esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization
## 3 Experiments
### Case Study
#### 3.2.1 Dynamic Communities during Morning Rush Hours  
During the morning rush hour, people purposefully went to work from the residential areas. Therefore, the communities around residential areas and work places usually changed more drastically. Some examples are given in Figure 15.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**MethodSnapshot** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** \\ \hline EACO & 4.6 & 4.5 & 4.5 & 4.3 & 4.6 & 4.5 & 4.4 & 4.5 & 4.3 & 4.3 & 4.3 & 4.3 & 4.4 & 4.3 & 4.2 \\ FacetNet [19] & 3.8 & 2.8 & 3.1 & 3.0 & 3.2 & 4.0 & 2.8 & 3.2 & 2.6 & 2.1 & 2.8 & 2.2 & 3.2 & 2.4 & 1.7 & 1.3 \\ DYNMOGA [20] & 4.1 & 4.1 & 4.1 & 4.1 & 4.1 & 4.1 & 4.1 & 4.0 & 4.0 & 4.1 & 4.0 & 4.1 & 4.1 & 4.1 & 4.0 & 4.0 \\ RWLA [7] & 4.3 & 4.2 & 4.2 & 4.2 & 4.2 & 4.3 & 4.2 & 4.2 & 4.2 & 4.2 & 4.2 & 4.2 & 4.1 & 4.1 & 4.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average frequency of OD pairs between road segments within the same community.  
Figure 14: The evolution of the communities identified by EACO in the real-world dataset.  
Community **C7** at 8:00-9:00 contains some commercial areas and workplaces (e.g., Chongwennen business district, Wangfujing business district, Tiananmen Square, and the R&F work area). When people are already working, the intensity of the human activity in **C7** decreases. Therefore, **C7** in Figure 15a gradually shrinks over time (**C12** and **C9** in Figure 15b,c). Community **C3** at 8:00-9:00 is near the Chaoqing business district. As people continue to come to the business district, Community **C3** gradually grows over time (**C24** and **C16** in Figure 15b,c). Community **C17** at 8:00-9:00 is near the residential area of Ding'anli. Since people leave this area to go to the nearby workplaces during the morning rush hour, **C17** in Figure 15a grows into **C19** in Figure 15b (near Ding'anli residential area and Aegegan business district) at 9:00-10:00. At 9:00-10:00, community **C23** near Fengtai Science and Technology Park (Figure 15b) is separated from the large community **C5** identified at 8:00-9:00. At 8:00-9:00, people go to work in Fengtai Science and Technology Park; therefore, a large community is identified around this place. After people come to their workplaces, the spatial interactions within Fengtai Science and Technology Park become strong. We can see that the dynamic communities identified by EACO can be used to reveal the effect of the morning commuting patterns on the spatial interactions between road segments.  
Figure 15: Some communities identified by EACO: (**a**) 8:00–9:00; (**b**) 9:00–10:00; (**c**) 10:00–11:00.
# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 5 Experiment
### Study Area and Implementations
#### 5.1.2 Experimental Implementations  
As mentioned in Section 3.1, we prepared the benchmark datasets including 37 land cover categories. Then, we extended the number of training images through the data  
Figure 5: Visualization of study area: (**A**) study area map; (**B**) samples of coastal land covers.  
augmentation methods introduced in Section 3.2. Based on the training images regarding waters, we fine-tuned and implemented the SAM model to conduct semantic segmentation to extract water areas in each optical satellite image. The SAM used for segmentation can be accessed via [[https://github.com/facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything)]([https://github.com/facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything)) (access date: 29 September 2024). Moreover, we utilized the training images regarding other types of land cover to fine-tune ViTAE-RVSA. Based on the fine-tuned ViTAE-RVSA, we generated the image gridding and conducted multi-label land cover classification. The ViTAE-RVSA used for segmentation can be accessed via [[https://github.com/ViTAE-Transformer/Remote-Sensing-RVSA7](https://github.com/ViTAE-Transformer/Remote-Sensing-RVSA7) tab-readme-ov-file]([https://github.com/ViTAE-Transformer/Remote-Sensing-RVSA7](https://github.com/ViTAE-Transformer/Remote-Sensing-RVSA7) tab-readme-ov-file) (access date: 29 September 2024). Finally, we used the VSM and semantic similarity to convert the classification results into land use semantics.
