esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Modal Sonar Mapping of Offshore Cable Lines with an Autonomous Surface Vehicle
## 2 ASV Development
### Control Architecture  
Autonomous navigation approaches for tracking pre-assigned waypoints are fundamentally required to achieve various tasks autonomously. Thus, a closed-loop controller was applied to control the course and direct the vehicle towards the waypoint.  
Waypoints were assigned based on the required tasks. Because the vehicle was developed as a nonholonomic system, its motion model for guidance and control approaches was simplified as a three-degree-of-freedom (3-DOF) kinematic model. In addition, a guidance approach for minimizing line-of-sight (LOS) and cross-track (CT) errors was employed. In this regard, a conventional proportional-derivative (PD) controller was designed and implemented to control the course of the vehicle toward the waypoints. During task activa  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Description** \\ \hline Dimension & 4.1 \(\times\) 2.5 \(\times\) 2.0 (L \(\times\) W \(\times\) H) m \\ Platform weight & 400 kg \\ Payload & up to 300 kg \\ Batteries & Lithium battery (25.9 VDC/ 104 Ah) \(\times\) 3 \\ Propulsive power & 5 HP \(\times\) 2 \\ Max. propeller speed & 1300 rpm \\ Speed & up to 6.35 knots \\ Endurance & 8 h @ 3 knots \\ Wireless Comm. & up to 1 km \\ Motion sensors & GPS compass (Hemisphere V113) \\  & AHRS (Advanced Navigation Spatial FOG) \\ Mapping sensors & MBES (Imagenex 837B Delta T 260 kHz) \\  & SSS (Klein UUV3500 450/900 kHz) \\ \hline \end{tabular}
\end{table}
Table 1: ASV Specifications.  
Figure 2: Equipped multi-modal sonar sensors. MBES for point cloud generation and SSS for sonar imaging.  
tion, a control input (\(\delta_{\tau}\)) of each thruster, determined by the LOS and CT guidance, can be expressed as follows:  
\[\delta_{\tau}=\rho(k_{\text{LOS}}^{p}e_{\text{LOS}}+k_{\text{LOS}}^{d}e_{\text{ LOS}})+(1-\rho)(k_{\text{CT}}^{p}e_{\text{CT}}+k_{\text{CT}}^{d}e_{\text{CT}}) \tag{1}\]  
where \(k_{\text{LOS}}^{p}\) and \(k_{\text{LOS}}^{d}\) are the proportional and differential gain coefficients for the LOS guidance, respectively, and \(k_{\text{CT}}^{p}\) and \(k_{\text{CT}}^{d}\) are the proportional and differential gain coefficients for the CT guidance, respectively. The variables \(e_{\text{LOS}}\) and \(e_{\text{LOS}}\) denote the heading angle error of the vehicle and its differential value. Moreover, \(e_{\text{CT}}\) and \(\dot{e}_{\text{CT}}\) represent the CT error and its differential value, respectively. In addition, \(\rho\) is defined as the ratio of the distance to the current goal waypoint from the vehicle position to the distance between the current and the previous waypoints. If \(\rho\) is larger than 1, then \(\rho\) is set to 1. Accordingly, the weighting of each guidance approach was adjusted using \(\rho\). The definitions of these tracking performance indices are presented in Figure 3.
# A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification
## 2 Related Works
### Graph-Based ReID  
Graph Convolutional Networks (GCNs) [26], with their ability to model abstract node relationships, have recently been applied in the ReID domain to capture temporal and spatial information in video sequences. In pedestrian images, body parts inherently possess structural relationships, and GCNs capitalize on this prior knowledge by encoding spatial or semantic similarities between pedestrian image features through edge weights, fortifying the model's ability to learn pedestrian characteristics.  
