esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Clustering Efficient Learning Network for Motion Recognition Based on Self-Attention Mechanism
## 1 Introduction
At this stage, researchers have proposed several deep learning methods to construct the structure of group context, and these methods have achieved better performance than traditional methods. For example, they use recurrent neural networks (such as LSTM) to model individual behaviors and group activities sequentially, input images as a whole, classify behaviors, build effective classifiers, and conduct feature dominance, in which the maximum average pool is used to aggregate human features. Although different people should receive the same degree of attention, at a certain time, different people's contributions to the whole group's activities are different. For example, in football, the player's \"free kick\" action should be more worthy of attention than other players, and the captured details should also be greater so that the type of sports can be better analyzed and judged, and the efficiency of reasoning and judgment can be improved. Based on the problem background of the above scheme, we specifically solved this problem. The contributions of this paper can be summarized as follows:  
1. Through the LSTM, this network can not only solve the problem of gradient disappearance and explosion in the recurrent neural network (RNN), but also capture the internal correlation between multiple people in the sports field for identification, etc. 2. On the basis of (1), the DEC is added to integrate the motion coding information in key frames to improve the judgment efficiency. 3. With the self-attention mechanism, it can not only analyze the whole process of the whole sports video macroscopically, but also focus on the specific attributes of the movement to capture more important details, extract the key posture features of athletes, and further enhance the features, effectively reducing the parameters of the self-attention mechanism in the calculation process, reducing the computational complexity while maintaining the ability to capture details, and improving the accuracy and efficiency of reasoning and judgment. Through verification on large video datasets of mainstream sports, we achieved high accuracy and improved the efficiency of detection and recognition. The logical structure of this paper is as follows: In Section 2, we introduce the related work, and mainly discuss the hottest group behavior detection technology at present. In Section 3, we introduce the specific methods and measures of this paper, build the overall algorithm flow chart of the full text, and elaborate the mechanisms of DEC, LSTM and self-attention in detail. In Section 4 of the experimental module, we compare and evaluate the effectiveness of DEC, and experiment with a pedestrian dataset and football dataset in the algorithm incorporating the self-attention mechanism, and compare with other mainstream network architectures. In Section 5, we discuss the full text and describe the advantages and limitations of this method. In the final conclusion, we summarize the full text and look forward to the future work direction.
## 3 Results
### Static Balance (COP)
#### 3.5.1 Eyes-Open  
Under the eyes-open condition, the experimental group showed a significant reduction in sway area, from 31.13 \(\pm\) 37.63 mm\({}^{2}\) to 9.47 \(\pm\) 17.12 mm\({}^{2}\) (\(p\)! 0.05), whereas the control group's decrease from 18.47 \(\pm\) 37.05 mm\({}^{2}\) to 3.93 \(\pm\) 7.32 mm\({}^{2}\) was not statistically significant (\(p\)! 0.05). Among these variables, only sway distance demonstrated a significant group \(\times\) time interaction, suggesting that the experimental group experienced a greater degree of improvement compared to the control group.  
The main effect of time was statistically significant, while the group \(\times\) time interaction effect was not (F = 0.360, \(p\) = 0.553).  
Sway distance showed a reduction from 6.99 \(\pm\) 2.11 cm to 4.13 \(\pm\) 1.18 cm in the experimental group and from 6.26 \(\pm\) 2.19 cm to 5.21 \(\pm\) 1.19 cm in the control group. The improvement was statistically significant over time, and a significant group \(\times\) time interaction was found (F = 7.177, \(p\) = 0.012), indicating superior gains in the experimental group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Experimental Group** & **Control Group** & **Group X Time** \\  & **(_n = 15_)** & **(_n = 15_)** & **F(df), \(p\)** \\ \hline BBS (score) & & & \\ Pre & 23.07 \(\pm\) 13.06 & 30.93 \(\pm\) 2.93 & \\ Post & 46.60 \(\pm\) 8.63 & 43.93 \(\pm\) 11.01 & \\ Change & 23.53 \(\pm\) 9.94 & 13.00 \(\pm\) 9.53 & F(1,28) = 8.767, \(p\) = 0.006 \\ LOS (mm\({}^{2}\)) & & & \\ Pre & 6673.71 \(\pm\) 4430.90 & 6749.42 \(\pm\) 4393.63 & \\ Post & 13,091.13 \(\pm\) 6896.00 & 10,014.97 \(\pm\) 5086.36 & \\ Change & 6417.42 \(\pm\) 4358.72 & 3265.55 \(\pm\) 2056.75 & F(1,28) = 6.415, \(p\) = 0.017 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pre- and post-treatment scores of dynamic balance measures (\(N\) = 30).  