## 4 Numerical Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & \(|x_{1}-a|\) & \(|x_{2}-a|\) & \(|x_{3}-a|\) & \(\rho\) \\ \hline Equation \((42)-PD,\gamma_{0}=-0.01\) & 0.10690E\(-\)2 & 0.10554E\(-\)12 & 0.24668E\(-\)58 & 4.5605896 \\ Equation \((43)-PD,\gamma_{0}=-0.01\) & 0.10690E\(-\)2 & 0.58225E\(-\)14 & 0.18875E\(-\)67 & 4.7487424 \\ Equation \((14)-(17),n=2,\lambda_{0}=0.5\) & 0.32719E\(-\)4 & 0.42649E\(-\)19 & 0.26035E\(-\)87 & 4.5827899 \\ Equation \((15)-(17),n=2,\lambda_{0}=0.5\) & 0.32719E\(-\)4 & 0.47493E\(-\)20 & 0.16676E\(-\)96 & 4.8272294 \\ Equation \((14)-(17),n=2,\lambda_{0}=1\) & 0.58111E\(-\)4 & 0.25364E\(-\)18 & 0.61743E\(-\)84 & 4.5691828 \\ Equation \((15)-(17),n=2,\lambda_{0}=1\) & 0.58111E\(-\)4 & 0.28197E\(-\)19 & 0.69228E\(-\)93 & 4.8066915 \\ Equation \((14)-(17),n=3,\lambda_{0}=1\) & 0.22673E\(-\)8 & 0.14247E\(-\)76 & 0.38886E\(-\)690 & 8.9963034 \\ Equation \((15)-(17),n=3,\lambda_{0}=1\) & 0.22673E\(-\)8 & 0.53419E\(-\)81 & 0.96778E\(-\)777 & 9.5795515 \\ Equation \((16)-(17),n=3,\lambda_{0}=1\) & 0.22673E\(-\)8 & 0.45910E\(-\)83 & 0.96092E\(-\)815 & 9.7957408 \\ Equation \((14)-(17),n=3,\lambda_{0}=1.5\) & 0.18012E\(-\)9 & 0.49194E\(-\)86 & 0.27126E\(-\)775 & 9.0024260 \\ Equation \((15)-(17),n=3,\lambda_{0}=1.5\) & 0.18012E\(-\)9 & 0.13193E\(-\)91 & 0.20518E\(-\)878 & 9.5794268 \\ Equation \((16)-(17),n=3,\lambda_{0}=1.5\) & 0.18012E\(-\)9 & 0.11706E\(-\)93 & 0.17692E\(-\)918 & 9.7974669 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Numerical results for \(f_{2}(x)\) by the methods without memory. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & \(|x_{1}-a|\) & \(|x_{2}-a|\) & \(|x_{3}-a|\) & \(\rho\) \\ \hline Equation \((4)n=2,\lambda=-1.5\) & 0.29673E\(-\)2 & 0.37452E\(-\)10 & 0.94752E\(-\)42 & 4.0001713 \\ Equation \((4)n=2,\lambda=-0.5\) & 0.27276E\(-\)4 & 0.11867E\(-\)19 & 0.42516E\(-\)81 & 4.0000025 \\ \(KM4,\beta=0.5\) & 0.37189E\(-\)2 & 0.32631E\(-\)9 & 0.19533E\(-\)37 & 3.9993916 \\ \(BRM8,h(t)=1+2t+5t^{2},\gamma=1\) & 0.84179E\(-\)4 & 0.62964E\(-\)31 & 0.61512E\(-\)248 & 8.0000456 \\ Equation \((4)n=3,\lambda=-1\) & 0.34838E\(-\)7 & 0.19030E\(-\)62 & 0.15080E\(-\)504 & 8.0000000 \\ Equation \((4)n=3,\lambda=-0.5\) & 0.11873E\(-\)7 & 0.80149E\(-\)66 & 0.34562E\(-\)531 & 8.0000000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical results for \(f_{1}(x)\) by the methods with memory.
## 3 Development of Robot Patient's Lower Limbs
### Robot Patient's Lower Limb
#### 3.2.3 Measurement of External Torque  
To satisfy the requirement of force sensing, the compliant unit was designed to be capable of measuring spring deformation. The relative movement between the compliant parts I and II resulted in deformation of the spring and an angular variation of the angular sensor, as shown in Figure 6. If no external force was present, the angular difference remained zero, indicating that there was no compression or deformation of the springs. If, however, an external force were applied, the springs would be compressed, causing a variation in the angular difference, which would be detected by the angle sensor of the compliant unit. The degree of angular difference allows the external torque \(\tau_{external}\) to be computed using the following Equations (2)-(4).  