Graph-based person re-identification leverages the highly structured human skeleton to extract semantic information at the image pose level, thereby suppressing noise interference through guiding or fusion mechanisms. PFD [17] divides images into overlapping fixed-size patches, followed by the employment of a transformer encoder to capture contextual relationships among these patches. Subsequently, pose-guided feature aggregation and a feature-matching mechanism are utilized to explicitly emphasize visible body parts. Finally, pose heatmaps and a decoder are leveraged as keys and values to enhance the discriminability of individual body parts. HOReID [11] introduces a learnable relation matrix, treating human key points obtained from pose estimation as nodes in a graph and, ultimately, forming a topological graph that mitigates noise disturbances. The PMFB [18] utilizes pose estimation to acquire confidence scores and coordinates of human key points. Subsequently, thresholds are set to filter out occluded regions, and visible parts are employed to constrain feature responses at the channel level, addressing the challenge of occlusion. PGMANet [19] employs human heatmaps to generate attention masks, synergistically eliminating noise interference through both element-wise multiplication with feature maps and guidance from higher-order relations. EcReID [12] consists of three modules: a mutual denoising module, an inter-node aggregation and update module, and a graph matching module. Among them, the graph matching module uses a graph matching method based on the human body's topology to obtain a more accurate calculation of the mask image similarity.  
The authors of some studies have partitioned each image's features horizontally [3], treating each segment as a node. However, human-related features may constitute a relatively small proportion of horizontal features in an image, and relying solely on horizontally segmented features can lead to interference from background characteristics in experimental outcomes. The authors of other works have integrated key point information, using human key points as graph nodes. However, he ratio of human content in features is unpredictable; this is particularly true in the case of occluded datasets, considering the fact that the impact of occluders on human features might result in missing key point information, undermining the model's robustness.
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 4 Statistical Analysis of Channel Propagation Characteristics
### Power-Related Characteristics
#### 4.2.3 Rician K-Factor  
This section illustrates the results for the K-factor for both operators and all locations. The CDF of the measured K-factor for the strongest PCI for the Op1 and Op2 at the measurement location 1 is shown in Figure 14. The measured K-factor for each operator and measurement location fits very well to the normal distribution. Based on the AIC estimator, normal distribution is the best approximation for the K-factor values for both operators Op1 and Op2 at location 1. Similar distribution fittings can be obtained for other measurement locations. The mean and standard deviation values for the K-factor are given in Table 6. The mean value of the K-factor for Op1 at locations 1 and 2, and Op 2 at locations 1, 4, and 6, is higher than other locations. At these locations, the K-factor value is far greater than 0 dB. In the case of Op1, locations 1 and 2 are closer to BS and less obstructed than other measurement points; as a result a significant part of the received power is associated to the dominant path, with a small number of paths. Similarly, locations 1, 4, and 6 have higher K-factor values, so it is more likely that there would be few paths, including a strong path that leads to a higher K-factor value. The main reason for high k-factor values at locations 4 and 6 is that the BS and locations 4 and 6 face directly without any obstruction by walls. The only obstruction was the glass window, opened at the time of measurement; hence the channel at locations 4 and 6, for Op2, is more LOS-alike; therefore, K-factor increases when the channel is more LOS-alike with a small number of weak reflected and scattered NLOS paths.  
At other measurement points, the signal is greatly obstructed and major portion of the power is received through NLOS components, with a very small direct path power. We also postulate here that the beam is not highly directive towards these locations, and a portion of power is received through the reflections due to the objects and nearby buildings.  
It can be observed that the K-factor varies with the location. The K-factor is particularly high when the beam is more directive toward the measurement location, and the distance between the base station and the measurement's location is small. Overall, the 5G system  
Figure 13: Box plot for path loss for Op1 and Op2 at all measurement locations, showing median value (red line), standard deviation (blue box), and outliers (red Plus signs). (**a**) Path loss for Op1. (**b**) Path loss for Op2.  
employing a single wide beam gives a higher K-factor at locations where the beam is more directive than the multiple SSBs-based transmission systems. Figure 15 shows the actual data points of the K-factor values and the presence of two peaks in the probability density function (pdf) of the K-factor due to channel variations.
