esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The reconfigurable intelligent surface (RIS) is one of the most innovative and revolutionary technologies for increasing the effectiveness of wireless systems. Deep learning (DL) is a promising method that can enhance system efficacy using powerful tools in RIS-based environments. However, the lack of extensive training of the DL model results in the reduced prediction of feature information and performance failure. Hence, to address the issues, in this paper, a combined DL-based optimal decoding model is proposed to improve the transmission error rate and enhance the overall efficiency of the RIS-assisted multiple-input multiple-output communication system. The proposed DL model is comprised of a 1-dimensional convolutional neural network (1-D CNN) and a gated recurrent unit (GRU) module where the 1-D CNN model is employed for the extraction of features from the received signal with further process over the configuration of different layers. Thereafter, the processed data are used by the GRU module for successively retrieving the transmission signal with a minimal error rate and accelerating the convergence rate. It is initially trained offline using created OFDM data sets, after which it is used online to track the channel and extract the transmitted data. The simulation results show that the proposed network performs better than the other technique that was previously used in terms of bit error rate and symbol error rate. The outcomes of the model demonstrate the suitability of the proposed model for use with the next-generation wireless communication system.  
reconfigurable intelligent surface (RIS); 1-dimensional convolutional neural network (1-D CNN); gated recurrent unit (GRU); bit-error rate (BER); symbol-error rate (SER)  
12 June 2023  
19 July 2023  
1
# Assessment of Modal Characteristics of Steel-Concrete Composite Grider Bridge with V-Shaped Piers
## 5 Analysis of Structural Self-Vibration Characteristics
### Effect of V-Shaped Pier Stiffness on Self-Vibration Characteristics  
The V-shaped piers' flexural stiffness was varied by \(-10\%\), \(-5\%\), \(5\%\) and \(10\%\) to analyze the changes in the self-vibration frequencies and vibration modes of the structure. The effects of the changes in the V-shaped piers' flexural stiffness on the self-vibration frequency and vibration characteristics of the modes are shown in Tables 7-9 and Figures 11-13.  
The original structure has no change in the first ten orders of vibration after changing the V-shaped piers' stiffness, and the self-vibration frequencies increase to some extent with the increase in the V-shaped piers' stiffness. The first-order self-vibration frequency changes most significantly after the V-shaped piers' stiffness increases from \(-10\%\) to \(10\%\); increasing by \(6.38\%\). The first-order vertical self-vibration frequency increased by \(3\%\). The results show that increasing the V-shaped piers' stiffness can effectively increase the full bridge stiffness, as the full bridge transverse stiffness undergoes a large degree of improvement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modal** & \multicolumn{5}{c}{**Self-Vibration Frequency**} \\
**Order** & \(-\)**10\%** & \(-\)**5\%** & **0** & **5\%** & **10\%** \\ \hline \(1\) & \(1.36\) & \(1.38\) & \(1.40\) & \(1.43\) & \(1.45\) \\ \(2\) & \(1.88\)  
Figure 11: Self-vibration frequencies for different V-shaped pier stiffnesses of the original structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modal** & \multicolumn{4}{c}{**Self-Vibration Frequency**} \\
**Order** & \(-\)**10\%** & \(-\)**5\%** & **0** & **5\%** & **10\%** \\ \hline
1 & 1.19 & 1.21 & 1.22 & 1.24 & 1.26 \\
2 & 1.42 & 1.43 & 1.43 & 1.43 & 1.44 \\
3 & 2.61 & 2.62 & 2.64 & 2.65 & 2.66 \\
4 & 3.37 & 3.37 & 3.38 & 3.39 & 3.39 \\
5 & 3.47 & 3.50 & 3.53 & 3.56 & 3.59 \\
6 & 3.55 & 3.56 & 3.57 & 3.58 & 3.59 \\
7 & 4.01 & 4.02 & 4.03 & 4.03 & 4.04 \\
8 & 4.09 & 4.12 & 4.15 & 4.17 & 4.20 \\
9 & 4.69 & 4.72 & 4.74 & 4.75 & 4.76 \\
10 & 5.06 & 5.07 & 5.08 & 5.11 & 5.14 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Self-vibration frequencies (Hz) for different V-shaped pier stiffnesses of Structure I.  
Figure 12: Self-vibration frequencies for different V-shaped pier stiffnesses of Structure I.  
