esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Capacity Trends Analysis
### Simulation Environment Description  
We combine the geometric satellite orbit model and the satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Simulation results in this section were carried out jointly using STK and MATLAB. Firstly, satellite orbital propagators and remote sensor locations of the initial epoch in a MATLAB script are loaded into STK through the interface between STK and MATLAB. Then, STK is used to calculate node parameters (e.g. position, velocity, _etc._) and link parameters (e.g., range, duration, azimuth, elevation, _etc._). Finally, node and link parameters generated from STK are exported to MATLAB for further processing and analysis.  
Our simulations extract node settings from a variety of sources to reflect realistic satellite and sensor networks. Satellite data settings are generated referring to the union of concerned scientists (UCS) satellite database [42] from [[http://www.ucsusa.org/](http://www.ucsusa.org/)]([http://www.ucsusa.org/](http://www.ucsusa.org/)). Information on remote sensors is drawn from the default datasets in STK.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 5 Resource Orchestration
### Orchestration Based on DRL
The space of actions was defined regarding the resources (i.e., uplink and downlink physical resource blocks in RAN, bandwidth in the transport network, and computing resources in edge servers) to assign to each slice at a time slot. Ref. [87] also presented a decentralized approach based on deep deterministic policy gradient (DDPG integrates DQN and actor-critic) to efficiently orchestrate end-to-end resources regarding overhead and delay minimization while considering SLA violations and resources limitation in edge slices. The coordinator agent managed the resource orchestration policies and multiple orchestration agents. The decentralized agents estimated the resource demands of network slices and allocated the orchestrated resources locally. All agents operated with a space of states modeled regarding the status of network slices in queue and performance information provided by the coordinator and orchestration agents to estimate the resource demanded by network slices and orchestrate resources. The action space was defined as the resources to assign to network slices in the base stations and edge servers.
# Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts
## 4 Experiments and Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Level 2** & **Level 3** \\ \cline{3-4}
**Descriptor** & **Level 1** & \multicolumn{2}{c}{**STDU +**} & \multicolumn{2}{c}{**STDU + WLD +**} \\ \hline BGC1 & 0.877 (SVM) & 0.908 (SVM-SGD) & 0.934 (SVM) \\ BGC2 & 0.903 (SVM) & 0.931 (SVM) & **0.969 (SVM)** \\ BGC3 & 0.857 (SVM) & 0.906 (SVM) & 0.931 (SVM) \\ GaborLBP & 0.834 (SVM) & 0.883 (SVM) & 0.903 (SVM) \\ GLCM & 0.923 (RF) & 0.957 (SVM) & 0.966 (SVM) \\ HOG & 0.846 (KNN) & 0.917 (SVM) & 0.94 (SVM) \\ LBP & 0.874 (SVM) & 0.906 (SVM) & 0.929 (SVM) \\ LQP & 0.911 (SVM) & 0.94 (SVM) & **0.969 (SVM)** \\ LTP & 0.889 (SVM) & 0.931 (SVM) & 0.96 (SVM) \\ MTS & 0.909 (SVM) & 0.94 (SVM) & 0.96 (SVM) \\ STU1 & **0.934 (SVM)** & — & — \\ STU2 & 0.914 (SVM) & 0.931 (SVM) & 0.96 (SVM) \\ WLD & 0.931 (SVM) & **0.969 (SVM)** & — \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Forward Subset Selection of descriptors applied to the whole image (also known as \(FSS4\times 4\)). Level 1 uses only one descriptor. The following levels concatenate the best descriptor from the previous level to the rest of the descriptors. The algorithm stops on level 4 because the evaluation measure is not improved from level 3 to 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Level 2** & **Level 3** \\ \cline{3-4}
**Descriptor** & **Level 1** & \multicolumn{2}{c}{**STDU +**} & \multicolumn{2}{c}{**STDU + MTS +**} \\ \cline{3-4}
**BGC1** & 0.845 (SVM) & 0.877 (SVM) & 0.897 (SVM) \\ BGC2 & 0.911 (SVM) & 0.954 (SVM) & 0.957 (SVM) \\ BGC3 & 0.843 (SVM) & 0.863 (RC) & 0.877 (SVM) \\ GaborLBP & 0.783 (SVM) & 0.849 (Bagging) & 0.869 (Bagging) \\ GLCM & 0.909 (SVM) & 0.92 (SVM) & 0.923 (SVM) \\ HOG & 0.846 (KNN) & 0.923 (SVM) & 0.909 (SVM) \\ LBP & 0.831 (SVM) & 0.886 (Bagging) & 0.889 (Bagging) \\ LQP & 0.897 (SVM) & 0.929 (SVM) & 0.931 (SVM) \\ LTP & 0.9 (SVM) & 0.951 (SVM) & **0.954 (SVM)** \\ MTS & 0.903 (SVM) & **0.96 (SVM)** & — \\ STU1 & 0.921 (SVM) & 0.94 (SVM) & 0.949 (SVM) \\ STU2 & 0.917 (SVM) & 0.929 (SVM) & 0.937 (SVM) \\ WLD & **0.94 (RF)** & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Forward Subset Selection of descriptors applied to \(8\times 8\) gridded image (also known as \(FSS8\times 8\)). Regarding the classifiers, in almost all the cases the best classifier is the SVM, which is more evident as the number of descriptors concatenated raises. This is because SVM works well with high dimensionality. Our recommendation is to use SVM trained with SMO.
## 5 Results
### Impact of Air-Gap Length on Coupling Coefficient  
The FEM simulations were accomplished for different air-gap lengths in the range of 10 \(\div\) 25 cm, which encompass Z Class 1 \(\div\) Z Class 3 classes according to SAE J2954 [35]. The results for the coil systems having circular and square geometries are presented in Figure 12(a) and Figure 12(b), respectively.  
Figure 11: Geometry of the coils with symmetrical Fe backing structure: both coils with ferrite plates using (**a**) circular coils; (**b**) square coils.  
Figure 12: Geometry of the coils with symmetrical Fe backing structure: both coils without ferrite backings using (**a**) circular coils; (**b**) square coils.  
Figure 13: Coupling coefficient vs. air-gap length: (**a**) circular coils; (**b**) square coils.  
The results indicate that the dependencies of the coupling coefficient vs. air-gap length for both circular and square coil systems have similar pattern, but the square coil systems show a slight superiority over the circular ones. The structures of the coils with cores having higher ferromagnetic volume produce the higher coupling coefficient values. However, it comes with the price of increased weight, space, and cost of the system. The structure composed of one coil with a ferrite plate and one coil with ferrite bars looks promising for vehicle charging applications.
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 5 Development Trend of Water Quality Monitoring Based on UV-Visible Spectroscopy
### Detection of Water Quality Parameters Based on Multi-Source Data Fusion Technology  
With deeper scientific research in recent years, many methods to detect water quality parameters have been proposed by many researchers. Each of these methods has its own advantages and disadvantages, and significant contributions have been made to the detection of water quality parameter concentrations [79]. However, the results of a single detection method would be biased. If there are human errors in the method, then it cannot accurately reflect the water pollution. Therefore, one of the development trends in water pollution detection in the future is to integrate all the information of water quality parameters obtained from investigation and analysis such that the information is unified by the evaluation method. Finally, a unified multi-source data fusion technology can be obtained. This technology integrates different data, absorbs the characteristics of different data sources, and then extracts better and richer information than a single data source [80]. The process of multi-source data fusion to detect water quality parameters was shown in Figure 7.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 3 Proposed Methodology
### Metrics Used by Our Proposed Algorithm  
In our proposed algorithm, we are including certain metrics on which basis we will select an optimal and efficient node as CH. Mostly every metric has its own importance and concern to select the nest nodes as CH.
