esa-sceva/satcom-chunk-collection huggingface dataset:
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 5 Conclusions  
In this paper, we propose a novel hypergraph position attention convolutional network for 3D point cloud segmentation. This method improves the performance of point cloud segmentation by taking advantage of the hypergraph position attention convolutional model, which makes full use of the inherent neighborhood information and spatial geometric structure information between point clouds. In addition, we propose a ResNet-like module that lightens the model and improves the network efficiency. A large number of experiments have proven the effectiveness of our method and provided new solutions in the field of point cloud segmentation. However, there are some limitations of our method. For example, we only utilize the spatial position information and color information of the point cloud without fully combining and utilizing other feature information, such as normal vectors. In the future, we will use this information further to explore the possibility of this network for point cloud segmentation performance enhancement. Meanwhile, we will use the network in other fields such as sentiment recognition and the recommender system to further prove the effectiveness of our network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Later** & **mIoU** & **OA** \\ \hline
1 & 63.8 & 88.6 \\
2 & 65.3 & 88.9 \\
3 & **66.8** & **89.3** \\
4 & 65.9 & 89.1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Influence of convolutional layers (%).  
Figure 7: Influence of the number of sampling points.  
**Author Contributions:** Y.R.: Conceptualization, methodology, software, writing--original draft. L.N.: Conceptualization, validation, writing--review and editing. Y.H.: Mentoring, obtaining funding. J.P.: Writing-review and editing. Z.H. and Z.L.: Formal analysis. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Guangxi Science and Technology Program, grant number Guike AD23026225, 2022 GXNSFAA035629 and GuikeAD23026298; the National Natural Science Foundation of China, grant number 62371149 and 61966007; the Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education, grant number CRKL230205.  
**Data Availability Statement:** The S3 IDS and ShapeNet Part datasets used in this article are publicly available datasets. The dataset links are [[http://buildingparser.stanford.edu/dataset.html](http://buildingparser.stanford.edu/dataset.html)]([http://buildingparser.stanford.edu/dataset.html](http://buildingparser.stanford.edu/dataset.html)) (accessed on 15 March 2023); [[https://shapenet.org/](https://shapenet.org/)]([https://shapenet.org/](https://shapenet.org/)) (accessed on 15 March 2023).  
**Acknowledgments:** The authors thank the referees whose comments helped to improve the manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Line-Detection Based on the Sum of Gradient Angle Differences
###### Abstract  
This paper presents a method to detect line pixels based on the sum of gradient angle differences (SGAD). The gradient angle differences are calculated by comparing the four pairs of gradients arising from eight neighboring pixels. In addition, a method to classify line pixels into ridges and valleys is proposed. Furthermore, a simple line model is defined for simulation experiments. Experiments are conducted with simulation images generated using the simple line model for three line-detection methods: second-derivatives (SD)-based method, extremity-count (EC)-based method, and proposed method. The results of the simulation experiments show that the proposed method produces more accurate line-detection results than the other methods in terms of the root mean square error when the line width is relatively large. In addition, the experiments conducted with natural images show that the SD- and EC-based methods suffer from bifurcation, fragmentation, and missing pixels. By contrast, for the original and the noise-contaminated versions of the natural images, the proposed SGAD-based line-detection method is affected by such problems to a considerably smaller extent than the other two methods.  
line-detection; gradient; angle difference +
Footnote †: journal: Applied Sciences
## 3 Results
Ultimately, the results confirm that routing optimization significantly enhances network performance, with higher data rates, reduced latency, and better adaptability to changing network conditions, particularly in mobile ad hoc and sensor networks. On the other hand, the throughputs of AODV, GPSR, ADOV:TOM, and GPSR:TOM improved for different vehicle speeds. AODV:DEM and AODV:TOM achieved an improvement over AODV by 21% (0.37 to 0.45 Mbps). GPSR:TOM improves the throughput by 100% (0.2 to 0.4 Mbps), while GPSR:DEM showed an improvement of 0.07 Mbps. All routing protocols maintained variation in an average range of 0.02 Mbps for all vehicle speeds except GPSR:DEM and GPSR, as their observed throughput is proportional to vehicle speed. The findings, as summarized in Table S1 of the Supplementary Materials, demonstrate that routing parameter preferences vary significantly between city and highway environments during the experimental study,  
Figure 16: Throughput and variation comparison between GPSR, GPSR:TOM, and GPSR:DEM in city and highway scenarios for different pause times. particularly in terms of MaxJitter and beacon interval settings, directly influencing delay and packet delivery performance. These insights highlight the necessity for adaptive routing protocol designs in VANETs. To enhance applicability, CM-AODV and CM-GPSR have been developed using the Chameleon method with TOM and DEM optimizations, ensuring improved routing efficiency and adaptability. While this study primarily relies on simulations, the extensive experimental framework provides a strong basis for real-world applicability, and future research will focus on incorporating actual vehicle data and field test scenarios to further validate the findings.
