esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data Used
### Data Preparation  
The quality of landslide inventory is very significant for landslide susceptibility modeling, and an accurate landslide inventory map is the foundation of landslide susceptibility modeling [63; 64]. In this study, three techniques were used to improve the reliability and accuracy of the landslide inventory map: historical report, aerial photo interpretation, and field survey using Global Navigation Satellite Systems (GNSS). According to the landslide inventory map in this area, 263 landslides were identified, and 184 landslide locations (70%) were randomly sampled as the training data and the other 79 landslide locations (30%) were used to validate models.  
Figure 1: Study area.  
After compiling the landslide inventory, it is necessary to choose the landslide conditioning factors to create the landslide susceptibility map [65]. The selection principle is to consider the mechanism and geo-environmental characteristics of landslide occurrence in the study area. Generally, the landslide conditioning factors used to evaluate landslide susceptibility include three categories: topographic factors, geological factors, and environmental factors. In this paper, 14 landslide conditioning factors were selected and transformed into the same resolution (\(30\times 30\) m), including elevation, slope, aspect, plan curvature, profile curvature, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), the normalized difference vegetation index (NDVI), land use, lithology, soil, distance to roads, and distance to rivers (Table 1, Figure 2).
## 4 Results
### Natural Frequencies
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\Omega_{1}\) & \(\alpha_{1}\) & \(\Omega_{2}\) & \(\alpha_{2}\) & & \multicolumn{6}{c}{**Nondimensional Frequency Parameters**} \\ \cline{5-8}  & & & & \(\beta_{1}\) & \(\beta_{2}\) & \(\beta_{3}\) & \(\beta_{4}\) & \(\beta_{5}\) \\ \hline \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{0} & \multirow{2}{*}{2.3587} & \multirow{2}{*}{3.9918} & \multirow{2}{*}{5.8927} & \multirow{2}{*}{8.4328} & \multirow{2}{*}{11.3698} \\  & & & & & 2.0652 & & & & \\  & & & & 1 & & 1.6155 & & & \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{1} & \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{2.0652} & \multirow{2}{*}{3.0312} & \multirow{2}{*}{5.3734} & \multirow{2}{*}{8.2727} & \multirow{2}{*}{11.3075} \\  & & & & & 1 & & & & \\  & & & & 1 & & & & & \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{1} & \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{1.6155} & \multirow{2}{*}{2.8248} & \multirow{2}{*}{5.3220} & \multirow{2}{*}{8.2578} & \multirow{2}{*}{11.3016} \\  & & & & & 1 & & & & \\  & & & & 1 & & & & & \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{0.1} & \multirow{2}{*}{1} & \multirow{2}{*}{0.1} & \multirow{2}{*}{2.0594} & \multirow{2}{*}{2.9811} & \multirow{2}{*}{5.2816} & \multirow{2}{*}{8.1918} & \multirow{2}{*}{11.2410} \\  & & & & 1 & & & & & \\  & & & & 1 & & & & & \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{0.1} & \multirow{2}{*}{1.7069} & \multirow{2}{*}{3.8892} & \multirow{2}{*}{5.8327} & \multirow{2}{*}{8.3506} & \multirow{2}{*}{11.3001} \\  & & & & & 1.5689 & & & & \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{0.1} & \multirow{2}{*}{1.5689} & \multirow{2}{*}{2.7814} & \multirow{2}{*}{5.2273} & \multirow{2}{*}{8.1763} & \multirow{2}{*}{11.2349} \\  & & & & 1 & & & & \\ \hline \multirow{2}{*}{0.5} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1.1859} & \multirow{2}{*}{2.3570} & \multirow{2}{*}{5.1659} & \multirow{2}{*}{8.1603} & \multirow{2}{*}{11.2286} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The first five nondimensional frequency parameters for different values of \(\Omega_{1},\alpha_{1},\ \Omega_{2},\ \alpha_{2}\).
## 4 Simulations and Results
### 4.1 Mean Time to First Failure (MTTFF)  
Mean Time to First Failure is the anticipated time amount that operations will last before the first failure. The empirical MTTFF PDF (exponentially distributed) and CDF of the RBS are depicted in Figure 8.  
