esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Analysis
### Scenario 1
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comparison** & \(\mathbf{X}\) & \(\mathbf{Y}\) & \(\mathbf{Z}\) & \(\mathbf{Phi}\) \\ \hline PID & 50 & 34 & 5 & 61 \\ LADRC & 15 & 62 & 50 & 35 \\ DDPG-LADRC & 1 & 6 & 1 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of transient performance of different control methods: peak time/s. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comparison** & \(\mathbf{X}\) & \(\mathbf{Y}\) & \(\mathbf{Z}\) & \(\mathbf{Phi}\) \\ \hline PID & 6\% & 12.5\% & 10\% & 5\% \\ LADRC & 2\% & 25\% & 5\% & 0 \\ DDPG-LADRC & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of transient performance of different control methods: overshoot. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Comparison** & \(\mathbf{X}\) & \(\mathbf{Y}\) & \(\mathbf{Z}\) & \(\mathbf{Phi}\) \\ \hline PID & 50 & 71 & 10 & 11 \\ LADRC & 14 & 40 & 3 & 35 \\ DDPG-LADRC & 1 & 5 & 1 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of transient performance of different control methods: settling time/s. The tracking error of the ROV trajectory tracking in Figure 9 is shown in Figure 10. Compared to the PID controller and the fixed parameter LADRC controller, the proposed DDPG-LADRC controller has a smaller steady-state error. The PID and fixed-parameter LADRC control schemes are unable to eliminate steady-state errors in a short time, which leads to an inability to track the desired trajectory. However, the DDPG-LADRC significantly improves the control accuracy of the system by introducing DDPG to achieve online tuning of LADRC parameters in response to environmental changes. This ensures that the ROV can maintain satisfactory control performance even in the presence of inaccurate model parameters and significant uncertain disturbances. After 60 s, data from 1000 sampling points should be collected to calculate the root mean square error for determining the stable accuracy of the control method, as presented in Table 7.  
In underwater robot control, better stability accuracy means that the robot can precisely reach the target position. Simulation results indicate that the designed DDPG-LADRC controller not only has robust performance but also possesses the ability to quickly track commands and suppress disturbances. Further comparisons show that the performance of \begin{table}
\begin{tabular}{c c c c c} \hline
**Comparison** & \(\mathbf{X}\) & \(\mathbf{Y}\) & \(\mathbf{Z}\) & _Phi_ \\ \hline PID & 0.351 & 0.497 & 0.381 & 0.004 \\ LADRC & 0.0285 & 0.3587 & 1.43 \(\times\) 10\({}^{-5}\) & 0.003 \\ DDPG-LADRC & 5.43 \(\times\) 10\({}^{-5}\) & 1.14 \(\times\) 10\({}^{-4}\) & 1.29 \(\times\) 10\({}^{-14}\) & 6.56 \(\times\) 10\({}^{-9}\) \\ \hline \end{tabular}
\end{table} 
Table 7: Stable accuracy (\(X\), \(Y\), \(Z\) = m, _Phi_ = rad). Figure 10: Comparison of tracking errors of different control methods for ROV. DDPG-LADRC surpasses that of PID and conventional fixed-parameter LADRC. Therefore, parameter optimization based on DDPG can enhance the control performance of LADRC.
## 2 An Optimized Handover Based on Context Information
### An Optimized Handover Scheme  
In this paper, a UE periodically checks the RSRP of neighboring BSs and it searches target cell list if the RSRP of currently associated BS is smaller than a predefined threshold, as defined in 3 GPP [35]. Then, it makes target cell list. In this paper, the speed of a UE is classified as either low or high, and the data rate of a service is classified as either low rate data traffic or high rate data traffic. Then, the speed and data rate of a UE are used to decide a target cell list. Similar to the work in [36], the condition of Equation (1) for call admission control is checked against cells in target cell list, where C', Creq, THHO, and C represent required capacity by active UEs, capacity required by a handover call, the ratio of reserved cell capacity for a handover call, and the total capacity, respectively. If the condition of Equation (1) is not satisfied at a cell, the cell is removed from the target cell list:  
\[\text{C'}+\text{Creq}\leq\text{TH}_{\text{HO}}\times\text{C} \tag{1}\]
# Improving the Resilience of High-Speed Rail Systems from a Configuration Perspective
## 6 Recommendation
### Improving System Resilience Attributes  
According to the core conditions, four variables are critical to improve system resilience attributes, i.e., DW, PA, FR, AT. Disturbance warning is useful for avoiding disasters [48; 81]. For example, the main reason for the Ningbo-Wenzhou Railway incident is the failure of early warning system. To identify disturbances early, installing a large number of automatic alarm systems for emergencies is a common way. Also, \"Internet + HSR\" application technology are helpful to achieve the overall safety, efficiency, and rapid response of a system.  
