esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine
## 2 Experimental Method
### Image Processing  
The image processing was accomplished by using a Matlab platform. The schematic diagrams of color image processing are shown in Figure 2a-e. An original metal vapor image is shown in Figure 2a. In order to reduce the amount of calculations and facilitate subsequent analyses, the original image was cut into 320 \(\times\) 320, and the key region was extracted, as shown in Figure 2b. Compared to RGB (red, green, blue) color space, the HSI (hue, saturation, intensity) color space was better due to two reasons. First, it uses three components of color image: hue (\(H\)), saturation (\(S\)), intensity (\(I\)), which are intimately related to the way in which human beings perceive color. Second, the geometric distance between two colors does not mean the visual differences between the two colors in the RGB color space, which means using perceptual color space is more suitable for human intuition than using the primary colors (RGB). The cut image was mapped into the HSI color space, as shown in Figure 2c.  
The most important step of image processing is image segmentation. It is the process of assigning a label to each pixel in an image so that pixels with the same label share certain features, such as color,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Beam Quality** & **Focal Length** & **Shutter Speed** & **Pixel Equivalent** & **Gas Flowing Rate** \\ \hline
12 mm-mrad & 300 mm & 0.5 ms & 11.4 pixel/0.1 mm & 40 L/min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Other laser welding conditions.  
Figure 1: Schematic diagram of the experimental system.  
intensity, or texture. There are various methods to implement segmentation, for example, thresholding methods, clustering methods, texture methods, transform methods, and so on. The image based on HSI color space was segmented with a thresholding technique, which replaced pixels in the image with a white pixel if the image intensity was greater than the threshold, or a black pixel if the image intensity was less than the threshold. The results of the repeated experiments showed that it was possible to segment accurately when choosing \(H=200\), \(S=50\), \(I=200\) as the threshold values. The result is shown in Figure 2d. In order to eliminate the spatters, we first began with the open operation, then used the close operation for further processing, and the final result was shown in Figure 2e. As can be seen, the morphologic features of the metal vapor plume were well preserved.
## 4 Security Analysis
### Scenario-Based Analysis  
In addition to the formal security analysis presented above, we informally analyze the security of our protocol for various attack scenarios.  
**Man-in-The-Middle Attack.** In this attack scenario, the adversary intercepts messages and performs data tampering in the communication between a vehicle and an RSU or the SA. However a MiTM attack will not succeed, since it requires from the adversary to forge the actual data sent be the vehicle, which are bind to the certificate of the vehicle via the use of a hash function.  
**Replay Attack.** In this attack scenario, the adversary replays the previously obtained legitimate signature to the receiver. Such attacks will not succeed, since the use of time stamps ensures message freshness.  
**Identity Revealing Attack.** The adversary attempts to reveal the real identity of a target vehicle. Then the adversary can illegally gather the personal data about the vehicle, which will threaten the privacy of the driver. That requires to win the IND-CPA property of the underlying cryptosystem.  
**Authority Abuse Attack.** In this scenario the CA attempts to arbitrarily issue certificates to illegal vehicles or revoke certificates of legal vehicles. Such attacks can be thwarted by employing a threshold CA scenario. In addition, revoking a legal vehicle must be accompanied by a transaction proving misbehaviour. That is equivalent to framing a vehicle which was proven impossible.  
In Table 2 we compare our scheme with the related work, in terms of their security and privacy characteristics. Our scheme is one of the few in the literature that provides unframeability and impersonation protection against corrupted authorities. At the same time it does not require maintaining revocation lists or expensive key-re-issuing after each revocation, while it maintains location privacy from honest but curious authorities.