The pattern of variation in structure I with the V-shaped piers' stiffness is similar to that of the continuous beam-continuous rigid structure wherein the first-order self-vibration frequency increases by 5.734% as the V-shaped piers' stiffness varies from \(-10\%\) to \(10\%\).  
The first-order self-vibration frequency increases the most with increasing V-shaped piers, with the first-order self-vibration frequency of the continuous rigid structure increasing by 4.07% when the V-shaped piers' stiffness increases from \(-10\%\) to \(10\%\).
## 5 Error Analysis and Correction
### Correction of 3D Reconstruction  
#### 5.2.1 Normalization  
In this paper, Equation (9) and the least squares method are used to calculate the underwater 3D reconstruction position. Moreover, the \(\lambda\) in Equation (9) is related to the camera position and the intersection of optical path of physical point and water surface. When \(y_{w}\) is approximately equal to \(y_{c}\), \(\lambda\) will be very large. While the principle of least squares method is to minimize the sum of the squares of error, if the weighting of a term is too large, the measured point will be tilted to it.  
Figure 11: Underwater 3D reconstruction with combinations of adjacent cameras.  
Figure 12: Y-Z plane of underwater 3D reconstruction with partial combined camera.
## 4 Evaluation
### Discussion
#### 4.3.6 Integration of HE and XAI  
In the proposed framework, HE using the CKKS scheme ensures the security and privacy of local data and gradients during training on edge devices. The encrypted local models are transmitted to the central server, where aggregation is performed using homomorphic operations, eliminating the need for decryption during this process. Once the global model is aggregated, it is decrypted at the server end for further processing. This decrypted global model is then subjected to XAI techniques, such as feature importance analysis or interpretability mapping, to extract insights that enhance model transparency and guide future local training iterations. This integration of HE and XAI ensures that privacy is maintained at the edge level while leveraging interpretability to refine the global model and improve overall performance.
## 1 Introduction
### Review of Energy Management Techniques for Extending the Lifespan of WWPM Systems
The authors failed to mention the energy consumption of the sensor nodes, but the results reported an approximately 50% energy reduction when they compared their proposed distributed approach with the centralized approach. References [34; 35] also implemented clustering whereby sensor nodes acquire vibration data from the pipe and transmit to a closely located cluster head node that performs local processing and finally transmits to a centralized base station that performs the leak detection decision. In another study, [PERSON] et al. [8] presented an end-to-end water leak localization system, which exploits compression as the data reduction approach for conserving energy consumption. The proposed system combined a lightweight edge anomaly detection algorithm based on Kalman filtering on compression rate stream. The results revealed that the proposed solution reduced the communication by 99% compared to the traditional periodic communication, thus enabling the use of battery-powered sensor nodes for an extended period. The limitation of this work is that the authors failed to mention the energy consumption of the sensor node and to what extent their proposed technique increases the WSN lifetime. Lastly, [PERSON] et al. [36] proposed a distributed computing solution for detecting leaks and reducing energy consumption in WWPM systems by implementing a distributed Kalmanfilter (DKF). The distributed computing technique performed leak detection locally on the sensor nodes and transmitted leak decisions to the base station only when a leak was detected on the pipeline. A 10-node linear WSN was simulated, where scenarios for both the proposed distributed approach and the centralized approach were tested. The reported results demonstrate that the fully distributed solution enhances both leak detection accuracy and reduces power consumption compared to the centralized approach.
