esa-sceva/satcom-chunk-collection huggingface dataset:
## Numerical Examples  
### Workflow for the Test Cases  
There are a series of steps included in the workflow of this study (see Figure 3). These main tasks are developing conceptual models, generating grids or meshes for different model concepts, simulating steady-state flow, and conducting particle tracking for the advective transport. The quantitative comparisons of head distribution and particle traces are then based on the results obtained from the DFN, ECPM, and the developed HD models. In this study, the flow and advective transport solutions of the DFN and ECPM model were obtained based on the FracMan and DarcyTools software. Notice that the DFN model (i.e., FracMan) only focuses on the fractures [25; 26], while the ECPM model (i.e., DarcyTools) considers the lumped behavior of the fractures and rock matrix [27; 28; 29]. In test Case I, the designed fractured rock is relatively simple. However, with the more complex feature of test Case II, the FracMan model (i.e., DFN) was excluded in this case because the feature to set fracture geometries and boundary conditions is not available in the DFN model.  
This study aims to assess the flow and advective transport based on the developed HD model. We used the solution from FracMan to obtain the DFN results and the solution from DarcyTools to obtain the ECPM results. The conceptual models for the test cases are similar to the used DFN and ECPM models. However, the boundary conditions and release of particles might be modified to fit the fractures in the DFN model and specify the cells in the ECPM model.
## 4 Next Forwarding Node Selection in UWSNs
### Taxonomy of Next Forwarding Node Selection in UWSNs
#### 4.2.1 Localization Routing Protocols
Moreover, PER consumes less energy and reduces the overhead by employing a forwarding tree-trimming mechanism that helps in reducing the number of duplicated packets. However, this protocol did not provide a method to avoid the communication void problem Moreover, the use of single sink architecture entails high energy consumption for nodes closer to the sink. Furthermore, it still needs an expensive location information that provided by GPS, which cannot be used in underwater environments. Figure 3: Virtual pipelines in HH-VBF. Different from VBF, an energy-efficient and topology routing protocol (SEANAR) for an underwater sensor network is designed based on topology information [39]. In general, this protocol uses spherical layers as shown in Figure 5 for dividing the forwarding area, whereas one sink is deployed in the center of the water's surface. Three types of neighbors have been introduced in SEANAR: inner layer (i.e., closest to the sink), aside layer (i.e., almost halfway to the sink), and outer layer (i.e., furthest from the sink). SEANAR is divided into two phases. The first phase is responsible for sharing information such as the node ID, residual energy, and location for the nodes that are located in the inner and aside layer by a broadcasted hello packet, whereas the nodes at the outer layer discard the hello packet. Next, the number of nodes in the inner and aside tables has been counted, and the degree of each node has been calculated based on this number. The second phase is responsible for selecting the next forwarding nodes by broadcasting a hello packet. The receiver nodes update the hello packet if the sender nodes are in the outer or aside layer. The sender node then calculates the weight after receiving all hello packets from its neighbors. The main advantage of this protocol is that the weight values are calculated based on multi-metrics. However, this is not efficient due to the lack of GPS information and unreliable links. Moreover, SEANAR did not provide any solution for the communication void problem. Furthermore, due to the huge number of hello packets that are used to calculate the weight value, the energy consumption increased. Also, the use of single sink architecture causes high energy consumption for the nodes placed closer to the sink. Figure 4: Deployment of PER in USWNs. Figure 5: Layering in SEANAR.
## 3 Offense and Defense Game Model
### Network Topology Diagram Model  
A simple undirected graph \(G(V,E)\) can be seen as an abstraction of a WSN, where \(V=\{V_{1},V_{2},\ldots,V_{N_{v}}\}\) is a set of nodes. \(N_{v}=|V|\) is the total number of nodes. Each \(V_{i}\) represents a sensor node in a WSN. \(E\subseteq V\times V=\{E_{1},E_{2},\ldots,E_{N_{v}}\}\) is a set of edges, where \(N_{e}\) is the total number of edges. And each \(E_{i}\) represents a transmission link through which data can be transmitted between two sensor nodes. The mapping abstraction is shown in the following Figure 1.  
