esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Scattering Matrix with Grouping Scheme and Power Allocation  
In this section, we derive the sum-rate problem for the RIS-assisted RSMA in the MU-MIMO wireless system and address the problem of grouping the RIS elements and power allocation to maximize the sum-rate performance. First, the common stream is decoded considering other private streams as interference. Then, each UE performs successive interference cancellation (SIC) to eliminate the common stream and decodes each private stream. The signal-to-interference-plus-noise ratio (SINR) of the common stream and private stream of the \(k\)-th UE is given as follows:  
\[\gamma_{c,k}=\frac{P_{c}\left|\mathbf{h}_{k}\mathbf{\Phi}\mathbf{G}\mathbf{p} _{c}\right|^{2}}{\sum_{j=1}^{N_{K}}P_{p}\left|\mathbf{h}_{k}\mathbf{\Phi} \mathbf{G}\mathbf{p}_{j}\right|^{2}+\sigma_{k}^{2}},\gamma_{p,k}=\frac{P_{p} \left|\mathbf{h}_{k}\mathbf{\Phi}\mathbf{G}\mathbf{p}_{k}\right|^{2}}{\sum_{j \
eq 1}^{N_{K}}P_{p}\left|\mathbf{h}_{k}\mathbf{\Phi}\mathbf{G}\mathbf{p}_{j} \right|^{2}+\sigma_{k}^{2}}. \tag{9}\]  
To ensure that common stream is decoded by all UEs, the SINR of the common stream is represented as \(\gamma_{c}=\min\gamma_{c,k}\). Then, the sum-rate is given as follows:  
\[R=\log_{2}(1+\gamma_{c})+\sum_{k=1}^{N_{K}}\log_{2}\Bigl{(}1+\gamma_{p,k} \Bigr{)}. \tag{10}\]  
In this paper, the scattering matrix with the grouping of the RIS elements and RS power ratio was optimized to maximize the sum-rate. The sum-rate problem for RIS-assisted RSMA in MU-MIMO can be formulated as follows:  
\[\max_{\mathbf{\Phi},\mathbf{\kappa}} R\] (11) s.t. \[\mathbf{\Phi}=\operatorname{diag}(\mathbf{\Phi}_{1},\mathbf{\Phi}_ {2},\cdots,\mathbf{\Phi}_{N_{G}}), \tag{12}\] \[\mathbf{\Phi}_{g}=(j\mathbf{X}_{I,g}+Z_{0}\mathbf{I})^{-1}(j \mathbf{X}_{I,g}-Z_{0}\mathbf{I}),\forall g,\] (13) \[\mathbf{X}_{I,g}=\mathbf{X}_{I,g}^{T}\forall g. \tag{14}\]  
To solve Equation (11), the alternative optimization algorithm is proposed. First, the scattering matrix optimization is performed, and then, the RS power ratio problem is solved.
# Study on the Influence of Groundwater Variation on the Bearing Capacity of Sandy Shallow Foundation
## 2 Experimental Tests
### Discussion  
In this study, a novel test device was designed that can simulate the effect of the groundwater level on the foundation bearing capacity. This opens up a new idea for the indoor model test of foundation bearing capacity. In addition, a new idea of numerical simulation was proposed. The problem was simplified by dividing the ground affected by groundwater harvesting into three sections with different soil saturation levels. Compared with the traditional foundation-bearing-capacity theory, this model has the advantage of considering the influence of the water-level fluctuation. In other theories, the water line is usually represented as a straight line. This is not accurate, because the soil capillary effect can make the water level uneven. The water line may not be static, after all. Fluctuating water levels are also common. Based on this, the formula was improved. A new expression form of pore water pressure was derived for sinusoidal fluctuation. The effective stress of the foundation soil was also changed.  
The simulation results were compared with the experimental results to verify the reliability of the conclusion. The error between the simulation calculation and the test is within the acceptable range. This proves the feasibility of the simulation method. Other scholars have studied the mechanical properties of unsaturated soil and found that its cohesion and internal-friction angle decrease with the increase in soil water content [30]. This is consistent with the conclusion of this paper. The reliability of the experiment and numerical simulation was verified by internal and external verification. This makes it possible to raise the groundwater-level warning in practical engineering  
The experiment had some limitations. As the bottom of the model box is impervious to water, the bottom of the foundation was regarded as an impervious layer in this experiment and simulation. In fact, the bottom of the model box is connected to the pump, so it cannot be considered impervious. In addition, the capillary effect makes it difficult to distinguish the water level of the foundation soil, so there are errors.  
