esa-sceva/satcom-chunk-collection huggingface dataset:
# Studies on the Electrochemical Behavior of Sulfite on Incoloy 800 in a Neutral Environment
## 3 Results
### Chronocoulometric Studies  
Having as a starting point the chronoamperometric studies, from the recorded data, to which is added the certainty that the only process that takes place on the Incoloy 800 electrode in the potential range of 0.4-1.5 V is sulfite oxidation, chronocoulometric data could be extracted. With the help of these data, using the laws of electrolysis, it was possible to calculate with a fairly good precision, the amount of sulfite anions (expressed in grams or moles) oxidized during the chronoamperometric studies. This amount, related to the initial amount of sulfite ions existing in the exact volume of solution used in the tests, made it possible to determine the degree of electrochemical transformation (\(r\)) of sulfite to sulfate through electrochemical oxidation. The chosen volume, 250 mL, is large enough so that the errors that appear as a result of the variation in the concentration of sulfite ions as a result of the oxidation process are as small as possible and do not influence the calculation of the degree of transfer. Precisely on the basis of these considerations, the experiments of chronocoulometric calculations were performed only for the cases in which the concentration of sulfite ions in the test solution was above 10\({}^{-1}\) mol L\({}^{-1}\). In Figure 5a are presented in graphic form, chronocoulometric data for the same value of the potential, +1.00 V at different sulfite concentrations added in base solution, and Figure 5b represents data for the same concentration when the determinations were conducted at different values of the oxidation potential.  
Figure 5: Chronocoulometric plots for electrooxidation of sulfite on Incoloy 800 electrode in neutral solution at different sulfite concentrations at +1.00 V (**a**) and in 1 mol L\({}^{-1}\) Na2 SO4 + 1 mol L\({}^{-1}\) Na2 SO3 at different potential values (**b**).  
According to the analysis of the plots, the degree of electrochemical transformation is lower if the determinations are conducted at the same value of the potential at different concentrations, and for the same concentration, the degree of electrochemical transformation increases with the increase of the oxidation potential.
## 4 Conclusions  
This paper uses numerical simulation for secondary development and applications through the use of a Mohr-Coulomb model, custom constitutive model comparison, and the verification of certain working conditions in the field to analyze the influence of different stress environments on the stability of the rock surrounding a high-stress-bearing laminated roadway. The main conclusions are as follows:  
1. The three-dimensional difference form of the damage body constitutive model was derived, and the constitutive equation was developed twice in FLAC3D software. The simulation results of the custom constitutive model fit well with the experimental data and are better than those of the Mohr-Coulomb constitutive model.
2. With the change in the lateral pressure coefficient of the surrounding rock, the trend of deformation calculated by the Mohr-Coulomb model and custom constitutive model is basically the same, but the difference in the deformation amount is significant. The deformation of the Mohr-Coulomb constitutive model is significantly smaller than that of the custom constitutive model. The maximum displacement of surrounding rock follows an exponential trend with the variation in a lateral pressure coefficient.
3. When the difference between vertical pressure and horizontal pressure increases, the plastic zone of the tunnel develops in a \"butterfly\" shape, and the plastic zone range of the custom constitutive model is larger than that of the Mohr Coulomb model.
4. For roadways with high horizontal stress, the deformation of the roadway floor is generally greater than that of the sidewall and roof. Attention should be paid to the support of the floor. For roadways with high vertical pressure, support should be strengthened to prevent excessive deformation of the sidewall, reduce the sliding and dislocation between the sidewall and the rock layer, and prevent the roadway from being in a high-stress concentration environment. Reducing the K value can improve the deformation development trend of the tunnel.  
[PERSON] and [PERSON] conceived and established the numerical model. [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scientific Research Foundation for High-Level Talents of Anhui University of Science and Technology (grant number 13210673), Natural Science Research Project of Anhui Educational Committee (grant number KJ2021A0453), National Natural Science Foundation of China (grant number 52204081, 52174105).  