## 4 Results and Discussion
Figure 11: \(Prec_{n}\) and \(Reca_{1}\) analysis of the AOBNN-BDNN approach with existing methodologies. Figure 10: ROC curve analysis of AOBNN-BDNN methodology. Figure 12 offers a comparative examination of the AOBNN-BDNN method with existing models with \(accu_{y}\) and \(F_{score}\). The experimental outcomes reported the betterment of the AOBNN-BDNN method. With \(accu_{y}\), the AOBNN-BDNN algorithm has gained increased \(accu_{y}\) of 99.72% whereas the SEODTL-BDC, ESD, LSVM, ESKNN, FKNN, and LD methodologies have reached reduced \(accu_{y}\) of 99.12%, 99.07%, 98.73%, 97.70%, 97.42%, and 97.92%, correspondingly. Additionally, concerning \(F_{score}\), the AOBNN-BDNN method has received increased \(F_{score}\) of 99.64% whereas the SEODTL-BDC, ESD, LSVM, ESKNN, FKNN, and LD methodologies have gained reduced \(F_{score}\) of 98.14%, 98.88%, 98.29%, 98.61%, 96.93%, and 96.99%, correspondingly. Finally, a classification time (CT) inspection of the AOBNN-BDNN model with recent models is carried out in Table 6 and Figure 13. The attained values implied the ESKNN method has shown poor results with a higher CT of 2.28 min. In the meantime, the ESD and FKNN methods have obtained slightly reduced outcomes with closer CT of 2.28 min and 2.24 min, respectively. \begin{table}
\begin{tabular}{l c} \hline \hline
**Methods** & **Classiffication Time (min)** \\ \hline AOBNN-BDNN & 1.01 \\ SEODTL-BDC & 1.18 \\ ESD model & 2.28 \\ LSVM model & 1.95 \\ ESKNN model & 2.80 \\ FKNN algorithm & 2.24 \\ LD algorithm & 1.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification time analysis of AOBNN-BDNN approach with recent algorithms. Figure 12: \(Accu_{y}\) and \(F_{score}\) analysis of the AOBNN-BDNN approach with existing methodologies. In addition, the LSVM and LD models have accomplished moderately reduced CT of 1.95 min and 1.75 min, respectively. However, the AOBNN-BDNN model has gained maximum outcome with minimal CT of 1.01 min. Therefore, the experimental values guaranteed that the AOBNN-BDNN method was found to be an effective tool compared to other approaches.
# Prediction of Tunnel Blasting Vibration Velocity Considering the Influence of Free Surface
###### Abstract  
The blast vibration attenuation will change as the tunnel cut hole is blasted to create a new free surface. To guarantee safe tunnel construction and optimize blast design parameters, it is crucial to understand the impact of the free surface on blast vibration attenuation. Previous studies have often only qualitatively evaluated the effect of the free surface on blast vibration, failing to quantify these impacts on vibration attenuation. In this paper, by analyzing the decay law of the blast vibration velocity under different free surfaces, we quantitatively assessed the effect of the number of free faces and burden distance on the peak vibration velocity. Then, we introduced free-face parameters to enhance the current formula for predicting blast vibration velocity. The results show that the peak vibration velocity decreases with the increasing free surface number and increases with increasing burden distance; the free surface factor cannot be ignored in the decay of vibration velocity and contributes about 21% more to the total decay factor than the scale distance factor. The average tolerance rises from 24.79% in the Sadovsky formula to 13.32% in the correction formula, and the correction formula more precisely predicts the peak vibration velocity. The accuracy of the correction formula provides a good scientific basis for the design of tunnel blasting parameters. The research results were successfully applied to the Jiujiawan proximity tunnel on the Duyun-Anshun highway in Guizhou, effectively guaranteeing blasting safety with a minimum clear distance of 17.2 m.  
t  
t nen engineering: blasting vibration; blasting free surface; vibration velocity prediction
## 2 Rigid-Flexible Coupling Dynamic Model of Cylindrical Roller Bearings
### Rigid-Flexible Coupling Dynamics Theory  
#### 2.3.1 Position of Arbitrary Points on Flexible Body  
Figure 5 shows the position coordinate system of the node \(P\) in the flexible body, where \(\mathbf{e}^{\prime}\) is the fixed inertial coordinate system and \(\mathbf{e}^{b}\) is the dynamic coordinate system that changes with the movement of the flexible body. The dynamic coordinate system changes with the displacement and rotation of the flexible body, and the position and direction of the dynamic coordinate system relative to the inertial coordinate system are defined as the reference coordinates.  
