esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Simulation in Virtual Environment  
For this work, the virtual environment CARLA [39] and the Robot Operating System 2 (ROS2) [40] were used to implement the proposed empiric models into a real-time simulation. Carla provides the simulated environment of a car in an urban traffic scenario and the associated sensor data, such as a semantic point cloud, where all points are annotated with the objects they represent. The point cloud is then handed over to ROS2, which acts as a middleware framework, providing a standardized API to enable communication between sensors and software. Within ROS2, all real-time calculations and evaluations are performed.  
Within Carla, the lidar was configured to resemble the Scala 2 system. The detection properties of the lidar were determined on the basis of publicly available data sheets [49; 50]. The semantic point cloud, containing both the spatial information and the beam incidence angle, was transferred from Cartesian coordinates into spherical coordinates. Within the spherical coordinates, the distance of each object to the lidar is represented by the parameter \(r\), which makes it easy to calculate the atmospheric loss for each laser beam.  
The weather effects on the reflected beam power were estimated using the empirical models for the extinction coefficient, the incident angle, and the object's reflection characteristics. Then, the backscattering noise was added by inserting additional points with the calculated intensity and annotating them as noise. The lidar built-in algorithm usually orders the echoes of the same beam direction by intensity to highlight echoes of interest. Finally, all values below a certain threshold will be filtered out, considering internal sensor losses, and this altered point cloud will be published in the ROS2 network. At this stage, the detection algorithm was not considered/implemented, so the detection performance for different adverse weather situations was calculated directly out of the two semantic point clouds with and without weather effects. These steps for the external and internal factors are presented in the flow chart in Figure 8.
# Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring
## 6 Radio Coverage of the LoRaWAN Area Speed Control System and Traffic Monitoring
The Received Signal Strength Indicator (RSSI) is the primary metric from the receiver's perspective. It measures the power of the signal received by the receiver within wireless networks. RSSI is commonly used in wireless communication technologies, including Wi-Fi, Bluetooth, LoRaWAN, and GSM. General RSSI ranges that can be applied to assess the quality of a signal in LoRa networks are shown below on Table 5:  
Figure 10: Radio coverage areas and values for six transmitting stations (GWs) within the preset area. Figure 9: Locations of transmitting stations (GWs) and distribution of area boundaries and roads. The values above, along with the corresponding signal quality assessments, are indicative and may vary in practice depending on numerous factors such as the environment, interference, or the specifications and parameters of the communication equipment. Nonetheless, an interesting aspect of analyzing the obtained results is the identification of areas covered by a signal with its adopted limit values within the preset area. The radio coverage of the area with signals above the thresholds of \(-100\), \(-90\), \(-80\), and \(-70\) dBm is shown in Figure 12. These figures indicate that full radio coverage of the preset area, with a signal ensuring reception even at a level defined as weak, was not achieved. However, simulation-based computations suggest that more than 80% of the roads within the preset area are within the range of the transmitting stations. \begin{table}
\begin{tabular}{l l} \hline \hline
**RRSI** & **Signal Reception** \\ \hline \(-60\) dBm and better & Excellent \\ \(-70\) dBm to \(-60\) dBm & Very good \\ \(-80\) dBm to \(-70\) dBm & Good \\ \(-90\) dBm to \(-80\) dBm & Satisfactory \\ \(-100\) dBm to \(-90\) dBm & Weak \\ \(-110\) dBm to \(-100\) dBm & Very weak \\ \(-120\) dBm to \(-110\) dBm & Signal absent or almost absent \\ \hline \end{tabular}
\end{table} 
Table 5: General RSSI ranges. Figure 11: Areas of radio coverage by individual GW transmitting stations.
