esa-sceva/satcom-chunk-collection huggingface dataset:
# Abstractive vs. Extractive Summarization: An Experimental Review
## 2 Related Work
### Evaluation Metrics  
For evaluation purposes, we employ two well-known families of metrics, namely _BLEU_ and _ROUGE_. In general, both assess the number of matching _n-grams_ (sequence of terms) between the machine generated summary and the human assigned one. _BLEU_[23], standing for _bilingual evaluation understudy_, is an automatic evaluation method that was originally created for the task of machine translation, but can also be applied for the automatic text summarization task, as suggested in [22; 23; 24]. It is based on the precision metric, which measures the number of words from the machine-generated (candidate) sentence that match the words in the human-written (reference) sentence, divided by the total number of words in the candidate sentence. Specifically, it utilizes a brevity penalty for short sentences and a modified precision. This precision calculates the geometric average of _n-gram_ precisions, while penalizing word repetition. Two variations of BLEU, namely _BLEU-1_ and BLEU-2, use unigram precision and both unigram and bigram precisions, respectively. In addition, we included another variation of the method, called _SACREBLEU_[60]. This metric tackles the problem of nonreproducibility in academic papers utilizing the original _BLEU_ method. This occurs due to differences in parametric setups for _BLEU_, which results in different scores across publications.  
_ROUGE_[22], which stands for _recall-oriented understudy for gisting evaluation_, was inspired by the success of the _n-gram_ overlap measure utilized by _BLEU_. In contrast to _BLEU_, _ROUGE_ was introduced as a recall-oriented metric. Specifically, the most common setups are: (i) _ROUGE-1_ and _ROUGE-2_ for unigrams and bigrams, respectively; (ii) _ROUGE-L_, which utilizes the _longest common subsequence_ (_LCS_) between the reference and the machine-generated summary.It is noted that a variation of _ROUGE-L_, called _ROUGE-LSUM_, is computed on the summary-level (contrary to _ROUGE-L_ that is computed on the sentence-level).
# A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation
## 2 Related Works and Methods
### Method
#### 2.2.4 Zero-Reference Loss Function  
The cornerstone of our method's efficacy in achieving zero-reference learning lies in the utilization of four reference-free loss functions drawn from the existing literature [13]. They encompass spatial consistency loss, exposure control loss, color consistency loss, luminance-smoothing loss, and a novel contribution from our work--the noise-estimation loss. Our network assesses the quality of the enhanced image by employing these loss functions, facilitating subsequent parameter adjustments. The expression of our noise-estimation loss function is as follows, and the other reference-free loss functions can be found in the literature [13].  
The noise-estimation loss can be used to maintain the signal-to-noise ratio (SNR) between the enhanced image and the input image, and the results of each calculation are fed back into the curve-estimation network to continuously adjust the curve parameters, and to ensure the quality of the enhanced image. The noise-estimation loss is defined as shown in Equation (9):  
\[L_{mol}=\frac{1}{S}{\sum{(SNR-W)^{2}}}. \tag{9}\]  
where \(W\) denotes the constant matrix and S denotes the size of the enhanced image.  
The final zero-reference depth profile estimates the loss of the network, as shown in Equation (10):  
\[L_{\text{total}}=W_{spa}L_{spa}+W_{\text{exp}}L_{\text{exp}}+W_{col}L_{col}+W_{ mol}L_{mol}+W_{tvA}L_{tvA}. \tag{10}\]  
where \(W_{spa}\), \(W_{\text{exp}}\), \(W_{col}\), \(W_{mol}\), and \(W_{tvA}\) denote the weights corresponding to spatial consistency, exposure control, color consistency, noise-estimation, and luminance-smoothing losses, respectively.  
Figure 3: Detailed architecture of depth-curve-estimation network.
