esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Discussion
### Failure Mode  
The failure modes commonly reported for the direct pull-out test by researchers are of three types [3; 16; 32; 40]: The first type of failure is the pull-out failure, in which the bar under consideration pulls out of the concrete specimen under loading. The second type of failure is the splitting of the concrete; this type of failure is characterized by the cracking and breaking of the concrete even before the bars pulls out under loading. The third type of failure is bar rupture, in which the reinforced bar yields or ruptures (in the case of FRP bars) and fails under loaded conditions [13]. Studies report that for reliable analysis of the bond performance with respect to bond strength, the expected type of failure is the pull-out of bars [41]. Pull-out failure is reported when the energy of the shearing bond strength between the bar and the geopolymer concrete exceeds the bonding capacity of the geopolymer concrete [42]. This gives a true depiction of the bond stresses acting on the pull-out specimens, and aids researchers in developing a better understanding of the bond performance of the composite structures [3; 16; 38]. It has been widely seen in previous research that the bond stress values reported for other types of failure, such as splitting of concrete or bar rupture, are often discarded in bond strength calculation owing to their inaccuracy in representing the bond stress distribution.  
In this study, the predominant failure mode exhibited by all of the test specimens was pull-out failure, irrespective of the bar diameter or the embedment length. In the pull-out failures of this study, both the steel and BFRP bars pulled out from the embedded concrete under relatively low stress levels, with the bar surface showing a slightly worn rib for the steel bars, and a sheared-off, torn fibre rib surface in the case of BFRP bars. It can also be inferred that during loading, the concrete shears with the wound surface of the BFRP bars, and the bond surfaces start to get damaged due to continuous stress from the loaded end [32]. The embedment length then extends until the free end reaches the lowest point and causes a sudden excessive slip, resulting in the pull-out of the bars. This scenario is consistent with observations from recent studies in this field, and confirms the behaviour of basalt FRP under direct pull-out tests [38]. The failure damage images show that the bars pulled out of the specimens, and that the pull-out load could not be resisted by the bar-concrete interface owing to lowered adhesion due to the smooth surface of the BFRP bars against the surface of the SCGC [32; 43; 44]. The pull-out failures of the specimens of all of the series are shown in Figures 11-15.  
Figure 11: Pull-out failure of 6-mm BFRP bar specimens.
## 4 Numerical Experiments and Results
### Example and Parameter Setting
#### 4.3.3 Convergence Analysis Based on the Total Cost and Carbon Emissions  
From the perspective of the current solutions, some current solutions that exceed the objective function value of the current optimal solution are also retained. With the increase in the number of iterations, the better solution is obtained again. This result indicates that the design of Strategy 1 can expand the search scope and help to find better solutions. From the perspective of the current optimal solutions, the total cost is gradually reduced, but the carbon emissions are slightly increased. This result indicates that the constructed model further considers the carbon emissions without sacrificing the total economic benefits. In addition, for both the current solution and the current optimal solution, the total cost and total carbon emissions decrease as the number of iterations increases. This result indicates that the designed algorithm has a certain optimization ability in solving MCVRP considering traffic congestion under the mixed carbon policy (Figure 8).  
Figure 7: Impact of different mixed carbon policy scenarios on the results of multi-compartment vehicle path planning. (**a**) Total costs under different mixed carbon policy scenarios. (**b**) Transportation costs under different mixed carbon policy scenarios. (**c**) Vehicle management and use costs under different mixed carbon policy scenarios. (**d**) Total driving distance under different mixed carbon policy scenarios. (**e**) Carbon emissions under different mixed carbon policy scenarios. (**f**) Fuel consumption and carbon emissions under different mixed carbon policy scenarios.  
In general, the two-stage VNS-TA algorithm can solve and optimize the MCVRP considering traffic congestion under the mixed carbon policy, which has the effects of cost saving and energy conservation and emission reduction.