\(A\left(a_{ij}\right)_{N_{v}\times N_{e}}\) is the diagonal matrix of graph \(G\), and \(a_{ij}\) represents the presence or absence of link connectivity between node \(V_{i}\) and \(V_{j}\). If nodes \(V_{i}\) and \(V_{j}\) have a message transmission link and assume that the number of packets transmitted, received, and forwarded in the link is \(S_{ij}\), and \(S_{ij}=S_{ji}\), then the link is assigned a weight \(a_{ij}=a_{ji}=S_{ij}=S_{ji}\). Otherwise, \(a_{ij}=a_{ji}=0\). Therefore, the definition of diagonal matrix \(A\) is defined in this paper as follows in Equation (13).  
\[a_{ij}=a_{ji}=\left\{\begin{array}{ll}S_{ij}=S_{ji}&\text{Node $i$ is connected to node $j$}\\ 0&\text{Node $i$ is unconnected to node $j$}\end{array}\right. \tag{13}\]  
Since the total number of packets transmitted, received, and forwarded by each node is not equal, the importance of each node in a WSN is different. \(I_{V_{i}}\) represents the importance of node \(V_{i}\). The definition is shown in Equation (14).  
\[I_{V_{i}}=a_{i1}+a_{i2}+\cdots+a_{iN_{v}} \tag{14}\]  
Similarly, each edge has a different level of importance. \(G_{max}\) is the maximum connectivity of the undirected graph \(G\). \(G_{E_{i}}\) is the maximum connectivity of the graph after removing an edge \(E_{i}\). Assume that \(E_{i}\) connects node \(V_{i}\) with node \(V_{j}\). Then \(I_{E_{i}}\) is the importance of that \(E_{i}\) edge, defined as in Equation (15). The calculation of link importance is divided into two parts. The first part is the proportion of the number of packets transmitted, forwarded, and received by the edge to the total number of packets transmitted, forwarded, and received by the whole network. The second part is the size of the change of the graph connectivity after removing the link. The larger it is, the more important the link is.  
Figure 1: Illustration of the process of abstracting a WSN into an undirected graph.  
\[I_{E_{i}}=\frac{a_{ij}+a_{ji}}{\sum_{i=1}^{N_{v}}\sum_{j=i}^{N_{v}}aij}+\frac{G_{ max}-G_{E_{i}}}{G_{max}} \tag{15}\]
## 4 Simulation And Discussion
In Figure 8, all agents with the traditional ILC algorithm completely track the leader's trajectory after the 100 th iteration, but the convergence rate is slower than that of the ILC algorithm based on fractional-power error signals with \(\alpha\in[0.7,1)\). As discussed above, it is discovered that fractional-power error signals show beneficial influences on the convergence rate. Figure 2: The communication digraph. Figure 4: Tracking process at 70 th iteration. Figure 5: The control input signals at 70 th iteration. Figure 3: Tracking process at 10 th iteration. Figure 8: Tracking error of traditional proportional differential (PD)-type ILC algorithm. Figure 6: Tracking error of ILC algorithm based on fractional-power error signals with \(\alpha=0.7\). Figure 7: Tracking error of ILC algorithm based on fractional-power error signals with \(\alpha=0.85\). In order to consider the external environmental effects, as well as model uncertainties of the multiagent systems, we use the following model to illustrate the robustness of the proposed ILC algorithm. \[\left\{ \begin{array}{l}\dot{x}_{i,k}(t)=A_{i}x_{i,k}(t)+f_{i}\left(x_{i,k}(t) \right)+B_{i}u_{i,k}(t)+w_{i,k}(t)\\ y_{i,k}(t)=C_{i}x_{i,k}(t)\end{array} \right.,i=1,2,3,4,5,6, \tag{34}\]  
where \(f_{i}\left(x_{i,k}(t)\right)\) represents unmodeled dynamics and \(w_{i,k}(t)\) denotes repetitive disturbance. Choose \(f_{1}\left(x_{1}\right)=x_{1}\), \(f_{2}\left(x_{2}\right)=\sin x_{2}\), \(f_{3}\left(x_{3}\right)=\cos x_{3}\), \(f_{4}\left(x_{4}\right)=x_{4}\), \(f_{5}\left(x_{5}\right)=\sin x_{5}\), \(f_{6}\left(x_{6}\right)=\cos x_{6}\), \(w_{1,k}(t)=w_{2,k}(t)=\left[ \begin{array}{c}\sin(10\pi t)\\ \cos(10\pi t)\end{array} \right]\), \(w_{3,k}(t)=w_{4,k}(t)=0.5\left[ \begin{array}{c}\cos(10\pi t)\\ 2\sin(10\pi t)\\ 3\sin(10\pi t)\end{array} \right]\), \(w_{5,k}(t)=w_{6,k}(t)=0.5\left[ \begin{array}{c}\sin(10\pi t)\\ 2\sin(10\pi t)\\ 3\cos(10\pi t)\\ 4\cos(10\pi t)\end{array} \right]\); other parameters remain unchanged. Figure 9 shows that the proposed ILC algorithm is sufficiently robust to model uncertainty and to repetitive disturbances.