This numerical simulation also has some limitations. First, the load-transfer mode and foundation-failure mode of deep foundation are quite different from those of a shallow foundation, so this model cannot be directly applied to a deep foundation. Second, the accuracy of the model in other types of foundation soil cannot be guaranteed, due to the differences in the water absorption and permeability of different types of soil.
# Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships
## 3 GPS Attack Designs and Spoofing Analysis
### GPS Attacks
#### 3.2.3 Designing a GPS Attack  
We design a GPS spoofing attack on a single GPS receiver, which can be seen in Figure 8. We assume that the attacker uses a GPS spoofer to attack a victim's GPS receiver. \(P=(x,y,z)\), \(P^{A}\), and \(\delta^{A}\) are the physical location of the receiver, the physical location of the attacker, and the transmission time offset of the attacker, respectively. From the attackers' perspective, the expected pseudo-range (\(R^{A}\)) that the receiver in location \(P\) calculates based on the attacker's signals is as follows.  
\[R^{A}=|P-P^{A}|+\delta^{A}V. \tag{25}\]  
In the perspective of the receivers, the pseudo-range (\(R^{\prime}\)) is calculated according to the signals of impersonated satellite location (\(L^{\prime}\)), the time offset (\(\delta^{\prime}\)), and receivers location determined by an attacker (\(L\)) as follows.  
\[R^{\prime}=|L^{\prime}-L|+\delta^{\prime}V. \tag{26}\]  
At the same time, the pseudo-range (\(R^{\prime}\)) can be calculated by transmission (\(T_{s}\)) and the receiving time (\(T_{r}\)) of GPS spoofing signals as follows.  
\[R^{\prime}=(T_{r}-T_{s})V+\delta^{\prime}V=|L^{\prime}-L|+\delta^{\prime}V. \tag{27}\]  
If the attacker (\(A\)) wants to spoof the GPS receiver (\(G\)) successfully, this requires \(R^{A}=R^{\prime}\).  
\[|P-P^{A}|+\delta^{A}V=|L-L^{\prime}|+\delta^{\prime}V. \tag{28}\]  
In the term of the attack on GPS C/A, the attacker can set the location of satellite \(L_{s}=(x_{s}\), \(y_{s}\), \(z_{s}\)) and the physical location of the attacker \(P_{0}=(x_{0}\), \(y_{0}\), \(z_{0})\) in advance. The attacker brings \(L^{\prime}=L_{s}\) and \(P^{A}=P_{0}\) into Equation (32) to solve for \(\delta^{\prime}\), and \(\delta=\delta^{\prime}\).
## 3 Performance Analysis
### Velocity Stability
The green points composed the stable region of the flight path angle. The feasible flight path angle was determined for each pitch angle. Piling them together, the flight path feasible zone for the whole flight mission was acquired. It is revealed in Figure 9b that the feasible zone of flight path angles shrinks with the pitch angle increasing. Specifically, the lower boundaries of the flight path angle are plotted in Figure 9c by the blue lines. Obviously, the lower boundaries decrease from \(\gamma=-9.3^{\circ}\) to \(\gamma=-4.6^{\circ}\). The method to establish the strategy parameter boundaries is similar to that to set the transition corridor [31] of multi-mode aircrafts such as composite wings and tilt wings. They all analyze the limiting factors to establish the boundaries and then search for appropriate parameters in the feasible zone. The conventional boundary construction methods treat each limiting factor singly and then synthesize them for a global feasible zone, while the attainable equilibrium set methods systematically think over all the factors and construct the zone directly, which is fairly versatile and robust and not confined to the specific forms of the study objects. However, the boundaries built by AES are not continuous, which costs a high calculation price for precision.