# Performance Analysis of P2P Networks with Light Communication Links: The Static Managed Case
## 7 Numerical Results  
This section presents the main results derived from the mathematical model derived for static nodes in P2P systems using both LiFi and WiFi communications. We also compare the analytical results derived from the Markov chain to simulation results to validate our model. Finally, we present a comparison between LiFi 2.0 and WiFi 7 technologies in centralized and P2P systems.
## 1 Introduction
In this structure, the standing-wave field formed by the forward transmission pump light and backward transmission Stokes light can enhance the stability of SBS generation, then improve the stability of the pulse compression; meanwhile, can also effectively compress the cell length [19; 20; 21]. Although many studies on SBS pulse compression technology have been carried out, most people have just paid attention to the pulse compression ratio. Moreover, most of the light sources used in their previous studies are commercial lasers, and there is no combination of the laser amplifier. To the best of our knowledge, little research on the beam quality, stability and applicability of the SBS pulse compression laser has been carried out. Figure 1: Temporal schematic diagram of simulated Brillouin scattering (SBS) pulse compression process (**a**) pump light incident to the SBS Cell; (**b**) backward Stokes light produced near the focus; (**c**) pump light energy transfer to the leading edge of the Stokes light; (**d**) compressed Stokes light obtained with a sharp rising edge. In this paper, we present a compact high beam quality hundred picoseconds laser system based on SBS pulse compression and aberration compensation techniques. Three-plan resonant reflector method is used to generate SLM Q-switched output combined with Cr\({}^{4+}\):Y\({}_{3}\)Al\({}_{5}\)O\({}_{12}\) (Cr\({}^{4+}\):YAG) crystal as saturable absorber. Double-pass amplifier is equipped with a compact single-cell SBS pulse compressor in our design. Multi-constituent medium FC-770 is used in the compressor for its high reflectivity and relatively low price. SLM seed pulses of 9 mJ energy with 7.8 ns pulse width are produced from the oscillator. After passing through the SBS pulse compressor, 7.8 ns pulses are then compressed to less than 500 ps by the SBS pulse compressor. The output energy and compressed pulse width as a function of operating voltages are given. Maximum energy 200 mJ is obtained with beam quality factor \(M^{2}\) less than 1.8 and energy fluctuation measured less than 3%.
## 2 Acquisition of Tunnel Face Images
### Method of Partitioned Image Acquisition
#### 2.1.3 Partitioned Shooting Plan and Timing for Tunnel Face Photography  
To obtain high-quality images, the camera should be placed 10-20 m in front of the tunnel face, perpendicular to it. The tunnel face is divided into sections to ensure comprehensive coverage.  
Various construction activities can obstruct the tunnel face and complicate photography. During drilling and charging, the drilling jumbo and the tunnel-lined platform car can block the view. During mucking, rubble covers the tunnel face, and high dust  
Figure 1: Image acquisition equipment.  
Figure 2: Tunnel-lined platform car light source.  
concentration makes photographing difficult. During the installation of steel arches and shotcrete application, the tunnel-lined platform car can again block the tunnel face, and the shotcrete process reduces visibility, affecting photo quality. Therefore, the optimal times for photography are after mucking and before installing steel arches, avoiding the adverse interferences shown in Figure 3 and ensuring clear visibility.  
This article relies on the Luanchuan-Lushi Expressway Tunnel in Henan and the Hangzhou-Wenzhou Railway Tunnel in Zhejiang (shown in Figure 4), where the tunnel face area is generally less than 100 square meters. Considering the onsite shooting conditions, the shooting plan shown in Figure 5 is adopted: the tunnel face is divided into six sections, and the camera is placed 10 m in front of the face. The optimal time for photography is after mucking and before installing steel arches. During this period, uniform lighting can be provided using the tunnel-lined platform car light source, improving the lighting quality for photographing the tunnel face.  
Figure 4: Map of tunnel locations.  
Figure 3: Adverse Interferences in Tunnel Face Photography. (The red boxes are where the tunnel face is obscured).
## 4 Qualitative Findings and Discussion
### Individualizing Needs and Balance of Design
#### 4.3.3 Safety  
Some participants had security concerns regarding the additional features of LSAD. Participant 7 expressed a particular fear of the device short-circuiting after cleaning: \"Especially during the summer, it needs frequent cleaning, but I worry about its waterproof performance.\". Additionally, some participants had concerns about the voltage devices inside LSAD.  
