esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### The Studies of the Behavioral Adjustment of HDV Drivers Interacting with CAVs
#### 2.2.3 Questionnaire Surveys
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Scenario** & **Method** & **Sample Size** & **Considering Individual Heterogeneity** & **Main Findings** \\ \hline
[56] & Car-following & Field test & 9 & No & Humans generally drive closer to their leader when following a CAV compared with the case when following another HDV. \\ \hline
[57] & Car-following & Field test & 10 & Yes/Trust & Drivers’ response to the lead vehicle is dependent on their subjective trusts on AV technologies. \\ \hline
[54] & Car-following & Field test & 9 & No & A driver following a CAV exhibits lower driving volatility in terms of speed and acceleration. \\ \hline  & & & & & Car-following behavior: No significant difference in the median headway when following a CAV compared with when following an HDV. \\ \multirow{5}{*}{[58]} & Car-following & & & & Gap acceptance behavior: human drivers adopt significantly smaller critical gaps when interacting with an approaching CAV when compared with an HDV. \\  & Gap acceptance & Field test & 18 & Yes/Trust & Overtaking behavior: drivers maintain a significantly shorter headway after overtaking a CAV in comparison to an HDV. \\ \multirow{5}{*}{[60]} & Car-following & & & & Positive information of CAVs: lead to closer interaction in comparison to HDVs. \\ \cline{2-6}  & Gap acceptance & & & & The increase in MPR lengthens the lane-changing preparation duration of HDVs. \\ \cline{2-6}  & Overtaking & & & & HDVs tend to exhibit more radical driving behavior when changing lanes into one with a CAV platoon. \\ \hline  & Car-following & & & & Human drivers are affected by CAV platoons in a dedicated lane and imitate the behavior of CAVs driving, car-following and lane-changing. \\ \multirow{5}{*}{[59]} & Lane-changing & & & & Age, gender, and education serve as significant factors in car-following behavior. \\ \cline{2-6}  & Lane-changing & & & & Drivers merged more frequently in front of a CAV at the unsignalized intersection, thus taking advantage of the law-abiding and cautious driving strategy of CAV. \\ \cline{2-6}  & On-ramp & & & & \\ \cline{2-6}  & Off-ramp & & & & \\ \cline{2-6}  & Basic segment & & & & \\ \hline  & Gap acceptance & & & & Drivers merged more frequently in front of a CAV at the unsignalized intersection, thus taking advantage of the law-abiding and cautious driving strategy of CAV. \\ \hline \hline  & & & & & Drivers merged more frequently in front of a CAV at the unsignalized intersection, thus taking advantage of the law-abiding and cautious driving strategy of CAV. \\ \end{tabular}
\end{table} 
Table 1: Studies focusing on the behavior adjustment of HDV drivers when facing CAVs.
# An Efficient Internet-Wide Scan Approach Based on Location Awareness
## 6 Conclusions  
In this study, we first experimentally analysed and found that when network scanning was performed using scanning sources from different countries, there was a significant difference in the time required for the scanning task, even though the target network was located in the same country. This phenomenon suggests that geographical location has a significant impact on scanning efficiency, especially in network scanning on a global scale. Differences in network environments in different countries can affect the accessibility of  
Figure 9: Impact of scanner clusters and number of target areas in coverage: **(a)** scan coverage of five scanner clusters; **(b)** scan coverage of ten scanner clusters; **(c)** scan coverage of twenty scanner clusters; **(d)** scan coverage of thirty scanner clusters. **Note**: All vertical axes indicate number of areas scanned, and all horizontal axes indicate coverage.  
