esa-sceva/satcom-chunk-collection huggingface dataset:
# IGBT Overvoltage Protection Based on Dynamic Voltage Feedback and Active Clamping
## 5 Experimental Analysis
### Influence of Capacitance on the Performance of a Dynamic Voltage Feedback Circuit  
To analyze the suppression effect of capacitance on the IGBT voltage spike and the influence of the gate signal in the dynamic voltage feedback circuit, set \(U_{ZS1}\) = 100 V, \(R_{4}\) = 100 \(\Omega\), \(U_{ZS2}\) = 300 V, where \(U_{ZS2}\) is the clamping voltage set by \(Z_{S2}\), \(Z_{S3}\) and \(Z_{S4}\) in the circuit. We obtain the waveform images of \(V_{ce}\) and \(V_{ge}\) in Figure 14 when \(C_{1}\) = 100 pF, and key parameters of IGBT shutdown in Table 2, where \(V_{ce}\) is measured with the \(\times\)10 probe of the oscilloscope. Figure 15 is a waveform image when only \(C_{1}\) = 500 pF is changed. A comparison shows that the change in \(C_{1}\) has a significant impact on the voltage spike of \(V_{ce}\). When \(C_{1}\) = 100 pF, the maximum value of \(V_{ce}\) is 382 V, and when \(C_{1}\) = 500 pF, the maximum value of \(V_{ce}\) is 346 V. First, the larger the value of \(C_{1}\) is, the longer the time from \(t_{0}\) to \(t_{3}\) will be, which will lead to a decrease in \(\mathrm{d}i_{c}/\mathrm{d}t\), and in turn reduces the voltage spike of \(V_{ce}\), which has a better suppression effect. At this time, the dynamic voltage feedback circuit behaves as a pair of \(\mathrm{d}i_{c}/\mathrm{d}t\) suppression effects of the IGBT.  
The main function of the feedback signal of the dynamic voltage feedback circuit is to increase the gate signal, which is manifested as the influence of increasing the slope of \(\mathrm{d}V_{ge}/\mathrm{d}t\), while the active clamp circuit manifests as a direct lift effect on the gate signal. When the IGBT turn-off voltage exceeds the \(U_{ZS2}\) set by the active clamp circuit, the gate signal will have a substantial rise. The experimental waveforms with \(C_{1}\) of 100 pF (Figure 14) and 500 pF (Figure 15) show that the larger the value of \(C_{1}\) is, the smaller the \(\mathrm{d}V_{ge}/\mathrm{d}t\), the longer the time for \(V_{ce}\) to reach the voltage peak, and the later the time to trigger turning on the active clamp circuit.  
Figure 13: IGBT turn-off experiment.
## 3 Results
### SMOTE Class Balancing  
The overall accuracy results from 5-fold cross-validation and unseen test data for each model type, by number of features and discretisation approach, are shown in Figures 16-18.  
Figure 14: Overall porosity prediction accuracy from 5-fold cross-validation and test data at 1.0 mm voxel size with random undersampling for class balancing. Each model has 3 or 7 input features and uses uniform or defect-centric discretisation.  
Figure 15: Overall porosity prediction accuracy from 5-fold cross-validation and test data at 2.0 mm voxel size with random undersampling for class balancing. Each model has 3 or 7 input features and uses uniform or defect-centric discretisation.  
Figure 16: Overall porosity prediction accuracy from 5-fold cross-validation and test data at 0.5 mm voxel size with synthetic minority oversampling for class balancing. Each model has 3 or 7 input features and uses uniform or defect-centric discretisation.  
Figure 17: Overall porosity prediction accuracy from 5-fold cross-validation and test data at 1.0 mm voxel size with synthetic minority oversampling for class balancing. Each model has 3 or 7 input features and uses uniform or defect-centric discretisation.  
Figure 18: Overall porosity prediction accuracy from 5-fold cross-validation and test data at 2.0 mm voxel size with synthetic minority oversampling for class balancing. Each model has 3 or 7 input features and uses uniform or defect-centric discretisation.  
At 0.5 mm voxel size (Figure 16), the nearest neighbours and bagged trees models achieved higher accuracies relative to random undersampling, by up to 4 and 8 percentage points for the nearest neighbours and bagged trees, respectively. However, the differences between validation and test accuracy were similar for the bagged trees and significantly increased for the nearest neighbours.  