## 5 Incremental Improvement of Market Mechanism
### Analyzing Agents' Equilibrium Behavior
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|} \hline
**Seller**\(\backslash\)**Bayer** & \multicolumn{2}{c|}{**CONR 0.2**} & \multicolumn{2}{c|}{**CONR 0.4**} & \multicolumn{2}{c|}{**CONR 0.6**} & \multicolumn{2}{c|}{**CONR 0.8**} & \multicolumn{2}{c|}{**CONR 1.0**} \\ \hline \multirow{2}{*}{MODE} & 7 & 96\% & 18,972 & 14 & 95\% & 18,763 & 20 & 94\% & 18,739 & 27 & 94\% & 18,739 & 33 & 94\% & 18,739 \\ \cline{2-11}  & \multirow{4}{*}{MODE} & **27,738** & \multirow{4}{*}{(280)} & 26,182 & & 24,826 & & 23,516 & & 22,218 \\  & & **(280)** & & \(-\)**7419** & & \(-\)6087 & & \(-\)4777 & & \(-\)3479 & \\  & **(101)** & & (107) & & (109) & & (112) & & & \\ \hline \multirow{2}{*}{TRUE} & — 0\% & \(-\)10,077 & 33 & 1\% & \(-\)9682 & 43 & 33\% & 1,008 & 51 & 73\% & 13,211 & 57 & 93\% & 18,473 \\ \cline{2-11}  & & 0 & & 302 & & & 7915 & & 15,238 & & **17,307** \\  & & (0) & & (148) & & (425) & & (232) & & (170) \\  & \(-\)10,077 & & \(-\)9999 & & & \(-\)7106 & & \(-\)2168 & & 1143 & \\  & (52) & & (69) & & (179) & & (185) & & (161) & \\ \hline \multirow{2}{*}{STEP} & — 0\% & \(-\)10,077 & 17 & 28\% & \(-\)443 & 28 & 93\% & 18,432 & 45 & 95\% & 18,828 & 57 & 94\% & 18,726 \\ \cline{2-11}  & \multirow{4}{*}{(0)} & \multirow{4}{*}{(460)} & 7810 & & **21,297** & & & & 19,401 & & 17,495 \\  & & & \(-\)90\% & & \(-\)4625 & & & \(-\)1086 & & & (204) \\ \cline{2-11}  & & (52) & & (108) & & (127) & & (139) & & (166) & \\ \hline \multirow{2}{*}{MONO} & — 0\% & 10,077 & 20 & 22\% & \(-\)2275 & 36 & 79\% & 14,749 & 50 & 91\% & 17,955 & 57 & 94\% & 18,684 \\ \cline{2-11}  & \multirow{4}{*}{(0)} & 0 & & 6238 & & 17,550 & & & **18,699** & & 17,416 \\ \cline{2-11}  & & & \(-\)9156 & & \(-\)**4081** & & \(-\)**568** & & **(283)** & & (188) \\ \cline{2-11}  & (52) & & (81) & & (124) & & (165) & & (172) & \\ \hline \end{tabular}
\end{table} 
Table 1: Payoff matrix in the low-risk market. MODE: modest strategy; TRUE: truth-telling strategy; STEP: step discount strategy; MONO: monotonous discount strategy. Table 1 shows that there are two Nash equilibria strategy profiles: (MODE, CONR 0.2), and (MONO, CONR 0.8). Among them, adopting the CONR 0.2 strategy is risky for buyers because it produces zero utility for them if sellers avoid using the MODE strategy, which produces a large loss for sellers even when buyers bid high values. Therefore, (MONO, CONR 0.8) is a more feasible strategy profile to be executed in the low-risk market with the penalized DA mechanism. From the above equilibrium analysis, it is found that buyers obtain more profits by strategically misreporting their value 20% less than the true valuation and the penalized DA mechanism fails to achieve quasi incentive compatibility for buyers.
# Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks
## 3 The Proposed Algorithm  
In this section, to make Problem (18) more tractable, a method based on optimization variables iteration is proposed. We decompose the original Problem (18) into the following two sub-problems iteratively. First, Sub-problem 1 optimizes the relay selection \(A\) and transmit power \(P\) with given fixed UAV trajectory \(Q\). Second, Sub-problem 2 optimizes the UAV trajectory \(Q\) with given relay selection \(A\) and power allocation \(P\). Finally, we present the overall algorithm and prove the convergence of this iteration algorithm. Without loss of generality, through the iteration of the two sub-problems, we can get a suboptimal solution to the original problem.
## 1 Introduction
The characteristics of the presented method are listed as: (1) a linear and direct relationship between strain and deflection is established to avoid error caused by the aforementioned double integral of curvature or transformation of response between time-domain and frequency-domain; (2) the method is suitable for both static and dynamic loads and external loads are irrelevant; (3) the method is applicable for different boundary conditions; (4) long-gauge strain sensors are utilized to ensure damage can be reflected and identified [17; 18; 19; 20; 21; 22], and therefore the method can be reasonably extended to damaged structures. Firstly, the relationship between long-gauge strain and deflection was theoretically derived based on the improved moment-area method. Secondly, a numerical simulation was conducted, and the influence of boundary conditions, load type and sensor gauge length on the method was investigated. Finally, a simply supported beam subjected to dynamic loads was designed to verify the method. Experimental results show that both the deflection time-history of arbitrary points of structures and deflection distribution along structures at a certain time can be obtained with high precision.
