esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Empirical Results
### Key Results
#### 4.2.1 Answer to RQ1 and H1  
Table 4 shows that the proposed VMD-Cascaded LSTM with Attention outperforms all benchmark models across all evaluation metrics. These include various configurations of LSTM with and without VMD, confirming the superior predictive power of the proposed hybrid architecture. Although classical models such as GARCH have historically been  
applied to volatility forecasting, this study focuses on deep learning-based approaches to ensure a consistent comparison framework. These results support H1 and affirm that the integration of VMD and Attention mechanisms significantly improves forecasting accuracy over standard LSTM-based models.
## 3 Results
### Peak Overpressure of the Shockwave  
The pressure sensor determines the amplitude of the incident shockwave. When driven by three times the theoretical enthalpy value, the peak overpressure of the shockwave with three hollow ratios and three diameters of Cu wires are shown in Figure 6a,b. The typical pressure waveform is shown in Figure 6c.  
Figure 6: Relationship between peak overpressure of an electrical explosion with different wire spatial structures. **(a)** The peak overpressure with different diameters and hollow ratios driven by triple enthalpy. **(b)** The relative size relationship between peak overpressure and driving energy. **(c)** The typical pressure waveform when \(D\) is 0.3 mm and \(a\) is 0.5. The difference between the maximum and the mean of the signal is the peak overpressure \(P_{p}\).  
As shown in Figure 6a, the diameter and hollow ratio of the wire have different intensity and form effects on the peak overpressure of the shockwave. In the study on the hollow ratio of Cu wire, the pressure soars with the increase of the hollow ratio when the diameter is a constant value. In the study on the diameter of Cu wire, the pressure decreases with the increase of the wire diameter when the hollow ratio, \(a\), is 0. The pressure increases slightly with the increase of the wire diameter when the hollow ratio, \(a\), is 0.5. The pressure increases significantly with the increase of the wire diameter when the hollow ratio, \(a\), is 0.7. The peak overpressure of the shockwave ranges from 25.2 MPa to 46.1 MPa. A wire with a larger diameter and bigger hollow ratio produces a stronger shockwave when driven by triple enthalpy. As shown in Figure 6b, blue and gray bubbles represent peak overpressure and driving energy, respectively. The driving energy is triple the enthalpy of the wire with the diameter and hollow ratio. The relative size relationship between blue and gray bubbles represents the absolute conversion efficiency of driving energy to peak overpressure. The Figure 6c indicates that the third peak in a typical waveform represents the peak overpressure of the shockwave, while the first two peaks are electromagnetic interference.  
The peak overpressure of the shockwave depends on the deposition efficiency of energy during the electrical explosion process of the Cu wire. The inhomogeneity of the heating phase transition limits the energy deposition efficiency. From the perspective of discharge energy, the uneven heating process prevents the copper wire from completely converting into plasma to deposit energy. The core position of the Cu wire can only be converted into metal droplets to consume the electric energy, which weakens the translation to the shockwave mechanical energy. From the perspective of discharge time, the ionization of the wire would occur on its surface firstly, and gradually evolve to the wire's core layer by layer. The non-uniform phase transition of the wire in space leads to the prolongation of the effective discharge time. Ultimately, the energy conversion power is reduced.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 6 Ongoing Developments
### Time-Sensitive Networking Profile for Automotive In-Vehicle Ethernet Communications  
The current lack of TSN profiles for automotive communications impairs interoperability and makes it difficult and costly for automotive manufacturers to define requirements and for suppliers to implement such requirements. To solve this issue, the ongoing project P802.1 DG \"Time-Sensitive Networking Profile for Automotive In-Vehicle Ethernet Communications\" defines TSN profiles for automotive communications in order to provide interoperability and connectivity for automotive applications on converged networks.  
The P802.1 DG project is the answer to the need for standardizing the selection and use of the IEEE 802 standards and features to deploy secure and highly reliable automotive converged networks. The automotive profiles defined by the P802.1 DG project will be used by car manufacturers and suppliers for designing and implementing deterministic IEEE 802.3 Ethernet networks able to support the entire range of in-car applications, including those requiring high availability and reliability, bounded latency, security, and maintainability. The P802.1 DG profiles [74] will favor interoperability and deployment and will be very beneficial to automotive manufacturers, suppliers, and users of networking services and automotive Ethernet components.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 2 Mathematical Model  
### Assumptions  
The ROV's dynamic model is complex and requires several parameters. Hence, the present study considered the following assumptions to simplify the mathematical model [4].  