**Institutional Review Board Statement:** Not applicable.
# A Four-Stage Mahalanobis-Distance-Based Method for Hand Posture Recognition
## 4 Experiments and Discussion
### Results  
In our experiments, we had to set the parameters of our method, \(AllowableMisNum\) and \(\varepsilon\), separately for SPAS and MU-ASL-digits to achieve the best performance. The value of \(AllowableMisNum\) was set to 1 for SPAS and 0 for MU-ASL-digits. \(\varepsilon\) was set to 9 for SPAS and 8.6 for MU-ASL-digits. The impact of parameter values on classification accuracy is discussed in Section 4.5. Our method achieved a recognition rate of 94.69% with a standard deviation of 3.54%.  
The confusion matrices are presented in Figure 5. For the SPAS dataset, the most frequently confused letter pairs are S-T (twelve times), O-S (ten times), and C-L (six times). All of the hand shapes corresponding to these letters are visually similar. In the case of MU-ASL-digits, the most frequent misclassifications are digits 4-8 (seven times) and 4-5 (six times). Interestingly, three times, the digit 7 was classified as 'no digit'. Such mistakes when a particular gesture is classified as 'no gesture' almost always result from the incorrectly detected skeleton by MediaPipe.  
We compared our approach with other methods found in the literature on the MU-ASL-digits dataset. For a fair comparison, we ensured that all the methods were validated by the same protocol. The results are presented in Table 2. Our method achieved the best recognition rate, outperforming the second best method (DeRefNets) by 1.3 percentage points.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Method** & **Recognition Rate [\%]** \\ \hline Non-Negative Matrix Factorization + Compressive Sensing [42] & 87.8 \\ AlexNet ’FC6’ + PCA and SVM [43] & 95 \\ Set of Geometric Features + Fisher Vector and SVM [44] & 95.3 \\ DeRefNets [45] & 96.14 \\ Our method & 97.44 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the proposed method with other existing approaches on MU-ASL-digits.  
Figure 5: Confusion matrices of the proposed method on the benchmark datasets: (**a**) SPAS and (**b**) MU-ASL-digits. The column named ’no’ represents the case where no gesture was recognized.
## 3 Results and Discussions
### TGA Analysis for the MWCNTs-g-Pmma  
The TGA curves of pristine MWCNTs, MWCNTs-alkyne, and MWCNTs-\(g\)-PMMA are presented in Figure 5. Two stages of significant mass losses are observed in the curves of pristine MWCNTs. The first weight loss of about 0.5% happens before 200 \({}^{\circ}\)C, which is due to the loss of adsorbed water. The second step at a weight loss of approximately 1.4% is due to the impurities of pyrolysis. For the MWCNTs-alkyne, because of the decomposition of organic groups on the surface, the final weight loss was increased to 5.27%. For MWCNTs-\(g\)-PMMAs received at various polymerization times during the click reactions, because the grafted PMMA chains decompose to different extents, the final weight losses for the reaction time at 6 h, 12 h, and 18 h are 22.4%, 25.1%, and 26.9%, respectively (Figure 5c-e). The curves for the MWCNT-\(g\)-PMMA show two main decomposition steps at 205 \({}^{\circ}\)C and 405 \({}^{\circ}\)C, which correspond to the side chains and PMMA chains respectively. As measured, the grafting percentage (GP) for a reaction time of 6 h, 12 h, and 18 h is 17.2%, 19.9%, and 22.0%, respectively.
