esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications to Benchmark Examples  
### Incompressible-Elastic Square Plate  
A convergence study of the recovery of errors in displacement or stress for an incompressible-elastic problem subjected to self-weight is used to test the effectiveness of the RPI-based error-recovery scheme. This problem is employed by [PERSON] et al. [30]. The absence of singularity in the illustrative plate problem enables the theoretical convergence rate of the ZZ recovery scheme to be compared to that of alternative recovery schemes. The analytical solutions for the illustrative plate problem are given in Equations (16)-(20).  
Domain and Boundary Conditions: \(\Omega\)[1, 1], \(u=v=0\) on \(\Gamma\).  
Analytical Solutions:  
\[u=2x^{2}\,y(1-x)^{2}(1-y)(1-2y), \tag{16}\]  
\[v=-2x\,y^{2}\,(1-x)(1-2x)\,\,(1-y)^{2}, \tag{17}\]  
\[p=x^{2}-y^{2}, \tag{18}\]  
Figure 1: Flow chart for adaptive finite element analysis of the incompressible-elasticity problem using least-square and RPI-based recovery techniques.  
Body Self-weight:  
\[b_{x}=4y\,\left(1-6x+6x^{2}\right)\left(1-3y+2y^{2}\right)+12x^{2}\left(1-2x+x^{ 2}\right)\left(-1+2y\right)-2x, \tag{19}\]  
\[b_{y}=-4x\left(1-6y+6y^{2}\right)\left(1-3x+2x^{2}\right)+12y^{2}\left(1-2y+y^{ 2}\right)\left(-1+2x\right)+2y, \tag{20}\]  
The adaptive analysis of the illustrative plate problem is  
1.1 Effect of Influence-Zone Shape and Radial Basis Function and Its Shape Parameters in RPI-Based Error Recovery  
In order the study the shape of influence zone and RBI in RPI-based recovery, the rectangular influence-zone shape (Figure 3) and thin-plate splint (TPS) RBI are also considered in incompressible-elastic finite element analysis. The influence of the shape parameters value of \(\eta\) =5 for TPS RBI is also studied. The computational results for RPI interpolation considering circular/rectangular zones and conventional patch with thin-plate splint (TPS) RBI and shape parameters are given in the following Tables 4-6 for regular and irregular mesh of quadrilateral elements.
# Machine Learning-Based DoS Amplification Attack Detection against Constrained Application Protocol
## 2 Related Work  
Securing the CoAP against DoS and DDoS has been extensively researched. Some research is based on the DTLS and enhanced DTLS for the CoAP security [9; 10; 11; 12]. However, DTLS was originally designed to protect web applications and is considered a heavy protocol that does not work well with IoT devices [10]. Software Defined Networking (SDN) has also been used to detect DDoS attacks against CoAP [9]. The authors provided two modules for the detection phase: the first module is to monitor the system and realize the anomalies between packets, and the second is to apply a machine learning algorithm to determine the DDoS packets. In [13], the authors compare different classifiers to detect the CoAP DDoS attacks and state that the support vector machine (SVM) algorithm outperformed Naive Byes, decision tree, and Random Forest with an accuracy of 99.88%. This paper only discussed the binary classification but not the multi-classification of the DDoS CoAP attacks. The anomaly detection of DDoS CoAP attacks was presented in [14]. This work claims an accuracy of 93% for classifying the malware. This research was confined to the attacks that target the CoAP server and did not cover attacks launched by CoAP clients against each other. The following section reviews the research that discussed DDoS attacks against IoT network.
# Stereo 3D Object Detection Using a Feature Attention Module
## 2 Related Work
### Feature Fusion Strategy for 3D Object Detection  
Researchers have studied the problem of improving the 3D object detection performance of multi-view or multi-modal information, and there are three main methods for information fusion: (1) early fusion, where the fusion of data occurs before feature extraction; (2) late fusion, where fusion is performed after extracting features from data of different modalities or perspectives; (3) deep fusion, where features of different scales are merged in the process of extracting features from image and point cloud data. Manyresearchers studied the important role of image information in point cloud-based detection methods. For example, F-PointNet [6], proposed by [PERSON] et al., first performs 2D detection on the image and then uses the Frustum PointNet model based on the 2D detection results to perform regression of the 3D object box.  
