esa-sceva/satcom-chunk-collection huggingface dataset:
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 5 Experiments
### Model Performance Comparison
#### 5.3.1 Quantitative Results
As shown in Table 5, ThinDyUNet has the smallest number of parameters, approximately four times fewer than TransUNet and about twenty times fewer than UNet, PAN, and PSPNet. This reduction is achieved by efficiently utilizing a fixed number of channels in each convolutional layer, making the model highly lightweight and efficient. Despite its compact size, ThinDyUNet remains competitive with larger models. To evaluate inference performance, we measured the average inference time per image using the validation dataset. ThinDyUNet recorded the fastest inference time among all models, thanks to its smaller size and lower computational complexity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Test Precision** & **Test Recall** & **Test Dice** & **Test mIoU** \\ \hline UNet & **0.95** & **0.821** & **0.846** & **0.79** \\ PAN & 0.914 & 0.332 & 0.393 & 0.327 \\ PSPNet & 0.896 & 0.757 & 0.793 & 0.691 \\ TransUNet & 0.74 & 0.493 & 0.487 & 0.404 \\ ThinDyUNet (proposed) & 0.872 & 0.75 & 0.744 & 0.646 \\ \hline \hline \end{tabular}  
* The best value is in bold.  
\end{table} 
Table 4: Model performance comparison on test set metrics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Val Precision** & **Val Recall** & **Val Dice** & **Val mIoU** \\ \hline UNet & 0.98 & 0.696 & **0.757** & 0.672 \\ PAN & **0.985** & 0.687 & 0.756 & **0.673** \\ PSPNet & 0.982 & 0.567 & 0.64 & 0.64 \\ TransUNet & 0.90 & 0.714 & 0.733 & 0.642 \\ ThinDyUNet (proposed) & 0.973 & **0.754** & 0.725 & 0.647 \\ \hline \hline \end{tabular}  
* The best value is in bold.  
\end{table} 
Table 3: Model performance comparison on validation set metrics. In contrast, UNet, PAN, PSPNet, and TransUNet recorded inference times of more than four milliseconds per image due to their significantly higher parameter counts. The PAN model has the slowest performance among all models, with a 6.01-millisecond inference time. Meanwhile, for other models, such as UNet, PSPNet, and TransUNet, it has around 4 ms of inference time. For the proposed ThinDyUNet model, it showed the fastest inference time of 2.45 ms. Due to the smallest number of model parameters, the amount of computation in the ThinDyUNet model is far less than other models, resulting in faster processing time for inference of one image frame.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 5 Threat Analysis and Risk Assessment  
DMSs are complex systems that require rigorous analysis to ensure their resilience against potential threats. TARA provides a structured approach to identifying, evaluating, and mitigating risks. It helps to systematically assess the security posture of a DMS, ensuring the system is protected against the most critical vulnerabilities. This methodology is integrated with the STRIDE framework to identify and categorize various types of threats.  
TARA is a cybersecurity methodology designed to evaluate the risks associated with each potential threat. Unlike traditional methods that focus solely on identifying threats, TARA takes a more holistic approach by evaluating the severity of each threat and its potential impact on the system. This enables system architects and security profession  
Figure 7: Logical diagram of the seat belt sensor.  
als to prioritize mitigations based on the most critical vulnerabilities, ensuring a robust security architecture.  
Within the TARA process for DMSs, the STRIDE threat-modeling framework is employed to systematically categorize potential threats into six distinct classes, each aligned with key cybersecurity principles such as confidentiality, integrity, availability, authentication, non-Repudiation, and authorization:  
* Spoofing: Unauthorized entities impersonate legitimate users or devices, often leveraging stolen credentials;
* Tampering: Malicious modification of data or components, undermining system integrity;
* Repudiation: Actions are performed without traceability, preventing accountability;
* Information Disclosure: Sensitive data is exposed to unauthorized parties, compromising confidentiality;
* Denial of Service (DoS): Attacks that disrupt service availability by overwhelming system resources;
* Elevation of Privilege: Gaining unauthorized access to higher system permissions, enabling broader control or damage.  
