esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Engineering Application of the ASRB Parameter Design Method
### Scheme Design and Effect Evaluation of ASRBs
#### 4.2.2 Determination of Design Parameters  
Combined with the design content of the ASRB scheme in Section 3.3, for the ASRB parameter design, the engineering focus was on the geometric parameters of the layout  
Figure 10: Maximum principal stress distribution simulation results and verification of monitoring results: (**a**) simulation results (unit: Pa); (**b**) on-site microseismic test results; (**c**) wave velocity test results [65].  
plan, including the length and aperture parameters. Previous studies have found that the distance parameter between boreholes also has a significant control effect on the energy released. Therefore, the angle, inclination, and spacing parameters need to be determined during the construction process. Hence, the factors of this ASRB parametric orthogonal test program were determined to be inclination, length, included angle, aperture, and spacing. The physical meaning of each factor is as follows:  
1. Inclination: the included angle between the ASRB axis and the tunnel axis (unit: \({}^{\circ}\)).
2. Spacing: the axial spacing of the tunnel between the ASRBs of adjacent rows (unit: m).
3. Length: the distance from the borehole bottom to the tunnel side wall in the ASRB (unit: m).
4. Included angle: the sharp angle between the two adjacent ASRBs on one side of the tunnel cross-section and the line connecting the tunnel center (unit: \({}^{\circ}\)).
5. Aperture: the diameter of the circle on the cross-section of the ASRB (unit: m) The physical meaning of the design parameters is shown in Figure 11.  
Figure 11: Schematic diagram of the physical meaning of factors selected in the orthogonal test: (**a**) top view of the model; (**b**) left view of the model.
## 3 Technical and Operational Challenges
### Scalability and Performance Trade-Offs  
Low-code platforms often sacrifice performance for rapid deployment. In automotive edge computing, apps built with drag-and-drop tools exhibit latency spikes when processing real-time telemetry from autonomous vehicles, as noted in [PERSON] et al.'s analysis [63] of CAN bus integrations. Similarly, energy grid simulations using low-code models show higher computational overhead compared to native C++ implementations, limiting their use in high-frequency trading systems.  
To address this, [PERSON] et al. [55] advocate for just-in-time (JIT) compilation techniques in low-code platforms, dynamically optimizing generated code for specific hardware architectures. Early adopters like Lockheed Martin report success with this approach, achieving a performance boost in satellite telemetry systems while retaining visual development benefits [1].
###### Abstract  
The high performance and efficiency of multiple unmanned surface vehicles (multi-USV) promote the further civilian and military applications of coordinated USV. As the basis of multiple USVs' cooperative work, considerable attention has been spent on developing the decentralized formation control of the USV swarm. Formation control of multiple USV belongs to the geometric problems of a multi-robot system. The main challenge is the way to generate and maintain the formation of a multi-robot system. The rapid development of reinforcement learning provides us with a new solution to deal with these problems. In this paper, we introduce a decentralized structure of the multi-USV system and employ reinforcement learning to deal with the formation control of a multi-USV system in a leader-follower topology. Therefore, we propose an asynchronous decentralized formation control scheme based on reinforcement learning for multiple USVs. First, a simplified USV model is established. Simultaneously, the formation shape model is built to provide formation parameters and to describe the physical relationship between USVs. Second, the advantage deep deterministic policy gradient algorithm (ADDPG) is proposed. Third, formation generation policies and formation maintenance policies based on the ADDPG are proposed to form and maintain the given geometry structure of the team of USVs during movement. Moreover, three new reward functions are designed and utilized to promote policy learning. Finally, various experiments are conducted to validate the performance of the proposed formation control scheme. Simulation results and contrast experiments demonstrate the efficiency and stability of the formation control scheme.  
deep reinforcement; formation control; formation generation; formation maintenance learning; multi-USV system +
Footnote †: journal: Applied Sciences
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Routing Protocol  
Because of the increasingly innovative layout, diverse network features, and many forms of communication, effective routing is difficult (i.e., V2V and V2I). The constant mobility of vehicles needs reliable network connectivity. An effective routing protocol must offer quality of service (QoS), improved traffic and network management, mobility, and delay-tolerant message delivery as a top priority. The protocol under consideration is topology-based, with routing data mostly kept in the routing domain. For good routing decisions from transmitter to recipient through a sufficient transceiver, an efficient routing system is required.
