esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 4 System Optimization Scheme Based on A Multi-Constraint Multi-Objective Genetic Algorithm
### Problem Formulation  
In the communication-twin-computing integration network, the network function is mapped to the docker containers according to the simulation requirements and the computing resources. The CPU, memory, and bandwidth of the docker containers can be viewed as fixed values before mapping. As shown in Figure 3, the resource in a docker container can be viewed as a box. Thus, we can view the problem of mapping the twin functions to the computing resources as a function-resource three-dimensional packing problem. However, unlike the ordinary three-dimensional packing problem, the function-resource three-dimensional packing problem can only be packed along the diagonal. When the resources of a certain dimension are consumed, it is not possible to continue restarting a new layer of packing. In order to find the optimal solution, we focus on three main objectives: maximizing function utilization, maximizing load balancing, and minimizing the total cost in the 6G satellite twin network.
## 5 Experiments
### Sensitivity Analysis
#### 5.3.3 Sensitivity Analysis on the Charter Ratio  
In this section, we conduct 6 experiments, aiming to illustrate the performance of the bi-objective MIP model in solving instances with different charter ratios. The ratio is denoted as \(\sigma\), where the original weekly charter-in cost and charter-out revenue for a ship of type s is \(c_{s}^{\text{in}}\) and \(c_{s}^{\text{out}}\), respectively. The actual charter-in cost and chareter-out revenue is \(\hat{c}_{s}^{\text{in}}\) and \(\hat{c}_{s}^{\text{out}}\), respectively. Then, \(\sigma=\hat{c}_{s}^{\text{in}}/c_{s}^{\text{in}}=\hat{c}_{s}^{\text{out}}/c_{ s}^{\text{out}}\). To investigate the impact of varying the charter ratio of all ship types on the model's effectiveness, we design instances by altering \(\sigma\) from 0.6 to 1.6 with a step size of 0.2 while keeping other parameters unchanged. Figure 8 shows the Pareto frontiers of all instances.  
The sensitivity analysis on the charter ratio reveals a distinct transformation in the shape of the Pareto frontier. As the charter ratio increases, the frontier becomes progressively flatter. When the charter ratio is low (e.g., \(\sigma=0.6\)), the frontier is steep, meaning that achieving a higher profit requires giving up a large amount of transport volume. In contrast, when the charter ratio is high (e.g., \(\sigma=1.6\)), the frontier is much flatter, allowing the company to improve its profit with a smaller reduction in volume. This change is driven by the direct link between charter-in costs and charter-out revenues. In a low charter ratio environment, chartering out yields less revenue. Therefore, the company's strategy focuses on deploying its fleet to serve a broad range of transport demands, resulting in higher transport volume. To improve profit from this state, the company must forgo large quantities of accepted demand, which explains the steep trade-off. In contrast, in a high  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(k\) & \(c_{p}=0\) & \(c_{p}=20\) & \(c_{p}=40\) & \(c_{p}=60\) & \(c_{p}=80\) & \(c  
character ratio environment, chartering out a ship becomes a very attractive alternative. As a result, the company's optimal strategy shifts: it serves only the demand that generates more profit than simply chartering out a ship. This reveals that a high charter ratio fundamentally changes the business priority from focusing on transport volume to strategically managing the fleet as a portfolio of assets, where chartering ships out can be a more profitable decision than using them to serve all but the most sufficiently valuable transport demands.
# Pedestrian Detection Based on Feature Enhancement in Complex Scenes
## 4 Experiments and Analysis
### Comparison Experiments  
In order to verify the detection performance and effectiveness of the pedestrian detection network PT-YOLO proposed in this paper, we compare the detection results of the network PT-YOLO with those of other mainstream networks. In this paper, the Faster R-CNN network, SSD network, YOLOv3 network, YOLOv7 network and YOLOv8 network were chosen to complete the comparison experiment. The experimental results are shown in Table 5.  