different countries, which in turn affects the efficiency of scanning. Therefore, we address this problem by proposing a geolocation-based scanning method that aims to improve scanning efficiency. Specifically, the method divides the scanner clusters using geographic locations and constructs a similarity matrix and then schedules the scanning tasks in an ordered manner based on this similarity matrix, by which it can realise the fastest scanning method to scan all the regions of the globe within a limited scanning area. However, simply relying on geographic location to distribute tasks still cannot completely solve the scanning efficiency problem, especially in large-scale network environments. To further optimise the scanning efficiency, we propose a load-balancing approach. This approach not only balances the load among different scanner clusters but also optimises the workload distribution of scanning nodes within the same cluster. By dynamically adjusting the task volume of each scanning node, we are able to avoid the idle state of some scanners, which leads to the waste of scanning resources. This improves the overall scanning efficiency. In the experimental part, we demonstrate the effectiveness of the proposed model and algorithm in improving scanning efficiency by validating them in a real network environment. By comparing with traditional scanning methods, our location-aware scanning method improves the scanning efficiency while ensuring the scanning coverage. It especially performs well in multinational network environments. This result suggests that geographic location factors play an important role in improving network scanning efficiency.
###### Abstract
Experimental testing with 10-Gbaud QPSK data was conducted under different simulated atmospheric turbulence intensities, FSO and MMW speed-ratio, and FEC overheads. In addition, we also validate improvements in terms of bit error ratio (BER), outage probability, and throughput performance. Figure 1: Proposed hybrid RF/FSO architecture with ADCT and HARQ for communication between the Earth and satellites [1; 3; 14].
## 4 Shaking Force Reduction of the 3 RRR PPM
### Optimal Trajectory Planning of the Common CoM
The corresponding trajectory of the end-effector of the manipulator is not straight; there is no linear relation between both  
Figure 4: Trajectory of the end-effector, point H, going from the initial position in dashed lines to the final position in solid lines: Blue trace corresponds to the trajectory followed by the tool, while the red trace corresponds to the trajectory followed by the manipulator’s Center of Mass (CoM). Figure 5: The “bang-bang” profile imposed to the global CoM of the manipulator. trajectories. Note that, doing this, it can only be assured the initial and the final position of the original trajectory of the tool. Once the positions of the manipulator are solved using Equations (30) and (37), it is possible to solve velocities and linear momentum, using Equations (31) and (47) simultaneously. Note that this new set of equations is linear; no iterative solution is required. Once the positions and velocities are known, it is possible to calculate the accelerations and the shaking force solving simultaneously Equations (33) and (48). Note again that this set of equations is linear. The result from guiding the CoM with a \"bang-bang\" profile, assuring a constant acceleration during its motion, is an elimination of the variation of the shaking force and a reduction in its magnitude as it can be seen in Figure 7.  
Figure 6: The tool trajectory (point \(H\)) after guiding the global CoM with a “bang-bang” profile. Figure 7: Comparison of the shaking force: The blue curve is the magnitude of the shaking force when the tool is guided with a cycloidal profile. The red line is the magnitude of the shaking force when the global CoM of the manipulator is guided with a “bang-bang” profile. Once the positions, velocities, and accelerations of the \"basic points\" are solved, it is possible to calculate the shaking moment using Equation (49). Although guiding the CoM, assuring a constant acceleration, is focused on the elimination of the variation of the shaking force, it can also contribute to the reduction of the shaking moment and its variation. A shaking moment comparison is presented in Figure 8. The moment is calculated with respect to the origin of the global reference frame shown in Figure 3.
# Focus Estimation Methods for Use in Industrial SFF Imaging Systems
## 3 Results
### Comparison of Results for Measurements Taken with Different Operator Kernel Sizes  
Two of six operators selected in the previous section have a fixed kernel size: DSL and DPT. These are classical filters with a kernel size of 3 \(\times\) 3. The modification proposed in this subsection does not affect their behaviour. The other operators allow the size of the kernel to be changed. For the \"tennengard\" operators, this changes the averaging region on the XY-plane. By changing the kernel size for the SSTD and SVAR operators, a different number of pixels is considered when calculating the corresponding statistical measure.  
Measurements were carried out for a range of window sizes from 3 \(\times\) 3 to 21 \(\times\) 21. The window size for the calculations was carried out with a step of 2, due to the odd size requirement. The comparison was performed for a metallic surface captured with a DL illuminator.  
For the maximum method, zero AFE error values were obtained for each operator over the entire range of mask sizes.  
