esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Discussion  
### Experiment Phenomenon and Deflection Displacement  
Figure 11 shows the failure mode of the end-plate joint component, and Figure 12 shows the failure mode of the jointless component. At the initial stage of loading, the components do not change significantly. When the external load increases to 60% of the ultimate strength, the steel tubes both represent obvious bending deformation. At the same time, the \(\pi\)-shaped ribs are subjected to the load, and obvious deformation occurs in the arc section. When loaded to the ultimate bearing state, the components deform significantly, the vertical load tends to be stable, and the bending springback is large after unloading.  
Figure 10: Layout of measuring points. (**a**) The end-plate joint component and (**b**) the jointless component.  
Figure 9: Schematic diagrams of loading test model of SRS components. (**a**) The end-plate joint component and (**b**) the jointless component.  
The end-plate joint component is welded to realize a fixed connection between the main limb steel tubes and the end-plate. The arch segments are fastened with high-strength bolts, and no brittle failure occurs during the loading process. The deformation of the end-plate in the tension zone is not obvious, and the axial force of the bolt on the tension side is large, but it does not break. Due to the absence of weak links, such as weld seams and bolted connections, the jointless component has good integrity, a simple force transmission mechanism during loading.  
Figure 13 shows the moment-mid-span deflection displacement curves of the end-plate joint component and jointless component. At the initial stage of loading, the mid-span deflection of the two kinds of components changes linearly with the bending moment. The growth rate of the deflection of the end-plate joint component is slow, showing higher deformation resistance and bending stiffness. When the load continues to be applied, the growth rate of the deflection gradually accelerates, and the components begin to enter the stage of plastic deformation. The plastic development period of the jointless component is longer, and the end-plate joint component first reaches the ultimate bearing state. At the later stage of loading, the deflection displacement continues to increase, while the external load remains relatively stable.  
The mid-span deflection of the end-plate joint component is approximately 46.0 mm when it reaches the ultimate bearing state, and the ultimate bearing capacity is 212.6 kN\(\cdot\)m. The maximum allowable deflection of jointless component is approximately 54.2 mm, and the ultimate bearing capacity is 209.5 kN\(\cdot\)m, which accounts for 98.5% of end-plate joint component, indicating that the end-plate joint component has good ductility and high bearing capacity.  
Figure 11: Failure mode of the end-plate joint component. (**a**) Elevation view and (**b**) axonometric drawing.  
Figure 12: Failure mode of the jointless component. (**a**) Elevation view and (**b**) axonometric drawing.
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.6 Hyperparameter Tuning  
This study explored three tuning methods to optimise hyperparameters: grid search, random search, and metaheuristics. In fact, XGBoost, as a robust machine learning algorithm, has numerous hyperparameters. This research focused on adjusting the following parameters [56]:  
* Learning Rate: Controls the step size for each boosting iteration. Smaller values can improve performance but increase training time.
* Max Depth: Determines the maximum depth of decision trees, affecting model complexity. Higher values may lead to overfitting, while lower values can result in underfitting.
* n Estimators: Sets the number of boosting iterations or trees in the model.  
Also, understanding hyperparameter tuning is crucial for enhancing machine learning model performance and preventing overfitting or underfitting. Grid search systematically explores a specified parameter grid, while random search randomly samples hyperparameter combinations. Inspired by natural phenomena, metaheuristic algorithms offer a more heuristic approach to finding optimal solutions. This research investigated the accurate tuning of XGBoost hyperparameters using various metaheuristic methods. The findings highlight the delicate balance required for optimal model performance in supervised learning problems. Hyperparameter tuning is also essential for maximising the effectiveness and generalisability of machine learning models.
# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 4 Experiments
### Comparison with the State-of-the-Art Methods  
To prove the superiority of our approach, we compared it with the state-of-the-art methods. Since many methods are performed on segmented hand images and most methods are based on the STB dataset, we also compared them on the segmented hand images of the STB dataset. Table 15 shows that, among all methods, our method achieved the best AUC value. [PERSON]'s method performs weak supervised learning by reducing keypoints into a depth image and can learn some implicit depth information through weak supervised learning, but the learned depth information is still less useful than that implied in the original RGB image. [PERSON], [PERSON], and [PERSON] use only 2D information to restore the 3D positions and lose some information from the RGB image. [PERSON]'s method restores the occluded hand areas through a GAN, but due to the error of the picture restored by the GAN, the error is magnified during intermediate error transmission. Since we have used the proposed CFAM module to take the information in the 2D score map and RGB image of the hand into account, our method achieved the best result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Methods** & **Wrist** & **Thumb** & **Index** & **Middle** & **Ring** & **Little** \\ \hline [PERSON] & Zimmermann [16] & **0** & 39.8 & 42.51 & 35.98 & 33.04 & 35.59 \\ Error (mm) & Ours & 0.21 & **32.83** & **34.43** & **29.81** & **26.85** & **29.02** \\ \hline Media & [PERSON] [16] & **0** & 29.46 & 34.66 & 29.8 & 27.98 & 28.7 \\ Error (mm) & Ours & 0.2 & **23.51** & **25.35** & **23.08** & **21.05** & **22.68** \\ \hline AUC (0–50 & [PERSON] [16] & 0.97 & 0.4 & 0.35 & 0.41 & 0.43 & 0.4 \\ mm) & Ours & 0.97 & **0.48** & **0.45** & **0.49** & **0.53** & **0.49** \\ \hline \hline \end{tabular}
\end{table}
Table 13: 3D hand pose estimation on the RHD dataset from the RGB image for each finger. The best results are highlighted in bold.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **[PERSON] [16]** & **Ours** \\ \hline AUC (0–50 mm) & 0.424 & **0.512** \\ Median Error (mm) & 28.69 & **22.04** \\ Mean Error (mm) & 35.61 & **29.14** \\ \hline \hline \end{tabular}
\end{table}
Table 14: 3D hand pose estimation on the RHD dataset from the RGB image. The best results are highlighted in bold.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **AUC (20–50 mm)** \\ \hline [PERSON] [19] (CVPR2018 workshop) & 0.923 \\ [PERSON] [23] (2018 WACV) & 0.941 \\ [PERSON] [20] (CVPR2018) & 0.965 \\ [PERSON] [18] (2018 CVPR) & 0.983 \\ [PERSON] [16] (2017 ICCV) & 0.985 \\ [PERSON] & **0.991** \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison of state-of-the-art methods in terms of AUC for the STB dataset. The best result is highlighted in bold.
## 9 Samples of the Results
### Further Case Studies and Real-World Applications of DRL  
Deep reinforcement learning (DRL) has been successfully applied to various real-world robotic tasks, enabling autonomous agents to perform complex decision-making and control operations in dynamic environments. This section highlights notable case studies that illustrate the practical deployment of DRL in robotics.  
One prominent application of DRL is in _robotic manipulation tasks_. [PERSON] [87] demonstrated the use of DRL for robotic arm control in medical applications, such as instrument-assisted surgery and object manipulation in constrained environments. Their approach maps raw pixel inputs from a camera to control commands using a deep reinforcement learning neural network, enabling precise manipulation with minimal manual intervention.  
Another area of successful DRL implementation is autonomous robot navigation. [PERSON] [88] applied DRL-based techniques to real-time mobile robot path planning, improving navigation efficiency in dynamic environments. By combining visual perception with reinforcement learning policies, their approach enabled robots to make real-time adjustments in response to obstacles, significantly reducing collision rates.  
DRL has also been integrated into _multi-agent robotic systems_ to enhance coordination and collaboration. [PERSON] and [PERSON] [89] explored _multi-agent deep reinforcement learning_ (MADRL) for multi-robot applications, demonstrating its effectiveness in cooperative tasks such as warehouse automation and swarm robotics. Their findings indicate that DRL-based  
Figure 23: Comparison of success rates between the RL-based approach and a traditional motion planner across varying pedestrian densities. The RL-based approach avoids the freezing robot problem and maintains higher success rates (Adapted from [49]).  
policies enable robots to learn from each other's experiences, improving task efficiency and adaptability.  
Additionally, DRL has proven effective in _motion control for industrial and agricultural robotics_. [PERSON] [90] showcased DRL-based trajectory optimization in robotic manipulators for automated fruit picking, achieving significant improvements in efficiency and precision. Their study also highlighted the challenges of transferring learned policies from simulations to real-world deployments, emphasizing the importance of domain adaptation techniques.
