esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 4 Trust-Based Proximal Policy Optimization (TBPPO)
### The Improved DFS Module  
Most deep reinforcement learning algorithms are designed based on Next Hop routing policies, as indicated in [44; 45; 46; 47]. However, it has been shown that such policies are prone to leading to routing loops, causing longer delays and some degree of packet loss [50]. These  
Figure 1: Schematic structure of the TBPPO algorithm.  
issues have not been adequately addressed in the aforementioned studies. To tackle these challenges, we introduce a path selection approach. The number of available paths in the network increases exponentially with the network's scale, which can be computationally prohibitive. Therefore, we introduce the concept of limiting the number of paths. We use the path selection approach in conjunction with the DFS algorithm to pre-select the K best paths, effectively sorting and selecting the optimal K paths from the available options. The initial topology structure, \(M_{topo}\), containing delay information, is used as the input to the network. K best paths are computed to form the preselected routing group. The delay values associated with these preselected routes effectively replace the delay matrix \(M(\tau)\) as part of the state representation. This preprocessing step results in a considerable reduction in the computational complexity of our algorithm. This preprocessing step can be expressed as:  
\[\mathbb{L}=DFS_{K}\big{(}M_{topo}\big{)} \tag{4}\]  
where, \(M_{topo}\) is the matrix topological structure, L represents the set of all possible paths generated after the application of the DFS algorithm, and its cardinality is denoted as \(L_{0}\). \(DFS_{K}\) signifies the utilization of an enhanced DFS algorithm to select the shortest K routes.
## 4 Experimental Results and Analysis  
In this section, we demonstrate the effectiveness of the proposed IGVMD-1 DCNN through experiments on different datasets. Compared with EMD and traditional VMD, the proposed IGVMD has a better feature extraction ability by using the bearing vibration signal under noise conditions. Then, compared with the neural network with the ReLU activation unit, the higher training efficiency of the 1 DCNN model with the PReLU activation unit designed in this paper is verified. Furthermore, we compared it with SVM, gcForest, LeNet-5, and Resnet.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 4 Approaches for Preventing Sticking of MXenes
### Integration with Polymer Matrices or Carbon Nanomaterials
#### 4.2.3 Hybrid Composites  
Combining polymer matrices and carbon nanomaterials with MXenes can create hybrid composites that offer synergistic effects to prevent sticking. In these composites, the MXenes are dispersed within a polymer matrix, while carbon nanomaterials are incorporated to enhance electrical conductivity, mechanical strength, and stability.  
The hybrid composite approach leverages the complementary properties of polymers, carbon nanomaterials, and MXenes. The polymer matrix provides encapsulation and dispersion stability, while carbon nanomaterials enhance the conduction pathways and  
Figure 17: A cross-sectional TEM image of MXene sheet embedded in the P(VDF-TrFE-CFE) matrix [76]. Reprinted from [PERSON] et al., ACS Nano., copyright (2018), ACS.  
mechanical integrity. Together, this integrated approach effectively prevents sticking and improves the overall performance of MXene-based supercapacitor electrodes.  
By integrating MXenes with polymer matrices or carbon nanomaterials, researchers can create composite structures that alleviate sticking issues, promote better dispersion, and enhance the stability and performance of MXene electrodes in supercapacitor devices. The choice of suitable polymers and carbon nanomaterials must be carefully considered to achieve optimal results.  
Heterostructures of MXene including transition metal dichalcogenides, such as MoS\({}_{2}\) for example, have been examined for enhanced stability. It has demonstrated good prevention of restacking of delaminated MXene nanosheets due to the favorable layered nature and tendency to easily form heterostructures with the MXene materials, thus decreasing the probability of their oxidation and restacking [78; 79].