## 3 Results and Discussion
For example, the maximum mean PPV value for EMM/EPS-20/80 mixture at SD \(\approx\) 16 m/kg\({}^{1/2}\) equals only 6.9 mm/s, for EMM/EPS-30/70 mixture at SD \(\approx\) 13 m/kg\({}^{1/2}\) equals 11.8 mm/s, etc. That means that SD which corresponds to PPV = 20 mm/s (Equation (18)), calculated by the best-fit equation, is \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{\(R\) (m)} & \multicolumn{2}{c}{**EME**} & \multicolumn{2}{c}{**EMM/EPS (50/50)**} & \multicolumn{2}{c}{**EMM/EPS (40/60)**} & \multicolumn{2}{c}{**EMM/EPS (30/70)**} & \multicolumn{2}{c}{**EMM/EPS (20/80)**} \\ \cline{2-11}  & \(W_{EP}\) **(g)** & \(W_{fit}\) **(g)** & \(W_{EP}\) **(g)** & \(W_{fit}\) **(g)** & \(W_{EP}\) **(g)** & \(W_{fit}\) **(g)** & \(W_{EP}\) **(g)** & \(W_{fit}\) **(g)** & \(W_{EP}\) **(g)** & \(W_{fit}\) **(g)** \\ \hline
1 & 5.85 & 5.85 & 9.54 & 13.36 & 11.68 & 14.17 & 13.56 & 21.88 & 15.22 & 50.27 \\
2 & 23.42 & 23.42 & 38.14 & 53.46 & 46.74 & 56.69 & 54.25 & 87.53 & 60.90 & 201.09 \\
3 & 52.69 & 52.69 & 85.82 & 120.28 & 105.16 & 127.55 & 122.07 & 196.95 & 137.02 & 452.45 \\
4 & 93.66 & 93.66 & 152.57 & 213.84 & 186.95 & 226.76 & 217.01 & 350.13 & 243.60 & 804.36 \\
5 & 146.35 & 146.35 & 238.39 & 334.12 & 292.11 & 354.31 & 339.08 & 547.07 & 380.62 & 1256.81 \\
6 & 210.74 & 210.74 & 343.28 & 481.14 & 420.64 & 510.20 & 488.28 & 787.79 & 548.09 & 1809.81 \\
7 & 286.84 & 286.84 & 467.25 & 654.88 & 572.54 & 694.44 & 664.60 & 1072.27 & 746.02 & 2463.35 \\ \hline \end{tabular} Note: \(W_{\rm fit}\), \(W_{\rm BF}\)—maximum permitted weights determined from experimental data using best-fit curve and estimated applying the equivalency principle, respectively.  
\end{table} 
Table 8: Experimental and predicted maximum permitted weights at different distances that generate PPV = 20 mm/s. Figure 6: Comparison of predicted and experimental W-R curves for fixed PPV value of 20 mm/s. significantly outside the range of experiments. The extrapolation outside the range of experimental data used for fitting, results in less accurate SD values derived based on experimental data. This is especially visible for EMM/EPS-20/80 mixture, for which the experimental PPV range is the narrowest (up to 6.9 mm/s), and which shows the largest difference between predicted and experimental PPV values (Figure 6).
# Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression
## 3 Results
### Parameter Settings and Steps of Experiments  
The results for the experiments depended on properly setting the types of the basis kernels and their parameters [53]. For SKSVR, we chose Gaussian kernel and performed the particle swarm optimization (PSO) [54] algorithm and ten-fold cross-validation procedure to find the optimal parameters for the data sets. The parameter \(\varepsilon\) was set to 0.001, the acceleration coefficients were c1 = c2 = 2, the population size was 30, the inertia weight was 0.9, the maximum number of iterations was 100, and parameters \(C\) and \(\sigma\) were forced to lie in the following intervals: \(C\in[10^{0},\,10^{3}]\), \(\sigma\in[10^{-2},\,10^{2}]\). For the BPNN, the structural parameter was 10-15-1, where the number of the input layer neurons was 10, the number of hidden layer neurons was 15, the number of output layer neurons was 1, the learning rate was 0.01, and it iterated 5000 times. For MKSVR, we used the SimpleMKL Toolbox ([[http://asi.insa-rouen.fr/enseignants/~arakoto/code/mklindex.html](http://asi.insa-rouen.fr/enseignants/~arakoto/code/mklindex.html)]([http://asi.insa-rouen.fr/enseignants/~arakoto/code/mklindex.html](http://asi.insa-rouen.fr/enseignants/~arakoto/code/mklindex.html))) to implement algorithms, and the multiple kernel function was composed of 32 different basis kernels including 28 Gaussian kernels with parameter \(\sigma\in\{0.01,\,0.02,\,\ldots,\,0.09,\,0.1,\,0.2,\,\ldots,\,0.9,\,1,\,2, \ldots,\,9,10\}\) and 4 polynomial kernels with parameter \(s\in\{1,\,2,\,3,\,4\}\).  