## 5 Empirical Study
### Statistical Tests  
We utilized the Wilcoxon effect size and Scott-Knott effect size difference as our statistical tests. We used Shapiro-Wilk test to verify the distribution of our study results, and results were not normally distributed. Thus, we used the Wilcoxon effect size [49] to answer the first and third research questions. Wilcoxon effect size is a non-parametric statistical test that can be used to determine the difference between two independent variables. The calculation of Wilcoxon effect size (\(r\)) is based on the \(Z\)-score and the number of samples (\(n\)), as shown in the following equation:  
\[r=\frac{Z}{\sqrt{n}} \tag{9}\]  
According to Cohen thresholds [50], the effect size (r) is interpreted as negligible, small, medium, or large. r is considered negligible if \(r\leq 0.1\), small if \(0.1<r\leq 0.3\), medium if \(0.3<r\leq 0.5\) and large if \(0.5<r\). The Wilcoxon effect size was used to measure the difference between the performance of the default and optimized models and thus answer the first research question. In addition, the Wilcoxon effect size was used to estimate the impact of combining undersampling with GAN-based methods, and thereby answer the third research question.  
In order to compare the performance of the traditional sampling techniques against GAN-based methods and answer the second research question, we utilized Scott-Knott ESD, which is a variant of the Scott-Knott test. This test uses a hierarchical clustering analysis to divide the methods into statistically different groups based on the mean  
\begin{table}
\begin{tabular}{c c} \hline
**Hyperparameter** & **Range of Values** \\ \hline Number of epochs & 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 \\ Batch size & 16, 32, 48, 64, 80, 94, 112, 128 \\ Learning rate & 0.0001, 0.0002, 0.0003, 0.0004, 0.0005 \\ \hline \end{tabular}
\end{table}
Table 4: Hyperparameters range for optimization of GAN-based methods.  
value [51]. If the statistical difference between the methods is negligible, the methods will be merged into one group and given the same rank. Furthermore, if the difference between the methods is significant, then the methods will be placed into different groups.
## 2 Experimental Materials and Methods
### Performance Testing and Characterization  
The sintered Si\({}_{3}\)N\({}_{4}\) ceramic was ground and polished; the bulk density was measured using the Archimedes drainage method; the theoretical densities of the Si\({}_{3}\)N\({}_{4}\) ceramics were calculated depending on the composition and density of the raw materials; and the relative density was calculated based on the theoretical density and bulk density. The relative density of all samples was greater than 99%. The phases present in the samples were identified by employing X-ray diffraction (XRD; D8 Advance; Bruker, Ettlingen, Germany) using Cu K\(\alpha\) radiation at an acceleration voltage of 40 kV, tube current of 20 mA, and scanning range of 10\(-\)60\({}^{\circ}\). After grinding and plasma-etching the ceramic, the microstructure was observed using a high-resolution field-emission scanning electron microscope (SEM; Nova, NanoSEM430; FEI, Eindhoven, The Netherlands). The volume resistivity of the composite material of \(\Phi\)30 mm \(\times\) 1 mm was measured using the three-electrode method with a Keithley-6517B high-resistance meter; the voltage was 1 kV, and the temperature range, 20\(-\)80 \({}^{\circ}\)C in air. The dielectric constant and dielectric loss of the sample were tested using the German Novocontrol Concept80 wide-frequency dielectric impedance spectroscope at room temperature and a testing frequency of 10\({}^{0}\)-10\({}^{5}\) Hz.
## 3 AMO Model Construction
### Model Solving Algorithm  
The Simulated Annealing (SA) algorithm [46] was adapted to solve the AMO model, and the steps of the proposed SA algorithm can be seen in Algorithm 1.  