At 1.0 mm voxel size (Figure 17), the accuracies are similar between the synthetic minority oversampling and random undersampling methods for the defect-centric discretisation approach but different for the uniform discretisation approach.
# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 3 Materials and Methods
### Datasets and Sources  
To evaluate the impact of AVs on traffic efficiency and emissions, a comprehensive dataset was constructed using both real-world and simulated sources. The raw datasets underwent standard preprocessing and cleaning procedures to ensure consistency and reliability. Redundant entries were removed, column names were normalized, and all timestamps were aligned to a consistent 24-hour format using UTC+3 to match the local time zone of Riyadh. Inconsistencies due to format differences between data providers were resolved through a parsing and validation script. Outlier handling was conducted using an interquartile range (IQR)-based method to detect and smooth anomalous traffic counts or travel times, especially those occurring due to sensor noise or atypical reporting. Detected outliers were corrected using a locally weighted smoothing function to preserve the general pattern without distorting peak or off-peak distributions. Intersections were geocoded and mapped to SUMO nodes using a Python-based matching algorithm that cross-referenced OpenStreetMap (OSM) data and official Saudi road maps. Vehicle types were proportionally  
Figure 1: Comparative analysis of the related literature across the categories of AV simulation, sustainability, smart cities, SUMO/TomTom use, and dataset relevance, based on [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21].  
allocated based on regional transport fleet distributions published by the Saudi Ministry of Transport and cross-referenced with urban mobility studies relevant to Riyadh's vehicle mix. While the dataset provides rich temporal coverage throughout the year, it does exhibit certain limitations. Traffic data collected during national holidays, religious events, and major public gatherings may not fully represent regular weekday patterns. These periods were flagged and considered in the analysis, but were not removed in order to preserve overall traffic behavior variance. These datasets were ultimately selected for their accuracy, comprehensiveness, and relevance to the specific traffic conditions in Riyadh. An overview of the dataset sources is illustrated in Figure 2, and more details are discussed in the following subsections.
###### Abstract  
With the growth of the internet of things (IoT), many challenges like information security and privacy, interoperability/standard, and regulatory and legal issues are arising. This work focused on the information security issue, which is one of the primary challenges faced by connected systems that needs to be resolved without impairing system behaviour. Information, which is made available on the Internet by the things, varies from insensitive information (e.g., readings from outdoor temperature sensors) to extremely sensitive information (e.g., video stream from a camera) and needs to be secured over the Internet. Things which utilise cameras as a source of information pertain to a subclass of the IoT called IoVT (internet of video things). This paper presents secured and unsecured video latency measurement results over the Internet for a marine ROV (remotely operated vehicle). A LabVIEW field programmable gate arrays (FPGAs)-based bump-in-the-wire (BITW) secure core is used to provide an AES (advanced encryption standard)-enabled security feature on the video stream of an IoVT node (ROV equipped with a live-feed camera). The designed LabVIEW-based software architecture provides an option to enable/disable the AES encryption for the video transmission. The latency effects of embedding encryption on the stream with real-time constraints are measured and presented. It is found that the encryption mechanism used does not greatly influence the video feedback performance of the observed IoVT node, which is critical for real-time secure video communication for ROV remote control and piloting. The video latency measurement results are taken using 128, 256 and 512 bytes block lengths of AES for both H.264 and MJPEG encoding schemes transmitted over both TCP and UDP transmission protocols. The latency measurement is performed in two scenarios (i.e., with matching equipment and different equipment on either end of the transmission).  
AES; IoT; video; IoVT; marine; FPGA; LabVIEW FPGA; RoV; IoRT +
Footnote †: journal: Sensors
# Development of a Staggered PCD End Mill for Carbon Fiber Reinforced Plastic
## 2 Design of Staggered PCD End Mill
### Structural Parameters of the Staggered PCD End Mill  
Tool fracture easily occurs when cutting with large-rake-angle PCD tools, due to the high strength and hardness of CFRP, so a smaller rake angle should be used. Considering the conditions of PCD welding and grinding, the rake angle \(\gamma_{f}\) is designed to be 3\({}^{\circ}\).  
If the relief angle is too small, fast tool wear will be found. If the relief angle is too large, the tool strength will be insufficient. To enhance the cutting edge and consider fiber resilience of CFRP and the impact of the feed on relief angle, the radial relief angle \(\alpha_{f}\) is designed to be 10\({}^{\circ}\) and the axial relief angle \(\alpha_{p}\) is designed to be 10\({}^{\circ}\).  