To improve persistence ability, maintaining a good level of safety and normal operation is critical. According to the Smart HSR 2035 research project funded by the Chinese Academy of Engineering, decision-making departments should have a high level of knowledge and competence, and strengthen daily emergency decision-making exercises to ensure that they can respond quickly and efficiently prevent the spread of disasters.  
To guarantee function restoration, several aspects can be considered. First, a backup line can be prepared. When a line is damaged, an HSR should still have a certain transportation capacity through the backup line [16]. Second, HSR operators should develop different emergency plans for different sections of each HSR line. Combining the local natural environment and cultural conditions, HSR operators can formulate plans for possible disruptions, and strictly implement various emergency drills in peacetime [88]. Third, HSR operators can set up a special emergency team for possible disruptions. This can efficiently avoid the impact of human factors on the operation of the HSR, and ensure that each incident can be quickly responded by a set of separate special plans. Furthermore, HSR operators should improve road accessibility for rescue teams and arrange the optimal location and allocation of relief trains to enhance the resilience level of the rail network [54; 89].  
In order to improve the adaptability and transformability of an HSR system, it is necessary to study and reflect on resources, management, and other aspects. First, under the premise that the foreseeable risks of each HSR are basically clear, human resources should be strengthened. Second, a database covering operating cases and accident cases should be established to improve the speed of information acceptance and feedback [35]. Third, a benign improvement mechanism should be formed based on consolidated theoretical study, to ensure that there are reasons for the transformation to be followed [5].
## 3 System Model
### PSO Model Review  
In WiFi indoor positioning, the user's location is initially unknown, and positioning needs to be performed within a designated area. This concept is similar to the idea behind PSO particle optimization [29]. PSO particles correspond to the physical coordinates of WiFi indoor positioning, representing particle coordinates. PSO initializes particles randomly within a designated area, iteratively optimizes them based on a fitness function, and seeks both personal and global best particle coordinates [28]. Through the continuous iteration of particle optimization, it gradually converges to the best k particle coordinates. Finally, the WKNN algorithm is employed to determine the final positioning coordinates.
# A Numerical Study of the Behavior of Micropile Foundations under Cyclic Thermal Loading
## 3 Results and Discussion
### _Pile axial thermal displacement_
#### 3.3.2 Influence of Surface Temperature Boundary Condition  
[PERSON] et al. (2021) [59] examined the effect of an \"inside\" surface temperature boundary condition on a thermally-activated large diameter pile in a cohesive medium, by introducing a periodic function similar to that described in Section 2.3. They found that tension was generated at the pile head, but this stabilized after a number of seasonal cycles and the overall effect in terms of pile head thermal movement and axial thermal load was similar to the case where the \"outside\" condition was maintained. Figures 11 and 12, and Tables 5 and 6, present and summarize the results of a similar assessment for the two micropile configurations considered in this study.  
Figure 11: Profiles of pile thermal vertical displacement, axial stress, and pile–soil interface shear stress after 10 cycles of balanced thermal loading for isolated pile (R/D = 30, top) and pile group (R/D = 4) with inside surface temperature boundary condition.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## Inverse Models
### Temporal Inversion
#### 4.3.3 A Second Method with a Geometric a Priori: The Subgraph Method  
In order to address the projection issue related to the Projected Disk method above, we designed a new method. Here, a mesh is considered as a graph that is, a set of nodes linked by edges (Figure 5). For instance, for the graph on the left-hand plot which is constituted of 7 nodes, the list of edges is \([(1,2),(1,3),(1,4),(4,5),(4,6),(6,7)]\). A matrix representing the distances between nodes in the graph can then be constructed from this list. The distance is expressed as the minimum number of edges required to draw a path between two nodes. For the left-hand plot (Figure 5), the matrix of distance has the following values.  