## 4 Experiments and Results
### Experimental Comparison with Existing Methods  
To further demonstrate the advantages of the proposed model, it was compared with several mainstream algorithms in the field of image classification, such as ConvNeXT, Resnet50, Resnet101, Vision Transformer-tiny (ViT-tiny), Swin Transformer-tiny,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Hyperparameters**} & \multicolumn{2}{c}{**Hyperparameters after Bayesian Optimization**} & \multicolumn{2}{c}{**Hyperparameters of Empirical Selection**} \\ \cline{2-4}  & **SOC** & **EOC** & **SOC** & **EOC** \\ \hline epoch & 180 & 180 & 150 & 150 \\ batch size & 16 & 8 & 16 & 16 \\ lr & 0.00390 & 0.00016 & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) \\ wd & 0.0335 & 0.0356 & 0.0500 & 0.0500 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between Bayesian optimization hyperparameters and empirical selection.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Accuracy**} \\ \cline{2-6}  & **SOC** & **EOC** & **SOC** & **EOC** \\ \hline ConvNeXt branch & 86.52\% & 69.01\% \\ +Swin Transformer branch & 95.13\% & 92.71\% \\ +SimAM & 96.21\% & 92.97\% \\ +Bayesian optimization & 99.26\% & 94.27\% & 94.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results of MSTAR dataset ablation.  
Table 8 reveals that, under SOCs, the lowest floating-point operations per second (FLOPs) of the T2t-ViT-14 network was 0.61 G, followed by 1.19 G in the proposed model. Table 9 shows that the classification task is more challenging under EOCs, while the Bayes-MFN achieved the highest recognition accuracy by a significant margin. Among the compared models, the proposed model had the smallest number of parameters and the highest recognition accuracy. In the SOC experiments, the parameters of the model and the accuracy were 6.68 M and 99.26%, respectively. In EOC experiments, the model had 6.67 M parameters and achieved 94.27% accuracy. In conclusion, the structure optimization of the proposed model presented in this paper was more effective. It can extract the local features and global dependency information of SAR images efficiently and simultaneously with fewer parameters and computational requirements to obtain more accurate results. Overall, the comprehensive performance of the proposed model was better than that of mainstream algorithms.
## 3 Results  
### Kinematics  
Within the normal ranges of knee and hip angle (\(30^{\circ}\leq\theta_{\rm knee}\leq 180^{\circ}\), \(60^{\circ}\leq\theta_{\rm hip}\leq 180^{\circ}\)), the knee angle \(\theta_{\rm knee}\) displays a monotonic change with \(y_{\rm hip}\). Therefore, in the following, \(\theta_{\rm knee}\) is regarded as the parameter that uniquely determines the configuration of the body in each exercise position, when the value of \(d\) is given.  
With knees fully extended (\(\theta_{\rm knee}=180^{\circ}\)) and \(d>0\), hips are in flexion (\(\theta_{\rm hip}<180^{\circ}\)) and ankles in plantarflexion (\(\theta_{\rm ankle}<0\)), and these two conditions are progressively enhanced by a gradual increase in the distance \(d\) (Figure 2). A progressive knee flexion yields a concurrent hip flexion, while the ankle displays a movement of dorsiflexion (increase of \(\theta_{\rm ankle}\)) followed by plantar flexion (decrease in \(\theta_{\rm ankle}\)). The ankle reverses the movement when the thigh is horizontal (\(\theta_{\rm thigh}=0\)), and this occurs at a knee angle that becomes progressively greater with the distance \(d\). For any given value of \(\theta_{\rm knee}\), the hip and ankle angle are decreasing functions of \(d\).  