Figure 3: Pre- and post-treatment comparisons of dynamic balance measures between groups.
## 3 Results and Discussion
### Effect of Origin on Quantitative Traits  
The effect of origin on agro-morphological and biochemical traits has been investigated in various crops [62]. Although different studies have evaluated the agro-morphological and biochemical traits of global barley collections, the effect of origin has been rarely investigated [20; 21; 23]. In this study, a total of 14 countries with accession numbers >10 and accounting for 91.83% of the total population studied were used to view the effect of origin on each parameter investigated. Our results found significant variation in all of the agronomical as well as biochemical traits analyzed (Figure 2 and Table S5). Nepal accessions were found to be early maturing with an average DM of 206.57 days while Turkey accessions were late maturing with an average DM of 219 days (\(p<0.05\)). Accessions from Nepal and Turkey also displayed the highest average CL (88.25 cm) and SL (8.22 cm), respectively, significantly different from most other countries. In contrast, the lowest average CL (62.02 cm) and SL (4.73 cm) values were in accessions from Morocco and Korea, respectively. Korean accessions also displayed the highest average GPP (49.26) and the lowest average TGW (35.88 g) (\(p<0.05\)). In contrast, the lowest average GPP (26.36) was found in accessions from Switzerland, while the highest average TGW (56.35 g) was observed in Morocco accessions (\(p<0.05\)). Regarding the biochemical traits, the average \(\beta\)-glucan content was the highest in accessions from Peru (4.45 g/100 g) and the lowest in accessions from Turkey (2.99 g/100 g) (\(p<0.05\)). On the other hand, accessions from Greece simultaneously displayed the highest average TPC (3.32 mg GAE/g), DPPH\({}^{\bullet}\) scavenging activity (3.22 mg AAE/g), ABTS\({}^{\bullet+}\) scavenging activity (6.34 mg AAE/100 g), and RP(3.21 mg AAE/g). Accessions from Nepal, on the other hand, displayed the lowest average TPC (2.52 mg GAE/g), while those from Afghanistan displayed the lowest average DPPH\({}^{\bullet}\) scavenging activity (2.24 mg AAE/g) and RP (2.27 mg AAE/g). The lowest average ABTS\({}^{\bullet+}\) scavenging activity was found in accessions from Peru (4.78 mg AAE/100 g). Overall, our results showed that origin had a significant effect on agronomical and biochemical traits and, hence, could be a determinant factor in the selection of barley genotypes. The effect of origin could be attributed to the difference in geological conditions, which impacts the chemical composition of plants over time [62]. Additionally, it was discovered that origins with higher TPC exhibited comparatively higher levels of antioxidant activities, which further emphasizes the role of barley polyphenols in regulating reactive radicals [6; 7; 8].
## 3 Discussion
Experimental exposure conditions maintained within the ICNIRP non-thermal safety standard of 20 mW/cm\({}^{2}\)[40]. Further, measurements of temperature rise demonstrated that effects were non-thermal in nature. A comparative analysis of changes in the growth profile of irradiated wild type and _Arad52_ (DNA damage repair) deletion cells revealed no directed detrimental effects on genomic stability arising from this non-ionizing radiation treatment. Analyses of dose dependent effects of MMW propagation and calibrations of cell density to exposure conditions revealed threshold parameters, which can be used for targeted inhibition of infectious microbes. Specifically, using an open-ended waveguide delivering a higher power density of 17.17 mW/cm\({}^{2}\) for 3-4 h exposure duration achieved complete termination of cellular proliferation in 50 cells/\(\mu\)L samples of _Saccharomyces cerevisiae_ yeast cells. Our results indicate that MMW irradiation can be useful for future application in medical treatments. The advantages in using non-ionizing MMW radiation enabling non-thermal control of targeted biological responses with or without chemical and biological drugs has promising potential for innovative biomedical procedures and devices. Invasive fungal infections remain a serious clinical problem. The current use of several classes of antifungal drugs have led to increased antimicrobial resistance of pathological species like _C. albicans_ by accelerating the development of mutations, overexpression of multidrug efflux pumps [41], etc. Moreover, _C. albicans_ as a pathogen depending largely on its ability to generate diversity at not only the genomic but also the morphological and physiological levels by its ability to switch from yeast to hyphal or pseudo hyphal forms [42, 43]; constitutes a major challenge in the development of new anti-fungal drugs and therapeutic strategies. A direct application of the MMW radiation effect mechanisms presented in this article for non-thermal exposure regimes can be used for treating pathogenic fungal infections (via cell division inhibition effect, see Figure 1), which are common in dermatology [44] and reproductive health [45]; and cancer lesion/tissue treatments (via cancer cell mortality effect [4; 5]) by appropriately scaling the exposure conditions described here to match clinical cell densities; besides various other promising purposes. Further, recent advances in endoscopic probes [46] operating in the Super High Frequency (SHF) 3-30 GHz bandwidths and similar technological trends will enable novel avenues of adapting such non-thermal techniques to treat internal body sites overcoming the burn hazard restrictions in application of conventional thermal ablation procedures. Our experiments demonstrate that non-thermal regimes of non-ionizing (85-105 GHz) MMW exposure have a promising potential not only for anti-cancer treatments [4; 5], but for targeted treatment of yeast related diseases.