* The ROV is symmetrical about its 3 planes.
* The center of buoyancy of the ROV is located on the geometric symmetry plane.
* The ROV was simulated as a rigid body; therefore, no geometrical and bending deformations were considered.
* The ROV's hydrodynamic coefficients were not variable.
## 2 Materials and Methods
### Quasi Static Compressive Test  
According to the ASTM D1621 [48]: Standard Test Method for Compressive Properties of Rigid Cellular Plastics, guidelines, all the specimens were subjected to compressive load testing. The test was conducted with a preload on a Universal Testing Machine (UTM): Instron 5969, equipped with a 50 kN load cell for lattice structures and 1000 kN for a solid plastic block. The strain rate was kept constant at 2.25 mm\(\cdot\)min\({}^{-1}\), in accordance with the ASTM D1621. The force-extension curves were analyzed and transformed into stress (\(\alpha\), N\(\cdot\)m\({}^{-2}\)) and strain (\(\epsilon\), [-]) using the procedures outlined in Appendix A of ASTM D695-02 [52], Additionally, stress was estimated from the experimental results by calculating the ratio between force and the overall cross-sectional area of the specimens. An elastic modulus (E, Pa) was also calculated by dividing the stress by the strain for all specimens [48].  
To obtain the results for lattice structures, the Gibson-Ashby model (represented by Equation (1) was used. This method involves plotting the relative density (\(\rho\)*/\(\rho_{\mathrm{s}}\)) against the normalised mechanical properties, with respect to the solid property. The SONY RX100 IV camera was used to capture the impressions during the compressive test. The stress and strain curve yielded important information about yield stress, stress at break, and Young's modulus of the specimens, which could be a function of the normalisation of density (\(\rho\)*/\(\rho_{\mathrm{s}}\)) or relative density. Additionally, a series of deformation impressions was expected to reveal significant patterns of collapsing, and the collapsed failure mode may depend on the \"n\" coefficient, which is a collapsing mode coefficient [53].  
\[\rho\)*/\(\rho_{\mathrm{s}}\propto(x^{\mathrm{s}}/x_{\mathrm{s}})^{\mathrm{n}} \tag{1}\]  
Not only were those normalised mechanical properties obtained with a quasi-static experiment, but energy absorption per volume (W, J\(\cdot\)kg\({}^{-1}\)) was another response that was estimated by integrating a force of up to 65 per cent of the strain rate in this study.
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
## 6 Conclusions  
Vehicle platooning is a prominent use case and a challenging scenario for vehicular communications. Several research studies have investigated the performance of cellular sidelink communications based on LTE-V2X Mode 4, but peculiar aspects such as multiple access interference in vehicle platooning scenarios have not yet been investigated. In this context, the resource allocation in sidelink operations in C-V2X has a major impact on the performance of the sidelink communication in real-world use-case scenarios. NR-V2X specifically addresses the requirements of platooning scenarios to overcome the limitations of LTE-V2X.  
In this work, we presented a comparative study of LTE-V2X and NR-V2X sidelink communications in a vehicle platooning scenario. We established and verified an analytical approach to estimate the performance of LTE-V2X Mode 4 and 5G-NR Mode 2 communications based on available resources, the number of interfering vehicles, and their relative positions. The moment matching approximation (MMA) method was enforced to approximate the statistics of the signal-to-interference-plus-noise ratio and allowed us to derive the packet success probability accounting for realistic propagation impairments described by a composite channel model.  
The comparison of the two technologies confirmed that the proposed approach provides a concise and accurate approximation of the attainable performance, confirming the superior performance with NR-V2X against LTE-V2X, particularly for high inter-vehicle distance and a large number of vehicles, thus enabling our framework to be effectively used for optimization and testing of the resource allocation for sidelink communications in 5G-V2X.  
In terms of future work, we intend to: (i) design a platform for both Mode 2 and Mode 4, where vehicles can switch between the two modes as needed and can select and modify the ideal RRI value based on their real-time vehicle density; (ii) expand our framework by incorporating a comprehensive analytical model of the physical access; and (iii) use experimental campaigns to strengthen the validation of the V2X analytical model.  
