esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Self-intersection Penalty Term
### Detection and Classification of Vertices  
To compute the partial derivatives of our proposed self-intersection penalty term, it is necessary to divide the vertices into three sets: \(V_{0}\), \(V_{in}\) and \(V_{out}\). A camera screen with pixels arranged in a square with \(H\) rows and \(W\) columns is set in front of the 3D mesh such that the orthogonal projection of the 3D mesh falls totally inside the screen. It is worth noting that the camera mentioned here is used only to emit detection rays, not for rendering and visualization. Detection rays are emitted from the center of each pixel to detect self-intersection, as is shown in Figure 3. The detail of the detection and classification is presented in Algorithm 1. To make our algorithm more intuitive, a schematic representation is given in Figure 4.  
Figure 4: An intuitive representation of our algorithm to detect self-intersection. The detection rays are emitted from the camera, the dashed area represents the self-intersection region. The vertices in red circle were detected to be in self-intersection according to the counter and orientation.  
Figure 3: Schematic representation of our approaches to detect self-intersection (the image of triangular mesh is rendered by Blender).  
```
Input: the height \(H\) and width \(W\) of the screen, \(V\) containing coordinates of all vertices, \(F\) containing vertex indexes of all triangles. Output:\(V_{0}\) containing vertices not in self-intersection, \(V_{out}\) containing vertices in self-intersection of the outer surface, \(V_{in}\) containing vertices in self-intersection of the inner surface.
1 initialize\(V_{0}\), \(V_{out}\) and \(V_{in}\) as empty sets initialize the linked list of each pixel as empty list for each triangle in \(F\)do
2 project the triangle onto the screen by orthogonal projection for each pixel in the screen do ifthis pixel located inside the triangle then insert this triangle into the linked list of this pixel in the order of smallest to largest in terms of depth
3 foreach pixel in the screen do ifthe linked list of this pixel is empty then continue
4 initialize\(counter\gets 0\) for each triangle in the linked list do ifthis triangle is facing the camera then \(count\gets counter+1\) else \(count\gets counter-1\) ifthis triangle is facing the camera then if\(counter=1\)then  append the three vertices of this triangle to \(V_{0}\) elseif\(counter=2\)then  append the three vertices of this triangle to \(V_{out}\) elseif\(counter=0\)then  append the three vertices of this triangle to \(V_{in}\) elseif\(counter=0\)then  append the three vertices of this triangle to \(V_{0}\) elseif\(counter=1\)then  append the three vertices of this triangle to \(V_{out}\) elseif\(counter=-1\)then  append the three vertices of this triangle to \(V_{in}\) return\(V_{0}\),\(V_{out}\) and \(V_{in}\)
```  
**Algorithm 1**Illustration of self-intersection detection and classification of vertices
# Enhancing E-Learning with Blockchain: Characteristics, Projects, and Emerging Trends
## 5 Discussion  
Our research focused on enhancing the e-learning through advanced technologies such as Blockchains. The findings highlight numerous advantages that Blockchain offers, including increased security, reliability, and transparency in educational systems. We evaluated various dedicated Blockchain projects specifically developed to revolutionize e-learning processes, such as IEEE Blockchain eLearning Modules, Interactive Learning Experience Platform (ILEP), APPII, and ODEM Blockcerts. These platforms aim to enhance different aspects of learning by providing trustworthy systems based on transparency, safety, and efficiency. They also enable secure data sharing for student attendance verification and performance tracking. However, our assessments also uncovered limitations, including scalability issues, immutability challenges, high operational expenses, and emerging regulatory obstacles, which hinder progress and user experiences. Despite these limitations and challenges, our study revealed significant potential benefits and opportunities associated with Blockchain technology in e-learning. For instance, further research can focus on leveraging Blockchain for secure identification (IDs), digital certificates, and badges to enhance student appeal in the job market. Furthermore, the possibility that this key technology could be used for facilitating student empowerment by enabling peer-to-peer learning and collaborations, as well as improving accessibility and affordability of education. By exploring these avenues, we can unlock the full potential of Blockchain in e-learning and shape the future of education. Our study has its own limitations as it focused on specific research questions and may not have explored the full range of applications and challenges of Blockchain technology in e-learning. Additionally, the dynamic nature of Blockchain technology means that new advances may have emerged since our research was completed that require constant exploration and adaptation. Furthermore, it is important to acknowledge the limitations related to the search process employed in our study. We restricted our search to articles indexed in ScienceDirect, Scopus, and Google Scholar, within the timeframe of 2016 to 2023. While these databases are widely recognized, they may not capture all relevant publications on the topic. Additionally, our focus on English language papers introduces a potential language bias and overlooks contributions from non-English sources. Considering these limitations, it is crucial to interpret the findings of this review within the context of these restrictions. To enable a more thorough analysis of the integration of Blockchain technology in the learning sector, future research should try to lengthen the search period, include a wider range of databases, and take publications in several languages into account. By addressing these limitations and conducting more inclusive research, we can have a better understanding of how Blockchain technology can affect online education.