\"The functionality of salt therapy is essentially a layer of salt with a 220 V voltage temperature control. Because I work in the safety field, I am really concerned about placing a 220 V voltage device around the waist. The consequences would be extremely serious in case of any mishap.\" (Participant 12)
## 5 Results and Discussion
Adjusted \(R^{2}\) values were consistent with \(R^{2}\) values, with Linear Regression exhibiting the highest adjusted \(R^{2}\) of 0.8511. Linear Regression and SVM-SVR exhibited the lowest MSLE values, indicating their effectiveness in minimizing logarithmic prediction errors. However, LightGBM showed the highest MSLE, suggesting higher variability in the accuracy of predictions. Regarding MBE, Linear Regression had a positive bias (0.0742), indicating slight overestimation, while other models exhibited varying degrees of bias. Overall, Linear Regression emerged as the top-performing model across multiple evaluation metrics, showcasing its robustness and effectiveness in predicting UCS values in oil and gas wells. However, XGBoost and SVM-SVR also demonstrated competitive performance, highlighting the importance of considering multiple models in predictive modelling tasks.
## 2 Problem Formulation
### Region Searching Model
#### 2.1.2 Probability Map Update  
It is known that the TEP \(P_{e}^{m,n}(k)\in[0,1]\) and the EU \(\chi_{e}^{m,n}(k)\in[0,1]\). If \(0\leq P_{e}^{m,n}(k)\leq 0.3\) and \(0\leq\chi_{e}^{m,n}(k)\leq 0.3\), the cell (m, n) is modeled as a known region. If \(0.3<P_{e}^{m,n}(k)\leq 0.7\) and \(0.3<\chi_{e}^{m,n}(k)\leq 0.7\), the cell (m, n) is modeled as a non-key region. And if \(0.7<P_{e}^{m,n}(k)\leq 1\) and \(0.7<\chi_{e}^{m,n}(k)\leq 1\), the cell (m, n) is modeled as a key region. Dangerous areas, such as hostile radar detection regions, peaks, etc., are designated as no-fly zones. Bayesian rule is used to update whether a cell exists a target [26]. The update equations for the probability that a target exists but is not detected and that the target does not exist but is reported are written as follows:  
\[P_{e}^{m,n}(k+1)=\frac{P_{e}^{m,n}(k)P_{e}^{m,n}(k)}{(P_{e}^{m,n}(k)-P_{f}^{m,n }(k))P_{e}^{m,n}(k)+P_{f}^{m,n}(k)}, \tag{2}\]  
\[P_{e}^{m,n}(k+1)=\frac{P_{e}^{m,n}(k)(1-P_{e}^{m,n}(k))}{(P_{f}^{m,n}(k)-P_{c} ^{m,n}(k))P_{e}^{m,n}(k)+1-P_{f}^{m,n}(k)}, \tag{3}\]  
where \(P_{c}^{m,n}(k)\in[0,1]\) is the detection probability of the airborne sensor to the target [27]. \(P_{f}^{m,n}(k)\in[0,1]\) is the false alarm rate, which indicates the probability that there is no target in the cell but a target is reported. \(\chi_{e}^{m,n}(k)\) decreases with the search number of UAVs, satisfying the following equation:  
\[\chi_{e}^{m,n}(k+1)=\frac{1}{2^{N_{f}(m,n)}}\chi_{e}^{m,n}(k), \tag{4}\]  
where \(N_{f}(m,n)\in\mathbb{N}\) is the number of times that a cell (m, n) searched by UAVs.
## 4 Result and Discussion
### Results for Throughput with Random UAV Movement  
Finally, the throughput of the UAV-GS communication system using bandwidth allocation estimated using the BRFFNN and LSGB models is compared, and the results are presented in Figure 10. This figure illustrates how each model impacts data transmission rates in the UAV-GS link. By evaluating the throughput achieved by both models, we can assess their effectiveness in sustaining high data rates under the given simulation conditions.  
