esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Attacks on the Contents  
### Attacks on the Digital Signatures and Watermarks  
Tampering detection by watermark is a similar approach to the digital signature [40] since they use hash data of the contents to ensure the integrity of the contents [37]. The difference is that the digital signature appends the encrypted hash as an apparent signature, while the watermarking-based tampering detection methods embed the hash (with or without encryption) into the contents secretly.  
When malicious attackers attack digitally-signed content, the main target of the attackers is to break the encryption of the signature by finding a collision of the hash [41] or estimating the public key [42]. On the other hand, when attackers attack the watermark for tampering detection, the target is different from the digital signature.  
The target of an ordinary attack on watermarks for copyright protection is to destroy the embedded copyright information without degrading the quality of the contents [43]. However, if an attacker destroys the watermark, that operation will be detected as tampering in the tampering detection scheme.  
The attack on the watermark for tampering has several steps. First, the attacker needs to determine if the hash is embedded in the contents since its existence is concealed. Second, the attacker should determine the parameters of embedding, such as the embedding method, frame length, and positions. Third, they need to forge the contents. Finally, they should embed the hash into the tampered parts to conceal the forgery.  
The detection of the embedded hash is the first step of the attack. This task is similar to steganalysis, which detects the covert data embedded in the contents. If we can prevent the detection of embedded information from the contents, it is beneficial to protect the contents from tampering attacks.
# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 5 Discussion
### Analysis of the Impact of Terrain on Soil Moisture Retrieval
The correlation coefficients of the phases with reference values decreased when the azimuth angles ranged between 111\({}^{\circ}\) and 155\({}^{\circ}\), and between 284\({}^{\circ}\) and 325\({}^{\circ}\), with average correlation coefficients of 0.19 and 0.22, respectively. Among these, the correlation coefficients for G19 and G26 gradually decreased as the azimuth angle increased, while the coefficients for G03 and G10 showed fluctuations. Overall, when the satellite trajectory azimuth angle was between 36\({}^{\circ}\) and 51\({}^{\circ}\), the correlation between the satellite phases and the reference values was the most stable, whereas the correlations at other azimuth angles exhibited instability. Figure 19 shows the mapping relationship between the correlation coefficients of different satellites at various azimuth angles and the corresponding normalized segment importance. The graph reveals that segments with higher correlation coefficients also had greater importance, indicating that the RF model based on MDI can recognize the quality of segment data during the retrieval process and assign appropriate weights. However, the graph also shows that some segments with high correlations are assigned lower weights than those with lower correlations, which could be a source of model error. Comparing the correlation between the phase data from different trajectory azimuth angles at the P041 station with soil moisture reference values against the DEM map reveals that, in areas with flatter terrain, the correlation between satellite phases and soil moisture reference values is higher and more stable. The MDI algorithm also shows high segment importance in these areas. However, in areas with significant terrain undulations, the correlation between phases and soil moisture reference values is lower, and while MDI may occasionally misjudge individual segments, the overall impor  
Figure 19: The upper \(Y\)–axis represents the normalized correlation coefficients between the phase of different satellites at various azimuth angles and the soil moisture reference values, while the lower \(Y\)–axis shows the importance of arc segments corresponding to those azimuth angles. low. Therefore, terrain factors indirectly affect the judgments made by the MDI algorithm. Fortunately, experimental results demonstrate that the MDI algorithm can effectively identify data with strong correlations, mitigating the impact of terrain on soil moisture retrieval and enhancing the reliability of the model's retrieval outcomes.
# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
## 3 Network Energy Models (Methods)
### System Models and Assumptions  
We considered a two-dimensional network model [17; 18] with sensor nodes, as well as the following assumptions:  
* All sensor nodes are considered stationary.
* In this study, we assumed there was one BS for which the data collected from the source IoT nodes are destined.
* The homogeneous SNs had similar processing and communication capabilities. In addition, we considered that the SNs are deployed with the same initial energy.
* The SNs deployed randomly are always located with their x and y coordinates in the topological area.
* The distance between the two neighboring SNs was evaluated using Euclidean distance.