[PERSON] et al. [22] studied the problem where the model partially relies on manually inputted features when generating 3D candidate boxes. They combined the top view and front view of visual and lidar information, applied 2D convolution to generate 3D candidate boxes, and proposed the MV3D model. The corresponding experiments were conducted using three kinds of fusion methods. [PERSON] et al. [13] were inspired by the MV3D model and found that BEV and image views are sufficient to interpret the 3D space well. They proposed the AVOD model, which removed the point cloud front-view processing branch and used an FPN (feature pyramid network) [23] to extract features from images and point clouds from top views, to crop out corresponding areas from the two feature maps, and to conduct feature fusion. The proposed candidate boxes are fused again, and the detection results are obtained. The model reduces the amount of calculations required while ensuring estimation accuracy. [PERSON] et al. [24] designed a continuous fusion layer and the ContFuse method, using a deep parameter continuous convolution network to fuse multi-scale image features into the multi-scale features of the BEV view and perform detection. [PERSON] et al. [25] found that fusion based on a point cloud from a top view and other perspectives will have insufficient accuracy due to the corresponding ambiguity in the fusion process. Based on ContFuse, the point coordinates of the BEV view were improved into the three-dimensional space representation of the point. Using the image as additional information and using the subnetwork for segmentation, the point cloud is projected onto the image to obtain the segmentation result corresponding to the point cloud to obtain the semantic information of the fused image, and, with the help of a learnable MLP (multi-layer perception), can fuse the features of adjacent points to form an attention mechanism for adjacent features. [PERSON] et al. [7] improved the model based on Point R-CNN [26] and proposed the EPNet model, which applied the additive attention mechanism to the deep fusion of features from point cloud and image data to improve the fusion effect.
## 4 Discussion
### Advantages and Limitations of the Study  
Air quality data have the characteristics of a function, and the use of discrete data for an analysis cannot well describe the dynamic characteristics and individual differences of air quality data. The functional data approach used in this study treated the observed data functions as an entity, not just as the order of individual observations. Through the unique method of functional data, more potential change laws can be elicited from a continuous perspective. The advantages of functional data are that it requires fewer assumptions, reduces the requirement for data acquisition frequency, and it can elicit more information. Moreover, in this study, the rough penalty method was also used to prevent overfitting and to ensure the smoothness of the function.  
To analyze the differences in air pollution in different regions, this study used functional clustering based on the principal component coefficient to classify areas of air pollution. The functional cluster analysis approach broadens the application scope of a traditional cluster analysis by resolving the problem that traditional clustering techniques are only used to address static issues. The difficulty of functional data is that the data belong to an infinite dimensional space, and a principal component analysis is an effective dimensionality reduction method, which can use a few synthetic variables to summarize the original variables. Combining a principal component analysis with a cluster analysis overcomes the problem of collinearity among indicators. Additionally, it can also address issues with greater data dimensionality and larger data volumes that traditional methods cannot handle.  
The functional linear model could be used to clarify the driving factors of air pollution. The functional linear model converted the regression coefficients in the traditional model into coefficient functions, thereby enriching the results of the model. The characteristics of the coefficient function could be used not only to explain the impact of the driving factors on air pollution, but also to obtain the trend of the degree of influence over time. However, the formation of air pollution is too complex to be addressed completely, and our limited data availability also prevented us from taking more environmental factors and anthropogenic activities into consideration. Therefore, such topics remain worthwhile items for further research.
# Design, Modeling, and Analysis of IEEE Std 80 Earth Grid Design Refinement Methods Using ETAP
###### Abstract  
The design of a compliant, safe, and reliable substation earth grid is not a straightforward process; in most cases, it requires some additional measures to be taken due to various constraints that differ from environment to environment, such as soil resistivity, a high fault level, a limited surface area, construction budget, etc. The IEEE Std 80-2013 proposes various refinement methods that can be applied to address different situations. For this study, the current limiting method, current diversion method, and touch and step voltage increment method were applied using the Electrical Transient Analysis Program (ETAP). A power system was designed, where a fault current generated by the supply transformers and back-fed by the power system's motors was exported to the earth grid. Using this fault current, various simulations were conducted to assess the performance of the earth grid. The analysis results show that the application of the current limiting method using neutral earthing resistors has a great impact on the design of the earth grid as this method significantly reduces the fault current injected into the earth grid. Furthermore, by applying the current diversion method, the amount of fault current injected into the earth grid is reduced by a fair amount, which improves the performance of the earth grid. Lastly, increasing the tolerable limits of touch and step voltages by reducing the fault clearance times significantly improves the compliance of the earth grid as the clearance time is reduced. From this study, it is therefore concluded that, by implementing the refinement methods depending on the design requirements and feasibility of the application, one can improve the compliance state of an earth grid.  