The inclination angle directly affects the magnitude of the axial force during milling. Axial milling force may result in delamination due to low binding force between CFRP layers. Considering the constraints of fabrication process, a small inclination angle is preferred. So in this work, the inclination angle is 3\({}^{\circ}\).  
The main structural parameters of the PCD end mill are shown in Table 1.
## 2 Methods for Determining the Number of Power Line and Transformer Outages
### Factors Influencing the Time of Failure Removal
Based on the data presented by the authors [125], the reliability coefficients were 0.59 trips per 100 km per year for 400 kV lines, 1.46 trips per 100 km per year for 220 kV lines, and 2.90 trips per 100 km per year for 110 kV lines, respectively. In the case of HV/HV transformers, the coefficient was statistically 0.12 trips per 100 transformers in operation over one year for 400/220 kV and 400/110 kV transformers and 0.11 trips per 100 transformers in operation in the case of 220/110 kV transformers. To determine the reliability coefficient of transformers, the relationship proposed by [PERSON] et al. [22] was utilized. Then, after taking into account the data obtained by the authors for one calendar year and assuming the occurrence of one failure in one year, the following results were obtained:  
\[\lambda=100\%\times\frac{\sum_{i}n_{i}}{\sum_{i}N_{i}}=100\times\frac{1}{213} =0.46 \tag{49}\]  
The indicator obtained then is 0.46 damage per 100 transformers per year. It can therefore be assumed that damage to a single transformer will occur on average every 3 years. In many works, it was also possible to find simple relationships for determining the number of transformer damages (28), as well as for determining the damage indicator per 100 working transformers (29):  
\[ \begin{array}{l}Number\ \mathrm{of\ failures}\ =\ [\mathrm{ Failure\ rate}]\ \times\ [\mathrm{population}],\\ Number\ \mathrm{of\ failures}=0.0047\times 213=1\\ \end{array}  \tag{50}\]  
\[ \begin{array}{l}Annual\ \mathrm{Failure\ Rate}=\frac{Failures}{Total\ \mathrm{in\ service\ transformers}}\times 100\%\\ Annual\ \mathrm{Failure\ Rate}=\frac{1}{213}\times 100\%=0.47\\ \end{array}  \tag{51}\]  
The authors also propose to use the relationship (50) with the damage index calculated for 100 HV/HV transformer:  
\[ \begin{array}{l}Number\ \mathrm{of\ failures}\ =\frac{[\mathrm{ Failure\ rate}]\ \times\ [\mathrm{population}]}{100}\,\\ Number\ \mathrm{of\ failures}\ =\frac{[0.47]\times[213]}{100}=1\\ \end{array}  \tag{52}\]In the case of system transformers located in North and South America, the outage rate is between 0.23 and 1.29 failures per 100 transformers according to the literature [113,126], then the outage rate for the Polish Power System, which has 213 system transformers, is between 1 and 3 transformer outages per year, which was calculated from the following relationship:  
\[ \begin{array}{l}k=\lambda\cdot p=0.23\cdot 2.13=0.49\\ k=\lambda\cdot p=1.29\cdot 2.13=2.74\end{array}  \tag{53}\]  
where \(k\) represents the number of shutdowns, \(\lambda\) is the outage rate per 100 transformers, and \(p\) is the total number of transformer groups counted in hundreds. In the case of HV/HV transformers located in Germany, the reliability rate is 0.03 failures per 100 transformers per year [125]. In the case of 400/130 kV transformers located in Sweden, this rate was 0.38 failures per 100 transformers per year in 2021 [127].
## 2 Simulation Methodology, Material, and the Experiments
### Finite Element Method (FEM)  
The FEM was employed in the simulation to calculate the electric field strength. The FEM is a numerical technique for finding approximate solutions to boundary value problems for differential equations. First, the problem domain is divided into finite elements, as shown in Figure 1, and each element is represented by a set of element equations to the original problem. To make the final calculation, all element equations are systematically recombined into a global system of equations, which can be numerically solved by known techniques from the initial values in the original problem.  
The FEM is the preferred method in structural mechanical analysis. In this study, the FEM is a suitable tool for obtaining a numerical solution for the spatial distribution of the electric field between the cathode and the anode.