## 3 Results
### Analysis of Feature Importance via SHAP
#### 3.3.3 Best Features Model Test  
Ultimately, an experiment was conducted using the same models employed initially in the combined dataset, but only with the top 10 most influential features according to SHAP for both the test-train split and the stratified \(k\)-fold validation methods. The results indicated that the Random Forest model continued to have a great performance, which is illustrated in Table 6, with F1 and accuracy scores of approximately 0.71. These scores closely resembled the results achieved when using all features in the model. However, this is surpassed by the Random Forest result using the cross-validation method, which achieved an accuracy of 0.81 shown in Table 7, remaining close to the original result with all features. In contrast, the other two models showed a considerable reduction in performance when the number of features was decreased.
# A Semantic Segmentation Method with Emphasis on the Edges for Automatic Vessel Wall Analysis
## 4 Discussion  
We evaluated the EVSegNet through ablation study by adding Resnet, DUC, HDC, and edge stream block. For the outer vessel wall contour segmentation, as can be seen in the first, third, and fourth row in the case of the MR vessel wall image segmentation results from Figure 9 and Table 2, the EVSegNet achieved superior quantitative results than the other methods for the segmentation of small-sized vessel walls in rows 1, 3, and 4.  
More careful observation suggests that adding the edge stream as an auxiliary output for the vessel wall segmentation task is vital for improving the accuracy. The use of a subnetwork of edge stream before doing the segmentation is beneficial; this approach improves the shape accuracy of the resulting segmentation. In particular, our edge stream does not require additional annotation, since edge information can be generated from the ground truth segmentation masks. This means that adding the edge module as prior knowledge into our method can further improve the segmentation accuracy by generating refined boundaries. Therefore, it improves the segmentation quality surrounding the edge, which results in the overall better segmentation performance achieved; our results are most consistent with ground truths.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Row1** & **Row2** & **Row3** & **Row4** & **Row5** \\ \hline \multirow{2}{*}{U-net} & Dice \% & 80.4 & 82.1 & 80.6 & 80.7 & 83.3 \\  & mIoU \% & 68.9 & 72.3 & 69.6 & 69.6 & 76.7 \\ \hline \multirow{2}{*}{U-net (Res)} & Dice \% & 81.0 & 85.3 & 83.4 & 84.0 & 86.2 \\  & mIoU \% & 71.1 & 79.7 & 76.7 & 77.5 & 80.9 \\ \hline \multirow{2}{*}{U-net (DUC)} & Dice \% & 82.2 & 86.8 & 84.5 & 84.5 & 87.4 \\  & mIoU \% & 72.4 & 81.4 & 77.8 & 77.8 & 82.0 \\ \hline \multirow{2}{*}{U-net (DUC + HDC)} & Dice \% & 83.6 & 87.7 & 85.5 & 86.1 & 88.2 \\  & mIoU \% & 75.6 & 81.8 & 79.6 & 80.7 & 82.5 \\ \hline \multirow{2}{*}{EVSegNet} & Dice \% & 85.7 & 91.5 & 86.1 & 86.9 & 90.4 \\  & mIoU \% & 80.3 & 85.3 & 80.8 & 81.6 & 83.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation Results of the Visualization Examples with Different Backbones (mean (std)).  
Figure 9: Visualization of the segmentation outputs with different backbone architecture, U-net (None), U-net (Res), U-net (DUC), U-net (DUC + HDC), and EVSegNet, respectively.
## 5 Evaluation Tasks and Metrics
### Evaluation Metrics  
To comprehensively assess the performance of the proposed methodologies, we employ a multi-faceted set of evaluation metrics. These metrics are designed to capture not only the quality of the generated solutions but also the stability, efficiency, and diversity of the optimization process. The metrics are broadly categorized as follows:  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Number of parts & \(p_{max}\) & 80 \\ Number of trays & \(t_{max}\) & 6 \\ Number of AGVs & \(n_{AGV}\) & 3 \\ Number of drones & \(n_{drone}\) & 1 \\ Total vehicles & \(n_{V}\) & 4 \\ Processing stations & \(n_{B}\) & 4 \\ Buffer locations & \(n_{C}\) & 3 \\ Part arrival interval & \(\tau_{A}\) & 30 \\ Processing time & \(\tau_{B}\) & 900 \\ Base travel time between A and B & \(t_{AB}\) & 400 \\ Base travel time between A and C & \(t_{AC}\) & 300 \\ Base travel time between B and C & \(t_{BC}\) & 200 \\ Drone speed factor & \(\alpha_{d}\) & 0.7 \\ Traffic jam probability & \(p_{jam}\) & 0.4 \\ AGV jam delay factor & \(\beta_{AGV}\) & 3.0 \\ Drone jam delay factor & \(\beta_{drone}\) & 1.5 \\ Processing failure probability & \(p_{fail}\) & 0.2 \\ Maximum battery capacity & \(E_{max}\) & 60 \\ Battery consumption rate & \(\gamma\) & 0.08 \\ Critical battery level & \(E_{crit}\) & 25 \\ Base charging time & \(\tau_{charge}\) & 300 \\ Per unit charging time & \(\tau_{unit}\) & 3 \\ Distance reward factor & \(f_{d}\) & 0.01 \\ Time reward factor & \(f_{t}\) & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initialization parameters for the AGV-drone scheduling task.