substation earth grid; IEEE Std 80-2013 refinement methods; touch voltage; step voltage; ground potential rise +
Footnote †: journal: Applied Sciences
# Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm
## 2 Background
[19] proposed a technique based on the fusion information for the hepatic classification by operating an ultrasonic image texture examination. Based on texture examination, a set of texture features are applied in the classification technique. A combination of the best-selected features associated with their weights is suggested. The total accuracy of this technique was 95%. The common phases of the CAD system based on feature extraction and classification stage were [6]: (1) feature extraction phase, numerous texture features were selected and extracted using the desired and significant features from the ultrasound images of the human liver tissues such as entropy [6], (GLCM) [18; 19; 26; 27; 28; 29], Grey Level Run Length Matrix (GLRLM) [18], FOS [18; 19; 30], Grey Level Difference Statistics (GLDS) [19; 31], Fourier Analysis (FA) [19; 27; 28], and Statistical Feature Matrix (SEM) [19; 27]; (2) Classification stage: many classifiers are proposed in the classification phase, for example, k-NN [27; 32], Naive Bayes (NB) [28], Decision Tree (DT) [29; 30], AdaBoost [31], Random Forest (RF) [33; 34], Fuzzy [35; 36] (SVM) [32; 37; 38; 39], Multilayered Perceptron (MLP) [29; 40], Probabilistic Neural Network (PNN) [41], Quadratic Discriminant Analysis (QDA), and Linear Discriminant Analysis (LDA) [6]. The most important purpose of this study is to achieve high recognition accuracy to discriminate between fatty/normal liver. Unlike the classical machine learning techniques that are used in this study, deep learning was proposed in [42]. Deep learning [43; 44] was not feasible to apply in our research work due to the limitation of the dataset. In our proposed work, there are four main contributions. Firstly, the ultrasound liver images were classified as fatty or normal without the segmentation stage. Secondly, the available dataset in previous works was insufficient compared to our proposed work. Thirdly, decreasing the total number of extracted features to only 26 features turned out to have low computational complexity. Fourthly, the achieved recognition accuracy of the voting-based classifier was better than other classifiers. The current paper is organized as follows. First, the technique of image acquisition used is described briefly in Section 2, and then Section 3 sets out the proposed work. After that, the experimental results and discussion are described in Section 4. As a final point, the conclusion is set out in Section 5.
## 3 Results
### Approximate Techniques
#### 3.2.1 Equal Weighting Technique  
The Equal Weighting Technique assumes that no information is known about the relative importance of preference attributes or that the information pertinent to discriminating among attributes based on preference is unreliable. Under these conditions, one can adopt maximum entropy arguments and assume that the distribution of true weights follows a uniform distribution ([PERSON], 2017).  
Given a set of N preference attributes, the Equal Weighting Technique assigns a weight \(w_{i}\) to each attribute as shown in Equation (4).  
\[w_{i}=1/N \tag{4}\]  
For example, consider our automobile purchase decision. Assuming no additional information is available to establish a preference ordering of the five problem attributes, an equal weight of \(1/5\) (0.20) is assigned to each.  
Strengths of This Approach  
The Equal Weighting Technique is the simplest of all weighting techniques, which includes both ratio assignment techniques and approximate techniques. The only prerequisite for applying the Equal Weighting Technique is a judgment that an attribute matters or is significant ([PERSON], 2017). The Equal Weighting Technique is a formal name for what is naturally done in the early stages of analysis.  
Limitations of This Approach  
The weights resulting from application of the Equal Weighting Technique may produce inaccurate rankings if the true weights associated with one or more criteria dominate the others. As with any technique based on mathematical principles, the weights obtainedvia the Equal Weighting Technique are only as good as its assumptions. The principle underlying the Equal Weighting Technique is the use of the uniform distribution constructed across all attributes. Alternative techniques should be used if this assumption is not applicable, or if more information exists that could assist in establishing a quantitative difference between attributes.  
When some information is available to help distinguish between attributes on the basis of importance, alternative techniques will produce better estimates of attribute weights. When the number of attributes is 10 or less, it is more useful to spend resources to first establish a rank ordering of the attributes using group discussion or pairwise ranking and then follow-up with an alternative approximate techniques.
# DMSO Improves the Ski-Slope Effect in Direct PCR
## 4 Conclusions  
Direct PCR system amplifies DNA directly from evidence samples and has the advantage of minimizing DNA loss during extraction, purification, or quantification processes. However, this advantage can lead to both non-specific amplification and the ski-slope effect. For this reason, a continuous improvement in direct PCR technique is required to successfully obtain DNA profiles. In this study, we developed a novel method employing the addition of DMSO in direct PCR that resulted in markedly increased amplification; this may result in accurate interpretation of DNA profiles. DMSO (3.75%, \(v/v\)) more markedly increased the PCR amplification yield of large-sized DNA sequences (>200 bp) than small-sized DNA sequences (<200 bp) in direct PCR. In addition, DMSO increased the RFU ratio of large-sized locus in STR genotyping and consequently reduced the ski-slope effect in direct PCR using 50 Korean buccal samples. In addition to reducing the ski-slope effect, this novel method for direct PCR can aid in more efficient amplification of DNA samples acquired from various pieces of crime scene evidence. Conceptualization, J.-Y.K. and J.Y.J. ; methodology, J.-Y.K. and D.-H.K. ; analysis and validation, J.-Y.K., J.Y.J., and D.-H.K. ; writing and original draft preparation, J.-Y.K. ; writing--review and editing, S.M., W.-H.L., B.-W.C., and D.-H.C. All authors have read and agreed to the published version of the manuscript. This work was supported by National Forensic Service (NFS2018 DNA02 and NFS2020 DNA02), Ministry of the Interior and Safety, Republic of Korea. The use of the samples and the analytical procedures involved were approved by the Institutional Review Board of the National Forensic Service of the Republic of Korea.
