esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Tax Fraud Modeling with Neural Networks
### Dimension Adjustment: Reduction of the Dimension According to the Main Components  
Our model features a series of quantitative independent variables, correlated with each other, that would trigger a multicollinearity problem in any model to be estimated. Consequently, it is imperative to reduce said variables to their uncorrelated main components. Adjusting the model to its components would eliminate the multicollinearity, in addition to reducing the impact of atypical values, and also bring about variable normality. Accordingly, after component adjustment, the model properties would be considered as optimum.  
In this case, the reduction can be taken to be legitimate because the matrix determinant, relative to the initial variables correlation, is practically null. Moreover, the commonalities of the variables are high, with many of them close to the unit. As a result of the analysis carried out, we have obtained 11 Ci main components (factors), which account for close to 85% of the initial variability of the data, thereby securing a satisfactory reduction. In more specific terms, the components account for 84.882% of the variability, after a VARIMAX rotation.  
Analysis of the factorial matrix revealed that the first component -C1- comprises the first 17 variables, which include earnings, bases, and quotas. The second component -C2- includes four variables related to the asset balances, tax and result of the filed tax return. The third component -C3- encompasses 5 variables related to capital gains tax and savings base tax. The fourth component -C4- contains 4 variables relative to fixed capital assets. Component C5 comprises 2 variables, namely regional government, and gift deductions. Component C6 comprises three variables pertaining to housing, and minimum personal and family deductions. Component C7 involves a single variable dealing with economic activities. Component C8 comprehends two variables related to taxable base and pension scheme deductions. Component C9 contains 3 variables pertinent to total deductible expenses and investment incentives deductions. Component C10 is a single variable relevant to positive net balance, on account of capital gains and losses. Lastly, component C11 comprises two variables pertaining to the net return regarding agrarian modules and other deductions. Hence, the factorial matrix makes it possible to express each of the 11 main components as a linear combination of the comprising initial variables and in a disjoint manner.  
The main components obtained are the input variables of the neural network model (independent variables). The output variable corresponds to the dichotomous mark variable, where value 1 indicates fraud, while zero value denotes the absence of fraud.
## 7 Results of the SSI Survey
### Human and Credential Exchange Layer  
[PERSON] et al. [32] have identified three attacks and generated for each of them an attack tree. In order to obtain fake credentials, a user may either create fake credentials at an issuer, spoof the issuer, amend issued credentials, or steal credentials. To create fake credentials at an issuer, the attacker either has to obtain admin credentials (malware or social engineering) or store fake credential data (accessing the issuer host or updating data). For spoofing, three variants exist: creating a replica issuer host, publishing a fake issuer DID to the SSI network, or reducing trust in the original issuer (by applying either the Sybil or Eclipse attack). To amend issued credentials, the attacker has to obtain the issuer's private key (i. e., access the issuer host and locate the private key, which could be done by malware or a social engineering attack) or sign the updated credential in the wallet. To steal credentials, the attacker has to either steal a wallet (steal the phone or attack the user's cloud storage) or impersonate the user (obtain credentials and request credentials).  
[PERSON] et al. [32] additionally describe the threat of obtaining personal data. Here, the authors identify three vectors: unauthorized access to the user's wallet, credential creep, and background data attack. To get unauthorized access to the wallet, the attacker needs to receive the user credentials (using malware or a social engineering attack) and access the wallet (stealing the phone or gaining remote access to the wallet). Credential creep is possible by requesting additional data or user profiling (multiple verifier requests for credentials and linking DID to identify the user). In a background data attack, the attacker has to obtain a sensitive dataset and link data via a verifier request.
# Covert Channel Based on Quasi-Orthogonal Coding
## 2 Radio Physical Layer Steganography  
### Creating a Covert Channel  
In mathematical terms, the process of creating a covert channel in the physical layer of a radio channel can be described as a superposition of a cover signal (cover) and a covert signal, which can be represented by the formula:  
\[s(t)=\sqrt{P_{1}}x_{1}(t)+\sqrt{P_{2}}x_{2}(t) \tag{1}\]  
where \(x_{1}\) and \(x_{2}\) are the cover signal and the covert signal, respectively, \(P\) (\(P=P_{1}+P_{2}\)) is the transmitter power. In order to reduce the probability of detecting a covert channel, the following conditions should be met:  
* the covert signal power should be significantly less than the cover signal (\(P_{1}\gg P_{2}\)).  