Figure 11 shows the AFE error values as a function of window size for operators with variable kernel sizes for the CoG method. For the CoG focus plane selection method, the AFE values are up to 1.2. The AFE values for the DSL and DPT filters do not depend on the window size and are also 0 for the height map considered. For such small AFE values, there is no observable trend between window size and error value.  
Figure 11: AFE values as a function of the size of the window size for the operators selected for comparison. Values were obtained for the CoG focus plane determination method.  
Figure 10: RMSE values as a function of the operator used for both focus plane selection methods. The results are grouped for the three considered surfaces.  
The DSL and DPT operators reached RMSE values for the maximum method of 7.7 and 4.9, respectively, and values for the CoG method of 7.3 and 4.8, respectively. Figure 12 shows the RMSE error values for the other operators. For both methods, the error values are below 8 over the entire range examined. The RMSE value decreases as the window size increases. The DPTT and DSLT operators achieve the best results in both cases. The changes in RMSE values are largest for smaller window sizes. The larger the window size, the smaller the impact of the size change on the RMSE value.  
When selecting the appropriate window size for a particular measurement application, consideration should also be given to the increase in required computing power, which increases as the window size increases.
# Kolmogorov-Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity
## 4 Results and Discussion
### Comparison of Time Cost and Model Performance  
To further evaluate the performance of the proposed KAN-based ROM, this section presents a comprehensive comparison with the DNN-based ROM proposed by [PERSON] et al. [24]. In this subsection, ROM1 refers to the proposed KAN-based reduced-order model, while ROM2 denotes the DNN-based reduced-order model. Both models utilize identical input signals and maintain exactly the same architectural configurations, including the number of layers and units per layer. Under these conditions, ROM1 comprises a total of 10,032 parameters, while ROM2 comprises 12,866 parameters. This represents an approximately 22.0% reduction in the total parameter count. Furthermore, due to its fewer parameters and inherently efficient network structure, ROM1 exhibits superior resistance to overfitting during training, thus achieving enhanced generalization performance.  
Table 2 compares the relative prediction errors of the two different ROMs with the direct CFD results for unsteady aerodynamic predictions. Figure 19 further illustrates a comparative analysis between the two ROMs' predictions of the limit cycle oscillations and the corresponding direct CFD computations. The results demonstrate that the proposed ROM1 achieves higher overall prediction accuracy compared to ROM2 in predicting both unsteady aerodynamic and LCO behaviors.  
The proposed reduced-order model demonstrates substantial computational efficiency compared to high-fidelity CFD simulations. In the present study, the most computationally intensive step involves the generation of datasets for model training and validation. Specifically, producing 7000 time steps, each encompassing 40 sub-iterations, requires approximately 9 h using 10 CPUs. Furthermore, an additional computational expense emerges during the model training phase, which typically takes around 4 h. However, once trained, the ROM significantly accelerates the prediction of unsteady aerodynamics and LCO responses. Specifically, predicting the unsteady aerodynamic of a single harmonic motion scenario takes approximately 0.7 s with the ROM, a stark contrast to the roughly 1 h required by equivalent CFD simulations. In predicting LCO responses, the ROM completes a prediction in approximately 120 s, while the corresponding high-fidelity CFD computations require approximately 8 h.  
Figure 18: Comparison of LCO trends with CFD results for the BACT model. (**a**) Plunging amplitude vs. \(V_{f}\). (**b**) Pitching amplitude vs. \(V_{f}\).  
These results clearly illustrate the high accuracy and efficiency of the proposed ROM in predicting unsteady aerodynamic and LCO responses. As the number of test scenarios increases, the computational advantage offered by the ROM becomes even more pronounced. Therefore, this approach is particularly beneficial in scenarios where computational resources are limited and rapid analysis turnaround is essential, underscoring the model's potential in streamlining unsteady aerodynamics and aeroelastic analyses.