By utilizing STRIDE within TARA, we can effectively assess the security risks associated with a DMS and take the necessary steps to mitigate them. To ensure a structured and reliable threat analysis, we employed a combination of software tools tailored to different phases of the assessment.  
The primary tool used was Ansys Medini Analysis, which was chosen for its robust capabilities in conducting risk and hazard analysis within the automotive context. This tool facilitated the structured identification of system assets, allowing us to map critical components such as sensors, actuators, ports, and dataflows. Ansys Medini enabled the automatic generation of threat lists based on the defined system architecture and allowed us to apply the STRIDE framework systematically to each identified asset.  
Furthermore, the Microsoft Threat Modeling Tool was used to represent the system architecture visually, identifying dataflows and potential entry points for malicious actors. This tool was particularly valuable for creating threat diagrams that depicted the relationships between system components and potential vulnerabilities.
## 4 Results and Discussion  
### Steady-State Performance  
The simplified double-integral sliding-mode-controlled PWM dc-ac buck converter circuit was built and simulated in MATLAB using Simscape Electrical. Figure 3 shows the Simscape Electrical model of the closed-loop dynamic buck converter using MATLAB, in which all the electrical elements are defined. As shown in Figure 3, the simplified control circuit is made up of three op-amps with seven resistors and a capacitor. The sinusoidal reference signal is generated using the MATLAB function that varies the reference waveform between 3 V and 23 V at 100 Hz. The pulse-width modulator is constructed using the MATLAB function that compares the control signal with a ramp waveform.  
MATLAB simulation result in Figure 4 shows the steady-state waveforms of the input voltage, \(v_{U}\), output voltage, \(v_{O}\), reference voltage, \(v_{R}\), and inductor current, \(i_{L}\). The ramp voltage, \(V_{T}\), switching frequency,\(f_{s}\), input voltage, \(V_{L}\), and load resistance, \(R\), of the electrical model of the closed-loop power converter are set to 4 V, 1 MHz, 28 V, and 75 \(\Omega\), respectively. It can be noticed that the closed-loop power converter converts 28 VDC input voltage to (3-23) VAC sinusoidal output voltage at 100 Hz. In addition, the operation of the power converter is maintained in continuous conduction mode, as shown in the inductor current waveform. The simulation result also shows the accurate tracking performance of the proposed control circuit.
# A Bimodal EMG/FMG System Using Machine Learning Techniques for Gesture Recognition Optimization
###### Abstract  
This study is part of a broader project, the Open Source Bionic Hand, which aims to develop and control, in real time, a low-cost 3D-printed bionic hand prototype using signals from the muscles of the forearm. This work is intended to implement a bimodal signal acquisition system, which uses EMG signals and Force Myography (FMG) in order to optimize the recognition of gesture intention and, consequently, the control of the bionic hand. The implementation of this bimodal EMG-FMG system will be described. It uses two different signals from BITlaino EMG modules and Flexiforce(tm) sensors from Tekscan(tm). The dataset was built from thirty-six features extracted from each acquisition using two of each EMG and FMG sensors in extensor and flexor muscle groups simultaneously. The extraction of features is also depicted, as well as the subsequent use of these features to train and compare Machine Learning models in gesture recognition through MATLAB's Classification Learner tool (v2.2.5 software). Preliminary results obtained from a dataset of three healthy volunteers show the effectiveness of this bimodal EMG/FMG system in the improvement of the efficacy on gesture recognition as it is shown, for example, for the Quadratic SVM classifier that raises from 75.00% with EMG signals to 87.96% using both signals.  