## 2 Material and Methods
### PTC Modeling  
For the present installation, the parabolic collector LS-2 is used, which is a commonly investigated device in the literature. The basic geometric values are the module width, which is equal to 5 m, and the module length, which is about 7.8 m, so a module aperture of 39 m\({}^{2}\) is determined. Moreover, the absorber tube's outer diameter is about 70 mm, the concentration ratio is about 22.74, and the maximum optical efficiency is equal to 73.3% [24]. Other geometrical and optical parameters can be found in Ref. [24], as well as the basic thermal modeling of the collector can be found in Ref. [24]. PTCs can exploit solar beam irradiation (\(\mathrm{G_{bn}}\)), so the available solar irradiation (\(\mathrm{Q_{solar}}\)) is calculated as [24]:  
\[\mathrm{Q_{solar}}=\mathrm{A_{a}}\cdot\mathrm{G_{bn}} \tag{1}\]  
The value (\(\mathrm{A_{a}}\)) is the collector aperture. In addition, the useful heat that is taken by the working fluid is defined as [24]:  
\[\mathrm{Q_{u}}=\mathrm{\dot{m}_{c}}\cdot\mathrm{c_{p,oil}}\cdot\left(\mathrm{T_ {c,out}}-\mathrm{T_{c,in}}\right) \tag{2}\]  
For the collector mass flow rate, the following estimation is considered, according to Ref. [13]:  
\[\mathrm{\dot{m}_{c}}=0.02\cdot\mathrm{A_{a}} \tag{3}\]  
The incident angle modifier (\(\mathrm{K(\theta)}\)) for the LS-2 PTC, considering the solar incident angle (\(\theta\)) in degrees, can be determined as below [24]:  
\[\mathrm{K(\theta)}=\cos(\theta)+0.000884\cdot\theta-0.00005369\cdot\theta^{2} \tag{4}\]  
In Ref. [25], other important expressions can be found which describe the heat convection inside the tube, also the heat losses between the absorber and the cover as well as  
Figure 1: Solar-driven cogeneration system that produces electricity and hydrogen.  
between the cover and the ambient. The energy balance of the thermal energy storage tank is expressed by the following equation [26]:  
\[\mathrm{Q_{st}=Q_{u}-Q_{in}-Q_{loss}} \tag{5}\]  
More specifically, the stored thermal energy (\(\mathrm{Q_{st}}\)) is equal to the heat input to the tank, which is equal to the collector's useful heat (\(\mathrm{Q_{u}}\)), minus the heat outputs from the tank, that are the heat transferred to the organic Rankine cycle system (\(\mathrm{Q_{in}}\)) and the heat losses to the ambient (\(\mathrm{Q_{loss}}\)). It is assumed that the storage tank does not store heat on the steady-state calculations and the thermal oil temperature is uniform in the entire tank volume. The tank heat loss coefficient (\(\mathrm{U_{T}}\)) value is considered at 0.5 W/m\({}^{2}\)K [13].
## 4 Discussion
### Larger Scale Network and Noisy Environment  
We develop a different and noisier scenario because large-scale sensor networks are also standard in practical applications, such as in industrial scenarios. We gradually increase the number of nodes and obtain the values of the metrics with the most significant impact. We do this on the PEGASIS protocol because it is the one that performs the best, we studied it as a success case and observed its reactivity when facing changes in the topology configuration. We performed the tests in two different environments: under normal network conditions and jamming with a jammer node located as centrally as possible in the topology. Table 6 shows the results of the network under normal conditions, and Table 7 shows the results under the presence of the jammer node.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Nodes** & **Availability Routes (\%)** & **Retransmissions** & **Resilience (Sec)** \\ \hline
100 & 66\% & 1.0 & 3.567 \\
150 & 62\% & 1.7 & 3.864 \\
200 & 59\% & 2.1 & 4.648 \\
250 & 51\% & 2.3 & 5.972 \\
300 & 39\% & 2.4 & 7.346 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics with relevant impact on energy for PEGASIS under normal conditions.  
