esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Electrostatic Field Characteristics  
The motion and distribution of space charges are insufficient to reveal the repetitive breakdown stability of needle electrodes. Therefore, this section further analyzes how the electrostatic field characteristics of the gap influence repetitive corona-stabilization breakdown by introducing the critical volume. It has been revealed that the propagation of streamer corona in SF\({}_{6}\) gaps is mainly influenced by the electrostatic field conditions [47; 48]. The critical volume is generally utilized to characterize the electrostatic field of highly inhomogeneous field gaps [15; 47]. It refers to a region near the high-curvature electrode: the electrons present in this region will trigger electron avalanches, causing them to reach  
Figure 18: Schematic diagram illustrating the propagation of shock waves and the transport mechanism of residual spatial charges in breakdown channels: (**a**) stage 1; (**b**) stage 2; (**c**) stage 3; (**d**) stage 4.  
the critical size and form a stable corona layer. In theory, the critical volume should be formed by two curved surfaces. The initial electrons accelerate from the inner surface that are close to the high curvature electrode, and their acceleration distance is just enough to form an electron avalanche of critical size. The outer surface of the critical volume is where the effective collision ionization coefficient is zero. The internal surface has little impact on the working performance of the corona-stabilized switch. Therefore, this article mainly focused on the external surface characteristics.  
This section analyzes the influence of the spatial electric field distribution on the repetitive breakdown stability and insulation recovery of corona-stabilized switches from an electrostatic field perspective. The field distribution of the switch with different needle electrodes was simulated using commercial software, and the boundary of the stable corona layer was defined based on the critical breakdown field strength \(\text{Ec}=885\ \text{kV}/(\text{cm}\cdot\text{MPa})\)[49; 50]. Figure 19 shows the corona envelope of needle electrodes with different structural features.  
Based on the results of the electrostatic field, the needle electrode exhibited good stability in repetitive breakdown and insulation recovery due to its unique geometry, which created a concentrated electric field not only at the tip, but also at the connection location between the tip and rod. A smaller cone angle of the needle electrode leads to a higher inhomogeneity of the electric field at its tip, resulting in stronger corona discharge intensity and a better stabilization effect on breakdown. When the cone angle of the electrode increases, although the electric field at its tip becomes more uniform, the electric field at the connection location between the tip and rod will also increase accordingly. This results in a corona layer forming over a larger area around the tip of the electrode, which still provides good shielding and stabilization effects (such as N4).
# Rendezvous Based Adaptive Path Construction for Mobile Sink in WSNs Using Fuzzy Logic
## 5 Performance Evaluation
### Path Length during the Network Life Time  
We assume that the MS energy is much more than the sensor node energy. The cost of energy dissipated as a result of the MS movement is represented by the length of the path that the MS will use to collect the data. Figure 10 shows the path length in meters in the DBRkM and FUZZY_RPs approaches. In this experiment, the number of sensor nodes was 150. As shown in the figure, the path length in the DBRkM approach is constant while the path construction is dynamic during the network lifetime in the FUZZY_RPs approach. As shown in the figure, the path length exceeds the DBRkM approach only in the last stage of the network, almost when the remaining energy in the network is low and most of the nodes are dead. As shown in Table 4, for the case when the number of sensor nodes is 150, the first dead node in the FUZZY_RPs approach occurs in round 1172 and for the DBRkM approach in round 655. However, during the lifetime of the network, the path length in the FUZZY_RPs approach is less than the path length in the DBRkM approach, especially in the first 2700 rounds.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Number of Rounds until First Node Dies**} \\ \cline{2-4}
**Number of Sensor** & **Fuzzy\_RPs** & **DBRkM** & **Improvement (\%)** \\
**Nodes** & **Approach** & **Approach** & \\ \hline
150 & 1172 & 655 & 78.93 \\
200 & 1044 & 704 & 48.30 \\
250 & 1037 & 575 & 80.35 \\
300 & 928 & 505 & 83.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network lifetime improvement with different number of sensor nodes.  
Figure 9: Network lifetime vs. number of sensor nodes.