Methodology, P.D.M., R.V. and F.S.; Investigation, R.V. E.C. and P.D.M.; Validation, A.R.; Writing--original draft, A.R.; Writing--review and editing, E.C., P.D.M. and F.S.; Project administration, F.S. All authors have read and agreed to the published version of the manuscript.  
The work of the authors was supported by the Centre of EXcellence on Connected, Geo-Localized and Cybersecure Vehicles (EX-Emerge), funded by the Italian Government under CIPE resolution n. 70/2017 (7 August 2017).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 The AMC and Antenna Designs
### Design of Circular Sector-Shaped Dipole Antenna with Meandered Arm  
Figure 3a presents the design concept for optimizing the meandered arm structure. The shape of the meandered arm is designed using the method proposed by [PERSON] et al. [26] to operate in the HF, VHF, and UHF bands. The design sequence begins with dividing the area where the meandered arm is positioned into nine segments of identical lengths. Each divided segment is designated as one column of the grid, which is marked in blue. The binary genetic algorithm (GA) is used to decide the optimal position of each segment, and the three bits are encoded to assign eight states. Two adjacent segments in the optimal position are connected by vertical lines marked in red. The size of the horizontal and vertical components of the meandered arm are \(13.2\times 13.2\) mm\({}^{2}\), and \(5.5\times 5.5\) mm\({}^{2}\), respectively. The optimally designed meandered arm is reduced by the scaling factor \(k\) = 0.9 and positioned in a space between the elements from \(d_{1}\) to \(d_{3}\). The optimized values for \(d_{1}\), \(d_{2}\), and \(d_{3}\) were 69.9 mm, 62.6 mm, and 62.4 mm, respectively.  
After optimizing the configuration and spacing parameters, the meandered arms are symmetrically duplicated as \(A_{n}^{\prime}\)(\(n\) = 1,2,3,4) (Figure 3b). The binary GA optimization process also optimizes the scaling factor \(k\) and spacing element \(d_{n}\)(\(n\) = 1,2,3), and each design parameter is assigned eight bits. Therefore, 59 bits are used in total during the binary GA optimization process. The binary GA optimization is performed using MATLAB programming, with the script interface connecting to the CST program. The 100 iterations of the binary GA take 20 populations into consideration, and use a 0.1 mutation rate through the single-point crossover scheme. The optimization condition is to satisfy the bandwidth of VSWR 3.5:1 or less as wide as possible in the HF, VHF, and UHF bands. Figure 3b shows the configuration of the designed dipole antenna. The dipole antenna's element consists of eight optimized meandered arms existing above and below the circular sector-shaped patch with a radius of \(P_{r}\) = 247 mm and a central angle \(\alpha\) = 97\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** &
\begin{tabular}{c} **Operating Frequency** \\ **Bandwidth** \\ \end{tabular} & **Size (\(\lambda^{3}\)\({}_{L}\))** \\ \hline
[19] & 1.89:1 & \(0.007\times 0.007\times 0.0006\) \\
[22] & 2:1 & \(0.009\times 0.009\times 0.0005\) \\
[25] & 7.23:1 & \(0.003\times 0.003\times 0.0015\) \\ Proposed AMC & 25.5:1 & \(0.006\times 0.006\times 0.0008\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the proposed AMC and AMC in previous studies (note that \(\lambda_{L}\) is the wavelength of the lowest operating frequency).  
Figure 2: Simulated reflection phase for the unit cell of the designed AMC.