# An Improved Differential Quadrature Time Element Method
## 2 Basic Rules and Formulations
### Discussion  
1. Unlike previous high-order methods, the present method is easily implemented, and the shape functions are no long necessary for constructing spatial element matrices, which are explicit and can be obtained by simple algebraic operations of weighing coefficient matrices of the DQ and Guass-Lobatto quadrature rules.
2. For any C\({}^{0}\) and C\({}^{1}\) problems, Lagrange polynomials are used as trial function; in addition, the Guass-Lobatto quadrature points are selected as nodes and collocated not uniformly.
3. The mass matrix of DQ finite element is diagonal, which is different from conventional diagonal lumped mass matrix, but the element mass matrix used in DQTEM0 is full.
4. Compared with DQTEM0, the dimension of the coefficient matrix \(\mathbf{G}\) of the present method is reduced and the present method is more efficient than DQTEM0, which are validated by numerical results in Section 4.
5. To avoid confusion, the comparison among DQTFEM, DQTEM0 and the improved DQTEM is presented in Table 1. In addition, these DQ time element methods seem similar to the standard spectral method in some sense, since they are all based on polynomial interpolation. The standard spectral method is global interpolation, in which the Chebyshev polynomials, the Legendre polynomials and the Fourier series or harmonic series are employed in general. While the DQ time element methods employ Lagrange polynomials, and if only one element is employed in time and/or spatial domain, they are global interpolation; if the domain is divided into several elements, the DQ time element methods are local interpolation.
## 1 Introduction
The payload-bus integrated flat panel-type unique mechanical design of S-STEP requires efficient thermal control to achieve an SAR imaging duration of 60 s in both left- and right-looking modes and keep all onboard components within allowable temperature thresholds in orbit. For lighter, smaller, better, and cheaper development of the S-STEP system, the proposed thermal design strategy is based on the optimization of environmental heat fluxes acting on the satellite by passive thermal control using surface coatings, MLI, and radiators. In addition, a lightweight flexible graphite sheet with high thermal conductivity is used as conductive thermal paths between some high-power instruments and dedicated radiators. The multifunctional structure of TRM assembly provides effective thermal dissipation capability through the integrated radiators during SAR imaging. These strategies contribute to minimizing the satellite mass budget and heater power consumption, which help realize the implementation of a \"lighter\" SAR satellite. To verify the effectiveness of the proposed thermal design strategy, an on-orbit thermal analysis of the S-STEP system was performed. Further, the effectiveness of thermal design on the key payload components and multifunctional TRM structure was experimentally evaluated in a space-simulated thermal vacuum environment.
# Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks
## 5 Simulation Setup and Experimental Results
### Actor/Sensor Recovery Time  
The actor recovery time is of high significance for network improvement and running applications on it. When the actor fails, then it is important to initiate the prompt recovery process to avoid the reduction in the network performance. Figures 11 and 12 show the actor recovery time of the proposed EAR algorithm and other competing approaches: RNF, DPCRA, ACR, and ACRA. In these experiments,  
Figure 10: The residual energy of EAR and other competing approaches: RNF, DPCRA, ACR, and ACRA based on 27 event-monitoring.  
Figure 9: The residual energy of EAR and other competing approaches: RNF, DPCRA, ACR, and ACRA based on 18 event-monitoring.  
we used two different network topologies: 1200 \(\times\) 1200 m\({}^{2}\) and 1400 \(\times\) 1400 m\({}^{2}\). In Figure 11, we used 1200 \(\times\) 1200 m\({}^{2}\) network topology with 48 connections.  
Based on the results, we observed that EAR has overall minimum actor/sensor recovery time. We determined an actor recovery time for maximum 27 failure nodes including 11 actors and 16 sensors nodes. At the maximum of 27 failure nodes, EAR has 3.25 s actor/sensor recovery time while other approaches have 3.6-4.7 s. The results show that EAR has 3.19-20% improvement over other competing approaches.  
In Figure 12, we used 1400 \(\times\) 1400 m\({}^{2}\) network topology with 60 connections. Based on the results, we observed that EAR has overall minimum actor recovery time. We determined an actor/sensor recovery time for maximum 27 failure nodes including 11 actors and 16 sensors nodes. At the maximum  
Figure 11: Number of failure actors/Sensors and required actor recovery time for EAR, RNF, DPCRA, ACR, and ACRA approaches with 1200 \(\times\) 1200.  
Figure 12: Number of failure actors/Sensors and required actor recovery time for EAR, RNF, DPCRA, ACR, and ACRA approaches with 1400 \(\times\) 1400.  
