esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Criteria  
In order to measure performance rate of classification models, some metrics such as accuracy, recall, precision, and f-measure were used in this study. Table 1 shows a confusion matrix for a binary classification problem.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted**} \\ \cline{3-4}  & & **Class “0”** & **Class “1”** \\ \hline \multirow{3}{*}{**TIRADS**} & **Class “0”** & TP & FP \\ \cline{2-4}  & **Class “1”** & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix explanation for a binary classification problem.  
Figure 1: Flow diagram of proposed algorithm.  
TP (True Positive) represents positive actual values correctly predicted by the model, FN (False Negative) is incorrectly predicted negative actual classes, TN (True Negative) is negative actual values correctly predicted by the model, and FP (False Positive) represents positive actual values incorrectly predicted by the model.  
Accuracy: It is the most used method among model performance criteria. It is obtained by dividing the number of samples that the model knows correctly by the total number of samples. The accuracy can be calculated from the ratio given in Equation (6).  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{6}\]  
Precision: It is calculated by dividing the number of positive classes that the model predicts correctly by the total number of positive classes. The precision value can be calculated from Equation (7).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
Recall: It is calculated by dividing the number of positive classes that the model predicts correctly by the total number of positive predicted classes. The sensitivity value can be calculated from Equation (8).  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
f-Measure: Sensitivity and precision values do not make sense alone. The f-measure is found by the harmonic mean of the precision and sensitivity values. It is the most commonly used evaluation criterion after accuracy value. The F-score value can be calculated as seen in Equation (9).  
\[\text{f}-\text{Measure}=\frac{(2\text{xRecall}*\text{Precision})}{(\text{ Recall}+\text{Precision})} \tag{9}\]  
ROC (Receiver Operator Characteristics) Area: Another commonly used criterion for classification problems is the ROC area. The ROC area is used to visualize performances in classification problems. In recent years, it is frequently used especially in medical decision making. The ROC curve has the FP ratio on the x-axis and the TP ratio on the y-axis. FP rate can be calculated from Equation (10) and the TP rate from Equation (11). It means a good classification is made in the ROC curve as the curve shifts to the upper left corner. As the curve shifts to the center, it means a poor classification.  
\[\text{FP Rate}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{10}\]  
\[\text{TP Rate}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]
## 3 Parametric Study for Determining the Optimal Sampling Strategy
### Sampling from a Single Point
#### 3.1.7 Effect of Soil Anisotropy  
According to the literature, the spatial variability of soil in the horizontal direction is much greater than the respective one in the vertical direction due to natural deposition and soil formation processes. In this respect, these studies mention that the horizontal spatial correlation length \((\theta_{h})\) is generally about 10 times the vertical one \((\theta_{v})\). For example, \(\theta_{h}\approx 9\theta_{v}\) for [PERSON] (2008), \(\theta_{h}\approx 10\theta_{v}\) for [PERSON] et al. (2008) and [PERSON] (2009), \(\theta_{h}\approx 12\theta_{v}\) for [PERSON] et al. (2010), \(\theta_{h}\approx 13\theta_{v}\) for [PERSON] and [PERSON] (2010), and \(\theta_{h}\approx 2\) to 7 times the \(\theta_{v}\) value for [PERSON] et al. (2011).  
Driven from these findings, the effect of soil anisotropy on the optimal sampling location will be investigated here by comparing the \(\theta_{h}=\theta_{v}\) case with the \(\theta_{h}=10\theta_{v}\) case. The reference wall-soil system with \(\theta_{v}/H=\theta_{h}/H=2.08\) will be compared with a respective one having \(\theta_{v}/H=2.08\) and \(\theta_{h}/H=20.8\). The variation of \(p_{f}\) with \(d_{p}/H\) for various \(\mathrm{x}/H\) values is shown in Figure 10. From this figure, it is inferred that the statistical error practically remains the same for horizontal sampling distances less than one wall height (\(\mathrm{x}\leq H\)) for both the translating and rotating wall cases. Although for \(\mathrm{x}\leq H\) the difference in the \(p_{f}\) values is very small, again, the optimal sampling location is at \(x/H=0\). Comparing Figure 10a with Figures 3c and 10b with Figure 3d, it can be said that the effect of the horizontal sampling location is significantly higher in the isotropic case. Regarding the optimal sampling location, the soil anisotropy has no effect on the optimal sampling depth both in the case of the translating and rotating wall.  