# Dynamic Behavior of Track Bridge on Serviced Long-Span Road/Railway Bridge
## 6 Conclusions  
To predict the behavior of the track and bridge structures of the Yeongjong Bridge (a section of an airport railway) with respect to preparing for future increases expected in train speeds, field measurements and numerical analyses were performed. The results were used as a basis for analyzing the dynamic track-bridge behavior according to increases in train speed (up to 200 km/h).  
1. According to the analysis of track behavior based on increases in train speed, no structural problems were observed with regard to track structure stability (track displacement and acceleration) and train operation stability (track support stiffness, track impact factor, derailment coefficient, wheel load reduction, track gauge widening and track twist).  
Figure 14: Result of internal and external load-displacement according to train type.  
Figure 13: Prediction of dynamic behavior of the bridge according to increasing train speed. (**a**) Bridge deck displacement (truss bridge); (**b**) Bridge deck acceleration (truss bridge); (**c**) Bridge deck displacement (suspension bridge); (**d**) Bridge deck acceleration (suspension bridge).  
An examination of the resonance effect of the bridge structure revealed no resonance effect due to increases in train speed (up to 200 km/h). From the perspective of the Yeongjong Bridge structure, no problems related to increased deflection or acceleration caused by an increase in speed up to 150 km/h (which is the present target speed for future airport railways) were observed.  
Conceptualization, J.-S.C. and J.-Y.C.; investigation, K.-Y.L.; formal analysis, J.-S.C.; field test, J.-Y.C. and K.-Y.L., writing--original draft preparation, J.-Y.C. and S.-H.K.; writing--review and editing, S.-H.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflicts of interest.
# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 2 Materials
### Data Source
Sentinel-2 and Landsat 8 scenes are available with different processing levels, containing raw at-sensor radiance values (L1A) as well as geometrically and radiometrically corrected sensor radiance values (L1B). These processing levels can be used to derive additional Level 2 data products, such as atmospherically corrected surface radiance or surface temperature and emissivity. We have processed the Sentinel-2 images in the bands of Near InfraRed (NIR: B8 with 0.84 um (S2A)/0.83 um (S2B) wavelength) and Short-Wave InfraRed (SWIR: B11, with 1.61 um (S2A, S2B) wavelength, and B12, with 2.20 um (S2A)/2.19 um (S2B) wavelength) Since NIR and SWIR Sentinel-2 bands have different spatial resolutions ranging between 10 m and 20 m, all S2 images were automatically resampled to the same spatial resolution of 20 m using the nearest neighbor method. Moreover, we have used the Landsat 8 images in the NIR (B5: 0.85-0.88 um wavelength) and SWIR (B6: 1.57-1.65 um wavelength, and B7: 2.11-2.29 um wavelength) bands. Additionally, the Landsat 8 images were resampled to the higher spatial resolution of 20 m. We have analyzed the Top-Of-Atmosphere (TOA) reflectance data from both sensors, a dimensionless measurement that describes the ratio between reflected and incident solar radiation on a given surface.
# Reliability Modeling and Analysis of Multi-Degradation of Momentum Wheel Based on Copula Function
## 4 Momentum Wheel Reliability Calculation Based on Copula Function
### Reliability Calculation Based on Copula Function  
After determining the marginal distribution of the remaining life of the momentum wheel under each single degeneration, the Copula function is used to calculate the joint distribution function of the remaining life of the momentum wheel. The procedure is described as follows:  
1. The lifetime marginal distribution \(F_{1}(t)\) of the known residual quantity of the lubricant and the lifetime failure margin distribution \(F_{2}(t)\) of the current, let \(U=F_{1}(t)\), \(V=F_{2}(t)\).
