esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions
### Control Effect of the Velocity-Dependence Parameter  
The velocity-dependence parameter \((a-b)\) in rate-and-state friction law controls the fault frictional behavior for constant normal stress. Three faults exhibited velocity-strengthening at room temperature, both in dry and wet samples with different injection pressures, that is, \((a-b)>0\) (see Table 3), indicating that the faults were not under the necessary condition for unstable slip. Thus, the stress drop and the accelerated sliding caused by the injection of 15 MPa pressurized water were direct responses of frictional strength to the reduction in the effective normal stress. Furthermore, the sliding behaviors were affected by factors such as injection pressure, injection rate, rock permeability, fault deformation property, etc. In the experiments, the stress drop was proportional to the actual pore pressure in the fault, which means that the magnitude of the induced earthquake was related to the injection parameters, such as injection volume and rate, in agreement with previous numerical simulations [45] and those recorded in the field cases [6; 46].  
Previous researches have shown that temperature can affect the velocity-dependent property of carbonate rocks [38; 47; 48]. The limestone fault exhibited velocity-weakening and regular stick-slip at 100 \({}^{\circ}\)C, in line with previous experiments [38; 47]. Based on the experiments of normal stress perturbation on fault sliding instability [49], when the shear stress of a velocity-weakening fault is close to the frictional strength, a stress perturbation above 0.03 MPa can induce the critically stressed fault dynamic slip. However, the injection of 1 MPa pressurized water did not cause a significant change in shear stress in this experiment, partly attributable to the low criticality of fault stress during injection. When the injection pressure increased from 1 MPa to 15 MPa, it was large enough to ignore the effect of criticality of the fault stress. Consequently, the injection immediately induced a stick-slip, producing a sizable stress drop. Due to the velocity-weakening property, the fault sliding was faster than at room temperature, implying that the high-pressure injection in a velocity-weakening fault is more likely to induce the fault dynamic slip, thereby triggering earthquakes. According to rate-and-state friction law, a fault characterized by velocity-weakening meets the conditions for earthquake nucleation [35; 37], which means that the dynamic slip induced by pressurized water injection may release the tectonic stress of the fault. Thus, the maximum magnitude of the induced earthquake may be affected by the tectonic parameters, such as fault length and the criticality of the fault stress, which has been demonstrated by previous experiments and numerical simulations [45; 50; 51].
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 6 Performance Evaluation and Discussions
### PDR Results
#### 6.1.2 Uniform Node Distribution Results  
In stark contrast with the random network, the uniformly distributed network exhibits remarkable resilience against the dropping attack. Here, even the dropping attack, known for its disruptive nature, has a minimal effect on the PDR. The average PDR for the uniform network only experiences a slight decrease, dropping from a near-perfect baseline of 0.999 to 0.982. See Figure 9.  
The combined attack, incorporating both dropping and sinkhole functionalities (black hole attack), demonstrates a significant impact on the PDR in both network distributions. However, the severity of the impact differs between the two network types:  
* Random network: The random network experiences a substantial decrease in the PDR under the combined attack, with the average PDR dropping from a baseline of 0.607 to 0.307. This represents a decrease of approximately 50.16%.
* Uniform network: While the uniformly distributed network maintains a higher PDR compared to the random network even under attack (0.419 vs. 0.307), the combined attack has a proportionally larger effect on its performance. The uniform network's PDR suffers a decrease from a near-perfect baseline of 0.999 to 0.419, representing a decline of approximately 57.54%.