## 3 Theoretical Analysis
### Nonlinear Dynamic Analysis  
The method of nonlinear dynamic analysis is the initial condition method. The initial condition method obtains the internal forces of the components to be removed under a static force before the failure of the structure, applies the reaction force after the removal of the components in the remaining structure, and applies the constant and live load in the overall structure. When the residual structure reaches static equilibrium, the reaction force of the component to be removed is suddenly removed to simulate the failure of the component [23].  
First, the load is applied to the original structure. The load combination is as follows:  
\[1.2 DL+0.5 LL \tag{1}\]  
where \(DL\) is the constant load and \(LL\) is the live load. The internal force of the original structure under the above load is calculated, and the unbalanced force at the end of the failure column in each working condition is obtained.  
Then, the simulated failure column in the original structure is removed. At the same time, the corresponding unbalanced force is applied to the node at the upper end of the simulated failure column to make the structure equivalent to the original structure. In the new model, the constant load and live load of the structure were loaded from \(t_{0}\) to \(t_{1}\) according to Figure 7a, and the unbalanced force was loaded according to Figure 7b. The failure of a single column was simulated and nonlinear dynamic analysis was carried out. A stability period (\(t_{1}\) to \(t_{2}\)) of 1.5 s was taken to ensure the initial equilibrium condition before removing the column. The column-removal time (\(t_{2}\) to \(t_{3}\)) was considered to be less than one-tenth of the period of vertical motion as per the GSA guidelines, and it was taken as 0.001 s for all of the scenarios [2]. \(P\) is the magnitude of the force while \(t\) is time.  
Figure 6: Bilinear isotropic hardening constitutive model.
## 2 Materials and Methods
### Control System Design  
#### 2.3.1 Automatic Line Pairing System Program Design  
In this system, Hirschmann IMC series PIC controllers are used, the programming language is ST, with CODESYS as the software platform. Pressing the button of the automatic alignment system will cause the automatic alignment mode to be initiated. The program design of the automatic alignment system includes the acquisition of the angle sensor signal of the touch alignment mechanism, the electric steering wheel motion control, and the steering wheel angle sensor signal acquisition. When the automated alignment system is operating normally, the system employs a fuzzy PID control method to alter the electric steering wheel deflection angle through CAN bus and subsequently regulate the harvester's travel direction. After the signal processing, the main control unit calculates the lateral deviation of the front-end touch mechanism through the kinematic model, and then fuzzes and defuzes it to acquire the three coefficients for controller adjustment and the final output control quantity. Simultaneously, following the steering wheel deflection, according to the feedback angle signal from the rear wheel angle sensor to determine whether the rear wheel meets the predetermined criteria, and then complete the automatic alignment system design process as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Operating voltage (V) & 9–16 \\ Max. output torque (N.M) & 16 \\ Max. speed (RPM) & 120 \\ Response delay (s) & \textless{}0.2 \\ Steering error with load (\({}^{\circ}\)) & \textless{}\pm 5\) \\ Communication protocol & CAN2.0B \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters of electric steering wheel.  
Figure 7: Electric steering wheel installation schematic (1) Steering wheel top cover (2) Steering wheel skeleton (3) Motor (4) Adaptability flange (5) Motor bracket (6) Socket head cap screw.
# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 4 Case Study
### Results
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **FFNN** & **FFNN + OSR** \\ \hline No. hidden layers & 3 & 3 \\ Nodes per layer & [500, 2000, 2000, 400, 2] & [500, 2000, 2000, 400, 2] \\ Nonlinearity & ReLU & ReLU \\ Batch size & 32 & 32 \\ Optimizer & RMSprop & RMSprop \\ Learning rate & \(1\times 10^{-4}\) & Cosine (\(1\times 10^{-3}\) to \(1\times 10^{-4}\)) \\ Learning rate warmup & - & 10\% of epochs (init. \(1\times 10^{-4}\)) \\ Epochs & 50 & 50 \\ Label smoothing & - & 0.1 \\ Accuracy & 99.99\% & 99.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: FFNN parameters. The neural networks used share the same architecture; however, one is trained using the standard procedure, while the other follows the OSR framework (incorporating cosine learning rate, learning rate warmup and label smoothing). which is extremely useful when working with unknowns. Therefore, the MLS approach is used to detect HIFs. The output of the classifier is not softmaxed, but the maximum element of this vector is used to represent the class. The FFNN and the FFNN with OSR enhancements are fed the same test and HIF samples. Figures 8 and 9 show the maximum logits of the test samples and the HIFs. (blue dots and orange triangles, respectively). The MLSs of the steady-state class (0) for FFNN without improvements are closely spaced, but for the load change class (1) they are widely scattered (Figure 8). The latter discrepancy arises from differences in the directions in which load changes occur, as well as variations in the magnitudes of load changes. The features characterizing steady states are similar, with the only difference being the 0 Hz component (representing different loading levels), which is removed during preprocessing. As expected, HIFs are misclassified because the NN always assigns a class to the inputs. Furthermore, the NN assigns HIFs to both classes, even though the HIF response is more similar to that of load change. Moreover, the MLSs of known classes and the HIF overlap, indicating that the classifier struggles to handle unknowns effectively. On the other hand, the results for the FFNN trained according to the OSR suggestions are shown in Figure 9. The scale of the logits shrank on the y-axis, but now the samples of both classes are grouped. The NN decided correctly that these samples are closer to the load change event than to the steady state, assigning all the HIFs to the class 1. More importantly, the MLSs of the HIFs are far from the rest of the MLSs, indicating that the input is from the unknown class. Thus, the HIF can be identified as an outlier and rejected by the classifier in a trivial manner. The OSR approach proves valuable in detecting outliers. The neural network, with these additions to the training procedure, demonstrates an enhanced ability to detect outliers in a straightforward manner. Figure 7: Loss and accuracy plots for FFNN with OSR improvements. Training and test loss converge, indicating a stable and successful training procedure. Accuracy rises rapidly and approaches near-perfect score.
## 5 Conclusions  
The theoretical model of a taut cable with two HDR-type dampers, i.e., an HDR damper and a TM-HDR-D, was established via the complex analysis method. Based on the theoretical model, the effects of the HDR damper and the TM-HDR-D on the cable vibration reduction under the individual and combined actions were studied. Finally, taking a real-world cable as an example, the combined design of two dampers was carried out. The following conclusions can be drawn:  
1. As a cable-end damper, the effectiveness of the HDR damper mainly depends on the damper's stiffness, loss factor, and location. In practice, the damper's stiffness needs to be optimized to achieve the best damping contribution to the cable system. The TM-HDR-D can be installed at any position of the cable, and through precise frequency tuning it can supplement the damping contribution of the cable-end HDR damper.
2. When the TM-HDR-D has a small mass, the combined damping effect of the HDR damper and the TM-HDR-D approximates a linear superposition of their respective effects.
3. In practice, a TM-HDR-D can be co-designed with an HDR damper to achieve multi-modal damping improvement. The TM-HDR-D can make up for the deficiency of the HDR damper and achieve the multimode vibration control of the cable.  
[PERSON] carried out the studies, participated in the derivation, and drafted the manuscript. [PERSON] conceived of the study, participated in its design and coordination, and helped to draft the manuscript. [PERSON] and [PERSON] participated in the design of the study. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Dampers’ design targeting the first two cable vibration modes (\(M~{}=~{}70\) kg, \(\varphi~{}=~{}0.5\), \(x_{1}/L=0.02\)) with different frequency ratios: (**a**) \(\rho=0.7\); (**b**) \(\rho=0.8\); (**c**) \(\rho=0.9\).  