Figure 9: \(p_{f}\) versus \(d_{p}/H\) example curves for different \(FS\) values for the case of (**a**) sliding and (**b**) overturning wall.
# Evaluation of the Machinability of Ti-6 Al-4V Titanium Alloy by AWJM Using a Multipass Strategy
## 1 Introduction
They found that the use of pure WJ could remove embedded abrasives more effectively with specific process conditions. [PERSON] [37] compared the use of three different abrasive types, namely garnet, olivine, and crushed glass during the AWJM of Ti-6 Al-4V. He found that garnet was the most effective for obtaining a greater depth of penetration, followed by olivine and crushed glass, but olivine led to larger focusing tube wear, followed by garnet and crushed glass. [PERSON], [PERSON], and [PERSON] [38] studied the surface roughness of slots produced by AWJM on a Ti-6 Al-4V workpiece, as well as grit embedment; and [PERSON], [PERSON], and [PERSON] [39] thoroughly explored the effect of traverse speed during the AWJM of the same alloy, finding that traverse speed considerably affects the surface morphology and kerf taper ratio. As can be seen from the above literature review, although some authors investigated various aspects of the multipass AWJM strategy, there have been no relevant studies that thoroughly analyzed the influence of the multipass strategy on the formation of slots under different AWJM parameters. Using a constant abrasive mass flow rate and SoD, and by changing the traverse speed, the jet pressure, and the number of passes (up to 4), this study aimed to define the changes of the depth of penetration and slot geometry, as well as the impact on productivity and cutting efficiency of a challenging and difficult to machine material, such as Ti-6 Al-4V. Therefore, in this study, the effect of multiple passes was investigated in relation to the depth of cut, kerf width, kerf taper angle, material removal rate, and cutting efficiency during the AWJM of a Ti-6 Al-4V alloy, and various useful conclusions were drawn.
## 2 Materials and Methods
### Hybrid Sensor Design and Edge AI Detection
The model was trained on a custom-labeled dataset obtained from controlled fire experiments involving various ignition materials such as paper, wood, and plastic, as well as non-fire interference scenarios including aerosol sprays, vaping, and cooking emissions. The complete dataset comprises 3900 training images, 1300 validation images, and 1300 test images. Sample images from each category are provided in Appendix B (Figure A1) to illustrate the dataset distribution used for training, validation, and testing. To enhance generalization and reduce overfitting, the dataset was augmented with Gaussian noise and temporal jitter. Such approaches, commonly used in CNN-based fire detection research, have demonstrated high accuracy--often exceeding 96%--while minimizing false alarms under complex real-world conditions [44; 45; 46]. Model inference is executed in real-time on a 32-bit low-power processor embedded within the sensor housing, enabling immediate fire event classification at the edge. This edge AI implementation significantly reduces detection latency compared to cloud-based systems, achieving an average response time of 5.8 s from ignition under standardized test conditions. Such performance aligns with recent studies demonstrating that lightweight deep learning models deployed on embedded devices can sustain high classification accuracy (up to 98%) while minimizing computational cost and energy consumption [47; 48; 49]. By eliminating the need for continuous high-volume data transmission to external servers, the system ensures network independence and energy efficiency--critical attributes for deployment in communication-limited or infrastructure-constrained environments [50; 51]. This capability also supports long-term operation in battery-powered or mobile platforms such as drones and wireless sensor networks. To enhance the interpretability and trustworthiness of the proposed fire detection system, explainable AI (XAI) functionality is incorporated using Gradient-weighted Class Activation Mapping (Grad-CAM). The system integrates input from camera imagery, infrared sensors, and photoelectric smoke detectors into a CNN-based inference module that classifies fire events in real time. Grad-CAM highlights the most influential portions of the input data contributing to the classification outcome, thereby providing human operators with visual justification for model decisions [24; 47]. This interpretability not only supports validation and auditability but also reinforces operational reliability in safety-critical scenarios. By combining dual-sensor data acquisition, edge-based deep learning inference, and interpretable output, the proposed framework ensures rapid and transparent fire detection across diverse environments such as smart buildings, industrial facilities, and remote monitoring sites.
