esa-sceva/satcom-chunk-collection huggingface dataset:
# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 6 Numerical Experiments
### Result of Numerical Experiment
#### 6.2.1 Effects of Different Communication Methods on Relief Aircraft Dispatch  
In Section 4, we proposed corresponding transport methods based on different information sharing levels. The dispatchment methods are based on the characteristics of different information sharing levels.  
Based on the simulation result, we can further analyze the most suitable scenario for each transport method. From Figure 9, we can see that the \"reconnaissance and rescue\" method is better when the Gini coefficient is 0.1 or 0.3. In this case, almost all the shelters have injuries to be evacuated. Even if there is no information from the shelter sites, as long as aircraft are dispatched to a shelter, they can bring some injuries back.  
The \"one destination\" method in level two is more suitable to scenarios where D/C is smaller than 1 and the Gini coefficient is 0.1 or 0.3. In these cases, the available aircraft exceed the demand, and the distribution of injuries is even. Although this is a simple method, it remains efficient.  
The \"multiple destinations\" method in level two is more suitable to scenarios where D/C is smaller than 1 and the Gini coefficient is 0.5 or 0.7. Due to the extremely uneven distribution of injuries, some shelters may have more injuries than the capacity of the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Time (min)**} & \multicolumn{3}{c}{**Gini = 0.1**} & \multicolumn{3}{c}{**Gini = 0.3**} & \multicolumn{3}{c}{**Gini = 0.5**} & \multicolumn{3}{c}{**Gini = 0.7**} \\ \hline \multirow{2}{*}{**DC**} & **Dispatch** & **Total** & **Finish** & **Waiting** & **Total** & **Finish** & **Waiting** & **Total** & **Finish** & **Waiting** & **Total** & **Finish** & **Waiting** \\  & **Method** & **Time** & **Time** & **Time** & **Time** & **Time** & **Time** & **Time** & **Time** & **Time** & **Time** & **Time** & **Time** \\ \hline \multirow{4}{*}{0.6} & One & 2332 & 743 & 115 & 1626 & 513 & 162 & 2381 & 795 & 173 & 4448 & 1480 & 220 \\  & Two-I & 1223 & 450 & 86 & 1289 & 446 & 94 & 1044 & 506 & 113 & 1668  
aircraft, while others may have few injuries. Therefore the \"multiple destinations\" method is more suitable for this extreme distribution.  
The \"dynamic transport\" method is best in every scenario. In fact, this method combines the advantages of the two methods in level two. In particular, it has the best performance for waiting times.
## 4 Performance Evaluations
### Simulations
#### 4.1.2 Reliability  
Under the above conditions, the processing results of different SNRs are simulated to verify the reliability of the proposed DAM-TEQ. In order to obtain convincing statistical rules, 100 simulations were carried out for different SNR groups, which effectively avoided the contingency of the results and increased the reliability and generality, as shown in Figure 6. The criterion for determining whether the information transmission is accurate is indicated by a processed BER of the order of magnitude \(\alpha\) (a BER at or below \(\alpha\) is considered to be an accurate transmission). The figure shows the accuracy of different criteria at different SNRs. When \(\alpha=10^{-2}\), the accuracy can reach more than 90% after the SNR reaches \(-2\). When \(\alpha=10^{-3}\), the accuracy can be maintained at more than 90% after the SNR reaches 3. When \(\alpha=10^{-4}\), the accuracy can be maintained above 90% after the SNR reaches 6. The results show that the proposed DAM-TEQ can demonstrate a high transmission accuracy at a low SNR, and with the increase in the SNR, the accuracy will continue to remain at a high level, which can ensure stable and reliable information transmission.  
Figure 5: DAM-TEQ processing results under different relative speeds.  
Figure 6: Accuracy statistical results.