Figure 16: Algorithm for jamming detection under PEGASIS, TEEN, LEACH, and HPAR.  
We observe that the negative impact on the different network arrangements begins to decline sharply when the number of nodes is larger than 200 nodes, worsening the network conditions by around 70% under normal conditions. When the network is under the jammer node's presence, the network conditions starts to deteriorate if the number of nodes is above 150, decreasing the performance by 85%. These environments present more challenging conditions for the wireless communications due to a high traffic load (this increases the overhead); in addition, the nodes' connections and disconnections become more frequent due to an increased packet loss caused by collisions. As a result, there is a direct impact on the route availability and the network's ability to recover the full link topology (without leaving isolated nodes).
## 1 Introduction
Existing path selection schemes all select one decoding result from \(L\) decoding results, wasting the other \(L-1\) decoding results. 5. Existing path selection schemes do not consider analyzing and ranking the weights of the \(L\) decoders. To address the challenges of UAV noise interference and limited hardware resources and the above issues, this paper proposes a path selection criterion based on information geometry for BPL decoding (Belief Propagation List Decoding Based on Information Geometry, BPLIG). The innovations of this paper are as follows:  
1. This paper utilizes information geometry to conduct mathematical analysis on polar code decoding results and establish a mathematical model. The soft information output by each BP decoder is mapped to a point on a statistical manifold. Mathematical tools of information geometry are used to analyze the statistical manifold and calculate its metrics, distances, and other properties. 2. The concept of the \"soft information centroid\" is proposed. Information geometry is used to perform equivalent calculations on \(L\) points on the statistical manifold, and a path selection criterion based on the soft information centroid is proposed, which neither wastes decoding results nor ignores the weight of each decoding result. 3. Geometric distance is used as the weight of the decoder, and a path selection criterion for polar code BPL decoding based on geometric distance is proposed. Simulation results show that the new path selection criterion proposed in this paper can improve the error correction performance of polar code BP decoding with fewer iterations and BP decoders, meeting the payload requirements of UAVs. The remainder of this paper is organized as follows. Section 2 reviews the BPL decoding process of polar codes. Section 3 establishes the mathematical framework for polar code BPL decoding, calculates the metric matrix of the statistical manifold, proposes the concept of the \"soft information centroid\", presents the path selection criterion for polar code BPL decoding based on the soft information centroid, and proposes a weight selection method based on geometric distance for this path selection criterion. Section 4 presents simulation analysis. Section 5 summarizes and discusses future directions. The main symbols used in this paper are listed in Table 1.
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 2 System Model and SWIPT-D2D Schemes  
Let us consider the PS-D2D-based HSTNs, in which a low earth orbit (LEO) satellite \(S\) broadcasts information to an energy-constrained UT \(U_{1}\) and a shadowed IoT device \(U_{2}\) with the assistance of a terrestrial AF relay \(R\), as shown in Figure 1. The location of the IoT device \(U_{2}\) is outside the coverage of relay node \(R\), and it cannot completely receive the information sent by satellite source node \(S\) due to factors such as the shielding effect. Thus, it is necessary to complete the information transmission from satellite \(S\) to IoT device \(U_{2}\) with the cooperation of UT \(U_{1}\). We assume that the satellite \(S\), UT \(U_{1}\), and IoT device \(U_{2}\) are equipped with a single antenna and are operating in half-duplex mode.  
In HSTNs, we assume that the channels between the satellite and the terrestrial nodes undergo independent shadowed Rician fading, which is commonly used in land mobile satellite (LMS) cooperative networks, and the channels among terrestrial nodes undergo independent Nakagami-\(m\) fading, which is commonly used in terrestrial communication networks. We also assume that the terrestrial nodes are inflicted by the additive white Gaussian noise (AWGN) with mean zero and variance \(\sigma^{2}\).