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 3 Link Budget Analysis
### Total Path Loss  
The path loss model encompasses all signal losses incurred during propagation from the transmitter to the receiver, essentially aggregating all individual losses. For the specific scenario in question, the path loss can be computed using the following formula:  
\[Path\ Loss=FSPL-10 log(f_{ds})-L_{gas}-L_{rain} \tag{11}\]  
In addition to received power, two additional metrics that can be utilized to assess the link budget analysis include the following:  
* The carrier-to-noise ratio: This ratio, represented in Equation (12), measures the received carrier strength to the received noise. This is also called the signal-to-noise ratio (SNR). \[SNR=\frac{P_{RX}}{Noise\ Power\ (N)}\] (12)
* Latency Latency denotes the delay that signals encounter as they pass through a communication system. Although latency is not usually included in standard link budget analyses, it plays a critical role in the planning and performance assessment of communication frameworks, particularly in situations requiring an immediate or time-sensitive exchange of data. The formula for latency (t) is given in Equation (13). \[t=\frac{Distance\ (d)}{c}\] (13) The latency equation presented does not factor in the UAV's speed. However, in practical scenarios, it would likely be influenced by the processing times at both the transmitter and receiver ends. Since we lack specific data regarding this, we assume that processing occurs instantaneously, without any delay. These equations are applied to perform the link budget analysis for the given scenario.
## 4 Results
### Chemico-Physical Characterization  
#### 4.2.1 DSC Measurements  
DSC measurements of NAC shows an endothermic peak (enthalpy change, \(\Delta\)H 153.4 \(\pm\) 0.4 J\(\cdot\)g\({}^{-1}\)) at 109.2 \(\pm\) 0.1 \({}^{\circ}\)C, attributable to the melting of the compound (Figure 1).  
DSC profiles both of physical mixture NAC:resveratrol:cyclodextrinetrin (10:15:75 w:w, MF) and of NACESOLTM show the same behavior with three different endothermic effects (Figure 2). The first one, with a T\({}_{\rm onset}\) 106.9 \({}^{\circ}\)C, could be due to the melting of NAC. The \(\Delta\)H of this peak in the physical mixture is about 15.0 J g\({}^{-1}\) and corresponds to the value that the compound should have (15.3 J g\({}^{-1}\)) if no interaction among the components occurs in the physical mixture. The integration of this peak, due to the lack of a return of the curve to the baseline, can only be approximate. The other two unresolved peaks in the thermal range 120-230 \({}^{\circ}\)C followed by an endothermic drift can be attributed to the interaction between cyclodextrin and resveratrol.  
Figure 1: Differential scanning calorimetry (DSC) profile of pure N-acetylcysteine (NAC).  
Figure 2: DSC profiles of NACESOLTM and physical mixture (MF) NAC:Resveratrol:cyclodextrinetrin (10:15:75 w:w:w).  
In fact, the analysis of pure resveratrol powder highlights the DSC profile with a melting point at 263.0 \({}^{\circ}\)C with a \(\Delta\)H of 261.4 J\(\cdot\)g\({}^{-1}\), while cyclodextrin treated with patented technology shows an enlarged and asymmetric endothermic peak with T\({}_{\text{onset}}\) of 127.4 \({}^{\circ}\)C and \(\Delta\)H equal to 158.7 J\(\cdot\)g\({}^{-1}\) (data not reported). The absence of the resveratrol melting peak suggests that in the physical mixture, Resveratrol is present in an amorphous form, probably included in a multi-composite with cyclodextrin.  
The same behavior is present in the DSC profile of NACESOLTM, in which three unresolved endothermic effects occur. The peak attributable to the NAC fusion is clearly distinguished, with T\({}_{\text{onset}}\) of 105.2 \({}^{\circ}\)C and \(\Delta\)H of 7.3 J\(\cdot\)g\({}^{-1}\).  
This value of \(\Delta\)H is definitely lower than the value measured in the physical mixture but still represents around 49%. The thermal data therefore suggests that, in NACESOLTM, a substantial percentage of NAC (about half of the quantity envisaged by the composition) is still present in crystalline form and therefore not included in multi-composite compound.
# Numerical Simulation on Dynamic Behavior of Slab-Column Connections Subjected to Blast Loads
## 2 Literature Review
According to the ASCE/SEI (2011), the connection should be designed to resist shear force, axial force, bending moment, and torsion [41]. The effects of rebound are also considered for all connections. The reinforcements in beam-column connections are supposed to comply with details of earthquake-resistant structures according to ASCE/SEI (2011) [41]. There are no other guidelines for the design of blast-resistant slab-columnconnections. Especially, the material properties in blast events are different in earthquake conditions because of the difference in strain rate. The strain rate is typically over \(10^{0}\) s\({}^{-1}\) in blast events and \(10^{-5}\) s\({}^{-1}\) in earthquakes [42]. Therefore, more accurate material properties are needed to design structures subjected to explosive loads comparing to structures subjected to seismic loads. According to UFC 3-340-02 issued by the US Department of Defense (DoD), support rotation and ductility are selected as criteria for evaluating the structural performance of RC structural members under explosive loads [43]. This criterion assumes that the structural member is effectively resisting the blast load when the support rotation is 2 degrees or less. In addition, ASCE/SEI 59-11 proposes the limit of support rotation for blast-resistant RC structures and also provides a level of protection (LOP) [41]. It is noteworthy that both DoD and ASCE are proposing support rotation as an evaluation factor for the behavior of blast-resistant structures.
# Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission
## 3 Energy Efficiency Optimization of Secure Multicast Transmission
### Dinkelbach's Transform  
At the \(\ell\) th iteration of subproblem in (28), the fractional function of the objective is still difficult to solve with convex optimition directly. We have \(R_{\text{ee}}\left(\mathbf{\Lambda}\right)=R_{\text{mc}}\left(\mathbf{\Lambda}\right)- \text{tr}\left\{\left(\frac{\partial}{\partial\mathbf{\Lambda}}R_{\text{ev,ub}} \left(\mathbf{\Lambda}^{(\ell)}\right)\right)^{T}\mathbf{\Lambda}\right\}\), which is a concave-minus-linear function. Thus, \(R_{\text{ee}}\left(\mathbf{\Lambda}\right)\) is a concave functions with respect to \(\mathbf{\Lambda}\). Meanwhile, \(P(\mathbf{\Lambda})\) is a linear function. [PERSON]'s transform is usually used to approach such concave-linear fractional program, whose advantage is that it does not have to include additional constraints. It has been proved that this algorithm possessed superlinear convergence [43; 44]. Besides, [PERSON]'s transform We express the powers allocation \(\mathbf{\Lambda}\) solved in (28) as \(\mathbf{\Gamma}\). Via invoking [PERSON]'s transform, we can solve the subproblem of each energy efficiency secure multicast optimization in (28) by iteratively solving the sequence of convex optimization problem as follows,  
\[\mathbf{\Gamma}^{(t+1)}=\underset{\mathbf{\Gamma}}{\arg\max} \quad WR_{\text{mc}}\left(\mathbf{\Gamma}\right)-W\text{tr}\left\{ \left(\frac{\partial}{\partial\mathbf{\Lambda}}R_{\text{ev,ub}}\left(\mathbf{\Lambda }^{(\ell)}\right)\right)^{T}\mathbf{\Gamma}\right\}-\eta^{(t)}P(\mathbf{\Gamma}),\] s.t. \[\quad\text{tr}\left\{\mathbf{\Gamma}\right\}\leq P_{\text{max}},\ \mathbf{\Gamma}\succeq\mathbf{0},\ \mathbf{\Gamma}\text{ diagonal}, \tag{30}\]  
where \(t\) denotes the iteration index. \(\mathbf{\Lambda}^{(\ell)}\) is the solution of the \(\ell\) th outer iteration of the MM method. Note that it is treated as a constant and will not update in the inner iteration in (30). \(\eta^{(t)}\) denotes an auxiliary variable introduced by [PERSON]'s transform, which can be updated as follows,  
\[\eta^{(t)}=W\frac{R_{\text{mc}}\left(\mathbf{\Gamma}^{(t)}\right)-\text{tr}\left\{ \left(\frac{\partial}{\partial\mathbf{\Lambda}}R_{\text{ev,ub}}\left(\mathbf{\Lambda }^{(\ell)}\right)\right)^{T}\mathbf{\Gamma}^{(t)}\right\}}{P(\mathbf{\Gamma}^{(t)})}. \tag{31}\]  
By optimizing \(\mathbf{\Gamma}\) and \(\eta\) alternately, the generated solution sequence \(\left\{\mathbf{\Gamma}^{(t)}\right\}_{t=0}^{\infty}\) in (30) is also proved to converge and get the optimal solution of the subproblem in (28).