## 1 Introduction and Background
Following the time code, we joined traffic data and road weather data into a common database that formed the basis for mathematical modelling of the effects of traffic lane control. We assert that, with adequate and timely knowledge of the lane distribution traffic flow characteristics, we can detect traffic instabilities that may cause traffic conflicts and collisions. Then, we act accordingly, e.g., with overtaking bans for trucks. With appropriate measures of active traffic control of HGV in different weather conditions, we can improve the efficiency of traffic management. As we can influence traffic flow in this way, we can achieve higher traffic flow rates or better lane utilisation, thus reducing delays, which we demonstrated by using a microsimulation traffic model of a two-lane unidirectional carriage highway section. This is discussed in Section 3. The results of the impact of heavy vehicle traffic control are presented in Section 4. This section also includes a discussion comparing the delays in traffic flow in the case of a truck overtaking ban or, if this measure is not implemented, in different weather conditions. Lastly, Section 5 offers conclusions and discusses the findings of the study by presenting some directions for further research on this subject.
# Deep Learning-Based Optimization for Maritime Relay Networks
## 2 System Model  
In the maritime communication environment, a dual-hop network model is considered, which comprises a source node ship (S), a destination node ship (D) and K relay node ships. Each ship travels at varying speeds (\(v\)) and in diverse directions (\(\theta\)), thereby generating a dynamic network environment (depicted in Figure 1). At two distinct time points, \(t_{1}\) and \(t_{2}\), the positions of the ships and their communication links vary, reflecting the instability engendered by the movement of ships within maritime communication networks. In this scenario, data transmission is facilitated via relay nodes that employ the Decode-and-Forward (DF) protocol.  
The source node ship must transmit data to the destination node ship; however, the long distance between them makes direct communication impractical. To overcome this limitation, relay node ships serve as intermediaries to facilitate data transmission.  
To further expand, a multi-hop network model is considered, where data are required to pass through multiple relay clusters to reach the destination node (D). The network is composed of a source node (S), a destination node (D) and M relay clusters. These relay clusters are labeled \(C_{1}\), \(C_{2}\),..., \(C_{M}\), with each cluster containing multiple relay nodes denoted as \(R_{m1}\), \(R_{m2}\),..., \(R_{mK}\), where \(m=1,\ldots,M\). S and D cannot communicate directly,and except for the nodes in \(C_{M}\), other relay nodes also cannot establish direct connections with D.  
The transmission process begins with S, broadcasting data to the first relay cluster \(C_{1}\). Subsequently, a relay node from cluster \(C_{1}\) is selected to receive the data and forward them to the next cluster, \(C_{2}\). This process continues sequentially across the relay clusters until D successfully receives the data. The complete transmission path is formed by the relay nodes selected from each cluster, with the data being forwarded hop-by-hop along the path to ensure their successful delivery to the destination node.
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 3 Method  
### Proposed Structure  
Our goal is to use the LSTM model to learn the spatio-temporal information of Reference Signal Received Power (RSRP) and nearest Reference Signal Received Power (NRRSP), thereby reducing unnecessary handovers. Figure 2 shows the system scheme we proposed.  
In our research we employ the Federated Long Short-Term Memory (F-LSTM) model to predict future RSRP values and the RSRP of neighboring cells, which are then used to optimize handover events. The F-LSTM model is composed of a global model and multiple local models trained by various clients. Each client trains its LSTM model locally using its own data, capturing temporal dependencies in signal strength without sharing raw data. After local training, only the model weights are sent to a central server, where the FedAvg algorithm aggregates these updates into a global model. This global model is then redistributed to the clients for further training. By integrating Federated Learning and LSTM, our system enhances handover prediction while preserving privacy, allowing for real-time handover decisions through dynamic threshold adjustments based on predicted RSRP values. This approach effectively reduces unnecessary handovers and mitigates the ping-pong effect. In the revised manuscript, we will include more details on the LSTM architecture, data flow, and model synchronization to provide a clearer understanding of the implementation.  
Figure 2: Proposed system structure.  