The experiments demonstrate that the pedestrian detection network PT-YOLO exhibits superior overall performance compared to other object detection algorithms. The PT-YOLO  
Figure 11: Pedestrian detection results in actual scenes. (**a**) scene 1; (**b**) scene 2; (**c**) scene 3; (**d**) scene 4; (**e**) scene 5; (**f**) scene 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Precision\%** & **Recall\%** & **MAP@ MAP 0.5** & **Loss** & **Parameters** & **Flops** \\ \hline Faster R-CNN & 39.94 & 72.93 & 65.14 & 0.626 & 9,988,756 & 26.7 \\ SSD & 95.61 & 43.63 & 68.14 & 1.648 & 7,987,021 & 16.2 \\ YOLOv3 & 93.80 & 82.20 & 92.00 & 0.061 & 9,308,902 & 23.4 \\ YOLOv5 & 93.80 & 82.00 & 90.93 & 0.063 & 7,022,326 & 15.9 \\ YOLOv7 & 94.20 & 85.10 & 92.10 & 0.077 & 37,196,556 & 105.1 \\ YOLOv8 & 94.90 & 85.10 & 92.30 & 0.067 & 11,097,853 & 28.7 \\ PT-YOLO & 95.20 & 85.20 & 93.40 & 0.054 & 10,810,495 & 22.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the mainstream detection network.  
pedestrian detection network achieves the highest MAP and the lowest loss. However, the detection accuracy of the SSD network is slightly higher than that of the pedestrian detection network PT-YOLO, and the analysis reveals that the SSD network uses several feature maps at different scales for pedestrian detection. The SSD network predicts the location and category of the target on the feature map at different levels so that it can better adapt to the different sizes of the target. In contrast, the pedestrian detection network PT-YOLO uses single-scale feature mapping, which may limit its detection performance for small or large objects. Because the YOLOv7 network uses more convolutional layers, the YOLOv7 network achieves better results in detection precision and recall, but it has higher losses and flops. We found that the YOLOv8 network also has good detection but with larger parameters. A comparison of the experimental results revealed that our proposed method is optimal. The network PT-YOLO can obtain accurate detection results with low complexity.
# Vibration Response Analysis of Overhead System Regarding Train-Track-Bridge Dynamic Interaction
## 3 Coupling Vibration Analysis of Train-Track-Overhead System  
### Response Performance Evaluation of Train-Bridge System  
The overhead system, train, and track are all considered in the coupling vibration analysis of the train-track-overhead system. The coupling vibration of the train bridge is influenced by a variety of factors. In this study, three variables--track irregularity level, speed, and damping coefficient--are used to analyze the dynamic response of the vehicle-bridge coupling system. This study examines the dynamic behavior of a freight train and a bus traveling at standard speed through an 80 m continuous beam overhead system with 10 spans. Bus and freight train axle weights are 12.5 t and 25 t, respectively. Table 5 provides a summary of the corresponding dynamic performance evaluation index limits. Referring to [17; 18; 19; 20], Table 1 summarizes the corresponding dynamic performance evaluation index limits.
# Toward Adaptability of E-Evaluation: Transformation from Tree-Based to Graph-Based Structure
## 4 Proposed Competence Tree Transformations to Graph-Based E-Evaluation Structure
### TBCG Transformation for Shortest Path E-Evaluation  
In the TBCG case, a contextual graph is started from the action of the upper node (see node 1 in Figure 3), and then a contextual element with two branches is created. One branch refers to the actions that will be performed if the learner performs the task correctly, and the other branch points to the actions that will be performed if the task is performed incorrectly. If the task is completed correctly, e-evaluation is complete. It means the student can solve the most complex task in the test, the task which evaluates all competencies associated with the test, and, therefore, there is no need to solve other tasks.  
In case the learner incorrectly solves the first task, they will need to solve all the tasks in difficulty level 2 (tasks 4, 3, and 2 in Figure 3). This corresponds to a situation when a student is not able to apply multiple competencies at the same time; therefore, it is worth evaluating whether they are able to demonstrate each competence separately. When the learner completes all difficulty level 2 tasks correctly, e-evaluation will be completed. If they fail in any of those tasks, the evaluation path will be supplemented with lower-level competence tasks of the failed task. Therefore, after each task, the evaluation path is forked, and additional tasks are presented in case the student fails. This is repeated while the lowest levels of the competence tree are added to the graph.  
The TBCG transformation result for the example competency tree (see Figure 3) is presented in Figure 4, while the pseudocode of the transformation is presented in Algorithm 1.  
Figure 4: Result of TBCG transformation for competence tree, presented in Figure 3.  
Figure 3: Example of competence tree, used for transformation to graph-based solutions.  
