esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Conversion Strategies for Effective Aviation Technical Support as a Service
## 4 Discussion
### Cybersecurity Risks and Mitigation Strategies for ATSaaS Architectures
Additionally, decentralizedthreat intelligence sharing among ATSaaS participants can facilitate early detection and response to emerging cyber threats, while secure gateways and endpoint protection solutions at each decentralized node can prevent unauthorized access and mitigate the risk of cyberattacks. A comparison of cybersecurity risks highlights the fundamental differences between centralized and decentralized architectures (Table 5). Centralized systems face a higher risk of a single catastrophic failure and large-scale data breaches but benefit from easier access control management and regulatory compliance. Decentralized systems, on the other hand, distribute risk across multiple nodes, making them less vulnerable to a single attack, but introduce greater complexity in managing security policies and ensuring consistent data integrity across stakeholders. Given the distinct security risks in both architectures, a hybrid cybersecurity approach may be the most effective solution for ATSaaS implementation. This approach could combine centralized security oversight with decentralized resilience, ensuring robust security governance while allowing flexibility for individual stakeholders. A federated cybersecurity governance model would enable centralized enforcement of security policies while granting localized security autonomy to different ATSaaS participants. Collaboration with aviation cybersecurity organizations, such as EASA's Cybersecurity Strategy and FAA Cybersecurity Programs, can further align ATSaaS security measures with industry best practices. Additionally, continuous threat assessment and AI-driven adaptive security frameworks can dynamically respond to evolving threats, ensuring proactive risk mitigation and maintaining system resilience. By integrating these cybersecurity principles into the ATSaaS framework, both centralized and decentralized architectures can achieve a secure, reliable, and resilient technical support system for aviation stakeholders. Addressing cybersecurity risks from the early stages of ATSaaS implementation is essential for ensuring long-term sustainability and trust within the industry.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 4 Performance Analysis
### Simulation Results  
The simulations are carried out by varying source transmission rate, node density, and maximum node velocity. The simulation results for these three cases are discussed next.
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 4 Datasets  
The classification performance of the four mentioned thresholding methods namely the Probabilistic Thresholding Method, the Type-I and Type-II error method, the ROC method and the Box-Cox transformation method will be tested against 20 different datasets (refer to Table 5) of which 18 of them are obtained from standard benchmark datasets based on evolutionary learning (KEEL) repository [36]. The standard benchmark datasets are originally from the UCI machine learning repository which is utilized by many for the studies of binary or two classes of classification problems (normal vs. abnormal in this case). Each sample in the datasets is randomly selected (based on their class attributes of normal or abnormal), and assigned to a Training set or a Testing set accordingly. The quantity of samples in the training and the testing set that corresponds to their class attributes is roughly divided by a 50-50 percent basis [37]. The training sets are the datasets of which the optimized number of variables (reduced variables) as well as the optimum thresholding value (MD\({}_{\text{T}}\)) are sought using the MTS procedures and the four thresholding methods respectively.  
The additional two datasets namely the Medical diagnosis of liver disease [38] and the Taguchi's charactear recognition [23] datasets are also included. The following section will briefly describe these two additional datasets.
# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
## 3 TPC-CQF Improved from CQF
### TPC-CQF Mechanism
For onboard queue buffer constraints in LEO satellite networks (\(K_{Q}\leq K_{\mathrm{max},Q}\)), the fragmentation effect of satellite-borne buffers intensifies with increasing queue count \(N\). Assuming TSF packet sizes follow a bimodal distribution, the overflow probability for bursty packets is:  
\[P_{overflow}=\exp\left[-\frac{\left(K_{\mathrm{max},Q}-\mu\right)^{2}}{2\sigma^ {2}}\right] \tag{9}\]  
Figure 3: TPC-CQF scheduling mechanism. When \(N>3\), \(P_{overflow}\) exhibits exponential growth, violating the low packet loss rate design objective. Therefore, the three-queue architecture constitutes the optimal solution for balancing packet loss rate, latency, and complexity, a conclusion that will be validated in subsequent simulations. Figure 4 illustrates that in the presence of packet collisions, TPC-CQF may slightly increase end-to-end delay but significantly improves resource utilization, enabling the transmission of more TSFs. For instance, in this scenario, two packets of TSF1 <1,2>, two packets of TSF2 <1,2>, and an additional packet <2,1> are all scheduled in Q8 for centralized forwarding in the subsequent time slot.