## 5 Experiments
### Experiment 2: Static Splitting Factor with No Stopping Criteria
Time-wise, splitting factors of 6, 8, and 10 are consistently the best-performing factors across the considered connection pools. However, it is worth noting that in cases of 5% and 10% pollution factors, splitting factors of 14, 18, and 20 sporadically exhibit the highest efficiency for pools of 2K, 5K, and 8K connections, respectively. Regarding resource utilization, the optimal choice across all connection pools is consistently a splitting factor of 2. Similarly to the case of 1% of pollution factor, it is evident that there is a trade-off between time and resource requirements. For instance, a splitting factor of 10 requires 19.91 min and 1364 nodes in the case of a 10% pollution factor and 10K connections. On the contrary, a splitting factor of 2 needs \(\approx\)31% more time, but it spawns \(\approx\)18% less nodes. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Num. of connections**} & \multicolumn{1}{c}{**SF = 2**} & \multicolumn{1}{c}{**SF = 4**} & \multicolumn{1}{c}{**SF = 6**} & \multicolumn{1}{c}{**SF = 8**} & \multicolumn{1}{c}{**SF = 10**} & \multicolumn{1}{c}{**SF = 12**} & \multicolumn{1}{c}{**SF = 14**} & \multicolumn{1}{c}{**SF = 16**} & \multicolumn{1}{c}{**SF = 18**} & \multicolumn{1}{c}{**SF = 20**} \\ \hline
1000 & 3.03 & 2.15 & 2.23 & 4.0
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 3 Traffic Adaptive Data Dissemination
### Store-Carry-Foruard Mechanism  
TrAD assigns the role of SCF-agent to the vehicles at a specific location in a VANET, which is beneficial to fill the connectivity gaps among vehicular clusters. The SCF-agent stores and carries data messages from one cluster to another, and then it will forward (broadcast) the stored data to the new coming vehicles. That is, TrAD uses the temporal delay to complement the spatial partition. Moreover, we designed a SCF-agent rebroadcast constraint technique that is a particular operation to _further reduce_ the redundant broadcasts. In order to avoid data redundancy, the protocol compares the message list of a SCF-agent with its neighbors'. Only the data messages required by the neighbor will be scheduled by using the SCF-agent rebroadcast constraint technique.
## 4 Proposed Approach for GPS+Galileo Solution  
For the G and E, the best results were obtained for Function_3 and Function_5, respectively. Therefore, a new observation weighting approach for the GPS+Galileo positioning was proposed. It uses different weighting functions for both systems. For this new solution, named GE_new, Function_3 was used for GPS and Function_5 for Galileo. This solution used the same precision of GPS and Galileo observations as the GE solution. The results of this solution were compared with the results obtained for the GE and GE1 for Function_2, for which the best results were obtained. The results are shown in Figure 11.  
For the GE_new, the obtained 3D accuracy was inferior compared to the GE and GE1. The accuracy for the GE_new was 21 \(\pm\) 7 mm, whereas for the GE and GE1 it was 17 \(\pm\) 7 mm. However, when analyzing the convergence time and ZPD, an improvement was achieved. For the GE_new, the convergence time was 21 \(\pm\) 5 min, whereas for the GE and GE1 it was 24 \(\pm\) 13 min and 22 \(\pm\) 10 min, respectively. The significantly smaller standard deviation is particularly noticeable here. When analyzing ZPD for the GE_new, a value of 4.1 \(\pm\) 6.3 mm was obtained, whereas the values for the GE and GE1 were 5.7 \(\pm\) 8.3 mm and 6.0 \(\pm\) 8.2 mm, respectively.  
Adopting two different weighting functions for GPS+Galileo solutions, separately for each system, gave better results than using the same function for both systems. In addition, in the literature [33; 38], it was shown that using different weighting methods for different systems gave better results. The obtained positioning accuracy was worse, but only by 4 mm. On the other hand, a shorter convergence time and higher accuracy of the estimated ZPD were obtained, particularly with a smaller standard deviation. An improvement of about 5% was obtained for the convergence time and about 30% for the tropospheric delay.  
Figure 11: Mean accuracy 3D (**left**), convergence time (**center**) and ZPD (**right**) from all stations and all periods for the proposed weighting method—GE_new (purple), GE (red) and GE1 (green) for Function 2.
## 5 Performance Evaluation
### Simulation Parameters and Performance Metrics  
The performance of the proposed work was evaluated using simulations conducted in MATLAB. The model used for simulations was discussed in Section 3. In this simulation, task offloading with different task types was performed. These task sizes are ranged from 5 kb to 20 kb for sensitive tasks, 12 kb to 30 kb for traffic management tasks, and 20 kb to 50 kb for infotainment tasks. The coverage area of a fog node was taken as 200 m.  
The data rate for the downlink between the fog node and the vehicle is 8 Mbps. However, the downloading data rate of a vehicle from a cloud server is 2 Mbps. A fog computing node has limited processing capability and can execute 500 kBytes of tasks simultaneously, However, the cloud has unlimited processing capacity.  
A list of salient simulation parameters are shown in Table 1. In this work, a Monte-Carlo-based simulation was performed for fair comparative analysis of \(ETE_{\text{FN}}\). The results are obtained as an average of \(10^{3}\) iterations.