\[\tau_{external}=\tau_{deform} \tag{2}\]  
\[\Delta\theta_{c}=\Delta\theta_{ang}\ /\ R \tag{3}\]  
\[\tau_{deform}= n\cdot\Delta S\cdot k_{compliant}=n\cdot d\cdot\Delta\theta_{ang} \cdot k_{spring} \tag{4}\]  
Here, \(\tau_{deform}\) is the torque after the spring has been compressed; \(\theta_{c}\) is the angular difference between compliant unit parts one and two, and \(\theta_{angle}\) is the angle measured from the angular position sensor. Additionally, \(R\) is the ratio of the two gears (1:4) and the ratio between \(\Delta\theta_{c}\) and \(\Delta\theta_{ang}\). The quantity \(d\) is the radius (29.5 mm) from the center to the spring, and \(k_{compliant}\) and \(k_{spring}\) are the stiffness of the compliant unit and the elasticity coefficient of the spring, respectively. Furthermore, \(n\) is the number of springs placed in the compliant unit (four in the current study). \(\Delta S\) and \(\Delta l_{c}\) are the variations in arc length and linear distance when the springs are deformed. The design of the compliant unit allows the maximum deformation of the spring when \(\Delta\theta_{ang}\) equals 10 \({}^{\circ}\). Therefore, the measurable torque ranges from 0 to 42 Nm.  
Figure 5: Mechanical design of a hollow compliant unit.  
Figure 6: Operating principle of compliant unit when external torque is applied.
## 3 Analysis of the DQL Method
### Benchmark for Step Strategies
#### 3.2.3 Discussion on the Experiment Results  
The performance profile for all the DQL strategies is shown in Figure 2. From Figure 2, we can see that the performance profile formed three clusters. The best performing strategy combinations were strategies 123, 122, 121, 120, and 124. The underperforming strategy combinations were strategies 102, 103, 101, 100, and 104. In addition, this ranking held for any \(\tau\). We therefore drew the following conclusions.  
* Quadratic step strategy 2 outperformed quadratic step strategy 1, which outperformed disabling the quadratic step. This showed that the quadratic step led to a performance improvement.
* Linear step strategy 4 was the worst strategy in every cluster. This strategy slowed down the performance. In addition, linear step strategies 1, 2, and 3 and disabling the linear step showed a mixed performance. Their performance differences were too small to find a clear winner.  
These conclusions above gave us the insight to develop the Smart DQL method. In the Smart DQL method, we allow the method to choose the appropriate strategy dynamically and adaptively. First, since both quadratic step strategies were better than disabling the quadratic step, we decided to include both quadratic step strategies in the Smart DQL method. For the linear step strategies, we decided to remove linear step strategy 4 and we allowed the method to choose appropriate linear step strategies. In addition, we developed a better rotation strategy that selected the rotation direction using the results of previous iterations. The Smart DQL method is discussed in the next section.
## 2 Mobility Models in FANETs
### Time-Dependent Mobility Model
#### 2.3.3 Smooth Turn  
The Smooth Turn (ST) model enables nodes to travel in various directions while maintaining a connection between their acceleration in spatial and temporal variables [60]. In ST, every UAV selects a location in the flying sky and revolves about it until it finds a new turning point. Moreover, to achieve a smooth trajectory, it chooses a point that is  
Figure 12: The trajectory of FANETs using BSA models.  
Figure 13: The trajectory of FANETs using GM models.  
perpendicular to the UAV's direction. The waiting time interval is exponentially distributed, according to the model. Figure 14 illustrates the trajectory of this mobility model. Without adding any additional constraints, ST accurately depicts the smooth movement patterns of UAV aircraft. ST model is designed to support FANETs monitoring operation [61]. Further, it can be used in the Internet of Things (IoT) integrated with UAV Networks [62].
# Research on the Impact of Flexible Working Hours on Reducing Traffic Delays in the City
## 2 Materials and Methods
### Simulation of Different Variants  
The calibrated traffic model assesses the current state of the evaluated traffic network and simulates various scenarios involving defined adjustments. The primary adjustment involved modifying the demand model to spread the peak traffic intensity over a longer period-modeling the temporal distribution of peak traffic while maintaining the same number of vehicles passing through the network. The morning peak hour (MPH) was from 7:15 to 8:15, with the peak quarters measured just before 8:00. It can be assumed that this morning peak is due to the start of the work shift at 8:00. With flexible WHs, some residents might choose a different start time for work. For this scenario, it is assumed that residents will be allowed to start their work shift at 9:00.  
Three scenarios were simulated:  
* Variant 0: The current state.
* Variant 1: A state where 10% of the peak traffic intensity is shifted to the period immediately following the current peak. Specifically, 10% of the intensity from the period 7:15-8:15 is shifted to 8:00-9:15, and 10% of the intensity from 14:00-16:15 is shifted to 16:15-17:15.