# Free Vibration of a Taut Cable with Two Discrete Inertial Mass Dampers
###### Abstract  
Recently, inertial mass dampers (IMDs) have shown superior control performance over traditional viscous dampers (VDs) in vibration control of stay cables. However, a single IMD may be incapable of providing sufficient supplemental modal damping to a super-long cable, especially for the multimode cable vibration mitigation. Inspired by the potential advantages of attaching two discrete VDs at different locations of the cable, arranging two external discrete IMDs, either at the opposite ends or the same end of the cable is proposed to further improve vibration mitigation performance of the cable in this study. Complex modal analysis based on the taut-string model was employed and extended to allow for the existence of two external discrete IMDs, resulting in a transcendental equation for complex wavenumbers. Both asymptotic and numerical solutions for the case of two opposite IMDs or the case of two IMDs at the same end of the cable were obtained. Subsequently, the applicability of asymptotic solutions was then evaluated. Finally, parametric studies were performed to investigate the effects of damper positions and damper properties on the control performance of a cable with two discrete IMDs. Results showed that two opposite IMDs can generally provide superior control performance to the cable over a single IMD or two IMDs at the same end. It was also observed that attaching two IMDs at the same end of the cable had the potential to achieve significant damping improvement when the inertial mass of the IMDs is appropriate, which seems to be more promising than two opposite IMDs for practical application.  
Keywords:stay cable; vibration control; hybrid control; inertial mass damper; viscous damper +
Footnote †: journal: Applied Soft. 2019, 9, 3919; doi:10.3390/app9183919
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 3 Erosion Mechanism in Cavitating Jets
### Effect of Exit Velocity  
It was shown [53] that as the cavitation number decreases or as the exit-jet velocity increases, the mass loss, the erosion rate, and the eroded area increase. The strong relationship between the jet exit velocity and the erosion rates was also stated in [39]. The distribution of cavitation bubbles along the jet path and along its radial cross-section is also a function of many parameters, such as the upstream and downstream pressures, the nozzle diameter, the standoff distance, the nozzle geometry, and the flow pattern.  
Figure 10: Erosion rate vs. angle of attack [52].
## 2 Literature Review
### Digital Twin Application  
#### 2.2.1 Data  
Various types of data must be acquired in the process of initiating a digital twin. Data can be largely divided into three types: sociodemographic information, facility or geographic information, and sensor information. Sociodemographic information consists of population, demographic, and economic data as well as information on the floating population and management of facilities subject to the use of a digital twin [21]. Facility and geographic information, which is used in the modeling of a digital twin, is an essential element in the process whereby actual facilities are converged to virtual facilities [24]. Lastly, information on actual facilities and related data are collected through diverse sensors such as LiDAR [10].
## 3 Mission Analysis  
To conduct some tasks for IoST applications (AIS and fire detection) in certain regions of developing and emerging countries--South Asia, the Middle East, and North Africa--waiting times must be reduced to obtain the required temporal coverage. For the AIS mission, the straits between these countries have the densest traffic in the entire world (Malacca, Hurmuz, Bab Mandab, Suez Canal, Bosphorus, and Gibraltar). Then, for fire detection, some important forests may be targeted in Southeast Asia (in China's mainland and Borneo rainforest), the Middle East (in Turkey, and Lebanon), and North Africa (in Algeria and Tunisia). The processes of the two proposed applications and their targeted area are illustrated in Figures 4 and 5.  
Figure 3: Autonomous ground segment with sensor network for fire detection.  
In these studied applications, as shown in Figures 4 and 5, the nanosatellite missions are operated by the primary ground station at Beihang University. The developing countries support ground stations and the radio amateur community can receive transmitted data from the space segment. Data from support ground stations and radio amateurs can be  
Figure 4: Ground/Space segment interactions for managing maritime transportation systems with the potential inclusion of other developing and emerging countries.  
Figure 5: Ground/Space segment interactions for IoST application based on fire detection.  
supplied to the Beihang ground station data center via the internet. Payload users will supply payload operation requests to developing and emerging countries and will have access to their payload data stored in the Beihang data center to manage or perform the required missions.  