Detailed Working of the Proposed Structure
* **Data Collection and Preprocessing** Clients collect RSRP and NR-RSRP data over time, capturing the dynamic changes in signal strength. The data are preprocessed to handle missing values and normalize the range, ensuring consistency across different clients.
* **Local Model Training** Each client uses an LSTM network to train on its local dataset. The LSTM model captures the temporal dependencies in the RSRP data, learning the patterns that indicate signal strength variations. Dropout layers are included to prevent overfitting and enhance generalization.
* **Federated Learning Process** After local training, each client sends its model updates (e.g., weights) to a central server. The server aggregates these updates to form a global model, which benefits from the diverse data distributions of all clients.
* **Global Model Distribution** The updated global model is then redistributed to the clients. This iterative process ensures that the global model continuously improves and adapts to new data without requiring centralized data storage.
* **Handover Optimization** The global F-LSTM model predicts the future RSRP and NR-RSRP for each client. The predictions are fed into a dynamic handover algorithm, which adjusts handover thresholds based on predicted signal strengths and patterns. The algorithm reduces unnecessary handovers by considering both the predicted future RSRP and the current signal environment, thus enhancing overall network performance.
## 5 Results and Discussion
### Wall Pressure  
Figure 6 shows the comparison of the wall pressure between the rigid and FSI model of the left coronary artery at systolic and diastolic phase of the cardiac cycle. The pressure drop was maximum across the 90% area stenosis as compared to other regions of stenosis. The maximum pressure drop was found as 100,077 Pa for both the rigid and FSI models, whereas a slight difference in wall pressure was noted across the bifurcation in both the models. The pressure drop across the 70% and 80% area stenosis were 11,727 Pa and 10,784 Pa, respectively. Similarly, the wall pressure for the CFD and FSI models of the left coronary artery was plotted for the diastolic phase of the cardiac cycle. It can be seen clearly that the wall pressure drop was maximum across the 90% area stenosis present at the LCX branch as compared to 70% and 80% at the LAD branch. It was found that the pressure drops to 8871 Pa across 90% of the area of stenosis for the diastolic phase of the cardiac cycle for both the models. A slight variation across the bifurcation regions was observed in wall pressure between the rigid and FSI models.  
Figure 5: Mesh independent study for pressure along the diastolic condition (0.55 s).
# Intra-System Reliability Assessment of 2-Dimensional Shear Wave Elastography
## 3 Results
### Measurement Repeatability  
Repeatability ranges using calculations of RC and CV from all operators are summarised in Table 2. Both depth and target type influenced the ability of the system to provide consistent repeated measures. The smallest variation in repeated measurements resulted from the 10 kPa and 17 kPa targets. The RC and CV ranged from 0.06-0.24 m/s  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Operator Type**} & **ICC at 3 cm** & **ICC at 6 cm** \\  & **(95\% CI)** & **(95\% CI)** \\ \hline
3 Operators & 0.976 (0.971–0.986) & 0.918(0.878–0.945) \\ \hline
2 Operators (experienced + less experienced) & 0.978 (0.967–0.985) & 0.88 (0.823–0.919) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intraclass correlation coefficient (ICC) of the measured values of elasticity according to operator experience at two different depths.  
Figure 3: Box and whisker plot of shear wave velocities (\(n\) = 900) plotted against target stiffness (kPa), using 10 mm ROI. The coloured region represents the 25 th to 75 th percentile range.  
and 1.3-5.7% for both these targets at 3 cm and 6 cm. The greatest variability in repeated measurements resulted from the 45 kPa and 85 kPa target. The largest variability in repeat measurements resulted from the 85 kPa target at 3 cm depth.
# Hybrid RF/VLC Network Architecture for the Internet of Things
## 4 System Design
### Sensor and Actuator Integration  
The main objective of this architecture design is providing a supportive multi-technology hybrid network to communicate with sensors and actuators. This has been achieved by defining three different HNF purposes. 0x01 and 0x02 purposes correspond to Board Sensor data and External Sensor data transmission, respectively. The associated subframes have the same packet structure (Figure 9), but this division has been included to advance future necessities.  
