esa-sceva/satcom-chunk-collection huggingface dataset:
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 5 Results
### Effect of Referencing Depth Map with Segmentation
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Approach** & **Gaming** & **Gesticulating** & **Greeting** & **Lower Stretching** & **Patting** & **Reacting** & **Talking** & **Upper Stretching** & **Walking** & **All** \\ \hline xR-EgoPose [8] & 138.1 & 117.1 & **96.9** & 146.9 & **135.9** & **130.7** & 105.4 & 149.7 & 148.6 & 146.1 \\ xR-EgoPose 8 depth & 138.6 & 112.4 & 111.9 & 136.6 & 138.4 & 131.9 & 105.7 & 137.8 & 133.1 & 135.6 \\ xR-EgoPose 8v seg. depth & **133.1** & **104.8** & 116.8 & **129.4** & 140.7 & 133.2 & **103.5** & **131.6** & **130.1** & **130.1** \\ \hline Mo2 Cap2 [10] & 118.9 & 100.2 & 110.8 & 141.4 & 127.8 & 132.5 & 106.3 & 151.3 & 152.2 & 145.8 \\ Mo2 Cap2 W depth & **109.4** & 100.7 & **90.2** & 127.9 & 116.2 & 122.7 & **87.6** & 138.6 & **120.2** & 131.0 \\ Mo2 Cap2 W seg, depth & 110.2 & **96.8** & 117.7 & **125.1** & **114.7** & **122.7** & 95.7 & **135.0** & 140.2 & **130.9** \\ \hline Ours & 61.0 & 52.8 & **48.9** & 67.5 & **74.5** & 61.8 & **46.8** & 74.6 & 75.0 & 71.6 \\ Ours W depth & **58.6** & **50.9** & **54.2** & 65.4 & 79.0 & **61.2** & **48.5** & 70.5 & 69.8 & 68.2 \\ Ours W seg, depth & 66.7 & 57.2 & 55.8 & **62.9** & 78.2 & 64.9 & 53.4 & **66.7** & **67.1** & **65.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of MPJPE for our method, Mo2 Cap2, and xR-EgoPose, categorized by action types and the average, with or without depth and/or segmentation regression using the SegDepth module.
# Side-Channel Power Analysis Based on SA-SVM
## 5 Discussion  
It can be observed from Tables 2 and 3 that the accuracy decreased when the number of interest points surpassed a certain threshold. The reason is that when the number of POIs is too large, redundant features increase data error, resulting in an ill-conditioning matrix. It is very difficult to solve the equations with an ill-conditioned matrix. For many  
Figure 5: Guessing entropy for SA-SVM and SVM; (**a**) guessing entropy, SA-SVM, 4 POIs; (**b**) guessing entropy, SVM, 4 POIs; (**c**) guessing entropy, SA-SVM, 53 POIs (**d**) guessing entropy, SVM, 53 POIs; (**e**) guessing entropy, SA-SVM, 117 POIs; (**f**) guessing entropy, SVM, 117 POIs.  
machine learning models, the complexity sharply increases with the increase in the number of features, resulting in an increase in time and cost. In the application of these models, selecting the appropriate feature points can effectively reduce the extent of model training while increasing the classifier's generalizability.  
[PERSON] et al. [18] compared the performance of an SVM model using an RBF kernel, a Fourier kernel, and a wavelet kernel under the same penalty factor. Although they used an SVM-based technique with a wavelet kernel for the first time in SCA, they did not compare the performance of the SVM that was based on these kernels with a model using optimal parameters. This paper provides additional evidence that the wavelet kernel indeed outperforms other kernels when SVMs are used in SCA. A question that arises is why does the Morel-RBF kernel work well with a small number of POIs? We believe that although mapping is performed twice to readily separate data in high dimensions, its effect is limited.  
Table 4 outlines the results of a comparison of the accuracy of machine learning algorithms used in the DPAv4.1 dataset. [PERSON] et al. [15] presented the results for the DPAv4.1 dataset when considering 50 of the most important features. It can be seen that Naive Bayes, MLP, XGBoost, RF, and CNN algorithms have poor accuracy in small datasets. [PERSON] \(X\) et al. [12] used the SMOTE algorithm to solve data imbalance issues, improving the accuracy of the RF algorithm. Table 4 shows that when the number of datasets and POIs is comparatively small, the SA-SVM model outperforms other machine learning algorithms.  