* constellation points of the covert signal should have a pseudo-random (noise) characteristic. The receiver input signal is represented by the following simplified formula: \[y(t)=h\sum_{i=1}^{2}\sqrt{P_{i}}x_{i}(t)+w(t)\] (2) where \(h\) (\(h\sim\mathrm{CN}(0,1)\)) is a channel gain and \(w\) is a Gaussian noise.  
In an ideal case, when \(w(t)=0\) and the value of the parameter \(h\) is known in the receiver, the recovery process of the signal \(x_{2}(t)\) would proceed as follows (ideal SIC):  
\[x_{2}(t)=\frac{y(t)-\sqrt{P_{1}}x_{1}(t)}{\sqrt{P_{2}}}+w(t) \tag{3}\]  
Transmission and reception of the cover and covert signal was presented in Figure 1.
## 2 Design of Real-Time Communication Subsurface Mooring System
### System Software Design  
The software of the real-time communication subsurface mooring system is designed with a state machine architecture divided into four working states: sleep, standby, data acquisition, and satellite communication according to its workflow, as shown in Figure 9. The system is in a sleep state most of the time; the main control circuit board will be in sleep mode at this time, most peripherals are in the power-off state, and the power consumption of the whole machine architecture can be reduced to about 60 uA@11V. When the system serial port receives a legal command, it will move to the standby state; various working parameters of the system can be configured at this time. During the data acquisition state, the inductive modem module will poll and collect all sensor info and also verify and compress the data [27]. After the acquisition is completed, it will enter into the satellite communication state immediately; during this process, the system will enable the satellite communication module to encapsulate and encrypt the current coordinates and sensor data and transmit them to the ground station. This system can also directly enter the satellite communication state at the set period of each day. At the same time, the system can remotely receive instructions from the shore station and complete operations such as status query, parameter modification, and historical data reissue.  
Figure 8: Hardware block diagram of this system.
# Coverage-Based Classification Using Association Rule Mining +
## 6 Experimental Evaluation
Statistically significant counts (wins/losses) of J&B against other rule-based classification models on classification rules is shown in Table 9.  
Table 9 proves that J&B produced a statistically smaller classifier than DTNB and SA methods on 10 datasets, and DT, FR and CBA methods on 7 (or more than 7) datasets out of 12. The most importantly, J&B generated statistically smaller classifiers than all other models on bigger datasets, which was our main goal in this research. Experimental evaluations on bigger datasets (over 10,000 samples) are shown in Figure 2. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Dataset** & **DTNB** & **DT** & **C4.5** & **PT** & **FR** & **RDR** & **CBA** & **SA** & **J\&B** \\ \hline Breast.Can & 122 & 22 & 10 & 20 & 13 & 13 & 63 & 20 & 47 \\ Balance & 31 & 35 & 35 & 27 & 44 & 22 & 77 & 45 & 79 \\ Car.Evn & 144 & 432 & 123 & 62 & 100 & 119 & 72 & 160 & 41 \\ Vote & 270 & 24 & 11 & 8 & 17 & 7 & 22 & 30 & 13 \\ Tic-Tac-Toe & 258 & 121 & 88 & 37 & 21 & 13 & 23 & 60 & 14 \\ Nursery & 1240 & 804 & 301 & 172 & 288 & 141 & 141 & 175 & 109 \\ Hayes-root & 5 & 8 & 22 & 14 & 11 & 10 & 34 & 45 & 34 \\ Lymp & 129 & 19 & 20 & 10 & 17 & 11 & 23 & 60 & 29 \\ Spect.H & 145 & 2 & 9 & 13 & 17 & 12 & 4 & 50 & 11 \\ Adult & 737 & 1571 & 279 & 571 & 150 & 175 & 126 & 130 & 97 \\ Chess & 507 & 101 & 31 & 29 & 29 & 30 & 12 & 120 & 24 \\ Connect4 & 3826 & 4952 & 3973 & 3973 & 403 & 341 & 349 & 600 & 273 \\ Average (\%) & 618 & 674 & 409 & 411 & 93 & 75 & 79 & 125 & 64 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The number of classification rules generated by the classifiers. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **DTNB** & **DT** & **C4.5** & **PT** & **FR** & **RDR** & **CBA** & **SA** \\ \hline
**W** & 10 & 7 & 6 & 6 & 8 & 5 & 7 & 10 \\
**L** & 2 & 5 & 4 & 6 & 4 & 5 & 3 & 2 \\
**W-L** & 0 & 0 & 2 & 0 & 0 & 2 & 2 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Statistically significant wins/losses counts of J&B method on rules. Figure 2: Comparison of rule-based classification methods on the average number of rules. Figure 2 proves the advantage of the J&B method: it produced the smallest classifier among all rule-based classification models on selected datasets. Our experiment on relevance measures (average results) such as \"Precision\", \"Recall\" and \"F-measure\", is highlighted in Figure 3. The detailed result for each dataset can be found in Appendix A.