* Variant 2: A state where 20% of the peak traffic intensity is shifted to the period immediately following the current peak. Specifically, 20% of the intensity from the period 7:15-8:15 is shifted to 8:00-9:15, and 20% of the intensity from 14:00-16:15 is shifted to 16:15-17:15.  
It is not possible to accurately determine how traffic will change after the introduction of flexible WHs. However, we assume that people will choose times when they experience  
Figure 5: Example of simulation model calibration. Source: processed by authors.  
Figure 6: Example of subpath for traffic flow no. 7: red section—selected subpath, purple—detectors, arrows—traffic markings, triangles—traffic signs. Source: processed by authors.  
less delay. A 10% and 20% shift from the traffic peak were chosen to allow an estimation of how the situation would change if the traffic peak were reduced by, for example, 15%.  
The traffic survey data were collected in 15-min intervals, as were the input values into Aimsun, and the simulation evaluation was performed every minute. A 10% reduction in the traffic peak means reducing the value of each 15-min interval by 10%, meaning the number of vehicles by which the traffic peak is reduced differs in each interval.
###### Abstract  
The damage evolution process of layered rock is influenced by its fine structure, lamination direction, and confining pressure, exhibiting significant anisotropic characteristics. This study focuses on shale as the research object, employing indoor tests and theoretical analysis to define damage variables and initial damage based on ultrasonic wave velocity. This research investigates the damage evolution law of layered rock under varying confining pressures and dip angles. The findings reveal that damage variables defined using transverse wave velocity effectively reflect the damage evolution process. Additionally, confining pressure significantly affects damage evolution, with increasing pressure causing a rightward shift in the damage variable-strain curve and an increase in initial damage. The slab inclination angle also influences damage evolution; samples with 45\({}^{\circ}\) and 60\({}^{\circ}\) inclinations are more susceptible to damage, with initial damage showing a trend of increasing and then decreasing. To accurately describe the relationship between damage variables and strain during the loading process, this paper establishes a segmented damage evolution equation characterized by wave velocity. Initially, an inverse proportional function is employed to characterize the strain before crack closure. Subsequently, a logistic function represents the curve from crack strain to peak strain. This combined approach provides a comprehensive depiction of the damage evolution. This study underscores the importance of considering confining pressure and laminar inclination in the analysis of rock stability and integrity. These results provide critical insights into the damage evolution characteristics of layered rocks, offering valuable references for engineering safety assessments.  
rock mechanics; layered rock; initial damage; ultrasonic wave velocity; damage evolution
## 4 Conclusions and Prospects
### Intelligent systems for multi-field coupling:  
The increasing complexity of deep mining and resource extraction necessitates the development of intelligent mining strategies that can handle the dynamic interplay between temperature, water flow, stress, and chemical fields in rock masses. To address this, researchers need to focus on the creation of intelligent theories and control systems. For instance, strategies involving stress-dominated energy control, high-temperature management, and water resource optimization are essential for the efficient utilization of deep mineral deposits. Furthermore, by leveraging advanced sensing technologies and machine learning, a more precise understanding of the multi-field environment can be achieved. This would enable the development of intelligent systems capable of identifying potential hazards and improving the safety and efficiency of resource extraction in complex underground environments.