The TBCG transformation does not require data on child competency serial dependency. Relative difficulty is not necessary (all child competency tasks will be given to solve if the parent competency task failed); however, it would be a benefit to keep the idea of presenting tasks from more complex to lower level complexity tasks.
# Dynamic Response Analysis of Structures Using Legendre-Galerkin Matrix Method
## 3 Legendre-Galerkin Matrix Method
### Approximation of the Function Using Shifted Legendre Polynomials  
* Legendre polynomials: introduced by \(L_{m}(t)\), the Legendre polynomials are defined in the interval \([-1,1]\) and can be obtained using the following recursive equations: \[\begin{array}{l}L_{0}(t)=1,\\ L_{1}(t)=t,\\ L_{2}(t)=\frac{3}{2}t^{2}-\frac{1}{2},\\ \vdots,\\ L_{m+1}(t)=\frac{2m+1}{m+1}t\,L_{m}(t)-\frac{m}{m+1}L_{m-1}(t).\end{array}\] (16)  
For further application of these polynomials, they are shifted to the interval \([0,L]\) with the change of variable \(\frac{2}{2}t-1\). Therefore, these shifted polynomials which are shown by \(L_{m}^{*}(t)\) are represented as the following:  
\[L_{m}^{*}(t)=L_{m}(\frac{2}{L}t-1)\qquad t\in[0,L]. \tag{17}\]
* Inner product of two functions: the inner product of two functions _f(t)_ and _g(t)_ is represented by \(\langle f(t),g(t)\rangle\). If the functions are known and continuous in the interval \([0,b]\), then: \[\langle f(t),g(t)\rangle=\int_{a}^{b}f(t)g(t)w(t)dt.\] (18)
* Orthogonality of two functions: two functions _f_(t) and _g_(t) are orthogonal with respect to the weight function \(w(t)\) on \([a,b]\) if: \[\langle f(t),g(t)\rangle=0.\] (19)  
The shifted Legendre polynomials are orthogonal with respect to the weight function \(w(t)=1\) in the interval \([0,L]\). This means that:  
\[\int_{a}^{b}L_{m}^{*}(t)L_{n}^{*}(t)dt=\left\{\begin{array}{ll}0&m\
eq n,\\ \frac{L}{2m+1}&m=n.\end{array}\right. \tag{20}\]  
Orthogonality of the functions has wide application in the function approximation theory. Any continuous function such as _f_(x) can be approximated in the interval \([0,L]\) using these polynomials as below:  
\[f(t)=\sum_{m=0}^{\infty}f_{m}L_{m}^{*}(t), \tag{21}\]  
where _f_m can be obtained from:  
\[f_{m}=\frac{\langle f(t),L_{m}^{*}(t)\rangle}{\langle L_{m}^{*}(t),L_{m}^{*}( t)\rangle}=\frac{2m+1}{L}\int_{0}^{L}f(t)L_{m}^{*}(t)dt. \tag{22}\]Only the first \(N+1\) terms of Equation (21) are used in practice. Therefore:  
\[f(t)=\sum_{m=0}^{N}f_{m}L_{m}^{*}(t)=\mathbf{F}^{T}\mathbf{\Phi}_{N}(t), \tag{23}\]  
where \(\mathbf{F}\) and \(\mathbf{\Phi}_{N}(\mathrm{t})\) are factors of the shifted Legendre vectors expressed as:  
\[\mathbf{F}=[f_{0}\ f_{1}\ \ldots\ f_{N}]^{T}, \tag{24}\]  
\[\mathbf{\Phi}_{N}(t)=[L_{0}^{*}(t)\ L_{1}^{*}(t)\ \ldots\ L_{N}^{*}(t)]^{T}. \tag{25}\]
## 4 Angle Selection and Ray Categorization
### Angle Discretization and Selection
The pentagon shapes lay on the triangular edges of the basic icosahedron that are referred to as the pentagonal points. Furthermore, subdividing the triangles and projecting them onto the TS surface defines a Delaunay grid that forms finer hexagon and pentagon shapes. We then construct a Voronoi polygon on top of the Delaunay grid by connecting the centroids of neighboring triangles as shown in Figure 4c. Hence, the sphere can be potentially covered with both hexagon and pentagon cells to insure full surface coverage. The triangular subdivision process mentioned above can be repeated until the hexagon and pentagon size is close to the transducer footprint. Once the Voronoi polygons size and shape closely match the footprint, the angle discretization process concludes, and each polygon defines a channel with the transmission angle being the angle connecting the polygon centroid to the transmitting node (center of the TS).
# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 4 Experimental Results and Analyses
### The Evaluation of Traffic Information Estimation Methods
#### 4.2.3 The Evaluation of Vehicle Speed Estimation  
For the evaluation of vehicle speed estimation, the estimated vehicle speeds \(u_{i,ha}\), \(u_{i,ha}\), \(u_{i,hp}\) and \(u_{i,hp}\) can be generated via the estimated traffic flow from HO events (\(q_{i,h}\)) Equation (3), the estimated traffic flow by using NLU events (\(q_{i,n}\)) Equation (4), the estimated traffic density by using CA events (\(k_{i,a}\)) and the estimated traffic density by using PLU events (\(k_{i,p}\)) by Equations (9)-(12). For instance, the average vehicle speed of the first road segment \(u_{1,ha}\) can be estimated as 78 km/h (_i.e._, \(u_{1,ha}=q_{1,h}/k_{1,a}\)) at 8 a.m. in accordance with the estimated traffic flow by using HO events (_i.e._, \(q_{1,h}=7560\) car/h) and the estimated traffic density by using CA events (_i.e._, \(k_{1,a}=97\) car/km). This study calculated the accuracies of traffic density estimation as \(1-\dfrac{|U_{i}-u_{i,ha}|}{U_{i}}\), \(1-\dfrac{|U_{i}-u_{i,na}|}{U_{i}}\), \(1-\dfrac{|U_{i}-u_{i,hp}|}{U_{i}}\) and \(1-\dfrac{|U_{i}-u_{i,hp}|}{U_{i}}\). In accordance with the results in Sections 4.2.1 and 4.2.2, Table 6 shows that the average accuracies of vehicle speed estimation of the first road segment between 8 a.m. and 22 p.m. are 92%, 90%, 81% and 85% for the estimated vehicle speeds \(u_{i,ha}\), \(u_{i,na}\), \(u_{i,hp}\) and \(u_{i,np}\), respectively. As shown in Table 7, the results of the vehicle speed estimation show that the estimated traffic flow based on the amount of NLUs and estimated traffic density based on the amount of CAs can obtain the highest accuracy of vehicle speed estimation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{\(\mathbf{K_{1}}\)} & **The Amount** & \multicolumn{2}{c}{**The Amount**} & \multirow{2}{*}{\(\mathbf{k_{1,a}}\)} & \multirow{2}{*}{\(\mathbf{k_{1,p}}\)} & \multirow{2}{*}{\(1-\dfrac{|\mathbf{K_{1}-k_{1,a}}|}{\mathbf{K_{1}}}\)} & \multirow{2}{*}{\(1-\dfrac{|\mathbf{K_{1}-k_{1,p}}|}{\mathbf{K_{1}}}\)} \\  & & **of CAs** & & & & & \\ \hline
8 & 97 & 97 & 126 & 97 & 128 & 99\% & 67\% \\
9 & 88 & 86 & 112 & 86 & 103 & 97\% & 83\% \\
10 & 77 & 71 &
# Total Internal Reflection Lens for Optical Wireless Receivers
## 3 Measurements
### Data Transmission Setup  
Figure 6 shows the experimental setup for data transmission. For the validation of the TIR lens, we transmit a non-return-to-zero (NRZ) on-off keying (OOK) signal through an optical wireless channel with and without the TIR lens. This is close to the baud rate of 8B/10B-coded 1000 BASE-T networks that are highly relevant for industrial real-time communication protocols. A _Stratix V_ field-programmable gate array (FPGA) is used to generate a pseudo-random bit sequence (PRBS-7) at the transmitter. The transmitter converts the signal into the optical domain. Thus, a custom laser driver converts the voltage signal from the FPGA into a current and directly drives an edge-emitting laser diode (LD) to modulate the optical intensity. The center wavelength of the LD is 850 nm. The receiver printed circuit board (PCB) contains an APD for signal detection. The APD bias is varied within a control loop to adjust the APD gain depending on the incident optical power. For strong input signals, the bias and thus the APD gain is reduced to avoid saturation. The photocurrent is converted into a voltage using a transimpedance amplifier (TIA). The eye diagram is measured using a digital oscilloscope of type _Lecroy 10-36 Zi_. The _Stratix V_ FPGA is used to carry out a bit error rate test (BERT) at the receiver. The measurements are repeated for \(Z_{0}=50\) cm, \(Z_{0}=100\) cm and \(Z_{0}=150\) cm with and without the TIR lens. Table 1 summarizes the optical wireless link parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Wavelength & \(\lambda\) & 850 & nm \\ Calculated points & \(N\) & 1200 & per surface \\ PD & - & Si-APD & - \\ PD diameter & \(d_{\text{PD}}\) & 0.5 & mm \\ Responsivity (M = 1) & \(R\) & 0.5 & A/W \\ \hline Lens input diameter & \(d_{\text{l}}\) & 8.8 & mm \\ Acceptance angle & \(\theta_{\text{a}}^{\text{m}}\) & \(\pm 5\) & \({}^{\circ}\) \\ Calculated points & \(N\) & 1200 & per surface \\ \hline Bit rate & \(R\) & 1.289 & Gbit/s \\ Modulation & - & NRZ-OOK & - \\ Pattern & - & PRBS-7 & - \\ Probe bandwidth & \(f_{\text{probe}}\) & 13 & GHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the optical wireless link.  