## 3 Use Case: UMTS Networks
### Data Selection
Table 1 summarizes the selected KPIs and highlights their match with the requirements that were established by the methodology. The table shows that they fulfill the requirements to be used as variables towards obtaining the composite indicator. Note that the selection is similar to the one that is presented in [20]. Slightly different versions of the selected KPIs are also available in LTE, e.g., INTER-RAT HO in LTE refers to HOs to UMTS technology. Because of this, the extension of the proposed methodology to LTE is straightforward.
# Association between Motivational Climate, Emotional Intelligence, and Bicycle Use in Schoolchildren
## 5 Limitations and Future Perspectives  
This study has several limitations that should be considered when interpreting its findings. First, although the sample size was sufficient, the use of a descriptive and cross-sectional design may have introduced biases. Additionally, the reliance on self-reported data reflects the participants' perceptions of their behavior, which may not always align with objective reality. Second, the majority of the participants were between the ages of 9 and 13 and belonged to only two primary schools located in the same autonomous city, limiting the generalizability of the findings to other age groups, educational settings, or broader geographical areas. Consequently, future research should aim to validate these results in diverse cultural contexts and age groups. Although this study identified an association between moderate cycling practice and the development of a task climate, further longitudinal research is needed to address the long-term effects of cycling on MC improvement and EI development in children. Additionally, it would be interesting to explore the relationship between cycling and other factors, such as anxiety, student satisfaction, and the promotion of active mobility habits among schoolchildren, especially in girls. Moreover, future studies should also examine EI and MC through other types of physical activity beyond cycling to better understand how different forms of exercise contribute to these outcomes.  
Despite these limitations, the study's findings are valuable as it is the first to provide a detailed analysis of the relationship between MC, EI, and schoolchildren's cycling habits in primary school students. From a practical perspective, this research suggests that policymakers should support PE teachers in integrating bicycle-based intervention programs into PE sessions. Such programs can help achieve higher levels of self-determined motivation, foster active mobility habits, acquire greater physical and emotional well-being, and enhance the development of perceptual and motor skills among primary school students.
# Novel Fluidic Oscillator Evaluation Considering Dimensional Modifications
## 3 Mesh and Boundary Conditions
### Three-Dimensional Baseline Model
The boundary conditions considered for the present 3D simulations are summarized in Table 2. The turbulent intensity at the domain inlet was set to \(I=0.1\%\), with the fluid velocity being \(U=10\) m/s. The following equations were used to determine the turbulence properties at the computational domain inlet: \(k=(3/2)U^{2}I^{2}\); \(\epsilon=C_{\mu}(k^{3/2}/L)\), where \(C_{\mu}=0.09\); \(\omega=\epsilon/(C_{\mu}k)\); and \(v_{t}=k/\omega\). Using these equations, the following values for the different turbulence parameters were obtained. Turbulence kinetic energy, \(k=1.5\times 10^{-4}\) (m\({}^{2}\)/s\({}^{2}\)); turbulence dissipation rate, \(\epsilon=2.05\times 10^{-6}\) (m\({}^{2}\)/s\({}^{3}\)); the specific dissipation range, \(\omega=0.152\) (s\({}^{-1}\)); and the turbulence kinematic viscosity, \(v_{t}=9.9\times 10^{-4}\) (m\({}^{2}\)/s). In the present study, the fluid used was air at 15 degrees Celsius; it was considered as turbulent, incompressible, and isothermal. The fluid dynamic viscosity and its density were \(\mu=1.813\times 10^{-5}\) kg/(m s) and \(\rho=1.225\) kg/m\({}^{3}\), respectively. The finite volumes package in OpenFOAM version 5.0 was used to perform all simulations; the platform ParaView was used as the post-processor. The Navier-Stokes equations were solved using the pressure implicit with splitting operators (PISO) solver. The time-step employed was \(4\times 10^{-7}\) s, and a second-order spatial discretization was considered. In order to consider the simulations as converged, the residuals for the fluid velocity components, the turbulent kinetic energy, the specific dissipation rate, and the turbulent kinematic viscosity had to be on the order of \(10^{-6}\). After all simulations, the residuals for the pressure variable were on the order of \(10^{-4}\).