27 failure nodes, EAR has the same time of 3.25 s as obtained with 1200 \(\times\) 1200 m\({}^{2}\) network topology with 60 connections. It is confirmed that the increase in the network topology does not affect the actor/sensor recovery time while other approaches have 3.62-4.75 s. The result show that EAR has 3.21-20.8% improvement over other competing approaches.  
The results confirm the soundness EAR in terms of an actor recovery time due to contention-free forwarding capacity of the nodes. In addition, particular RSSI value is selected for traffic forwarding process that makes the process of actor recovery much easier. As, all of the existing approaches either attempt to recover the failure actor or try to reduce the overhead, but our proposed approach reduces the power consumption and delivers the data without contention. Furthermore, it improves the backup node selection process in case of node failure or being disjoint. These characteristics of EAR help reduce the actor recovery time as compared with other approaches.
## 7 Experimentation and Results
### Comparisons between the Four Algorithms Using Statistical Analysis  
Table 11 summarizes the objective functions, namely, energy efficiency, packet throughput, end-to-end delay and end-to-end latency, from ten runs for each algorithm. The statistical analysis using one-way ANOVA (Tukey's test) outlined in Table 12 shows that MOSFET significantly outperforms SPEA2 in which, MOSFP substantially increase the energy efficiency (0.116, \(p\leq 0.001\)) and packet throughput (0.394, \(p\leq 0.001\)), and decrease the end-to-end delay (\(-0.265\), \(p\leq 0.001\)) and end-to-end latency (\(-2.718\), \(p\leq 0.001\)) compared to SPEA2. These mean differences also represent that MOSFP outperforms SPEA2 by 41%, 99%, 24% and 41% in term of energy efficiency, packet throughput, end-to-end delay and end-to-end latency respectively. However, no significant mean difference is observed between MOSFET and the rest of the algorithms i.e., OMOPSO and NSGA-II for all objective functions. This indicates that MOSFP outperforms OMOPSO and NSGA-II with a small mean difference between them in the range of 2% to 9%.  
Another aspect that will be highlighted in this sub-section is the consistency of the algorithm to perform between runs. A more stable algorithm will result in a smaller standard deviation of the objective function. From the experiments, SPEA2 has results in a more consistent performance for three objective functions between runs among the algorithms. The standard deviations of SPEA2 are approximately 3%, 23% and 7% much smaller compared to others for energy efficiency, packet throughput, and end-to-end delay respectively. In end-to-end latency, MOSFET has shown a more consistent performance where its standard deviations are 8%, 4% and 3% smaller than SPEA2, OMOPSO and NSGA-II, respectively.  
Overall, the MOSFET algorithm obtained the best average of all objective functions while the OMOPSO algorithm is in the second, followed by NSGA-II and SPEA2. In terms of performance consistency, the SPEA2 results are shown to be more consistent in energy efficiency, packet throughput, and end-to-end delay whereas MOSFP is shown to be more consistent in end-to-end latency.
# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
## 3 Construction of Dataset and Training of Models
### Input and Output of Neural Network Model  
The training set used to train the ANN included 80% of the data randomly selected from the divided three cluster datasets. The remaining 20% of the data and 582 acceleration records obtained by IDA served as the test set. For the prediction models of the linear structure system, the input was \(\tilde{a}_{g}=[\tilde{a}_{g}^{1},\tilde{a}_{g}^{2},\tilde{a}_{g}^{3},\ldots, \tilde{a}_{g}^{n}]\) and the output was a response \([u_{si},\dot{u}_{si},\ddot{u}_{si}]_{i=1,2,3,4,5,6}\) of layers 1-6. For the nonlinear structure system, the input of the models was \(\tilde{a}_{g}=[\tilde{a}_{g^{\prime}}^{1},\tilde{a}_{g^{\prime}}^{2},\tilde{a }_{g^{\prime}}^{3}\ldots,\tilde{a}_{g}^{n}]\) and the output was the response \([u_{s6},\dot{u}_{s6},\ddot{u}_{s6},u_{h},\dot{u}_{b},\ddot{u}_{b},u_{d},\dot{ u}_{d},\ddot{u}_{d}]\) of layer 6, linear component \(m_{b}\), and nonlinear component \(m_{d}\). The input and output variables of each model are shown in Table 4. The server platform and environment configuration used for all model training in this study are shown in Table 5.