esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Model-Free Adaptive Nonlinear Control and Its Automatic Application
## 3 Model-Free Adaptive Control Design with Output Error Rate (MFAC-OER)
### Mimo-Mfac-Oer  
Considering that most of the practical systems are MIMO systems, in order to improve the applicability of the method in this paper, the MFAC-OER control scheme is further extended to MIMO systems.  
Consider the following MIMO discrete-time nonlinear systems  
\[\mathbf{y}(t+1)=g\big{(}\mathbf{y}(t),\;\cdots,\;\mathbf{y}(t-n_{y}),\mathbf{u}(t),\;\cdots, \;\mathbf{u}(t-n_{u})\big{)} \tag{14}\]  
where \(\mathbf{u},\mathbf{y}\) are the input and output of the system at time \(t\), respectively; \(n_{u}\), \(n_{y}\) are the orders of input and output, respectively; and \(g(\cdot)\) is an unknown nonlinear time-varying function.  
Define \(\mathbf{H}(t)\in\mathbf{R}^{mLy+mL_{u}}\) as a matrix consisting of control inputs within the sliding time window \([t-L_{u}+1,t]\) and outputs within the sliding time window \([t-L_{y}+1,t]\), as follows:  
\[\mathbf{H}(t)\!=[\Delta y^{\mathrm{T}}(t),\cdots,\Delta y^{\mathrm{T}}(t-L_{y}+1),\Delta u^{\mathrm{T}}(t),\cdots,\Delta u^{\mathrm{T}}(t-L_{u}+1)]^{\mathrm{T}} \tag{15}\]  
where \(0\leq L_{y}\leq n_{y},1\leq L_{u}\leq n_{u}\) are the length constants of the linearization of the system output and control input, respectively, also called pseudo-orders.  
Figure 1: The improved control system block diagram.  
According to the SISO scheme (13), the FFDL-MFAC-OER scheme of MIMO is derived  
\[\left\{\begin{array}{c}\Delta\mathbf{u}(t)=\frac{\hat{\mathbf{\Phi}}_{2}^{T}(t)(\theta+ \frac{1-\theta}{T^{2}})(\mathbf{y}_{d}(t+1)-\mathbf{y}(t))-\hat{\mathbf{\Phi}}_{2}^{T}(t) \hat{\mathbf{\Phi}}_{1}(t)(\theta+\frac{1-\theta}{T^{2}})\Delta\mathbf{y}(t)-\hat{\mathbf{ \Phi}}_{2}^{T}(t)\frac{1-\theta}{T^{2}}\mathbf{e}(k)}{\lambda+(\theta+\frac{1- \theta}{T^{2}})\left\|\hat{\mathbf{\Phi}}_{2}(t)\right\|^{2}}\\ \hat{\mathbf{\Phi}}(t)=\hat{\mathbf{\Phi}}(t-1)+\frac{\beta\big{(}\Delta\mathbf{y}(t)-(t-1 )\Delta\mathbf{H}(t-1)\big{)}\Delta\mathbf{H}(t-1)^{T}}{\mu+\left\|\Delta\mathbf{H}(t-1) \right\|^{2}},\\ \hat{\mathbf{\Phi}}(t)=\hat{\mathbf{\Phi}}(1)\;\;\text{if}\;\;\left\|\hat{\mathbf{\Phi}}(t )\right\|\leq\varepsilon\;\;\text{or}\;\left\|\Delta\hat{\mathbf{H}}(t-1)\right\|^ {2}\leq\varepsilon\\ \text{or}\;\;sign\Big{(}\hat{\phi}_{L_{y}+1}(t)\Big{)}\
eq sign\Big{(}\hat{ \phi}_{L_{y}+1}(1)\Big{)}\end{array}\right. \tag{16}\]  
where, at any time \(t\), the time-varying parameter matrix \(\mathbf{\Phi}(t)=[\mathbf{\Phi}_{1}(t),\mathbf{\Phi}_{2}(t)]\) is bounded, and \(\mathbf{\Phi}_{n}(t)\in\mathbb{R}^{n\times m}\) is the corresponding submatrix, \(n=1,\cdots,L_{y}+L_{u}\) ; \(\hat{\mathbf{\Phi}}_{1}(t),\hat{\mathbf{\Phi}}_{2}(t)\) is the estimated value of \(\mathbf{\Phi}_{1}(t),\mathbf{\Phi}_{2}(t)\).