As shown in Figure 3, the Morlet-RBF wavelet kernel, which required the optimization of three parameters, took significantly longer in the SVM model than in the SA-SVM model. This is because grid search is essentially a violent search. Grid search will iterate through various parameter combinations to find the best set based on the set scoring mechanism. When the number of hyperparameters increases, its computational complexity will increase exponentially. This also proves that SA-SVM has better performance in optimizing more than two parameters.  
However, our method also has the following shortcomings: in this study, we only carried out experiments on a single bypass power analysis dataset, and these experiments were not extended to other datasets.
# Action Recognition Based on the Fusion of Graph Convolutional Networks with High Order Features
###### Abstract  
Skeleton-based action recognition is a widely used task in action related research because of its clear features and the invariance of human appearances and illumination. Furthermore, it can also effectively improve the robustness of the action recognition. Graph convolutional networks have been implemented on those skeletal data to recognize actions. Recent studies have shown that the graph convolutional neural network works well in the action recognition task using spatial and temporal features of skeleton data. The prevalent methods to extract the spatial and temporal features purely rely on a deep network to learn from primitive 3D position. In this paper, we propose a novel action recognition method applying high-order spatial and temporal features from skeleton data, such as velocity features, acceleration features, and relative distance between 3D joints. Meanwhile, a method of multi-stream feature fusion is adopted to fuse these high-order features we proposed. Extensive experiments on Two large and challenging datasets, NTU-RGBD and NTU-RGBD-120, indicate that our model achieves the state-of-the-art performance.  
human action recognition; graph convolution; high-order feature; spatio-temporal feature; feature fusion +
Footnote †: journal: Applied Sciences
## 2 Methods
### Graph-Based Wall Topology Analysis
#### 2.2.2 Graph-Based Wall Connecting and Correction  
The main task of this section is to determine the location of intersected corner points and connect the adjacent wall line segments to the closed polygonal house boundary under the guidance of a WLTG. The problem is that, similar to the RTG-based methods, topology errors are inevitable in a WLTG. Short line segments may be lost, and spurious connections will appear when two non-intersecting walls are too close. Moreover, as shown in Figure 3e, sometimes the intersected points may not be the endpoint of the line segments, but in the middle part. Therefore, additional processing is required to distinguish the connecting type and correct the errors. We further categorize the initial connections between walls into four types, and the definition and corresponding operation are described below:  
1. [label=()]
2. Spurious edge, where the average point density is below the pre-given threshold \(Th_{num}\). We simply delete the corresponding graph edge.
3. \(L\) type edge, where if two line segments are vertical and both need to be extended to the corner point or the length of exceeding part does not exceed a \(Th_{len}\). The two line segments are directly intersected at the corner.
4. \(T\) type edge, where two line segments are vertical but do not form an \(L\) type edge. The bottom edge is extended to the intersected corner, while the top one is decomposed at the corner point and similar operations are adopted on the WLTG.
5. \(Z\) type edge, where two line segments are parallel. We add the missed short vertical edges, and, meanwhile, added one graph vertex and two graph edges to the WLTG.  
The average point density in (a) is defined as below:  
\[density=\frac{Num_{pts}}{2\times len\times Th_{dis}} \tag{5}\]  
where \(Num_{pts}\) is the number of line inlier points, \(len\) is the length of the line segments, and \(Th_{dis}\) is the distance threshold in RANSAC-based line detection.
## 1 Introduction
### Contribution  
To address the above-mentioned issues, the main contributions of this paper are summarized as follows:  
* Investigation of MG communication requirements based on MGMS and DMS requirements.
* Study of relevant communication standards from the perspective of smart MG and standard revision requirements in a way facilitating wireless deployment in MG communication.
* Study of the deployment of wireless communication applications in MG through the classification of literature based on applications and methods.
* Determination of wireless technology applications in MG concerning communication requirements and standards of each application.
* Investigation using new wireless technologies such as 5G to attain a roadmap for wireless application in new MGMS trends such as MAS and multi-MG.  