# The Evolution of Wikipedia's Norm Network
## 3 Results
### Overlap and Semantic Coherence  
Over the course of network construction, core norms are drawn apart topologically. At the same time, the semantic coherence of their neighborhoods rises.  
Figure 3 shows the average pairwise overlap between the top ten pages in our network (since some norms are created later, the number of norms in this final set is lower early on). Early in the system history, when the network is small, overlap is very high. The creation of new pages leads to a rapid decline in overlap; even in 2006, when all core norms are in place, the overlap continues to decline. Figure 3 also shows the evolution of semantic coherence, which rises rapidly and stabilizes early.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Rank** & **Name** & **Classification** & **Creation Date** \\ \hline
1 & Neutral\_point\_of\_view & User-content & 24 December 2001 \\
2 & Verifiability & User-content & 2 August 2003 \\
3 & Identifying\_reliable\_sources & User-content & 28 February 2005 \\
4 & What\_Wikipedia\_is\_not & User-user/user-content & 24 September 2001 \\
5 & Biographies\_of\_living\_persons & User-content & 17 December 2005 \\
6 & Consensus & User-user & 11 July 2004 \\
7 & Policies\_and\_guidelines & User-user/user-content & 1 November 2001 \\
8 & Administators & User-admin & 16 May 2001 \\
9 & No\_original\_research & User-content & 21 December 2003 \\
10 & Citing\_sources & User-content & 19 April 2002 \\
11 & Assume\_good\_faith & User-user & 3 March 2004 \\
12 & Notability & User-content & 7 September 2006 \\
13 & Blocking\_policy & User-admin & 8 June 2003 \\
14 & Dispute\_resolution & User-user/user-admin & 12 January 2004 \\
15 & Redirect & User-content & 25 February 2002 \\
16 & Civility & User-user & 5 February 2004 \\
17 & Arbitration\_Committee & User-admin & 16 January 2004 \\
18 & Vandalism & User-content & 29 March 2002 \\
19 & Edit\_warring & User-user & 26 April 2003 \\
20 & Talk\_page\_guidelines & User-user & 15 April 2005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Core norms. Top twenty pages, by eigenvector centrality, in 2015. All are either policy or guideline pages, and all were in place by the end of 2006. The majority of these core norms were created before 2004, when the population was less than 3% of its peak.  
Network growth could have been imagined to drive a knitting together of distinct principles. Instead, the opposite happens: core norms slowly draw apart as page creation leads to distinct spheres of influence. Rather than nucleating around a set of densely-connected core principles, the norm network continues to condense around multiple points.  
We note that the local clustering coefficient, a measure of the extent to which two nodes, linked to the same node, tend to also link together, remains essentially constant over the span of the data (see Appendix E, Figure E.1). The ways in which editors link together small groups of pages changes little while their cumulative effect produces large and lasting changes both in attention inequality and page overlap.
# Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables
## 4 On-Site Bridge Experiment
### Experimental Results and Analysis  
#### 4.2.1 Recognition Results for Condition 1  
Condition 1 was in a state after the broken support was completely removed. The time interval of 10:10-10:20 of the acceleration signals were selected for calculation on 11 October 2019. The acceleration signal and the frequency spectrum are shown in Figure 15, a period of 400 s of acceleration data was selected for calculation. The fundamental frequency basically changed in the range of 0.76 Hz to 0.79 Hz. The cable force results calculated based on the variational mode decomposition and the HHT method are shown in Figure 15. The cable force fluctuated relatively in the range of [3.5 \(\times\) 10\({}^{3}\) kN, 4 \(\times\) 10\({}^{3}\) kN]. The main reason is that the side beam was unconstrained after the downstream support was removed.
## 3 Results and Discussion
### Determining the Optimum Extraction Conditions
#### 3.3.1 Extraction pH
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multirow{2}{*}{**Gelatins**} & \multicolumn{3}{c}{**Extraction Conditions**} & \multirow{2}{*}{**Yields (\%)**} \\ \cline{3-4}  & & \multicolumn{1}{c}{**pH**} & \multicolumn{1}{c}{**Temperature (\({}^{\circ}\)C)**} & \multicolumn{1}{c}{**Time (h)**} \\ \hline  & & & & 0.5 & 7.30 \(\pm\) 1.30 \({}^{\text{a}}\) \\  & & & 35 & 1 & 7.26 \(\pm\) 0.99 \({}^{\text{a}}\) \\  & & & & 3 & 7.83 \(\pm\) 1.79 \({}^{\text{a}}\) \\  & Type A & 7 & & 0.5 & 6.17 \(\pm\) 0.70 \({}^{\text{a}}\) \\  & & & 50 & 1 & 6.24 \(\pm\) 0.76 \({}^{\text{a}}\) \\  & & & & 3 & 6.18 \(\pm\) 0.92 \({}^{\text{a}}\) \\  & & & & 0.5 & 6.85 \(\pm\) 1.22 \({}^{\text{a}}\) \\  & & & 65 & 1 & 7.56 \(\pm\) 0.70 \({}^{\text{a}}\) \\ Temperature & & & & 3 & 7.08 \(\pm\) 0.13 \({}^{\text{a}}\) \\ and time & & & & 0.5 & 8.46 \(\pm\) 1.13 \({}^{\text{a}}\) \\  & & & 35 & 1 & 8.21 \(\pm\) 0.54 \({}^{\text{a}}\) \\  & & & & 3 & 8.83 \(\pm\) 0.60 \({}^{\text{a}}\) \\ \cline{3-4}  & Type B & 8 & & 0.5 & 8.07 \(\pm\) 0.39 \({}^{\text{a}}\) \\  & & & 50 & 1 & 9.29 \(\pm\) 0.47 \({}^{\text{a}}\) \\  & & & & 3 & 8.79 \(\pm\) 1.25 \({}^{\text{a}}\) \\ \cline{3-4}  & & & & 0.5 & 8.79 \(\pm\) 0.56 \({}^{\text{a}}\) \\  & & & 65 & 1 & 8.99 \(\pm\) 0.50 \({}^{\text{a}}\) \\  & & & & 3 & 9.98 \(\pm\) 0.11 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} All values are the mean \(\pm\) standard error of three replicates. Different letters in the superscript of each column show significant difference (\(p<0.05\)) for each extraction process at different conditions.  
\end{table} 
Table 2: _Cont._bands were always lower in type A gelatin than type B, suggesting the higher quality of the type A gelatin.
# Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset
## 3 Methodology
### Feature Extraction  
We introduced the AndroVul dataset in [9] and the interested reader can find full details in that publication. In this section, we propose a brief overview of the feature extraction process and output as applied to an app. In short, we used well-known static analysis tools (Apktool, AngroBugs) to extract three kinds of features, i.e., dangerous permissions from the app's manifest file, code smells from the app's Smali code representation, and AndroBugs vulnerabilities from the APK files. In Algorithm 2, we describe the general process for extracting the vulnerability features from an apk file. It starts with the reverse engineering process (line 2), followed by the extraction of the desired features from their respective files (lines 3-5). The extracted vulnerabilities features are then mapped to values and written into a single csv file. The values mapped to the features are determined as shown in the equations below:  
\[\textit{permissions}\rightarrow\begin{cases}\textit{Requested\_permission}&1 \\ \textit{Other\_permissions}&0\end{cases}\]  
\[\textit{CodeSmells}\rightarrow\begin{cases}\textit{Weight(security\,code\, smell)}\end{cases}\]  
\[\textit{AngroBugs\_vulnerabilities}\rightarrow\begin{cases}\textit{Critical}&1 \\ \textit{Worning}&0.5\\ \textit{Other}&0\end{cases}\]  
```
Input: Apk files; apps Output: Dataset in CSV_file
1 forall apps in Datasetdo
2\(\textit{APK}_{file}\leftarrow\textit{Open(file)}\)\(\triangleright\) Reverse engineering APK
3\(\textit{Permissions}_{list}\leftarrow\textit{Get\_Distinct\_Permissions}(manifest_{File})\)\(\triangleright\) Extracting permissions from manifest file
4\(\textit{Code\_Smell}_{list}\leftarrow\textit{Get\_CodeSmell}(\textit{Smali}_{Files})\)\(\triangleright\) Extracting code smell from Smali files
5\(\textit{AngroBugs\_vulnerabilities}_{list}\leftarrow\textit{Get\_AngroBugs\_vulnerabilities}(\textit{AndroBugs\_report})\)\(\triangleright\) Extracting AngroBugs_vulnerabilities from AndroBugs_report
6 foreachappdo
7\(\textit{Permission}\leftarrow\textit{App[i]\_Permission}\)\(\triangleright\) Mapping permissions
8\(\textit{Code\_Smell}\leftarrow\textit{App[i]\_CodeSmell}\)\(\triangleright\) Mapping code smells
9\(\textit{AndroBugs\_vulnerabilities}\leftarrow\textit{App[i]\_AngroBugs\_vulnerabilities}\)\(\triangleright\) Mapping AndroBugs_vulnerabilities
10 end for
11
12 end for
13
14 end for
15\(\textit{CSV}_{(file)}\leftarrow\textit{Append}(\textit{CSV}_{(file)},\textit{ Concat}(\textit{Vector}_{(\textit{Permission})},\textit{Vector}_{(\textit{Code\_Smell})},\textit{Vector}_{(\textit{AngroBugs\_vulnerabilities})}))\)\(\triangleright\) Concatenating all vulnerabilities features in CSV file return (\(\textit{CSV}_{(file)}\))
```  
**Algorithm 2**Feature Extraction Algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**App** & **Samples** \\ \hline Benign & 11,971 \\ Flagged Malware & 2831 \\ VirusShare Malware & 3978 \\ Total & 18,780 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset description.
# Power Sharing Control in a Grid-Tied DC Microgrid: Controller Hardware in the Loop Validation
## 2 MGIS: Description, Modeling, and Design
### Photovoltaic Array Description and Modelling  
For PA modeling, a Keysight E4360A photovoltaic panels emulator was used as a reference. As mentioned in [43; 44; 45; 46; 47; 48], the device is used to substitute a real PA, presenting identical behavior. The emulator reproduces the characteristic current-voltage photovoltaic curve, based on parameters such as the open-circuit voltage \(V_{\infty}\); the voltage and current in the maximum power point \(V_{mpp}\) and \(I_{mpp}\), respectively; and the short-circuit current \(I_{sc}\)[49], while adding two extra variables (\(R_{s}\) and \(N\)), which are dependent on the four previously mentioned parameters. Equation (1) defines the relationship of the PA current \(I_{p}\) and its voltage \(V_{p}\).  
\[I_{p}=\frac{V_{p0}\big{(}I_{p}\big{)}-V_{p}}{R_{p}} \tag{1}\]  
Figure 1: Main grid interconnection system topology.  