## 3 Results and Discussions  
In this section, performance parameters such as return loss impedance bandwidth, gain, radiation efficiency and radiation pattern of the antenna are presented and analyzed. The results were extracted from the simulation, which was done using CST.
# Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
## 2 Methodology
### Texture Feature Selection  
In this section, the exploration, analysis, and selection of texture features are presented. There are previous works that use texture features as tools to describe different faults in IM. In the study presented by [PERSON] et al. (2014) and [PERSON] et al. (2015) the contrast and homogeneity texture features were used to detect bearing faults in IM. In order to evaluate the viability of these features and include others, combination tests were carried out and their feasibility was qualitatively evaluated. Figure 3 shows an empirical analysis that shows examples of combinations of the previously mentioned texture features. From Figure 3a,c, the features are overlapping, making it difficult to create a decision boundary that allows separation of the unbalance and healthy classes. In Figure 3b the samples show a small separation allowing the creation of a model that classifies these faults. However, the accuracy achieved with these texture features is less in comparison with the features  
Figure 2: Vibration signals used to extract texture features, (**a**) healthy class, (**b**) Unbalance fault.  
used in this study. Figure 3d shows a separation generated by the correlation and variance texture features. This separation has a non-linear behavior, hence a classifier that allows the manipulation of the non-linearity is required.
## 2 Stability Analysis of Ultra-Large Span Open-Off Cutting Areas
### Analysis of Factors Influencing Stability in the Open-Off Cutting Areas  
The 81309 working face at Baode Coal Mine is characterized as a large-scale, high-cut, comprehensive mining operation with pronounced susceptibility to the influence of substantial mechanical equipment. The design parameters specify a net width of 9.8 m at the cutting location, and an exceptional feature of this working face is the maximum span of the roadway, which reaches 11.8 m at the cutting installation chamber. The instability phenomenon is easy to occur in the middle of the roof of the roadway with such a large span. The thickness of the coal seam in the position of cutting holes fluctuates greatly, the roof structure is complicated, the coal seam contains many dirt layers, and the surrounding rock of the roadway is broken and difficult to support. Baode Coal Mine mining alternating work is relatively perfect; during the 81308 mining face, 81309 working roadway has completed the excavation work, and the service cycle of cutting the hole is as long as two years, further aggravating the difficulty of roadway support.  
In light of the comprehensive analysis provided, it is evident that the stability control of the cutting area in the 81309 working face is influenced by multiple factors, with the significant impact of the expansive open-off cutting span being particularly prominent. Notably, certain sections of the excavation exhibit span up to 11.8 m, thereby presenting formidable challenges in terms of maintenance. The pronounced subsidence of the mid-section of the roadway roof, coupled with the inadequacy of the existing support strength, underscores the imperative need to explore effective methodologies for actively supporting the surrounding rock in ultra-large-span cutting areas to ensure the safety and integrity of mining operations within the coal mine.