Figure 2: Dependence of the hip and ankle angle (\(\theta_{\rm hip}\) and \(\theta_{\rm ankle}\)) on the knee angle (\(\theta_{\rm knee}\)) for different values of the horizontal ankle-to-hip distance \(d\) (\(d=0.2\), \(0.3\), \(0.4\), \(0.5\) m). The knee flexion angle is given by \(180^{\circ}-\theta_{\rm knee}\). Data refer to a subject 175 cm tall.
## 4 Materials and Methods  
### Materials  
Collagen-related triple-helical peptides were synthesized as C-terminal amides and purified by reverse-phase high-performance liquid chromatography [44; 45]: H-GPC(GPO)\({}_{3}\)GFG GER(GPO)\({}_{5}\)GPC-NH\({}_{2}\) (GFOGER-GPO), H-GPC(GPP)\({}_{5}\)GFOGE(GPP)\({}_{5}\)GPC-NH\({}_{2}\) (GFOGER-GPP), cross-linked collagen-related (GPO)\({}_{\rm n}\) peptide (CRP-XL), GPC(GPO)\({}_{5}\)GAOGER(GPO)\({}_{3}\)GPC-NH\({}_{2}\) (GAOGER-GPO), collagen type III-derived VWF-binding peptide VWF-III (VWF-BP), H-GPC(GPP)\({}_{5}\)GPRGQOGVMGFO(GPP)\({}_{5}\)GPC-NH\({}_{2}\)[46]. Collagen-I Horm derived from equine tendon  
Figure 7: Schematic platelet adhesion and activation by collagen under flow or in suspension. (**A**) Under flow conditions, immobilized collagen-H interacted with VWF to capture platelets via GPlb-V-IX and activate platelets via GFVI and integrin \(\alpha_{2}\)\(\beta_{1}\). Thrombin built up through the recruitment of flowing platelets interacting with collagen/VWF-adhered platelets. Syk inhibition suppressed initial platelet activation and platelet aggregate formation. (**B**) Collagen-H added to a suspension of platelets transiently interacted with GFVI, resulting in Syk-dependent Ca\({}^{2+}\) rises. Autocrine agonists stimulated non-adhered platelets, responding through Syk-independent signals.  
(collagen-H) was obtained from Nycomed ([PERSON], the Netherlands). Human placenta-derived collagen-III (C4407), collagen-IV (C7521), and fibrillar collagen-I (C7774) came from Sigma-Aldrich (Zwijndrecht, the Netherlands). The latter was used to prepare monomeric collagen-I through pepsin treatment, as described in Reference [47]. The selective spleen tyrosine kinase (Syk) inhibitor PRT-060318,2-((1,2,5)-2-aminocyclohexylamino)-4-(\(m\)-tolylamino)pyrimidine-5-carboxamide (Syk-IN), came from Bio-Connect (Huissen, the Netherlands). Used for fluorescence staining were Alexa Fluor (AF)647-labeled antihuman CD62P mAb (304918, Biolegend, London, UK), FITC-labeled fibrinogen (F0111, Dako, Amstelveen, the Netherlands), and AF568-labeled annexin A5 (A13202, ThermoFisher, Eindhoven, the Netherlands). Fura-2 acetoxymethyl ester and pluronic were from Invitrogen (Carlsbad, CA, USA). Human \(\alpha-\)thrombin was from Kardia (Leiden, the Netherlands). Stable ADP (Me-S-ADP) and MRS-2179 were from Sigma-Aldrich. Other materials were from sources described before [48].