## 3 Results and Discussion
### PP Fibers Effect on Tensile Strength of Foamed Geopolymers  
Figure 8 shows the PP fiber effect on the tensile strength of foamed geopolymers. A significant increase in tensile strength was observed when the geopolymer contains foamed and PP fiber compared with the control geopolymer. The enhancement was observed more than 10 times from the control geopolymer. The result shows that the trend tends to increase with the amount of PP fiber contained in the foamed geopolymer. The tensile strength of 40% foamed geopolymer with reinforced increases 6.7% and 44.6% compared to without the PP fibers. This similar trend was shown for the tensile strength of 60% foamed geopolymer, which increases as the fiber is included in the matrix. This may be due to the cohesiveness between the fibers and the geopolymer matrix, which improves strength. Besides that, the high tensile strength properties of PP fibers give a major contribution to the tensile strength of geopolymers. According to [27], the increase in tensile strength occurred when PP fiber from 0.3% to 0.5% was added into the geopolymer specimen. This is a consequence of the fiber's ability to connect the cracks and prevent the crack-face separation whenever cracks occur and spread. A similar trend has been reported, for example [28], which confirmed that the addition of the ideal bemp fiber content makes an incredible enhancement in the tensile strength of the geopolymer composite.  
Figure 8: Tensile strength of foamed geopolymer.  
Figure 7: Dry density of fly ash foamed geopolymer with different PP fibers.
## 4 Experimental Setup and Procedures  
### Timber Specimens  
In the experimental test, the timber is pine wood from North America, which has a density of 420 kg/m\({}^{2}\) and moisture content of around 19.2%. The experimental samples' dimensions are 0.04 m broad, 0.15 m long and 0.08 m deep. As shown in Figure 4, epoxy was used to install two PZT patches at the predefined positions on a total of six specimens. As a result, the interface between PZT and the surface of the timber was covered with a thin layer of epoxy resin. A PZT material layer is sandwiched between two electrode layers in a PZT sensor [44]. The diameter of the PZT sensor was 10 mm and the thickness was 0.2 mm. The total of six specimens were divided into three groups. As manifested in the Figure 4, some artificial structural damage was applied to all specimens before monitoring. A crack existed on each specimen for Group A (specimens 1 and 2). A hole was drilled in each specimen for Group B (specimens 3 and 4) and C (specimens 5 and 6). The PZT patches' locations and pre-configured damage are shown in Figure 4.  
Groups A, B, and C were subjected to three types of artificial damage, different crack depths, different depths and diameters of the holes under different circumstances, in this experimental test. For Group A, the crack width of both specimens was fixed at 1.5 mm while the artificial crack damage depths varying from 4 to 10 mm were conducted; in Group B, for both specimens, the holes' diameter was set at 4 mm while the effect was researched by varying the artificial hole depths in 4 and 10 mm under two circumstances; As for the last Group C, to look into the impact of this structural flaw, the depth of the hole for the two test specimens was set at 4 mm and the hole diameters were varied between 3 and 9 mm. Table 1 illustrates the test cases of specimens in this research.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Group A** & **Case 1** & **Case 2** & **Group B** & **Case 3** & **Case 4** & **Group C** & **Case 5** & **Case 6** \\ \hline Crack width & 1.5 & 1.5 & Hole diameter & 4 & 4 & Hole diameter & 3 & 9 \\ Crack depth & 4 & 10 & Hole depth & 4 & 10 & Hole depth & 4 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test circumstances of specimens for three groups.  
Figure 4: Timber specimens of three groups. (**a**) Specim of Group A; (**b**) Specim of Group B; (**c**) Specim of Group C.
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 3 Karman Vortex Street-Based Energy Harvesting  
A Karman vortex street (or a von Karman vortex street) is a recurring pattern of whirling vortices created by vortex shedding, which is responsible for the unstable separation of flow around obstacles [39]. When fluid is flowing around the bluff body, a vortex is formed in the path behind the bluff body periodically. The vortex's shedding frequency, \(f=\frac{uS}{L}\), depends on the Strouhal number \(S_{t}\) (taken as a constant within a given Reynolds number range), the characteristic length \(L\) and the incoming flow velocity \(u\). The vortex that is formed behind the bluff body creates an asymmetric pressure field around it, which is subsequently exposed to alternating aerodynamic forces, resulting in vibrations of the bluff body with limited amplitude. When the vortex shedding frequency is near to the bluff body's natural frequency, resonance takes place, and the bluff body vibrates with large amplitude. At the same time, the phenomenon of frequency lock-in may occur, usually known as the synchronization region, as illustrated in Figure 3a, where the frequency of vortex shedding will no longer fluctuate with the flow velocity within a given range of incoming flow velocities [40]. Such unsteady fluid flows in the wake behind a cylinder are called Karman vortex streets, as depicted in Figure 3b. They consist of a wobbled group of vortices which shed periodically and rotate in alternating directions, both at the top and bottom of the cylinder.