bionic hand; electromyography; force myography; feature extraction; gesture recognition; classification models +
Footnote †: journal:  
0000-000-0001-5885-2233][PERSON]
###### Abstract
(2004) [25] used numerical methods to simulate and calculate the constitutive behavior of specimens under different end conditions and compared the differences in detail to obtain the true elastoplastic constitutive relationship of the specimens. [PERSON] et al. (2005) [26] studied the effect of the end restraint on the nonhomogeneous behavior of structured soils in triaxial tests using a finite element model based on the Structured Cam Clay model with Biot consolidation. The destruction phase of the structured soil was not significantly influenced by the end restraint, but after destruction, during hardening, the stress-strain behavior of the soil was significantly influenced by the end restraint. [PERSON] et al. (2009) [27] proposed a self-learning simulation analysis framework to discuss sand drainage behavior in a triaxial compression test under a fully frictional platen, and it was found that the entire specimen exhibited rotation of the principal stress, variation in the intermediate principal stress, and uneven volume changes. Many experimental and numerical studies on the end-restraint effect in triaxial tests have been performed in the literature. Neither the lubricated sample cap method nor the digital image measurement method can effectively eliminate the end-restraint effect. Previous investigations of the end-restraint effect in triaxial tests have mainly focused on small-scale triaxial tests of sand and clay, but few have focused on large-scale triaxial tests of coarse-grained soil. In this study, large-scale triaxial shear tests of coarse-grained soil with end-restraint and end-free (microfriction) conditions were carried out using a large-scale high-pressure triaxial shear apparatus and a self-developed microfriction load-transfer plate for large triaxial tests ([PERSON] et al., 2014) [28]. In conclusion, using data from traditional triaxial tests to estimate the stress and deformation of high earth-rockfill dams may cause significant mistakes. In order to successfully address the end restraint problem of triaxial tests and precisely determine soil mechanical characteristics, geotechnical microfriction triaxial test technology must be adopted. In this study, the strength and deformation characteristics of coarse-grained soil under normal end-restraint and end-free (microfriction) conditions were studied. The influence of the end restraint on the stress-strain characteristics of the coarse-grained soil was analyzed. The results provide a theoretical foundation and scientific basis for the safety and stability analysis of high earth-rockfill dams.
## 3 Results  
### Characteristics of Maturation of Rubber Compound  
The results of the delta torque (\(\Delta_{\text{S}}\)), curing time (t\({}_{90}\)), and scorching time (ts\({}_{2}\)) tests of the VBP-01 rubber compound using ZnO and SA as control formulas were higher than VBP-02 to VBP-05. The difference in the results of the delta torsion test was caused by the difference in the activity of ZnO and stearic acid (VBP-01) with ZS (VBP-02 to VBP-05) in the vulcanization process to accelerate the formation of cross-linking reactions. Increasing the ZS ratio sped up the reaction time of forming cross-links with polysulfide accelerators. ZnS, sulfur and a vulcanization accelerator accelerated the rubber molecule's cross-linking reaction. ZS in the vulcanization process on the rubber matrix was the first element directly involved in increasing the accelerator's performance, which formed cross-link reactions. According to [PERSON] et al. [22], the reaction between ZnO and stearic acid speeds up the cross-linking reaction, which contains elemental sulfur in the rubber matrix. ZnO in the vulcanization process [23] stabilizes curing by activating sulfur vulcanization, increasing torque, and reducing drying time and scorching time [15].  
Torsion delta describes the formation of cross-links during vulcanization. If the torque delta increases, the cross-links formed also increase. According to [PERSON] et al. [24], the greater the torque increase, the more the cross-links density increases. Torsion delta is often used as a reference to determine crosslink density [25]. The density of the formed cross-links affected the molecular structure and mechanical properties of vulcanized rubber. Cross-link density affected hardness, abrasion resistance, permanent compression, and other mechanical properties. In addition, cross-link density affected torque delta, curing time, and scorching time. According to [PERSON] and [PERSON] [26], the density of cross-links affects the differences in the mechanical properties of vulcanized rubber.  