```
Input: initial temperature \(T\), temperature reduction rate \(K\), maximum iteration count \(IterM\), upper bound for variable values \(UB\), lower bound for variable values \(LB\), number of variables \(Xnum\), perturbation probability \(P\), neighborhood change magnitude \(S\), maximum consecutive success count N\({}_{success}\), maximum consecutive failure count \(N_{fail}\).
1 Initialization: \(X_{current}\) is the current solution. \(X_{best}\) is the current best solution. \(Y_{current}\) and \(Y_{best}\) are objective function values. Iteration counter \(n=0\). Success count \(success=0\). Failure count \(fail=0\).
2 Generate an initial solution: \(X_{current}=LB+rand(0,1)*(UB-LB)\), \(Y_{current}=getObjBySimulation(X_{current})\), set \(X_{best}=X_{current}\), \(Y_{best}=Y_{current}\), \(n=n+1\).
3 while \(n<IterM\)
4 for\(i=1\): \(Xnum\)
5 if\(rand(0,1)<\) P
6 \(X_{Next\{i\}}=X_{current\{i\}}*rand(0,1)*rand(0,1)*(UB\{i\}-X_{current\{i\}})*S\) \(+(randint(0,1)-1)*(rand(0,1)*(X_{current\{i\}}-LB\{i\})\times S)\)
7 else
8 \(X_{Next\{i\}}=X_{current\{i\}}\)
9 \(Y_{next}=getObjBySimulation(X_{next})\)
10 if\(Y_{next}>Y_{best}\)
11 \(X_{best}=X_{next}\)
12 \(Y_{best}=Y_{next}\)
13 \(success=success+1\)
14 \(fail=0\)
15 else
16 \(\Delta=Y_{best}-Y_{next}\)
17 \(rAccept=exp(-\Delta/T)\)
18 \(rand(0,1)<rAccept\)
19 \(X_{current}=X_{next}\)
20 \(success=0\)
21 \(fail=fail+1\)
22 if\(success>N_{success}\)
23 \(P=P*0.5\)
24 \(success=0\)
25 if\(fail>N_{fail}\)
26 \(P=P*2\)
27 \(fail=0\)
28 \(T=T*K\)
29 \(n=n+1\) Output: \(X_{best}\), \(Y_{best}\)
```  
**Algorithm 1** The Main flow of SA algorithm
# An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
## 3 Inductive Link Circuit Modeling
### Power Link (Downlink) Transfer Functions  
When analyzing the power link in Figure 3, the source of the uplink can be considered as a short circuit. By leveraging the mesh-current technique [22], the voltage and current transfer functions of the power link are derived as shown in Equations (1) and (2), where \(M=k\sqrt{L_{\text{o}}L_{\text{i}}}\) is the mutual inductance between the coils and \(s\) is the Laplacian. Equations (1) and (2) will be used to calculate the PTE in Section 4.1.3. The relation between \(V_{\text{o}}(s)\) and \(V_{\text{load}}(s)\) is denoted in Equation (3). Equation (3) will be used to calculate the power carrier amplitude on the out-body coil in Section 4.1.1.  