2.3.2. Velocity and Acceleration of Flexible Body at Arbitrary Points  
A flexible body moves from the P1 position to the P2 position, including rigid translation to rigid rotation and deformation motion. The position vector of the node P on the flexible body in space can be expressed as follows:  
\[r=r_{0}+A\big{(}s_{p}+u_{p}\big{)} \tag{13}\]  
where \(r\) is the position vector of point P in the bearing's inertial coordinate system. \(r_{0}\) is the position vector of the cage's floating coordinate origin in the bearing's inertial coordinate system. \(A\) is the direction cosine matrix. \(s_{p}\) is the position vector in the floating coordinate system of point P when the flexible cage is not deformed. \(u_{p}\) is the relative deformation vector, and its expression is as follows:  
\[u_{p}=\Phi_{p}q_{f} \tag{14}\]  
where \(\Phi_{p}\) is the assumed deformation mode matrix of a point conforming to the Ritz basis vector condition. \(q_{f}\) is the generalized coordinate of deformation.  
The first derivative and the second derivative of time are obtained by the Formula (13), and the velocity vector and acceleration vector of any point on the flexible body can be obtained as shown below.  
\[\dot{r}^{p}=\dot{r}_{0}+\dot{A}\big{(}s_{p}+u_{p}\big{)}+A\Phi_{p}\dot{q}_{f} \tag{15}\]  
\[\ddot{r}^{p}=\ddot{r}_{0}+\ddot{A}\big{(}s_{p}+u_{p}\big{)}+2\dot{A}\Phi_{p} \dot{q}_{f}+A\Phi_{p}\ddot{q}_{f} \tag{16}\]
## 3 Analysis and Results of CGAN-IFCM
### Performance Evaluation of CGAN-IFCM
#### 3.1.1 Binary Detection Model using CGAN-IFCM  
Consider that the proposed CGAN-IFCM, the number of clusters \(N_{c}\), and conditioning variables \(\omega_{n}\) and \(\omega_{0}\) highly influence the joined degree of membership \(u_{VDC,pc}\) and cluster center \(v_{VDC,c}\) and thus correlate to the performance (TNR and TPR) of the detection model. The evaluation is first performed on the binary detection model using CGAN-IFCM with the SVD database. Table 2 summarizes the TNR and TPR of the selected scenarios with \(N_{c}=[3,10]\), \(\omega_{n},\omega_{0}=[2,2.4]\), with step size of 1 and 0.1, respectively. Key observations can be drawn from this process:  
1. (\(\omega_{n}=2\)): The membership \(u_{VDC,pc}\) is more dominated by \(\omega_{0}=2\). Therefore, \(\omega_{0}\) increases as the TNR and TPR decrease. 2. (\(\omega_{n}=[2.1,2.4]\)): The highest TNR and TPR are obtained at \(\omega_{0}=2\). This can be explained by the case that if \(\omega_{n}>\omega_{0}\). Recall that \(X_{p}\) and its neighbor \(X_{p}\) share similar characteristics and tend to group under the same \(v_{VDC,c}\). However, when \(\omega_{n}\leq\omega_{0}\), both TNR and TPR decrease. 3. The performance of CGAN-IFCM is better when \(N_{c}=[3,5]\) compared to \(N_{c}=[6,10]\) and deteriorates significantly when \(N_{c}>5\), as some clusters could be redundant and lead to errors in voice disorder detection. As a result, the voice disorder detection model achieves higher accuracy when \(\omega_{n}>\omega_{0}\), which reveals the effectiveness and necessity of the membership function \(u_{neighbor,pc}\) and weighting factor \(\omega_{n}\). On the other hand, when \(\omega_{n}\leq\omega_{o}\), \(u_{VDC,pc}\) tends to be dominated by \(\omega_{0}\), which is close to the performance of existing FCM algorithms. Next, we focus on the binary detection model with the VOICED database. Similarly, Table 3 summarizes the TNR and TPR of the selected scenarios with \(N_{c}=[3,10]\) and \(\omega_{n},\omega_{0}=[2,2.4]\), with step sizes of 1 and 0.1, respectively. Besides following the same observations shown in Table 2, there are two extra observations. 1. The detection model is dominated by a class of candidates with voice disorders because the number of samples of voice disorders remains larger than the number of samples of healthy candidates after the adoption of CGAN. In other words, as shown in Table 3, there is a notable gap (on average, 3.94% versus 0.79%, as shown in Table 2) between TNR and TPR, where TPR is higher than TNR. 2. The overall accuracy of the binary detection model with the VOICED database is less than that of the SVD database. This could be explained in two ways: the imbalanced dataset in SVD and the number of samples in SVD, which is 10 times that in VOICED.