## 4 Performance Evaluation  
In this section, we evaluate the performance of our protocol MAR-RiMAC using simulations. We compare MAR-RiMAC with the original RI-MAC ([9] protocol, which exploits a traditional backoff as a collision resolution mechanism. Our experiments use the same MAC layer parameters as [9].
###### Abstract
The weak softening type curve (Figure 1a) has a low softening degree and a small difference between the peak strength and the residual strength; in contrast, the strong softening curve (Figure 1b) has a high softening degree and a large difference between the peak strength and the residual strength. By searching the keyword 'ring shear' from 2000 to 2020 in the web of science database, 32 valid papers about ring shear tests were retrieved, presenting 104 groups of soil ring shear test data. Among them, 54 groups belong to the weak softening curve, accounting for 52%; 50 groups belong to the strong softening curve, accounting for 48%. The curves of different softening types reflect the different damage evolution characteristics in the process of soil shear, which may affect the applicability of the selected damage distribution function. Therefore, more distribution functions are needed to verify the damage distribution model suitable for different slip zone soil curves. Figure 1: Two kinds of softening characteristic shear stress–shear displacement curves. (**a**) Weak softening curve; (**b**) strong softening curve; normal stress: \(\sigma_{\text{n1}}<\sigma_{\text{n2}}<\sigma_{\text{n3}}<\sigma_{\text{n4}}\). Focusing on the selection of damage distribution in constructing the shear constitutive model of slip zone soil, this study investigates the possible damage distribution functions for describing the damage evolution process of the soil. The fitting method is adopted instead of the previous method of solving model parameters through peak value. This method can better show the fitting effect of the constitutive model on the shear curve, especially in the strain softening stage. Then, the applicability of various distribution functions for two groups of shear curves in slip zone soil were analyzed with different softening degrees. Finally, the optimal damage distribution suitable for different softening types of curves was determined.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 4 Polarization-Reconfigurable Antennas  
The polarization mismatch between antennas in receivers and transmitters in communication systems creates power loss. The mismatch is because of the alteration of position and orientation of the two antennas. The polarization reconfigurability is a feature that compensates this power loss by matching the antenna's polarization [101]. Polarization reconfigurability can be achieved by changing the shape of the radiating patch [101], switching between different radiators [102] and changing the antennas shape [103; 104; 105; 106; 107].  
In Reference [101], three polarization modes are created by controlling the truncations on the patch by using four diodes at 2.4 GHz. The proposed textile-based antenna is placed on felt substrate. The performance of the antenna over a phantom is analyzed to verify its suitability for BAN applications. In another proposed textile-based substrate-integrated waveguide (SIW) circular ring-slot antenna [108], the inclination of the linear polarization is controlled in the range of angles 30\({}^{\circ}\) by changing the position of the short strip to three places on the slot ring.  
In Reference [102], by switching between four sector radiating elements located at 0\({}^{\circ}\), 360\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\), which are interconnected by pin diode switches to the center, the antenna exhibits dual-polarization with a combination of vertical and horizontal polarizations with a omnidirectional radiation pattern. The proposed antenna is placed on felt and conductive textile (E-textile) and is used for the ground plane and radiating elements. The polarization of the antenna is reconfigured between two circular states at 1.85 GHz to 2.75 GHz (unfolded, H = 106 mm) and a linear state at 1.4 GHz and 3.7 GHz (folded state, H = 53 mm) in an origami conical spiral antenna [107]. Another mechanical polarization reconfigurable antenna based on origami and skeleton scaffoldings is presented in [103; 104; 105]. The proposed bifilar segmented helical antennas reconfigure between right-hand circular polarization and left-hand circular polarization by retaining around their central axis (Figure 7) [103]. Paper with 100 \(\upmu\)m thickness is used as the origami base for this structure. In other mechanical polarization reconfigurable textile patch antennas, the conductive Velcro tape and zip fastener is used separately to switch between linear to circular polarizations at 2.3-2.5 GHz [106]. Both antennas are made of felt and conductive cloth. Two antenna's axial ratio repeatability ability to interchanging polarization modes are compared in this research. The primary advantage of this research is its ability to adjust the antenna characteristics.