# Real-Time Conveyor Belt Deviation Detection Algorithm Based on Multi-Scale Feature Fusion Network
## 2 Materials and Methods
### Deviation Detection Module  
When the conveyor belt deviates, the deviation of the center line position of the two edges of the conveyor in the result and the label is used as the fault feature information. Assuming \((x_{it},y_{it})\) is the coordinate of the starting point of the left edge in the result, while \((x_{ib},y_{ib})\) is the coordinate of the starting point of the right edge, and the abscissa of midpoint between two edge \(x_{\rm A}\) and \(x_{\rm B}\) can be expressed as:  
\[x_{\rm A}=\frac{\sum\limits_{i=1}^{j}x_{it}}{j}, \tag{2}\]  
\[x_{\rm B}=\frac{\sum\limits_{i=1}^{n}x_{ib}}{n-j}, \tag{3}\]  
where \(j\) represents the pixel width of left edge and \(n-j\) represents the pixel width of right edge, then the abscissa of the center line of the left and right starting point are as follows:  
\[d_{1}=\frac{|x_{\rm B}+x_{A}|}{2}. \tag{4}\]  
The criteria for judging the deviation of conveyor is defined as:  
\[|d_{1}-d_{2}|>\alpha\omega. \tag{5}\]  
In Equation (5), \(d2\) is the abscissa of the center line of the two edges of conveyor belt in normal operation, \(w\) represents the width of belt and \(\alpha\) is constant. When the belt deviates, the deviation in the width direction will exceed 5% of the bandwidth, so the value of \(\alpha\) is set to 0.1. By comparing \(|d_{1}-d_{2}|\) and 0.1\(w\), it can be judged whether the conveyor belt is running a fault.
## 5 Results and Discussion
### Testing Results  
The testing process of a DQN involves deploying the intelligent agent to a new testing environment after training and evaluating whether the learned strategies can adapt to the new setting while obtaining reasonable rewards. The quality of the test results reflects the agent's generalization ability and its level of robustness. Throughout the training process of a DQN, the agent interacts with the environment and continually updates its Q-value function and strategy to achieve the objective of maximizing cumulative rewards.  
For the simulation of arterial intersections, three scenarios are established: Scenario 1, without the implementation of self-driving TSP; Scenario 2, with traditional ATSP; and Scenario 3, with arterial TSP controlled by the DQN algorithm. These three scenarios are simulated.  
The results of the comparison as to the average delay at the intersection are presented in Figure 7, and the comparison for the average queue length at the intersection is shown in Figure 8. The findings indicate that Scenario 3 demonstrates a 27.77% optimization in delay compared to Scenario 1, and a 30.55% optimization compared to Scenario 2. Additionally, the average queue length in Scenario 3 is optimized by 33.41% when compared to Scenario 1, and by 38.21% when compared to Scenario 2.  
Figure 5: Total delay training analysis.  
Figure 6: Average queue length training analysis.  
Figures 7 and \(8\) demonstrate that the real-time signal-priority control of self-driving buses using DQN proves to be particularly effective during the morning and evening peak hours when traffic volume is high. This approach significantly reduces delay and queuing for private vehicles, and importantly, the delay and queuing do not produce excessive fluctuations with varying traffic volumes. This method is suitable for arterial intersections with heavy traffic flow, as it helps alleviate traffic congestion during peak hours and enhances overall road capacity.
## 2 Proposed Methodology of This Study
### Support Vector Machines  
Support Vector Machines are among the most popular learning algorithms, since in terms of relatively small training data, they are efficient and reliable [12]. The aim of these machines is to build a separator for the data in the form of a hyperplane, with linear equation [13]  
\[\mathbf{w}\cdot\mathbf{x}+b=0, \tag{1}\]  
parametrized by vector \(\mathbf{w}\) and constant \(b\), to binary classify any entry \(\mathbf{x}\). By mapping both the entry data as well as the parameter vector into a higher dimensional space through a transform \(\Phi(\mathbf{x})\), the problem can be moved in a more fit environment for optimized classification, with the separation hyperplane function in the new form [13]  
\[f(\mathbf{x})=\sum_{j=1}^{m}\,\alpha_{j}\Phi\big{(}\mathbf{x}_{j}\big{)}\cdot \Phi(\mathbf{x})+b, \tag{2}\]  
where a crucial component emerges, the _kernel function_:  
\[K(\mathbf{x}_{j},\mathbf{x})=\Phi\big{(}\mathbf{x}_{j}\big{)}\cdot\Phi( \mathbf{x}). \tag{3}\]  
Few different kernels are adopted in this work, and they are shown in Section 4.  
As previously stated, SVMs are binary classifiers, which means that they can potentially distinguish data into one of only two classes. Classification on a number of classes \(N\) higher than 2 is feasible, but only by building many sub-classifiers, as seen in Figure 4, trained either to distinguish between a class and all others (One-vs-All) or between all possible couplings of classes (One-vs-One). In this research, multi-class classification is approached in a One-vs-One philosophy.
# Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder
## 4 Results and Discussion
### Frame-Based Parallel Algorithms Analysis
\begin{table}
\begin{tabular}{l r r r r r r r r} \hline \hline  & \multicolumn{4}{c}{**PartyScene**} & \multicolumn{4}{c}{**PeopleOnStreet**} \\ \hline
**QP** & \multicolumn{4}{c}{**10 Processes**} \\ \hline  & N \(\times\) R & \(1\times 10\) & \(2\times 5\) & \(5\times 2\) & \(10\times 1\) & \(1\times 10\) & \(2\times 5\) & \(5\times 2\) & \(10\times 1\) \\ \hline
37 & 94\% & 97\% & 96\% & 96\% & 89\% & 92\% & 92\% & 91\% \\
32 & 94\% & 97\% & 96\% & 97\% & 89\% & 94\% & 93\% & 92\% \\
27 & 94\% & 98\% & 97\% & 97\% & 89\% & 95\% & 93\% & 93\% \\
22 & 95\% & 97\% & 97\% & 97\% & 88\% & 94\% & 93\% & 93\% \\ \hline  & \multicolumn{4}{c}{**10 Processes**} \\ \hline  & N \(\times\) R & \(2\times 8\) & \(4\times 4\) & \(8\times 2\) & \(16\times 1\) & \(2\times 8\) & \(4\times 4\) & \(8\times 2\) & \(16\times 1\) \\ \hline
37 & 93\% & 94\% & 94\% & 92\% & 84\% & 85\% & 85\% & 84\% \\
32 & 93\% & 94\% & 94\% & 94\% & 85\% & 85\% & 85\% & 85\% \\
27 & 94\% & 95\% & 95\% & 95\% & 85\% & 85\% & 85\% & 85\% \\
22 & 94\% & 94\% & 95\% & 94\% & 85\% & 85\% & 85\% & 88\% \\ \hline  & \multicolumn{4}{c}{**20 Processes**} \\ \hline  & N \(\times\) R & \(2\times 10\) & \(4\times 5\) & \(5\times 4\) & \(10\times 2\) & \(2\times 10\) & \(4\times 5\) & \(5\times 4\) & \(10\times 2\) \\ \hline
37 & 91\% & 94\% & 92\% & 93\% & 80\% & 81\% & 80\% & 83\% \\
32 & 92\% & 95\% & 94\% & 94\% & 80\% & 82\% & 82\% & 83\% \\
27 & 92\% & 95\% & 95\% & 95\% & 81\% & 83\% & 83\% & 83\% \\
22 & 92\% & 95\% & 95\% & 94\% & 82\% & 82\% & 82\% & 83\% \\ \hline  & \multicolumn{4}{c}{**20 Processes**} \\ \hline  & N \(\times\) R & \(3\times 8\) & \(4\times 6\) & \(6\times 4\) & \(8\times 3\) & \(3\times 8\) & \(4\times 6\) & \(6\times 4\) & \(8\times 3\) \\ \hline
37 & 90\% & 91\% & 90\% & 93\% & 73\% & 77\% & 77\% & 76\% \\
32 & 92\% & 92\% & 93\% & 93\% & 77\% & 78\% & 78\% & 78\% \\
27 & 93\% & 94\% & 92\% & 94\% & 78\% & 78\% & 79\% & 78\% \\
22 & 93\% & 94\% & 93\% & 94\% & 78\% & 78\% & 78\% & 78\% \\ \hline  & \multicolumn{4}{c}{**FourPeople**} & \multicolumn{4}{c}{**ParkScene**} \\ \hline
**QP** & \multicolumn{4}{c}{**10 Processes**} \\ \hline  & N \(\times\) R & \(1\times 10\) & \(2\times 5\) & \(5\times 2\) & \(10\times 1\) & \(1\times 10\) & \(2\times 5\) & \(5\times 2\) & \(10\times 1\) \\ \hline
37 & 94\% & 97\% & 96\% & 95\% & 88\% & 92\% & 91\% & 90\% \\
32 & 94\% & 97\% & 96\% & 96\% & 90\% & 93\% & 92\% & 91\% \\
27 & 93\% & 95\% & 95\% & 95\% & 92\% & 93\% & 93\% & 93\% \\
22 & 95\% & 97\% & 98\% & 97\% & 94\% & 96\% & 95\% & 95\% \\ \hline  & \multicolumn{4}{c}{**16 Processes**} \\ \hline  & N \(\times\) R & \(2\times 8\) & \(4\times 4\) & \(8\times 2\) & \(16\times 1\) & \(2\times 8\) & \(4\times 4\) & \(8\times 2\) & \(16\times 1\) \\ \hline
37 & 94\% & 95\% & 96\% & 93\% & 87\% & 89\% & 89\% & 88\% \\
32 & 94\% & 95\% & 95\% & 94\% & 89\% & 89\% & 90\% & 89\% \\
27 & 93\% & 94\% & 94\% & 93\% & 90\%
## 3 Results and Discussion
### Experimental Results of Mechanical Properties
#### 3.3.2 Flexural Strength  
Figure 13 shows the flexural strength variation with volume content of RWG in concrete at 28 th day of curing. With 50% volume content of RWG in concrete, it can be observed that the flexural strength decreased by about 34.10% with respect to RC. The variation tendency is similar to the one observed for compressive strength at 28 th day of curing, namely between 0 to 20% (Figure 12). From 20 to 50% of RWG, one can notice that the flexural strength decreased significantly due to the adherence between cement paste and glass particles that becomes lower as the RWG is more and more incorporated into concrete. The same result was reported by several researchers [29; 30; 32]; for instance, [PERSON] et al. [18] found that, with addition 30% of volume percentage of RWG, the flexural strength decreased by 11.3%.