## 4 Simulation Results Analysis
Figure 11 compares the sum rate of the two strategies at high inter-beam interference for different numbers of served users. We have assumed a channel bandwidth is 1 MHz. It can be observed from the figure that the inter-beam interference becomes more severe as the number of served users increases. The use of the adaptive transmit diversity strategy reduces the spectral efficiency of the system; however, the strategy is able to convert the interference signal into signal gain and improves the SINR. Therefore, the sum-rate performance of the system with the adaptive transmit diversity strategy is better than that of the conventional scheme and is especially significant in the case of a large number of users.
# Research on a Strategy for Prediction Methods of Submarine Self-Propulsion Hydrodynamic Performance
## 2 Numerical Simulation Basics
### Geometric Model  
The study object used in this study was the fully appended model of the SUBOFF submarine. The SUBOFF submarine model project was proposed by the Defense Advanced Research Projects Agency (DARPA) of the United States [30]. The fully appended model of the SUBOFF submarine consisted of an axisymmetric main body, a sail, and four tail rudders. The geometric parameters of the submarine model were as follows: total length of the submarine hull \(L_{sub}\) = 4.356 m, distance from the propeller disk to the bow \(L_{pro}\) = 4.261 m, length of the forebody \(L_{bow}\) = 1.016 m, length of the parallel middle body \(L_{par}\) = 2.229 m, maximum diameter of the middle body \(B\) = 0.508 m, height of the sail \(H_{h}\) = 0.206 m, distance from the sail leading edge to the bow \(L_{tan}\) = 0.924 m, distance from the sail trailing edge to the bow \(L_{tra}\) = 1.293 m, maximum thickness of the sail \(Hth\) = 0.066 m, and the wetted surface area of the submarine model \(S_{m}\) = 6.348 m\({}^{2}\). The propeller used was the E1619 high-skew propeller, specifically designed by the INSEAN tank in Italy for submarines [14; 24]. For the open water numerical validation of the propeller, the original model size was used. For the submarine with propeller numerical simulation, the propeller was scaled based on the hub size at the propeller disc of the submarine. The main geometric parameters of the propeller are as follows: propeller diameter \(D\) = 0.485 m (0.262 m during self-propulsion), number of blades \(z\) = 4, pitch ratio \(P/D\)(0.7 \(R\)) = 1.15, chord length \(b\)(0.75 \(R\)) = 6.8 mm (3.67 mm during self-propulsion), expanded area ratio \(A_{E}/A_{0}\) = 0.608, hub diameter ratio \(D_{h}/D\) = 0.226, and a right-handed rotation direction. The entire submarine's self-propulsion model is shown in Figure 2.
## 3 Controller Design
### Sliding-Mode Control
: _Here, \(\eta>0\) needs to be chosen so that control amplitudes do not become excessively large, which may lead to reaching actuator saturations in the application at hand._  
The nonlinear control law, shown in Figure 6, is given by rearranging Equation (23) into  
\[u_{\text{SM}}=\frac{1}{b}\left[-a_{31}x_{1}-a_{32}x_{2}-a_{33}x_{3}+\dot{x}_{3,\text{d}}+\alpha_{1}(-x_{3}+\dot{x}_{2,\text{d}})+\alpha_{0}(-x_{2}+\dot{x}_ {1,\text{d}})+\eta\cdot\tanh\left(\frac{S(\tilde{\mathbf{x}})}{p}\right)\right] \tag{24}\]  
with the substitution \(\tanh\left(\frac{S(\tilde{\mathbf{x}})}{p}\right)\) for the term \(\text{sign}(S(\tilde{\mathbf{x}}))\) as a regularization to avoid discontinuities in the control signal for operating states crossing the sliding manifold and to minimize chattering [19; 22]. For compensating possible gain errors, which result from model uncertainties, the steady-state offset  
\[\Delta\dot{n}_{\text{H2}}= \begin{cases}\frac{P_{\text{el,m}}-P_{\text{el,d}}} {K_{s}}&\text{for}\ \ \dot{P}_{\text{el,d}}=0\\ 0&\text{for}\ \ \dot{P}_{\text{el,d}}\
eq 0\end{cases}  \tag{25}\]  
with  
\[K_{s}=\frac{P_{\text{el,d}}}{\dot{m}_{\text{H2,m}}} \tag{26}\]  
is added to the input variable as soon as constant operating points are detected. This compensation becomes necessary due to the fact that the regularized switching function in Equation (24) typically only guarantees convergence to a bounded domain around \(S(\mathbf{\tilde{x}})=0\)[10; 22].