## 5 Discussion
### Key Findings and Some Unsupported Hypotheses
For example, some users may still prefer the immediate feedback and tangible interface of physical devices to digital alternatives. In addition, different interaction expectations between physical and virtual SSTs are likely to influence this relationship. While location convenience drives the use of physical SSTs, mobile applications are evaluated based on interface design and functionality rather than geographic proximity. This may reflect that consumers' evaluation criteria for physical and mobile SSTs are in separate cognitive frameworks. This also explains why convenience in one domain does not necessarily translate into positive attitudes in the other. Consumers' satisfaction with physical SST (H2b) and continued use of physical SST (H1) had no significant effect on consumers' willingness to download and use virtual SST. Based on the argument from the habit theory in the technology adoption literature [25; 35], we inferred that one possible reason is the usage habits of consumers. This implies that offline habits may negatively affect online transfer intentions. In this sense, lack of habit formation may be the primary reason for this disconnect. Despite satisfaction with physical kiosks, users may not have developed sufficient habitual engagement to motivate the adoption of complementary digital channels. For example, occasional SST users may be satisfied with their infrequent interactions but lack the behavioral motivation to extend their usage to a mobile app. Next, a perceived lack of need could explain this result; consumers may view the mobile app as redundant if their service needs are already adequately met by physical SSTs. Finally, resistance to channel switching could also be a reason. Even satisfied physical SST users may be reluctant to switch to virtual alternatives due to switching costs, learning curves, or a general aversion to technological change. This resistance manifests itself as inertia. Despite being satisfied, consumers continue to use familiar physical interfaces because the perceived effort of adopting a new technology outweighs the potential benefits of the mobile app. Perspective from consumer efficiency theory [53] provides similar explanations as consumers favor channels that can reduce their costs, such as Internet search costs. Taiwan has a high density of 24 h convenience stores. Consumers are already accustomed to using convenience stores for various services and purchases, including physical SST. When consumers are unsure about how to use SST, some may prefer to seek assistance from staff immediately. In such cases, the convenience and satisfaction derived from using offline SST may hinder them from adopting online SST.
###### Abstract  
To investigate the effects of the strain rate and confinement on characteristic stresses and strength criterion in granite under static to quasi-static loading, triaxial compression tests were systematically conducted across strain rates of \(10^{-6}\) to \(10^{-2}\) s\({}^{-1}\) and confining pressures of 0-40 MPa. Stress-strain curves, characteristic stresses, macro-fracture patterns, and dynamic strength criterion were analyzed. The experimental results indicate the following: (1) crack damage stress (\(\sigma_{\rm cd}\)) and peak stress (\(\sigma_{\rm p}\)) show strong linear correlations with logarithmic strain rate, while crack initiation stress (\(\sigma_{\rm ci}\)) exhibits weaker rate dependence; (2) linear regression establishes characteristic stress ratios \(\sigma_{\rm ci}=0.58\sigma_{\rm p}\) and \(\sigma_{\rm cd}=0.85\sigma_{\rm p}\); (3) macroscopic fractures transition from Y-shaped shear patterns under low confinement and strain rate conditions to X-shaped shear failures at higher confinement and strain rate; (4) the Mohr-Coulomb criterion effectively characterizes dynamic strength evolution in granite, with cohesion increasing 22% across tested strain rates while internal friction angle remains stable at around 50\({}^{\circ}\); (5) variations in microcrack activity intensity during rock deformation stages result in the dynamic increase factor for characteristic stresses (CSDIF) of \(\sigma_{\rm ci}\) being lower than \(\sigma_{\rm cd}\) and \(\sigma_{\rm p}\). More importantly, \(\sigma_{\rm cd}\) and \(\sigma_{\rm p}\) exhibit CSDIF reductions as confining pressure increases. This differential behavior is explained by confinement-enhanced shear fracturing dominance during crack propagation stages, combined with the lower strain rate sensitivity of shear versus tensile fracture toughness.  
Keywords:quasi-static; strain rate effects; characteristic stresses; strength criterion; macro-fracture patterns; fracture toughness +
Footnote †: journal: Applied Sciences
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 4 Data Post-Processing
### Primary Detection of Defects
#### 4.3.1 Improvement of Detection Results  
The sensitive region for further detection can be selected from the edge of the detected defect base on the primary detection of defect edge as seen in Figure 16e. To alleviate the effect of heating inhomogeneity of the specimen, the average value of standard deviation of multiple sensitive areas is calculated instead of the single one. Two sensitive region selection methods are discussed:  
1. Uniform selection (Figure 18a): sensitive regions are followed one by one along the edge of the defect. The total number of sensitive regions is determined by the perimeter of the defect extracted at the primary detection. The size of the sensitive region is set to \(10\times 15\) pixels.