## 4 Case Study  
### Data Preparation  
In order to further illustrate the functionality of the system's modules with a practical case, Chapters 18-22 of the national standard document 'Code for Design of Metro' (GB 50157-2013) [45] were selected as the case study. The raw data contained a large number of irrelevant elements, such as spaces, chapter numbers, and symbols, which were unrelated to the main content. To ensure the quality of the data, regular expressions were employed to remove these extraneous elements, following the data cleaning principles outlined earlier. As the requirements text is usually presented in paragraph form, regular expressions were applied for filtering and processing, as shown in Table 4. The table illustrates the raw form of the requirement document and the form after processing with regular expressions. This processing split one requirement into two distinct requirements  
Figure 5: Node extraction algorithm flowchart.  
and removed irrelevant chapter numbers, such as 18.3.4, that were not needed for the input model. After data cleaning, a total of 516 requirements were collected.  
After completing the data cleaning process, the SRs were categorized into five types: functional requirements, interface requirements, performance requirements, physical requirements, and design constraints. Based on the definitions of these five requirement types, the research team members annotated the collected requirements. During the annotation process, each requirement was carefully reviewed, and the corresponding category label was assigned according to the SysML requirement definition standard. To ensure label accuracy, other team members verified the preliminary classification results, ensuring the correctness and consistency of the requirement labels. In total, 516 requirement data entries were collected, of which 198 (38.37%) were labeled as functional requirements, 50 (9.69%) as interface requirements, 57 (11.05%) as performance requirements, 109 (21.12%) as physical requirements, and 102 (19.33%) as design constraints. A sample of the data is presented in Table 5, which includes the requirement description text and its corresponding requirement category.
## 4 Failure Envelopes in the \(V\)-\(H\)-\(M\) Domain
### Approximate Expression of the Bearing Capacity in the V-H-M Domain  
Based on the parametric study, it can be concluded that both the sand layer thickness \(T_{\rm s}/L\) and the soil properties \(\tan(\varphi)\gamma^{\prime}sD_{\rm P}/s_{\rm sum}\) influence the shape of failure envelopes under the combined \(V\)-\(H\)-\(M\) loadings. To estimate the bearing capacities conveniently, the failure envelopes normalized by the corresponding \(H_{0}\) and \(V_{\rm ult}\) are shown in Figure 12 (\(H_{0}\) is the uniaxial horizontal capacity; \(V_{\rm ult}\) is the vertical capacity of the foundation), and consider the mobilization of the sand layer thickness and the soil properties. Inheriting the format of the empirical formula by [PERSON] (2011) for composite foundations in pure sand, Equation (2) is proposed to determine the shape of failure envelopes in the combined \(V\)-\(H\)-\(M\) domain. \(A\), \(B\), \(C\), \(D\), and \(E\) are coefficients related to \(T_{\rm s}/L\) and \(\tan(\varphi)\gamma^{\prime}sD_{\rm P}/s_{\rm sum}\), which are derived in Table 2. (\(h_{0}\) is the dimensionless uniaxial horizontal capacity; \(v_{0}\) is the dimensionless vertical load)  