Curing time shows the maturation time of rubber compounds up to 90% [27]. Short curing time in the vulcanization process is an option to determine production time. Using ZS in the vulcanization process of rubber compound into rubber vulcanizate from the test data accelerated the drying time (t\({}_{90}\)) between 58.3% and 69%.  
The scorch time test (ts\({}_{2}\)) results for the VBP-01 formula were longer than the rubber compound using ZS. In addition, the use of ZS 7.00 phr (VBP-04) to 9.00 phr (VBP-05) in the vulcanization process accelerated the scorch time between 22.4% and 95.5% (Table 2). This happened because ZS can accelerate and complement each other during the vulcanization process [28; 29]. According to [PERSON] et al. [24], the vulcanization rate is affected by the ratio of ZnS added. The use of ZS from the results of this study increased the rate of vulcanization. This can be seen from the results of testing the characteristics of rubber compound ripening (Table 1).
## 3 Experiments and Results
### Experimental Results
#### 3.2.3 Spoofing Experiments Results  
The following shows the experimental results under the spoofing condition. The scene of the spoofing experiment is shown in Figure 8. Two spoofing experiments were conducted on 27 December 2022, and the experimental results are shown in Figures 17a and 17b, respectively.  
Figure 16: (**a**) represents the result of the first experiment without spoofing, and (**b**) represents the result of the second experiment.  
Figure 18 shows the details of G14 and G21 in the first spoofing experiment, and the part between the two red dotted lines indicates the time period when the spoofer device is turned on. Figure 19 shows the changes in the orientation of all satellites in the airspace for normal and spoofing situations.  
Figure 17: (**a**) represents the result of the first experiment with spoofing, and (**b**) represents the result of the second experiment.
## 3 Simulation Results and Discussions  
The simulation results of the RTN amplitude dependence on gate voltage at single charge trapping in the oxide layer and at the interface are shown in Figure 6. It is seen that for both single oxide and interface defects, the RTN amplitude dependence on the gate voltage overload is not influenced by the considered form of the single defect. Figure 6 also indicates that a single defect trap in the oxide layer can be modeled by a homogeneously charged sphere (or circle at the interface) or by a simple cell.  
Figure 6: Random telegraph noise (RTN) amplitude dependence on the gate voltage overload generated by single charged defect with different forms: in the oxide layer (**a**) and at the interface (**b**).  
The RTN amplitude dependence on the radius of spherical or circular area of defects in the oxide or at the interface, correspondingly, was simulated to study the influence of the defect size.  
One has considered the radius range between 0.1 and 0.5 nm, which covers practically all featured linear sizes of atomic radius and interatomic distances in Si, SiO\({}_{2}\), and HfO\({}_{2}\). When changing the radius only, the charge density is modified because the trapped charge remains single and elementary. The results show that the RTN amplitude depends linearly, but very weakly, on the radius of the defect and practically does not change in the radius range under consideration (see Figure 7). When increasing the radius of the defect area, at a constant trapped charge, the RTN amplitude is practically not changed in all considered ranges of the gate voltage overload. The RTN amplitude in the subthreshold region is not as high as it is reported in some experimental works [14; 25]. To find out a possible reason for creating the large RTN amplitudes in the subthreshold region, as observed in our experiments, we have studied the RTN amplitude dependence on single charge quantity in the spherical defect area at the constant radius 0.15 nm. We considered the repeatedly charged defect with constant size. In this case, the RTN is appreciably changed in the subthreshold as well as in the near-threshold region (see Figures 8 and 9a). The RTN amplitude in the subthreshold region is considerably increased when increasing the number of single charges N in the defect (see Figure 9b). Obviously, the main reason for creating the high RTN amplitude is not the size as well as the shape of the defects but the density or the number of trapped charges in the defect.  
Figure 8: RTN amplitude dependence on the gate overload for different numbers of the single charges trapped N in the defect area.  