## 3 Design Optimization
### Parametric Study  
To analyze the effect of the design variables on the multi-objective function and constraints, the analysis of means (ANOM) was performed, as shown in Figure 5. Consequently, it can be seen that the rib thickness has a greater influence on the multi-objective function and constraints compared to the inner angle between the PM. In particular, the standard deviation of torque according to the change of rib thickness was 1.264, which suggests the largest effect on torque among the multi-objective function and constraints.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Items** & **Unit** & **DOE1** & **DOE2** & **DOE3** & **DOE4** & **DOE5** & **DOE6** & **DOE7** & **DOE8** & **DOE9** \\ \hline Inner angle between & \(\circ\) & 130 & 130 & 130 & 136 & 136 & 136 & 142 & 142 & 142 \\ permanent magnets & & & & & & & & & & \\ Rib thickness & mm & 1 & 1.5 & 2 & 1 & 1.5 & 2 & 1 & 1.5 & 2 \\ \hline Average torque & N-m & 62.791 & 61.169 & 59.714 & 63.147 & 61.514 & 60.046 & 63.411 & 61.764 & 60.302 \\ Total harmonic distortion of & \% & 4.066 & 3.616 & 3.547 & 4.075 & 3.599 & 3.519 & 4.082 & 3.590 & 3.488 \\ the back EMF & \% & 91.766 & 91.571 & 91.385 & 91.809 & 91.614 & 91.429 & 91.839 & 91.645 & 91.463 \\ Torque ripple & \% & 7.836 & 7.342 & 6.928 & 7.816 & 7.325 & 6.951 & 7.767 & 7.298 & 6.962 \\ \hline \hline \end{tabular}
\end{table}
Table 2: DOE results. DOE: design of experiments.  
Figure 5: The analysis of means results.
## 3 Theoretical Background
### Received (RX) Signal Level Power  
The received signal power (dBm) expressed on the receiver is given as follows [12]:  
\[R_{X}^{\mathrm{[dBm]}}=P_{T}^{\mathrm{[dBm]}}+G_{T}^{\mathrm{[dBi]}}+G_{R}^{ \mathrm{[dBi]}}-L_{FS}^{\mathrm{[dB]}} \tag{2}\]  
where \(P_{T}\) is the power of the transmitter, \(G_{T}\) is antenna gain of the transmitter, \(G_{R}\) is the antenna gain of the receiver, and \(L_{FS}\) the free space loss.
# IFF-Net: Irregular Feature Fusion Network for Multimodal Remote Sensing Image Classification
###### Abstract  
In recent years, classification and identification of Earth's surface materials has been a challenging research topic in the field of earth science and remote sensing (RS). Although deep learning techniques have achieved some results in remote sensing image classification, there are still some challenges for multimodal remote sensing data classification, such as information redundancy between multimodal remote sensing images. In this paper, we propose a multimodal remote sensing data classification method IFF-Net based on irregular feature fusion, called IFF-Net. The IFF-Net architecture utilizes weight-shared residual blocks for feature extraction while maintaining the independent batch normalization (BN) layer. During the training phase, the redundancy of the current channel is determined by evaluating the judgement factor of the BN layer. If this judgment factor falls below a predefined threshold, it indicates that the current channel information is redundant and should be substituted with another channel. Sparse constraints are imposed on some of the judgment factors in order to remove extra channels and enhance generalization. Furthermore, a module for feature normalization and calibration has been devised to leverage the spatial interdependence of multimodal features in order to achieve improved discrimination. Two standard datasets are used in the experiments to validate the effectiveness of the proposed method. The experimental results show that the IFF-NET method proposed in this paper exhibits significantly superior performance compared to the state-of-the-art methods.  
classification; IFF-Net; multimodal; remote sensing (RS); BN; judgment factor +
Footnote †: journal: Medical Image Classification  
1
Footnote 1: [[https://doi.org/10.3390/app14125061](https://doi.org/10.3390/app14125061)]([https://doi.org/10.3390/app14125061](https://doi.org/10.3390/app14125061))
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 6 Evaluation
### Leakage Frequency Detection  
In order to evaluate the leakage frequency detection algorithm, experiments were conducted against the aforementioned dataset captured using the experimental setup. At the end of each experiment, the dissimilarity metrics for each pair of the considered network traffic patterns were plotted. With the plotted values, it is easier to identify valleys  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Total Size** & 350 GB \\ \hline
**Compressed Size** & 42 GB \\ \hline
**Frequency Range** & 30–220 Mhz \\ \hline
**Data Description** & For each frequency from 30 to 220 Mhz, data set contains EMR trace files for each packet pattern and corresponding _pap_ files. \\ \hline
**Total Samples** & 190 \(\times\) 6 _pap_ files, 190 \(\times\) 6 IQ files \\ \hline
**Total Time** & 4 h 30 min Approximately \\ \hline
**Hardware Setup** & UTP cable, HackRF One SDR, H-Loop antenna, SLT Router, MacBook Pro \\ \hline
**Software Setup** & Python3.7, Scapy Library, GNU Radio Library, h5 py Library for data compression \\ \hline \hline \end{tabular}
\end{table}
Table 2: EMR data set details.  