## 5 Overview of Algorithm  
The combination of the aforementioned Sections 4.3-4.5 is considered as a novel solution generation mechanism for MOEA/D-MFAR. Corresponding to Figure 3, the complete algorithm flow is delineated in Algorithm 1.  
The complexity of Algorithm 1 is mainly derived from Step 3. The complexity of 3.1 is \(O(M)\), and the complexity of 3.1.1 is related to the number of UAVs \(N^{U}\) as well as the types of IoT devices \(T\), which is \(O(MN^{U})+O(MT)\). The complexity of 3.1.2 is \(O(MN^{I})\). The complexity of 3.1.3 includes the fine-tuning of UAV positions, the fine-tuning of bandwidth, as well as the crossover and mutation operations of UAV-IoT association, with the complexities of these four parts being \(O(MN^{U})+O(MT)+O(MT)+O(MN^{I})\), respectively. For 3.2, the complexity is related to the number of objectives \(Q\), which is \(O(QM)\). For 3.3, the complexity is related to the field size \(S\) and the number of objectives \(Q\), which is \(O(QSM)\). For 3.4, assuming that each individual can generate new and better solutions, the worst-case complexity is \(O(M^{2})\). Considering that \(N^{I}\) is larger than \(T\) and \(N^{U}\), the worst-case complexity for Step 3 is \(O(MNI)+O(QMS)+O(M^{2})\). Taking into account the total number of iterations \(E\), the worst-case complexity of the algorithm is \(O(EMN^{I}+EMQS+EQM^{2})\). Under normal circumstances, \(N^{I}\), \(Q\), and \(S\) are all smaller than \(M\), so the main complexity of the algorithm comes from the non-dominated sorting, which is \(O(EQM^{2})\). This is also the current state of most multi-objective optimization algorithms based on non-domination.  
```
1:(1) INPUT:
2:\(E\) : Number of iteration steps, \(M\) : population size;
3:\(S\) : The size of the neighborhoods; some scene parameters;
4:(2) INITIALIZATION:
5: (2.1) Initialize \(EP\) = \(\emptyset\), and \(PF\) = \(\emptyset\);
6: (2.2) Randomly initialize \(M\) individuals;
7: (2.3) Calculate \(FV_{1},\ldots,FV_{M}\);
8: (2.4) Initialize \(z^{*}=(z_{1},\ldots,z_{\mathbb{Q}})\);
9: (2.5) Generate \(\lambda^{1},\ldots,\lambda^{M}\), and \(\mathbf{B}(m)\);
10:(3) UPDATE:
11:for\(m=1,\ldots,M\)do
12: (3.1) Generate new solutions \(\Gamma^{{}^{\prime}}_{m}\) according to 4.3 and 4.4;
13: (3.2) Update \(z^{*}\): For each \(q_{1},\ldots,Q\), if \(fv_{q}(\Gamma^{{}^{\prime}}_{m})>z_{q_{i}},z_{q}=fv_{q}(\Gamma^{{}^{\prime}}_ {m})\);
14: (3.3) Update field solutions in \(\mathbf{B}(m)\): For each sequence number \(i\in\mathbf{B}(m)\), if \(g^{le}(\Gamma^{{}^{\prime}}_{m}|\lambda^{i},z^{*})\leq g^{le}(\Gamma_{m}| \lambda^{i},z^{*})\), then set \(\Gamma_{m}=\Gamma^{{}^{\prime}}_{m}\) and \(FV_{m}=FV_{m}^{\prime}\);
15: (3.4) Update \(EP\) and \(PF\):
16:\(EP\gets EP\cup\{\Gamma^{{}^{\prime}}_{m}\}\)\(\triangleright\) Add \(\Gamma^{{}^{\prime}}_{m}\) to \(EP\)
17: Remove dominated solutions from \(EP\)
18:\(PF\leftarrow\) Update \(PF(EP)\)\(\triangleright\) Update \(PF\) based on the new \(EP\)
19:endfor
20:UNTIL the termination condition is met.
21:OUTPUT\(EP\) the \(PF\).