## 4 Results  
### Estimation of Foliage Profile  
An example of the process used to estimate the forest foliage profile of the plot is shown in Figure 5. The scanning angle, flying altitude, and pulse density in the example were 20\({}^{\circ}\), 2000 m, and 1 pulse/m\({}^{2}\), respectively. The red line in Figure 5 was the dividing line between the ground and the canopy. Figure 5a shows the return energy profile of the plot. We can see that the height range of the canopy return energy was larger than the height range of the ground return energy, and the canopy return energy varied with the increase in tree height. The canopy closure profile is shown in Figure 5b. Figure 5b indicates that the canopy closure increased as the tree height decreased, and the rate of change was different at different heights. The total canopy closure was 0.74. Figure 5c represents the cumulative \(LAI\) profile, and it showed that the cumulative \(LAI\) increased as the tree height decreased. The total \(LAI\) was 4.94. Therefore, the difference between the simulated total \(LAI\) and the retrieved total \(LAI\) was 0.08. Figure 5d represents the forest foliage profile, and it demonstrated that the single-layer foliage area index varied with the increase of tree height. The single-layer foliage area index at the middle heights was larger than at the lower or higher heights, and the largest value was 0.33.
## 6 Influence Analysis of Construction Parameters and Field Verification
### Discussion  
1. In this study, the surface settlement of the water-rich soft stratum in Tianjin was fitted using the Peck formula. It should be noted that homogenous stratum is one of the application conditions for the Peck formula. The upper-soft and lower-hard stratum in this paper is treated as homogeneous stratum equivalently.
2. In the current study, only a preliminary investigation of the ground deformation characteristics caused by the shield tunneling in water-rich soft stratum was carried out with a refined numerical model, where the hydraulic parameters were set as fixed values. In fact, the hydraulic parameters are related to the tunneling process and have obvious nonlinearity. Thus, it is thought that the research will pave the way for more in-depth studies.
3. This paper did not consider the stratum damage caused by groundwater seepage, and a depth analysis related to the impact of non-homogeneous stratum on shield  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Monitoring** \\ **Section** \\ \end{tabular} } & \multirow{2}{*}{\(P_{s}^{*}\)} & \multirow{2}{*}{\(F_{f}^{*}\)} & \multirow{2}{*}{\(V_{g}^{*}\)} & \multicolumn{2}{c}{**Maximum Surface Settlement (mm)**} & \multicolumn{4}{c}{**Lining Quality**} \\ \cline{5-10}  & & & & **Measured** & **Calculated** & **Value** & **Error** & **Measured** & **Calculated** & **Value** & **Error** \\ \hline TY-56 & 0.91 & 0.33 & 1.01 & \(-9.68\) & \(-10.11\) & 4\% & 13.52 & 16.05 & 19\% \\ TY-66 & 0.92 & 0.33 & 1.11 & \(-5.04\) & \(-5.93\) & 18\% & 12.21 & 14.71 & 20\% \\ TY-290 & 1.20 & 0.45 & 0.99 & \(-13.1\) & \(-12.46\) & 5\% & 14.85 & 16.61 & 12\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the measured values and the simulated values at monitoring sections.  
Figure 30: Lining ovality at TY-56.  
Figure 29: Surface settlement at TY-56.  
tunneling in water-rich soft stratum was not carried out. Therefore, these contents can be regarded as fundamental for future research.
# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
### Challenges  
Although previous research has achieved outstanding successes, there still exist several challenges as follows:  
* The ability to combine signal processing with deep learning lies in the effective extraction and utilization of discriminative features from raw signals. Traditional signal processing methods, such as Fourier transform and wavelet analysis, often require extensive domain expertise and may struggle to handle complex, non-stationary signals. While deep learning models can automatically learn features from raw data, they are highly sensitive to noise and variations in signal quality. Balancing the strengths of both approaches to achieve robust feature extraction remains a critical challenge, especially in real-time applications.
* Deep learning model compression is particularly relevant in resource-constrained environments, such as edge devices or embedded systems. Deep learning models, despite their high diagnostic accuracy, often suffer from high computational complexity and large memory requirements, making them difficult to deploy in practical industrial settings. Techniques like pruning, quantization, and knowledge distillation are commonly used to compress models, but these methods often lead to a trade-off between model size and diagnostic performance. Ensuring that compressed models retain their generalization capabilities while reducing computational overhead is a non-trivial task, especially when dealing with complex fault patterns.
* Cross-domain diagnosis and domain adaptation arises from the inherent differences between source and target domains. In real-world industrial scenarios, the distribution of data can vary significantly due to changes in operating conditions, such as load, speed, and environmental noise. This domain shift can severely degrade the performance of fault diagnosis models trained on one domain when applied to another. Domain adaptation techniques aim to align the feature distributions of source and target domains, but achieving effective adaptation without extensive labeled data in the target domain remains a major hurdle. Additionally, the dynamic nature of fault patterns across domains further complicates the development of robust and adaptive diagnostic models.