###### Abstract  
Maintaining high system performance is critical for a multi-hop flying mesh network (FlyMesh) to perform missions in different environments. Although the Poisson point process (PPP) has been widely used for the performance analysis of FlyMesh, it still has flaws in describing the spatial distribution of the UAVs since it does not restrict the minimum distance between them. The spatial deployment of FlyMesh varies depending on the environment. Considering the relevance and practicality, we modeled the multi-hop FlyMesh using the \(\beta\)-Ginibre point process (\(\beta\)-GPP) and equipped each UAV with a directional antenna. Under the condition of the decode-and-forward protocol, we derived the connection probability and ergodic capacity of a multi-hop FlyMesh utilizing the Laplace transform of interference. Then, we calculated an approximate expression for the interference Laplace transform based on the diagonal approximation and further obtained the coverage probability. Finally, the numerical simulation results verified the correctness of the theoretical derivation, indicating that it is possible to optimize the system's performance based on the expressions derived in this paper.  
umanned aerial vehicles; \(\beta\)-Ginibre point process; multi-hop; directional antenna; performance analysis +
Footnote †: journal:  
Article
# Optimization of Multistage Coilgun Based on Neural Network and Intelligent Algorithm
## 2 Simulation Model
### Current Filament Model
The force \(F_{x}\) acting on the projectile in the axial direction is as follows:  
\[F_{z}=\frac{dM}{dz}I_{d}I_{p} \tag{4}\]  
If the mass of the projectile is \(m\), then according to [PERSON]'s second law, it can be obtained:  
\[\frac{d\upsilon}{dt}=\frac{F}{m}=\frac{1}{m}\frac{dM}{dz}I_{d}I_{p} \tag{5}\]  
Since the external circuit capacitor voltage \(U_{d}\) satisfies the following conditions when time \(t\) is greater than time \(t_{0}\), it follows that  
\[\frac{dU_{d}}{dt}=-\frac{1}{C}I_{d} \tag{6}\]  
By combining Equations (2), (3), (5) and (6), the differential equation group of a single-turn driving coil and the projectile current filament loop can be obtained and then generalized to multi-stage driving coils and expressed in matrix form as follows:  
\[\left\{ \begin{array}{l}\frac{dI}{dt}=(L+M)^{-1}\Big{(}U-\upsilon\frac{dM}{dx }I-RI\Big{)}\\ \frac{dU_{d}}{dt}=-\frac{I_{d}}{\upsilon}\\ \frac{d\upsilon}{dt}=\frac{1}{m}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}I_ {di}I_{ai}\frac{dM_{u,j}}{dx}\\ \frac{dx}{dt}=\upsilon\end{array} \right. \tag{7}\]  
Based on Equation (7), it is possible to calculate the electromagnetic and motion characteristics of any MSSICG, where \(R\), \(L\), and \(I\) are the resistance matrix, inductance matrix, and current column vector of the driving coil \(d_{1}\)-\(d_{m}\) and current filament \(a_{1}\)-\(a_{n}\), respectively; \(U\) is the initial voltage column vector; \(U_{d}\) is the sub matrix of \(U\); \(M\) is the mutual inductance matrix; and \(c\), \(m\), \(\upsilon\), and \(x\) represent capacitance, mass, velocity, and displacement, respectively.
## 5 Discussion
### Two Main Directions of Anisotropy Varying with Depth
#### 5.2.4 Electrical Anisotropy Direction Related to the Fluid-Filled in Oriented Fractures  
In this section, the author discusses if the stress field that induces electrical anisotropy direction is likely a possible mechanism that can also generate fluid-filled oriented fractures or in shear zones in the study area. In the transgressional region, existence of shear zones with preferred orientation inducing electrical anisotropy direction is not unexpected. Indeed, in the early stage of transgression region, the degree of progressive shearing zones interconnected along strike may increase with the degree of electrical anisotropy and hence shears may act as elements of fluid-driven weakening and growth [56]. Upper mantle electrical anisotropy is mainly related to the presence of bound water allowing the diffusion of hydrogen [57]. Although, others mechanism as the presence of aligned high conductive mineral such wet olivine or hydrous olivine may cause electrical anisotropy in the upper mantle [48]. In contrast, dry olivine is only weakly anisotropic [58]. Finally, it is surprising to note that electrical anisotropy directions for both short and long periods determined in this study (Figures 3 and 7) are in agreement with the electrical anisotropy direction determined in central Germany by [59]. The latter authors found that electrical anisotropy for the lithosphere is close to E40\({}^{\circ}\)N and electrical anisotropy direction of the asthenosphere is close to E72\({}^{\circ}\)N. Moreover, in the same area of central Germany, the origin of high electrical anisotropy observed, was interpreted to be likely controlled by hydrogen diffusion [59; 60]. Thus, it is possible to hypothesize that the same condition mechanism associated with the hydrogen diffusion can exist in the context of Asal rift. Electrical anisotropy direction was found in the shear zone of Great Slave Lake in Canada [52] and the graphitic films along shear zones and wet olivine may be the causes of enhanced electrical anisotropy. Moreover, another possible explanation is that subsequent retrograde metamorphism can immobilize graphite during fluid-based leaching and redeposit it as vein zones or tabular textures [52].  