# An Initial Case Study of the Computational Modeling of Cord-Rubber Segments
## 2 Materials and Methods
### Computational Modeling  
#### 2.3.1 Multibody Modeling of the Cord-Rubber Coupling Assembly  
The cord-rubber coupling is located in the drivetrain of the electric railway vehicle and is used to transmit the torque between the electric motor and the gearbox. Its primary role consists of the flexible connection of the units mentioned above the drivetrain; it acts as a stiffness and damping element in the radial, axial and tangential directions. The tangential forces are crucial for the transmission of the torque. Concerning the nature of the movement of the electric motor and gearbox relative to each other, the bending stiffness of the unit must be considered. Due to the installation of individual segments in the coupling assembly, the load tensor expressed for each segment shows a complex character, changing periodically with the pitch angle of the drive mandrels to which the cord-rubber segment is attached.  
To investigate the nature of loading that occurs on individual segments, a computational model of the coupling assembly based on a multibody was built (Figure 5). This computational model allows parametrically changing the basic conceptual properties of the coupling and provides comprehensive information about the clamping pulley displacements on the individual cord-rubber segment during the actual operation of the coupling in real operating conditions. Prescribed limits of coupling operating conditions in terms of radial and axial displacement indicate the maximal deformation rate of each segment, where the combination of tension, bending and twisting is present. The model is intended for use in two stages of the computational approach:  
Initial stage--here, the simulation with limited operational conditions is performed. The main goal is to determine the maximal values of the individual segment deformation. This information will be used in the future to determine the boundary conditions of FEM simulations, which will serve to determine the stiffness characteristics of the segment.  
Final stage--here, the stiffness characteristics determined based on FEM will be entered into the multibody model. Simulation results from this stage will provide information about the reacting force course on each segment clamping pulley. Considering the real operating conditions (radial and axial displacements of the output shaft relative to the input shaft), the resulting force effects on the individual cord-rubber segments will not be identical. For this reason, periodically fluctuating forces inside the coupling are expected. This phenomenon occurs without carrying the torque through the coupling and vice versa. This causes torque loss in the coupling during the operation. The course of periodically fluctuating forces is crucial for the fatigue-life aspects evaluation.  
Figure 4: The force–deformation curve from the uniaxial tension test (**left side**) and the cord–rubber segment in specially designed clamping jaws during the tension test (**right side**).
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 6 \(\text{Sillcom}_{trd}\): Customized Optimization with Computation/ Communication Tradeoff  
As previously mentioned, the design of \(\text{Sillcom}_{3R}\) aims to reduce the number of communication rounds based on the premise that, when the computational cost is relatively low, communication rounds have a greater impact on end-to-end execution time. Specifically, [52; 53] states that the amortized time for each invocation of fixed-key AES is only 10 clock cycles, whereas real-world network latency typically exceeds 0.1 ms. This disparity of several orders of magnitude allows \(\text{Sillcom}_{3R}\) to minimize the impact of communication latency as the bottleneck when \(m\) is small.  
However, as \(m\) increases, the computation of \(O(m^{2})\) DDCF keys, which corresponds to \(O(m^{2}\cdot l)\) fixed-key AES computations, gradually approaches or even exceeds the communication delay. To address this computation bottleneck, we propose an alternative design, \(\text{Sillcom}_{trd}\), which reduces computational overhead by increasing the number of communication rounds. This tradeoff enables faster end-to-end query times in scenarios with a larger number of RPs. The core of \(\text{Sillcom}_{trd}\) lies in the design of an efficient top-\(k\) circuit. Leveraging the fact that \(k\) is small in indoor localization scenarios, we design an optimized top-\(k\) circuit, which we describe in detail below.