Node ID indicates the identifier of the data producer, which in this work corresponds to the ID stored by the ZigBee network coordinator. Num Sensors describe how many different sensor data are included in the frame. Sensor ID is the node's internal identifier related to the sensor. The application layer will have the task of interpreting the type of sensor associated to this ID. The following field comprises 4 bits to indicate the sizes of each sensor data field, and 4 bits reflecting the signs of each one of them (positive: 0; negative: 1). Hence, the maximum number of sensor data allowed is 4. However, if fewer sensor data are being transmitted, the corresponding data fields set to 0x00 and the sign bit to 1 will show that there is no information.  
Besides sensor data packets, the 0x04 purpose indicates interaction with actuators. This frame definition states Node ID, Actuator ID, and Data (Figure 10).  
Figure 8: Visible light communication (VLC) packet structure with the HNF as payload.  
Figure 9: Board Sensor data and External Sensor data subframe structure.
## 2 System and Channel Model
\tag{19}\]  
According to Formula (9.301) in Reference [14], the Meijer-G function is expanded in the form of a first-order Mellin Barnes integral, resulting in the following:  
\[F_{\gamma_{2}}(\gamma_{2}) =\frac{\xi^{2}}{r\Gamma(a)}\int_{0}^{\gamma_{2}}x^{-1}\frac{1}{2 \pi i}\int\limits_{L}\frac{\Gamma\bigg{(}\frac{\xi^{2}}{c}-s\bigg{)}\Gamma(a- s)}{\Gamma\bigg{(}\frac{\xi^{2}}{c}+1-s\bigg{)}}\left(\frac{-\frac{c}{r}}{ \frac{c}{x}r}\right)^{s}dsdx \tag{20}\] \[=\frac{\xi^{2}}{r\Gamma(a)}\frac{1}{2\pi i}\int\limits_{L}\frac{ \Gamma\bigg{(}\frac{\xi^{2}}{c}-s\bigg{)}\Gamma(a-s)\Gamma(s)}{\Gamma\bigg{(} \frac{\xi^{2}}{c}+1-s\bigg{)}}\left(\frac{\frac{c}{r}}{ \frac{\rho_{r}}{(bA_{0})^{c}}}\right)^{s}[\underbrace{\int_{0}^{\gamma_{2}}x \frac{c\xi}{r}^{-1}dx}_{\frac{c}{r}^{\gamma_{2}}\sigma_{2}^{\gamma_{2}}}]ds\] \[=\frac{\xi^{2}}{c\Gamma(a)}\frac{1}{2\pi i}\int\limits_{L}\frac{ \Gamma\bigg{(}\frac{\xi^{2}}{c}-s\bigg{)}\Gamma(a-s)\Gamma(s)}{s\Gamma(s) \Gamma\bigg{(}\frac{\xi^{2}}{c}+1-s\bigg{)}}\left(\frac{\frac{c}{\rho_{r}}r \gamma_{2}\frac{c}{r}}{\frac{(bA_{0})^{c}}}\right)^{s}ds,\]  
In the above equation is the Gamma function operator \(\Gamma(\cdot)\). To enclose the complex integral line \(L\) by utilizing the properties of the Gamma function \(s\Gamma(s)=\Gamma(s+1)\), it can be concluded that  
\[F_{\gamma_{2}}(\gamma_{2})=\frac{\xi^{2}}{c\Gamma(a)}\cdot\frac{1}{2\pi i} \int\limits_{L}\frac{\Gamma\bigg{(}\frac{\xi^{2}}{c}-s\bigg{)}\Gamma(a-s) \Gamma(s)}{\Gamma(1+s)\Gamma\bigg{(}\frac{\xi^{2}}{c}+1-s\bigg{)}}\Bigg{(} \frac{1}{\frac{(bA_{0})^{c}}{c}}\bigg{(}\frac{\gamma_{2}}{|\rho_{r}}\bigg{)}^{ \frac{c}{r}}\Bigg{)}^{s}ds. \tag{21}\]  
The definition formula of the Meijer-G function expressed by Formula (9.301) in Reference [14], after observation and comparison of parameters, can be obtained as follows:  
\[F_{\gamma_{2}}(\gamma_{2})=\frac{\xi^{2}}{c\Gamma(a)}G_{2,3}^{2,1}\Bigg{[} \frac{1}{(bA_{0})^{c}}\bigg{(}\frac{\gamma_{2}}{\rho_{r}}\bigg{)}^{\frac{\xi} {\xi}}\Bigg{|} \begin{array}{c}\frac{1}{\xi^{2}}+1\\ \frac{\xi^{2}}{c},a,0\end{array} \Bigg{]}. \tag{22}\]
## 4 Discussion and Result Analysis
\begin{table}
\begin{tabular}{c c c c} \hline
**Total** & **\% of Successful** & **\% of Failed** & **Average** \\
**Authentications** & **Authentications** & **Authentications** & **Authentication Time** \\ \hline
425 & 70.35\% & 29.64\% & 19.75 s \\ \hline \end{tabular}
\end{table} 