## 4 Results and Discussion
### Discussion on the Statistical Tests of H1 and H2
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**(a) Hypothesis Test Summary**} \\ \hline  & \multicolumn{2}{c}{**Null Hypothesis (H0) Challenging the**} & \multicolumn{2}{c}{**Test**} & \multicolumn{1}{c}{**Sig.**} & \multicolumn{1}{c}{**Decision**} \\  & \multicolumn{2}{c}{**Corresponding i-th Hypothesis (Hi)**} & \multicolumn{1}{c}{} & & \\ \hline \multirow{2}{*}{**H0\({}_{1}\) vs. H1**} & The distribution of profit is the same across categories of area & Independent Samples Kruskal–Wallis Test & 0.151 & Retain the null hypothesis \\ \hline \multirow{2}{*}{**H0\({}_{2}\) vs. H2**} & The distribution of profitability is the same across categories of area & Independent Samples Kruskal–Wallis Test & 0.828 & Retain the null hypothesis \\ \hline \multirow{2}{*}{**(b) Hypothesis Test Summary**} & \multicolumn{2}{c}{**Null Hypothesis (H0) Challenging the**} & \multicolumn{2}{c}{**Test**} & \multicolumn{1}{c}{**Sig.**} & \multicolumn{1}{c}{**Decision**} \\  & \multicolumn{2}{c}{**Corresponding i-th Hypothesis (Hi)**} & \multicolumn{1}{c}{} & & \\ \hline \multirow{2}{*}{**H0\({}_{3}\) vs. H3**} & The distribution of profit is the same across categories of main type of bike & Independent Samples Kruskal–Wallis Test & 0.001 & Reject the null hypothesis \\ \hline \multirow{2}{*}{**H0\({}_{4}\) vs. H4**} & The distribution of profitability is the same across categories of main type of bike & Independent Samples Kruskal–Wallis Test & 0.001 & Reject the null hypothesis \\ \hline \hline \end{tabular}
\end{table} 
Table 8: (a) Kruskal–Wallis H test of area-specific factors and policies. (b) Kruskal–Wallis H test of project-specific factors. bike delivery organizations as well. Finally, such delivery services tend to be more effective, efficient and, hence, with a higher profitability in those countries than in others.
## 3 Results and Discussion
### Various Scan Path
#### 3.3.2 Reduction of Line Width  
In order to reduce the line width at the elliptical scan path, which was advantageous for the dispersion of laser energy, the minor axis length A was reduced to 5 \(\upmu\)m. When the major axis length B was set to 50 \(\upmu\)m, as shown in Figure 9a, a conductive structure with a significant incomplete deposition area in the middle of the line was formed. Accordingly, as a result of the experiment by reducing the major axis length B to 25 \(\upmu\)m, a significant part of the incomplete deposition area was improved, as shown in Figure 9b, and a conductive structure with a line width of 130 \(\upmu\)m was obtained. This was because the elliptical scan path was more advantageous in dispersing the laser energy than the scan path based on the previous vertical line. Even when the line width was reduced, as in this experiment, it was possible to suppress unnecessary ablation by minimizing laser beam overlapping.  
Figure 8: Deposition with elliptical scan path: (**a**) schematic diagram; (**b**) experimental result.  
Figure 7: Deposition with triangular scan path: (**a**) schematic diagram; (**b**) experimental result.  
Figure 9: Result of reducing the minor axis length according to major axis length B: (**a**) 50 \(\upmu\)m; (**b**) 25 \(\upmu\)m.