According to [36], the revisit times for AIS decoding and fire detections are 2 h and 3 h, respectively. The revisit time for early forest fire detection should ideally be 60 min or less [37]. Table 3 presents the remaining detailed usage requirements for threshold performance of the proposed IoST missions, which will be utilized to set the operating orbit parameters.
###### Abstract  
In the present study, we aimed to determine whether the shelf life of effervescent tablets may be assessed during storage using total directional hemispherical reflectance (THR). We also analyzed selected pharmacokinetic parameters of the tablets and used X-ray microtomography to assess the internal structure of the tablets. Two types of effervescent tablets of one commercial product containing magnesium and vitamin B6 (expired and unexpired) were analyzed. In addition, randomly selected unexpired and expired tablets were dried in a vacuum oven for 24 h at 50 \({}^{\circ}\)C. The expired effervescent tablets disintegrated in a significantly shorter time than the unexpired tablets (68 s vs. 83 s, \(p\) = 0.016). The remaining pharmacopeial parameters did not differ between the two types of tablets. We observed that expired effervescent tablets showed lower mean values of THR in five spectral ranges, which indicates that a greater amount of radiation beam was transmitted into their inner structure than for unexpired tablets. The density of the inner structure assessed on the microtomographic scans differed significantly between all the analyzed tablets (\(p\) < 0.001). The highest mean density was observed in the case of the unexpired tablets dried in a vacuum oven (1.273 g/cm\({}^{3}\)), while the lowest density was observed in the case of the expired tablets (1.261 g/cm\({}^{3}\)). The expired tablets had a larger pore diameter compared to the unexpired tablets (0.095 mm vs. 0.074 mm, respectively; \(p\) = 0.007). Additionally, the percentage of porosity was higher in the expired tablets compared to the unexpired tablets (\(p\) < 0.001).  
effervescent tablets; solid dosage forms; directional hemispherical reflectance; reflectometer; X-ray microtomography; homogeneity; characteristics of tablets +
Footnote †: journal: Applied Sciences
## 3 Validation
### Case Study 2  
In order to further verify the rationality and applicability of the approach proposed in this work, a comparison between the predictions and the results generated by [PERSON] et al. [41] in perspective of the lateral response for pile shaft was also conducted; the relative parameters of the pile shaft for examining are listed as below, in Table 2:  
Specifically, in this case, the pile shaft is located in the rock slope, the corresponding rock layers for the passive and active segments are the strongly weathered rock and slightly weathered rock, with the thickness of 10 m and 20 m, respectively. Therefore, according to the national code [48], the horizontal resistance proportional coefficients for those two types of rock layers are determined as \(m_{p}=5\) MN/m\({}^{4}\) and \(m_{a}=50\) MN/m\({}^{4}\), respectively. In this case, the influence of the vertical friction in the passive segment of the pile shaft was taken into account. By assuming that the other parameters are constant and ignoring the slope effects on the vertical resistances in the passive segment of the pile shaft, the vertical subgrade coefficients of the foundation can be determined as \(k_{\text{pp}}=3\times 10^{5}\) kN/m\({}^{3}\)[48]. Hence, the comparison results are obtained as illustrated in Figure 9. In detail, in Figure 9, it can be observed that the predictions generated by the proposed method are extremely closed to that of [PERSON] et al.'s [41] work for both the lateral deformation (Figure 9a) and the bending moment (Figure 9b). The errors between predicted results and [PERSON] et al.'s [41] work of maximum lateral displacement and maximum ending moment of pile shaft is about 4.99% and 7.52%, respectively. It is also understood from Figure 9 that there are marked reduction effects on the lateral response of the pile shaft due to the vertical friction in the passive segment. It is illustrated that the horizontal deformation for the pile-top is reduced by 25.36% and the maximum bending moment for the pile shaft is reduced by 19.40% by dint of the effects caused by the vertical friction, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Type of soil stress distribution & Rectangle \\ Dameter of pile shaft (m) & 2.0 \\ Length of pile shaft (m) & 30.0 \\ Elastic modulus (Gpu) & 18.0 \\ Concrete density for the pile shaft (kN/m\({}^{3}\)) & 25.0 \\ Subjected axial force at top of pile shaft (kN) & 9100.0 \\ Subjected bending moment at top of pile shaft (kN \(\times\) m) & 1000.0 \\ Subjected horizontal force at top of pile shaft (kN) & 179.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed parameters in work of [PERSON] et al. [41].  