## 2 Materials and Methods
### Numerical Study  
MWA involves inserting a slender microwave antenna into tissue and heating. This work's physical model is composed of a microwave antenna and biological tissue inserted by the antenna (Figure 1). The width and height of biological tissue are 20 mm and 40 mm, respectively. As the distance between the ablation antenna and the thermometric antenna was set as 2 mm, the biological heat transfer focus area was defined as 10 mm from the ablation antenna center. The structure and scale of the ablation antenna are shown in Figure 1a. The antenna's geometric structure is composed of a slender coaxial cable (slot, conductor, and dielectric) and a conduit with 1.79 mm diameter. The coaxial cable has a slot with 1 mm length at z = 6.5 mm, which is set according to antenna size. Because this model has rotational symmetry, a 2D axisymmetric structure is used to construct geometric shapes to simulate the 3D heat effect. A free triangular grid is used for meshing (Figure 1b), with 3 mm maximum element size, 0.0024 mm minimum element size, 1.3 maximum element growth rate, 0.3 curvature factor, and one narrow area resolution. The established coordinate system is a schematic plan view of cylindrical coordinates, which is used to analyze time-averaged power flow in the next subsection. Due to the larger temperature gradient near the ablation antenna, its grid is denser. As the distance from the antenna became further, the grid gradually became thinner. For ablation antenna, the dielectric part is the key to the transmission of microwave energy. Therefore, a denser grid is used for dielectric, and the maximum unit of the grid was set to 0.15 mm.  
Figure 1: Schematic diagram of biological tissue model. (**a**) Geometric structure and parameters of biological tissue model. (**b**) A free triangular grid in biological tissue model. Its cylindrical coordinate mainly analyzes d and z directions.
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 4 Predictive Power Manager for EH-WSN
### Optimal Working Point  
As can be seen above, the total power loss in a wind-powered WSN system is composed of two parts: DC-DC dissipation \(P_{D\_system}\) and supercapacitor leakage \(P_{leak}\). The DC-DC power loss \(P_{D\_system}\) relates to the output current \(I_{O1},I_{O2}\) and \(W_{Boost},W_{Buck-Boost}\). The leakage power is determined by supercapacitor voltage \(V_{SC}\). Among these parameters, \(I_{O2}\) is task-specific and primarily decided by the transmission power \(P_{Tran}\). \(V_{SC}\) could be calculated by \(V_{SC}=V_{In}/(1-D_{1})\). \(V_{Out}\) must be set to \(V_{DD}\) and it is always a constant voltage, it can be computed by \(V_{Out}=V_{SC}\cdot(t_{1}^{\prime}/t_{2}^{\prime})\). Therefore, configuring the DC-DC converters to maximize the harvested energy and minimize the total energy dissipation simultaneously is of great importance for the proposed EH-WSN, it can be analyzed as follows:  
First, Step 1 refines the configuration of Boost converter (i.e., decides the optimal value of parameters \(T_{Boost}\), \(L_{1}\) and \(t_{1}\) to match \(R_{Optimal}\), which is to accomplish maximal energy harvesting; then Step 2 calculates the tradeoff parameters of two converters by considering the energy efficiency under TPC scheme; finally Step 3 computes the corresponding \(V_{SC}\) and the remaining parameters.