## 1 Introduction
Traditional methods, while customizable to an extent, do not provide the level of personalization and adaptability necessary to meet these varied needs [10]. As medicine moves towards patient-centric care, the demand for personalized surgical methods increases [11]. Surgeons need to tailor surgical plans to the individual patient's anatomy and desired outcomes. The need for greater surgical precision and predictable outcomes is paramount in improving patient satisfaction rates and reducing complications [12]. Utilizing advanced technology can help achieve this by improving surgical planning, execution, and follow-up care. Incorporating 3D technology in surgical procedures can aid in better visualization of the surgical area, enhancing precision during surgery [13]. Furthermore, the ability to simulate different surgical scenarios can lead to better preparedness and more predictable outcomes. By enabling patients to visualize their surgical outcomes beforehand through virtual surgical planning, we can manage their expectations better and potentially enhance satisfaction rates [14]. Moreover, less invasive surgery due to precise planning can lead to quicker recovery times and less post-operative discomfort, further improving the patient experience [15]. This review seeks to critically examine the evolution and limitations of traditional methods in orthognathic and oral maxillofacial surgery and explore the potential advantages of integrating advanced technological tools, such as 3D technology, into surgical procedures. Our aim is to highlight the importance of precise planning, predictability of outcomes, and personalized surgical approaches for optimizing patient satisfaction, reducing complications, and improving overall surgical results. We will delve deep into the current practices, discuss their inherent challenges, and assess the promise held by innovative techniques that are reshaping the future of cranio-maxillofacial surgical interventions.
## 4 Results and Discussion
### Pollutant Distribution
However, this enhanced adsorption capacity does not always translate directly into more effective remediation, as an increase in the surfactant concentration, while increasing the solubility of pollutants, may also lead to increased costs and other potential environmental risks when the concentration is excessively high. Notably, when the Freundlich adsorption constant is reduced below 0.001 mol/Kg, we observe that further decreases in the constant contribute minimally to the improvement of remediation effects. This is clearly demonstrated in Figures 5-7, which describe the relationship between the total concentrations of different pollutants and the Freundlich adsorption constant. These diagrams reveal a similar trend for three types of pollutants--VOC, SVOC, and PAH--in response to changes in the adsorption constant. Initially, a relatively small reduction in the adsorption constant leads to a significant enhancement in remediation effects, but when the adsorption constant drops below \(10^{-5}\), the total concentrations of pollutants follow a similar trend over time, indicating that further reductions in the adsorption constant do not significantly enhance remediation. This pattern suggests that in the remediation process, we need to find an optimal value for the adsorption constant to maximize cost-effectiveness and environmental benefits. Figure 4: Remediation results for different Freundlich adsorption constants after 55 days. After a 55-day remediation period, the final content of VOC in relation to different adsorption constants further confirms this point. The gradual reduction in the adsorption constant initially leads to an enhancement of remediation effects, but when the adsorption constant falls below a certain threshold, this enhancement begins to weaken. According to the analysis in Figure 8, we find that the optimal range for the adsorption constant is between 0.001 and 0.0001, where it can effectively promote the removal of pollutants while avoiding the weakening of remediation effects due to an overly small adsorption constant. Therefore, when designing soil remediation strategies, we must not only consider the optimal balance point of the adsorption constant but also the sustainability of long-term remediation effects. This means finding a balance between improving pollutant removal efficiency and controlling costs while ensuring that the remediation process does not impose new burdens on the environment. Figure 5: Relationship between total VOC and adsorption constant. Figure 6: Relationship between total SVOC and adsorption constant.