The remainder of this paper is organized as follows. Section 2 is about the concept of MG focusing on electrical and communication structures and specifications. Section 3 introduces communication standard protocols and their revision requirements to facilitate wireless deployment in MGMS. Communication technologies and their applications in each supervisory level of the MGMS are discussed in Section 4. To provide a roadmap for using new technologies in MG focusing on wireless technology and more specified enhanced ones such as 5G in Section 5, we classified the literature based on its objectives, MG application, and method qualification when using wireless technologies in MG environments. After opening new horizons for our paper research area in Section 6, our attempt is finalized in Section 7.
## 2 Nutritional Properties of Sweet and Sour Cherries
### Polyphenols in cherries  
In cherry, as in other red fruits, the ripening process is related to a change from the initial green color to red, with accumulation of polyphenolic compounds, anthocyanins and degradation of chlorophyll. Phenolic compounds are concentrated in the skin and contribute to sensory and organoleptic qualities of fruits, such as taste and astringency. Furthermore, it has been demonstrated that they are bioactive compounds. The phenols contained in sour and sweet cherries have been characterized [13]. Cyanidin 3-glucoside, cyanidin 3-rutinoside, cyanidin 3-sophoroside, pelargonidin 3-glucosidase, pelargonidin 3-rutinoside, 3-glucose, and peonidin 3-rutinoside have been identified in sweet and sour cherries [14-18].  
Among phenolic acids, hydroxycinnamates (neochlorogenic acid and p-coumaroylquinic acid) have been quantified either in sweet and sour cherries. Flavonols and flavan-3-ols such as catechin, epicatechin, quercetin 3-glucose, quercetin 3-rutinoside, and kaempferol 3-rutinoside were also  
\begin{table}
\begin{tabular}{|l|c|c|} \cline{2-3} \multicolumn{1}{c|}{} & **Sweet cherries** & **Sour cherries** \\ \hline
**Vitamin C** & 7 mg & 10 mg \\
**Niacin** & 0.2 mg & 0.4 mg \\
**Pantothenic Acid** & 0.2 mg & 0.1 mg \\
**Vitamin A** & 64 IU & 1283 IU \\
**Vitamin E (Alpha Tocopherol)** & 0.1 mg & 0.1 mg \\
**Vitamin K** & 2.1\(\mu\)g & 2.1 \(\mu\)g \\
**Beta Carotene** & 38 \(\mu\)g & 770 \(\mu\)g \\
**Lutein+Zaaxanthin** & 85 \(\mu\)g & 85 \(\mu\)g \\
**Total Phenols** & 109.8 mg & 228.9 mg \\ \hline \end{tabular}
\end{table}
Table 1: Phytonutrients in sweet and sour cherries (in 100 g).  
found in sweet and sour cherries [15; 16]. The higher levels of total phenolics in sour cherries have been attributed to higher concentrations of anthocyanins and hydroxycinnamic acids [19] (Table 2).  
As summarized in Table 2, different cultivars of sweet and sour cherries show a high variability of phenol compound levels. Total anthocyanins of sweet cherries are between 30 (cv. Black Gold) and 79(cv. Cristalina) mg cyanidin-3-glucoside equivalents (CGE)/100 g, whereas total anthocyanins of sour cherries were between 45(cv. Balaton) and 109 (cv. Sumadinka) mg CGE/100g [13; 19].
# The Boundary Element Method in Acoustics: A Survey
## 5 Extending the Boundary Element Method in Acoustics
### Aero-Acoustics  
While the boundary element method has been applied to coupled fluid-structure interaction problems for almost as long as the method has been around, the BEM in aero-acoustics is a much more recent development. The standard exterior BEM in a vibro-acoustic settling has been applied in aircraft noise [210; 211]. The wave Equation (1) does not include convective flow and is only an adequate model in aero-acoustics in the special case of insignificant flow and the formulations and methods outlined in this work are no longer directly applicable. The Navier-Stokes equations model fluid flow and their solution by numerical methods is termed _computational fluid dynamics_ (CFD). There are reports of applying the standard acoustic BEM and CFD to aircraft [47; 48; 302; 303; 304] and, similarly, to underwater vehicles [305; 306].  
To model the noise from aircraft, the domains are typically large and significant resolution is required to capture the higher frequencies, and hence domain methods come with a high computational cost. Computational aero-acoustics [11] has arisen for developing and applying numerical methods in this particular area. The attraction of the BEM in computational aero-acoustics is the same as it is in standard acoustics, a significant reduction in meshing and hence the potential for faster computation.  