Figure 7: Matlab script example for PDF and CDF: MTBF MC.  
Data collector monitors are applied in the different transitions preceded by **BF** for each component (BFMC in the example of Figure 6 and **BFRBS** for the whole system, Figure 5). This monitor will record only the first time-to-failure equal to the first **wait** value.
## 5 Results and ANN Model Validation
### Effects of a Relief Well
#### 5.1.1 Without a Relief Well  
The CO2 injection rate was optimized to maximize the mass of cumulative CO2 injection without a relief well. The pore pressure behaviors at the faults are shown in Figure 11a. The solid and dashed curves indicate the pore pressure at the top layers of aquifers A and B, respectively. The colors specify faults EF1 (black) and EF2 (blue). The pore pressure values were measured at the closest points on faults EF1 and EF2 from the injection well. Before injection (time = 0), the pore pressure in aquifer B was higher than that of aquifer A as aquifer B is located in a deeper formation. In addition, although the pore pressure in EF1 and EF2 of aquifers A had a difference of 0.2 MPa, the pore pressure of each fault in aquifer B did not have a big difference because the depths of the faults at the top layer of aquifer B were almost identical (746.5 m, 749.8 m). As the injection process proceeded, the pore pressures in both aquifers increased. The pore pressure increase in aquifer A was larger than that of aquifer B, and the pore pressure of aquifers A exceeded aquifer B after 0.17 years and 0.21 years of injection at EF1 and EF2, respectively. This is because the average permeability of aquifer A (30 md) is higher than that of aquifer B (11 md). The injection process stopped at 6.85 years as the pore pressure of EF1 in aquifer A reached the fault reactivation pressure of 11.7 MPa, while the pore pressures at the other monitoring points were still lower. As shown in Figure 11b, which depicts the pressure distribution in aquifers A and B at the end of the injection, the pore pressure of aquifer A was distributed over a wider area than that of aquifer B due to the higher injectivity of aquifer A. Consequently, if the pore pressure in aquifer A is well managed by a relief well, it is possible to achieve a larger CO2 storage volume with a safe injection process in terms of the potential fault reactivation.
# Ergonomics Study of Musculoskeletal Disorders Among Tram Drivers
## 2 Materials and Methods
### Driver's Working Posture in the Tram Cabin  
In addition to the assessment of working posture, specific characteristics of the tram driver's cabin were considered, as they directly influence body alignment, range of motion, and overall ergonomic load. The cabin is generally compact, with limited space for repositioning during prolonged sitting. The driver's seat is adjustable in height and longitudinal direction; however, the degree of adjustability is often insufficient to accommodate drivers of varying anthropometric profiles. The backrest is slightly inclined but lacks dynamic lumbar support, which can lead to passive spinal loading over time.  
For example, the tram driver's control panel is typically divided into left, central, and right sections. The central section includes a monitoring display, while the left side houses are functions less frequently used. The most frequently used controls--such as door buttons and emergency braking--are positioned on the right side. The driving lever (T-handle), located on the left, plays a central role in operational control. The ergonomic analysis considered the relative reach, visibility, and adjustability of these components for each morphotype, ensuring that the driver's posture remains within safe biomechanical limits throughout the workday (Figure 4).  
Figure 3: Steps of the REBA research method with a scheme for determining the REBA score.  
The control interface, including the T-handle lever and dashboard, is fixed in position, typically located at a horizontal distance of approximately 32 cm from the seat reference point, and at a vertical height of 73 cm relative to the floor. This requires the operator to maintain a forward-reaching posture. The left armrest, where the T-handle is located, is positioned at a height of 70 cm, which may or may not align optimally with the driver's elbow height, depending on body dimensions. The right armrest is positioned at a height of 60 cm, particularly with the dominant arm moving forward when needed to reach the right control panel. Legroom is restricted laterally, often causing drivers to keep their knees and feet close together throughout the shift.  
Overall, the spatial configuration and fixed elements of the cabin contribute to reduced postural variability and limited ergonomic adaptation, increasing the potential for static muscular loading, particularly in the lumbar spine, shoulders, and neck regions.