\[\frac{H_{\rm ult}}{H_{0}}-A\times\left(\frac{V}{V_{\rm ult}}\right)^{B}\times \left(C-D\times\left(\frac{V}{V_{\rm ult}}\right)^{E}\right)=1 \tag{2}\]  
\[h_{0}=a_{1}+b_{1}\times D_{\rm w0}+(c_{1}+d_{1}\times D_{\rm w0})\times e_{0}+ (e_{1}+f_{1}\times D_{\rm w0}+(g_{1}+h_{1}\times D_{\rm w0})\times e_{0}) \times\varphi_{0} \tag{3}\]  
\[v_{0}=a_{2}+b_{2}\times D_{\rm w0}+(c_{2}+d_{2}\times D_{\rm w0})\times\varphi_ {0} \tag{4}\]  
\[h_{0}=\frac{H_{0}}{2\pi R^{3}\gamma^{\prime}s},\ v_{0}=\frac{V_{\rm ult}}{2\pi R ^{3}\gamma^{\prime}s},\ D_{\rm w0}=\frac{D_{\rm w}D_{\rm P}}{L^{2}},\ \varphi_{0}=\frac{\tan(\varphi)\gamma^{\prime}sD_{\rm P}}{s_{\rm sum}},\ e_{0}= \frac{e}{D_{\rm P}}.\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(T_{\rm s}/L\) & \(\tan(\varphi)\gamma^{\prime}sD_{\rm P}/s_{\rm sum}\) & \(A\) & \(B\) & \(C\) & \(D\) & \(E\) \\ \hline
0.1 & 0.754 & 1 & 1.177 & \(-\)0.505 & 0.439 & 3.784 \\
0.1 & 0.848 & 1 & 1.847 & \(-\)0.790 & 0.209 & 27.125 \\
0.1 & 0.949 & 1 & 2.200 & \(-\)0.790 & 0.210 & 45 \\
0.1 & 1.096 & 0.739 & 1.913 & \(-\)0.551 & 0.774 & 4.49 \\
0.3 & 0.754 & 1 & 2.172 & 0.508 & 1.411 & 0.194 \\
0.3 & 0.848 & 1 & 2.8 & 2.725 & 3.659 & 0.201 \\
0.3 & 0.949 & 1 & 1.85 & 0.351 & 1.307 & 1.55 \\
0.3 & 1.096 & 1 & 1.355 & 0.322 & 1.314 & 2.552 \\
0.5 & 0.754 & 1 & 1.256 & 0.914 & 1.653 & 0.891 \\
0.5 & 0.848 & 1 & 1.831 & 14.907 & 15.665 & 0.084 \\
0.5 & 0.949 & 1 & 0.961 & 0.305 & 1.300 & 3.593 \\
0.5 & 1.096 & 1 & 1.718 & 1.598 & 2.623 & 1.621 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of \(A\), \(B\), \(C\), \(D\) and \(E\).  
Figure 12: Typical FE results of the normalized bearing capacity in the \(V\)-\(H\)-\(M\) domain.
## 3 Results
### Textural and Chemical Characteristics of Spent GAC and Its Regenerated Products  
SEM and EDS analyses were employed to examine the surface morphology and elemental composition of the spent and regenerated GACs. Figure 4 shows the SEM images of the spent GAC and two of the regenerated samples (KN-900-00 and KNC-900-00) at 1000\(\times\) and 3000\(\times\) magnifications. It can be seen that fine particles were loosely deposited or packed on the GAC surface. Although the samples exhibited similar porous textures, the CO\({}_{2}\)-activated product (KNC-900-00) appeared more porous, as seen in Figure 4c, which corresponds with its higher measured pore properties compared to those of spent GAC and pyrolysis-regenerated GAC (i.e., KN-900-00).  
Figure 4: SEM images of (**a**) spent GAC (KN), (**b**) an N\({}_{2}\)-pyrolyzed GAC product (KN-900-00), and (c) a CO\({}_{2}\)-activated GAC product (KNC-900-00).  
Energy dispersive X-ray spectroscopy (EDS) was used in this work in order to acquire the spectra for the purpose of determining the changes in the elemental compositions of spent GAC and its regenerated products [33]. The analysis showed the carbon content of spent GAC to be as high as 61.20 wt% (Figure 5a). The spent GAC also contained significant amounts of other non-carbon elements like oxygen (O, 30.14 wt%), iron (Fe, 3.03 wt%), silicon (Si, 3.28 wt%), and aluminum (Al, 2.34 wt%). In comparison, analysis showed the typical regenerated GAC products (KN-900-00 and KNC-900-00) (Figure 5b,c), to have higher carbon and lower oxygen content. These results could be attributed to the desorption and the activation (charring) of adsorbed constituents, thus releasing some oxygen-containing gases (e.g., H\({}_{2}\)O, CO, CO\({}_{2}\)) during the thermal regeneration process.  