\[\left\{\begin{array}{l}\frac{V_{\text{load}}(s)}{V_{\text{dl}}(s)}=\frac{sM} {\left(\frac{1}{L_{\text{i}}}\left(s\text{L}_{\text{i}}+R_{\text{i}}\right)+1 \right)\left(\left(s\text{L}_{\text{o}}+R_{\text{o}}\right)\left(1+\frac{1}{ L_{\text{o}}\text{Gd}}\right)+\frac{1}{L_{\text{o}}}\right)-s^{2}M^{2}\frac{1}{L_{ \text{i}}}\left(1+\frac{1}{L_{\text{o}}\text{Gd}}\right)}\\ Z_{\text{i}}=R_{\text{load}}||\frac{1}{s\text{C}_{\text{i}}}||\left(R_{\text{pa+ }}+R_{\text{pa--}}\right);Z_{\text{d}}=\frac{1}{s\text{C}_{\text{d}}}+R_{\text {d}}\end{array}\right., \tag{1}\]  
\[\frac{I_{\text{load}}\left(s\right)}{I_{\text{o1}}\left(s\right)}=\frac{Z_{ \text{d}}}{Z_{\text{d}}+sL_{\text{o}}+R_{\text{o}}-\frac{s^{2}M^{2}}{sL_{ \text{i}}+R_{\text{i}}+Z_{\text{i}}}}\cdot\frac{sM}{sL_{\text{i}}+R_{\text{i} }+Z_{\text{i}}}\cdot\frac{Z_{\text{i}}}{R_{\text{load}}}, \tag{2}\]  
\[\frac{V_{\text{load}}\left(s\right)}{V_{\text{o}}\left(s\right)}=\frac{sMZ_{ \text{i}}}{\left(sL_{\text{i}}+R_{\text{i}}+Z_{\text{i}}\right)\left(sL_{ \text{o}}+R_{\text{o}}-\frac{s^{2}M^{2}}{sL_{\text{i}}+R_{\text{i}}+Z_{\text {i}}}\right)}. \tag{3}\]
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 2 Preliminaries and Problem Formulation
### Distributed Pinning-Based Consensus Control  
Pinning control is a strategy that involves pre-setting target convergence values for multiple agents within a network to facilitate communication between adjacent agents. This method employs controllers installed at key nodes, effectively managing the entire network and significantly reducing the number of controllers required, making it highly suitable for distributed control scenarios in complex networks. In the context of microgrids, pinning control effectively integrates multiple distributed generators (DGs), achieving distributed cooperative control. This strategy enhances the stability and robustness of the microgrid by using key nodes as reference points for control, thereby simplifying the management of distributed energy resources. This research employs pinning control for secondary voltage distributed management across microgrids.  
It aims to establish a suitable protocol, \(\tilde{v}_{i}(\tilde{k})\), for ensuring the tracking of a predefined target voltage. Here, \(\tilde{v}_{i}(\tilde{k})\) depends solely on input from the DG and neighboring DGs. Moreover, each DG is subject to these criteria:  
\[\lim_{\tilde{z}_{0}\rightarrow\infty}(|\tilde{z}_{0}(\tilde{\kappa})-\tilde{ z}_{i}(\tilde{\kappa})|)\leq\epsilon \tag{3}\]  
where, \(\tilde{z}_{0}(\tilde{\kappa})\) denotes the microgrid's preset reference value, known as the virtual leader, and \(\epsilon\), a sufficiently small positive constant, is close to zero. For the \(i\)-th DG, the tracking error, \(\delta_{i}(\tilde{k})\), equals \(\tilde{z}_{0}(\tilde{\kappa})-\tilde{z}_{i}(\tilde{\kappa})\).
## 3 O-RAN with 200 m FSO Link  
### FSO Setup and Background  
In the proposed scheme, 200 m of the FSO link are arranged utilizing pairs of commercially available FSO devices, as illustrated in the upper section of Figure 6. It should be clarified that the commercial FSO equipment can reach a transmission distance of at least 3 km, but to facilitate the setup and collection of experimental data in the urban area, it is only installed at a length of 200 m. In this work, 1330 nm small form-factor pluggable (SFP) transceivers are used as the barrel hoop for the network switches and the FSO devices, and the FSOs automatically convert the 1330 nm beam to a 1550 nm beam, which has lower propagation attenuation and has less impact on the human eye.  
Figure 5: At various steering angles—specifically, (**a**) 45\({}^{\circ}\), (**b**) 30\({}^{\circ}\), (**c**) 15\({}^{\circ}\), (**d**) 0\({}^{\circ}\), (**e**) \(-15^{\circ}\), (**f**) \(-30^{\circ}\), and (**g**) \(-45^{\circ}\)—the agent can take actions to achieve the best phase convergence effect within a specific time period. The subfigure is a representation of a 2D overlay.