Distances from node 1 to all the other nodes correspond to the top line of the matrix and are indicated in blue in Figure 5, distances from node 2 to all other nodes correspond to the second line and so forth. A mesh of triangular elements like the one represented in Figure 5B is considered as a graph, from which the matrix distance can easily be computed with Matlab. Using this matrix, it is straightforward to pick nodes at a given edge distance from any specific node in order to select a subpart of the original mesh, which would constitute the pressurized source.  
This method requires less parameters than the Projected Disk method as that part of the mesh which is submitted to pressure is fully described by only three parameters: a node number \(n\), a distance \(d\) expressed as a number of edges and an overpressure \(P\). The new mesh is a subpart of the original mesh centered on the selected node \(n\) and containing all the nodes located at a distance of less than \(d\). For this method to lead to a circular pressurized area (the minimum level of a priori), triangular mesh elements need to be roughly equilateral.
## 4 Results and Discussion  
The Spatiotemporal Variation Characteristics of Pressure and Pressure Gradient during Compression Wave Propagation  
Figure 11 shows the generation mechanism of the initial compression waves and their propagation characteristics in the tunnel.  
Point a in Figure 10(a) represents the starting point of the initial compression wave front, point b denotes the generation of the initial compression waves as the train enters the tunnel, and point c signifies the endpoint of the initial comp  
Figure 11: Characteristics of generation and propagation of initial compression waves: (**a**) generation of initial compression waves; (**b**) propagation of initial compression waves.  
point a, the pressure had already begun to rise, indicating that disturbance had been created in the air inside the tunnel before the high-speed train entered it. Until the leading edge of the high-speed train's nose reaches the entrance of the tunnel, the pressure increment reaches \(\Delta P_{\text{b}}\). When the rear end of the high-speed train's nose reaches the entrance of the tunnel, the pressure reaches its maximum value, designated as \(\Delta P_{\text{c}}\). As the high-speed train continues to enter the tunnel, the friction between the train's surface and the air causes the pressure to increase gradually, resulting in the pressure increment from point c to point d. The segment from point a to point c on the curve is defined as the initial compression waves.  
In Figure 11b, the left and right sides of the diagram illustrate the variations in pressure amplitude and pressure gradient of the compression waves, respectively. The solid and dashed red lines represent the running trajectories of the train's head and tail, while the solid and dashed black lines represent the propagation trajectories of the waves' front and the waves' trail of the initial compression waves. As the compression waves propagate in the tunnel, the pressure amplitude of the compression waves continuously decreases with increasing propagation distance. In undisturbed tunnel regions, the pressure remains at 0 kPa. The pressure gradient of the compression waves rapidly increases within a propagation distance of 5 km, reaching its maximum value at 5 km before gradually decreasing thereafter. During the whole propagation process, the pressure gradient of the compression waves always remains greater than the pressure gradient of the initial compression waves.
## 4 Result and Discussion
### Performance Evaluation Measure  
The proposed CNN models are evaluated using an independent test dataset from the training data in each cross-validation fold. Accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) curves are used as measures for the comprehensive evaluation of the CNN models. The accuracy, sensitivity, and specificity scores are calculated as follows (Table 1):  
\[\text{accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FN}+ \text{FP}}\]  
\[\text{sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]  
\[\text{specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}\]  
TP and TN denote the numbers of images correctly predicted as abnormal and normal, respectively. FP and FN denote the numbers of images incorrectly predicted as abnormal and normal from normal and abnormal, respectively. The measure \"accuracy\" refers to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{Real} \\ \cline{3-4}  & & Abnormal & Normal \\ \hline \multirow{2}{*}{Prediction} & Abnormal & TP (True positive) & FP (False positive) \\  & Normal & FN (False Negative) & TN (True negative) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation metrics for classification.  