# Seismic Study of An Isolated Cable-Stayed Bridge under Near-Fault Ground Motions
## 4 Pounding Mitigation Performance of Seismic-Reduction System  
### Selection of Input Ground Motions  
Recently, near-fault ground motions have gradually been recognized as the most destructive loads that structures may encounter throughout their service life [30]. A set of ten ground motions, each consisting of both horizontal and vertical components, are selected for the same site conditions from the Pacific Earthquake Engineering Research Center's strong motion database. The selected motions have magnitudes ranging from 6.53 to 7.62 and shear wave velocities varying from 192 m/s to 566 m/s. Detailed characteristics of these selected ground motions are provided in Table 1.  
Figure 4: Finite element model.  
Figure 5 illustrates the unscaled horizontal acceleration response spectra of the ground motion. For the current analyses, earthquake excitation for the prototype bridge is confined to the longitudinal direction. The peak ground accelerations (PGAs) of these ground motions are adjusted to 0.56 g and used as rare seismic events for the subsequent study.
# A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering
## 2 Materials and Methods
### Theoretical Framework
#### 2.2.2 CAN Message  
The CAN (Controller Area Network) is a high-speed serial bus technology for communication, which is suitable for high-speed real-time data transmission and remote sensor applications. The CAN Bus has the characteristics of high reliability, real-time, long communication distance and fast data transmission speed, and has been widely used in industrial control, automotive electronics, mechatronics and other fields. In this study, the millimeter-wave radar of ARS 408-21 XX outputs the target-related data detected by the CAN message. CAN message is a standard message transmission protocol, which consists of 4 parts: frame header, ID (identifier), data and parity code. The frame header is the first byte of the standard width on the CAN bus and usually includes the physical characteristics of the cable connected to the signal line. ID is an identifier that uniquely identifies a specific node on the CAN bus. Data is the actual data on the CAN bus, which can contain one or more data bytes. The specific amount is determined by different applications. The check code is used to check whether the CAN message is transmitted and received correctly and by comparing the checksum calculus to figure out whether the original data is incorrect. In the target output mode of ARS 408-21 XX millimeter-wave radar, the data in the CAN message is expressed in the form of hexadecimal, which needs to be analyzed according to the given communication protocol in order to get the intuitive information of the target. Therefore, you need to understand specific communication protocols when parsing CAN packets to correctly identify data, parse data formats, calculate data values, and convert data into readable target information.
# Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System
## 6 Conclusions and Future Works  
In this paper, we proposed (i) an original performance SPN model, (ii) its refined model with an absorbing state and (iii) an availability SPN model, for performance and availability assessment of an MEC infrastructure. Comprehensive analyses were performed to assimilate different aspects of the system operations in terms of performance and availability. Performance metrics were analyzed in the original performance model including MRT, discard rate, and utilization of master and slave nodes with regard to arrival delay. Two case-studies using the refined model with an absorbing state were conducted to investigate the completion time of application execution. An availability model was first introduced for a base-line MEC with the basic configuration of a master and a slave node, while an extensional one was proposed when redundancy of slave nodes was taken into account. Availability metrics were analyzed including operational availability and downtime hours in a year. Availability sensitivity wrt. MTTFs and MTTRs were performed in a comprehensive manner. The analysis results pinpoint essential system parameters which incur significant impacts on performance and availability of an MEC. As a result, this study helps comprehend operational characteristics of an MEC regarding performance and availability metrics, and thus helps design actual MEC architectures and plan in advance economic operations of practical MEC systems.  
Conceptualization, F.A.S.; methodology, F.A.S.; software, C.B., L.R, and B.S.; validation, F.A.S. and L.F.; formal analysis, C.B., L.R, B.S. and L.F.; investigation, F.A.S. and T.-A.N.; data curation, C.B., L.R, and B.S.; writing--original draft preparation, C.B., L.R, and B.S.; writing--review and editing, T.-A.N., D.M., J.-W.L. and F.A.S.; visualization, C.B., L.R, and B.S.; supervision, D.M., J.-W.L. and F.A.S.; project administration, F.A.S. and T.-A.N.; funding acquisition, D.M. and J.-W.L. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the National Council for Scientific and Technological Development--CNPq, Brazil, through the Universal call for tenders (Process 431715/2018-1). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2020R1A6A1A03046811). This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2020-2016-0-00465) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) This research was funded and conducted under \"The Competency Development Program for Industry Specialist\" of the Korean Ministry of Trade, Industry and Energy (MOTIE), operated by Korea Institute for Advancement of Technology (KIAT). (No. N0002428).  