## 3 Numerical Analysis
and \(\rm f_{si}\) is static load of vehicle acting on i-th wheel-rail contact point. \(\rm F_{Hi}\) (\(\rm i=1~{}\sim~{}4\)) are the contact forces by Hertzian spring between the wheels and rail, and can be obtained as follows [23]. The boundary conditions are simulated by two points. The starting point is from 0 m to the 200 m point of the rail as a first layer. \[\rm F_{Hi}=\left\{ \begin{array}{cc}\frac{K_{H}}{1+\alpha(x_{ci})K_{H}}\ \{y_{wi}-~{}y_{r}(x_{ci})\}&\quad\rm if\ \{y_{wi}-~{}y_{r}(x_{ci})\}>0\\ \\ 0&\quad\rm else\end{array} \right. \tag{9}\]  
where \(\rm K_{H}\) is the linearized Hertzian spring coefficient satisfying the following characteristic equation:  
\[\rm\alpha(x_{ci})K_{H}^{3}+~{}K_{H}^{2}-~{}c_{H}^{2}\ \{y_{wi}-~{}y_{r}(x_{ci})\}=0 \tag{10}\]  
And \(\rm\alpha(\bar{\rm\epsilon}_{c})\) is given by:  
\[\rm\alpha(x_{ci})=\frac{\left\{x_{ci}-(L-x_{ci})\left[4\ x_{ci}^{2}(L-x_{ci})^ {2}+\left(x_{ci}L^{3}-x_{ci}^{2}L^{2}+L^{4}\right)\psi_{s}+\psi_{s}^{2}\right] \right\}}{12 EI(1+\psi_{s})} \tag{11}\]  
where:  
\[\rm\psi_{s}=\frac{12 EI}{GA_{r}L^{2}} \tag{12}\]  
and EI, G, \(\rm A_{r}\), and L are the flexural stiffness, shear modulus, cross-sectional area of the rail, and element length, respectively. In this paper, the precast slab track systems were composed by the slab panel with a short length (approximately 5-m length per panel) to ensure rapid installation. Moreover, there was a distance (75 mm) between panels without using the dowel bar to join the slabs. Thus, the discontinuous points will occur while operating the train. Besides, the transition zone (ballasted track-slab track) was also considered and modeled in this study. By using the APATSI program, the structural responses, driving safety, and load transfer efficiency of two types of the track were evaluated. Using the above mathematical model, the train-track interaction analysis was performed while a train moved at 110 km/h. The train we used is an urban trainset, which is being operated in South Korea, and consists of 6 cars with 3 motor cars and 3 passenger cars, as shown in Figure 6. Since the train is symmetric concerning the longitudinal direction, the half models are used, which is summarized in Table 2.  