## 7 Simulations Results
However, the feedback-only controller fails to guide the camera to its desired pose and falls into local minima, as evident from the third point's coordinates (\(u_{3},\;ur_{3},\;v_{3}\)), which do not match the target at steady state. This issue arises from the overdetermined nature of the stereo-based visual servoing system, where the number of output constraints (9) exceeds the degrees of freedom (DoFs) available for control (6). As a result, the feedback controller can only match six out of nine image coordinates, leaving the rest coordinates unmatched. In contrast, the feedforward-and-feedback controller avoids local minima and accurately moves the camera to the target pose. This is because the feedforward component directly controls the robot's joint angles rather than image features. Since the joint angles (6 DoFs) uniquely correspond to the camera's pose (6 DoFs), the feedforward controller helps the system reach the global minimum by using the desired joint configurations as inputs. When comparing performance, the system with the feedforward controller exhibits a shorter transient period (less than 2 s) compared to the feedback-only system (less than 3 s). However, the feedforward controller can introduce overshoot, particularly in the presence of disturbances. This occurs because feedforward control provides an immediate control action based on desired setpoints, resulting in significant initial actuator input that causes overshoot. Additionally, a feedforward-only system is less robust against disturbances and model uncertainties. Fine-tuning the camera's movement under these conditions requires a feedback controller. Therefore, the combination of feedforward and feedback control ensures fast and accurate camera positioning. The feedforward controller enables rapid convergence toward the desired pose, while the feedback controller improves robustness and corrects errors due to disturbances or uncertainties. Together, they work cooperatively to achieve optimal performance.
## 4 Results and Discussion
### Thaw Settlement Simulation  
During the natural thawing process, the temperature of the frozen soil wall curtain rises gradually from the outside and the tunnel tube sheet to the inside of the frozen soil wall. The law of change in soil thawing temperature is similar to the law of change in frost heave temperature and is therefore not discussed specifically. The record of thaw settlement starts with the state of the shield tunnel tube sheet after it has been installed. The computed displacement field of soil thaw settling is shown in Figures 15 and 16. From the start of thawing to 10 days of thawing, only a small part of the frozen soil wall is thawed at this stage due to the influence of the low-temperature frozen soil wall, and the soil thaw settlement displacement grows more slowly at this time. From 11 to 45 days after thawing, the frozen soil wall is gradually thawed from the outermost soil and the soil in contact with the tunnel, when the soil thaw settlement displacement grows at a faster rate. After 45 days of thawing, the frozen soil wall has been thawed and the natural thaw is complete.  
The ground surface displacement distributions of the thaw settlement are shown in Figures 17 and 18. The thaw settlement displacement increases with increasing natural thawing time, and the displacement is generally symmetrical about the center of the tunnel, which is the same as the frost heave shown in Figure 11. After the complete thawing of the frozen soil wall, the maximum surface thaw settlement displacement reached 18 mm, and the surface settlement displacement was significantly greater than the surface frost heave displacement. The distribution of thaw settlement displacement from the top of the tunnel to the surface is shown in Figure 19. The soil displacement at the top of the tunnel is the largest, which is the same as the frost heave shown in Figure 13.  