## 2 Methods  
### Process Parameters and Deposition Patterns  
This study used an Insstek(r) MX-1000, a DMT metal 3D printer employing a 2 kW Ytterbium Fiber Laser (Figure 1). A molten pool is generated by the laser beam focused on the substrate, while the powder was deposited at the same time into the focal area of the laser through coaxial nozzles with flowing argon gas. The argon gas serves as a shield to protect the molten pool from oxidation. The nozzle head is positioned at a location 10 mm above the build and the beam diameter at the part surface is 1.2 mm.  
Powder materials and the substrate were chosen as SM45C steel and SUS304. If the substrate material and the deposited material are different, residual stress and distortion may occur due to different thermal expansion coefficients. However, since the substrate material is cut after manufacture and discarded, industry practice was taken into account that always considers inexpensive materials for the substrate. The test sample deposited on each substrate is 1.8 mm height, 4.0 mm wide, and 60.0 mmlong, and the substrate is 3.0 mm thick, 50.0 mm wide, and 100.0 mm long. The substrates were fully clamped with a jig for the 20 mm longitudinal direction (Figure 2).  
The test samples are deposited with the target bead height of 0.45 mm and hatching space of 0.8 mm. The target bead height is experimentally determined with the process parameters described in Table 1.  
Six patterns were designed using long raster patterns, short raster patterns, and combinations of both. To reduce the time difference between the completion of one layer deposition and the start of the next layer's deposition for all cases, the long raster pattern consists of two tool paths A and B. The short raster pattern consists of two tool paths C and D (Figure 3). Pattern 1 applies only the long raster pattern to deposit four layers and pattern 2 only applies the short raster pattern. Pattern 3-6 are designed to apply the short raster pattern and long raster pattern twice in four-layer deposition,  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value** \\ \hline Nominal Laser power (kW) & 0.6 \\ Travel speed (m/min) & 0.85 \\ Mass feed rate (g/min) & 8.0 \\ Hatching space (mm) & 0.8 \\ Layer thickness (mm) & 0.45 \\ Beam diameter (mm) & 1.2 \\ Substrate material & SM45C \\ Powder material & SUS316 \\ \hline \end{tabular}
\end{table}
Table 1: Process parameters.  
Figure 1: Insstek® MX-1000, a Direct Metal Tooling (DMT) metal 3D printer.  
Figure 2: Schematics of the substrate and test sample dimensions.  
respectively. (Table 2). Although the tool paths were selected to minimize the time difference between the pattern, there is a slight difference in the total deposition time according to the pattern. In particular, Patterns 3 and 4 require switching from the previous layer's ending point to the current layer's starting point. This extra movement allows extra cooling time between these layers and increases the total deposition time by approximately 5-8%.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 2 Smart Meter Overview
In a scenario of several millions of customers and daily readings with fine granularity, considering best-practice checks for validation, it can take many hours before the data become available to the suppliers for the billing process. Some SMs can even send raw, non-validated measurements directly to the final customer in real time for awareness purposes and, possibly, home automation. Thanks to a set of IHDs communicating directly with the SM or through a Home Area Network (HAN), metering data can be visualized on a local display or smartphone application, giving consumption feedback and tips for reducing electricity absorption and thus saving money. In addition, an SM capable of two-way communications can receive requests from the IHD asking for specific data or to execute operations. Such systems are inherently more subject to electrical- and cyber-security issues, which must be taken into account.
## 4 Constrained SAC Algorithm  
### Constrained Markov Decision Process Formulation  
In a normal DRL framework, the problem should be first defined by a tuple \((\mathcal{S},\mathcal{A},P,r)\) to solve, which consists of state space \(\mathcal{S}\), action space \(\mathcal{A}\), transition probability \(P\) and reward \(r\). Similarly, a constrained DRL also needs this turtle but with an extra cost function \(\mathcal{C}\). So, we give the following definitions.  