The detailed steps of the procedure we followed are listed as follows:  
**Step 1**: Import the pre-prepared dataset of grain storage parameters.  
**Step 2**: Divide the whole dataset into the training (80%) and test (20%) sets randomly.  
**Step 3**: Train the MLR model, ANN model, SKSVR model (by PSO algorithm), and MKSVR model (by SimpleMKL algorithm) with the training set.  
**Step 4**: Use the test set to test the performances of the MLR model, ANN model, SKSVR model, and MKSVR model.  
**Step 5**: Repeat steps 2-5 until the number of experiment repetitions is reached (20 times).
# Assessing the Stress Induced by Novel Packaging in GaN HEMT Devices via Raman Spectroscopy
###### Abstract  
Micro-Raman spectroscopy was carried out to evaluate the localized residual stresses in commercial Gallium-Nitride-based devices, specifically, AlGaN/GaN high-electron-mobility Transistors (HEMTs) with a novel packaging design provided by STMicroelectronics S.r.l. (Catania, Italy). The packaging plays a key role in protecting the device core against the external environment, thus minimizing damages caused by mechanical shocks, exposure to light, and contact with chemicals, conjointly achieving an efficient heat dissipation rate. Even though the packaging is a required step for the proper functioning of ready-to-use electronic devices, its application typically may introduce mechanical stress to AlGaN/GaN HEMTs, which can result in various reliability issues. In this paper, we investigate the impact of packaging on residual stress by analyzing the frequency shift of the E2 Raman peak along GaN layers and at the GaN/Si interface. An extensive evaluation was conducted using both a packaged device and a wafer-level device. The correlation between Raman frequency shifts of the E2 mode was accurately quantified, revealing a stress mitigation of approximately 0.1 GPa. This reduction is ascribed to the compressive stress introduced by the packaging, which partially offsets the intrinsic tensile stress of the wafer-level device. The proposed methodology could, in principle, be implemented to improve the development of packaging.  
AlGaN/GaN high-electron-mobility transistor; micro-Raman spectroscopy; power electronic; packaged device; wafer level +
Footnote †: journal: Applied Sciences
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## 1 Introduction
### Related Work
This is particularly true in more complex system environments. Moreover, the over-relaxation detectors like AOR, SOR, and GS do not have a systematic way to choose these parameters, which causes performance loss in large antenna systems and different channel conditions [12]. The existence of these research gaps in massive MIMO systems highlights the necessity for advanced innovative signal detection approaches that can effectively address the selection of optimal parameters by lowering computational complexity for high-dimensional MIMO conditions.
## 2 Materials and Methods  
### Materials Used to Build the Database  
Research was carried out for cementitious mortars modified with different types and amounts of additives. A reference mortar was modified with siliceous fly ash and granite powder waste in a 0-30% amount of cement mass. Figure 1 presents the chemical composition of the cement, fly ash, and granite powder used in the research, which was obtained by performing SEM analyses. The 11 different series compositions, whose composition is shown in Table 1, were prepared for the study. The tests were performed and analyzed after 4, 8, 12, and 16 cycles for two samples of each series to prevent outlier results. Overall, 88 sets of results were subjected to the study.  