Figure 8: Diagram of five-fold cross-validation.  
the proportion of correctly classified numbers in the total data. The ratio of abnormal data among the abnormal data that have received abnormal determination is known as the \"sensitivity\", and the ratio of the data that have been determined to normal among normal data is known as the \"specificity\". The ROC curve is a graphical plot that illustrates the diagnostic ability of a binary classifier system when its discrimination threshold is varied. The ROC curve is plotted by the true positive rate against the false positive rate at various thresholds (see Figure 9). Additionally, the area under the ROC curve (AUC) is calculated. A larger AUC represents better performance.  
Additionally, to overcome the weakness of the deep learning model as a non-interpretable black box model, a visual explanation technique such as gradient-weighted class activation mapping (Grad-CAM) [58] is used to validate the effectiveness of the models. In recent studies, several researchers have attempted to understand AI models through visual explanations using the VGG16 model and Grad-CAM [59; 60; 61; 62]. To apply Grad-CAM, we use the following values for each model; the output of the last convolutional layer, and the weight of the fully connected layer that follows it. Grad-CAM enables one to understand which features in each Rol are observed and learned by the models. It shows the emphasized important areas used for classifying normal and abnormal images.
# Transmission Power Adaption for Full-Duplex Relay-Aided Device-to-Device Communication
## 2 System Model
### FD Relay-Aided D2D Communication Scheme
From this, we know that the power of receiving signal at D2D relay is:  
\[E\Big{\{}\left|r(i)\right|^{2}\Big{\}}=p_{B}\left|h_{BR}\right|^{2}+p_{R}\left| h_{L}\right|^{2}+\sigma^{2} \tag{9}\]  
\(g\) represents the normalized amplify factor by the D2D relay with adopting amplify forward protocol which means that the relay will amplify the input signal by factor \(g\). It can be given by:  
\[g=\sqrt{\frac{\alpha p_{R}}{p_{B}\left|h_{BR}\right|^{2}+p_{R}\left|h_{L} \right|^{2}+\sigma^{2}}} \tag{10}\]  
in which \(\alpha p_{R}\) is denoted as the power used for forwarding cellular user' signals. Naturally, we also have the aggregate power consumption at D2D relay as \(E\Big{\{}\left|t(i)\right|^{2}\Big{\}}=p_{R}\). Thus, the signal-to-interference and noise ratios (SINR) \(\gamma_{R}\) of the first hop at the relay can be formulated as:  
\[\gamma_{R}=\frac{p_{B}\left|h_{BR}\right|^{2}}{p_{R}\left|h_{L}\right|^{2}+ \sigma^{2}} \tag{11}\]  
Then we let the numerator and denominator be divided by \(\sigma^{2}\), the SINR can be rewritten as:  
\[R^{re}>W\log_{2}(1+\gamma_{BC}) \tag{3}\]0From the Equation (8), we can see that the transmitting signals of D2D relay are composed by two portions. The first portion contains the signal \(x(i)\) transmitting for cellular user and the second portion is the signal component sending to the D2D receiver using the remaining \((1-\alpha)\) fraction power. When decoding respective signals at the receivers, the cellular user and D2D receiver will treat the signal component designated for the other as interference. We get the receiver signal at cellular user and D2D receiver as follows:  
\[R^{re}>W\log_{2}(1+\gamma_{BC}) \tag{3}\]1 \[R^{re}>W\log_{2}(1+\gamma_{BC}) \tag{3}\]2  
In FD mode, the cellular user decodes the relayed signal \(t(i)\) from \(y_{\text{C}}(i)\) and the weak DT link signal \(x(i)\) is treated as interference. Therefore, as above, the SINR of the second hop at the cellular user \(\gamma_{\text{C}}(i)\) can be formulated as:  
\[R^{re}>W\log_{2}(1+\gamma_{BC}) \tag{3}\]3  
The instantaneous end-to-end SINR of one symbol transmitting from BS to cellular user in FD mode can be expressed as [28]:  
\[R^{re}>W\log_{2}(1+\gamma_{BC}) \tag{3}\]4  
In our model, these expressions we deduce are explicitly including the effects of the self-interference and the overheard direct link. Then to guarantee the rate requirement of the cellular user with the proposed spectrum sharing protocol, the SINR of the system should be greater or equal to the requirement of SINR in the conventional DT mode as follows:  
\[R^{re}>W\log_{2}(1+\gamma_{BC}) \tag{3}\]5  
As before, the SINR of D2D receiver can also be formulated as:  
\[R^{re}>W\log_{2}(1+\gamma_{BC}) \tag{3}\]6  
Finally, we can get the achievable rates for the cellular and D2D links respectively, as:  
