</td><td>0.01</td></tr><tr><td>-267.16</td><td>52321.86</td><td>2.76</td><td>0.23</td><td>0.16</td><td>0.18</td><td>0.02</td><td>0.00</td><td>0.00</td></tr><tr><td>-267.16</td><td>52321.86</td><td>3.85</td><td>0.33</td><td>0.23</td><td>0.25</td><td>0.02</td><td>0.00</td><td>0.00</td></tr></tbody></table>
<table>
  <tr>
    <td>-267.16</td>
    <td>52321.86</td>
    <td>4.39</td>
    <td>0.37</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-267.16</td>
    <td>52321.86</td>
    <td>5.50</td>
    <td>0.47</td>
    <td>0.32</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-267.16</td>
    <td>52321.86</td>
    <td>6.57</td>
    <td>0.56</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-267.16</td>
    <td>52321.86</td>
    <td>8.18</td>
    <td>0.71</td>
    <td>0.49</td>
    <td>0.55</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1433.84</td>
    <td>50689.80</td>
    <td>2.80</td>
    <td>0.22</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1433.84</td>
    <td>50689.80</td>
    <td>3.90</td>
    <td>0.30</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1433.84</td>
    <td>50689.80</td>
    <td>4.46</td>
    <td>0.35</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1433.84</td>
    <td>50689.80</td>
    <td>5.58</td>
    <td>0.44</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1433.84</td>
    <td>50689.80</td>
    <td>6.67</td>
    <td>0.53</td>
    <td>0.40</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1433.84</td>
    <td>50689.80</td>
    <td>8.31</td>
    <td>0.66</td>
    <td>0.50</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1823.64</td>
    <td>50604.44</td>
    <td>2.81</td>
    <td>0.21</td>
    <td>0.17</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1823.64</td>
    <td>50604.44</td>
    <td>3.91</td>
    <td>0.30</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1823.64</td>
    <td>50604.44</td>
    <td>4.47</td>
    <td>0.34</td>
    <td>0.27</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1823.64</td>
    <td>50604.44</td>
    <td>5.59</td>
    <td>0.43</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1823.64</td>
    <td>50604.44</td>
    <td>6.69</td>
    <td>0.52</td>
    <td>0.40</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1823.64</td>
    <td>50604.44</td>
    <td>8.33</td>
    <td>0.65</td>
    <td>0.50</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7132.44</td>
    <td>47184.05</td>
    <td>2.74</td>
    <td>0.16</td>
    <td>0.29</td>
    <td>0.15</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>7132.44</td>
    <td>47184.05</td>
    <td>3.82</td>
    <td>0.22</td>
    <td>0.41</td>
    <td>0.21</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7132.44</td>
    <td>47184.05</td>
    <td>4.36</td>
    <td>0.26</td>
    <td>0.47</td>
    <td>0.24</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7132.44</td>
    <td>47184.05</td>
    <td>5.46</td>
    <td>0.32</td>
    <td>0.59</td>
    <td>0.30</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7132.44</td>
    <td>47184.05</td>
    <td>6.52</td>
    <td>0.39</td>
    <td>0.71</td>
    <td>0.36</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7132.44</td>
    <td>47184.05</td>
    <td>8.11</td>
    <td>0.49</td>
    <td>0.89</td>
    <td>0.45</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9251.14</td>
    <td>46569.24</td>
    <td>2.74</td>
    <td>0.15</td>
    <td>0.27</td>
    <td>0.14</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9251.14</td>
    <td>46569.24</td>
    <td>3.81</td>
    <td>0.21</td>
    <td>0.38</td>
    <td>0.19</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9251.14</td>
    <td>46569.24</td>
    <td>4.35</td>
    <td>0.24</td>
    <td>0.44</td>
    <td>0.22</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9251.14</td>
    <td>46569.24</td>
    <td>5.44</td>
    <td>0.30</td>
    <td>0.56</td>
    <td>0.28</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9251.14</td>
    <td>46569.24</td>
    <td>6.50</td>
    <td>0.37</td>
    <td>0.67</td>
    <td>0.34</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>994.46</td>
    <td>50786.15</td>
    <td>2.79</td>
    <td>0.22</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>994.46</td>
    <td>50786.15</td>
    <td>3.88</td>
    <td>0.31</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>994.46</td>
    <td>50786.15</td>
    <td>4.43</td>
    <td>0.36</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>994.46</td>
    <td>50786.15</td>
    <td>5.55</td>
    <td>0.45</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>994.46</td>
    <td>50786.15</td>
    <td>6.63</td>
    <td>0.54</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>994.46</td>
    <td>50786.15</td>
    <td>8.26</td>
    <td>0.67</td>
    <td>0.49</td>
    <td>0.55</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-294.16</td>
    <td>49876.90</td>
    <td>2.77</td>
    <td>0.21</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-294.16</td>
    <td>49876.90</td>
    <td>3.86</td>
    <td>0.29</td>
    <td>0.22</td>
    <td>0.26</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-294.16</td>
    <td>49876.90</td>
    <td>4.41</td>
    <td>0.33</td>
    <td>0.25</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-294.16</td>
    <td>49876.90</td>
    <td>5.52</td>
    <td>0.42</td>
    <td>0.32</td>
    <td>0.37</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-294.16</td>
    <td>49876.90</td>
    <td>6.59</td>
    <td>0.50</td>
    <td>0.38</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-294.16</td>
    <td>49876.90</td>
    <td>8.21</td>
    <td>0.63</td>
    <td>0.48</td>
    <td>0.55</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-141.32</td>
    <td>49619.49</td>
    <td>2.74</td>
    <td>0.20</td>
    <td>0.15</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-141.32</td>
    <td>49619.49</td>
    <td>3.82</td>
    <td>0.28</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-141.32</td>
    <td>49619.49</td>
    <td>4.36</td>
    <td>0.32</td>
    <td>0.25</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-141.32</td>
    <td>49619.49</td>
    <td>5.46</td>
    <td>0.41</td>
    <td>0.31</td>
    <td>0.37</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-141.32</td>
    <td>49619.49</td>
    <td>6.52</td>
    <td>0.49</td>
    <td>0.37</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
</table>
<table>
  <tr>
    <td>-141.32</td>
    <td>49619.49</td>
    <td>8.11</td>
    <td>0.61</td>
    <td>0.47</td>
    <td>0.55</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>62.80</td>
    <td>49403.87</td>
    <td>2.78</td>
    <td>0.20</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>62.80</td>
    <td>49403.87</td>
    <td>3.87</td>
    <td>0.28</td>
    <td>0.22</td>
    <td>0.26</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>62.80</td>
    <td>49403.87</td>
    <td>4.42</td>
    <td>0.32</td>
    <td>0.25</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>62.80</td>
    <td>49403.87</td>
    <td>5.54</td>
    <td>0.41</td>
    <td>0.31</td>
    <td>0.37</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>62.80</td>
    <td>49403.87</td>
    <td>6.61</td>
    <td>0.49</td>
    <td>0.38</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>62.80</td>
    <td>49403.87</td>
    <td>8.24</td>
    <td>0.61</td>
    <td>0.47</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>590.99</td>
    <td>49150.82</td>
    <td>2.82</td>
    <td>0.20</td>
    <td>0.16</td>
    <td>0.19</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>590.99</td>
    <td>49150.82</td>
    <td>3.93</td>
    <td>0.28</td>
    <td>0.22</td>
    <td>0.26</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>590.99</td>
    <td>49150.82</td>
    <td>4.48</td>
    <td>0.32</td>
    <td>0.25</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>590.99</td>
    <td>49150.82</td>
    <td>5.61</td>
    <td>0.40</td>
    <td>0.31</td>
    <td>0.38</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>590.99</td>
    <td>49150.82</td>
    <td>8.36</td>
    <td>0.60</td>
    <td>0.47</td>
    <td>0.57</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9420.56</td>
    <td>47271.11</td>
    <td>2.70</td>
    <td>0.14</td>
    <td>0.26</td>
    <td>0.13</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9420.56</td>
    <td>47271.11</td>
    <td>3.76</td>
    <td>0.20</td>
    <td>0.37</td>
    <td>0.19</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9420.56</td>
    <td>47271.11</td>
    <td>4.30</td>
    <td>0.23</td>
    <td>0.42</td>
    <td>0.21</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9420.56</td>
    <td>47271.11</td>
    <td>5.38</td>
    <td>0.30</td>
    <td>0.54</td>
    <td>0.27</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9420.56</td>
    <td>47271.11</td>
    <td>6.42</td>
    <td>0.36</td>
    <td>0.65</td>
    <td>0.33</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9420.56</td>
    <td>47271.11</td>
    <td>8.00</td>
    <td>0.45</td>
    <td>0.82</td>
    <td>0.41</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9982.63</td>
    <td>47023.92</td>
    <td>2.72</td>
    <td>0.14</td>
    <td>0.26</td>
    <td>0.13</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9982.63</td>
    <td>47023.92</td>
    <td>3.79</td>
    <td>0.20</td>
    <td>0.36</td>
    <td>0.18</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9982.63</td>
    <td>47023.92</td>
    <td>4.33</td>
    <td>0.23</td>
    <td>0.42</td>
    <td>0.21</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9982.63</td>
    <td>47023.92</td>
    <td>5.42</td>
    <td>0.29</td>
    <td>0.53</td>
    <td>0.27</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9982.63</td>
    <td>47023.92</td>
    <td>6.47</td>
    <td>0.35</td>
    <td>0.64</td>
    <td>0.32</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>9982.63</td>
    <td>47023.92</td>
    <td>8.05</td>
    <td>0.44</td>
    <td>0.81</td>
    <td>0.41</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>877.94</td>
    <td>49063.31</td>
    <td>2.82</td>
    <td>0.19</td>
    <td>0.15</td>
    <td>0.19</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>877.94</td>
    <td>49063.31</td>
    <td>3.93</td>
    <td>0.27</td>
    <td>0.22</td>
    <td>0.26</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>877.94</td>
    <td>49063.31</td>
    <td>4.49</td>
    <td>0.31</td>
    <td>0.25</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>877.94</td>
    <td>49063.31</td>
    <td>5.62</td>
    <td>0.39</td>
    <td>0.31</td>
    <td>0.38</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>877.94</td>
    <td>49063.31</td>
    <td>6.72</td>
    <td>0.47</td>
    <td>0.38</td>
    <td>0.45</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>877.94</td>
    <td>49063.31</td>
    <td>8.37</td>
    <td>0.59</td>
    <td>0.47</td>
    <td>0.57</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1164.89</td>
    <td>48975.81</td>
    <td>2.80</td>
    <td>0.19</td>
    <td>0.15</td>
    <td>0.19</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1164.89</td>
    <td>48975.81</td>
    <td>3.91</td>
    <td>0.26</td>
    <td>0.21</td>
    <td>0.26</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1164.89</td>
    <td>48975.81</td>
    <td>4.46</td>
    <td>0.30</td>
    <td>0.24</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1164.89</td>
    <td>48975.81</td>
    <td>5.59</td>
    <td>0.38</td>
    <td>0.31</td>
    <td>0.38</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1164.89</td>
    <td>48975.81</td>
    <td>6.68</td>
    <td>0.46</td>
    <td>0.37</td>
    <td>0.45</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1164.89</td>
    <td>48975.81</td>
    <td>8.32</td>
    <td>0.57</td>
    <td>0.47</td>
    <td>0.57</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10068.03</td>
    <td>46895.82</td>
    <td>2.72</td>
    <td>0.14</td>
    <td>0.26</td>
    <td>0.13</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10068.03</td>
    <td>46895.82</td>
    <td>3.79</td>
    <td>0.20</td>
    <td>0.36</td>
    <td>0.18</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10068.03</td>
    <td>46895.82</td>
    <td>4.33</td>
    <td>0.23</td>
    <td>0.42</td>
    <td>0.21</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10068.03</td>
    <td>46895.82</td>
    <td>5.42</td>
    <td>0.29</td>
    <td>0.53</td>
    <td>0.27</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10068.03</td>
    <td>46895.82</td>
    <td>6.47</td>
    <td>0.35</td>
    <td>0.64</td>
    <td>0.32</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10068.03</td>
    <td>46895.82</td>
    <td>8.05</td>
    <td>0.44</td>
    <td>0.81</td>
    <td>0.41</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>10260.17</td>
    <td>46725.03</td>
    <td>3.78</td>
    <td>0.20</td>
    <td>0.36</td>
    <td>0.18</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10260.17</td>
    <td>46725.03</td>
    <td>4.31</td>
    <td>0.23</td>
    <td>0.41</td>
    <td>0.21</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
</table>
<table>
  <tr>
    <td>10260.17</td>
    <td>46725.03</td>
    <td>5.40</td>
    <td>0.29</td>
    <td>0.52</td>
    <td>0.26</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10260.17</td>
    <td>46725.03</td>
    <td>6.45</td>