The authors would like to thank anonymous reviewers for their constructive comments and suggestions.
## 3 Literature Classification and Analysis
### Fractional-Order Models
#### 3.5.1 New Approach to the Variable Fractional-Order Model of a Robot Arm  
This is dedicated to research on mathematical models of the robot arm, including a detailed analysis of two specific models--one based on the Newton-Euler formalism, and the second using variable fractional-order difference equations [43]. The idea was also to compare these models with real measurements to determine their effectiveness in simulation. Newton-Euler formalism is a traditional method that has provided a long-standing basis for modeling robotic systems, essentially based on classical mechanics principles. The variable fractional-order difference equations model involves the use of fractional calculus, which enables a superior representation of systems with memory and hereditary properties. The final model structure integrates nonlinear behavior and variations in input signal characteristics [44]. The detailed experiments were conducted on a robot arm as part of a larger mobile platform project. The robot utilizes a DC electric drive motor with harmonic gears controlled via a single-phase PWM inverter. The study analyzed the torque profiles (M0, M1) necessary for various positions of the manipulator over time, focusing on the starting phase (increasing torque), steady phase (constant torque), reduction phase (decreasing torque to zero), and stop phase (zero torque). The results of the robot arm were obtained through simulations and real measurements [45]. Two performance indices were used, IAE and ISE. The proposed fractional-order variable model gave a better response than the classical model by presenting higher accuracy in the realization of the real arm. Graphical results were obtained representing the response of the robotic arm. Notable figures included plots of measured responses versus simulated results for both models, demonstrating the superiority of the fractional-order approach. This determined the correct order of functions to relate real-time signals of the system. The research corroborates that the variable fractional order difference equation model provides a much better conceptual and estimated framework to understand and predict the dynamics of a robot arm compared to other classical methods [46]. This gives another degree of freedom for possible enhancement of robotic control systems in a real environment.
## 4 Discussion
### Test3  
In this experiment, we compared ACO-NN to ACO [50], ABC [50], DTSA [50], and CHA [51] for the instance Eil101. As shown in Table 6, the ACO-NN method has good results with the rank 3, and it proves a better experimental result for standard deviation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Alg.** & **Nb.Node** & **Optimal** & **Mean** & **R-Mean (\(\sigma\))** & **Std.Dev** & **Rank** \\ \hline ACO [50] & 101 & 629 & 693 & 0.0923 & 6.80 & 2 \\ ABC [50] & 101 & 629 & 1315 & 0.5216 & 35.28 & 5 \\ DTSA [50] & 101 & 629 & 689 & 0.0870 & 4.47 & 1 \\ CHA [51] & 101 & 629 & 862 & 0.2703 & N/R & 4 \\ ACO-NN & 101 & 629 & 695 & 0.0949 & 3.70 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of ACO-NN to various approaches for Eil101.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Instances** & **Nb.Node** & **Optimal** & **Alg.** & **Best** & **Mean** & **Std.Dev** & **Time (s)** \\ \hline Eil76 & 76 & 538 & ACO-NN & 562 & 567 & 2.8946 & 5.04 \\  & & Elitist-AS [46] & 538 & 551 & 0 & 42 \\ \hline Ch150 & 150 & 6528 & ACO-NN & 6846.45 & 6914.88 & 40.11 & 9.29 \\  & & Elitist-AS [46] & 6528 & 6550 & 11.22 & 53 \\ \hline D198 & 198 & 15,780 & ACO-NN & 17,054.09 & 17,123.04 & 60.83 & 12.48 \\  & & Elitist-AS [46] & 15,888 & 15,940 & 64.83 & 103 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of ACO-NN to Elitist-AS for Eil76, Ch150, and D198.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Alg.** & **Nb.Node** & **Optimal** & **Mean** & **R-Mean (\(\sigma\))** & **Std.Dev** & **Rank** \\ \hline ACO [50] & 76 & 538 & 594 & 0.0942 & 40.2152 & 3 \\ PSO [50] & 76 & 538 & 975 & 0.4482 & 152.4061 & 7 \\ GA [50] & 76 & 538 & 652 & 0.1748 & 122.0972 & 4 \\ BH [50] & 76 & 538 & 659 & 0.1836 & 152.1754 & 5 \\ DTSA [50] & 76 & 538 & 588 & 0.0850 & 5.7296 & 2 \\ CHA [51] & 76 & 538 & 687 & 0.2168 & N/R & 6 \\ ACO-NN & 76 & 538 & 567 & 0.0511 & 2.8946 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of ACO-NN to various approaches for Eil76.