2. The Copula function contains an unknown parameter \(\gamma\), so parameter estimation is needed. Since the margins of lubricant residuals and currents are known, the Canonical maximum likelihood method (CML) is used to estimate the parameters \(\gamma\). \[\hat{\gamma}=\text{argmax}\sum_{i=1}^{n}\ln c(u_{i},v_{i};\gamma)\] (13)
3. Calculate Copula density function \(c_{\gamma}(u,v)\) and Copula distribution function \(C_{\gamma}(u,v)\).
4. Calculate the momentum wheel residual life probability function \(R(t)\) based on the Copula distribution function according to the joint probability distribution formula in probability theory. \[\begin{array}{ll}R(t)&=P(y_{1}(t)\geq D_{1},y_{2}(t)\leq D_{2})\\ &=1-P(y_{1}(t)<D_{1})-P(y_{2}(t)>D_{2})\\ &+P(y_{1}(t)<D_{1},y_{2}(t)>D_{2})\\ &=1-F_{1}(t)-F_{2}(t)+C_{\gamma}(u,v)\\ &=R_{1}(t)+R_{2}(t)+C_{\gamma}(u,v)-1\end{array}\] (14) where \(F_{1}(t)\) and \(F_{2}(t)\) are the momentum wheel failure distribution functions based on the remaining quantity of lubricant and the current; \(R_{1}(t)\) and \(R_{2}(t)\) are the corresponding momentum wheel reliability functions; \(C_{\gamma}(u,v)\) is the Copula joint distribution function.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter \(\gamma\) Range** & \(\mathbf{C_{\gamma}(u,v)}\) \\ \hline Gumble & \([1,\infty)\) & \(\exp\Bigl{\{}-\bigl{[}(-\ln u)^{\gamma}+(-\ln v)^{\gamma}\bigr{]}^{\frac{1}{ \gamma}}\Bigr{\}}\) \\ Clayton & \([-1,\infty)/\{0\}\) & \(\max\bigl{[}(u^{-\gamma}+v^{-\gamma}-1)^{-\frac{1}{\gamma}},0\bigr{]}\) \\ Frank & \((-\infty,\infty)/\{0\}\) & \(-\frac{1}{\gamma}\ln\Bigl{[}1+\frac{(e^{-\gamma-1})(e^{-\gamma-1})}{e^{-\gamma -1}}\Bigr{]}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The three most common binary copula functions.
# Electro-Optical Imaging Technology Based on Microlens Array and Fiber Interferometer
## 4 Numerical Simulation of the Imaging System
The frequency information was also concentrated in the low-frequency part; therefore, the sampling density was increased to obtain a high-quality image. This resulted in a corresponding increase in system frequency. Figure 5: Relationship between the variance and entropy of the imaging result and the sampling interval. Figure 6: (**a**) Light intensity distribution of the source; (**b**) airplane image spectrum center slice. Figure 7: Image reconstruction result of digital simulation experiment when (**a**) \(\triangle\)u = 0.33, N\({}_{\text{ap}}\) = 200; (**b**) \(\triangle\)u = 0.40, N\({}_{\text{ap}}\) = 166; (**c**) \(\triangle\)u = 0.50, N\({}_{\text{ap}}\) = 134.  
complexity. A satellite was used as the detection target; from the simulation results (Figure 8), it can be seen that the spectrum information of the satellite image is concentrated in the low-frequency part. We attempted to improve the image quality without increasing the number of microlenses, i.e., the complexity of the system. Then, the complex rectangular sub-aperture arrangement method was adopted to adjust the sampling density at high and low frequencies separately. In the results presented in Figure 8, the sampling interval is uniformly set to \(\triangle\)u = 0.33. In Figure 9, the sampling spectrum is divided into two parts, a high frequency and a low frequency, and the sampling intervals are respectively set to \(\triangle\)u1 = 0.19, \(\triangle\)u2 = 0.38. Similarly, the sampling intervals are set to \(\triangle\)u1 = 0.33, \(\triangle\)u2 = 0.66, \(\triangle\)u3 = 1.3 in Figure 10. The above settings ensure that the number of microlenses used in the three sampling modes is nearly equal. The simulation results are shown in Figures 8-10. Visual comparison shows no difference in image quality. This shows that the complex rectangular sub-aperture arrangement method proposed in this paper is feasible. We use variance and entropy to evaluate the subtle differences between the images. The results are presented in Table 2, which proves that higher-quality images could be obtained with the same number of microlenses.