2. Random selection (Figure 18b): thirty sensitive areas are randomly selected from the edge of the defect. The size of the sensitive region is randomly decided within the range of \((10,15)\times(10,15)\) pixels.  
Figure 17: Discrete distribution of relative deviation of defect area for 100 times primary detection.  
Figure 19 shows the standard deviations for the two defect regions for the whole set of the infrared image sequence (1000 frames). The results yield smoother curves than the traditional measurement (see Figure 14), and the peak value of a given curve becomes easier to identify. For example, for uniform selection (Figure 19a), defect #1 reaches its peak standard deviation 45.37 at Frame 164, while defect #2 reaches its peak standard deviation 26.22 at Frame 183; for random selection (Figure 19b), defect #1 reaches its peak standard deviation 45.99 at Frame 166, while defect #2 reaches its peak standard deviation 26.08 at Frame 186.  
It can be seen that for each defect, two methods produce similar results, which suggests both methods work well. However, such results differ a lot with the two defects because the two defects have different depths. The peak standard deviation indicates that the defect and non-defect areas have reached the maximum degree of differentiation in that frame. As a result, that frame should be selected to extract the defect boundary. Therefore, we consider that Frame 164 is reasonable for calculating defect #1 and Frame 183 is reasonable for calculating #2, by uniform selection of sensitive regions, while Frame 166 is suitable for defect #1 and Frame 186 is suitable for defect #2 by random selection. The secondary detection results are shown as statistics in Table 3, which improve the result of primary detection (Table 2).  
Figure 19: The MSDSRM to select a suitable frame for each defect: (**a**) Curves of standard deviation vs. time based on uniform selection of sensitive regions; (**b**) Curves of standard deviation vs. time based on random selection of sensitive regions.
# V2X-Based Highly Reliable Warning System for Emergency Vehicles
###### Abstract
In order to overcome the life loss within and outside of EVs during their movement, the movements have to be monitored and to control their traversing routes. By smarting traffic signals to monitor and control the flow of traffic and to change the sequence of signals, we can enhance EV movement. We propose a jitter-to-highly reliable (J2H) approach of customizing the traffic signals and an alert passer mechanism to alert other vehicles. Once an EV is started, its source, destination and level of emergency will be updated on the network, and based on the traffic density, the fastest route to reach the destination is determined. The V2X system in the J2H approach passes an alert to all the traffic signals on that route. The traffic signals will continuously monitor the position of the vehicle by using the Global Positioning System (GPS). Based on the position of the vehicle, the distance between the vehicle and the traffic signal on that route is periodically updated. Once the vehicle comes within the range of the closest traffic signal, based on constraints such as number of lanes, emergency level, types of roads, traffic density, number of EVs approaching, and time of arrival of the vehicles, the traffic signal will be customized. The V2X then passes the information to all the traffic signals that are available along the route of the EV. The alert passer mechanism warns about the approaching EV to other vehicles on that route. The remainder of this paper is organized as follows. In Section 2, we discuss some existing related works. The system model is introduced in Section 3, and then, it follows with our proposed solution in Section 4. Simulation results are presented to validate the performance of our proposed method in Section 5. Finally, this paper is concluded in Section 6.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 3 Stratifying Privacy-Preserving Solutions
### Data Protection Mechanisms  
The basic mechanisms to preserve privacy in IoT-based healthcare systems are Data Protection Mechanisms. These techniques, including encryption and anonymization, are  
Figure 3: Classification framework for privacy preservation solutions.  
Figure 2: Five elements of a robust privacy-preserving solution.  
applied to patients' data during several stages: data in transit, data at rest, and data in processing.
* Data in Transit: Refers to data actively moving between systems, devices, or networks (e.g., during communication between IoT devices and cloud servers).