## 5 A Computational Model of Network Leadership
### Summary of the Parameters  
In the section, we summarize and discuss the parameters of our computational model of Network Leadership. In Table 1, for each parameter, we provide a brief description and the default values we also experimented on in the use-case study later discussed in Section 7. As already remarked in Section 5, the computational models of network leadership parameters should be set in agreement with managerial strategies of a company, as a complementary support for decisions on career advancements. For example, thanks to \(\alpha_{1}\) and to \(\alpha_{2}\), managers can find their better trade-off between the leadership centrality and the brokerage ranks. Similarly, it is possible to control the importance given to _commenters_ (\(\pi_{1}\), \(\pi_{2}\)) and _rates_ (\(\pi_{3}\),\(\pi_{4}\)). With the exceptions of \(\alpha^{i}\), \(\alpha^{e}\), and \(\alpha^{c}\) parameters, which have been originally set to 0.85 by the authors in Reference [22], \(\beta\) and \(\gamma\) parameters enable an even, fine-grained control of the model by calibrating the mutual influence between the three (i.e., trust, collaboration, and empowerment) layers. Indeed, the capability to adapt our model to the strategic needs of managers is one of the relevant features of our proposed framework.  
Finally, we empirically tuned the \(\phi\) and \(\Theta\) parameters (see Section 5.4) and evaluated that \(\phi=3\) and \(\theta=0.80\) are optimal thresholds for topic leaders analysis with our dataset.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Parameter** & **Description** & **Default or Tuned Value** \\ \hline \(\alpha_{1}\), Equation (1), Section 5 & to control the weight of leadership centrality & 0.5 \\  & rank in the computation of the Network Leadership & \\ \hline \(\alpha_{2}\), Equation (1), Section 5 & to control the weight of brokerage rank in the computation of the Network Leadership Rank & 0.5 \\ \hline \(\pi_{1}\), \(\pi_{2}\), Equation (3), Section 5.2 & to control the importance of comments & 0.5 \\ \hline \(\pi_{3}\), \(\pi_{4}\), Equation (7), Section 5.2 & to control the importance of trust & 0.5 \\ \hline \(\alpha^{i}\), \(\alpha^{e}\), \(\alpha^{c}\), Equations (7)–(9), Section 5.2 & _teleporting factors_ for the _trust_, _empowerment_, and _collaboration_ layers, respectively & 0.85 \\ \hline \(\beta\), \(\gamma\), Equations (8) and (9), Section 5.2 & to control the mutual influence between _collaboration_, _empowerment_, and _trust_ layers & 1.0 \\ \hline \(\phi\), Section 5.4 & threshold frequency of the elements in concept & 3.0 \\  & cliques & \\ \hline \(\Theta\), Section 5.4 & threshold for the cosine-similarity score between context and category vectors & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the parameters of our computational model of Network Leadership. We remark that \(\phi\) and \(\Theta\) values were tuned to optimally perform Topic Leaders analysis with our dataset.
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 3 The Proposed Predictive Handover Management Approach  
In this section, we present information regarding the proposed predictive handover management approach. Figure 1 shows the overall procedure of the handover management approach. This approach is composed of two main parts: \((1)\) user mobility prediction and \((2)\) predictive handover procedure analysis. For the first step, the main idea is to predict the future trajectory of the user using a hybrid model based on the VAR and GRU models. For the second step, we exploited the obtained prediction information from the last step to optimize the handover signaling procedure for the both horizontal and vertical handovers. In the following, we provide the details regarding each step.