that provide the similarity between the patterns. The lower the similarity, the better the corresponding pair of network traffic patterns to represent digital 0 and 1 symbols. Out of the three similarity metrics considered, cosine similarity and Pearson correlation demonstrated similar results, while cross-correlation indicated opposite results to the previous two. With these results, further analysis was narrowed down to a few optimal frequencies. Figure 3 illustrates the selected frequencies from the cosine and Pearson correlations: 42-50 MHz, 91-99 MHz, and 200-213 MHz. Similarly, the selected frequencies from cross-correlation are also shown in Figure 4: 30-35 MHz, 120-125 MHz, and 148-152 MHz.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
###### Abstract  
In heterogeneous wireless sensor networks (HWSNs), optimizing energy efficiency presents significant challenges due to variations in node energy levels and the complexity of the network environment. This paper introduces an energy efficiency optimization algorithm for HWSNs based on the Deep Q-Network (HDQN). The algorithm aims to address these challenges by selecting the optimal information transmission path. The HDQN leverages energy differences between nodes and real-time environmental data to enhance network efficiency. Its reward function takes into account node distance, remaining energy, and relay node count to balance node participation and minimize overall energy consumption. The Deep Q-Network (DQN) uses the mean squared error for precise reward estimation, and an improved packet header structure supports effective routing decisions. Simulation results show that the HDQN significantly outperforms existing algorithms--EEHCHR, 2L-HMGEAR, NCOGA, DEEC, and SEP--in terms of energy efficiency, network lifetime, and robustness, demonstrating its potential to advance the performance of HWSNs. The research results of the paper provide a theoretical basis for future energy efficiency research in wireless communication and contribute to the study of the new generation of wireless networks.  
heterogeneous wireless sensor networks; Deep Q-Network (DQN); energy efficiency; path optimization; reward function; routing algorithm +
Footnote †: journal: Electronics Electronics
# Exploiting Frequency Characteristics for Boosting the Invisibility of Adversarial Attacks
## 4 Experimental Results and Analysis
### Visualization Analysis
#### 4.3.2 Difference Analysis  
To further analyze the impact of attacks on images, we visualize the adversarial perturbations using an absolute difference map.  
Taking the adversarial examples in untargeted attack scenario on the Tiny-ImageNet dataset as an example, we differentiate the adversarial example from the original image, take the absolute values, and visualize the absolute difference maps on the three RGB channels. Due to the constraints of the \(L_{\infty}\) norm, the maximum perturbation of all the adversarial examples is set to \(16/255\). We normalize all the absolute difference maps to \(0\)\(\sim\)\(255\), where \(0\) and \(255\) represent black and white, respectively. The larger the perturbation value of that point, the brighter the pixel point.  
In Figure 7, we show the absolute difference maps of perturbations in the three channels of the image for the comparative methods and our proposed method. The perturbations generated by the comparative methods in each channel exhibit meaningless noise patterns, with the perturbation strength in each region being approximately uniform, especially evident in the maps of PGD. In contrast, our absolute difference maps exhibit clear distribution patterns of perturbations in all three channels. Our perturbations are mainly concentrated in regions of the image with complex contents, but have little effect on the smooth regions of the image.  
From the above analysis, it can be concluded that our method adapts the perturbations to the image content and significantly reduces the influence on the smooth regions of the image, i.e., the regions where the low-frequency component accounts for more. We transferred the perturbations to high-frequency regions where the human visual system is less sensitive, thus making the generated adversarial examples have minimal visual differences compared to the original images.  
Figure 7: Absolute difference maps of the adversarial examples and clean image, where each column from top to bottom is the perturbation of the three channels R, G, and B. All absolute difference maps are normalized from \(0\) to \(255\), where \(0\) and \(255\) are represented in black and white.