However, presence of over pressureful fluid and hydrated clays at the interface of tectonic plates inferred from 3D MT inversion were suggested to be the major factor controlling plate coupling [61]. Recently, in the Otway Basin of Australia, electrical anisotropy direction inferred from MT data was interpreted in terms of fluid-filled fractures oriented favourably for reactivation in the current stress field that resulted in enhanced electrical and hydraulic conductivity [62].
## 5 Experimental Analysis
### Tuning of Hyperparameters  
The hyperparameters that have been used in this study are the number of epochs, learning rate, and batch size. To justify the tuning of these hyperparameters, classification results for various values of hyperparameters were calculated. In this section, results for dataset-2 have been presented. Figure 7 illustrates the accuracy vs. number of epochs graph when the learning rate and batch size are fixed as mentioned in experimental settings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **3 \(\times\) 3** & **5 \(\times\) 5** & **7 \(\times\) 7** & **Overall** \\
**Name** & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** \\ \hline Dataset-1 & 98.57\% & 98.81\% & 98.85\% & **99.01\%** \\ Dataset-2 & 98.30\% & 98.43\% & 98.37\% & **98.97\%** \\ Dataset-3 & 97.66\% & 97.67\% & 97.38\% & **98.35\%** \\ Dataset-4 & 94.87\% & 94.67\% & 95.01\% & **96.07\%** \\ Dataset-5 & 94.85\% & 94.87\% & 94.62\% & **95.89\%** \\ Dataset-6 & 97.79\% & 97.05\% & 97.54\% & **97.79\%** \\ Dataset-7 & 96.84\% & 93.03\% & 95.90\% & **97.82\%** \\ Dataset-8 & 94.42\% & 94.83\% & 94.80\% & **95.71\%** \\ Ekush Basic & 95.18\% & 96.06\% & 94.99\% & **98.36\%** \\ Ekush Numerals & 97.44\% & 98.13\% & 97.74\% & **99.13\%** \\ BanglaLekha Basic & 91.86\% & 90.64\% & 88.74\% & **96.09\%** \\ BanglaLekha Numerals & 96.27\% & 96.41\% & 96.68\% & **98.11\%** \\ NumtaDB Numerals & 96.93\% & 96.92\% & 96.64\% & **97.50\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of performance among individual and BengaliNet architectures for the considered datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Datasets** & **Number of Classes** & **BengaliNet Accuracy** \\ \hline Dataset-1 & 10 & 99.01\% \\ Dataset-2 & 50 & 98.97\% \\ Dataset-3 & 171 & 98.35\% \\ Dataset-4 & 171 & 96.07\% \\ Dataset-5 & 199 & 95.89\% \\ Dataset-6 & 7 & 97.79\% \\ Dataset-7 & 256 & 97.82\% \\ Dataset-8 & 256 & 95.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall Performance of CMATERdb datasets in terms of overall accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Datasets** & **Number of Classes** & **Overall Accuracy** \\ \hline Ekush Basic & 50 & 98.36\% \\ Ekush Numerals & 10 & 99.13\% \\ BanglaLekha Basic & 50 & 96.09\% \\ BanglaLekha Numerals & 10 & 98.11\% \\ NumtaDB Numerals & 10 & 97.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall Performance of Ekush, BanglaLekha and NumtaDB datasets in terms of overall accuracy.  
Figure 8 illustrates the accuracy vs. batch size graph when the learning rate and the number of epochs are set to the values mentioned in experimental settings. Finally, Figure 9 shows the accuracy for different learning rates. It can be noticed that better accuracy can be achieved for the proposed setup.  
Figure 8: Accuracy vs. batch size graph for dataset-2.  
Figure 7: Accuracy vs. number of epochs graph for dataset-2.