## 5 Performance Evaluation
### Performance Metrics  
The performances of the proposed routing and the clustering protocols, HPA-SR and ASCS, are evaluated in terms of the following metrics:  
* Packet delivery ratio (PDR): it is defined by the ratio of the number of the received data packet at the destination node over the number of the transmitted data packet at the source node.
* Delay: it is defined by the average latency to establish the route per one session.
* Control overhead: it is defined by the average number of control packets to establish a route per session per node.
* The average number of the cluster head change: it is defined by the average number of cluster heads changes in per cluster per session [33].
* Packet loss ratio: it is defined by the ratio of the number of packets loss to the total number of sent packets [48].
# Cattle Weight Estimation Using Fully and Weakly Supervised Segmentation from 2D Images
## 2 Materials and Methods  
### Database  
Research data were obtained from the Gangwon-do Livestock Research Institute in Hoengseong, Gangwon-do, Republic of Korea. Because it is a research institution, this institute measures the body shape and weight of its cattle every three or six months. The guideways, equipment, and staff needed to measure body shape and weight were prepared for the experiment.  
A camera was installed on the ceiling above the center of a scale to collect images during weight measurement. Examples of the collected images are shown in Figure 3a. We collected video data at 1280 \(\times\) 720 (HD) resolution for a total of 43 cattle. Most were recorded between 10:00 h and 14:00 h, so the lighting was sufficiently bright. The actual weights ranged between 158 kg and 651 kg, with an average of 340.46 kg and a standard deviation of 148.73 kg. The weight distribution is shown in Figure 3b. Of the data for 43 cattle, the data for 23 cattle were used for training, and the remaining data were used for testing. To construct the datasets, all 43 cattle were first classified by weight into two groups: light and heavy. The average weight of the light and heavy groups was 260.33 kg and 421.23 kg, respectively. So that the average cattle weight of the training and testing data were randomly selected in the same ratio  
Figure 2: Flow chart of our cattle weight estimation system.  
from each group. The weight distribution can be biased if a dataset is randomly distributed without considering the weight. Since this cannot be seen as a generalized performance of the study, the data sets were distributed in groups according to weight in this study. The average weights of the training and testing data were 327.78 kg and 355.05 kg, respectively.
# Side-Channel Vulnerabilities of Unified Point Addition on Binary Huff Curve and Its Countermeasure
## 1 Introduction
In order to show that unified point addition actually has these weaknesses, we implemented unified point addition on a binary Huff curve on an ARM cortex-m4 processor that performs field multiplications depending on the secret bit value, repeatedly. Then, we analyzed a power consumption trace collected from the implementation by using our attack methods. As a result of the actual experiments, we were able to find secret bit values more than 94% of the time, which proves that this unified point addition operation is indeed vulnerable to our attacks, and the single trace attack is a practical threat. To provide security against our attack methods, we propose a new countermeasure using an equivalence class for unified point addition. By using the equivalence class, even though two input points of the unified point addition operation are in the same class, the two points can be different projective coordinate values. In addition, to provide perfect security against our attack methods, we reconfigured the operations of the unified point addition formula. The proposed unified point addition method for the binary Huff curve using the equivalence class is just about 2\(\sim\)4.4% slower than the existing unified point addition method from [8; 9]. In addition, the proposed method is about 8.5\(\sim\)17.5% faster than an existing countermeasure that provides same security, i.e., unified point addition using blinding operands of a field multiplication [10]. We applied the aforementioned attacks to the unified point addition formulae of other elliptic curves and confirmed that most unified point addition formulae have these vulnerabilities. This paper is organized as follows. Section 2 introduces basic knowledge of binary Huff curves and a description of ROSETTA and HCCA. In Sections 3 and 4, we explain the vulnerabilities of the unified point addition formulae and describe the experimental results of applying these methods. Section 5 proposes our method to make unified point addition secure against our attacks. In Section 6, we compare the proposed method with previous methods. Finally, Section 7 addresses our conclusions. In addition, we explain the vulnerabilities of several unified addition formulae and their countermeasures in the Appendix A.