Figure 9: Comparison of lateral displacement and bending moment in Case Study 2 ZHAO et al. [41]. (**a**) Lateral displacement/mm; (**b**) bending Moment/kN \(\times\) m.
# The Multi-Branch Deep-Learning-Based Approach to Heart Dysfunction Classification
## Appendix C Detailed Classification Metrics  
Classification metrics were calculated on results received from running our algorithm with the LSTM as the core network. LSTM has two layers and seven hidden sizes. Training for LSTM employed a learning rate equal to 0.01, the Adam optimizer, and a batch sizeequal to 500. The training process runs for a maximum of 30 epoch iterations with early stopping after 5 epochs without improvement. We employed the BCEWithLogitsLoss criterion and positive weights. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Class** & **Branch \(\mathbf{\kappa}\)** & **Branch \(\mathbf{\beta}\)** & **Branch \(\mathbf{\gamma}\)** & **Branch \(\mathbf{\delta}\)** & **Branch \(\mathbf{\epsilon}\)** & **Branch \(\mathbf{\zeta}\)** \\ \hline BBB & **0.413470** & 0.321300 & 0.371671 & 0.409911 & 0.291602 & 0.395289 \\ PR & **0.505117** & 0.247499 & 0.484876 & 0.387639 & 0.305979 & 0.422467 \\ PVC l VPB & 0.899264 & 0.884987 & 0.773748 & 0.894591 & **0.921404** & 0.904347 \\ LAD & 0.430800 & 0.288247 & 0.600014 & **0.618377** & 0.285261 & 0.328825 \\ RAD & 0.208520 & 0.092792 & **0.90567** & 0.703333 & 0.140264 & 0.152924 \\ RBBB l CRBBB & 0.933160 & 0.932577 & 0.882603 & 0.899832 & **0.94122** & 0.936367 \\ Tlnv & 0.610578 & 0.58808 & 0.641881 & 0.673373 & 0.590317 & **0.764585** \\ LOT & 0.602917 & 0.674092 & 0.723692 & **0.744892** & 0.700981 & 0.704567 \\ AF & 0.475622 & 0.421919 & 0.30222 & 0.544573 & 0.467661 & **0.602218** \\ AFL & 0.453395 & 0.394781 & 0.354915 & 0.508478 & 0.480628 & **0.606689** \\ CLBBB l LBBB & 0.326211 & 0.270363 & 0.374664 & **0.375518** & 0.263575 & 0.278739 \\ QAb & **0.87734** & 0.755670 & 0.743421 & 0.82156 & 0.710558 & 0.860728 \\ Tab & 0.803205 & 0.8080515 & 0.886186 & **0.905691** & 0.7757084 & 0.889573 \\ LPR & 0.534839 & 0.365901 & 0.299062 & 0.515563 & 0.445649 & **0.556087** \\ LQRSV & 0.232231 & 0.133646 & **0.383355** & 0.366094 & 0.189878 & 0.259811 \\ IAVB & 0.690174 & 0.657462 & 0.630925 & 0.704802 & **0.739515** & 0.68493 \\ PAC & 0.954296 & 0.734406 & 0.770217 & 0.839615 & 0.90467 & **0.992045** \\ PRWP & 0.901047 & 0.864718 & 0.876276 & **0.912428** & 0.844026 & 0.892487 \\ SB & 0.331723 & 0.23137 & 0.353656 & **0.415658** & 0.235260 & 0.367976 \\ Brady & 0.407006 & 0.331323 & 0.269448 & 0.337268 & 0.389516 & 0.498495 \\ NSR & 0.321648 & 0.193221 & 0.264152 & **0.507028** & 0.167270 & 0.287732 \\ STach & 0.277925 & 0.195767 & **0.392103** & 0.260803 & 0.207330 & 0.317156 \\ \hline \end{tabular}
\end{table} 
Table 4: Recall at each branch per class presented as the average of five folds. Significantly better results are marked in bold.