* Data at Rest: Refers to data stored in a static state, such as on databases, servers, or local devices.
* Data in Processing: Refers to data actively being used or manipulated by applications or systems, such as during computations or analytics.
1. Encryption Techniques: The process of using encryption techniques involves preserving the privacy of patient data through data encryption, such as symmetric, asymmetric, and homomorphic encryption, to convert the patient's Personally Identifiable Information (PII) and PHI to a format that cannot be read or understood by unauthorized parties [14]. The encrypted PII/PHI, including texts or images, can then be securely sent, stored, and accessed by individuals with access privileges.
2. Anonymization Techniques: Anonymization techniques are used for modifying or concealing healthcare data attributes to safeguud patient privacy while preserving the feasibility of data analysis. These techniques, such as k-anonymity, l-diversity, and differential privacy, are used to ensure that sensitive data remain protected while still being useful for research and analysis [13].
## 5 Results
### Movement and Navigation Tests
#### 5.2.2 Traction Test  
The torque exerted by each wheel was not provided by the manufacturer, so a traction test was carried out, as shown in Figure 19. In this test, a 0.4 m long bar was attached to the axle of the wheel, and a mass of 586.13 g was placed on the bar. The mass was then moved along the bar (from the furthest separation to the closest distance) until the wheel was able to lift the bar to the horizontal position.  
The applied lever arm was 0.4 m, while the weight lifted at its end was 0.58 kg. Additionally, the bar had a mass of 0.08 kg, which was distributed almost uniformly along its length. The maximum torque, \(T_{wheel}\), developed by each wheel is calculated with Equation (14):  
\[T_{wheel}\ =\ F_{weight}\cdot d_{mass}+F_{bar}.\frac{d_{bar}}{2} \tag{14}\]  
where:  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Surface**} & \multirow{2}{*}{**Payload**} & \multirow{2}{*}{**Slope**} & \multicolumn{2}{c}{**Max. Lineal**} & **Distance to** & **Braking** \\  & & & **Speed** & **Top Speed** & **Distance** \\ \hline \multirow{2}{*}{Bituminous} & 0 kg & 0\({}^{\circ}\) & 17.64 km/h & 2.37 m & 1.1 m \\  & 15 kg & 0\({}^{\circ}\) & 15.97 km/h & 2.51 m & 1.2 m \\ \hline \multirow{2}{*}{Grass} & 0 kg & 0\({}^{\circ}\) & 12.68 km/h & 2.40 m & 0.7 m \\  & 15 kg & 0\({}^{\circ}\) & 9.78 km/h & 2.37 m & 0.7 m \\ \hline \multirow{2}{*}{Gravel} & 0 kg & 0\({}^{\circ}\) & 11.76 km/h & 2.32 m & 0.8 m \\  & 15 kg & 0\({}^{\circ}\) & 9.47 km/h & 2.41 m & 0.85 m \\ \hline \multirow{2}{*}{Mud} & 0 kg & 0\({}^{\circ}\) & 11.84 km/h & 2.55 m & 0.5 m \\  & 15 kg & 0\({}^{\circ}\) & 0 km/h & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum ground speed, acceleration, and braking tests, performed for different payloads and surfaces.  
Figure 18: (a) Hydrogen tank and battery subsystem SoC under Profile 3. (b) Hygrobot power profile.  
\(T_{\mathit{v}hel}\) is the maximum torque developed by each wheel (Nm).  
\(F_{\mathit{w}eight}\) is the weight force applied by mass, with \(\mathit{mass}\ =\ 0.58\) kg and \(g=\frac{9.81\ N}{1\ kg}\).  
\(d_{\mathit{mass}}\) is the applied lever arm (0.2 m).  
\(F_{\mathit{bar}}\) is the weight force applied by the bar (bar mass = 0.08 kg).  
\(d_{\mathit{bar}}\) is the bar length (0.4 m).  
This results in \(T_{\mathit{w}hel}\) = 2.35 Nm.  