# Evaluation of Different Simulation Methods for Analyzing Flood Scenarios in the Danube Delta
## 1 Introduction
Therefore, the main objective of this study was the evaluation of two simulation methods to assess extent of flooding for the settlements in the Danube Delta, by using low-cost equipment and less time-consuming simulation methods, to obtain flood maps corresponding with different flood scenarios. The flood extension maps depict the potentially flooded areas of the settlements, as a result of the occurrence of a breach (rupture) in the dyke. Furthermore, a comparison was made between the results obtained using two methods that analyze different scenarios of breaching the dyke. The analysis will be applied to the settlements on the course of the Sulina branch, located near the area where the Danube discharges into the Black Sea.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 1 Introduction
### Scope and Contributions  
The primary purpose of this article is to deliver a discussion on NTN structure and its key components while narrowing the topic down from a satellite perspective into UAV-based NTN, i.e., FANET. After that, a thorough literature study on the most recent advances in FANET technology is presented. In this study, we aim to provide a high-level overview of the FANET and its most recent discoveries, which will include its benefits and drawbacks, as well as new research prospects for both current and future applications in 5G and 6G. We also detail the various specifications needed for FANETs and the key limitations that prevent its widespread deployment. Furthermore, we design a framework for a DTN-assisted FANET, while simultaneously covering well-known routing approaches in FANET. Applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) tools, in FANET are also discussed. With this as our main goal, the five important contributions that we made to this work are as follows:  
* This paper presents detailed NTN with UAVs, i.e., FANET, including features, existing and emerging applications, and its constraints. It sheds light on the distinctions that exist between the NTN components and gives a comprehensive survey of 218 FANET-related papers.
* The holistic overview of most recent advancements in relation to the emerging FANET technology is provided in terms of communication standards, physical layer, UAV role management, trajectory optimization, and routing protocols.
* DTN-routing protocols are taken into specific consideration for FANET due to its nature of intermittent connectivity and a DTN-assisted FANET framework is described and evaluated.
* The applications of AI/ML/DL techniques in FANET are thoroughly discussed.
* Finally, we take into account FANET's potential and investigate its unique characteristics and advantages over existing approaches for dealing with challenging FANETproblems. We use this knowledge to foresee the paths future research will go and the obstacles that will need to be overcome to enable FANET in wireless networks.  
We also emphasize the advantages of our survey while summarizing the characteristics of other important surveys of FANET in Table 1 to further elucidate the importance of this study. We highlight that, despite the previous important studies, our study primarily focuses on providing and bringing together a broad overview of NTN components, applications, communication standardization process, physical layer issues, network management, trajectory optimization, routing protocols, DTN-assisted FANET, and applications of ML in FANET.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 5 Evaluation Methodology  
This section introduces the methodology and the simulations set up to evaluate the proposed handover scheme for high mobility users in a VLC network. During the vehicle trajectory, the system needs to detect when the vehicular node is leaving a cell to execute a handover. Our methodology uses a detailed representation of the MAC protocols and a realistic channel model to emulate the actual conditions of a V2I communication for a vehicle moving into a tunnel. It includes the use of the MMC method to simulate the channel impulse response, which takes into account the different transmitters' radiation patterns, i.e., the vehicle's headlamps and tunnel lamps. The evaluation setup is divided into three parts. First, we describe the methodology and the tools used, then the layout of the scenario and the parameters are introduced, and finally, we explain the metrics used for the performance evaluation.  
We consider three hypothetical system configurations to validate the proposed handover solution:  
* **Configuration 1:** Perform the handover process using only uplink information, i.e., satisfy the \"Condition 1\" criterion, in a conventional network topology.
* **Configuration 2:** Perform the handover process with the \"Condition 1\" criterion in the 2.5 layer network topology.
* **Configuration 3:** Perform the handover process using both uplink and downlink information, i.e., satisfy \"Condition 1\" and \"Condition 2\" criteria, in the 2.5 layer network topology, see Figure 8.