State space is a set of representations of the environment observed by the agent. These observations have all the relevant information the agent needs to make a decision. In the ISTN, the SINRs, rates and energy efficiency can be calculated according to the channel fading coefficients, thus, we select them as the state. The current state space is defined as:  
\[s^{(t)}=\Big{\{}h_{1}^{(t)},h_{2}^{(t)},\cdots,h_{K}^{(t)}\Big{\}}. \tag{15}\]  
Action space is a set of all valid actions of choices available to the agent. In this scenario, precoders are allocated to users to calculate energy efficiency with channel fading coefficients. So the current action space is given as  
\[a^{(t)}=\{\mathbf{w}\}, \tag{16}\]  
where \(\mathbf{w}\) is defined in Section 3.  
Reward is a feedback from the environment based on the last action that agent took. It utilizes this feedback to improve its action policy. Usually, we can directly use energy efficiency as a reward; however, the QoS constraint cannot be incorporated in this case. Therefore, we introduce the Lagrangian relaxation method to convert the original problem into an unconstrained one.  
According to references [41; 42], the following constrained optimization  
\[\max_{x}\,f(x). \tag{17}\]  
\[\text{s.t.}\,g_{i}(x)\geq 0, \tag{18}\]  
is equivalent to the problem  
\[\min_{\mathbf{\lambda}>0}\,\max_{x}\,\hat{f}(x,\mathbf{\lambda},\rho)=f(x)+\sum_{i} \lambda_{i}g_{i}(x), \tag{19}\]  
where \(\hat{f}(x,\mathbf{\lambda})\) is the Lagrangian relaxed form of original \(f(x)\). The Lagrangian multiplier \(\lambda_{i}\) adjusts the relative importance of constraint \(g_{i}(x)\), which is called a cost function as well, against the objective function. Then, with the following equations at the \(t\)-th iteration, it can be gradually updated.  
\[\lambda_{i}^{(t+1)}=\lambda_{i}^{(t)}+\mu_{\lambda}g_{i}\Big{(}x^{(t)}\Big{)}, \tag{20}\]  
where \(\mu_{\lambda}\) is the learning rate of the Lagrangian multiplier.  
With the above information, the current instantaneous reward in Lagrangian relaxed form is defined as:  
\[r\Big{(}s^{(t)},a^{(t)}\Big{)}=EE+\sum_{k=1}^{K}\lambda_{k}^{(t)}\Big{(}R_{k}^ {(t)}-R^{\text{th}}\Big{)}, \tag{21}\]  
in which the cost function is denoted as \(c_{k}^{(t)}=R_{k}^{(t)}-R^{\text{th}}\). The reward will decline when the QoS constraint is not satisfied.
# Risk Identification Method and Application of Roof Water Inrush Under Multi-Working Face Mining
## 3 Engineering Application
### Engineering Theoretical Identification  
The theoretical identification of the key strata position in the 120501 working face was performed using the key strata identification method, and the results are presented in Figure 8.  
The theoretical calculation of the critical dimension for key strata failure in the 120501 working face was performed using the critical dimension calculation formula. The results of the calculations are presented in Table 2.  
Based on the theory of Key Strata and Full Mining Disturbance, and with reference to the comprehensive physiochemical parameters of the strata at the working face, it is predicted that when the mining height is 4 m, the maximum development height of the water-conducting fracture zone in the 120501 working face will reach 60 m. In contrast, the 110504 working face is not expected to face the risk of delayed water  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Lithology** & **Elastic Modulus** & **Bulk Density** & **Tensile Strength** & **Compressive Strength** \\  & **/GPa** & **/(KN\(\cdot\)m\({}^{-3}\))** & **/MPa** & **/MPa** \\ \hline coal & 1.56 & 14.10 & 0.23 & 12 \\ mudstone & 6.00 & 25.35 & 1.64 & 15 \\ silty mudstone & 10.00 & 24.96 & 2.20 & 42 \\ silstone & 18.00 & 26.30 & 3.86 & 46 \\ medium sandstone & 26.00 & 25.28 & 4.95 & 43 \\ fine sandstone & 22.00 & 26.74 & 6.22 & 44 \\ fine-grained sandstone & 18.00 & 25.48 & 3.56 & 37 \\ siltstone 2 & 7.20 & 24.95 & 2.08 & 35 \\ coarse sandstone & 12.98 & 23.89 & 2.24 & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comprehensive physical and mechanical parameters of the working face strata.  