# Phase Reversal Method for Damage Imaging in Composite Laminates Based on Data Fusion
###### Abstract  
This paper proposes a phase-reversal method (PRM) for damage imaging in plate structures. The PRM is a novel Lamb-wave-based method that mainly focuses on phase spectrum information of scattering waves reflected from a defect. The PRM reverses the phase angle along the propagation path by using the inverse Fourier transform first, and then the reversal reconstruction of the wave field in the frequency domain is performed for damage imaging. The proposed method analyzes the scattered wave field without using the baseline data and structural parameters. Moreover, dispersion characteristics and anisotropy are not involved in the process of damage positioning, thus making the PRM suitable for damage monitoring of composite laminates. To improve the PRM accuracy further, a combined addition and multiplication method of the correlation coefficient (CAMM) is proposed, which can reduce the effects of phase and noise artifacts and distortion. The results of the finite element simulations and experiments show that the combination of the PRM and CAMM methods can accurately locate damage in composite structures. Therefore, the PRM and CAMM methods have great application potential in damage imaging in composite laminates.  
phase-reversal method; composite laminate; data fusion; Lamb wave; damage imaging +
Footnote †: journal: Applied sciences
# Blockchain-Based Multimedia Content Protection: Review and Open Challenges
## 2 Taxonomy of Blockchain-Based Multimedia Content Protection Systems
### Multimedia Content Protection Techniques
#### 2.6.1 Multimedia Encryption  
The process of encoding plaintext messages into ciphertext messages is called encryption, and the reverse process of transforming ciphertext back to plaintext is called decryption. This technique is expected to provide one or more of the following properties:  
* Confidentiality: It refers to limiting data access or disclosure to authorized users and preventing access or disclosure to unauthorized ones.
* Integrity: It refers to protecting data against modification, or alteration, whether by accident or deliberately.
* Authenticity: It refers to enabling the receiver of data to ascertain its origin.  
In the naive approach, the entire multimedia content is encrypted using standard encryption methods such as symmetric (e.g., Advanced Encryption Standard (AES), Rivest Cipher (RC5), etc.) and asymmetric (Rivest-Shamir-Adleman (RSA), Digital Signature Algorithm (DSA), etc.) cryptographic algorithms. Since the multimedia content such as audio or video data is typically very large in size, the naive approach becomes computationally demanding. Nowadays, many new encryption algorithms for audio and video have been proposed to avoid the naive approach and gain better efficiency. These new algorithms can be divided into various categories: full encryption, selective encryption, joint compression and encryption, syntax-compliant encryption, scalable encryption and multi-access encryption.  
Another important form of public-key cryptography is the homomorphic cryptosystem, which allows certain types of operations (addition/multiplication (partially homomorphic), or both (fully homomorphic) to be carried out over encrypted data while yielding the same encrypted results as if the operations were run on plaintext. Homomorphic cryptosystems are useful for copyright protection applications as they allow content owners to make computations directly to the ciphertext without exposing the keys. This property prevents the buyers' privacy-sensitive information from being exposed.
## 2 Related Work
Despite the technological advancements that have been made, the constantly evolving nature of security threats and the specific characteristics of mobile ad hoc networks (MANETs) require continued research and innovation in this crucial area of network security. The impact of these malicious activities on network performance has been the subject of extensive examination by numerous researchers. In [19], the authors discussed the effects of malicious nodes on multicast routing protocols in vehicular delay-tolerant networks, a specific application of MANETs. Their findings highlight the need for attack-resilient multicast protocols in highly mobile network scenarios. These diverse research efforts collectively demonstrate the multifaceted nature of MANET security challenges and the ongoing need for innovative, integrated solutions that can address the complex interplay of factors such as energy efficiency, reliability, mobility, and scalability while effectively mitigating sophisticated attacks like black hole and gray hole intrusions.