```  
**Algorithm 1** The flow of MOEA/D-MFAR algorithm.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 4 Results
### Validation of the SVM Classification Model  
Peak NDVI and TIN of 83 irrigated and rainfed wheat survey samples were used as feature vectors during classification. The variables \(C\) and \(\gamma\) in SVM were estimated through a five-fold cross-validation using a grid search (Figure 5). Then, based on this pair of global optimal parameters (\(C\) = 1.4142 and \(\gamma\) = 2.8284), the SVM model was used for the classification of irrigated and rainfed wheat in south central Shanxi Province.  
Figure 4: Spatial patterns of wheat sowing (**a**) and maturity (**b**) dates in south central Shanxi Province.  
Figure 5: SVM parameter selection (**a**) and accuracy of five-fold cross-validation (**b**) for training samples. (**a**) shows the pair of global optimal parameters (\(C\) and \(\gamma\)) through the grid search; (**b**) is the visualization of classification of irrigated and rainfed wheat when the training data were mapped into a higher dimensional feature space using the RBF kernel function.  
An additional 774 testing samples (388 irrigated and 386 rainfed wheat) were randomly selected from Google-Earth and used to validate the capacity of the SVM model. The overall accuracy was 92.4% for the NDVI time series-based classification model, while the overall accuracy was 96.0% for the peak NDVI and TIN classification model (Table 4). It is inevitable to use the same start and end period for the NDVI time series in the NDVI time series-based method. Therefore, discrepancies in the growing period (sowing and maturity dates) and growing length of irrigated and rainfed wheat are greatly ignored. This may cause uncertainty in the classification results, especially in arid and semi-arid regions, such as south central Shanxi Province in this study. The peak NDVI and TIN values can reflect crop growth characteristics under different water conditions. Thus, the use of peak NDVI and TIN as feature vectors for classification has a large advantage over the NDVI time series-based method and may result in higher classification accuracy.
# Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
## 3 Methods
### Fisher's LDA  
The objective of [PERSON]'s LDA [4] is to find the feature subspace which maximizes the linear separability of a given dataset. [PERSON]'s LDA achieves the objective by minimizing the within-class variance and maximizing the between-class variance simultaneously.  
To describe the Fisher's LDA formally, let us consider an input matrix \(X\in\mathbb{R}^{m\times n}\) where \(m\) is the number of data points, \(n\) is the input dimension, and \(x_{i,j}\in\mathbb{R}^{n}\) is a \(j\)-th data point in the \(i\)-th class. The within-class scatter matrix \(S_{w}\in\mathbb{R}^{n\times n}\) is defined as follows:  
\[S_{w}=\sum_{i=1}^{c}\sum_{j=1}^{m_{i}}(x_{i,j}-\overline{x}_{i})(x_{i,j}- \overline{x}_{i})^{T}.\]  
Here, \(c\) is the number of classes, \(m_{i}\) is the number of data points in the \(i\)-th class, and \(\overline{x}_{i}\in\mathbb{R}^{n}\) is the mean of data points in the \(i\)-th class. This formulation can be interpreted as the sum of class-wise scatter matrices. A small determinant of \(S_{w}\) indicates that data points in the same class exist densely in a narrow area, which may lead to high class separability.  
Next, the between-class scatter matrix \(S_{b}\in\mathbb{R}^{n\times n}\) is defined as follows:  
\[S_{b}=\sum_{i=1}^{c}m_{i}(\overline{x}_{i}-\overline{x})(\overline{x}_{i}- \overline{x})^{T},\]  
where \(\overline{x}\) is the mean of entire data points in the given dataset. A large determinant of \(S_{b}\) indicates that the mean vector \(\overline{x}_{i}\) of each class is far from the \(\overline{x}\), another condition hinting for high class separability.  
Using these two matrices, we can describe the objective of [PERSON]'s LDA as follows:  
\[\Phi_{lda}\in\operatorname*{arg\,max}_{\Phi}\frac{|\Phi^{T}S_{b}\Phi|}{|\Phi^ {T}S_{w}\Phi|}. \tag{1}\]  
Here, \(\Phi_{lda}\in\mathbb{R}^{n\times d}\) is the projection matrix where \(d\) is the dimension of feature subspace (in general, we choose \(d\ll n\)). The column vectors of projection matrix \(\Phi_{lda}\) are the axes of feature subspace, which maximize the class separability. The term in the objective function is also known as the [PERSON]'s criterion. By projecting \(X\) onto these axes, we obtain a \(d\)-dimensional projection of the original data \(X^{\prime}\in\mathbb{R}^{m\times d}\):  
\[X^{\prime}=X\Phi_{lda}.\]  
In general, if \(S_{w}\) is an invertible matrix, we can calculate the projection matrix which maximizes the objective of the [PERSON]'s LDA by eigenvalue decomposition.