    <td>0.35</td>
    <td>0.63</td>
    <td>0.32</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10260.17</td>
    <td>46725.03</td>
    <td>8.03</td>
    <td>0.44</td>
    <td>0.80</td>
    <td>0.40</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>10767.99</td>
    <td>46624.27</td>
    <td>2.72</td>
    <td>0.14</td>
    <td>0.25</td>
    <td>0.13</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10767.99</td>
    <td>46624.27</td>
    <td>3.79</td>
    <td>0.19</td>
    <td>0.35</td>
    <td>0.18</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10767.99</td>
    <td>46624.27</td>
    <td>4.33</td>
    <td>0.22</td>
    <td>0.41</td>
    <td>0.21</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10767.99</td>
    <td>46624.27</td>
    <td>5.42</td>
    <td>0.28</td>
    <td>0.52</td>
    <td>0.26</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10767.99</td>
    <td>46624.27</td>
    <td>6.47</td>
    <td>0.34</td>
    <td>0.63</td>
    <td>0.32</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10767.99</td>
    <td>46624.27</td>
    <td>8.06</td>
    <td>0.43</td>
    <td>0.79</td>
    <td>0.40</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>24.97</td>
    <td>52251.32</td>
    <td>2.79</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>24.97</td>
    <td>52251.32</td>
    <td>3.89</td>
    <td>0.33</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>24.97</td>
    <td>52251.32</td>
    <td>4.44</td>
    <td>0.38</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>24.97</td>
    <td>52251.32</td>
    <td>5.55</td>
    <td>0.48</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>24.97</td>
    <td>52251.32</td>
    <td>6.63</td>
    <td>0.58</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>24.97</td>
    <td>52251.32</td>
    <td>8.26</td>
    <td>0.72</td>
    <td>0.49</td>
    <td>0.55</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11219.92</td>
    <td>46270.26</td>
    <td>2.72</td>
    <td>0.13</td>
    <td>0.24</td>
    <td>0.12</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11219.92</td>
    <td>46270.26</td>
    <td>3.78</td>
    <td>0.19</td>
    <td>0.34</td>
    <td>0.17</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11219.92</td>
    <td>46270.26</td>
    <td>4.32</td>
    <td>0.22</td>
    <td>0.40</td>
    <td>0.20</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11219.92</td>
    <td>46270.26</td>
    <td>5.41</td>
    <td>0.28</td>
    <td>0.51</td>
    <td>0.26</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11219.92</td>
    <td>46270.26</td>
    <td>6.46</td>
    <td>0.34</td>
    <td>0.61</td>
    <td>0.31</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11219.92</td>
    <td>46270.26</td>
    <td>8.04</td>
    <td>0.42</td>
    <td>0.77</td>
    <td>0.39</td>
    <td>0.12</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>-362.83</td>
    <td>51616.50</td>
    <td>2.80</td>
    <td>0.23</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-362.83</td>
    <td>51616.50</td>
    <td>3.90</td>
    <td>0.32</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-362.83</td>
    <td>51616.50</td>
    <td>4.46</td>
    <td>0.37</td>
    <td>0.26</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-362.83</td>
    <td>51616.50</td>
    <td>5.58</td>
    <td>0.46</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-362.83</td>
    <td>51616.50</td>
    <td>6.66</td>
    <td>0.56</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-362.83</td>
    <td>51616.50</td>
    <td>8.30</td>
    <td>0.70</td>
    <td>0.50</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>340.33</td>
    <td>52176.03</td>
    <td>2.79</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>340.33</td>
    <td>52176.03</td>
    <td>3.88</td>
    <td>0.34</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>340.33</td>
    <td>52176.03</td>
    <td>4.43</td>
    <td>0.39</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>340.33</td>
    <td>52176.03</td>
    <td>5.55</td>
    <td>0.48</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>340.33</td>
    <td>52176.03</td>
    <td>6.63</td>
    <td>0.58</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>340.33</td>
    <td>52176.03</td>
    <td>8.26</td>
    <td>0.73</td>
    <td>0.49</td>
    <td>0.55</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12176.51</td>
    <td>45961.44</td>
    <td>2.73</td>
    <td>0.13</td>
    <td>0.23</td>
    <td>0.12</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12176.51</td>
    <td>45961.44</td>
    <td>3.80</td>
    <td>0.18</td>
    <td>0.33</td>
    <td>0.17</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12176.51</td>
    <td>45961.44</td>
    <td>4.34</td>
    <td>0.21</td>
    <td>0.38</td>
    <td>0.19</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12176.51</td>
    <td>45961.44</td>
    <td>5.43</td>
    <td>0.27</td>
    <td>0.49</td>
    <td>0.25</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12176.51</td>
    <td>45961.44</td>
    <td>6.49</td>
    <td>0.33</td>
    <td>0.60</td>
    <td>0.30</td>
    <td>0.10</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12176.51</td>
    <td>45961.44</td>
    <td>8.07</td>
    <td>0.41</td>
    <td>0.75</td>
    <td>0.38</td>
    <td>0.12</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>608.57</td>
    <td>52112.00</td>
    <td>2.78</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>608.57</td>
    <td>52112.00</td>
    <td>3.88</td>
    <td>0.34</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>608.57</td>
    <td>52112.00</td>
    <td>4.42</td>
    <td>0.39</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>608.57</td>
    <td>52112.00</td>
    <td>5.54</td>
    <td>0.49</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>608.57</td>
    <td>52112.00</td>
    <td>6.62</td>
    <td>0.59</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
</table>
<table>
  <tr>
    <td>608.57</td>
    <td>52112.00</td>
    <td>8.24</td>
    <td>0.73</td>
    <td>0.49</td>
    <td>0.55</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1232.00</td>
    <td>50991.00</td>
    <td>2.75</td>
    <td>0.23</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1232.00</td>
    <td>50991.00</td>
    <td>3.83</td>
    <td>0.32</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1232.00</td>
    <td>50991.00</td>
    <td>4.37</td>
    <td>0.36</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1232.00</td>
    <td>50991.00</td>
    <td>5.47</td>
    <td>0.46</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1232.00</td>
    <td>50991.00</td>
    <td>6.53</td>
    <td>0.55</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1232.00</td>
    <td>50991.00</td>
    <td>8.12</td>
    <td>0.69</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1314.00</td>
    <td>50993.00</td>
    <td>2.75</td>
    <td>0.23</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1314.00</td>
    <td>50993.00</td>
    <td>3.82</td>
    <td>0.32</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1314.00</td>
    <td>50993.00</td>
    <td>4.36</td>
    <td>0.37</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1314.00</td>
    <td>50993.00</td>
    <td>5.46</td>
    <td>0.46</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1314.00</td>
    <td>50993.00</td>
    <td>6.52</td>
    <td>0.55</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1314.00</td>
    <td>50993.00</td>
    <td>8.11</td>
    <td>0.69</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1621.50</td>
    <td>50930.00</td>
    <td>2.74</td>
    <td>0.23</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1621.50</td>
    <td>50930.00</td>
    <td>3.81</td>
    <td>0.33</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1621.50</td>
    <td>50930.00</td>
    <td>4.35</td>
    <td>0.37</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1621.50</td>
    <td>50930.00</td>
    <td>5.44</td>
    <td>0.47</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1621.50</td>
    <td>50930.00</td>
    <td>6.49</td>
    <td>0.56</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1621.50</td>
    <td>50930.00</td>
    <td>8.07</td>
    <td>0.70</td>
    <td>0.48</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1787.00</td>
    <td>51080.00</td>
    <td>2.74</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1787.00</td>
    <td>51080.00</td>
    <td>3.81</td>
    <td>0.33</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1787.00</td>
    <td>51080.00</td>
    <td>4.34</td>
    <td>0.38</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1787.00</td>
    <td>51080.00</td>
    <td>5.44</td>
    <td>0.47</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1787.00</td>
    <td>51080.00</td>
    <td>6.49</td>
    <td>0.57</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1787.00</td>
    <td>51080.00</td>
    <td>8.07</td>
    <td>0.71</td>
    <td>0.48</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1892.00</td>
    <td>51611.00</td>
    <td>2.74</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1892.00</td>
    <td>51611.00</td>
    <td>3.82</td>
    <td>0.34</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1892.00</td>
    <td>51611.00</td>
    <td>4.36</td>
    <td>0.39</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1892.00</td>
    <td>51611.00</td>
    <td>5.45</td>
    <td>0.48</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1892.00</td>
    <td>51611.00</td>
    <td>6.51</td>
    <td>0.58</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1892.00</td>
    <td>51611.00</td>
    <td>8.10</td>
    <td>0.72</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1847.00</td>
    <td>51682.00</td>
    <td>2.74</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1847.00</td>
    <td>51682.00</td>
    <td>3.82</td>
    <td>0.34</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1847.00</td>
    <td>51682.00</td>
    <td>4.36</td>
    <td>0.39</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1847.00</td>
    <td>51682.00</td>
    <td>5.46</td>
    <td>0.49</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1847.00</td>
    <td>51682.00</td>
    <td>6.52</td>
    <td>0.58</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1847.00</td>
    <td>51682.00</td>
    <td>8.11</td>
    <td>0.73</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1624.00</td>
    <td>51729.00</td>
    <td>2.75</td>
    <td>0.25</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1624.00</td>
    <td>51729.00</td>
    <td>3.83</td>
    <td>0.34</td>
    <td>0.23</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1624.00</td>
    <td>51729.00</td>
    <td>4.37</td>
    <td>0.39</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1624.00</td>
    <td>51729.00</td>
    <td>5.47</td>
    <td>0.49</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1624.00</td>
    <td>51729.00</td>
    <td>6.54</td>
    <td>0.59</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1624.00</td>
    <td>51729.00</td>
    <td>8.14</td>
    <td>0.74</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1040.00</td>
    <td>51851.00</td>
    <td>2.75</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
</table>
<table>
  <tr>
    <td>1040.00</td>
    <td>51851.00</td>
    <td>3.82</td>
    <td>0.34</td>
    <td>0.23</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1040.00</td>
    <td>51851.00</td>
    <td>4.36</td>
    <td>0.39</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1040.00</td>
    <td>51851.00</td>
    <td>5.46</td>
    <td>0.49</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1040.00</td>
    <td>51851.00</td>
    <td>6.52</td>
    <td>0.58</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>992.50</td>
    <td>51853.00</td>
    <td>2.74</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>992.50</td>
    <td>51853.00</td>
    <td>3.82</td>
    <td>0.34</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>992.50</td>
    <td>51853.00</td>
    <td>4.36</td>
    <td>0.39</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>992.50</td>
    <td>51853.00</td>
    <td>5.46</td>
    <td>0.48</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>992.50</td>
    <td>51853.00</td>
    <td>6.52</td>
    <td>0.58</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>992.50</td>
    <td>51853.00</td>
    <td>8.11</td>
    <td>0.72</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>953.00</td>
    <td>51844.50</td>
    <td>2.74</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>953.00</td>
    <td>51844.50</td>
    <td>3.82</td>
    <td>0.34</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>953.00</td>
    <td>51844.50</td>
    <td>4.36</td>
    <td>0.39</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>953.00</td>
    <td>51844.50</td>
    <td>5.46</td>
    <td>0.48</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>953.00</td>
    <td>51844.50</td>
    <td>6.51</td>
    <td>0.58</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>953.00</td>
    <td>51844.50</td>
    <td>8.10</td>
    <td>0.72</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>720.00</td>
    <td>51763.00</td>
    <td>2.74</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>720.00</td>
    <td>51763.00</td>
    <td>3.82</td>
    <td>0.34</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>720.00</td>