# A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors
## 1 Introduction
They also have the advantage of using wireless network interfaces instead of power-hungry wireless links while simultaneously maintaining an extremely small footprint. ECG monitoring systems produce large volumes of data that must be compressed for efficient processing, storage, and transmission. Many wavelet transform-based methods have been proposed for use with the lossy data compression technique [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23], and these have fine visual qualities that minimize reconstruction errors. Such methods achieve high compression ratios (CRs) and have no diagnostic-affecting features relating to reconstructed signals. Lossless compression schemes [24; 25] employ original signals that are not reconstructed, and because no signal distortion can occur with their use, they are preferable for diagnostic applications. In this respect, [PERSON] [26] used LZ77, complex extraction, and Huffman coding to achieve lossless ECG compression; [PERSON] et al. [27] proposed a lossless compression scheme using contexts and R-R interval estimation; and [PERSON] and [PERSON] [28] proposed a lossless compression method using vector quantization and wavelet transforms. The encoding scheme proposed in this study addresses real-time application under resource-limited conditions for field-programmable gate array (FPGA), ASIC, or chip implementation, and the primary objective of this design was to achieve easy circuit integration (Figure 1). The ECG signal-acquiring module uses an Analogy-to-Digital converter (ADC) chip to convert an analogy signal to digital raw data for processing. This implementation requires less modification when inserting the proposed chip between the ADC chip and the processor, because it uses the existing sample clock from the ADC data-ready signal as the only operation clock. This chip encodes raw data from the ADC output, and sends it for processing using the same connection interface. The encoding scheme comprises an arithmetic logic unit, such as subtraction or rotation, and a counter unit for minimal power consumption. A PEM uses this encoding scheme by employing the existing ASIC at no additional cost. This approach offers two operation modes: fixed-block mode and dynamic-block mode. Fixed-block mode has one prefix parameter, a \"split resolution\", which affects the CR and was experimentally applied to the MIT/BIH Arrhythmia Database [29] to identify parameters for optimal compression. Dynamic-block mode uses the selected \"split resolution\" from the fixed-block mode to compress ECG data while simultaneously producing a quantity of continue flat data, which could be as heartbeat detection. The final CR exceeds 2.1; however, and the heartbeat detection accuracy reaches 98% with a normal beat.
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 1 Introduction
In these previous studies on single-thruster fault diagnosis and in studies using actual USVs, analyses were performed on a frequency domain using wavelet or Fourier transform. In this study, we investigated a new fault diagnosis method based on multiple data correlation analysis for fault conditions and visualization. The fault conditions chosen for this study were the breakage and entanglement of the thruster's blades, which are common faults caused by external factors [25]. By segmenting the faults and conducting an experiment with these cases, the type and degree of faults were identified and analyzed. In the data-driven method, a fault is diagnosed based on data patterns, which makes it important to select appropriate fault features with sensitive responses to faults and which allow for the proper description of system characteristics. Fault features were selected with reference to previous studies in which the validity of fault features in the underwater thrusters of USVs were investigated [26]. To verify whether the selected fault features sensitively respond to fault conditions and properly reflect system states, experiments using an ocean engineering water tank were performed. For the analysis of the correlation of data in the fault condition, different from other studies, the principal component analysis (PCA) algorithm, which allows the reduction of dimensionality and visualization while minimizing the loss of information in terms of structure and characteristics, was applied. Additionally, Shannon entropy was used for quantification to perform fault detection and diagnosis of USV thrusters. The main contributions of our research can be described as follows:In this study, data on vibration, current consumption, rotational speed and input voltage from the water-tank experiment and actual ship data were selected as fault features. 2. A new study of applying the fault diagnosis method for multiple thruster data correlation under fault conditions using a visualization scheme. 3. A fault diagnosis method is introduced that classifies fault types and is accessible for tuning in unstable-environment field practice. 4. The performance of the proposed fault diagnosis algorithm for an unmanned surface vehicle's thruster was verified by analyzing the results of each fault condition.