# Grousers Effect in Tracked Vehicle Multibody Dynamics with Deformable Terrain Contact Model
## 1 Introduction
In this paper a MTB model of a tracked machine with a deformable terrain model was developed, also thanks to the authors' research group previous experience in MTB contact [32] and vehicle modelling [33]. The main assumption of the model considers the terrain deformation located in a close area around the vehicle tracks. Thus, the soil deformation can be kinematically correlated to the track motion. The soil mechanics equations were implemented on each rigid body used to model the rubber tracks. Each link is connected to each contiguous one by means of bushing elements aimed to replicate a realistic track elastic characteristic. Soil mechanics equations were implemented through a specifically designed routine which solved only the equations for the track links in contact with the terrain. The algorithm defined at each iteration the forces applied to each track link in contact with the terrain. Moreover, the effects of grousers in the overall tractive performance of the machine were investigated. To model grousers effects, soil mechanics equations related to passive terrain failure [34; 35] were considered and machine draw-bar pull was evaluated for different types of soil.
## 2 Materials and Methods
### HPO-SRU Training  
The SRU deep learning model is a model proposed by [PERSON] et al. [50] based on the research on LSTM, GRU, and other models. It introduces parallel processing to reduce training time and complexity while maintaining the accuracy. The structure of the SRU model is shown in Figure 7.  
The network structure of SRU is as follows:  
\[\begin{cases}\widetilde{x_{t}}=Wx_{t}\\ f_{t}=\sigma\Big{(}W_{f}x_{t}+b_{f}\Big{)}\\ r_{t}=\sigma(W_{r}x_{t}+b_{r})\\ c_{t}=f_{t}\odot c_{t-1}+(1-f_{t})\odot\widetilde{x_{t}}\\ h_{t}=r_{t}\odot g(c_{t})+(1-r_{t})\odot x_{t}\end{cases} \tag{7}\]  
As can be seen from the above formulas, \(h_{t}\) does not rely on \(h_{t-1}\), so the program can be parallelized. The last two formulas can perform calculations very quickly and concisely. Their operations are all between corresponding elements.  
Simultaneously, matrix multiplication can be batch processed, which can significantly improve the intensity of computation and GPU utilization. In the above formula, three weight matrices can be merged into one large matrix:  
\[U^{T}=\begin{pmatrix}W\\ W_{f}\\ W_{\tau}\end{pmatrix}[x_{1},...,x_{t}] \tag{8}\]  
In order to improve the accuracy of the SRU in photovoltaic power prediction, the four main parameters of the optimal SRU should be sought, namely hidden size, learning rate, network layers, and batch size. The HPO algorithm is used to optimize the four parameters  
Figure 7: The structure of the SRU.  
of the SRU model. The fitness function is set as the MAE between the predicted and observed values. The steps for photovoltaic power prediction based on the HPO-SRU are as follows:  
1. Standardize the data processed with outliers, normalize the entire dataset to [0, 1], and divide it into a training set and a test set according to the ratio of 8:2.
2. Parameter initialization: Initialize the parameters of the HPO algorithm, including the number of search agents N and the maximum number of iterations T, and set the upper and lower boundaries of the HPO algorithm and map them to the upper and lower bounds of the SRU parameters' hidden size, learning rate, network layers, and batch size.
3. Obtain the initial optimal fitness value through SRU training and prediction.
4. Adjust the positions of the hunters and prey according to the rules of the HPO, simultaneously updating the fitness values of members whose positions have been adjusted.
5. Obtain the best solution to the problem and output the optimal parameters of the SRU. Build a model using the optimal parameters for prediction.
# Virtual Flux Control Methods for Grid-Forming Converters: A Four-Method Comparison
## 4 HIL Results and Comparison
### Phase Jump  
In this test, a 20\({}^{\circ}\) phase jump is induced in the grid at t = 0.5 s, and the results are presented in Figure 11. The first plot illustrates the grid frequency measured through a PLL, which experiences a temporary disturbance at the moment of the phase jump before stabilizing back to its nominal value. The second and third plots display the active and reactive power measured during the test, respectively. Similar to the previous case, the reactive power dynamics remain almost identical across all four control algorithms, with the FBC method exhibiting slightly higher oscillations compared to the others.  
Figure 11: Frequency, active and reactive power during a phase jump.  
Figure 10: Instantaneous currents during a frequency variation.  