# Robust Quadratic Optimal Control for Discrete-Time Linear Systems with Non-Stochastic Noises
## 3 Robust Optimal Control Strategy
### Robust Control Strategy
\begin{array}{cc}W^{-1}(k)&0\\ 0&0\end{array} \right]\left[ \begin{array}{c}w(k)\\ \eta(k)\end{array} \right]\leq 1.\]  
Let  
\[M_{1}(k)=\left[ \begin{array}{cc}W^{-1}(k)&0\\ 0&0\end{array} \right],\]  
it yields  
\[\rho^{T}(k)M_{1}(k)\rho(k)\leq 1,\]  
namely,  
\[\left[ \begin{array}{c}1\\ \rho(k)\end{array} \right]^{T}\left[ \begin{array}{cc}1&0\\ 0&-M_{1}(k)\end{array} \right]\left[ \begin{array}{c}1\\ \rho(k)\end{array} \right]\geq 0, \tag{20}\]  
the constrain in (19)  
\[\eta^{T}(k)P^{-1}(k)\eta(k)\leq 1,\]  
can be rewritten in the following form  
\[\left[ \begin{array}{c}w(k)\\ \eta(k)\end{array} \right]^{T}\left[ \begin{array}{cc}0&0\\ 0&P^{-1}(k)\end{array} \right]\left[ \begin{array}{c}w(k)\\ \eta(k)\end{array} \right]\leq 1.\]  
Let  
\[M_{2}(k)=\left[ \begin{array}{cc}0&0\\ 0&P^{-1}(k)\end{array} \right],\]  
it yields  
\[\rho^{T}(k)M_{2}(k)\rho(k)\leq 1,\]  
namely,  
\[\left[ \begin{array}{c}1\\ \rho(k)\end{array} \right]^{T}\left[ \begin{array}{cc}1&0\\ 0&-M_{2}(k)\end{array} \right]\left[ \begin{array}{c}1\\ \rho(k)\end{array} \right]\geq 0. \tag{21}\]  
And the constrain in (19)  
\[\bar{f}(k)\leq\lambda,\]  
namely,  
\[\lambda-z^{T}z-2h^{T}\rho(k)-2z^{T}F\rho(k)-\rho^{T}(k)\widetilde{G}\rho(k) \geq 0,\]  
can be rewritten as follows  
\[\left[ \begin{array}{c}1\\ \rho(k)\end{array} \right]^{T}\left[ \begin{array}{cc}\lambda-z^{T}z&-h^{T}-z^ {T}F\\ -h-F^{T}z&-\widetilde{G}\end{array} \right]\left[ \begin{array}{c}1\\ \rho(k)\end{array} \right]\geq 0. \tag{22}\]According to Lemma 1 (S-procedure), for all \(\rho(k)\) that satisfies (20) and (21), the Equation (22) holds if there exist  
\[\tau_{i}\geq 0,i=1,2,\]  
such that  
\[\left[ \begin{array}{cc}\lambda-z^{T}z&-h^{T}-z^{T}F\\ -h-F^{T}z&-\widetilde{G}\end{array} \right]-\sum_{i=1}^{2}\tau_{i}\left[ \begin{array} []{cc}1&0\\ 0&-M_{i}(k)\end{array} \right]\geq 0,\]  
namely,  
\[\left[ \begin{array}{cc}\lambda-\sum_{i=1}^{2}\tau_{i}&-h^{T}\\ -h&-\widetilde{G}+\sum_{i=1}^{2}\tau_{i}M_{i}(k)+F^{T}F\end{array} \right]-[ \begin{array}{cc}z&F\end{array} ]^{T}[ \begin{array}{cc}z&F\end{array} ]\geq 0.\]  
According to Lemma 2 (Schur complement), it yields  
\[\left[ \begin{array}{cc}I&z&F\\ z^{T}&\lambda-\sum_{i=1}^{2}\tau_{i}&-h^{T}\\ F^{T}&-h&-\widetilde{G}+\sum_{i=1}^{2}\tau_{i}M_{i}(k)+F^{T}F\end{array}  \right]\geq 0.\]  
This completes the proof. The proposed control law \(u(k)=-\widetilde{B}^{-1}b+\widetilde{B}^{-1/2}z\), let \(\Gamma(k)=\widetilde{B}^{-1}[B^{T}(k)Q^{T}(k+1)A(k)]\), then the control law can be rewiritten as \(u(k)=-\Gamma(k)\hat{x}(k)+\widetilde{B}^{-1/2}z\), which contains two parts: the estimated states feedback and uncertainties compensation. For the first part, the feedback gain \(\Gamma(k)=[B^{T}(k)Q(k+1)B(k)+R(k)]^{-1}[B^{T}(k)Q^{T}(k+1)A(k)]\) can be computed off-line. For the second part \(\widetilde{B}^{-1/2}z\), it denotes the compensation for the estimated states error and noises which can be obtained by solving the semi-definite programming problem (17).