A 200 m length of track was simulated, which is 100 m of ballasted track and 100 m of precast slab track, as shown in Figure 7. The details of structures and track systems were presented in Table 3. Two types of precast slab tracks use the same kinds of the superstructure. The differences are the elastic devices attached at the bottom of each type of slab track and the distance between the devices as shown in Figures 3 and 4. Figure 6: The urban railway trainset of six cars.
# Deep Learning of Diffuse Optical Tomography Based on Time-Domain Radiative Transfer Equation
## 2 Methodology
### LSTM Deep Learning Method
The learning steps are taken as three methods as follows:  
\[E_{t} =\sigma\Big{(}W_{E}\cdot y_{t-1}+U_{E}\cdot x_{t}+b_{E}\Big{)}\]1 A schematic view of our deep learning scheme based on LSTM. Boxes and circles represent layers and pointwise operations, respectively. Blue circles are input gates and yellow circles are output gates. In addition, a forget gate shown by red circles is embedded, which allows the network to retain or forget some information. See text for the details of the algorithm. For example, the text \(\sigma\) means a sigmoid function and tanh means a tangent hyperbolic function, respectively, which are written in Equations (7)–(12). * Multi-step classification: First, we divide the 28 positions into 8 groups and identify an appropriate group using a deep learning model for the groups. Then, an absorber in the group is identified with a different deep learning model;
* Data subtraction method, where we subtract the data of a firstly detected absorber and then reanalyze the remaining data. In the case of an absorber larger than the size used for the training data, we put the data into the deep learning model twice, where the machine predicts two domains to express the big absorber;
* Multi-beam injection method: we use time-domain data for beams with different injection places. The accuracy of detection is improved especially for multi absorbers, that is, improving accuracy for the prediction. All detailed explanations will be shown in the later section.
## 2 The Proposed Method
### Minimum Sampling Number  
Suppose \(n\) times of the independent random sampling are carried out, in which the probability of correctly selecting the best fitting Bezier curve for each success is \(p\), and the probability of failure is \(1-p\); if X represents the success in \(n\) independent random sampling times, then \(X\) is a binomial random variable with parameters \((n,p)\), and its probability density function is:  
\[p(i)=C^{i}_{n}p^{i}(1-p)^{n-i} \tag{9}\]  
Then the mathematical expectation of the binomial random variable \(X\) is:  
\[\begin{array}{l}E[X]\ \ =\sum\limits_{i=0}^{n}ip(i)=\sum\limits_{i=0}^{n}iC^{i}_{ n}p^{i}(1-p)^{n-i}\\ \ \ =\sum\limits_{i=0}^{n}\frac{in!}{(n-i)!i!}p^{i}(1-p)^{n-i}\\ \ \ =\sum\limits_{i=0}^{n}\frac{n!}{(n-i)!(i-1)!}p^{i}(1-p)^{n-i}\\ \ \ =np\sum\limits_{i=0}^{n}\frac{(n-1)!}{(n-i)!(i-1)!}p^{i-1}(1-p)^{n-i}\\ \ \ =np\sum\limits_{i=0}^{n}C^{k}_{n-1}p^{k}(1-p)^{n-1-k}\\ \ \ =np[p+(1-p)]^{n-1}\\ \ \ =np\\ \ =np\end{array} \tag{10}\]  
Figure 7: Distribution of control points.  
That is, the average number of successes of \(n\) times of independent random sampling is \(n\) multiplied by the probability of each success, so if \(n\) times of independent random sampling are done, at least one success can be expressed as:  
\[E[X]\geq 1\Leftrightarrow np\geq 1\Leftrightarrow n\geq\tfrac{1}{p} \tag{11}\]  
If a control point falls within a \(5\times 5\) neighborhood centered on the correct control point position, it is approximately correct, and the sampling points are uniformly distributed in the sampling space. The minimum required sampling times are then:  
\[n\geq\frac{1}{p}=\frac{N_{w}\times N_{h}}{5\times 5} \tag{12}\]  
where \(N_{w}\) is the width of the sampling region and \(N_{h}\) is the height of the sampling region.