## 5 Supplementary Experimental Studies with PV-ISM
### The Art Images Dataset
A configuration using eight transformer blocks and a multi-layer perceptron with intermediate capacity achieved an F1 score of 92.40 percent. Increasing the number of transformer blocks to sixteen and thirty-two led to further gains, with the highest performance reaching an accuracy of 93.16 percent and an F1 score of 93.66 percent. These results suggest that deeper transformer architectures can better capture the subtle differences between authentic and AI-generated images. Along with the high results in the metrics, we can observe the steady and robust learning process through the epochs in Figure 16.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**IS** & **PS** & **TBs** & **MLP Layer** & \begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular}  & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline
224 & 14 & 4 & (2048, 1024) & 78.42 & 77.91 & 80.53 & 79.20 \\
224 & 14 & 8 &  
In contrast, increasing the depth without a sufficiently expressive feed-forward layer led to diminished returns. For example, when thirty-two transformer blocks were used with a smaller multi-layer perceptron, the F1 score dropped to 89.60 percent. This outcome underscores the importance of balancing transformer depth with adequate feed-forward capacity. To further evaluate the effectiveness of the proposed PV-ISM architecture, a comparative analysis was conducted against two widely adopted convolutional neural network models: ResNet50 and ResNet101. The models were evaluated on the same dataset under identical training and testing conditions. Table 8 presents the performance metrics, including accuracy, precision, recall, and F1 score, for each model. The ResNet50 model achieved an accuracy of 88.98 percent, with a precision of 83.50 percent, a recall of 93.77 percent, and an F1 score of 88.34 percent. The deeper ResNet101 model showed improved performance, reaching an accuracy of 90.94 percent, precision of 86.46 percent, recall of 94.97 percent, and an F1 score of 90.52 percent. However, the proposed PV-ISM model, which is based on a Vision Transformer backbone with optimized architectural parameters, outperformed both the ResNet variants across all evaluation metrics. Specifically, PV-ISM attained an accuracy of 93.16 percent, a precision of 88.80 percent, a recall of 99.09 percent, and an F1 score of 93.66 percent. This demonstrates a substantial improvement in classification capability, particularly regarding recall and F1 score, suggesting superior sensitivity and overall model balance.
## 5 Vulnerability Detection
### Detailed Comparative Analysis on Vulnerability Detection Methods
#### 5.2.1 Machine Learning and Deep Learning Approaches  
This cluster has shown remarkable promise due to its ability to recognize complex vulnerability patterns beyond conventional analysis. Techniques involving attention mechanisms [105; 106] and transfer learning allow models to focus on the most relevant parts of the code, increasing detection accuracy. Approaches such as those presented in [54] transform contract bytecode or abstract syntax trees into graphs, using graph neural networks (GNNs) to identify structural patterns indicative of vulnerabilities. Studies such as [55; 64] incorporate attention layers (e.g., self-attention, dual-attention) to highlight critical code fragments--often referred to as \"hotspots\"--where bugs are more likely. Moreover, frameworks such as [67; 68; 69] leverage distributed machine learning to detect new (zero-day) vulnerabilities while safeguarding sensitive code and data. Transfer learning further addresses the data scarcity problem by reusing feature representations learned from large code corpora. Hybrid approaches, such as combining CNNs with RNNs or merging symbolic execution with deep learning, have shown promise in balancing interpretability with accuracy.  
Issues in the Existing Approaches  
Despite these advances, the computational intensity and complexity of these models remain significant challenges for large-scale practical deployment. In addition, ensuring high-quality, representative training data is essential, and strategies for enhanced data collection, pre-processing, and augmentation are needed. Moving beyond simplistic binary classification to more nuanced detection strategies can further improve detection accuracy.
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 4 Results
### Edge Computing Suitability  
To validate the practical deployment potential of the proposed ADDAEIL framework in edge computing environments, we conducted a comprehensive evaluation of runtime efficiency and resource consumption on the **NVIDIA Jetson Orin NX 16 GB** platform. This platform is a compact and power-efficient computing module designed specifically for edge computing scenarios, featuring an 8-core Arm Cortex-A78 AE v8.2 64-bit CPU (2 MB L2 + 4 MB L3) and 16 GB of 128-bit LPDDR5 memory (102.4 GB/s bandwidth), and operating within a 10 W-25 W power envelope.  
The experiment was conducted on the CEMACS dataset, which features the longest time series in our dataset. The experimental results are summarized as follows:  
* **Training Time**: 0.117 s.
* **Latency per window (i.e., testing time)**: 0.15-0.21 s.
* **Maximum Memory Usage**: 340 MB.
* **Model Size**: 0.24 MB.  