## 4 Experimental Setup
### Dataset Description  
As seen in Table 3, we performed experiments with two real-world maritime datasets, the same as the previous case study [14], and six open source software (OSS) projects which were published by [PERSON] et al. [7]. For the maritime projects, we call these two datasets dataset A and dataset B, respectively. The two datasets are extracted from defects recorded in domain software applied to large ships over 15 years. The numbers in parentheses mean the defect ratio, but the industrial dataset is omitted for security reasons. Additionally, for OSS projects, we shortened their full name into BU, CO, JD, PL, MO, PO, respectively.  
Table 4 summarizes the description of 14 change-level metrics [7], which are widely used in JIT-SDP researches. Each instance of the dataset contains 14 metrics and a class label. At the time of testing, stratified 10-fold cross-validation was introduced where the first nine folds were used as training data and the last fold was used as test data while preserving the dataset class distribution such that each fold has the same class distribution  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Languages** & **Description** & **Period** & \begin{tabular}{c} **Number of** \\ **Features** \\ \end{tabular} & \begin{tabular}{c} **Total Number of** \\ **Changes** \\ \end{tabular} \\ \hline A & C/C++ & Maritime Project A & 10/2004–08/2019 & 14 & \(931\) (\(-\)) \\ \hline B & C/C++ & Maritime Project B & 11/2015–08/2019 & 14 & 8498 (\(-\)) \\ \hline BU & \multirow{4}{*}{**Datasets were extracted by [PERSON] et al. [7]**} & Bugzilla & 08/1998–12/2006 & 14 & 4620 (36\%) \\ \cline{3-5} CO & & Columba & 11/2002–07/2006 & 14 & 4455 (31\%) \\ \cline{2-5} JD & \multirow{2}{*}{\
\begin{tabular}{c} **Datasets were extracted by [PERSON] et al. [7]** \\ \end{tabular} } & Eclipse JDT & 05/2001–12/2007 & 14 & 35,386 (14\%) \\ \cline{2-5} PL & \multirow{2}{*}{
\begin{tabular}{c} **Eclipse Platform** \\ \end{tabular} } & Eclipse Platform & 05/2001–12/2007 & 14 & 64,250 (14\%) \\ \cline{2-5} MO & & Mozilla & 01/2000–12/2006 & 14 & 98,275 (5\%) \\ \cline{2-5} PO & & PostgreSQL & 07/1996–05/2010 & 14 & 20,431 (25\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Project description [14].  
Figure 6: Brief summary of harmony memory applying parameters.  
as the initial dataset. The model was then trained using the first nine training data and its performance was assessed using the last test data. In the same manner, a new model was iteratively built, while the other nine folds became the training dataset. At the end of training, the performances were consolidated for all 10 outcomes [14; 36].  
Our experiments were performed on Google Colaboratory, which is free and online python environment accessible via the Chrome web browser. Datasets were uploaded to Google drive and accessible by python optimization source code on Colaboratory. Client environment was a personal laptop with Intel Core i7 processor @1.8 GHz, 16 GB memory and installing Windows 10 OS and the Chrome web browser.