## 3 Proposed Methodology
### Proposed Architecture Using Concatenating Features
#### 3.3.3 Naive Bayes  
The inclusion of Naive Bayes (NB) in our framework is motivated by its computational efficiency and effectiveness in text classification tasks. Despite its simple assumption of feature independence, NB has proven remarkably successful in hate speech detection [60]. Its probabilistic nature aligns well with our multi-feature approach, particularly in handling the uncertainty inherent in offensive language detection. The model can work well in uncertain datasets by calculating the probability of each situation and making decisions with the help of maximum a posteriori estimation.  
We have calculated the posterior probabilities of classes \(C_{l}\) using Equation (26).  
\[p(C_{l}\mid f)=\frac{p(C_{l})p(f\mid C_{l})}{p(f)} \tag{26}\]  
where, \(p(C_{l}|f)\) refers to the conditional probability of class label \(C_{l}\) given the input features f. It represents the probability of observing class \(C_{l}\) on a given specific input f, and \(p(C_{l})\) represents the prior probability of class \(C_{l}\), which is the probability of randomly selected sample from class \(C_{l}\) without considering the input features f. Similarly, \(p(f|C_{l})\) denotes the likelihood or probability of observing the input features f given the class \(C_{l}\). It representsthe probability distribution of the input features f conditioned on class \(C_{l}\). A decision rule is used to construct the classifier. The class with the highest probability is typically chosen using Equation (27).  
\[\hat{y}=\operatorname*{argmax}_{l\in\{1,\dots,L\}}(C_{l})\prod_{i=1}^{n}p(f_{i} \mid C_{l}) \tag{27}\]  
where \(\hat{y}\) represents the predicted class label, and \(\operatorname*{argmax}_{l\in\{0,1,2\}}\) is an operator that returns the argument (class index \(X\) l) that maximizes the given expression.The term \(p(C_{l})\) denotes the prior probability of class \(C_{l}\), representing the likelihood of observing class \(C_{l}\) regardless of the input features f. \(\prod_{i=1}^{n}\) represents the product operator, iterating over the training example from i = 1 to n. \(p(f_{i}\mid C_{l})\) denotes the likelihood or probability of observing the input features \(f_{i}\) on given class \(C_{l}\). Equation (27) states that the predicted class label \(\hat{y}\) is determined by selecting the class index l that maximizes the probability \(p(f_{i}\mid C_{l})\) and \(l\in\{0,1,2\}\). In other words, we have to choose the class for which the observed input features f are most likely given that class.
## 5 Conclusions  
In this work, the data-driven spatio-temporal deep learning (SDL) model is used to rapidly compute ultrasonic wave propagation for modelling forward and reflected ultrasonic wave propagation in the different geometrical domains as well as varying frequencies and cycles. The SDL is trained with simulation-assisted FE simulation data. Here, we have used two types of training datasets to teach two individual SDL models, using multiple point sources of wave simulation in a single domain and reflected wave propagation from boundaries with different geometries. The SDL can learn representations of the time-domain ultrasonic wave propagation phenomenon from the training datasets, thus employing a data-driven approach to understand the underlying physics to build an AI predictive model to simulate ultrasonic wave propagation and reflection from boundaries. The SDL model can be used for modelling forward and reflected ultrasonic wave propagation simulations, in the different geometrical domains and varying incident wave parameters, such as frequencies and number of cycles with significantly reduced time (20\(\times\)) and computation resources, which compare well with the FE model simulations. Furthermore, the SDL models have, traditionally, been successfully used for creating conventional 2D wave propagation simulations. Understanding the seismic wave propagation in the geological structure is complex, due to various geological and geometrical structures, to take into account that the various numerical methods are used to model seismic wave propagation in the geological structure but need huge numerical costs and memory storage for handling larger domains with higher frequencies. However, the proposed SDL model can be a direct replacement, where time and a high-processor computer are the constraints for seismic wave propagation simulation. Hence, the SDL model approach may be further extended for modelling the scattering effects of the defects and virtual source imaging in nondestructive evaluation and biomedical imaging.  
[PERSON] and [PERSON], together, conceptualized and developed the methodology. [PERSON] generated the training datasets from a numerical simulation. [PERSON] helped with the validation and interpretation of results. All authors have equally contributed to the theoretical and numerical foundation of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by SERB, DST, the government of India, and a CII partnership with Baker Hughes, under the prime minister's Fellowship Scheme for Doctoral Research, 2020.  
Not applicable.  
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.