    <td>51763.00</td>
    <td>4.36</td>
    <td>0.38</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>720.00</td>
    <td>51763.00</td>
    <td>5.45</td>
    <td>0.48</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>720.00</td>
    <td>51763.00</td>
    <td>6.51</td>
    <td>0.58</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>720.00</td>
    <td>51763.00</td>
    <td>8.10</td>
    <td>0.72</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>674.00</td>
    <td>51763.00</td>
    <td>2.74</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>674.00</td>
    <td>51763.00</td>
    <td>3.82</td>
    <td>0.34</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>674.00</td>
    <td>51763.00</td>
    <td>4.36</td>
    <td>0.38</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>674.00</td>
    <td>51763.00</td>
    <td>5.45</td>
    <td>0.48</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>674.00</td>
    <td>51763.00</td>
    <td>6.51</td>
    <td>0.58</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>674.00</td>
    <td>51763.00</td>
    <td>8.09</td>
    <td>0.72</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>138.50</td>
    <td>51865.00</td>
    <td>2.74</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>138.50</td>
    <td>51865.00</td>
    <td>3.82</td>
    <td>0.33</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>138.50</td>
    <td>51865.00</td>
    <td>4.36</td>
    <td>0.38</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>138.50</td>
    <td>51865.00</td>
    <td>5.45</td>
    <td>0.48</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>138.50</td>
    <td>51865.00</td>
    <td>6.50</td>
    <td>0.57</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>138.50</td>
    <td>51865.00</td>
    <td>8.08</td>
    <td>0.71</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>105.60</td>
    <td>51824.00</td>
    <td>2.74</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>105.60</td>
    <td>51824.00</td>
    <td>3.82</td>
    <td>0.33</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>105.60</td>
    <td>51824.00</td>
    <td>4.36</td>
    <td>0.38</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>105.60</td>
    <td>51824.00</td>
    <td>5.45</td>
    <td>0.48</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>105.60</td>
    <td>51824.00</td>
    <td>6.50</td>
    <td>0.57</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>105.60</td>
    <td>51824.00</td>
    <td>8.08</td>
    <td>0.71</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-8.40</td>
    <td>51250.70</td>
    <td>2.75</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-8.40</td>
    <td>51250.70</td>
    <td>3.82</td>
    <td>0.33</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-8.40</td>
    <td>51250.70</td>
    <td>4.36</td>
    <td>0.38</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-8.40</td>
    <td>51250.70</td>
    <td>5.45</td>
    <td>0.47</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
</table>
<table>
  <tr>
    <td>-8.40</td>
    <td>51250.70</td>
    <td>6.51</td>
    <td>0.57</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-8.40</td>
    <td>51250.70</td>
    <td>8.09</td>
    <td>0.70</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1047.15</td>
    <td>52011.69</td>
    <td>2.74</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1047.15</td>
    <td>52011.69</td>
    <td>3.81</td>
    <td>0.34</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1047.15</td>
    <td>52011.69</td>
    <td>5.45</td>
    <td>0.49</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1047.15</td>
    <td>52011.69</td>
    <td>6.51</td>
    <td>0.58</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1047.15</td>
    <td>52011.69</td>
    <td>8.11</td>
    <td>0.73</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>326.03</td>
    <td>49231.62</td>
    <td>2.80</td>
    <td>0.20</td>
    <td>0.16</td>
    <td>0.19</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>326.03</td>
    <td>49231.62</td>
    <td>3.90</td>
    <td>0.28</td>
    <td>0.22</td>
    <td>0.26</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>326.03</td>
    <td>49231.62</td>
    <td>4.46</td>
    <td>0.32</td>
    <td>0.25</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>326.03</td>
    <td>49231.62</td>
    <td>5.58</td>
    <td>0.40</td>
    <td>0.31</td>
    <td>0.37</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>326.03</td>
    <td>49231.62</td>
    <td>6.66</td>
    <td>0.48</td>
    <td>0.38</td>
    <td>0.45</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>326.03</td>
    <td>49231.62</td>
    <td>8.30</td>
    <td>0.60</td>
    <td>0.47</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1486.70</td>
    <td>51915.27</td>
    <td>2.78</td>
    <td>0.25</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1486.70</td>
    <td>51915.27</td>
    <td>3.88</td>
    <td>0.35</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1486.70</td>
    <td>51915.27</td>
    <td>4.43</td>
    <td>0.40</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1486.70</td>
    <td>51915.27</td>
    <td>5.54</td>
    <td>0.50</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1486.70</td>
    <td>51915.27</td>
    <td>6.62</td>
    <td>0.60</td>
    <td>0.40</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1486.70</td>
    <td>51915.27</td>
    <td>8.25</td>
    <td>0.75</td>
    <td>0.50</td>
    <td>0.55</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10802.30</td>
    <td>38371.00</td>
    <td>2.84</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>10802.30</td>
    <td>38371.00</td>
    <td>4.06</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>10802.30</td>
    <td>38371.00</td>
    <td>4.59</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>10802.30</td>
    <td>38371.00</td>
    <td>5.68</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>10802.30</td>
    <td>38371.00</td>
    <td>6.80</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>10802.30</td>
    <td>38371.00</td>
    <td>8.49</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.10</td>
    <td>0.01</td>
    <td>0.05</td>
  </tr>
  <tr>
    <td>6989.46</td>
    <td>40327.50</td>
    <td>2.71</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>6989.46</td>
    <td>40327.50</td>
    <td>3.77</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.14</td>
  </tr>
  <tr>
    <td>6989.46</td>
    <td>40327.50</td>
    <td>4.30</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.16</td>
  </tr>
  <tr>
    <td>6989.46</td>
    <td>40327.50</td>
    <td>5.39</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.02</td>
    <td>0.20</td>
  </tr>
  <tr>
    <td>6989.46</td>
    <td>40327.50</td>
    <td>6.44</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.03</td>
    <td>0.23</td>
  </tr>
  <tr>
    <td>6989.46</td>
    <td>40327.50</td>
    <td>8.03</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.08</td>
    <td>0.03</td>
    <td>0.29</td>
  </tr>
  <tr>
    <td>7157.14</td>
    <td>40260.10</td>
    <td>2.72</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.09</td>
  </tr>
  <tr>
    <td>7157.14</td>
    <td>40260.10</td>
    <td>3.79</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.01</td>
    <td>0.13</td>
  </tr>
  <tr>
    <td>7157.14</td>
    <td>40260.10</td>
    <td>4.33</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.15</td>
  </tr>
  <tr>
    <td>7157.14</td>
    <td>40260.10</td>
    <td>5.43</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.02</td>
    <td>0.19</td>
  </tr>
  <tr>
    <td>7157.14</td>
    <td>40260.10</td>
    <td>6.48</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.03</td>
    <td>0.23</td>
  </tr>
  <tr>
    <td>7157.14</td>
    <td>40260.10</td>
    <td>8.08</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.08</td>
    <td>0.03</td>
    <td>0.29</td>
  </tr>
  <tr>
    <td>6520.27</td>
    <td>40523.75</td>
    <td>2.70</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.11</td>
  </tr>
  <tr>
    <td>6520.27</td>
    <td>40523.75</td>
    <td>3.76</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.15</td>
  </tr>
  <tr>
    <td>6520.27</td>
    <td>40523.75</td>
    <td>4.29</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.17</td>
  </tr>
  <tr>
    <td>6520.27</td>
    <td>40523.75</td>
    <td>5.38</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.02</td>
    <td>0.21</td>
  </tr>
  <tr>
    <td>6520.27</td>
    <td>40523.75</td>
    <td>6.43</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.03</td>
    <td>0.26</td>
  </tr>
  <tr>
    <td>6520.27</td>
    <td>40523.75</td>
    <td>8.01</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.04</td>
    <td>0.32</td>
  </tr>
  <tr>
    <td>7308.66</td>
    <td>40195.80</td>
    <td>2.74</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.09</td>
  </tr>
</table>
<table>
  <tr>
    <td>7308.66</td>
    <td>40195.80</td>
    <td>3.81</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.01</td>
    <td>0.13</td>
  </tr>
  <tr>
    <td>7308.66</td>
    <td>40195.80</td>
    <td>4.35</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.15</td>
  </tr>
  <tr>
    <td>7308.66</td>
    <td>40195.80</td>
    <td>5.45</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.02</td>
    <td>0.18</td>
  </tr>
  <tr>
    <td>7308.66</td>
    <td>40195.80</td>
    <td>6.51</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.02</td>
    <td>0.22</td>
  </tr>
  <tr>
    <td>7308.66</td>
    <td>40195.80</td>
    <td>8.12</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.08</td>
    <td>0.03</td>
    <td>0.28</td>
  </tr>
  <tr>
    <td>8122.45</td>
    <td>39721.79</td>
    <td>2.74</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.07</td>
  </tr>
  <tr>
    <td>8122.45</td>
    <td>39721.79</td>
    <td>3.81</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.01</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>8122.45</td>
    <td>39721.79</td>
    <td>4.35</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.01</td>
    <td>0.12</td>
  </tr>
  <tr>
    <td>8122.45</td>
    <td>39721.79</td>
    <td>5.45</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.02</td>
    <td>0.15</td>
  </tr>
  <tr>
    <td>8122.45</td>
    <td>39721.79</td>
    <td>6.52</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.02</td>
    <td>0.18</td>
  </tr>
  <tr>
    <td>8122.45</td>
    <td>39721.79</td>
    <td>8.13</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.03</td>
    <td>0.23</td>
  </tr>
  <tr>
    <td>7740.77</td>
    <td>39969.37</td>
    <td>2.77</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>7740.77</td>
    <td>39969.37</td>
    <td>3.85</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.01</td>
    <td>0.12</td>
  </tr>
  <tr>
    <td>7740.77</td>
    <td>39969.37</td>
    <td>4.40</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.01</td>
    <td>0.13</td>
  </tr>
  <tr>
    <td>7740.77</td>
    <td>39969.37</td>
    <td>5.51</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.02</td>
    <td>0.17</td>
  </tr>
  <tr>
    <td>7740.77</td>
    <td>39969.37</td>
    <td>6.59</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.02</td>
    <td>0.20</td>
  </tr>
  <tr>
    <td>7740.77</td>
    <td>39969.37</td>
    <td>8.22</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.03</td>
    <td>0.25</td>
  </tr>
  <tr>
    <td>8546.14</td>
    <td>39434.60</td>
    <td>2.75</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.07</td>
  </tr>
  <tr>
    <td>8546.14</td>
    <td>39434.60</td>
    <td>4.38</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.01</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>8546.14</td>
    <td>39434.60</td>
    <td>5.49</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.01</td>
    <td>0.13</td>
  </tr>
  <tr>
    <td>8546.14</td>
    <td>39434.60</td>
    <td>6.57</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.02</td>
    <td>0.16</td>
  </tr>
  <tr>
    <td>8546.14</td>
    <td>39434.60</td>
    <td>8.19</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.02</td>
    <td>0.20</td>
  </tr>
  <tr>
    <td>6193.46</td>
    <td>40660.45</td>
    <td>2.66</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.11</td>
  </tr>
  <tr>
    <td>6193.46</td>
    <td>40660.45</td>
    <td>3.70</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.16</td>
  </tr>
  <tr>
    <td>6193.46</td>
    <td>40660.45</td>
    <td>4.22</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.18</td>
  </tr>
  <tr>
    <td>6193.46</td>
    <td>40660.45</td>
    <td>5.29</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.02</td>
    <td>0.22</td>
  </tr>
  <tr>
    <td>6193.46</td>
    <td>40660.45</td>
    <td>6.32</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.03</td>
    <td>0.27</td>
  </tr>
  <tr>
    <td>6193.46</td>
    <td>40660.45</td>
    <td>7.87</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.04</td>
    <td>0.34</td>
  </tr>
  <tr>
    <td>6370.76</td>
    <td>40589.00</td>
    <td>2.69</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.11</td>
  </tr>
  <tr>
    <td>6370.76</td>
    <td>40589.00</td>
    <td>3.75</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.15</td>
  </tr>
  <tr>
    <td>6370.76</td>