Figure 7: RTN amplitude dependence on the radius of (**a**) the oxide, and (**b**) the interface single charged defects at a constant trapped charge.
## 3 Parameter Estimation Method for the Weak PRBC-PAM Signal
### Parameter Estimation
At this time, the signal \(p(t)\) obtained by adjusting the value of 0 of \(Sdf(t)\) is the pseudorandom code, and the ratio of the time taken by a value of 1 in \(Sdf(t)\) to that in \(p(t)\) is the duty cycle of the pulse signal, which is called \(z_{1}\). By averaging the smallest numbers in the duration of 1 or \(-1\) in \(p(t)\), the code width \(T_{\rm c}\) of the pseudorandom code can be obtained, which is also equal to the repetition period \(T_{\rm m}\) of the pulse signal. Finally, the PAM pseudorandom sequence can be obtained by multiplying the estimated pseudorandom sequence by the pulse signal with a known duty cycle and deburring. Roughly speaking, if the frequency to be estimated is known, the underestimated pseudorandom code of the PRBC-PAM signal can be directly obtained from the ternary result \(Sdf(t)\); if the frequency to be estimated is unknown, we should first estimate the frequency to be tested through \(z_{\rm m}\), and then obtain the pseudorandom code of the PRBC-PAM signal as we know the frequency.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 3 Adaptive Fault Location Algorithm for Active Distribution Systems
### Step 5: Iterative Process to Determine the Fault Current
#### 3.5.1 Estimation of the Fault Current by Using the Information Provided by IEDs  
When the information provided by IEDs is available, the estimation of the fault current is straightforward. For this case, the current feeding the fault from the downstream circuit \(\mathbf{I}_{F,k+1}^{f}\) is assumed to be \(\mathbf{I}_{F,k+1}^{f}=\mathbf{I}_{DER_{k-1,k}}^{f}\), where \(\mathbf{I}_{DER_{k-1,k}}^{f}\) is the current estimated in step 2.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 4 Negotiation-Free Encryption Method
### Algorithm Implementation
#### 4.2.2 Data Encryption Transmission  
After getting the key, the system uses the key and timestamp to encrypt the data to ensure the transmission security. The format of the message is shown in Figure 3.  
\[Message=ID_{r}+Enc_{key}(ID_{s}+data)+TS \tag{6}\]  
\(V_{s}\) uses a key to encrypt the \(Data\) and gets the ciphertext. Then \(V_{s}\) transmits the ciphertext and the time-stamp to \(V_{r}\).
# The Impact of Spatial Resolutions on Nature-Based Solution Suitability Mapping for Europe
###### Abstract  
Flooding events, like in Germany in 2021, highlight the need for re-naturalising banks of rivers and streams to naturally mitigate future flooding. To identify potential areas for Nature-Based Solutions (NBS), the NBS Toolkit--a decision-support tool for Europe--was developed within the H2020 OPERANDUM project. The tool builds on suitability mapping, which is progressively adopted for pre-assessing areas for Nature-Based Solutions. The NBS Toolkit operates with European open-source data, which is available at different spatial resolutions. In this study, we performed a GIS-based analysis to examine the impact of different resolution data on the resulting suitability maps. The results suggest that for large-scale measures such as riparian forest buffers, coarser resolutions are sufficient and may save processing time and capacities. However, fine resolution datasets can bring added value to urban suitability mapping and are of greater importance for small-scale, local Nature-Based Solutions.  
natural flood management; land use and land cover; GIS; flood risk reduction
# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 4 Architecture Overview
### Detailed Design
* 5 Decision-making: The forwarding process verifies whether the node on the receiving side is in the cluster with the highest energy level and the topic of interest. This work extends GPDMI by adding steps 3, 4, and 5. We used training data to define low energy or high energy to train the clusters by energy level, defined by \(E=\) [Energy_lower_, Energy_upper_]. The interests are globally represented, and every node knows the interests of all the other nodes. The node interest did not change during the simulation.