Table 2: Authentication process information. Figure 9: Failed authentication attempts information. predefined types of relations, and only 19% using user-set types of relations. Taking this into account, we could determine that there was no problem in letting the user set their own type of relations, as long as users had a good set of predefined types that could actually be used when relating the images. This is also supported by the fact that only 35% of all users created and used their own relation types, the majority of them using the predefined types. Prior to testing, it was believed that giving the user the freedom to establish relations, using their own types of relations (other than the predefined types that were given as an option) would cause a high number of failed attempts at authentication, due to the user forgetting not only the relation between images, but also the type of relation. Nevertheless, no evidence was found to support this. Regarding the qualitative aspects of the mechanism, the data collected from the survey after the testing process showed that most users found both the set up and authentication process relatively easy, as depicted in Figure 11.  
In the same survey, users were also asked if they would use the proposed MFA mechanism instead of commonly used ones, such as OTP. Figure 12 shows that 55% of users said that they would use it, with most users approving the mechanism as something usable and applicable. Figure 11: User experience results (1 being very hard, and 10 very easy). Figure 10: Information about the user established relations.
## 1 Introduction
### Main Contribution  
This work presents a novel approach that integrates a human-robot interface (HRI) with network congestion control in an application-layer ROS package for image transmission in robot teleoperation scenarios where a stable and robust connection cannot be guaranteed. Examples of these scenarios include wireless-based communications (WiFi, VLC, and acoustic) where the bandwidth is limited and there is a necessity for image and point-cloud transmission. The transmission of this large amount of data in a non-robust network leads to a congested communication buffer, resulting in the loss of control of the intervention robot, which can often be fatal.  
The proposed ROS package provides a technique to prevent the congestion of the network by adapting the compression parameters of the transmitted image to the network conditions. This ensures that the network remains stable and responsive during critical teleoperation tasks. Additionally, the system is designed to handle periods of communication loss, reducing the quality and scale of the image, converting the image to grayscale, or even stopping image transmission automatically. In this case, the algorithm can be customized to run autonomous behavior that moves the robot to a position where communication is guaranteed.  
The proposed algorithm was tested in different use cases to evaluate its efficiency and performance. Both scenarios used an underwater robot that establishes a link to a GCS using VLC modems, while the operator uses an HRI designed and adapted to each use case to teleoperate the robot and perform an inspection or an intervention task. The experiments were conducted in an underwater tank where conditions such as distance, orientation, and light were variable, affecting the robustness of the connection. Results are divided into three parts. The first part contrasts the manufacturer specifications of the VLC modems, such as bandwidth and field of view, with some network performance tests conducted while the robot performed predefined trajectories in position and orientation. Second, the image transmission ROS package was tested in a non-disturbance condition (no robot movement). Finally, the algorithm was tested in two use cases: an underwater wireless pipe inspection and intervention, where the efficiency, robustness, and performance of the algorithm allowed the operator to perform the missions without losing control of the underwater robot. These experiments are the second stages of the ones reported in [18].