    <td>40589.00</td>
    <td>4.28</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.17</td>
  </tr>
  <tr>
    <td>6370.76</td>
    <td>40589.00</td>
    <td>5.37</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.02</td>
    <td>0.22</td>
  </tr>
  <tr>
    <td>6370.76</td>
    <td>40589.00</td>
    <td>6.41</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.03</td>
    <td>0.26</td>
  </tr>
  <tr>
    <td>6370.76</td>
    <td>40589.00</td>
    <td>7.98</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.04</td>
    <td>0.33</td>
  </tr>
  <tr>
    <td>6694.62</td>
    <td>40453.50</td>
    <td>2.71</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>6694.62</td>
    <td>40453.50</td>
    <td>3.77</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.14</td>
  </tr>
  <tr>
    <td>6694.62</td>
    <td>40453.50</td>
    <td>4.31</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.17</td>
  </tr>
  <tr>
    <td>6694.62</td>
    <td>40453.50</td>
    <td>5.40</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.02</td>
    <td>0.21</td>
  </tr>
  <tr>
    <td>6694.62</td>
    <td>40453.50</td>
    <td>6.45</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.03</td>
    <td>0.25</td>
  </tr>
  <tr>
    <td>6694.62</td>
    <td>40453.50</td>
    <td>8.03</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.03</td>
    <td>0.31</td>
  </tr>
  <tr>
    <td>10569.90</td>
    <td>38430.90</td>
    <td>2.90</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>10569.90</td>
    <td>38430.90</td>
    <td>4.13</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>10569.90</td>
    <td>38430.90</td>
    <td>4.68</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>10569.90</td>
    <td>38430.90</td>
    <td>5.80</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.01</td>
    <td>0.04</td>
  </tr>
</table>
<table>
  <tr>
    <td>10569.90</td>
    <td>38430.90</td>
    <td>6.94</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.08</td>
    <td>0.01</td>
    <td>0.05</td>
  </tr>
  <tr>
    <td>10569.90</td>
    <td>38430.90</td>
    <td>8.67</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.10</td>
    <td>0.01</td>
    <td>0.07</td>
  </tr>
  <tr>
    <td>8924.91</td>
    <td>39174.88</td>
    <td>2.80</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.06</td>
  </tr>
  <tr>
    <td>8924.91</td>
    <td>39174.88</td>
    <td>3.90</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.01</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>8924.91</td>
    <td>39174.88</td>
    <td>4.45</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.01</td>
    <td>0.09</td>
  </tr>
  <tr>
    <td>8924.91</td>
    <td>39174.88</td>
    <td>5.58</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.01</td>
    <td>0.12</td>
  </tr>
  <tr>
    <td>8924.91</td>
    <td>39174.88</td>
    <td>6.67</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.02</td>
    <td>0.14</td>
  </tr>
  <tr>
    <td>8924.91</td>
    <td>39174.88</td>
    <td>8.32</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.02</td>
    <td>0.17</td>
  </tr>
  <tr>
    <td>9320.55</td>
    <td>38915.00</td>
    <td>2.84</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.05</td>
  </tr>
  <tr>
    <td>9320.55</td>
    <td>38915.00</td>
    <td>3.97</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.01</td>
    <td>0.07</td>
  </tr>
  <tr>
    <td>9320.55</td>
    <td>38915.00</td>
    <td>4.58</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.01</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>9320.55</td>
    <td>38915.00</td>
    <td>5.68</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.01</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>9320.55</td>
    <td>38915.00</td>
    <td>6.79</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.01</td>
    <td>0.12</td>
  </tr>
  <tr>
    <td>9320.55</td>
    <td>38915.00</td>
    <td>8.47</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.10</td>
    <td>0.02</td>
    <td>0.15</td>
  </tr>
  <tr>
    <td>9803.28</td>
    <td>38672.50</td>
    <td>2.87</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>9803.28</td>
    <td>38672.50</td>
    <td>4.01</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.01</td>
    <td>0.05</td>
  </tr>
  <tr>
    <td>9803.28</td>
    <td>38672.50</td>
    <td>4.63</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.01</td>
    <td>0.06</td>
  </tr>
  <tr>
    <td>9803.28</td>
    <td>38672.50</td>
    <td>5.73</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.01</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>9803.28</td>
    <td>38672.50</td>
    <td>6.86</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.01</td>
    <td>0.09</td>
  </tr>
  <tr>
    <td>9803.28</td>
    <td>38672.50</td>
    <td>8.56</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.10</td>
    <td>0.01</td>
    <td>0.12</td>
  </tr>
  <tr>
    <td>5649.32</td>
    <td>40578.37</td>
    <td>2.41</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.25</td>
  </tr>
  <tr>
    <td>5649.32</td>
    <td>40578.37</td>
    <td>3.34</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.35</td>
  </tr>
  <tr>
    <td>5649.32</td>
    <td>40578.37</td>
    <td>3.81</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.40</td>
  </tr>
  <tr>
    <td>5649.32</td>
    <td>40578.37</td>
    <td>4.76</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.07</td>
    <td>0.50</td>
  </tr>
  <tr>
    <td>5649.32</td>
    <td>40578.37</td>
    <td>5.71</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.60</td>
  </tr>
  <tr>
    <td>5649.32</td>
    <td>40578.37</td>
    <td>7.09</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.11</td>
    <td>0.75</td>
  </tr>
  <tr>
    <td>5652.58</td>
    <td>40577.76</td>
    <td>2.41</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.25</td>
  </tr>
  <tr>
    <td>5652.58</td>
    <td>40577.76</td>
    <td>3.34</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.35</td>
  </tr>
  <tr>
    <td>5652.58</td>
    <td>40577.76</td>
    <td>3.81</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.40</td>
  </tr>
  <tr>
    <td>5652.58</td>
    <td>40577.76</td>
    <td>4.76</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.07</td>
    <td>0.50</td>
  </tr>
  <tr>
    <td>5652.58</td>
    <td>40577.76</td>
    <td>7.09</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.11</td>
    <td>0.75</td>
  </tr>
  <tr>
    <td>5302.61</td>
    <td>40366.38</td>
    <td>2.37</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.26</td>
  </tr>
  <tr>
    <td>5302.61</td>
    <td>40366.38</td>
    <td>3.29</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.36</td>
  </tr>
  <tr>
    <td>5302.61</td>
    <td>40366.38</td>
    <td>3.75</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.05</td>
    <td>0.41</td>
  </tr>
  <tr>
    <td>5302.61</td>
    <td>40366.38</td>
    <td>4.67</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.07</td>
    <td>0.52</td>
  </tr>
  <tr>
    <td>5302.61</td>
    <td>40366.38</td>
    <td>5.61</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.08</td>
    <td>0.62</td>
  </tr>
  <tr>
    <td>5302.61</td>
    <td>40366.38</td>
    <td>6.97</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.10</td>
    <td>0.77</td>
  </tr>
  <tr>
    <td>10128.60</td>
    <td>38552.10</td>
    <td>2.89</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>10128.60</td>
    <td>38552.10</td>
    <td>4.03</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>10128.60</td>
    <td>38552.10</td>
    <td>4.65</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.01</td>
    <td>0.05</td>
  </tr>
  <tr>
    <td>10128.60</td>
    <td>38552.10</td>
    <td>5.77</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.01</td>
    <td>0.06</td>
  </tr>
  <tr>
    <td>10128.60</td>
    <td>38552.10</td>
    <td>6.90</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.08</td>
    <td>0.01</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>10128.60</td>
    <td>38552.10</td>
    <td>8.62</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.10</td>
    <td>0.01</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>2453.60</td>
    <td>42517.92</td>
    <td>2.70</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.14</td>
  </tr>
</table>
<table>
  <tr>
    <td>2453.60</td>
    <td>42517.92</td>
    <td>3.76</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.19</td>
  </tr>
  <tr>
    <td>2453.60</td>
    <td>42517.92</td>
    <td>4.36</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.22</td>
  </tr>
  <tr>
    <td>2453.60</td>
    <td>42517.92</td>
    <td>5.39</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.10</td>
    <td>0.27</td>
  </tr>
  <tr>
    <td>2453.60</td>
    <td>42517.92</td>
    <td>6.44</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.12</td>
    <td>0.33</td>
  </tr>
  <tr>
    <td>2453.60</td>
    <td>42517.92</td>
    <td>8.02</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.15</td>
    <td>0.41</td>
  </tr>
  <tr>
    <td>4973.06</td>
    <td>39924.90</td>
    <td>2.34</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.27</td>
  </tr>
  <tr>
    <td>4973.06</td>
    <td>39924.90</td>
    <td>3.24</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.38</td>
  </tr>
  <tr>
    <td>4973.06</td>
    <td>39924.90</td>
    <td>3.70</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.05</td>
    <td>0.43</td>
  </tr>
  <tr>
    <td>4973.06</td>
    <td>39924.90</td>
    <td>4.60</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.06</td>
    <td>0.54</td>
  </tr>
  <tr>
    <td>4973.06</td>
    <td>39924.90</td>
    <td>5.53</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.08</td>
    <td>0.65</td>
  </tr>
  <tr>
    <td>4973.06</td>
    <td>39924.90</td>
    <td>6.87</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.10</td>
    <td>0.81</td>
  </tr>
  <tr>
    <td>2719.06</td>
    <td>49944.59</td>
    <td>2.83</td>
    <td>0.20</td>
    <td>0.19</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>2719.06</td>
    <td>49944.59</td>
    <td>4.50</td>
    <td>0.32</td>
    <td>0.30</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>2719.06</td>
    <td>49944.59</td>
    <td>5.64</td>
    <td>0.40</td>
    <td>0.37</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>2719.06</td>
    <td>49944.59</td>
    <td>6.74</td>
    <td>0.48</td>
    <td>0.45</td>
    <td>0.44</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>2719.06</td>
    <td>49944.59</td>
    <td>8.40</td>
    <td>0.60</td>
    <td>0.57</td>
    <td>0.55</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>2981.59</td>
    <td>49886.72</td>
    <td>2.83</td>
    <td>0.20</td>
    <td>0.19</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>2981.59</td>
    <td>49886.72</td>
    <td>3.94</td>
    <td>0.27</td>
    <td>0.27</td>
    <td>0.25</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>2981.59</td>
    <td>49886.72</td>
    <td>4.50</td>
    <td>0.31</td>
    <td>0.30</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>2981.59</td>
    <td>49886.72</td>
    <td>5.63</td>
    <td>0.39</td>
    <td>0.38</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>2981.59</td>
    <td>49886.72</td>
    <td>6.73</td>
    <td>0.47</td>
    <td>0.46</td>
    <td>0.43</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>2981.59</td>
    <td>49886.72</td>
    <td>8.39</td>
    <td>0.59</td>
    <td>0.58</td>
    <td>0.54</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>3503.95</td>
    <td>49738.68</td>
    <td>2.81</td>
    <td>0.19</td>
    <td>0.20</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>3503.95</td>
    <td>49738.68</td>
    <td>3.91</td>
    <td>0.27</td>
    <td>0.28</td>
    <td>0.25</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>3503.95</td>
    <td>49738.68</td>
    <td>4.47</td>
    <td>0.31</td>
    <td>0.32</td>
    <td>0.28</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>3503.95</td>
    <td>49738.68</td>
    <td>5.59</td>
    <td>0.39</td>
    <td>0.40</td>
    <td>0.35</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>3503.95</td>
    <td>49738.68</td>
    <td>6.69</td>
    <td>0.46</td>
    <td>0.48</td>
    <td>0.43</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>3503.95</td>
    <td>49738.68</td>
    <td>8.33</td>
    <td>0.58</td>
    <td>0.61</td>
    <td>0.53</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>4814.58</td>
    <td>49441.08</td>
    <td>2.77</td>
    <td>0.18</td>
    <td>0.22</td>
    <td>0.17</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>4814.58</td>
    <td>49441.08</td>
    <td>3.86</td>
    <td>0.25</td>
    <td>0.31</td>
    <td>0.23</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>4814.58</td>
    <td>49441.08</td>
    <td>4.41</td>
    <td>0.29</td>
    <td>0.36</td>
    <td>0.27</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>4814.58</td>
    <td>49441.08</td>
    <td>5.52</td>
    <td>0.37</td>
    <td>0.45</td>
    <td>0.34</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>4814.58</td>
    <td>49441.08</td>
    <td>6.60</td>
    <td>0.44</td>
    <td>0.54</td>
    <td>0.40</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>4814.58</td>
    <td>49441.08</td>
    <td>8.22</td>
    <td>0.55</td>
    <td>0.68</td>