The throughput is calculated using two machine learning models, the BRFFNN and LSGB, for bandwidth allocation under 5G standard specifications. As shown in Figure 10, the average throughput for the BRFFNN model, represented by the red line, is approximately 3.841 million pps. In comparison, the LSGB model achieves an average throughput of around 3.272 million pps. This indicates that the BRFFNN model outperforms the LSGB model in terms of throughput. The notable difference in the bandwidth estimates between the BRFFNN and LSGB models will be further discussed in the next section.
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 5 Environmental Impacts on Hardware-Based LQEs
### Environmental Impacts on Mapping Models between Distance and \(PRR\)  
#### 5.1.1 Communication Distance and PRR in Different Environments  
The relationships between communication distance and \(PRR\) in different environments are shown in Figure 4. It can be seen that there is no consistent mapping relationship between the communication distance and \(PRR\) in different environments. The starting and ending distance of the connected region, the transitional region, and the disconnected region are totally different in the three environments. For example, the communication range from 70 m to 90 m belongs to the disconnected region of the runway, while this range belongs to the connected region of the corridor and transitional region of the artificial lawn, respectively. Even the range of the transitional region is also different in the three environments. Moreover, the difference between \(PRR\) at the same distance is even up to 90%. That is to say, the relationship between communication distance and \(PRR\) will be greatly affected in changing environments.
## 4 Results
### Performance Comparison  
For a more comprehensive and fair comparison, a traditional first-order CNN, a bilinear based second-CNN, and a local feature SIFT method are chosen [16,25]. The bilinear CNN based on the traditional bilinear method with same parameters as the proposed method is constructed and the same for the first-order CNN (detailed in Table 1). The results are shown in Figure 11. It can be seen that the accuracies of MoAS-CNN are significantly higher than those of other methods on all sensor scales, especially for smaller-scale pressure arrays. This result illustrates the effectiveness of the proposed MoAS-CNN for low-resolution haptic image. For 16 \(\times\) 16, the MoAS-CNN obtained 95.73%, while the traditional bilinear CNN was 92.46%, traditional CNN 89.79%, and local feature method 67.24%. Since the inputs of both 16 \(\times\) 16 and 20 \(\times\) 20 scales are too small to be applied in SIFT, which can only extract 3-5 key points, the recognition effect is very limited.  
For the samples that the model failed to identify, the true labels and predicted probabilities are shown in Figure 12, marked in orange. These haptic images are collected in a random pressure range of 0.5-3 kPa, angle and position. The inference value of some letter categories with similar shapes are very close, indicating that the differences between features are not obvious. As a comparison, 10 volunteers (7 males and 3 females, age [20,30] are invited to identify the misclassified samples and the results are marked in green. It can be observed that some samples were not recognized by the neural network or humans, as shown in Figure 12a-f, and others were recognized by humans, but not by neural networks, as in Figure 12g-i. No volunteer can completely reclassify all failed samples. The misclassification can be mainly attributed to the following: firstly, the restricted sensor density leads to fine edges not being captured; secondly, the elastic coupling of the flexible sensors causes the unpressed pixels around the pressed pixels to be deformed, producing pseudo-outputs. Thirdly, features are not sufficiently mined through the MoAS-CNN and the features of similarly shape categories are not learned separately.  
Figure 11: Performance comparison with other first-order, second-order and traditional local feature methods.
## 4 Discussion
### Knowledge Distillation for Lightweight Accuracy Preservation  
Aiming at the problems that the lightweight model has limited expressive ability in complex remote sensing backgrounds and is prone to blurred boundaries and missed detection of small targets, this paper further introduces a feature-level oriented knowledge distillation strategy. Different from traditional distillation methods based on output soft labels, this strategy guides the shallow model to enhance its discriminative ability for key areas while maintaining a compact structure by semantically aligning the intermediate features at the encoder stage of the deep network. In order to verify the actual effect of this strategy in the task of remote sensing water body segmentation, this paper further conducts a visual comparison of the attention heatmaps of the deep teacher model and the lightweight model at different levels, as shown in Figure 12. The results show that the deep model retains richer spatial and semantic information in each layer, while the lightweight model, despite its compact structure, can still effectively capture the main contours of the water bodies and exhibits an attention distribution similar to that of the deep model. This verifies the significant role of this strategy in enhancing the integrity of semantic representation and segmentation accuracy and provides effective support for high-performance water body recognition under low-resource conditions.  