\[R^{re}>W\log_{2}(1+\gamma_{BC}) \tag{3}\]7  
\[R^{re}>W\log_{2}(1+\gamma_{BC}) \tag{3}\]8
## 2 Welding Area Image Acquisition and Defect Classification
### Defect Classification of the Safety Vent  
The defect classification of the safety vent's welding area focuses on three categories: Two of the most common defect types and one normal type. All data are from a real production floor, as shown in Figure 2. The Normal type refers to a welding area that has no defect. The Porosity defect type refers to a void (the radius is approximately 1 mm) in the welding area. The Level Misalignment defect type occurs when the heights of the two pieces of metal welded are not well aligned (the height difference is approximately 1 mm). The Normal type images represent qualified products and the other two categories represent defective products. Each image was taken from the surface of each individual safety vent. A total of 8941 images were obtained, which consisted of 1715 Normal images, 3879 Porosity images, and 3347 Level Misalignment images. Generally, the welding area images were similar in most of the areas, but each defect type had unique characteristics. For example, the Porosity type typically had a hole in the welding seam, but the position of the hole is uncertain. Except for the black holes in Figure 2b, there was almost no difference in appearance between the Porosity images and Normal images. Additionally, the Level Misalignment type could also have this type of black hole; that is, these classifications are not strictly defined. Taking into consideration this complicated scenario, it is difficult to set a template for inspection using a feature extraction algorithm.  
Figure 1: Welding area image acquisition setup.  
Figure 2: _Cont_.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 6 Performance Study
### Adaptation to Requirement Changes  
#### 6.1.1 Effects of Required DCSR Changes  
Figure 4 illustrates the effect of the required DCSR, \(p_{s}\), which varies from 80% to 97%. Figure 4a shows the collected DCSR for different required DCSRs and also the average E2E delay when using the proposed algorithm. The results show that the required delay-constrained success ratio is satisfied and the collected DCSR is higher than the required DCSR. This is attributed to the fact that the duty cycle interval is calculated in the worst case in which the transmission hop is six and the set of random variables \(S_{j}\) is identically distributed. For example, when the requirement is set to 80%, 91.7% of the packets can reach the sink within 20 s. In addition, we can see that the gap between the collected and required DCSR tends to become smaller when the required DCSR increases, as shown in Figure 4a.  
Moreover, as shown in Figure 4b, the calculated duty cycle interval decreases as the required DCSR increases. This means nodes wake up more frequently; in other words, packets tend to be forwarded earlier. Since other parameters remain unchanged (e.g., node density, delay requirement), the duty cycle interval drop results in the decreased average E2E delay, as shown in Equation (12). For example, when the required DCSR varies from 80% to 97%, the duty cycle interval and the average E2E delay gradually fall from 23.29 s to 17.49 s and 12.01 s to 9.14 s, respectively. Figure 4b also shows that our algorithm can achieve a high packet delivery ratio, approximately 100% over the range of required DCSR. From Figure 4, the results also indicate that the duty cycle interval plays a key role in the E2E delay. Thus, we estimated packet delay as a function of duty cycle interval in the proposed algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline Radius of total circular area & 300 m \\ Transmission range of a node & 75 m \\ Group width & 37.5 m \\ Data packet size & 46 bytes \\ Beacon message size & 6 bytes \\ Duty cycling & 6\% \\ Simulation time & 3000 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation setup.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Range** & **Default Value** \\ \hline Required DCSR (\%) & \{80 85 90 95 97\} & 95 \\ Delay bound (s) & \{10 20 30 40 50\} & 20 \\ Node density & \{3 4 6 8 10 12\} & 8 \\ Event rate (packets/s) & \{0.1 0.5 1.0 1.5 2.0 2.5 3.0\} & 0.5 \\ The number of sources & \{1 2 4 8 12 16 20\} & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network parameters. DCSR, delay-constrained success ratio.