Work on the adaption of the BEM to a wider scope of problems has been developed, for example by the dual and multiple reciprocity method, which has also been applied to variants of the Helmholtz equation [46; 47; 48; 103; 131; 148]. A generalisation of the boundary element method in acoustics that includes convection, is applicable to problems with uniform flow, but this can also form a useful approximation method with the mean flow rate substituting the value for the uniform flow rate [307]. Similar to the approach in half-pace problems, discussed in Section 5.1.2, aero-acoustic problems are adapted for the BEM by revising the Green's function [308; 309; 310]. Recently the BEM in acoustics has been adapted to model viscous and thermal losses [311; 312; 313].  
Methods in aero-acoustics that use the BEM generally involve setting a fictitious surface, enclosing the significant effects such as noise generation and turbulence, within which typical methods of CFD are used to model the Navier-Stokes equations; the sound generation and sound propagation are modelled separately. The boundary element method models the outer domain, but requiring the fictitious surface to be meshed, modelling the uniform flow and the radiation condition from the boundary and into the far-field. There are several reported implementations and aero-acoustic and related applications of these methods [307; 314; 315; 316; 317; 318; 319; 320; 321; 322; 323; 324; 325; 326].
# Performance Investigation of Coated Carbide Tools in
## 3 Results
### Cutting Tool Performance Optimization  
The developed ANN model was also utilized to calculate the optimal values of the milling parameters. Specifically, a routine is implemented to identify the machining conditions that maximize the lifespan of the coated inserts. Since a standard optimization routine might only identify local maxima of the objective function, a repetitive procedure was necessary to locate the global maximum. To address this, a loop with multiple starting points within the boundaries of the machining parameters was applied. This allowed the optimization algorithm to run repeatedly, computing various local maxima. During each  
Figure 10: FEM-based calculation of the coated tool’s lifespan based on the maximum stress of the cutting edge.  
iteration, the calculated local maximum was compared to the global maximum, and if an improvement was found, the global maximum was updated. The pseudocode for this methodology is provided in Algorithm 1. As a result of this process, the values of the milling parameters yielding the longest lifespan for the cutting tool were determined.  
```
Input: \(V_{\text{c}min}\), \(V_{\text{c}max}\): min., max. Cutting speed \(a_{\text{c}min}\), \(a_{\text{c}max}\): min., max. Radial depth \(f_{\text{c}min}\), \(f_{\text{c}max}\): min., max. Feed rate \(V_{\text{s}top}\), \(a_{\text{s}top}\), \(f_{\text{s}top}\): Step increments
1: initialize: \(\text{V}_{\text{c}}=\text{V}_{\text{c}min}\), \(\text{a}_{\text{e}}=\text{a}_{\text{c}min}\), \(\text{f}_{\text{z}}=\text{f}_{\text{z}min}\)
2: global_max = 0
3: for\(\text{V}_{\text{c}}=\text{V}_{\text{c}min}\) && \(\text{V}_{\text{c}}\leq\text{V}_{\text{c}max}\)
4: for\(\text{a}_{\text{e}}=\text{V}_{\text{c}min}\) && \(\text{a}_{\text{e}}\leq\text{a}_{\text{e}max}\)
5: for\(\text{f}_{\text{z}}=\text{f}_{\text{z}min}\) && \(\text{f}_{\text{z}}\leq\text{f}_{\text{z}max}\)
6: tensor_data = X (\(\text{V}_{\text{c}}\), \(\text{a}_{\text{e}}\), \(\text{f}_{\text{z}}\))
7: Tool_Life_Prediction = Model(tensor_data)
8: if Tool_Life_Prediction > global_max then
9: \(\text{V}_{\text{c}opt}=\text{V}_{\text{c}}\), \(\text{a}_{\text{e}opt}=\text{a}_{\text{e}}\), \(\text{f}_{\text{z}opt}=\text{f}_{\text{z}}\)
10: global_max = Tool_Life_Prediction
11:endif
12: \(\text{f}_{\text{z}}=\text{f}_{\text{z}}+\text{f}_{\text{s}top}\)
13: endfor
14: \(\text{a}_{\text{e}}=\text{a}_{\text{e}}+\text{a}_{\text{s}top}\)
15: endfor
16: \(\text{V}_{\text{c}}=\text{V}_{\text{c}}+\text{V}_{\text{s}top}\)
17: endfor
18: return global_max, V\({}_{\text{c}opt}\), \(\text{a}_{\text{e}opt}\), \(\text{f}_{\text{z}opt}\) Output: Optimized Manufacturing Conditions (\(\text{V}_{\text{c}}=100\), \(\text{a}_{\text{e}}=20\), \(\text{f}_{\text{z}}=0.2\))
```  
**Algorithm 1**Pseudocode for the parameter's optimization pipeline  
Figure 11: Comparison between the experimental results and the numerical and AI predictions on the cutting tool’s performance.
# Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing
## 3 Computer Vision-Based Control
Grasping of unknown objects requires model-free grasping approaches that are focused on finding a spot in the region of interest that could be graspable. Such methods are more appealing as they can generalize to unseen objects. Model-free methods skip the pose-estimation step and are directly estimating the grasp pose. Grasps across 1500 3D object models were analyzed in [37] to generate a dataset that contains 2.8 million point clouds, suction grasps and robustness labels. The generated dataset (Dex-Net 3.0) was used to train a Grasp Quality Convolutional Neural Network (GQ-CNN) for classifying robust suction targets in single object point clouds. The trained model was able to generalize to basic (prismatic or cylindrical), typical (more complex geometry), and adversarial (with few available suction-grasp points) with respective success rates of 98%, 82%, and 58%. A more recent implementation of [38] (Dex-Net 4.0) containing 5 million synthetic depth images was used to train policies for a parallel-jaw and vacuum gripper resulting in 95% accuracy on gripping novel objects. In [59], Generative Grasping Convolutional Neural Network (GG-CNN) is proposed, where the quality and pose of grasps is predicted at every pixel, achieving an 83% grasp success rate on unseen objects with adversarial geometry. Thus, these methods are well suited for closed-loop control up to 50 hz, meaning the methods can enable successful manipulation in dynamic environments where objects are moving. Multiple grasp candidates are provided simultaneously, and the highest quality candidate ensures relatively high accuracy in previously unknown environments. Often the object grasping problem has been focused just on the grasping phase; however, in industrial settings, full pick and place cycle is required. Post gripping [60] is as important as the gripping phase and eventually grasping strategies should be addressed accordingly. For model-free methods, the post-gripping process is extremely complicated and mostly cannot be executed without additional operations after picking up the object. Some exceptional cases such as picking of potentially tangled objects are present where proposed solutions [61; 62] only partially address this problem, mostly by avoiding the grasping of such objects.
## 3 Numerical Calculation  
### Data Analysis  
In the previous section, an analytical collimating lens can be designed for a UV-LED based on two parameters, \(r_{0}\) and \(\zeta\), and a ray-tracing program is developed for analyzing the divergent angles and remaining powers of all the LED's rays being collected and collimated by the designed lens. The light collimating performance as well as the power efficiency of the designed lens for a given LED light source can be quantitatively evaluated in a prompt and efficient manner. For each of the six chosen commercial UV-LEDs, typically 1 million rays have been used in the numerical simulation for searching for the best lens design with the optimal optical performance. Based on the practical considerations, the range of the variables are set to be 1 mm \(\leq r_{0}\leq\) 30 mm and 0.1 \(\leq\xi\leq\) 3, such that the designed lenses are with reasonable sizes and optical power intensity for lens manufacturing and applications. The numerical simulation is carried out with a searching step size of 1 mm for \(r_{0}\) and 0.1 for \(\xi\). Therefore, for each of the six commercial UV-LED light sources, a 30 \(\times\) 30 dual-variable local search model with a total of 900 groups is built. The analytic lens design with the best light collimation ability and optical efficiency is established by determining the values of \(r_{0}\) and \(\xi\).  
Figure 4 shows the contours of the divergence angle of the six commercial UV-LED light sources after analytic lens surface design and optical ray tracing. The \(x\)-axis and \(y\)-axis represent \(r_{0}\) and \(\zeta\), respectively. The grayscale value of the color bar represents the weighted mean \(\overline{\gamma}\) of divergence angle for each combination of \(r_{0}\) and \(\zeta\). With an increasing value of \(r_{0}\), the light collimation ability in this contour map shows a trend of improvement. It indicates that when the UV-LED light-emitting area is relatively much smaller than the lens aperture size, better collimation ability can be achieved since the LED light source is closer to a point source to the lens.