Figure 12: Visualization of attention maps at each downsampling stage for the lightweight and deep models. Warm colors (yellow to red) represent higher feature responses, while cool colors (blue) indicate lower responses.
## 5 Results and Discussion
### Discussion and Result
#### 5.2.4 Research Questions and Answers  
Q1: What are the current issues that threaten user privacy and security in centralized IDMSs?  
Central identity management systems suffer from certain privacy issues, as discussed in the previous section. One of the most important problems is centralization, since it relies upon one central party, which results in the high risk of an SPOF. Third-party control is considered one of the most important threats in centralized systems, since the user is under the control of a third party, which can compromise their privacy, such as monitoring their movements and studying their behavior.  
Q2: Will decentralizing identity management by using distributed-ledger technology solve user privacy problems, and if so, why?  
Decentralization of identity management by using distributed ledger technology addresses the problem of a SPOF because copies of the system are distributed over multiple peers. As the peers constantly compare and verify the validity of the copies, when one fails, the rest discover the error and recopy the system in the correct chain. Furthermore, technology provides the smart contract, which plays a major role in limiting the control of third parties, as tasks are assigned to the smart contract, and the tasks are automatically executed without the intervention of any third parties.  
Q3: What are the blockchain-based technologies that may be utilized to enhance user privacy?  
Using the smart contract as an intermediary to carry out tasks between the parties enhances the privacy of the parties, since, for example, users can send tokens through the smart contract to a service provider, whose tokens have attributes certified by third parties. Since a smart contract acts as an intermediary, third parties and service providers cannot  
Figure 3: Blockchain Types.  
track user relations or actions. Additionally, the user can control how much data is shown in each token created for a service provider through a smart contract. The smart contract can also be used to track all the viewers of the token data by recording their addresses and the time they viewed it. So, yes, this technology enhances user privacy.  
Q4: What is the most efficient blockchain-based development platform for IDMSs?  
As a result of the research, most of the applications used the public blockchain (Ethereum) because it is open source and has smart contract technology. Furthermore, Ethereum works with a special currency called Ether, and has a special programming language called Solidity.
###### Abstract  
This study investigates differences in lower limb muscle activity during isometric external hip rotation while standing using static and dynamic models within the AnyBody Modeling System. Thirty-three participants performed controlled isometric rotations using a custom-designed device capable of simultaneously measuring rotational moments and ground reaction forces. Both static and dynamic simulations were conducted for each subject using personalized biomechanical models. Muscle activity values at the point of peak rotational moment were analyzed for twelve key muscles involved in hip rotation and stabilization of the knee joint, and statistical differences were assessed for significance. Muscles from the glutest group (_Glutteus minimus, medius_, and _maximus_) generally showed lower activation in dynamic simulations, although this trend was not statistically significant for all muscles or test conditions. The mean difference in muscle activity values between static and dynamic simulations was between 0.03 and 0.08 for the glutest group muscles and up to 0.15 for the _liopsosa_. Static models overestimated the role of stabilizers. Significant differences (\(p\leq 0.05\), Wilcoxon signed-rank test) were observed between the two approaches in terms of predicted muscle activation. In conclusion, discrepancies in muscle activity predictions between static and dynamic simulations highlight the need for task-specific simulation design and careful result interpretation.  
musculoskeletal system; lower limb; isometric rotation; mathematical modeling; AnyBody Modeling System +
Footnote †: journal: Applied sciences  
_Article_
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 5 Performance Evaluation
### Barcelona Scenario
A remarkable difference with respect to the other protocols is observed for AE traffic, where Q-RPL achieves up to a 17% improvement compared to ML-RPL, which is the next best performer.