The terms \(V_{p0}\big{(}I_{p}\big{)}\) and \(R_{p}\) are calculated according to Equations (2) and (3).  
\[V_{p0}\big{(}I_{p}\big{)}=\frac{V_{oc}\bigg{\{}V_{oc}ln\bigg{[}2-\Big{(}\frac{I_{ p}}{I_{oc}}\Big{)}^{N}+ln(2)R_{s}I_{sc}\bigg{]}\bigg{\}}}{ln(2)[V_{oc}+R_{s}I_{sc}]} \tag{2}\]  
\[R_{p}=\frac{V_{oc}R_{s}}{V_{oc}+R_{s}I_{sc}} \tag{3}\]  
In contrast, Equations (2) and (3) depend on the variables \(R_{s}\), \(a\) and \(N\), which are a function of the input parameters (\(V_{oc}\), \(V_{mpp}\), \(I_{sc}\)_and_\(I_{mpp}\)), calculated with Equations (4)-(6).  
\[R_{s}=\frac{V_{oc}-V_{mpp}}{I_{mpp}} \tag{4}\]  
\[a=\frac{V_{mpp}\Big{(}1+\frac{R_{s}I_{sc}}{V_{oc}}\Big{)}+R_{s}\big{(}I_{mpp}- I_{sc}\big{)}}{V_{oc}} \tag{5}\]  
\[N=\frac{ln(2-2^{a})}{ln\Big{(}\frac{I_{mpp}}{I_{sc}}\Big{)}} \tag{6}\]
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 4 Experiment
### The Proposed Train Dynamic Model
#### 4.1.2 Comparison with Parametric Based Train Dynamic Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Velocity** & **Acceleration** & **Velocity** & **Acceleration** & **Velocity** & **Acceleration** & **Velocity** & **Acceleration** \\ \hline
3 & 1.081497 & 5 & 1.113422 & 7 & 1.113031 & 9 & 1.112617 \\
11 & 1.112179 & 13 & 1.111718 & 15 & 1.111233 & 17 & 1.110725 \\
19 & 1.110193 & 21 & 1.109638 & 23 & 1.109059 & 25 & 1.108456 \\
27 & 1.107831 & 29 & 1.107181 & 31 & 1.106508 & 33 & 1.105811 \\
35 & 1.105091 & 37 & 1.104348 & 39 & 1.10358 & 41 & 1.10279 \\
43 & 1.101975 & 45 & 1.101138 & 47 & 1.100276 & 49 & 1.099391 \\
51 & 1.098483 & 53 & 1.097551 & 55 & 1.096595 & 57 & 1.095616 \\
59 & 1.094614 & 61 & 1.093587 & 63 & 1.074573 & 65 & 0.9804486 \\
67 & 0.950206 & 69 & 0.893474 & 71 & 0.8668448 & 73 & 0.8167437 \\
75 & 0.7704815 & 77 & 0.7276618 & 79 & 0.6879385 & 81 & 0.6335031 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Relationship between the velocity and the acceleration of PTDM-I. The comparison results of the LSTM based train dynamic model, PTDM-I, PTDM-II and the original data are presented in Figure 6. We only draw the operation curves of these approaches from Xingcheng Station to Magezhuang Station for clarity. As expected, the proposed dynamic model clearly outperformed two traditional parametric models: PTDM-I and PTDM-II. As can be seen in Figure 6, PTDM-I has the highest output errors. In actual applications, before launching a new metro line, the relational table should be adjusted by experienced engineers to adapt to the real environment. As an optimized version of PTDM-I, the model performance was significantly improved in PTDM-II. The reason is that the parameters were tuned using the fruit fly optimization algorithm. More specifically, for each station interval, this algorithm was repeatedly employed for parameter optimization. For example, according to Table 6, \(c_{0}\), \(c_{1}\) and \(c_{2}\) are, respectively, adjusted as 2.908890237, 0.014551371 and 0.00002373932 from Caoziwu Station to Yancun Station. Furthermore, these parameters change to 1.078270769, 0.005545171 and 0.0003830150 between Yancun Station and Xingcheng station. The fruit fly optimization algorithm belongs to a kind of interactive evolutionary computation (refer to [54] for more information). However, PTDM-II is shown to be less effective in the train coasting phase and the train cruising phase. One can also find that the performance of the proposed model was better than PTDM-II in the train coasting phase and the train cruising phase due to its learning ability. Similar to Figure 5, the proposed model could generate very close output to the original data. The experimental results demonstrate that the LSTM based model could remarkably enhance the output accuracy compared to the parametric based models.