## 3 Estimation of Reinforcement Strain
### Estimation Results  
The strain of the steel reinforcement, estimated using the crack width for the two specimens, is shown in Figure 7. The formula for crack width consists of components based on when the crack \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Base Type** & **Crack Width** & \(\varepsilon_{sm}-\varepsilon_{cm}\) & **Name** \\ \hline EC 2 Part.1-1 & \(w_{k}=S_{r,max}(\varepsilon_{sm}-\varepsilon_{cm})\) & First 1  & E1-1 \\  & \(S_{r,max}=3.4c+0.425k_{1}k_{2}d_{b}/\rho_{e}\) & Second 2  & E2-1 \\ EC 2 Part.2 & \(w_{k}=S_{r,max}(\varepsilon_{sm}-\varepsilon_{cm})\) & First 1  & E2-1 \\  & \(S_{r,max}=d_{b}/3.6\rho_{e}\) & Second 2  & E2-2 \\ \hline Euro-Design Handbook  & E2-2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Formulas for calculation of the crack width. occurs. Hence, there is a minimum value that exists, which depends on the material properties and coefficient. Therefore, there is an offset of about \(1000\times 10^{-6}\) mm/mm in the first-order-based formula and about \(10\times 10^{-6}\) mm/mm in the second-order-based formula in Figure 7. The estimated strain of each specimen was compared with the measured strain at S3, which was closest to the crack that was measured for each specimen. For the integral specimen, the estimated strain was greater than the measured strain at S3 before the yield load was reached. After the yield load was reached, the measured strain increased sharply and was similar to the strain estimated using the first-order formulas (E1-1, E2-1, EH-1). For the modular specimen, the estimated strain was greater than the measured strain and increased sharply after the yield load was reached. Tables 5 and 6 present the estimated strains at the yield load and ultimate load. The estimated strain at the yield load was compared with \(2000\times 10^{-6}\) mm/mm, \(\varepsilon_{y}\), which is the theoretical yield strain of the steel reinforcement, and the estimated strain at the ultimate load was compared with the strain at the yield load. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{5}{c}{**Estimated Strain (\(\chi 10^{-6}\) mm/mm)**} \\ \cline{2-6}  & & **At Yield Load (\(\epsilon_{y\#}^{*}\))** & **Ratio (\(\epsilon_{y\#}^{*}\)/\(\varepsilon_{y}\))** & **At Ultimate Load (\(\epsilon_{\text{aM}}^{*}\))** & **Ratio (\(\epsilon_{\text{aM}}^{*}\)/\(\epsilon_{y\#}^{*}\))** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} First-order \\ formula \\ \end{tabular} } & E1-1 & 2720.67 & 1.360 & 4063.4 & 1.494 \\  & E2-1 & 2782.88 & 1.391 & 4175.75 & 1.501 \\  & EH-1 & 2730.10 & 1.365 & 4405.73 & 1.614 \\  & & 2744.55 & 1.372 & 4214.96 & 1.536 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Second-order \\ formula \\ \end{tabular} } & E1-2 & 1666.07 & 0.833 & 3008.76 & 1.806 \\  & E2-2 & 1728.28 & 0.864 & 3121.1 & 1.806 \\ \cline{1-1}  & EH-2 & 2079.09 & 1.040 & 3754.68 & 1.806 \\ \cline{1-1}  & Average & 1824.48 & 0.912 & 3294.85 & 1.806 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Estimated strains of the modular specimen at the yield and ultimate loads.
# Hybrid Mode Reconfigurable Antenna with V-Shaped Extrudes for Cognitive Radio Applications
## 1 Introduction
Then, a different, unoccupied spectrum that is not being used by PUs is sent to the SUs. Following this process enables seamless and simultaneous communication between PUs and SUs. Additionally, the CR includes numerous intelligent characteristics, such as the ability to immediately detect the presence of PUs and effectively utilise unused bands. For channel sensing, either a broadband or a reconfigurable narrowband antenna can be utilised. On the other side, for the communication process to be able to accomplish the dynamically exposed white spaces in a spectrum, it requires a reconfigurable antenna. A sensing antenna is used to begin this cycle by watching the channel action, as shown in Figure 1.  
We can utilise a reconfigurable antenna after detecting an unoccupied spectrum. In the subsequent part of the sequence, a cognitive processor selects the optimal region of the spectrum for communication. The CPU then tells the communicating, designed antenna how to perform in order to establish the essential mode of communication. In the final stage, the processor attains cognition by accumulating knowledge from previous channel activity. The cognitive radio technology is able to select the best communication mode and adjust its hardware as a result of this cycle [51]. New technologies and development also require a spectrum, which has previously been allotted to different types of services. In most wealthy nations, the majority of the accessible spectrum has already been assigned. Despite the fact that the bulk of this spectrum has already been allotted, study from the Federal  
Figure 1: Cognitive radio cycle. Communications Commission (FCC) demonstrates that the allotted bandwidth often goes underutilised, leading to spectrum gaps also known as white space, which is spectrum that is left unused. Hence, spectrum scarcity is not caused by a shortage of spectrum but rather by its improper allocation. The inspiration to create CR, which can opportunistically utilise the spectrum and delivers effective spectrum utilisation, comes from the stark disparity between spectrum allotment and usage. The arrangement of various wireless devices which cohabit with main users while causing the least amount of interference with licenced users is predicted to result in significant spectral efficiency. The cognitive radio system completely explained in Figure 1 has the abilities of multifunctionality, efficient spectrum utilization, and interpretability and can be used in RFID, ISM Band applications, satellite communications, radar and military services, and 4G and 5G communication.