    <td>0.51</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>5203.06</td>
    <td>49413.91</td>
    <td>2.79</td>
    <td>0.18</td>
    <td>0.23</td>
    <td>0.17</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>5203.06</td>
    <td>49413.91</td>
    <td>3.88</td>
    <td>0.25</td>
    <td>0.32</td>
    <td>0.23</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>5203.06</td>
    <td>49413.91</td>
    <td>4.43</td>
    <td>0.29</td>
    <td>0.37</td>
    <td>0.27</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>5203.06</td>
    <td>49413.91</td>
    <td>5.55</td>
    <td>0.36</td>
    <td>0.47</td>
    <td>0.34</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>5203.06</td>
    <td>49413.91</td>
    <td>6.63</td>
    <td>0.44</td>
    <td>0.56</td>
    <td>0.40</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>5203.06</td>
    <td>49413.91</td>
    <td>8.26</td>
    <td>0.55</td>
    <td>0.71</td>
    <td>0.50</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>5931.82</td>
    <td>49361.61</td>
    <td>2.80</td>
    <td>0.18</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>5931.82</td>
    <td>49361.61</td>
    <td>3.89</td>
    <td>0.25</td>
    <td>0.34</td>
    <td>0.23</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>5931.82</td>
    <td>49361.61</td>
    <td>4.45</td>
    <td>0.29</td>
    <td>0.39</td>
    <td>0.26</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>5931.82</td>
    <td>49361.61</td>
    <td>5.57</td>
    <td>0.36</td>
    <td>0.49</td>
    <td>0.33</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
</table>
<table>
  <tr>
    <td>5931.82</td>
    <td>49361.61</td>
    <td>6.65</td>
    <td>0.43</td>
    <td>0.59</td>
    <td>0.40</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>5931.82</td>
    <td>49361.61</td>
    <td>8.29</td>
    <td>0.54</td>
    <td>0.75</td>
    <td>0.50</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>6958.05</td>
    <td>49109.45</td>
    <td>2.76</td>
    <td>0.17</td>
    <td>0.26</td>
    <td>0.16</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>6958.05</td>
    <td>49109.45</td>
    <td>3.84</td>
    <td>0.24</td>
    <td>0.36</td>
    <td>0.22</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>6958.05</td>
    <td>49109.45</td>
    <td>4.39</td>
    <td>0.27</td>
    <td>0.42</td>
    <td>0.25</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>6958.05</td>
    <td>49109.45</td>
    <td>5.50</td>
    <td>0.35</td>
    <td>0.53</td>
    <td>0.32</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>6958.05</td>
    <td>49109.45</td>
    <td>6.56</td>
    <td>0.41</td>
    <td>0.63</td>
    <td>0.38</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>6958.05</td>
    <td>49109.45</td>
    <td>8.17</td>
    <td>0.52</td>
    <td>0.80</td>
    <td>0.48</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7232.84</td>
    <td>47929.08</td>
    <td>2.76</td>
    <td>0.16</td>
    <td>0.28</td>
    <td>0.15</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>7232.84</td>
    <td>47929.08</td>
    <td>3.84</td>
    <td>0.23</td>
    <td>0.40</td>
    <td>0.21</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7232.84</td>
    <td>47929.08</td>
    <td>4.38</td>
    <td>0.26</td>
    <td>0.46</td>
    <td>0.24</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7232.84</td>
    <td>47929.08</td>
    <td>5.49</td>
    <td>0.33</td>
    <td>0.58</td>
    <td>0.31</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7232.84</td>
    <td>47929.08</td>
    <td>6.55</td>
    <td>0.40</td>
    <td>0.69</td>
    <td>0.37</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7232.84</td>
    <td>47929.08</td>
    <td>8.16</td>
    <td>0.50</td>
    <td>0.87</td>
    <td>0.46</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1717.45</td>
    <td>42915.30</td>
    <td>2.76</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.05</td>
    <td>0.11</td>
  </tr>
  <tr>
    <td>1717.45</td>
    <td>42915.30</td>
    <td>3.85</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.15</td>
  </tr>
  <tr>
    <td>1717.45</td>
    <td>42915.30</td>
    <td>4.46</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.09</td>
    <td>0.18</td>
  </tr>
  <tr>
    <td>1717.45</td>
    <td>42915.30</td>
    <td>5.51</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.11</td>
    <td>0.22</td>
  </tr>
  <tr>
    <td>1717.45</td>
    <td>42915.30</td>
    <td>6.59</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.13</td>
    <td>0.26</td>
  </tr>
  <tr>
    <td>1717.45</td>
    <td>42915.30</td>
    <td>8.22</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.16</td>
    <td>0.33</td>
  </tr>
  <tr>
    <td>7387.58</td>
    <td>48889.82</td>
    <td>2.77</td>
    <td>0.17</td>
    <td>0.27</td>
    <td>0.16</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>7387.58</td>
    <td>48889.82</td>
    <td>3.86</td>
    <td>0.24</td>
    <td>0.38</td>
    <td>0.22</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7387.58</td>
    <td>48889.82</td>
    <td>4.41</td>
    <td>0.27</td>
    <td>0.43</td>
    <td>0.25</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7387.58</td>
    <td>48889.82</td>
    <td>5.52</td>
    <td>0.34</td>
    <td>0.54</td>
    <td>0.32</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7387.58</td>
    <td>48889.82</td>
    <td>6.60</td>
    <td>0.41</td>
    <td>0.65</td>
    <td>0.38</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7387.58</td>
    <td>48889.82</td>
    <td>8.21</td>
    <td>0.52</td>
    <td>0.82</td>
    <td>0.47</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7169.97</td>
    <td>47567.15</td>
    <td>2.75</td>
    <td>0.16</td>
    <td>0.29</td>
    <td>0.15</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>7169.97</td>
    <td>47567.15</td>
    <td>3.83</td>
    <td>0.23</td>
    <td>0.41</td>
    <td>0.21</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7169.97</td>
    <td>47567.15</td>
    <td>4.38</td>
    <td>0.26</td>
    <td>0.47</td>
    <td>0.24</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7169.97</td>
    <td>47567.15</td>
    <td>5.48</td>
    <td>0.33</td>
    <td>0.59</td>
    <td>0.30</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7169.97</td>
    <td>47567.15</td>
    <td>6.55</td>
    <td>0.39</td>
    <td>0.71</td>
    <td>0.36</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12227.05</td>
    <td>45938.96</td>
    <td>2.73</td>
    <td>0.13</td>
    <td>0.23</td>
    <td>0.12</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12227.05</td>
    <td>45938.96</td>
    <td>3.79</td>
    <td>0.18</td>
    <td>0.33</td>
    <td>0.17</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12227.05</td>
    <td>45938.96</td>
    <td>4.33</td>
    <td>0.21</td>
    <td>0.38</td>
    <td>0.19</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12227.05</td>
    <td>45938.96</td>
    <td>5.42</td>
    <td>0.27</td>
    <td>0.48</td>
    <td>0.24</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12227.05</td>
    <td>45938.96</td>
    <td>6.48</td>
    <td>0.32</td>
    <td>0.59</td>
    <td>0.30</td>
    <td>0.10</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12227.05</td>
    <td>45938.96</td>
    <td>8.06</td>
    <td>0.41</td>
    <td>0.74</td>
    <td>0.37</td>
    <td>0.12</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>12366.69</td>
    <td>45901.23</td>
    <td>2.72</td>
    <td>0.12</td>
    <td>0.22</td>
    <td>0.11</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12366.69</td>
    <td>45901.23</td>
    <td>3.79</td>
    <td>0.17</td>
    <td>0.32</td>
    <td>0.16</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12366.69</td>
    <td>45901.23</td>
    <td>4.32</td>
    <td>0.20</td>
    <td>0.37</td>
    <td>0.19</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12366.69</td>
    <td>45901.23</td>
    <td>5.41</td>
    <td>0.26</td>
    <td>0.47</td>
    <td>0.24</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12366.69</td>
    <td>45901.23</td>
    <td>6.46</td>
    <td>0.31</td>
    <td>0.57</td>
    <td>0.29</td>
    <td>0.10</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12366.69</td>
    <td>45901.23</td>
    <td>8.05</td>
    <td>0.39</td>
    <td>0.72</td>
    <td>0.36</td>
    <td>0.13</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>12273.18</td>
    <td>45184.86</td>
    <td>2.74</td>
    <td>0.10</td>
    <td>0.19</td>
    <td>0.10</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
</table>
<table>
  <tr>
    <td>12273.18</td>
    <td>45184.86</td>
    <td>3.81</td>
    <td>0.15</td>
    <td>0.27</td>
    <td>0.14</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12273.18</td>
    <td>45184.86</td>
    <td>4.35</td>
    <td>0.17</td>
    <td>0.31</td>
    <td>0.16</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12273.18</td>
    <td>45184.86</td>
    <td>5.44</td>
    <td>0.22</td>
    <td>0.39</td>
    <td>0.20</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12273.18</td>
    <td>45184.86</td>
    <td>6.50</td>
    <td>0.26</td>
    <td>0.48</td>
    <td>0.24</td>
    <td>0.13</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>12273.18</td>
    <td>45184.86</td>
    <td>8.10</td>
    <td>0.34</td>
    <td>0.61</td>
    <td>0.31</td>
    <td>0.15</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>12199.46</td>
    <td>44729.15</td>
    <td>2.77</td>
    <td>0.09</td>
    <td>0.17</td>
    <td>0.08</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12199.46</td>
    <td>44729.15</td>
    <td>3.86</td>
    <td>0.13</td>
    <td>0.24</td>
    <td>0.12</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12199.46</td>
    <td>44729.15</td>
    <td>4.40</td>
    <td>0.15</td>
    <td>0.27</td>
    <td>0.14</td>
    <td>0.10</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>12199.46</td>
    <td>44729.15</td>
    <td>5.51</td>
    <td>0.19</td>
    <td>0.35</td>
    <td>0.18</td>
    <td>0.12</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>12199.46</td>
    <td>44729.15</td>
    <td>6.58</td>
    <td>0.23</td>
    <td>0.42</td>
    <td>0.21</td>
    <td>0.14</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>12199.46</td>
    <td>44729.15</td>
    <td>8.20</td>
    <td>0.30</td>
    <td>0.54</td>
    <td>0.27</td>
    <td>0.17</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11988.25</td>
    <td>44006.90</td>
    <td>2.80</td>
    <td>0.07</td>
    <td>0.13</td>
    <td>0.06</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11988.25</td>
    <td>44006.90</td>
    <td>3.90</td>
    <td>0.10</td>
    <td>0.18</td>
    <td>0.09</td>
    <td>0.10</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11988.25</td>
    <td>44006.90</td>
    <td>4.45</td>
    <td>0.12</td>
    <td>0.21</td>
    <td>0.11</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11988.25</td>
    <td>44006.90</td>
    <td>5.57</td>
    <td>0.15</td>
    <td>0.27</td>
    <td>0.14</td>
    <td>0.14</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11988.25</td>
    <td>44006.90</td>
    <td>6.66</td>
    <td>0.18</td>
    <td>0.32</td>
    <td>0.16</td>
    <td>0.17</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11988.25</td>
    <td>44006.90</td>
    <td>8.29</td>
    <td>0.23</td>
    <td>0.42</td>
    <td>0.21</td>
    <td>0.21</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11705.11</td>
    <td>43225.58</td>
    <td>2.79</td>
    <td>0.05</td>
    <td>0.09</td>
    <td>0.04</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11705.11</td>
    <td>43225.58</td>
    <td>3.89</td>
    <td>0.07</td>
    <td>0.12</td>
    <td>0.06</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11705.11</td>
    <td>43225.58</td>
    <td>4.44</td>
    <td>0.08</td>
    <td>0.14</td>
    <td>0.07</td>
    <td>0.13</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11705.11</td>
    <td>43225.58</td>
    <td>5.56</td>
    <td>0.10</td>
    <td>0.18</td>
    <td>0.09</td>
    <td>0.16</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11705.11</td>
    <td>43225.58</td>
    <td>6.65</td>
    <td>0.12</td>
    <td>0.22</td>
    <td>0.11</td>
    <td>0.19</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11705.11</td>
    <td>43225.58</td>
    <td>8.29</td>
    <td>0.15</td>
    <td>0.28</td>
    <td>0.14</td>
    <td>0.24</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11560.90</td>
    <td>42340.71</td>
    <td>2.80</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11560.90</td>
    <td>42340.71</td>
    <td>3.90</td>
    <td>0.03</td>
    <td>0.06</td>
    <td>0.03</td>
    <td>0.13</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11560.90</td>
    <td>42340.71</td>
    <td>4.46</td>
    <td>0.04</td>
    <td>0.07</td>
    <td>0.03</td>
    <td>0.15</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11560.90</td>
    <td>42340.71</td>
    <td>5.58</td>
    <td>0.05</td>
    <td>0.08</td>
    <td>0.04</td>
    <td>0.18</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11560.90</td>
    <td>42340.71</td>
    <td>6.68</td>
    <td>0.06</td>
    <td>0.10</td>
    <td>0.05</td>
    <td>0.22</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11560.90</td>
    <td>42340.71</td>
    <td>8.33</td>
    <td>0.07</td>
    <td>0.13</td>
    <td>0.06</td>
    <td>0.28</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>11541.28</td>
    <td>42218.97</td>