## 3 Proposed Improved Hybrid SSA with an EDO (ISSAEDO) Algorithm
### Exponential Distribution Optimization (EDO) Algorithm
* After the conclusion of the optimization technique, it is observed that all of the solutions converge towards the global optimum solution. The anticipated values for the mean and variance of the optimal solution are expected to be low, while the value of the scale parameter is projected to be high. Following an overview of the procedures involved in the proposed EDO algorithm as described earlier, Algorithm 2 presents the pseudo-code that delineates the original EDO method. ```
1:\(N\)--the population size
2:\(Maxtime\)--the maximum time
3:\(lb\)--the lower bound
4:\(wh\)--the upper bound
5:\(Xwinners_{best}\)--the optimal solutions obeying the exponential solution
6:Start
7: Initialize a population of \(N\) solutions representing the multiple Exponential Distribution models \(Xwinners_{i}\) (\(i=1,2,\ldots,N\)) using Equation (11);
8: Define fitness vector to store the fitness of all solutions obeying the Exponential Distribution;
9: Find the best fitness (\(bestfitness\)) and \(Xwinners_{best}\);
10:\(time=1\);
11: Construct the memory-less matrix such that \(memoryless=Xwinners\);
12:while(\(time<Maxtime\)) do
13: Define \(V\) matrix of size \(S\);
14: Rank the solutions in \(Xwinners\) population in ascending order of their fitness values;
15: Calculate \(Xguidize^{time}=(Xwinners_{best1}^{time}+Xwinners_{best2}^{time}+Xwinners_{best3}^ {time})/3\);
16: Define the EDO adaptive parameters \(a,b,c,d\) and \(f\);
17:for\(i=1:N\)do
18:if\(x<0.5\)then\(\triangleright\) Apply exploitation phase
19:if\(Xwinners_{i}^{time+1}=memoryless_{i}^{time}\)then
20: Update \(V_{i}^{time+1}=a(memoryless_{i}^{time}-\sigma^{2})+bXguide^{time}\);
21:else
22: Generate \(\phi\in[0.1]\);
23: Update \(V_{i}^{time+1}=b(memoryless_{i}^{time}-\sigma^{2})+\log(\phi)Xwinners_{i}^{ time}\);
24:else
25: Update \(V_{i}^{time+1}=Xwinners_{i}^{time}-M^{time}+(cZ_{1}+(1-c)Z_{2})\);
26: Check bounds of \(V_{i}^{time+1}\);
27: Copy \(V\) to \(memoryless\) matrix;
28: Define \(newFitness\) vector to store the fitness of all solutions of \(memoryless\) matrix;
29:for\(i=1:N\)do
30:if\((newFitness_{i}=Fitness_{i})\)then
31: Update \(Xwinners_{i}=V_{i}\) and \(Fitness_{i}=newFitness_{i}\);
32:if\((Fitness_{i}<bestfitness)\)then
33: Update \(Xwinners_{best}=Xwinners_{i}\) and \(bestfitness=Fitness_{i}\);
34:\(time++\);
35:End
```  
**Algorithm 2** The original EDO algorithm
## 5 Results
### Continuous Auditing Based on Advanced Data Analytics  
To assist in executing audit activities, interviewees reinforced the continuous auditing strategy based on data, representing an innovative and proactive approach to protecting digital assets and sensitive information. Unlike traditional audits performed periodically, continuous auditing uses data analysis, which can even be in real-time, to monitor and evaluate security controls and processes continuously.  
Regarding this context unit, interviewee INT12 made the following comment: _\"Decisions based on concrete data make cyber risk management more reliable. Using analytical tools for auditing and monitoring cyber risks can significantly increase the ability to detect and respond to emerging risks. Using data and metrics to support recommendations and actions improves the credibility and effectiveness of security initiatives\"_.  
At this point, the interviewees expressed another point of disagreement. One side highlighted the high degree of precision and the complete and detailed view of the statistical analyses on the population universe. In this context, the INT03 registration unit on the subject stands out: _'In the existence of a thousand critical assets, if auditors test nine hundred assets, I cannot say with any degree of certainty, although statistically correct, that the organization is safe since only one vulnerable asset needs to trigger a chain of catastrophic events'_.  
As a counterpoint, the other side highlighted the speed and efficiency in statistical analyses performed on samples, as long as they correctly represent the entire universewith quality and representativeness. Regarding this context unit, interviewee INT06 made the following contribution: _'In a context of resource scarcity, it may be interesting to select a representative sample to represent the whole or even prioritize what may be most exposed or most critical for the organization'_.  
The data-driven continuous audit strategy was also highlighted in the works of [1; 5; 12; 62]. In a complementary manner, ref. [12; 69] pointed out that by leveraging data analysis, the third line can identify trends and anomalies indicative of a cyber threat, thus enabling a proactive response and decision-making that makes cyber risk management more reliable.  
Ref. [39] highlights that continuous auditing transforms traditional practices by enabling real-time monitoring through digital technologies. According to [40], adoption remains slow because benefits, such as improved risk detection, take time to manifest. This necessitates that organizations be patient and have clear objectives when implementing these tools.