## 3 Proposed Sparse SLMS Algorithms
### Third Proposed Algorithm: SLMS-R1  
In addition to the RZA in (23), RL1 function was also considered as an effective sparse constraint in the field of compressive sensing (CS) [19]. By choosing a suitable reweighted factor, \(\delta_{RL1}\), RL1 could approach the optimal \(\ell_{0}\)-norm (L0) constraint. Hence, the LMS-RL1 algorithm has been considered an attractive application of sparse channel estimation. The LMS-RL1 algorithm [5] was developed as  
\[G_{RL1}\left(\mathbf{w}(n)\right)=\left(1/2\right)e^{2}(n)+\lambda_{RL1}\left| \left|\left.\mathbf{f}(n)\mathbf{w}(n)\right|\right|\right|_{1} \tag{24}\]  
where \(\lambda_{RL1}\) denotes positive regularization parameter and \(\mathbf{f}(n)\) is defined as  
\[\left[f(n)\right]_{i}=\frac{1}{\delta_{RL1}+\left|\left[\mathbf{w}(n-1)\right]_{i }\right|},\ i=0,1,\cdots,N-1 \tag{25}\]where \(\delta_{RL1}>0\) and then \(\left[f(n)\right]_{i}>0\). By taking the derivation of Equation (24), the LMS-RL1 algorithm was updated as  
\[\begin{split}\mathbf{w}(n+1)&=\mathbf{w}(n)+\mu\frac{\partial G _{RL1}(\mathbf{w}(n))}{\partial\mathbf{w}(n)}\\ &=\mathbf{w}(n)+\mu e(n)\mathbf{x}(n)-\rho_{RL1}\text{sgn}\left(f(n)\mathbf{w }(n)\right)\mathbf{f}^{T}(n)\\ &=\mathbf{w}(n)+\mu e(n)\mathbf{x}(n)-\rho_{RL1}\text{sgn}\left(\mathbf{w}(n) \right)\mathbf{f}^{T}(n)\\ &=\mathbf{w}(n)+\mu e(n)\mathbf{x}(n)-\frac{\rho_{RL1}\text{sgn}(\mathbf{w}( n))}{\delta_{RL1}+\left|\mathbf{w}(n-1)\right|}\end{split} \tag{26}\]  
where \(\rho_{RL1}=\mu\lambda_{RL1}\). The third step of derivation can be obtained since \(\text{sgn}\left(f(n)\right)=\mathbf{1}_{1\times N}\) and then \(\text{sgn}\left(\mathbf{f}(n)\mathbf{w}(n)\right)=\text{sgn}\left(\mathbf{w}(n)\right)\). Here the cost function \(G_{RL1}(n)\) is convex due to the fact that it does not depend on \(\mathbf{w}(n)\). Likewise, sign function is adopted for constraint \(e(n)\) and then robust SLMS-RL1 algorithm is obtained as  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu\text{sgn}\left(e(n)\right)\mathbf{x}(n)-\frac{\rho_{RL1 }\text{sgn}\left(\mathbf{w}(n)\right)}{\delta_{RL1}+\left|\mathbf{w}(n-1)\right|} \tag{27}\]
# Design and Mechanical Properties of Negative Poisson's Ratio Structure-Based Topology Optimization
###### Abstract  
Scholars have shown significant interest in the design and investigation of mechanical metamaterials with a negative Poisson's ratio as a result of the rapid progress in additive manufacturing technology, giving rise to the concept of metamaterials. The mechanical properties of structures with a negative Poisson's ratio, including Poisson's ratio, elastic modulus, and impact performance, have received growing scrutiny. This paper introduces the design of a novel concave beetle-shaped structure with a negative Poisson's ratio. The structure is developed using the variable density topology optimization method, with the design parameters adjusted to achieve optimal results from six datasets. The mechanical properties of the concave beetle-shaped structure are comprehensively assessed with the integration of mathematical models derived from mechanics theory, quasi-static compression tests, and finite element analyses. This study's findings indicate that the intrinsic parameters of the structure significantly influence its properties. The structure's Poisson's ratio ranges from \(-0.267\) to \(-0.751\), the elastic modulus varies between 1.078 and 5.481 MPa, and the specific energy absorption ranges from 1.873 to 2.634 kJ/kg, demonstrating an improvement of up to 40%.  
concave beetle-shaped structure; negative Poisson's ratio; topology optimization; additive manufacturing; finite element analysis Article
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## 4 Simulation Result
### Max Current Test  
In the following the control system is tested by driving the AUV as before, but introducing the effect of the tidal current acting on the vehicle movement. The dynamics have been set to provide the worst case operating conditions (according to the specifications provided) by considering the maximum angular orientation (\(\beta=-0.1745\) rad) with respect to the final docking position and the maximum current speed (\(V_{T}=0.514\) m/s) acting on the opposite direction with respect to the vehicle's orientation. The performance is shown in Figure 8, which reveals the variables controlled with respect to the set-point positions. Compared with the previous results, the performance indicates the effect of the current that increases oscillations. Despite this effect, the control system has the ability to control the system in this worst case scenario, maintaining the settling time and the steady-state performance. Figure 9 shows the main thruster control signal, where the current disturbance effect includes the additional oscillations.  
Figure 6: No current test: rear side thrusters’ control signals (blue plot) with respect to the saturation values (dashed red plot).  
Figure 7: No current test: rear side thrusters’ control signals (blue plot) with respect to the saturation values (dashed red plot).  
Figure 8: Max current test: controlled variables (blue plot) with respect to the target position (dashed black plot).  
Figure 9: Max current test: main thruster control signal (blue plot) with respect to the saturation and backslash values (dashed red plot).  
Due to the presence of the current forces, these control signals are more aggressive. In fact, when the vehicle reaches zero forward speed, only the side thrusters and main thruster are used to maintain the required positions opposing the current forces.