Cementitious mortars were prepared using a cement CEM I 42.5R (Gorazdze, Poland), fine aggregate (Byczen, Poland), water from a water supply, granite powder waste (Strzegom, Poland), and siliceous fly ash (Hranice, Czech Republic). The lowest value of the bulk density was obtained for fly ash and was approximately 1.15 g/cm\({}^{2}\), and it was lower than for cement, which was 1.18 g/cm\({}^{2}\), and for granite powder, which was 1.22 g/cm\({}^{2}\). It was quite different in the case of the specific surface area, as the cement was about 3700 cm\({}^{2}\)/g and was lower than the specific surface area of granite powder, for which it was 3950 cm\({}^{2}\)/g, and for fly ash, which was approximately 4150 cm\({}^{2}\)/g. Sieve size development of granular materials used is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**No.** & **Series** & **Cement** & **Water** & **w/c** & **w/b** & **FA** & **GP** & **Binder** & **Sand** \\ \hline
1 & Ref & 1 & 0.5 & 0.50 & 0.50 & 0 & 0 & 1 & 3 \\
2 & FA10 & 0.9 & 0.5 & 0.56 & 0.50 & 0.1 & 0 & 1 & 3 \\
3 & FA20 & 0.8 & 0.5 & 0.63 & 0.50 & 0.2 & 0 & 1 & 3 \\
4 & FA30 & 0.7 & 0.5 & 0.71 & 0.50 & 0.3 & 0 & 1 & 3 \\
5 & GP10 & 0.9 & 0.5 & 0.56 & 0.50 & 0 & 0.1 & 1 & 3 \\
6 & GP20 & 0.8 & 0.5 & 0.63 & 0.50 & 0 & 0.2 & 1 & 3 \\
7 & GP30 & 0.7 & 0.5 & 0.71 & 0.50 & 0 & 0.3 & 1 & 3 \\
8 & 5 FA + 5 GP & 0.9 & 0.5 & 0.56 & 0.50 & 0.05 & 0.05 & 1 & 3 \\
9 & 10 FA + 10 GP & 0.8 & 0.5 & 0.63 & 0.50 & 0.1 & 0.1 & 1 & 3 \\
11 & 10 FA + 20 GP & 0.7 & 0.5 & 0.71 & 0.50 & 0.1 & 0.2 & 1 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: The series compositions of the cementitious mortar mixes.  
Figure 1: Chemical composition of ingredients used in the research. (**a**) Cement, (**b**) siliceous fly ash, and (**c**) granite powder.
## 3 Results and Discussion  
Removal of Heavy Metals As(V) and Zn(II) and Turbidity According to Solution pH Using the Ceramic Membrane  
The removal rates of As(V) and Zn(II) by the ceramic membrane were investigated under various pH conditions (Figure 2). When only As(V) was present in the solution, its removal at solution pH values of 2, 4, and 6 was negligible. Only 7% of As(V) was removed by the ceramic membrane in solution pH 8. Meanwhile, the As(V) removal rate was increased to 50% at solution pH 8 in a solution containing As(V) and Zn(II) (As + Zn). This is due to the precipitation of As(V) with Zn(II) and/or adsorption of As(V) on the zinc hydroxide carbonate precipitate [22, 23]. In a solution containing metal ions and turbidity (As + Zn + Kaolin), the removal rate of As(V) increased to 31% at solution pH 6 and reached 54% at solution pH 8. Here, the geochemical modeling with Visual Minteq 3.1 suggests that adsorption was the predominant reaction of As(V) removal at pH 6 whereas precipitation was the main mechanism at pH 8 (Figure 3a). The saturation index (Table 1) calculated using Visual Minteq 3.1 confirms that Zn\({}_{3}\)(AsO\({}_{4}\))\({}_{2}\)\(\cdot\)2.5H\({}_{2}\)O(s) was the main species of As(V) precipitate at solution pH 8. The positive values of the saturation index indicate the supersaturated condition due to the formation of a particular product [24, 25].  
Figure 3: Fractions of (**a**) As(V) and (**b**) Zn(II) removal from the As + Zn + Kaolin solution according to solution pH.  
Figure 2: (**a**) As(V) and (**b**) Zn(II) removal according to solution pH using the ceramic membrane.  
The ceramic membrane was unable to remove Zn(II) at solution pH values of 2 and 4 but could remove it at solution pH values of 6 and 8 with removal rates of 14% and 90%, respectively (Figure 2b). The removal rates of Zn(II) at solution pH values of 6 and 8 were 21% and 97% from the As + Zn solution, which further increased to 31% and 98% from the As + Zn + Kaolin solution, respectively. The removal fractions of Zn(II) from the As + Zn + Kaolin solution were 14% phase-change, 8% precipitation, and 9% adsorption, at solution pH 6 and 91% phase-change and 7% precipitation, but without adsorption, at solution pH 8 (Figure 3b). The saturation index value confirmed that Zn(II) was phase-changed mainly to Zn(OH)\({}_{2}\) (am, beta, delta, epsilon, gamma) at pH 8 (Table 1). The turbidity caused by kaolin was completely removed by the ceramic membrane at all solution pH values (data not shown).