Figure 7: Comprehensive lithological column diagram of the working face.  
The thickness of the protective strata should be no less than six times the thickness of the mined coal seam. Based on a mining height of 4 m, the protective strata thickness should not be less than 24 m.  
The bedrock thickness of the 120501 working face ranges from 160 to 200 m. Given the predicted maximum height of the water-conducting fracture zone of 60 m, the protective strata thickness is estimated to range between 100 and 140 m. This exceeds the minimum requirement of 24 m, suggesting a low risk of water inrush from the roof.
## 4 Discussion
Although the influence of experience and body composition on peak EMG trends was modest, the results suggest that less experienced athletes have a diminished ability to increase maximum muscle activation in key muscles, such as the RF, VM, and VL. Furthermore, it is important to highlight that our study provides additional and well-founded evidence that aligns with previous studies [21, 22] on fat control and muscle gain in the performance of Taekwondo athletes. It is important to highlight that, although the fixed effects have an influence, there is a high level of individual variability in the muscle activation data. Among the main strengths of this study is the use of a within-subject design, which increases statistical power by reducing inter-individual variability and allowing for a more accurate assessment of neuromuscular adaptations across repeated kicks. Another important strength was the inclusion of both novice and advanced Taekwondo athletes who were comparable in age, body weight, standing height, and BMI. This homogeneity helped to minimize potential confounding variables and strengthened the internal validity of our comparisons. Our findings present several practical applications for Taekwondo training. Coaches and practitioners should prioritize exercises targeting antagonistic muscle groups to enhance the performance and efficiency of the roundhouse (_bandal chagui_) kick. Novice athletes may benefit from drills designed to strengthen and improve the coordination of the quadriceps and from incorporating controlled co-contraction exercises to enhance joint stability during kicking. The limitations of this study include the small sample size, which restricts its external validity. Nonetheless, the statistical significance and large effect sizes observed underscore the importance of the differences identified in our research. Participants performed repeated roundhouse (_bandal chagui_) kicks from a static position, which differs from real combat conditions. However, the study aimed to use this setup as an experimental model to simulate task-specific demands and assess performance changes with repetition. Additionally, muscle fatigue was not directly measured. Fatigue is a key factor that can distinguish between novice and expert athletes, as experts often demonstrate greater fatigue resistance and more efficient motor unit recruitment. Well-established EMG-based fatigue estimation methods, such as median frequency or zero-crossing rate analysis [31], are typically applied to sustained or cyclic contractions and may be less reliable in dynamic, high-intensity tasks like consecutive kicks. Nonetheless, future studies are encouraged to incorporate these or alternative fatigue indicators to better characterize performance dynamics and muscle function over time. Finally, the binary classification of athletes into novice and advanced groups may not fully reflect the continuous nature of skill development. Differences in neuromuscular patterns could be more closely related to training volume and cumulative experience rather than to group labels alone.
## 2 Materials and Methods
### Implementation
#### 2.2.3 Routine for Picking a Container from the Shelf  
This routine consists of detecting and picking a requested container placed on a designated shelf. This process is an adaptation of the previous routine for picking a container from a table, adjusted for picking from elevated positions. It follows the flowchart shown in Figure 10 and the sequence of images illustrating the task in Figure 11. Starting with the robot positioned in front of the shelf where the container is located, the steps are as follows:  
Figure 8: Finite state machine for the process of centering the gripper with the ArUco marker.  
Figure 9: Grasping scheme.  
1. Look for the container: This sub-process is the same as the one used for picking from the table and follows the finite state machine shown in Figure 7.
2. Correct base position: Again, this sub-process is the same as the one implemented before.
3. Gripper centering: This sub-process includes a modification. First, it moves backward 0.3 m to create space for raising the gripper to an elevated position. Then, the previously presented finite state machine from Figure 8 is executed. Once the more precise location of the container is obtained, the robot moves forward 0.3 m.
4. Pick up the container: The 'pick' described before is executed by grabbing the container with the end-effector and then retreating from the shelf, as shown in Figure 10(c).
5. Post-pick: Finally, the end-effector is commanded to predefined intermediate safe poses between the high pose and the final retreated pose of Figure 10(d). For this trajectory, a constraint of maintaining the orientation is set via MoveIt.