## Literature Review
### ANN-GIS Approach  
ANNs has been introduced as a mapping tool to GIS to perform a predictive capability for joint operations [45]. Although GIS in combination with ANNs was popular in the fields of geoscience, irrigation, meteorology and Agriculture, it has been tested in the field of road safety by applying deep learning models using a Recurrent Neural Network (RNN) to predict the injury severity of traffic crashes for the North-South Expressway-Malaysia [46]. Previously this technique had been applied for sediment prediction in Gothenburg harbor [45], landslide susceptibility using the landslide occurrence factors produced with the help of a ANNs model [47], detection of flood hazards in the Blue Nile, White Nile, Main Nile, and River Atbara [48], macrobethnos habitat potential mapping regarding _Macrophthalmus dilatatus_, _Cerithidegpsilla cingulata_, and _Armandia lancolate_[49], learning the patterns of development in the region [50], tunneling performance prediction required in routine tunnel design works and performance in terms of stability as well as impact on surrounding environment [51], and deforestation maps production to determine the relationship between deforestation and various spatial variables such as the vicinity to roads and to expenditures, forest disintegration, elevation, slope, and soil type [52].
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 5 Validation
### Tests Details  
During the integration and the final demonstration, a lot of tests were conducted. Regarding the validation for the MTRR, a total of 45 missions were registered. The basic details of each mission are shown in Table 3, including the total number of actions in the mission, how many were high-level ones, and how many were low-level, the vehicles assigned for the mission, and whether the mission was real or simulated.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicle Name** & **Type** & **Codename** \\ \hline Noptilus-1 & AUV & PORTO1 \\ Noptilus-2 & AUV & PORTO2 \\ Fridtiof & AUV & NTNU \\ Telemetron & USV & MAROB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details for the vehicles used during the tests involving the MTRR.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mission** & **Start Time** & \begin{tabular}{c} **High Level** \\ **Actions** \\ \end{tabular} & \begin{tabular}{c} **Low Level** \\ **Actions** \\ \end{tabular} & \begin{tabular}{c} **Total** \\ **Actions** \\ \end{tabular} & \
\begin{tabular}{c} **Assigned Vehicles (Codename)** \\ **Mission** \\ \end{tabular} &
\begin{tabular}{c} **Type of** \\ **Mission** \\ \end{tabular} \\ \hline
1 & 10:01:27 & 9 & 14 & 23 & NTNU, PORTO2, MAROB & Real \\
2 & 10:17:56 & 9 & 0 & 9 & NTNU, PORTO2, MAROB & Real \\
3 & 11:57:17 & 9 & 14 & 23 & NTNU, PORTO2, MAROB & Simulated \\
4 & 12:57:58 & 9 & 34 & 43 & NTNU, PORTO2, MAROB & Real \\
5 & 13:25:55 & 9 & 34 & 43 & NTNU, PORTO2, MAROB & Real \\
6 & 15:31:05 & 9 & 34 & 43 & NTNU, PORTO2, MAROB & Simulated \\
7 & 17:10:09 & 12 & 50 & 62 & NTNU, PORTO1, PORTO2, MAROB & Real \\
8 & 17:58:57 & 3 & 16 & 19 & MAROB & Real \\  
\hline \hline \end{tabular}
\end{table}
Table 3: Details for each registered mission.  
The approach used for the mission assignment for all the missions was by individual tasks due to the constraints in the Robotic System On-board Architecture (RSOA) used in the vehicles. This means that when a mission starts, only the first action for each vehicle is sent to them. The rest of the actions in the mission plan are sent when a _COMPLETED_ status report is received from a vehicle for its current action.  
Mission 27 was aborted after sending the first action in the mission plan to each vehicle as there were unexpected underwater obstacles that prevented them to fulfill the mission. Missions 35, 36, 44 and 45 were those performed during the SWARMs final audit.