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 4 Results, Analysis and Discussion
### Energy Observables Result  
#### 4.2.1 Gentle Wind Scenario  
The energy observables (EMR and EDR) extracted from August 3 to August 5 are shown in Figure 6b,c with \(\textit{Threshold2}=0.3\). To study the relationship between the observables and in situ wind speed, we resampled the in situ data to match the temporal resolution of the observables. From Figure 6b,c, both observables show the same evolution with in situ wind speed.  
The scatter plots between wind speed and EMR are presented in Figure 7c, in which a strong linear dependence on wind speed of EMR can be observed. A simple linear polynomial is employed to describe their relationships.  
\[WS=m\cdot EMR+n \tag{11}\]  
where \(m\) and \(n\) are coefficients that can be obtained using least squares to fit EMR and in situ wind speed. To evaluate the wind speed retrieval performances, two metrics including the root mean square (RMSE) and the coefficient of determination were computed. As shown in Table 3, the correlation between EMR and wind speed is 0.769, with a RMSE of 0.89 m/s.  
As shown in Figure 7d, the scatter plots also show a strong linear relationship between wind speed and EDR. A similar linear polynomial was employed to describe their relationship.  
\[WS=a\cdot EDR+b \tag{12}\]  
Figure 6: Comparison between in situ wind speed measurement and observables in the gentle wind scenario. (**a**) In situ wind speed. (**b**) EMR. (**c**) EDR. With \(\textit{Threshold2}=0.3\), both EMR and EDR are computed from the normalized power waveform.  
where \(a\) and \(b\) are fitted coefficients. The fitting result indicates a correlation of 0.765, with a RMSE of 0.90 m/s.  
Two metrics of wind speed retrieval including coherent time and relative amplitude (RA) [18; 19] are employed to compare the retrieval performances with the new observables. The coherent time is the time that the autocorrelation of the complex value in specular delay point decays to \(1/e\) of its maximum. The RA is the amplitude ratio of a delay point in the trailing edge and specular point. Figure 7a,b illustrate the relationships between the wind speed, RA and the coherent time, respectively. As presented in Table 3, both methods show good retrieval performances in the gentle wind scenario.
# Fractional Order Controller Design for Wind Turbines
## 2 Materials and Methods  
### Two-Mass Wind Turbine Model  
Thanks to mechanical improvements that have been developed over recent years, wind turbines can transform the kinetic energy in wind into electrical power rapidly and efficiently. Consequently, the development of robust and intelligent controllers is also required. Although aeroelastic simulators exist, an analysis of their highly complex structures was unnecessary for this study. Furthermore, in this section, we present our simplified model that focused on controlling the speed of wind turbines. In the literature [23], the dynamics of wind turbines has been commonly examined using two-mass models. These models are specifically chosen due to their generalization of the derived control laws, which can be applied for wind turbines of various sizes. Generally, the structure of a variable speed wind turbine consists of a gearbox, a generator and an air turbine, as displayed in Figure 1. Furthermore, a set of nonlinear ordinary differential equations are listed, which were used during the design of our final model. Equation (1) was used to calculate the aerodynamic power of the rotor, where \(\rho\) is the density of air, \(R\) is the rotor radius, \(v\) is the wind speed and \(C_{p}\) is the power coefficient, which depended on the tip speed ratio \(\lambda\) and pitch angle \(\beta\) of the blade:  
\[P_{t}=\frac{1}{2}\rho R^{2}C_{p}(\lambda,\beta)v^{3} \tag{1}\]  
As can be seen in Equation (2), the tip speed ratio \(\lambda\) was defined as the difference between the rotor speed multiplied by the rotor radius and the wind speed:  
\[\lambda=\frac{\omega_{t}R}{v} \tag{2}\]  
In this study, the power coefficient \(C_{p}\) of the wind turbine blades was analytically estimated as in [24]:  
\[C_{p}(\lambda,\beta)=0.5176\bigg{(}\frac{116}{\lambda_{j}}-0.4\beta-5\bigg{)}e ^{-\frac{21}{\lambda_{j}}}+0.0068\lambda \tag{3}\]  
Figure 1: A mechanical schematic of a two-mass wind turbine system. \[\frac{1}{\lambda_{i}}=\frac{1}{\lambda+0.08\beta}-\frac{0.035}{\beta^{3}+1} \tag{4}\]  
Equation (5) was presented by [PERSON] et al.