Figure 15: Vertical displacement of the soil: (**a**) thawing for 10 days; (**b**) thawing for 25 days; (**c**) thawing for 35 days; (**d**) thawing for 45 days.  
Figure 16: Horizontal displacement of the soil: (**a**) thawing for 10 days; (**b**) thawing for 25 days; (**c**) thawing for 35 days; (**d**) thawing for 45 days.  
Figure 17: Surface vertical displacement simulation value.  
A comparison of the thaw settlement displacement monitoring values of the vertical displacement of the ground surface directly above the center of the tunnel for the first 45 days with the simulated values is shown in Figure 20. The figure confirms the strong correlation between the field results and the numerical simulation. However, follow-up grouting was carried out during the thaw settlement process, limiting the development of ground surface displacements, and it is difficult for the numerical simulation to realize this process. There is still a little gap between the measured and simulated values, and the method still needs some small enhancements and refinements.  
Figure 19: Soil thaw settlement displacement map directly above the tunnel.  
Figure 20: Thaw settlement displacement comparison between simulation and actual measurement.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Qos-Based Routing
#### 3.5.1 Designing Lightweight QoS Routing Protocol (LRPD)  
The LRPD protocol proposed in [PERSON] et al. [32] is specially designed to optimize delay Qos, the protocol adopts a modular approach, and the modules closely cooperate to achieve goal optimization, as shown in Figure 12. The data from the upper layer are first entered into the data classification module, for priority division. The data are divided into general packets (GP) and delay sensitive packets (DP). The highest priority belongs to DP. Then, the DP and GP data are entered into the general module and the delay module, respectively, and are eventually sent to the queue processing module. The high priority data need not wait for direct transmission, while the low priority data need wait until an appropriate transmission time, in order to ensure the non-delay transmission of DP data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Sink Quantity** & **Characteristic** & **Communication Mode** & **Delay** & **Energy** \\ \hline DSCB & \multirow{2}{*}{2} & Next hop: & Emergency data: single hop & \multirow{2}{*}{low} & \multirow{2}{*}{high} \\ (2017) & & \(C.F_{n}=\frac{d}{E_{nomication+1}.F_{n}}\) & & General data: multi-hop & \\ \hline CRPBA & \multirow{2}{*}{2} & CH selection: & Emergency data: single hop & \multirow{2}{*}{low} & \multirow{2}{*}{medium} \\ (2018) & & \(Function(d,E)=\frac{d}{E_{max}}\) & General data: two hops & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cluster-based routing protocols comparison.  
The LRPD protocol theoretically analyses the total delay, the possible total delay from the source node to the destination node is as follows:  
\[D^{tot}=D^{q}+D^{tr}+D^{pr}+D^{proc} \tag{12}\]  
\[minimize(\mathrm{D}_{ij}^{tot}),\forall j\in NH_{i} \tag{13}\]  
where \(D^{q}\) is the queue delay, \(D^{tr}\) is the transmission delay, \(D^{pr}\) is the transmission delay, \(D^{proc}\) is the processing delay, \(NH_{i}\) is the next hop set of node \(i\). The simulation data prove that the protocol reduces the end-to-end delay and meets the Qos requirements.
# Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints
## 4 Results and Analysis  
### Results of the Proposed Hybrid ML Model  
After completing the hyperparameter tuning, the SMA-MLP model can be applied to predict joint shear strength in the dataset. To facilitate comparison, this section also presents the prediction results of the original MLP model (i.e., the MLP model without SMA optimization). Figure 8 illustrates the comparison between the measured joint shear strength values from the direct shear tests and the predicted results from the two ML models. The closer the data points are to the actual measurements, the better the predictive performance of the model. Perfectly overlapping data points indicate a complete match between the predicted and experimental results. As shown in Figure 8, under the same training and test sets, the SMA-MLP model demonstrates superior predictive performance compared to the original MLP model, with smaller discrepancies between the predicted values and experimental data. This indicates that SMA effectively enhances the predictive capability of the original MLP model, making it more reliable and accurate in forecasting the joint shear strength.  