# Estimating Significant Wave Height from SAR with Long Integration Times
###### Abstract  
Synthetic aperture radar (SAR) is an important means of estimating significant wave height with obvious advantages of all-day, all-weather, high resolution and wide swath coverage. At present, the estimation methods of significant wave height are based on visible ocean waves in SAR images. However, due to the characteristic of long integration time for low-frequency SAR (such as P-band, L-band), the ocean waves are usually invisible in SAR images. In addition, in the case that there are multiple wave systems, significant wave height of only one wave system can be estimated for the reason that only a blurred wave system can be observed in SAR images. In order to solve the above two problems, a method of estimating significant wave height from SAR with long integration times is proposed in this paper. Firstly, each ocean wave system is refocused from single-look complex (SLC) data, respectively. Then, without any additional processing, the 180\({}^{\circ}\) ambiguity of wave propagation direction is removed based on the optimum focus setting. Finally, significant wave height is estimated in combination with azimuth cutoff, wavelength and propagation direction of ocean waves. This method is applied to two airborne SAR field data with long integration times. One case is that ocean waves are invisible in SAR images, the other is that there are two wave systems on the real ocean surface, but only one is visible in the SAR images. The results show that the proposed method can estimate significant wave height in the cases of invisible ocean waves and multiple ocean waves. The estimation results of significant wave height are compared with the European Centre for Medium-Range Weather Forecast (ECMWF) data, and the error is basically stable within 0.2 m, which verifies the effectiveness of the proposed method.  
s 29 January 2022  
Accepted: 22 February 2022  
Published: 23 February 2022
## 2 Multi-Dimensional User Importance Indicator System and Ranking Model
### User Importance Ranking Based on the Improved AHP-EW Method
#### 2.2.1 User Importance Ranking Based on the AHP Algorithm  
In this section, we use the AHP to rank the user importance proposed in Section 2.1, where we assume that there are \(m\) indicators and \(n\) evaluation objects, and the general steps of hierarchical analysis are divided into the following parts:  
(1) Constructing the judgment matrix. The judgment matrix is constructed to compare the importance between two indicators as follows:  
\[\mathrm{A}=\left(\mathrm{a}_{\mathrm{ij}}\right)_{n\times n^{\prime}}\mathrm{ a}_{\mathrm{ij}}>0,\mathrm{a}_{\mathrm{ij}}=\frac{1}{\mathrm{a}_{\mathrm{ji}}} \tag{14}\]  
where \(\mathrm{a}_{\mathrm{ij}}\) describes the importance ranking between indicator \(i\) and indicator \(j\).  
(2) Single order of hierarchy. We find the maximum eigenvalue of judgment matrix \(\mathrm{A}\) in (1) and normalized eigenvector \(\mathrm{W}\) that corresponds to the maximum eigenvalue \(\lambda_{\mathrm{max}}\) and satisfies \(\mathrm{AW}=\lambda_{\mathrm{max}}\mathrm{W}\). The standardized eigenvectors are the corresponding weight ratios of different indicators obtained via the AHP.  
(3) Consistency test. Since the judgment matrix is strongly subjective, a consistency test is required to ensure that the resulting judgment matrix is internally reasonable and to prevent self-contradiction in the matrix. The inconsistency indicator \(\mathrm{CI}\) is defined as follows:  
\[\mathrm{CI}=\frac{\lambda_{\mathrm{max}}(\mathrm{A})-n}{n-1} \tag{15}\]  
where \(n\) is the order of matrix \(A\).  
The consistency ratio \(\mathrm{CR}\) is defined as follows:  
\[\mathrm{CR}=\frac{\mathrm{CI}}{\mathrm{RI}} \tag{16}\]  
where \(\mathrm{RI}\) is taken as the n-order matrix \(\mathrm{H}\), and the rules for taking individual values of the matrix are identical to those for judgment matrix \(\mathrm{A}\). When sufficiently many \(\mathrm{H}\) matrices have been taken, the average of the largest eigenvalues of matrix \(\mathrm{H}\) is the value of \(\mathrm{RI}\).  
When \(\mathrm{CI}\) is greater than 0.1, the consistency test can be considered to have been passed; if it is not, other schemes must be used.  