    <td>2.83</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11541.28</td>
    <td>42218.97</td>
    <td>3.94</td>
    <td>0.03</td>
    <td>0.05</td>
    <td>0.02</td>
    <td>0.13</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11541.28</td>
    <td>42218.97</td>
    <td>4.56</td>
    <td>0.03</td>
    <td>0.06</td>
    <td>0.03</td>
    <td>0.15</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11541.28</td>
    <td>42218.97</td>
    <td>5.65</td>
    <td>0.04</td>
    <td>0.07</td>
    <td>0.04</td>
    <td>0.19</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11541.28</td>
    <td>42218.97</td>
    <td>6.76</td>
    <td>0.05</td>
    <td>0.08</td>
    <td>0.04</td>
    <td>0.23</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11541.28</td>
    <td>42218.97</td>
    <td>8.43</td>
    <td>0.06</td>
    <td>0.11</td>
    <td>0.05</td>
    <td>0.29</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>11455.29</td>
    <td>41702.48</td>
    <td>2.86</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11455.29</td>
    <td>41702.48</td>
    <td>3.99</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.12</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11455.29</td>
    <td>41702.48</td>
    <td>4.61</td>
    <td>0.03</td>
    <td>0.05</td>
    <td>0.03</td>
    <td>0.14</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11455.29</td>
    <td>41702.48</td>
    <td>5.71</td>
    <td>0.04</td>
    <td>0.06</td>
    <td>0.03</td>
    <td>0.17</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11455.29</td>
    <td>41702.48</td>
    <td>6.83</td>
    <td>0.04</td>
    <td>0.08</td>
    <td>0.04</td>
    <td>0.21</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11455.29</td>
    <td>41702.48</td>
    <td>8.53</td>
    <td>0.05</td>
    <td>0.10</td>
    <td>0.05</td>
    <td>0.26</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>11351.75</td>
    <td>40978.71</td>
    <td>2.87</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11351.75</td>
    <td>40978.71</td>
    <td>4.01</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11351.75</td>
    <td>40978.71</td>
    <td>4.63</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.12</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
</table>
<table>
  <tr>
    <td>11351.75</td>
    <td>40978.71</td>
    <td>5.74</td>
    <td>0.03</td>
    <td>0.06</td>
    <td>0.03</td>
    <td>0.15</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11351.75</td>
    <td>40978.71</td>
    <td>6.87</td>
    <td>0.04</td>
    <td>0.07</td>
    <td>0.03</td>
    <td>0.18</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11351.75</td>
    <td>40978.71</td>
    <td>8.58</td>
    <td>0.05</td>
    <td>0.09</td>
    <td>0.04</td>
    <td>0.23</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>11253.16</td>
    <td>40318.81</td>
    <td>2.87</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11253.16</td>
    <td>40318.81</td>
    <td>4.00</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11253.16</td>
    <td>40318.81</td>
    <td>4.62</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11253.16</td>
    <td>40318.81</td>
    <td>5.73</td>
    <td>0.03</td>
    <td>0.05</td>
    <td>0.02</td>
    <td>0.13</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11253.16</td>
    <td>40318.81</td>
    <td>6.86</td>
    <td>0.03</td>
    <td>0.06</td>
    <td>0.03</td>
    <td>0.16</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11253.16</td>
    <td>40318.81</td>
    <td>8.56</td>
    <td>0.04</td>
    <td>0.07</td>
    <td>0.04</td>
    <td>0.20</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>11154.80</td>
    <td>39684.72</td>
    <td>2.83</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11154.80</td>
    <td>39684.72</td>
    <td>4.04</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11154.80</td>
    <td>39684.72</td>
    <td>4.57</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11154.80</td>
    <td>39684.72</td>
    <td>5.66</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11154.80</td>
    <td>39684.72</td>
    <td>6.77</td>
    <td>0.03</td>
    <td>0.05</td>
    <td>0.02</td>
    <td>0.13</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11154.80</td>
    <td>39684.72</td>
    <td>8.46</td>
    <td>0.03</td>
    <td>0.06</td>
    <td>0.03</td>
    <td>0.17</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>11079.78</td>
    <td>39173.00</td>
    <td>2.89</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11079.78</td>
    <td>39173.00</td>
    <td>4.11</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11079.78</td>
    <td>39173.00</td>
    <td>5.77</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.10</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11079.78</td>
    <td>39173.00</td>
    <td>6.91</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.12</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11079.78</td>
    <td>39173.00</td>
    <td>8.62</td>
    <td>0.03</td>
    <td>0.05</td>
    <td>0.03</td>
    <td>0.15</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>11001.83</td>
    <td>38634.00</td>
    <td>2.90</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>11001.83</td>
    <td>38634.00</td>
    <td>4.12</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11001.83</td>
    <td>38634.00</td>
    <td>4.67</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>11001.83</td>
    <td>38634.00</td>
    <td>5.79</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11001.83</td>
    <td>38634.00</td>
    <td>6.93</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.10</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>11001.83</td>
    <td>38634.00</td>
    <td>8.65</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.12</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>10961.33</td>
    <td>38334.25</td>
    <td>2.92</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>10961.33</td>
    <td>38334.25</td>
    <td>4.16</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>10961.33</td>
    <td>38334.25</td>
    <td>4.70</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.02</td>
  </tr>
  <tr>
    <td>10961.33</td>
    <td>38334.25</td>
    <td>5.84</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>10961.33</td>
    <td>38334.25</td>
    <td>6.99</td>
    <td>0.02</td>
    <td>0.03</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.03</td>
  </tr>
  <tr>
    <td>10961.33</td>
    <td>38334.25</td>
    <td>8.73</td>
    <td>0.02</td>
    <td>0.04</td>
    <td>0.02</td>
    <td>0.11</td>
    <td>0.00</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>1805.23</td>
    <td>50142.63</td>
    <td>2.86</td>
    <td>0.20</td>
    <td>0.17</td>
    <td>0.19</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1805.23</td>
    <td>50142.63</td>
    <td>3.98</td>
    <td>0.29</td>
    <td>0.24</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1805.23</td>
    <td>50142.63</td>
    <td>4.60</td>
    <td>0.33</td>
    <td>0.28</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>1805.23</td>
    <td>50142.63</td>
    <td>5.70</td>
    <td>0.41</td>
    <td>0.34</td>
    <td>0.38</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1805.23</td>
    <td>50142.63</td>
    <td>6.81</td>
    <td>0.49</td>
    <td>0.41</td>
    <td>0.45</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1805.23</td>
    <td>50142.63</td>
    <td>8.49</td>
    <td>0.62</td>
    <td>0.52</td>
    <td>0.57</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>2185.26</td>
    <td>50060.27</td>
    <td>2.81</td>
    <td>0.20</td>
    <td>0.17</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>2185.26</td>
    <td>50060.27</td>
    <td>3.92</td>
    <td>0.28</td>
    <td>0.24</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>2185.26</td>
    <td>50060.27</td>
    <td>4.48</td>
    <td>0.32</td>
    <td>0.28</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>2185.26</td>
    <td>50060.27</td>
    <td>5.61</td>
    <td>0.40</td>
    <td>0.35</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>2185.26</td>
    <td>50060.27</td>
    <td>6.71</td>
    <td>0.48</td>
    <td>0.42</td>
    <td>0.44</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>2185.26</td>
    <td>50060.27</td>
    <td>8.36</td>
    <td>0.60</td>
    <td>0.53</td>
    <td>0.55</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
</table>
<table>
  <tr>
    <td>3200.57</td>
    <td>49840.23</td>
    <td>2.82</td>
    <td>0.19</td>
    <td>0.19</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>3200.57</td>
    <td>49840.23</td>
    <td>3.94</td>
    <td>0.27</td>
    <td>0.27</td>
    <td>0.25</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>3200.57</td>
    <td>49840.23</td>
    <td>4.49</td>
    <td>0.31</td>
    <td>0.31</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>3200.57</td>
    <td>49840.23</td>
    <td>5.63</td>
    <td>0.39</td>
    <td>0.39</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>3200.57</td>
    <td>49840.23</td>
    <td>6.73</td>
    <td>0.47</td>
    <td>0.47</td>
    <td>0.43</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>3200.57</td>
    <td>49840.23</td>
    <td>8.38</td>
    <td>0.59</td>
    <td>0.59</td>
    <td>0.54</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>3888.19</td>
    <td>49613.99</td>
    <td>2.80</td>
    <td>0.19</td>
    <td>0.21</td>
    <td>0.17</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>3888.19</td>
    <td>49613.99</td>
    <td>3.90</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.24</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>3888.19</td>
    <td>49613.99</td>
    <td>4.45</td>
    <td>0.30</td>
    <td>0.33</td>
    <td>0.28</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>3888.19</td>
    <td>49613.99</td>
    <td>5.58</td>
    <td>0.38</td>
    <td>0.41</td>
    <td>0.35</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>3888.19</td>
    <td>49613.99</td>
    <td>6.66</td>
    <td>0.46</td>
    <td>0.50</td>
    <td>0.42</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>3888.19</td>
    <td>49613.99</td>
    <td>8.30</td>
    <td>0.57</td>
    <td>0.63</td>
    <td>0.53</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>4235.08</td>
    <td>49497.69</td>
    <td>2.79</td>
    <td>0.19</td>
    <td>0.21</td>
    <td>0.17</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>4235.08</td>
    <td>49497.69</td>
    <td>3.89</td>
    <td>0.26</td>
    <td>0.30</td>
    <td>0.24</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>4235.08</td>
    <td>49497.69</td>
    <td>4.44</td>
    <td>0.30</td>
    <td>0.34</td>
    <td>0.27</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>4235.08</td>
    <td>49497.69</td>
    <td>5.56</td>
    <td>0.37</td>
    <td>0.43</td>
    <td>0.34</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>4235.08</td>
    <td>49497.69</td>
    <td>6.65</td>
    <td>0.45</td>
    <td>0.52</td>
    <td>0.41</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>4235.08</td>
    <td>49497.69</td>
    <td>8.28</td>
    <td>0.56</td>
    <td>0.65</td>
    <td>0.52</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>6493.04</td>
    <td>49312.90</td>
    <td>2.81</td>
    <td>0.18</td>
    <td>0.26</td>
    <td>0.16</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>6493.04</td>
    <td>49312.90</td>
    <td>3.92</td>
    <td>0.25</td>
    <td>0.36</td>
    <td>0.23</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>6493.04</td>
    <td>49312.90</td>
    <td>4.47</td>
    <td>0.28</td>
    <td>0.41</td>
    <td>0.26</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>6493.04</td>
    <td>49312.90</td>
    <td>5.60</td>
    <td>0.36</td>
    <td>0.52</td>
    <td>0.33</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>6493.04</td>
    <td>49312.90</td>
    <td>6.69</td>
    <td>0.43</td>
    <td>0.62</td>
    <td>0.39</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>6493.04</td>
    <td>49312.90</td>
    <td>8.33</td>
    <td>0.54</td>
    <td>0.78</td>
    <td>0.49</td>
    <td>0.08</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7348.59</td>
    <td>48595.37</td>
    <td>2.76</td>
    <td>0.17</td>
    <td>0.27</td>
    <td>0.15</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>7348.59</td>
    <td>48595.37</td>
    <td>3.84</td>
    <td>0.24</td>
    <td>0.38</td>
    <td>0.22</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7348.59</td>
    <td>48595.37</td>
    <td>4.39</td>
    <td>0.27</td>
    <td>0.44</td>
    <td>0.25</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7348.59</td>
    <td>48595.37</td>
    <td>5.50</td>
    <td>0.34</td>
    <td>0.55</td>
    <td>0.31</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7348.59</td>
    <td>48595.37</td>
    <td>6.56</td>
    <td>0.41</td>
    <td>0.67</td>
    <td>0.37</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7348.59</td>
    <td>48595.37</td>
    <td>8.17</td>
    <td>0.51</td>
    <td>0.84</td>
    <td>0.47</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7277.86</td>
    <td>48188.24</td>
    <td>2.77</td>
    <td>0.17</td>
    <td>0.28</td>
    <td>0.15</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>7277.86</td>
    <td>48188.24</td>
    <td>3.85</td>
    <td>0.23</td>
    <td>0.39</td>