As shown in Table 2, the comparison of the performance evaluation indicators between the two models further verifies the impact of SMA on the predictive capability of the MLP model. The results indicate that the optimized SMA-MLP model exhibits superior predictive performance and higher computational efficiency across multiple performance evaluation indicators, demonstrating the significant role of SMA in enhancing the predictive ability of the MLP model. Specifically, on the test set, the R\({}^{2}\) of the SMA-MLP model reaches 0.96869, indicating an excellent fit between the predicted and actual values. Moreover, compared to the unoptimized MLP model, the SMA-MLP model achieves 0.09699 and 0.06677 in RMSE and MAE, respectively, significantly minimizing prediction errors. The SMA-MLP model outperforms the MLP model across all four key performance evaluation indicators,  
Figure 8: Comparisons between the predicted and measured \(\tau_{\text{P}}\).  
highlighting its superior generalization capability and robustness in joint shear strength prediction. Furthermore, the SMA-MLP model effectively captures the complex nonlinear relationships of joint shear strength, resulting in more accurate and stable predictions. Given its exceptional predictive performance, this model serves as a reliable approach for joint shear strength prediction, providing valuable insights for rock mass engineering design and geological hazard assessment.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 4 Experiments
### Evaluation Metrics  
In pose estimation tasks, the algorithm outputs a rotation matrix \(\mathbf{R}\) and translation vector \(\mathbf{t}\), which describe the rigid transformation of the target object from the world coordinate system to the camera coordinate system. The accuracy of this transformation can be quantified using the Average Distance (ADD) metric, which measures the average Euclidean distance between the predicted and ground-truth positions of all 3D model points.  
Given the ground-truth rotation matrix \(\mathbf{R}\) and translation vector \(\mathbf{t}\), and the predicted rotation matrix \(\mathbf{R}_{p}\) and translation vector \(\mathbf{t}_{p}\), the ADD is calculated as follows:  
\[\text{ADD}=\frac{1}{m}\sum_{x\in M}\big{\|}\big{(}\mathbf{R}x+\mathbf{t}\big{)} -\big{(}\mathbf{R}_{p}x+\mathbf{t}_{p}\big{)}\big{\|}, \tag{21}\]  
Here:  
* \(M\) is the set of m 3D points of the object in the world coordinate system.
* \(\mathbf{R}x+\mathbf{t}\) represents the true position of a point in the camera coordinate system.
* \(\mathbf{R}_{p}x+\mathbf{t}_{p}\) represents the predicted position of the same point.  
The ADD metric evaluates the precision of the pose estimation by averaging the Euclidean distances between the predicted and true positions of all model points. This provides a comprehensive measure of how well the estimated pose aligns with the ground truth.  
For objects with symmetry, the ADD metric may not accurately reflect the performance of pose estimation algorithms, as symmetry can lead to multiple visually indistinct poses. To address this, DenseFusion introduced the ADD-S metric, which accounts for object symmetry by measuring the average minimum distance between transformed points in the predicted pose and the nearest points on the ground-truth model.  
The ADD-S is calculated as follows:  
\[\text{ADD-S}=\frac{1}{m}\sum_{x_{1}\in M}\min_{x_{2}\in M}\left\|(\mathbf{R}x_ {1}+\mathbf{t})-\left(\mathbf{R}_{p}x_{2}+\mathbf{t}_{p}\right)\right\| \tag{22}\]  
The ADD-S metric addresses the limitations of ADD when evaluating symmetric objects by measuring the average minimum distance from transformed points in the predicted pose to the nearest points on the ground-truth model. Specifically, instead of calculating point-to-point distances, ADD-S computes the average of the minimum distances between each transformed point in the predicted pose and the closest point on the ground-truth model. If this average distance is less than 10% of the object's diameter, the pose estimation is deemed correct.