(4) Total hierarchy permutation. For a single indicator, the importance of different users is constructed as matrix \(\mathrm{B}\). \(\mathrm{B}\) is constructed in the same manner as matrix \(\mathrm{A}\). To find the maximum eigenvalue and its corresponding maximum eigenvector, at this time, the eigenvector is the score of different loads under the indicator.  
(5) Consistency test. For matrix \(\mathrm{B}\), the same consistency test as for matrix \(\mathrm{A}\) is used. If it does not pass the consistency test, it is replaced with another solution.  
Finally, the upper-level criterion weights calculated via hierarchical analysis are \(\beta=\{\beta_{1},\beta_{2},\beta_{3},\cdots,\beta_{m}\}\), and the sub-criteria layer weights are \(\phi=\{\varphi_{1},\varphi_{2},\varphi_{3},\cdots,\varphi_{n}\}\).
# Flexible Dispatch for Integrated Power and Gas Systems Considering Power-to-Gas and Demand Response
## 4 Solution Methodology
### Flexible Economic Dispatch for the Power System  
The flexible dispatching model of the power system is given in Part A, Section III. It belongs to a two-stage mixed integer nonlinear optimization problem. In order to simplify the solution process, this paper first uses a linearization method to deal with the non-linear function and constraints in the model, which are converted into a two-stage mixed integer linear optimization problem. The linearization method has been studied in many papers, so this article will not repeat them. On this basis, the Benders decomposition method is used to iteratively solve the two-stage flexible economic dispatch model. Specifically, for the main problem and the sub-problem, considering that both are linear programming problems after the transformation, the CPLEX commercial solver with high efficiency is directly used for solving.  
The optimized solution is obtained through the \(v\)-th iteration of the main problem. Mainly the following variables: \(f^{\text{u}(v)}_{\text{g},t}\), \(f^{\text{d}(v)}_{\text{g},t}\), \(f^{\text{u}(v)}_{\text{d},m,t}\), \(f^{\text{d}(v)}_{\text{d},t}\), \(f^{\text{u}(v)}_{\text{lcur},b,t}\), \(f^{\text{d}(v)}_{\text{wcur},k,t}\). Bring these results into the sub-problem to obtain the up and down adjustable capacity deficits \(f^{\text{u},s(v)}_{\text{i},t}\) and \(f^{\text{d},s(v)}_{\text{i},t}\) in each scenario of the \(v\)-th iteration. And judge whether to add Benders cut by (47):  
\[f^{\text{u},s(v)}_{\text{i},t}\leq\varepsilon,f^{\text{d},s(v)}_{\text{i},t}\leq\varepsilon \tag{47}\]  
where \(\varepsilon\) is a sufficiently small real number. If the adjustable capacity shortages obtained in each scenario satisfy Equation (47), it indicates that the power generation plan and adjustable capacity allocation in the main problem have sufficient flexibility, no Benders cut is generated, and the obtained optimization solution of \(v\)-th iteration is the final optimization result. Otherwise, add Benders cut:  
\[\sum_{i\in\Omega_{\text{G}}}\lambda^{\text{u},s(v)}_{i,t}\cdot\left(f^{\text{ u}}_{\text{g},t}-f^{\text{u}(v)}_{\text{g},t}\right)+\sum_{m\in\Omega_{\text{ DRR}}}\theta^{\text{u},s(v)}_{m,t}\cdot\left(f^{\text{u}}_{\text{d},m,t}-f^{ \text{u}(v)  
\[-M\cdot v_{ij,t}\leq-\widehat{f}_{ij,t}^{2}-\left(C_{ij}^{sf}\right)^{2}\left(p_{ i,t}^{2}-p_{j,t}^{2}\right)\leq M\cdot v_{ij,t} \tag{52}\]  
where \(v_{ij,t}\) is a binary variable and \(M\) is a sufficiently large number that can be taken as the maximum value of the pipe flow.  
The equivalent pipeline flow equation only contains the nonlinear element of the square term. Therefore, \(\widehat{f}_{ij,t}^{2}\), \(p_{i,t}^{2}\) and \(p_{j,t}^{2}\) can be processed by the incremental linearization model with the best calculation efficiency. The specific process can be found in [35].