    <td>0.21</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7277.86</td>
    <td>48188.24</td>
    <td>4.40</td>
    <td>0.27</td>
    <td>0.45</td>
    <td>0.25</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7277.86</td>
    <td>48188.24</td>
    <td>5.50</td>
    <td>0.34</td>
    <td>0.57</td>
    <td>0.31</td>
    <td>0.06</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7277.86</td>
    <td>48188.24</td>
    <td>6.57</td>
    <td>0.40</td>
    <td>0.68</td>
    <td>0.37</td>
    <td>0.07</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>7277.86</td>
    <td>48188.24</td>
    <td>8.19</td>
    <td>0.51</td>
    <td>0.86</td>
    <td>0.46</td>
    <td>0.09</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>1389.00</td>
    <td>43103.28</td>
    <td>2.78</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.05</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>1389.00</td>
    <td>43103.28</td>
    <td>3.97</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.14</td>
  </tr>
  <tr>
    <td>1389.00</td>
    <td>43103.28</td>
    <td>4.49</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.09</td>
    <td>0.16</td>
  </tr>
  <tr>
    <td>1389.00</td>
    <td>43103.28</td>
    <td>5.55</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.11</td>
    <td>0.19</td>
  </tr>
  <tr>
    <td>1389.00</td>
    <td>43103.28</td>
    <td>6.64</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.13</td>
    <td>0.23</td>
  </tr>
  <tr>
    <td>1389.00</td>
    <td>43103.28</td>
    <td>8.29</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.16</td>
    <td>0.29</td>
  </tr>
  <tr>
    <td>1995.35</td>
    <td>42765.86</td>
    <td>2.69</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.05</td>
    <td>0.12</td>
  </tr>
  <tr>
    <td>1995.35</td>
    <td>42765.86</td>
    <td>3.74</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.16</td>
  </tr>
</table>
<table>
  <tr>
    <td>1995.35</td>
    <td>42765.86</td>
    <td>4.34</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.19</td>
  </tr>
  <tr>
    <td>1995.35</td>
    <td>42765.86</td>
    <td>5.36</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.10</td>
    <td>0.24</td>
  </tr>
  <tr>
    <td>1995.35</td>
    <td>42765.86</td>
    <td>6.40</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.12</td>
    <td>0.28</td>
  </tr>
  <tr>
    <td>1995.35</td>
    <td>42765.86</td>
    <td>7.98</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.15</td>
    <td>0.36</td>
  </tr>
  <tr>
    <td>650.44</td>
    <td>43541.65</td>
    <td>2.84</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.06</td>
    <td>0.07</td>
  </tr>
  <tr>
    <td>650.44</td>
    <td>43541.65</td>
    <td>4.06</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.08</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>650.44</td>
    <td>43541.65</td>
    <td>4.58</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.09</td>
    <td>0.11</td>
  </tr>
  <tr>
    <td>650.44</td>
    <td>43541.65</td>
    <td>5.68</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.11</td>
    <td>0.13</td>
  </tr>
  <tr>
    <td>650.44</td>
    <td>43541.65</td>
    <td>6.80</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.14</td>
    <td>0.16</td>
  </tr>
  <tr>
    <td>650.44</td>
    <td>43541.65</td>
    <td>8.48</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.17</td>
    <td>0.20</td>
  </tr>
  <tr>
    <td>906.15</td>
    <td>43380.01</td>
    <td>2.82</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.06</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>906.15</td>
    <td>43380.01</td>
    <td>4.02</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.08</td>
    <td>0.11</td>
  </tr>
  <tr>
    <td>906.15</td>
    <td>43380.01</td>
    <td>4.55</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.09</td>
    <td>0.13</td>
  </tr>
  <tr>
    <td>906.15</td>
    <td>43380.01</td>
    <td>5.63</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.11</td>
    <td>0.16</td>
  </tr>
  <tr>
    <td>906.15</td>
    <td>43380.01</td>
    <td>6.74</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.13</td>
    <td>0.19</td>
  </tr>
  <tr>
    <td>906.15</td>
    <td>43380.01</td>
    <td>8.40</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.17</td>
    <td>0.23</td>
  </tr>
  <tr>
    <td>1169.49</td>
    <td>43219.70</td>
    <td>2.81</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.06</td>
    <td>0.09</td>
  </tr>
  <tr>
    <td>1169.49</td>
    <td>43219.70</td>
    <td>4.01</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.08</td>
    <td>0.13</td>
  </tr>
  <tr>
    <td>1169.49</td>
    <td>43219.70</td>
    <td>4.53</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.09</td>
    <td>0.14</td>
  </tr>
  <tr>
    <td>1169.49</td>
    <td>43219.70</td>
    <td>5.60</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.11</td>
    <td>0.18</td>
  </tr>
  <tr>
    <td>1169.49</td>
    <td>43219.70</td>
    <td>6.70</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.13</td>
    <td>0.21</td>
  </tr>
  <tr>
    <td>1169.49</td>
    <td>43219.70</td>
    <td>8.36</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.17</td>
    <td>0.27</td>
  </tr>
  <tr>
    <td>232.63</td>
    <td>43801.12</td>
    <td>2.86</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.06</td>
    <td>0.05</td>
  </tr>
  <tr>
    <td>232.63</td>
    <td>43801.12</td>
    <td>4.08</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.08</td>
    <td>0.07</td>
  </tr>
  <tr>
    <td>232.63</td>
    <td>43801.12</td>
    <td>4.61</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.10</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>232.63</td>
    <td>43801.12</td>
    <td>5.71</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.12</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>232.63</td>
    <td>43801.12</td>
    <td>6.84</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.14</td>
    <td>0.12</td>
  </tr>
  <tr>
    <td>232.63</td>
    <td>43801.12</td>
    <td>8.54</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.18</td>
    <td>0.15</td>
  </tr>
  <tr>
    <td>5617.41</td>
    <td>40899.73</td>
    <td>2.44</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.24</td>
  </tr>
  <tr>
    <td>5617.41</td>
    <td>40899.73</td>
    <td>3.39</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.33</td>
  </tr>
  <tr>
    <td>5617.41</td>
    <td>40899.73</td>
    <td>3.86</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.37</td>
  </tr>
  <tr>
    <td>5617.41</td>
    <td>40899.73</td>
    <td>4.82</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.07</td>
    <td>0.47</td>
  </tr>
  <tr>
    <td>5617.41</td>
    <td>40899.73</td>
    <td>5.78</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.56</td>
  </tr>
  <tr>
    <td>5617.41</td>
    <td>40899.73</td>
    <td>7.19</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.11</td>
    <td>0.70</td>
  </tr>
  <tr>
    <td>4206.46</td>
    <td>41569.60</td>
    <td>2.54</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.19</td>
  </tr>
  <tr>
    <td>4206.46</td>
    <td>41569.60</td>
    <td>3.53</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.27</td>
  </tr>
  <tr>
    <td>4206.46</td>
    <td>41569.60</td>
    <td>4.03</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.31</td>
  </tr>
  <tr>
    <td>4206.46</td>
    <td>41569.60</td>
    <td>5.06</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.39</td>
  </tr>
  <tr>
    <td>4206.46</td>
    <td>41569.60</td>
    <td>6.04</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.10</td>
    <td>0.46</td>
  </tr>
  <tr>
    <td>4206.46</td>
    <td>41569.60</td>
    <td>7.51</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.13</td>
    <td>0.58</td>
  </tr>
  <tr>
    <td>5215.37</td>
    <td>41067.90</td>
    <td>2.48</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.23</td>
  </tr>
  <tr>
    <td>5215.37</td>
    <td>41067.90</td>
    <td>3.44</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.31</td>
  </tr>
  <tr>
    <td>5215.37</td>
    <td>41067.90</td>
    <td>3.92</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.36</td>
  </tr>
  <tr>
    <td>5215.37</td>
    <td>41067.90</td>
    <td>4.93</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.08</td>
    <td>0.45</td>
  </tr>
</table>
<table>
  <tr>
    <td>5215.37</td>
    <td>41067.90</td>
    <td>5.87</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.09</td>
    <td>0.54</td>
  </tr>
  <tr>
    <td>5215.37</td>
    <td>41067.90</td>
    <td>7.30</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.12</td>
    <td>0.67</td>
  </tr>
  <tr>
    <td>23.12</td>
    <td>51212.77</td>
    <td>2.75</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>23.12</td>
    <td>51212.77</td>
    <td>3.82</td>
    <td>0.33</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>23.12</td>
    <td>51212.77</td>
    <td>4.36</td>
    <td>0.38</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>23.12</td>
    <td>51212.77</td>
    <td>5.46</td>
    <td>0.47</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>23.12</td>
    <td>51212.77</td>
    <td>6.51</td>
    <td>0.56</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>23.12</td>
    <td>51212.77</td>
    <td>8.09</td>
    <td>0.70</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>131.71</td>
    <td>51138.31</td>
    <td>2.75</td>
    <td>0.24</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>131.71</td>
    <td>51138.31</td>
    <td>3.82</td>
    <td>0.33</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>131.71</td>
    <td>51138.31</td>
    <td>4.36</td>
    <td>0.37</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>131.71</td>
    <td>51138.31</td>
    <td>5.46</td>
    <td>0.47</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>131.71</td>
    <td>51138.31</td>
    <td>6.51</td>
    <td>0.56</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>131.71</td>
    <td>51138.31</td>
    <td>8.10</td>
    <td>0.70</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>151.47</td>
    <td>51099.18</td>
    <td>2.74</td>
    <td>0.23</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>151.47</td>
    <td>51099.18</td>
    <td>3.82</td>
    <td>0.33</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>151.47</td>
    <td>51099.18</td>
    <td>4.36</td>
    <td>0.37</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>151.47</td>
    <td>51099.18</td>
    <td>5.45</td>
    <td>0.47</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>151.47</td>
    <td>51099.18</td>
    <td>6.51</td>
    <td>0.56</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>151.47</td>
    <td>51099.18</td>
    <td>8.09</td>
    <td>0.70</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>146.08</td>
    <td>51045.68</td>
    <td>2.75</td>
    <td>0.23</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>146.08</td>
    <td>51045.68</td>
    <td>3.82</td>
    <td>0.33</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>146.08</td>
    <td>51045.68</td>
    <td>4.36</td>
    <td>0.37</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>146.08</td>
    <td>51045.68</td>
    <td>5.46</td>
    <td>0.47</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>146.08</td>
    <td>51045.68</td>
    <td>6.52</td>
    <td>0.56</td>
    <td>0.39</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>146.08</td>
    <td>51045.68</td>
    <td>8.11</td>
    <td>0.70</td>
    <td>0.49</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>4684.56</td>
    <td>41312.00</td>
    <td>2.50</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.21</td>
  </tr>
  <tr>
    <td>4684.56</td>
    <td>41312.00</td>
    <td>3.48</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.29</td>
  </tr>
  <tr>
    <td>4684.56</td>
    <td>41312.00</td>
    <td>3.97</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.33</td>
  </tr>
  <tr>
    <td>4684.56</td>
    <td>41312.00</td>
    <td>4.98</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.08</td>
    <td>0.42</td>
  </tr>
  <tr>
    <td>4684.56</td>
    <td>41312.00</td>
    <td>5.94</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.10</td>
    <td>0.50</td>
  </tr>
  <tr>
    <td>4684.56</td>
    <td>41312.00</td>
    <td>7.39</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.12</td>
    <td>0.62</td>
  </tr>
  <tr>
    <td>791.05</td>
    <td>50919.53</td>
    <td>2.76</td>
    <td>0.23</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>791.05</td>
    <td>50919.53</td>
    <td>3.84</td>
    <td>0.31</td>
    <td>0.23</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>791.05</td>
    <td>50919.53</td>
    <td>4.39</td>
    <td>0.36</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>791.05</td>
    <td>50919.53</td>
    <td>5.49</td>
    <td>0.45</td>
    <td>0.32</td>
    <td>0.36</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>791.05</td>
    <td>50919.53</td>
    <td>6.56</td>
    <td>0.54</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>791.05</td>
    <td>50919.53</td>
    <td>8.16</td>
    <td>0.68</td>
    <td>0.49</td>
    <td>0.55</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>3886.51</td>
    <td>41742.70</td>
    <td>2.59</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.19</td>
  </tr>
  <tr>