## 4 Analysis and Discussion of Results
### Test No. 5: Collision Detection  
Test no. 5 was the analysis of the technique and method used to detecting collisions. To perform this test, positions were traversed in an automatic mode, in which the trained artificial neural network estimates the maximum current reached in each axis based on the error, the weight attached to the effector and the percentage of execution speed. The network was trained with 290 data obtained with robots in test execution mode. Figure 18 represents the graph with the maximum current estimated by the network and measured during execution.  
The first five positions (P1... P5) during the test were reached with the speed of 20% and a weight of 200 g, and the last five were reached with a speed at 35% and 300 g of weight.  
During the test no collision was detected, as expected, but graphically, it is observed that the current estimated by the network increases with increasing weight and speed. In contrast, these values are very high in relation to the current peaks reached during the run, which means the robot cannot detect the collision even if it occurs. The current measured during the test suffered from a little oscillation due to speed and weight, but includes an error associated with the common mode gain of the differential assembly.  
Given the unsatisfactory result obtained during the test, and to protect the robotic arm if it collided, the 290 data obtained were used, and the maximum current obtained in each axis was calculated by adding another 2% and defined as the maximum safe current for collision. The maximum safe collision current was set at 946 mA for axis one, 948 mA for axis two and 915 mA for axis three.
### Modern Research Methods  
For correct modeling of heat and mass transfer and evaporation in the interaction of droplets, data on the instantaneous velocity fields inside droplets are required. In recent years, optical non-contact methods have been rapidly developing, and their resolution and accuracy have been increasing. The following methods are now widely used for the diagnostics of multiphase flows: Particle Image Velocimetry (PIV) [31; 32; 33], Particle Tracking Velocimetry (PTV) [34], and Laser-Induced Phosphorescence (LIP) [35]. The main features of specified methods application in drops and thin films are considered in [36; 37; 38; 39].  
Thus, the literature analysis has shown that most of the experimental studies of droplet falling on film or on the liquid layer are associated with the measurements of the following key parameters: the geometry of crown splashing (rim crown wall, cavity below film surface, crown diameter and height, crown shape, crown height evolution), ejecta sheet and multi-drop impact, and the rates ofdroplet scatter at crown formation [40] with the use of PIV. In modeling the interaction of droplets, as a rule, the effects of buoyancy and Marangoni forces on convection generation inside the liquid are not taken into account. It is also unclear how the inertia forces affect the velocity field inside a droplet. Basically, the thermal and dynamic aspects concern the interaction of the droplet with the wall when the dynamic and thermal time ratio is considered. In the study of the properties of solutions with surfactants, the emphasis is placed on the study of wettability, contact angle dynamics and mapping the droplet evaporation modes. The effect of wettability with a high temperature is considered in [41]. High-temperature non-isothermal desorption was considered in [42]. The amalgamation and separation of droplets of different liquids leads to different flow regimes in mini- and microchannels [43; 44; 45]. A change in component concentrations during the amalgamation leads to free convection in the liquid.
###### Abstract  
Over the past five years, the proliferation of virtual reality platforms and the advancement of metahuman technologies have underscored the importance of natural interaction and emotional expression. As a result, there has been significant research activity focused on developing emotion recognition techniques based on speech data. Despite significant progress in emotion recognition research for the Korean language, a shortage of speech databases applicable to such research has been regarded as the most critical problem in this field, leading to overfitting issues in several models developed by previous studies. To address the issue of overfitting caused by limited data availability in the field of Korean speech emotion recognition (SER), this study focuses on integrating the data augmentation-saturation (DA-S) technique into a traditional ResNet model to enhance SER performance. The DA-S technique enhances data augmentation by adjusting the saturation of an image. We used 11,192 utterance numbers provided by AI-HUB, which were converted into images to extract features such as pitch and intensity of speech. The DA-S technique was then applied to this dataset, using weights of 0 and 2, to augment the utterance numbers to 33,576. This augmented dataset was utilized to classify four emotion categories: happiness, sadness, anger, and neutrality. The results of this study showed that the proposed model using the DA-S technique overcame overfitting issues. Furthermore, its performance for SER increased by 34.19% compared to that of existing ResNet models not using the DA-S technique. This demonstrates that the DA-S technique effectively enhances model performance with limited data and may be applicable to specific areas such as stress monitoring and mental health support.  
Keywords:ResNet; speech emotion recognition; deep learning; data augmentation