**Funding:** This research was founded by the Project of Industry Foresight and Key Core Technologies (Grant No. BE2021021) and the Special Project on Transformation of Scientific and Technological Achievements in Jiangsu Province (No. BA2022009), for which the authors are grateful.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data, models, and codes to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Experimental Research on the Load Transfer Mechanism of Tie Plates for 400 km/h High-Speed Turnouts
## 2 Materials and Methods
### Design of Experiments
#### 2.2.1 Experiment of Integral Stiffness of Plate Structure
Vertical displacement was applied continuously until a displacement of 10 mm was achieved; during this process, the loads applied to the cover and tie plates and their displacement curves were recorded. In Experimental conditions 4, the tie plate was assembled as shown in Figure 6b. The rubber-washer side of the cover plate was placed face up, with the metal surface pressed on one rubber boss of the tie plate, and the cover plate was not in contact with the steel sleeve; in addition, the rubber washer was removed. The round tool was set above to connect with the vertical loading device. The other end of the tie plate was pressed with a pressing plate, and the bolt fixing the pressing plate was secured tightly to prevent the tie plate from tilting at one end during the experiment. Another displacement meter was installed to measure the displacement of the washer below the cover plate. During the experiment, the assembly was subject to displacement control, and the cover plate was considered to be in contact with the tie plate rubber boss when the former was under a force of 20 N; at this point, the applied load and displacement were reset. Vertical displacement was applied continuously until a displacement of 8 mm was achieved (the vertical displacement is reduced as the height of the rubber boss is lower than that of the rubber washer); during this process, the loads applied on the cover and tie plates and their displacement curves were recorded. The new tie plate with the rubber boss and the original tie plate with the rubber washer are remarkably different in terms of the transfer and distribution laws of the bolt preload force from the cover plate to the tie plate under different bolt torques in the assembled state. However, because the compressions of the rubber washer or rubber boss and the tie plate under the rated torque of the bolt in the assembled state are still unknown, the effective components of the stiffness curves and the working state points are unknown.
## 5 Communication Overhead Analysis
### Proposed RCA Method  
The message structure of the proposed authentication RCA method is shown in Figure 10c, we analyzed the RCA message structure using Equation (23). RCA method support multi-zone authentication based on the aggregated security parameters of the communicated zones.  
\[m=\left\{L_{ZID},\leavevmode\
obreak\ pid_{i},m_{i},T_{s},pk_{agg},\sigma_{agg} \leavevmode\
obreak\ \right\} \tag{23}\]  
In RCA, the total communication overhead is \(\left|L_{ZID}\right|+\left|pid_{i}\right|+\left|T_{s}\right|+\left|pk_{agg} \leavevmode\
obreak\ \right|+\left|\sigma_{agg}\right|=1+4+4+64+32=105\) bytes.  
In SCA and RCA, we choose 1 byte for \(L_{ZID}\), as we assume that the maximum number of zones cannot exceed 256. While in the TCA method, we only send a single transmitter zone ID.  
Figure 11 compares the communication cost of the three proposed methods and the six previous methods in single-zone communication. It shows that the three proposed methods outperform all six previous methods, by reducing the communication cost by 50-80% in a single-zone scenario.  
In a multi-zone scenario, all previous methods repeatedly transmit the same message for multiple individual destination zones that require different signing keys. In contrast, the proposed authentication methods (TCA, SCA, RCA) send a single signed message to \(N\) zones and allow the receivers in multiple zones to verify the same message.  
Figure 12 compares the communication cost of the three proposed methods with three previous methods (3) [PERSON] et al. (2011), (4) [PERSON] et al. (2012), and (6) [PERSON] et al. (2014). We choose to compare our methods with the previous (2011,222,24), only because we found that the other protocols introduce nearly the same communication overhead. We tried to show the differences between our methods and the previous methods.  
TCA and RCA incur a constant communication cost of 105 bytes per message, irrespective of the number of destination zones. In contrast, the proposed SCA method and the previous methods (2011,222,24) show the communication cost increasing along with the number of destination zones \(N\). However, the proposed SCA method shows much lower growth in the increase of the cost than the previous methods. For example, in the case  
Figure 11: Comparison of communication cost per message for the three proposed authentication methods and the compared security protocols for single-zone scenario.  
of \(N=5\), SCA has a cost of only 489 bytes, while the previous methods, [PERSON] et al. [24], [PERSON] et al. [22] and [PERSON] et al. [21] incur significantly higher communication cost, as high as 2740, 1005, and 1620 bytes, respectively. For multi-zone communication of 5 destination zones, the proposed TCA and RCA methods reduce the communication cost by 26 times, while the SCA method reduces the communication cost by 2-5 times, as compared to the three previous methods of [21; 22; 24].