# Medical Gesture Recognition Method Based on Improved Lightweight Network
## 1 Introduction
However, the complex model depth and the huge number of model parameters require high computing power, which is not conducive to practical applications. Firstly, the model is too large and faces the problem of insufficient memory, and, secondly, some special scenarios have requirements such as low latency and fast response time for gesture recognition, which makes it difficult to apply gesture recognition to practical deployment [20]. In recent years, lightweight networks such as GhostNet [21], ShuffleNetv1 [22], MobileNetv1 [23], MobileNetv2 [24], ShuffleNetv2 [25], SqueezeNet [26], and IGCv3 [27] have been proposed successively, whose recognition effect is comparable to that of deep neural networks while greatly reducing the network model parameters, among which the MobileNet series is more representative. In MobileNetv1, depthwise separable convolution is proposed instead of standard convolution to reduce the parameter calculation. MobileNetv2 proposes a linear bottleneck structure and inverted residual structure on this basis to solve the gradient disappearance problem and enhance the feature expression capability. Compared with deep neural networks, MobileNet series network models require less memory, occupy fewer resources, less computation, and fast computation, and can be run on mobile devices and embedded devices. Therefore, the emergence of lightweight networks provides a new direction for gesture recognition. However, when the network structure of the MobileNet series is applied to the gesture recognition task, due to its internal network design, optimization strategy and activation function, negative-valued feature information in input information will be ignored, which may be the key point of gesture recognition [28]. Therefore, it makes its feature extraction ability insufficient and affects its classification accuracy. To address this problem, we propose an improved MobileNetv2 model, called E-MobileNetv2, which uses the MobileNetv2 architecture as the basic skeleton of the model and adds an adaptive channel attention module, ECA [29], which avoids dimensionality reduction while also effectively capturing cross-channel interaction information, thus improving recognition accuracy. A new activation function R6-SELU is proposed to replace the original ReLU6 to prevent the phenomenon of neuron \"death\", which causes the loss of negative-valued feature information, and further improves the network recognition accuracy. Finally, we also adjust the width multiplier and resolution multiplier of the model and use the transfer learning method to make the network model obtain better initialization weight parameters, which work together to improve the recognition accuracy of the model.
## 3 Results and Discussion
### Influence of Discharge Area
#### 3.2.3. Minimum Oxygen Concentration  
Figure 7 shows the minimum oxygen concentrations of large and small atomizers under different HRR conditions. The minimum oxygen concentration indicates the minimum value in the curve denoting time variation in oxygen concentration measured by the gas analyzer at a certain sampling point.  
As shown in Figure 7a,b, the minimum oxygen concentrations for HRR at 5.23 kW are lower than those for HRR at 1.17 kW. Under most water and nitrogen flow rate conditions, the increases in both flow rates increased the minimum oxygen concentration. Additionally, at the higher nitrogen flow rate condition, the minimum oxygen concentration increased, and the dependency of minimum oxygen concentrations on water flow rates became weaker. However, for large atomizer in Figure 7a, at the low nitrogen flow rate condition (e.g., 20 L/min), the minimum oxygen concentration gradually increased from 0 to 85 g/min of water flow rates. They were then reduced with increases in water flow rates. These trends are also observed for small atomizer at the low nitrogen flow rate condition (e.g., 10 L/min), as shown in Figure 7b. A reason is that under these conditions, the extinguishing time became longer, and more oxygen was consumed.  
In Figure 8, the relation between the extinguishing time and minimum oxygen concentration for large and small atomizers, obtained from Figures 4 and 7, is shown. Increases in extinguishing times resulted in decreasing the minimum oxygen concentrations. In addition, the higher HRR case showed the shorter extinguishing times and lower minimum oxygen concentrations, as compared with the lower HRR case.  
Figure 8: Extinguishing time vs. minimum oxygen concentration for discharge area effect (note that the scales of \(x\)-axis in both figures are different): (**a**) large atomizer and (**b**) small atomizer.  
Figure 7: Minimum oxygen concentration for large and small atomizers under different HRR conditions: (**a**) large atomizer and (**b**) small atomizer.  
Based on Figures 4-8, under the higher nitrogen flow rate conditions, the extinguishing times were shorter, peak surface temperatures were lower, and minimum oxygen concentrations were higher, as compared with the cases under the lower nitrogen flow rate conditions. One of the reasons for these trends is enhanced cooling effect owing to decreased droplet size of water mist and increased velocity of discharged flow. Additionally, the increased nitrogen flow rates and tiny water droplets can be involved in the chemical reactions in the reaction zone. These combined effects caused by the increased nitrogen flow rate are considered to be related to the general trends of those experimental results.