## 4 Experiments and Results Discussion
### Models Evaluation
#### 4.2.3 Ablation Experiments for Modules  
To further understand the contribution of each module in the MDISE model to the overall network performance, ablation experiments were designed for the MD module, 1D Inception module, and SE module. The MD module and 1D Inception module were compared to 3 \(\times\) 1 standard convolutional layers as a control, and the presence or absence of the SE module was also used as a control. The Base model refers to the model where the MDISE model's MD and 1D Inception modules are replaced with 3 \(\times\) 1 standard convolutional layers, and the SE module is removed. Similarly, the data from the test set with a blackbody temperature of 353 K were used for model validation. The ablation experiment results are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Concentration**} & \multicolumn{2}{c}{**1 st Filter Channel**} & \multicolumn{2}{c}{**2 nd Filter Channel**} \\ \cline{2-5}  & _MAE_**/\%** & _PSE_**/\%** & _MAE_**/\%** & _PSE_**/\%** \\ \hline
10–30\% & 0.1368 & 0.2176 & 0.0781 & 0.5742 \\
40–60\% & 0.1235 & 0.2341 & 0.1213 & 0.5443 \\
70–90\% & 0.2318 & 0.6479 & 0.2093 & 0.5529 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Uncertainty in the concentration inversion of the MDISE model.  
As shown in Table 7, the model performance with the addition of any single module is better than that of the Base model, with the SE module showing the highest contribution. When the SE module is added alone, the _RMSE_ in the 1 st and 2 nd filter channels are 1.5079% and 1.9661%, respectively. When two modules are combined, the model performance is better than that of a single module, with the combination of the 1D Inception and SE modules showing the highest contribution, the _RMSE_ in the two channels are 0.6599% and 0.8107%, respectively. The ablation experiments demonstrate that the MD module, 1D Inception module, and SE module all contribute to the performance of the MDISE model.
## 4 Conclusions  
In this paper, the CC-SHADE-ML algorithm has been proposed for solving large-scale global optimization problems. CC-SHADE-ML changes the number of subcomponents and the number of individuals according to their performance in the last cycles. The experimental results show that the proposed strategy can improve the performance of CC-based algorithms. We have investigated the proposed metaheuristic using the LSGO CEC'13 benchmark set with fifteen benchmark problems. The CC-SHADE-ML algorithm outperforms, on average, CC-SHADE algorithms with a fixed numbers of subcomponents and a fixed population size. All experiments are proven by the Wilcoxon test. CC-SHADE-ML has been compared with fourteen state-of-the-art metaheuristics. The numerical results indicate that the proposed CC-SHADE-ML algorithm is an efficient and competitive algorithm to tackle LSGO problems. We have also proposed hybridization of CC-SHADE-ML and MTS-LS1 algorithms. CC-SHADE-ML-LS1 has a high potential for enhancement. In our further works, we will improve the performance of the CC-SHADE-ML-LS1 algorithm by modifying the optimizer. Additionally, we will perform the comparison on a real-world LSGO problems for which the compared metaheuristics do not have fine-tuned control in order to prove the effect of self-configuration.  
Conceptualization, A.V. and E.S.; methodology, A.V. and E.S.; software, A.V. and E.S.; validation, A.V. and E.S.; formal analysis, A.V. and E.S.; investigation, A.V. and E.S.; resources, A.V. and E.S.; data curation, A.V. and E.S.; writing--original draft preparation, A.V. and E.S.; writing--review and editing, A.V. and E.S.; visualization, A.V. and E.S.; supervision, E.S.; project administration, A.V. and E.S.; funding acquisition, A.V. and E.S. All authors have read and agreed to the published version of the manuscript.  
The reported study was funded by RFBR and FWF according to the research project No. 21-51-14003.  
Not applicable.  
The authors declare no conflict of interest.  
The complete code of the CC-SHADE-ML algorithm is available at GitHub ([[https://github.com/[PERSON]/CC-SHADE-ML](https://github.com/[PERSON]/CC-SHADE-ML)]([https://github.com/[PERSON]/CC-SHADE-ML](https://github.com/VakhninAleksei/CC-SHADE-ML)), accessed on 18 August 2022).  
Figure 10: The ranking of CC-SHADE-ML-LS1 and other state-of-the-art algorithms.