Figure 6: Experimental setup for the eye diagram and BER measurements.
## 2 Thermal Error Modeling
### Pelican Optimization Algorithm Principle  
The process of establishing the POA-CNN-LSTMNN model for forecasting thermal errors is described as follows:  
1. Establish the architecture of the CNN-LSTMNN.
2. Prepare the training dataset and test dataset.
3. Randomly assign initial values to the parameters and thresholds of CNN-LSTMNN.
4. Initialize the pelican population and configure the parameters of the pelican optimization algorithm, including: 1. The total number of iterations: This parameter influences both the program s runtime and the reliability of the model--here, the algorithm is configured to run for a total of 80 iterations: 2. The population size, represented by the total number of individuals N, specified as 50: 3. The dimensionality of the optimization problem space: To refine the parameters of the CNN-LSTMNN model, namely, the learning rate, the number of iterations, and the number of nodes in the hidden layer, the dimension of the algorithm is set to 3. 5. Compute the fitness score for each individual: Assess the fitness value of each specific individual based on the optimization objective of the problem. Subsequently, perform a sorting operation based on the fitness scores to identify the best position and the worst position found so far. The fitness value can be the objective function value of the problem or the index related to the objective function. Here, POA s fitness function is determined by the _root mean square error_ (_RMSE_) between actual values and the predictions. 6. Find the prey and move towards it: Each individual pelican adopts different search strategies to choose the next moving direction and distance according to its own position and fitness value. Update the member position according to Equation (21) to obtain the current optimal value. 7. Predation of prey: Update the pelican s position further with Equation (23). 8. Evaluation of fitness: Recalculate the fitness value of the moved individual. If the existing best value outperforms the earlier one, the best overall solution is updated. If not, the program returns to step 4 and continues iterating until a termination criterion is met. 9. After the algorithm s iterations are finished, the chosen optimal solution is used to determine the specific values for the parameters and thresholds in the CNN-LSTMNN model. 10. Figure 5 illustrates the workflow of the POA-CNN-LSTMNN thermal error prediction model.
## 2 Background and Related Works
### Related Works
#### 2.2.5 Explainability of Remote Electrical Antenna Tilt  
We apply our method to the remote electrical antenna tilt use case, which has previously been explored from different angles using state-of-the-art methods. [PERSON] et al. [30] applied a safe RL method to avoid the risk of performance degradation, especially in the exploration phase. In [31], [PERSON] et al. also introduced the offline RL method to learn the agent's optimal policy from the real-world data. Furthermore, [PERSON] et al. [32] used a contextual linear bandit method to find an optimal policy with fewer data than naive or rule-based algorithms. [PERSON] et al. [33] introduced an RL method that uses a predefined coordination graph to allow coordination among agents in controlling the antennas. Lastly, [PERSON] et al. [34] proposed graph-attention Q-learning where the graph-attention network [35] is combined with DQN [1] to capture broader network information without having a large state for the agent. All these previous works do not focus on the explainability of the agent. Our work addresses this matter, including how to detect and mitigate bias. We implement our method and analyze our results on this use case and it can be implemented in any other environment, such as for OpenAI gyms (Cartpole and Lunar lander examples are shown in Appendix D).