## 5 Result and Discussion
### Performance Evaluation with 38 BdSL Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Model Name** & **Segmented** & **Image Pixel** & **Training (\%)** & **Validation (\%)** & **Testing (\%)** \\ \hline
38 BdSL & [PERSON] et al. [11] & No & 224 \(\times\) 224 & 97.68 & 91.52 & 89.60 \\ \hline
38 BdSL &
\begin{tabular}{c} Proposed model \\ (BenSignNet) \\ \end{tabular} & yes & 124 \(\times\) 124 & 99.99 & 96.00 & **94.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: State of the Art Comparison for 38 BdSL Dataset. Figure 9: Confusion Matrix of the Proposed Model with 38 BdSL Dataset. performance accuracy. Among 38 classes, 35 classes produced more than 90% accuracy; one class has 80% accuracy, and only two class made below 80% accuracy.
## 4 Simulation Results
The energy greedy scheme always pursues a low-energy allocation method without considering any other influencing factors. The energy consumption of the random deployment strategy is the largest. Because the random deployment strategy does not consider the heterogeneous of the processing capacity of the edge computing nodes for different tasks, and it is difficult to achieve a reasonable correspondence between the tasks and the edge computing nodes. We give the load balancing degree of the network under different task numbers in Figure 5. The change of load balance with the number of tasks is not regular, but within a certain range. This is because the types of simulation tasks, data volume, etc., have a certain randomness. Random task generation results in jitters in energy balance. At the same time, different algorithms also lead to differences in the range of energy balance because of different goals and strategies. It can be observed that compared with the energy consumption greedy strategy and the random allocation strategy, the PPO resource scheduling we proposed can always achieve the best network load balancing. When the task number is 20, the network load balancing of PPO resource scheduling is 0.31, which is 0.37 lower than the greedy strategy of energy consumption and 0.13 lower than the random deployment strategy. This is because our proposed optimization takes energy consumption and load balancing as the dual goals. In order to enhance the cumulative reward, agents must ensure a low degree of load balancing while pursuing low network energy consumption. The energy consumption greedy strategy does not consider the network balance, and always tends to allocate tasks to nodes with low energy consumption, which greatly leads to unbalanced network load. The random assignment scheme may be approximated as a uniform distribution of tasks when the number of tasks is large, but it does not take into account the data size required by the task and the difference in task processing capabilities of edge computing nodes. Therefore, the heterogeneity of edge computing network and task requirements affects the load balancing of the network to a certain extent. Figure 4: Energy consumption with different number of tasks.
## 2 Sensors
### Camera
#### 2.3.2 Simultaneous Localization and Mapping (SLAM)  
Photogrammetric and 3D-vision principles provide a mathematical formulation to estimate locations and orientations of images in different settings without essentially requiring additional information from other sensors. Simultaneous localization and mapping (SLAM) is basically a wide concept that refers to finding the camera position and orientation while collecting geometric information, usually as a sparse point cloud of the scene [73]. It enables cameras to act as localization sensors, especially in situations where no other reliable source of acquiring location information is accessible. An image-based SLAM could be implemented in different ways, e.g., visual odometry methods such as bundle block adjustment (BBA) could be used to estimate camera and scene parameters through an optimization process [74]. In general, a SLAM problem could be addressed by diverse camera configurations and other sensors. In monocular (or single-camera image-based) SLAM, a set of images from only one camera are given, while the trajectory of the moving camera is in question [75]. Camera-based SLAMs could be enabled also with other camera settings, including stereo, RGB-D, and multi-cameras. Knowing parameters such as the interior orientation of cameras or the internal structure in the case of using multi-cameras is of high importance and could significantly increase the reliability of the output SLAM algorithms. Examples of camera-based SLAM could be seen, e.g., in works of [PERSON] et al. [76] or [PERSON] et al. [77].  
Image-based SLAM is usually implemented by sequentially finding tie points between images, creating stereo pairs, solving relative orientation between pairs of images, and combining image pairs to create a network of images in an efficient way. Usually, a set of image-based tie points is translated into a sparse point cloud that represents the environment of the SLAM system. Local optimization by employing BBA helps to reduce the effect of noise and build a robust estimation; however, geometric constraints such as loops could significantly help the SLAM system to mitigate the effect of additive errors.  
Deep learning gradually finds its way into image-based SLAMs, e.g., DROID-SLAM proposed by [PERSON] and [PERSON] [78] employed a neural network for monocular, stereo, and RGB-D SLAMs. An overview of deep-learning SLAMs could be seen, e.g., in [PERSON] et al. [79].