## 3 Analytical Solutions
### Analytical Solution Calculation
The contact condition in the displacement direction is that the normal displacement of the two elastic bodies on the contact surface is equal, and the tangential displacement is also equal, namely:  
\[\left\{ \begin{array}{l}\left(\sigma_{\rho 1}\right)_{\rho=R_{2}}=\left( \sigma_{\rho 2}\right)_{\rho=R_{2}}\\ \left(u_{\rho 1}\right)_{\rho=R_{2}}=\left(u_{\rho 2}\right)_{\rho=R_{2}}\\ \tau_{\varphi\varphi 1}=\tau_{\varphi\varphi 2}=0\end{array} \right. \tag{5}\]  
Solving the equation solved using the normal displacement boundary condition on the contact surface in Equation (5) and the axisymmetric displacement in the plane strain problem, the interaction force \(q_{2}\) of the steel corrugated plate-mold bag concrete interface can be obtained:  
\[q_{2}=q_{1}\Bigg{[}\frac{2(1-\mu_{1})R_{1}^{2}}{(R_{2}^{2}-2\mu_{1}R_{2}^{2}+R_ {1}^{2})+n_{1}n_{2}(R_{2}^{2}-2\mu_{2}R_{2}^{2}+R_{3}^{2})}\Bigg{]} \tag{6}\]  
where, \(n_{1}=\frac{E_{1}(1+\mu_{2})}{E_{2}(1+\mu_{1})}\) and \(n_{2}=\frac{R_{1}^{2}-R_{2}^{2}}{using Equation (6). However, the ultimate bearing capacity of steel corrugated plate can be obtained through relevant member tests. According to Chinese standard JGJ 120-2012 [30], when the supporting structural members are designed according to the ultimate bearing capacity state, the strength safety factor of the temporary supporting structure should not be less than 1.25. Then, for the support structure with a safety level of one, its structural strength safety factor should not be less than 1.1. The strength safety factor of each part of the supporting structure can be calculated using the following formula:  
\[\left\{ \begin{array}{l}K_{1}=\left|\frac{f_{\mathrm{c}}}{\varphi_{\mathrm{q1 }}}\right|=\left|\frac{f_{\mathrm{c}}\rho^{2}\left(R_{1}^{2}-R_{2}^{2}\right)} {\rho^{2}\left(q_{2}R_{2}^{2}-q_{1}R_{1}^{2}\right)-R_{1}^{2}R_{2}^{2}(q_{1}-q _{2})}\right|\\ K_{2}=\left|\frac{\sigma_{\mathrm{s}}}{\varphi_{\mathrm{q2}}}\right|=\left| -\frac{\sigma_{\mathrm{s}}\rho^{2}\left(R_{2}^{2}-R_{2}^{2}\right)}{q_{2}R_{ 2}^{2}\left(\rho^{2}+R_{3}^{2}\right)}\right|\end{array} \right. \tag{7}\]  
where \(K_{1}\) is the strength safety factor of mold bag concrete; \(f_{\mathrm{c}}\) is the design value of compressive strength of concrete; \(K_{2}\) is the strength safety factor of the steel corrugated plate; \(\sigma_{\mathrm{s}}\) is the yield strength of the steel. The structural material strength of the bottommost part of the circular shaft for the field craft tests in this paper was verified using Equation (7), and the strength safety factors for steel corrugated plate and mold bag concrete were calculated as: 321.2 and 699, respectively. The calculated \(K_{1}\) and \(K_{2}\) both meet the above requirements; therefore, each part of the support structure is safe. However, the value of \(K_{2}\) is much larger than \(K_{1}\), indicating that the steel corrugated plate has a high safety redundancy and that most of the load on the structure is borne by the mold bag concrete.
## 4 Performance Evaluation
### Energy Calculation Model  
In the energy calculation approach, we use the energy loss calculation model given in [32]. It includes formulas for calculating the energy loss for sending and receiving data and we will use these formulas in our simulation experiments, Equation (5) is the formula for calculating the energy consumed for sending and forwarding data.  
\[E_{Tx}(h,d)=\left\{\begin{array}{ll}hE_{ellec}+h_{efs}d^{2}&d<d_{0}\\ hE_{ellec}+h_{emp}d^{4}&d\geq d_{0}\end{array}\right. \tag{5}\]  
where \(E_{ellec}\) represents the energy consumed to send one bit of data, and \(h\) is the number of bits occupied by the sensed data packet. \(e_{fs}\) and \(e_{mp}\) represent the energy loss factor for data sending under the free space model and multi-path fading model, respectively, the calculation model needs to be specifically selected based on the distance between nodes. When the distance \(d\) between nodes is less than \(d_{0}\), the free space model is selected. Otherwise, when \(d\) is greater than or equal to \(d_{0}\), it is considered that the distance between nodes is far, and the multi-path fading model is used for data transmission. \(d_{0}\) is calculated by Equation (6), and its value is a constant value.  
\[d_{0}=\sqrt{\frac{e_{fs}}{e_{mp}}} \tag{6}\]  
The formula for the received energy calculation is represented by (7), which is equal to the product of the size of the received packet multiplied by \(E_{elec}\), it is independent of the distance between the nodes.  
\[E_{Rx}(h,d)=hE_{elec} \tag{7}\]