## 4 Results and Discussions
### Advantages of Using Immersive Reality for Safety Training and Incident Management
Additionally, collecting real-world data in hazardous manufacturing is costly and risky; however, immersive VR can simulate environments and procedures using synthetic data generated via game engines like Unity, using AI and ML, reducing time, cost, and effort while enabling tailored, scenario-specific datasets [17]. Compared to traditional safety systems, such as paper protocols or static training videos, emerging technologies like DTs and XR-based simulations provide dynamic and adaptive safety environments. However, these advantages are not universal. Although immersive systems can enhance training retention in complex scenarios, they require a robust digital infrastructure and user proficiency. In contrast, conventional technologies, although less flexible, are often more cost-effective, reliable, and easier to scale, especially within legacy systems. Therefore, transitioning to digital ecosystems requires a careful balance between innovation and practical feasibility, particularly in the case of SMEs. To conclude, embracing technological innovation provides businesses with substantial benefits, such as reduced costs, technological progress, better management, and improved employee safety. Among these innovations, immersive reality technologies stand out for their ability to help lower operational costs while simultaneously enhancing participants' behavioral skills and safety awareness, making them a smart strategic investment for forward-thinking organizations and a key enabler in transforming to Industry 5.0. The integration of DT, IM, XR, IoT, AI, CPS, and COBOTS forms a proactive OHS ecosystem that shifts safety management from reactive to predictive. This advanced digital ecosystem enables the continuous monitoring of physical and cognitive states, virtual scenario-based training, and early risk detection. Recent studies [6; 16; 36; 52; 59; 71] support the benefits of this approach, including improved worker engagement, predictive incident prevention, and ergonomic optimization. Moreover, these advanced digital ecosystems not only mitigate operational risks but also align safety innovations with the broader goals of Industry 5.0. Table 9 provides an overview of the key benefits of using these interconnected immersive reality technologies for safety training and incident management, answering RQ4. Finally, Table 10 concludes the roles of DTs, IM, and interconnected advanced digital technologies in enhancing OHS. It shows the key applications and OHS-related advantages of these technologies in a manufacturing context.
## 5 Simulation Results
### Open-Loop Test  
The open-loop test is conducted with a one-period sinusoidal steering maneuver with a frequency of 0.5 Hz and an amplitude of \(\pi/4\) rad. The vehicle conditions are (1) unloaded vehicle without DYC (baseline), (2) 80-kg loaded vehicle without DYC, and (3) 80-kg loaded vehicle with DYC. The constant vehicle velocity is set to 80 km/h. The time history of steering wheel angle, yaw rate, body side slip angle, lateral acceleration, and yaw moment control input (DYC input) are shown in Figure 10.  
Figure 9: Frequency response of lateral acceleration response with respect to the steering angle.  
The vehicle trajectory is shown in Figure 11, and the Lissajous diagram of the steering wheel angle and the yaw rate is shown in Figure 12.  
Figure 10: Comparison of vehicle behavior.  
As can be noticed from Figure 10, the vehicle response of the 80-kg loaded vehicle without DYC becomes worse, as the phase delay of yaw rate, side slip angle, and lateral acceleration, with respect to the steering wheel angle, become larger, compared to the case of the unloaded vehicle. In addition, the gain of vehicle behavior with respect to the steering wheel angle becomes larger. In contrast, when the proposed DYC is applied, the phase delay becomes smaller in the same level as the case of unloaded vehicle and the vehicle behaves in the same manner of unloaded vehicle, thus the controller performs as expected. This means that the vehicle becomes insensitive to the loading condition. Next, as can be confirmed from the vehicle trajectory in Figure 11 and the Lissajous diagram in Figure 12, the loaded vehicle with DYC activated behaves in the same manner as the unloaded vehicle, whereas the loaded vehicle without DYC moves in different trajectory with maximum deviation of \(\sim\)0.5 m  
Figure 11: Comparison of vehicle trajectory.  
Figure 12: Comparison of Lissajous diagram of steering wheel angle and yaw rate.  
compared to the unloaded vehicle. Small differences between the loaded vehicle with DYC and the unloaded vehicle can be observed here, showing that the sensitivity to the loading condition is not perfectly compensated. This is due to nonlinearity in the vehicle dynamics and tire model. This can be improved by tuning the feedback compensator in order to compensating the nonlinearity of the vehicle dynamics.