On the other hand, significant differences emerge in the active power response of the converter under this rapid disturbance, which directly impacts the APS. While all control schemes reach steady state in approximately the same timeframe, the VFOC and DC algorithms exhibit higher oscillations at the time when phase jump occurs, with VFOC being the most affected (reaching a peak of \(-4\) MW). In contrast, FBC proves to be the most resilient, only reaching a peak of less than \(3\) MW and displaying the least deviation during this type of disturbance.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 3 Results
### Detection of Tree Losses by Difference in Vegetation Heights  
Tree loss detection is performed based on the difference in vegetation heights (Delta DHM) between November 2018 and April 2019, the minimum tree height (25 m), and the average tree crown area (152 m\({}^{2}\)). Negative values represent tree losses that correspond to new clearing or tree burning (Figure 14). The identification of tree losses is illustrated on Figure 15. At Site 1, there are a minimum of 19 tree losses associated with an area of 0.4 ha between November 2018 and April 2019. At Site 6, there are a minimum of 16 tree  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multicolumn{3}{c}{**November 2018**} & \multicolumn{3}{c}{**April 2019**} \\ \cline{2-7}  & **Maximum** & **Average (m)** & **Standard** & **Maximum** & **Average (m)** & **Standard** \\  & **Height (m)** & & **Deviation (m)** & **Height (m)** & & **Deviation (m)** \\ \hline Site 1 & 57.05 & 34.83 & 6.04 & 59.52 & 35.30 & 6.34 \\ Site 6 & 59.59 & 34.92 & 6.26 & 59.62 & 35.38 & 6.38 \\ Site 8 & 61.29 & 34.29 & 6.27 & 61.16 & 34.63 & 6.40 \\ Site 9 & 65.06 & 35.43 & 7.21 & 64,07 & 35.55 & 7.15 \\ Site 11 & 63.41 & 37.00 & 7.14 & 62.69 & 36.88 & 7.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical distribution of tree heights at each site between November 2018 and April 2019 (minimum height of 25 m).  
Figure 12: Vegetation height frequency distribution histograms for Sites 1, 6, 8, 9, and 11 in November 2018 and April 2019.  
losses associated with an area of 0.2 ha. At Site 8, there are a minimum of 17 tree losses associated with an area of 0.3 ha. At Site 9, there are a minimum of 28 tree losses associated with an area of 0.5 ha. As for Site 11, there are at least 27 tree losses associated with an area of 0.6 ha.  
Figure 14: Distribution of tree heights in April 2019 and tree losses during the period of November 2018–April 2019.  
Figure 13: Tree crown area delineated on aerial images acquired by drone.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Number of Polygons** & **Minimum Area (m2)** & **Maximum Area (m2)** & **Average Surface Area (m2)** \\ \hline Site 1 & 22 & 26 & 373 & 166 \\ Site 6 & 31 & 23 & 451 & 127 \\ Site 8 & 20 & 7 & 487 & 164 \\ Site 9 & 27 & 12 & 385 & 113 \\ Site 11 & 24 & 25 & 838 & 206 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Tree crown area statistics for all study sites.
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
###### Abstract  
Due to limited computation resources of a vehicle terminal, it is impossible to meet the demands of some applications and services, especially for computation-intensive types, which not only results in computation burden and delay, but also consumes more energy. Mobile edge computing (MEC) is an emerging architecture in which computation and storage services are extended to the edge of a network, which is an advanced technology to support multiple applications and services that requires ultra-low latency. In this paper, a task offloading approach for an MEC-assisted vehicle platooning is proposed, where the Lyapunov optimization algorithm is employed to solve the optimization problem under the condition of stability of task queues. The proposed approach dynamically adjusts the offloading decisions for all tasks according to data parameters of current task, and judge whether it is executed locally, in other platooning member or at an MEC server. The simulation results show that the proposed algorithm can effectively reduce energy consumption of task execution and greatly improve the offloading efficiency compared with the shortest queue waiting time algorithm and the full offloading to an MEC algorithm.  
mobile edge computing; vehicular platooning; task offloading; Lyapunov optimization