Then, the total maximum torque that the Hygrobot platform can support is determined with Equation (15):  
\[T_{\mathit{Hygrobot}}\ =\ T_{\mathit{v}hel}\cdot n_{\mathit{w}hel} \tag{15}\]  
where:  
\(T_{\mathit{Hygrobot}}\) is the maximum torque developed by the Hygrobot platform (Nm).  
This results in \(T_{\mathit{Hygrobot}}\) = 14.80 Nm.  
In addition, outdoor traction tests were carried out, with the aim of evaluating the maximum slope and weight that the platform can overcome on different terrains, as well as the maximum braking gradient that Hygrobot achieves. The obtained results are shown in Table 7.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 5 Centralized Key Management Scheme for LoRaWAN
### Key Updating Scheme (KUS)
A priority list for full key updating methods is recommended as follows: CKMS manager triggers key update > Unusual Network Behavior > Aperiodic (Number-Pool System) > Counter Method > Fixed Periods Update. Hence, the CKMS should follow the priority list from top to bottom in implementing the key update scheme, where 'CKMS manager triggers key update' has the highest priority and 'Fixed Periods Update' has the least priority.
## 3 Results and Discussion
### In Silico Analysis of the Isolated Major Protein from 'Senorita' Banana Extracts
This trend in antioxidant activity, where there is an initial increase in activity followed by a gradual decline has also been observed to other studied [33; 34]  
Table 3 showed that the activity of the undigested crude sample after three hours and four hours of digestion increased and found to be not significantly different with the activity of ascorbic acid (positive control). This implies that the initially active proteins found in the undigested crude sample released peptides with high antioxidant activity. However, further hydrolysis of these peptides decreased the activity of the sample as observed in the activities of the 12 h and 24 h digests, showing that the peptides with high activity might have been degraded to smaller peptides with lower activity. Figure 5: Comparison of DPPH radical scavenging activity (RSA) of crude, partially purified and purified protein extracts at different digestion hours. (Note: DPPH RSA of crude at 24 h digestion time is not included due to highly negative value obtained, \(-\)107.355%). On the other hand, the observed general decrease in activity of the partially purified and purified protein samples as the digestion period was increased, could mean that during hydrolysis, the initially active proteins present in the undigested samples did not release any peptides with high antioxidant activity, rather they were continuously degraded to peptides with lower activity. This observation is contrary to the result of in silico analysis. Statistical analysis of the crude, partially purified, and purified sample activities at a specific digestion period showed that the activities of undigested samples where not significantly different from each other, whereas, for most digested samples, only the activity from the digested crude samples were found to be significantly different. For the undigested samples, it could be that the activities observed were solely from the extracted and purified major protein, hence, the activities were not significantly different from each other. Meanwhile, the observed significantly different activity of the crude digested samples compared to other digested samples could be the result of the activity of peptides released from the non-major proteins removed during purification. In silico analysis of the putative major protein (Figure 3) showed that only few antioxidant peptides will be released upon hydrolysis justifying the low activities of the digested partially purified and purified samples
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 5 Results and Evaluation  
### Model Training Results  
The loss curves after training are presented in Figure 5. Despite slight fluctuations in the early epochs, all three models demonstrated effective convergence following the learning rate adjustment. TSNetIQ exhibits the fastest and most stable convergence behavior. A sharp decline in loss is observed during the initial training stages, and the loss stabilizes around epoch 50, fluctuating within the range of 1.68 to 1.83. This indicates that the model is capable of rapidly capturing essential features while avoiding overfitting,reaching a minimal loss in fewer epochs. In contrast, ResNetIQ shows slower convergence, with a stable loss plateau emerging after approximately 100 epochs, fluctuating between 2.39 and 2.75. This suggests that the model requires more time for parameter adaptation and converges less efficiently compared to TSNetIQ. Although the CNN model demonstrates relatively fast initial convergence with moderate fluctuation, its final loss remains high, stabilizing between 4.15 and 4.49, indicating lower accuracy and larger variability. Overall, TSNetIQ outperforms the other two models by achieving faster and more stable convergence, with the loss reaching a plateau earlier and with fewer epochs. The trained models were evaluated through the following tests.