These results demonstrate that the ADDAEIL framework exhibits low resource consumption, with a maximum memory usage of 340 MB and a model size of 0.24 MB. The  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Parameter Setting** & **AUC** & **F1-Score** & **Recall** & **Precision** \\ \hline Default (baseline) & 0.92 & 0.77 & 0.98 & 0.75 \\ \hline
**Varying Window Size**\(W\) & & & & \\ \(W=24\) & 0.92 & 0.80 & 0.99 & 0.77 \\ \(W=96\) & 0.91 & 0.71 & 0.94 & 0.71 \\ \hline
**Varying Number of Base Detectors**\(N\) & & & & \\ \(N=50\) & 0.92 & 0.77 & 0.98 & 0.75 \\ \(N=200\) & 0.92 & 0.77 & 0.98 & 0.75 \\ \hline
**Varying Update Ratio Range**\([r_{\min},r_{\max}]\) & & & & \\ \([0.05,0.5]\) & 0.92 & 0.76 & 0.98 & 0.74 \\ \([0.2,0.8]\) & 0.93 & 0.79 & 0.98 & 0.77 \\ \hline \end{tabular}
\end{table}
Table 4: Sensitivity analysis of ADDAEIL on Yahoo A3 dataset. Default setting: \(W=48\), \(N=100\), \(r_{\min}=0.1\), and \(r_{\max}=0.6\).  
framework also supports efficient model initialization, achieving a training time of 0.117 s. These characteristics are critical for deployment in resource-constrained edge environments. The system maintains a consistent latency per window of 0.15 to 0.21 s across the long streaming sequence of the CEMACS dataset. This stability confirms that the system delivers consistent, timely anomaly detection outputs at a low computational cost.  
For practical applications, the observed latency and resource requirements meet the real-time demands of edge computing scenarios. These scenarios include environmental monitoring, represented by the CEMACS dataset, and IT system monitoring and online analytics, represented by the NAB dataset. The system consistently maintains a response time of 0.15 to 0.21 s per window. This capability ensures that the framework can process real-time data streams effectively without compromising detection performance or requiring high-end computational resources.
# Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV
## 4 Simulation Results
### Stationary Target  
Figure 4 shows the paths of stationary target tracking in the inertial reference frame with different initial heading angle \(\psi_{a}=30^{\circ}\) and \(\psi_{a}=130^{\circ}\). For both cases, the distance error \(\dot{r}\), sliding manifold \(s\) and the heading rate \(\omega_{\psi}\) with the heading angle \(\psi_{a}\) are shown in Figures 5 and 6, respectively.  
From Figure 4a, we can find that all three of the guidance laws will steer the UAV at a constant speed to circle around the target. It is proved that the convergence rate of NFTSM algorithm is faster than the TSM algorithm. However, an overshoot exists when enlarging the \(\alpha_{2}\) and \(\beta_{2}\) as shown in Figures 5a and 6a, due to the subitem \(\alpha_{2}sig^{\beta_{2}}(\cdot)\) dominating the third subitem defined in Equation (12). This is also the reason why the convergence rate of the TSM algorithm is slower than the others. When \(\alpha_{2}=\beta_{2}=0\), the FTSM is equal to the TSM.  
Figure 4b represents that, when the included angle of the the UAV initial heading \(\psi_{a}\) and the azimuth angle are large, it takes a long time to achieve the desired trajectory for TSM algorithm, but the NFTSM algorithm has reached the goal position in a shorter time already. In Figures 5b and 6b, the heading rate order of the NFTSM algorithm is restricted because it exceeds the maximum UAV turn rate \(\dot{\psi}_{\max}\) from 8 s to 17 s. In order to obtain quick convergence, the NFTSM algorithm needs a strong mobility, while the TSM algorithm's result is changing slowly. After closing at the desired position, all three methods can hold the state without divergence because they have the similar third subitem in Equation (12).  
Figure 4: Tracking a stationary target with different initial heading angles.  
Figure 5: The results of tracking a stationary target with \(\psi_{a}=30^{\circ}\).  
Table 3 gives the results of the stationary target tracking. For the case \(\psi_{a}=30^{\circ}\), the distance error \(r\) can converge to less than 0.0001 m in 31.2 s by the NFTSM algorithm, while closing at \(-0.742\) m by the TSM algorithm. Because of the existence of shock, the NFTSM\({}_{1}\) algorithm will converge in 37.3 s. For the case \(\psi_{a}=30^{\circ}\), due to adjusting the course from the large angle, the NFTSM algorithm will converge in 72.9 s, but the other two methods cannot converge in the 100 s simulation time. For the simulation of stationary target tracking, it can be obtained that the proposed algorithm \(NFTSM\) is preformed better than the TSM in convergence time, and the reasonable design parameters are needed to eliminate the shock.