Figure 5: Elemental compositions of (**a**) spent GAC (KN), (**b**) regenerated GAC product (KN-900-00), and (**c**) regenerated GAC product (KNC-900-00) by EDS analysis.
## 1 Introduction
So, the motivation behind this study was the need to further understand the effectiveness of combining large G particles with smaller G particles of the same kind in epoxy/G composite and to determine to which degree the in-plane electrical conductivity and the tensile strength could be improved. The epoxy resins were usually used due to their excellent adhesion to other materials, high mechanical strength and stiffness, and high thermal and chemical resistance, which meet the requirements for different types of composites and applications. [PERSON] et al. (2018) described, in his review on epoxy composites, some recent works on the effect of combinations of graphene particles with other fillers on the mechanical, electrical, and thermal properties. The other issue of interest in this study was the effect of processing conditions on the composite properties. A compromise between the electrical and mechanical properties of conductive polymer composites is still a target for many researchers. Other properties, such as the thermal stability of the composites and the resulting microstructural changes, were also investigated. Filler loading of 65 to 80 wt% was used in this study due to the low value of in-plane electrical conductivity obtained below this percentage range. It is worth to mention that the work carried out in this study represents an extension to a previous work done by the same author, [PERSON] et al. (2018), on epoxy/G composites, who reported a maximum in-plane electrical conductivity of about 28 S/cm and a maximum tensile strength of about 14 MPa by optimizing the curing conditions at 80 wt.% G content of 150 \(\upmu\)m particle size (G150).
# PickingDK: A Framework for Industrial Bin-Picking Applications
## 5 Cases of Study
### Use Case 4-Synthetic Data Generation for Parameter Optimization  
In this use case, parameter optimization with synthetic data using PickingDK is shown. The appropriate parameter configuration of an algorithm ensures its performance and efficiency. Traditionally, parameter fine-tuning is done manually based on certain physical meaning. Drawbacks of manual configuration are obvious:  
* It requests expertise in a specific algorithm, which limits the flexibility of applying an algorithm to different scenarios.
* It lacks of annotated data for qualitatively evaluating parameter settings.  
Thus, in PickingDK, virtual capture services are introduced to cope with the data generation problem. The real bin-picking scene is simulated and rendered to obtain synthetic data and ground truth annotations.  
Taking the VirtualCapture service as an example, synthetic data generation starts by throwing user-provided pieces with random initial poses into a container. Physics simulation is then performed until all the pieces reach the stationary state, this simulates a real picking scene with randomly posed pieces. Finally, a rendering process is employed to obtain multi-modal information from this virtual scene, such as color image, depth map, and point cloud, as well as the ground truth data, such as the segmentation mask and 6 DOF object poses. Figure 14 shows an example of the captured synthetic point cloud.  
With the synthetic data and its ground truth, a simple experiment is performed in order to optimize the sampling factor of the PPFLocalization service [4]. Appropriate setting for the sampling factor is searched with respect to the detection rate using the synthetic data. A sampling factor \(n\) defines that the algorithm samples 1 per \(n\) points in the input point cloud. Point pair features are computed within the neighborhood of these sampled points and compared with the trained model. A detection is considered valid when pose error between detected and ground truth is lower than certain threshold. Figure 15 shows the experiment result, where the parameter \"sampling factor\" is searched within the interval [10; 100] with the step length 10. A larger sampling factor reduces the overall processing time while providing less localization accuracy. The increase of detection rate from sampling factor 10 to 30 shows that sparser sampling, to some extent, provides higher robustness against the noise in the input point cloud.  
Figure 14: Synthetic point cloud captured with VirtualEnsenso service.
# Unified Friction Formulation from Laminar to Fully Rough Turbulent Flow
## 3 Switching Functions, Friction Factors, New Formulation, and Comparative Analysis
### Friction Factors  
Friction factors, which serve as inputs for the unified formula presented in Equation (1), will be explicated here in further detail. Specifically, we will present friction factors for laminar flow \((a)\), smooth turbulent flow \((c_{1})\), and fully developed rough turbulent flow \((c_{3})\) hydraulic regimes. Turbulent flow is a phenomena that still causes a stir among experts [29; 30]. Thus, the formula shown here is suitable for the flow of Newtonian fluids, whereas some restrictions are applied for gases [31].