    <td>3886.51</td>
    <td>41742.70</td>
    <td>3.60</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.26</td>
  </tr>
  <tr>
    <td>3886.51</td>
    <td>41742.70</td>
    <td>4.11</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.30</td>
  </tr>
  <tr>
    <td>3886.51</td>
    <td>41742.70</td>
    <td>5.16</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.09</td>
    <td>0.37</td>
  </tr>
  <tr>
    <td>3886.51</td>
    <td>41742.70</td>
    <td>6.16</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.11</td>
    <td>0.44</td>
  </tr>
  <tr>
    <td>3886.51</td>
    <td>41742.70</td>
    <td>7.67</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.13</td>
    <td>0.56</td>
  </tr>
</table>
<table>
  <tr>
    <td>3589.55</td>
    <td>41903.40</td>
    <td>2.62</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.18</td>
  </tr>
  <tr>
    <td>3589.55</td>
    <td>41903.40</td>
    <td>3.64</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.25</td>
  </tr>
  <tr>
    <td>3589.55</td>
    <td>41903.40</td>
    <td>4.15</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.28</td>
  </tr>
  <tr>
    <td>3589.55</td>
    <td>41903.40</td>
    <td>5.21</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.09</td>
    <td>0.35</td>
  </tr>
  <tr>
    <td>3589.55</td>
    <td>41903.40</td>
    <td>6.22</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.11</td>
    <td>0.42</td>
  </tr>
  <tr>
    <td>3589.55</td>
    <td>41903.40</td>
    <td>7.74</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.13</td>
    <td>0.53</td>
  </tr>
  <tr>
    <td>-491.80</td>
    <td>51048.23</td>
    <td>2.80</td>
    <td>0.22</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-491.80</td>
    <td>51048.23</td>
    <td>3.90</td>
    <td>0.31</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-491.80</td>
    <td>51048.23</td>
    <td>4.46</td>
    <td>0.36</td>
    <td>0.26</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-491.80</td>
    <td>51048.23</td>
    <td>5.58</td>
    <td>0.45</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-491.80</td>
    <td>51048.23</td>
    <td>6.67</td>
    <td>0.54</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-491.80</td>
    <td>51048.23</td>
    <td>8.30</td>
    <td>0.68</td>
    <td>0.49</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-384.25</td>
    <td>51026.92</td>
    <td>2.80</td>
    <td>0.22</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-384.25</td>
    <td>51026.92</td>
    <td>3.90</td>
    <td>0.31</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-384.25</td>
    <td>51026.92</td>
    <td>4.46</td>
    <td>0.36</td>
    <td>0.26</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-384.25</td>
    <td>51026.92</td>
    <td>5.58</td>
    <td>0.45</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-384.25</td>
    <td>51026.92</td>
    <td>6.67</td>
    <td>0.54</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-384.25</td>
    <td>51026.92</td>
    <td>8.30</td>
    <td>0.68</td>
    <td>0.49</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>33.35</td>
    <td>50979.21</td>
    <td>2.80</td>
    <td>0.22</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>33.35</td>
    <td>50979.21</td>
    <td>3.89</td>
    <td>0.31</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>33.35</td>
    <td>50979.21</td>
    <td>4.45</td>
    <td>0.36</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>33.35</td>
    <td>50979.21</td>
    <td>5.57</td>
    <td>0.45</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>33.35</td>
    <td>50979.21</td>
    <td>6.65</td>
    <td>0.54</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>33.35</td>
    <td>50979.21</td>
    <td>8.28</td>
    <td>0.68</td>
    <td>0.49</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>413.02</td>
    <td>50915.33</td>
    <td>2.80</td>
    <td>0.22</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>413.02</td>
    <td>50915.33</td>
    <td>3.91</td>
    <td>0.31</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>413.02</td>
    <td>50915.33</td>
    <td>4.46</td>
    <td>0.36</td>
    <td>0.26</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>413.02</td>
    <td>50915.33</td>
    <td>5.58</td>
    <td>0.45</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>413.02</td>
    <td>50915.33</td>
    <td>6.67</td>
    <td>0.54</td>
    <td>0.40</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>413.02</td>
    <td>50915.33</td>
    <td>8.31</td>
    <td>0.68</td>
    <td>0.50</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>701.67</td>
    <td>50855.45</td>
    <td>2.80</td>
    <td>0.22</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>701.67</td>
    <td>50855.45</td>
    <td>3.89</td>
    <td>0.31</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>701.67</td>
    <td>50855.45</td>
    <td>4.45</td>
    <td>0.36</td>
    <td>0.26</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>701.67</td>
    <td>50855.45</td>
    <td>5.57</td>
    <td>0.45</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>701.67</td>
    <td>50855.45</td>
    <td>6.65</td>
    <td>0.54</td>
    <td>0.39</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>701.67</td>
    <td>50855.45</td>
    <td>8.29</td>
    <td>0.68</td>
    <td>0.50</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-402.94</td>
    <td>50058.40</td>
    <td>2.75</td>
    <td>0.21</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-402.94</td>
    <td>50058.40</td>
    <td>3.83</td>
    <td>0.29</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-402.94</td>
    <td>50058.40</td>
    <td>4.37</td>
    <td>0.33</td>
    <td>0.25</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-402.94</td>
    <td>50058.40</td>
    <td>5.48</td>
    <td>0.42</td>
    <td>0.31</td>
    <td>0.37</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-402.94</td>
    <td>50058.40</td>
    <td>6.54</td>
    <td>0.50</td>
    <td>0.38</td>
    <td>0.44</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-402.94</td>
    <td>50058.40</td>
    <td>8.14</td>
    <td>0.63</td>
    <td>0.47</td>
    <td>0.55</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-557.25</td>
    <td>50240.06</td>
    <td>2.70</td>
    <td>0.21</td>
    <td>0.15</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-557.25</td>
    <td>50240.06</td>
    <td>3.76</td>
    <td>0.29</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
</table>
<table>
  <tr>
    <td>-557.25</td>
    <td>50240.06</td>
    <td>4.29</td>
    <td>0.33</td>
    <td>0.25</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-557.25</td>
    <td>50240.06</td>
    <td>5.38</td>
    <td>0.42</td>
    <td>0.31</td>
    <td>0.36</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-557.25</td>
    <td>50240.06</td>
    <td>6.42</td>
    <td>0.50</td>
    <td>0.37</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-557.25</td>
    <td>50240.06</td>
    <td>7.99</td>
    <td>0.63</td>
    <td>0.47</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>414.27</td>
    <td>43688.32</td>
    <td>2.88</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.06</td>
    <td>0.06</td>
  </tr>
  <tr>
    <td>414.27</td>
    <td>43688.32</td>
    <td>4.10</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.08</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>414.27</td>
    <td>43688.32</td>
    <td>5.75</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.12</td>
    <td>0.11</td>
  </tr>
  <tr>
    <td>414.27</td>
    <td>43688.32</td>
    <td>6.88</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.14</td>
    <td>0.14</td>
  </tr>
  <tr>
    <td>414.27</td>
    <td>43688.32</td>
    <td>8.59</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.18</td>
    <td>0.17</td>
  </tr>
  <tr>
    <td>2859.78</td>
    <td>42307.30</td>
    <td>2.67</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.15</td>
  </tr>
  <tr>
    <td>2859.78</td>
    <td>42307.30</td>
    <td>3.71</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.21</td>
  </tr>
  <tr>
    <td>2859.78</td>
    <td>42307.30</td>
    <td>4.24</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.08</td>
    <td>0.24</td>
  </tr>
  <tr>
    <td>2859.78</td>
    <td>42307.30</td>
    <td>5.32</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.10</td>
    <td>0.30</td>
  </tr>
  <tr>
    <td>2859.78</td>
    <td>42307.30</td>
    <td>6.35</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.11</td>
    <td>0.36</td>
  </tr>
  <tr>
    <td>2859.78</td>
    <td>42307.30</td>
    <td>7.91</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.14</td>
    <td>0.45</td>
  </tr>
  <tr>
    <td>-693.01</td>
    <td>50387.99</td>
    <td>2.71</td>
    <td>0.21</td>
    <td>0.16</td>
    <td>0.18</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-693.01</td>
    <td>50387.99</td>
    <td>3.78</td>
    <td>0.29</td>
    <td>0.22</td>
    <td>0.25</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-693.01</td>
    <td>50387.99</td>
    <td>4.31</td>
    <td>0.34</td>
    <td>0.25</td>
    <td>0.29</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-693.01</td>
    <td>50387.99</td>
    <td>5.40</td>
    <td>0.42</td>
    <td>0.31</td>
    <td>0.36</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-693.01</td>
    <td>50387.99</td>
    <td>6.45</td>
    <td>0.51</td>
    <td>0.38</td>
    <td>0.43</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-693.01</td>
    <td>50387.99</td>
    <td>8.03</td>
    <td>0.64</td>
    <td>0.47</td>
    <td>0.54</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>3318.39</td>
    <td>42050.10</td>
    <td>2.61</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.05</td>
    <td>0.17</td>
  </tr>
  <tr>
    <td>3318.39</td>
    <td>42050.10</td>
    <td>3.63</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.06</td>
    <td>0.23</td>
  </tr>
  <tr>
    <td>3318.39</td>
    <td>42050.10</td>
    <td>4.15</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.07</td>
    <td>0.26</td>
  </tr>
  <tr>
    <td>3318.39</td>
    <td>42050.10</td>
    <td>5.20</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.09</td>
    <td>0.33</td>
  </tr>
  <tr>
    <td>3318.39</td>
    <td>42050.10</td>
    <td>6.21</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.11</td>
    <td>0.40</td>
  </tr>
  <tr>
    <td>3318.39</td>
    <td>42050.10</td>
    <td>7.73</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.14</td>
    <td>0.50</td>
  </tr>
  <tr>
    <td>-0.17</td>
    <td>43953.22</td>
    <td>3.13</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.06</td>
    <td>0.04</td>
  </tr>
  <tr>
    <td>-0.17</td>
    <td>43953.22</td>
    <td>4.15</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.09</td>
    <td>0.06</td>
  </tr>
  <tr>
    <td>-0.17</td>
    <td>43953.22</td>
    <td>4.69</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.10</td>
    <td>0.06</td>
  </tr>
  <tr>
    <td>-0.17</td>
    <td>43953.22</td>
    <td>5.82</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.12</td>
    <td>0.08</td>
  </tr>
  <tr>
    <td>-0.17</td>
    <td>43953.22</td>
    <td>6.97</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.15</td>
    <td>0.10</td>
  </tr>
  <tr>
    <td>-0.17</td>
    <td>43953.22</td>
    <td>8.70</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.00</td>
    <td>0.18</td>
    <td>0.12</td>
  </tr>
  <tr>
    <td>-546.49</td>
    <td>50703.39</td>
    <td>2.81</td>
    <td>0.22</td>
    <td>0.16</td>
    <td>0.19</td>
    <td>0.02</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-546.49</td>
    <td>50703.39</td>
    <td>3.92</td>
    <td>0.31</td>
    <td>0.23</td>
    <td>0.26</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-546.49</td>
    <td>50703.39</td>
    <td>4.47</td>
    <td>0.36</td>
    <td>0.26</td>
    <td>0.30</td>
    <td>0.03</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-546.49</td>
    <td>50703.39</td>
    <td>5.60</td>
    <td>0.45</td>
    <td>0.33</td>
    <td>0.37</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.00</td>
  </tr>
  <tr>
    <td>-546.49</td>
    <td>50703.39</td>
    <td>6.69</td>
    <td>0.54</td>
    <td>0.39</td>
    <td>0.45</td>
    <td>0.04</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>-546.49</td>
    <td>50703.39</td>
    <td>8.34</td>
    <td>0.67</td>
    <td>0.49</td>
    <td>0.56</td>
    <td>0.05</td>
    <td>0.00</td>
    <td>0.01</td>
  </tr>
  <tr>
    <td>5466.96</td>
    <td>40441.00</td>
    <td>2.49</td>
    <td>0.00</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.02</td>
    <td>0.02</td>
    <td>0.19</td>
  </tr>
  <tr>
    <td>5466.96</td>
    <td>40441.00</td>
    <td>3.46</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.00</td>
    <td>0.03</td>
    <td>0.03</td>
    <td>0.26</td>
  </tr>
  <tr>
    <td>5466.96</td>
    <td>40441.00</td>
    <td>3.95</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.03</td>
    <td>0.03</td>
    <td>0.30</td>
  </tr>
  <tr>
    <td>5466.96</td>
    <td>40441.00</td>
    <td>4.92</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.04</td>
    <td>0.38</td>
  </tr>
  <tr>
    <td>5466.96</td>
    <td>40441.00</td>
    <td>5.90</td>
    <td>0.01</td>
    <td>0.02</td>
    <td>0.01</td>
    <td>0.04</td>
    <td>0.05</td>
    <td>0.45</td>
  </tr>
</table>