## 4 Discussion
Our point is that it may be reasonable and desired to sacrifice the accuracy of the prediction in order to obtain a simpler, transparent, and robust model composed of easy-to-understand variables (MIVs), components, and functions. The number of building blocks of a functional model must be limited by human perceptual capabilities, as overly complex models, although accurate, may become incomprehensible to users. The accuracy of technical systems is never perfect, while understanding the real nature of the phenomena is always an ultimate goal. Understanding functional dependencies in relational database systems (RDBSs) is critical for more than just technical reasons. It involves knowing the specific needs of different fields, making sure systems can handle unexpected changes and work with new data. Following ethical rules and laws is also of primary importance for building trust with users. Importantly, having experts check and assess the quality of these systems ensures they are reliable and useful and meet legal standards. This approach makes RDBBSs not only more effective but also trusted sources of information in our data-driven world. Our experiments present several challenges, particularly in the areas of dataset characterization, threshold optimization, and penalization of complexity and redundancy. An intrinsic limitation arises from the need to carefully define the characteristics of the target datasets, which is a prerequisite for tuning the operational parameters of the GE, including the optimization of thresholds that balance sensitivity and specificity. In addition, the penalty mechanism for complexity and redundancy is critical to refining the efficiency of the model but introduces additional layers of decision complexity. A major concern within this framework is the issue of GE convergence and reproducibility of results. The inherent variability in GE processes often prevents consistent results, raising questions about the stability and determinism of the approach. While this variability reflects the dynamic nature of evolutionary algorithms, it necessitates a deeper investigation into methods that could improve the predictability and reliability of results, thereby moving towards a more deterministic model. Addressing these challenges is critical to advancing the application of GE in model-driven XAI, ensuring that models not only capture the nuanced relationships within the data but also do so with high degrees of reliability and reproducibility.
## 1 Introduction
### The Issue of Dataset Generation  
A major issue is the difficulty of obtaining good quality images due to the complex geometries of the parts or very reflective or shiny materials, and the lighting variations that occur in a normal industrial environment. Thus, creating a stable robust machine vision solution is always a complex task [15; 16; 17].  
In addition, getting samples of products with defects is not always possible. Due to the high production quality achieved by the manufacturing companies, most of the components will be defect-free. It must also be considered that sometimes the production rate is very high and it makes it difficult to locate defective parts. This is a common problem when collecting datasets for training defect detection models applied to industrial parts [4]. Therefore, the generation of annotated synthetic sample sets is an increasingly promising research area for the machine vision community.
## Abbreviations  
The following abbreviations are used in this manuscript:  
COTS Commercially available off the shelf  
DS Digital shadow  
DT Digital twin  
IoT Internet of things  
IP Internet protocol  
KPIs Key Performance Indicators  
MI Maturity Index  
PP Public-private partnership  
R&D Research and development  
SMEs Small- and medium-sized enterprises  
TRL Technology Readiness Level  
TRM Technology roadmap
# Enzymatic Degumming of Soybean Oil for Raw Material Preparation in BioFuel Production
## 3 Results  
To achieve maximum efficiency of the process, experiments were conducted to determine the optimal parameters: enzyme dose, temperature, pH value, reaction time, and amount of added water. Enzymatic treatment was carried out on samples of crude soybean oil obtained after pre-pressing the seeds. The initial parameters of crude soybean oil were determined and were (phosphorus level 421 ppm and free fatty acids (acid number 0.49 mg KOH/g of oil) Table 1.  
To achieve maximum results, the following general enzymatic degumming conditions were observed:  
* Temperature: 50-70 \({}^{\circ}\)C, optimal for enzyme activity.
* pH value: 5.5-8.0, which ensures stable enzyme operation.  
* Reaction duration: 1-5 h, depending on the quality of the starting material.
* Enzyme dose: 50-250 ppm, determined according to the concentration of phospholipids in the raw material.  
The degumming process was accompanied by regular monitoring of the level of residual phosphorus and the acid number, which are important indicators when planning the technology for obtaining biofuels.
## 3 Application Results
### Condition Evaluation Based on a Sensitivity Matrix (Approach C)  
The experimental results for Z15 and Z20 are shown in Figure 11. These set-ups are obtained using the same operating conditions (hardness, normal force, lubricant, and lubrication interval).  
Figure 10: Establishing diagnosis-oriented data filtering and threshold-based classification based on two different test runs (Z15 and Z20); first row: time behavior of pressure equivalent value for a complete test run; second row: time behavior of accumulated damage increments (\(AS\)); third row: time behavior of smoothed \(AS\) gradient and, thresholds,last row: classified machine state (approach B).  
The filtering methods described above are used to determine the centered moving average (Figure 11 (first row)). Comparing this value to the LM-value (black line), the trend value (Figure 11 (second row)) is calculated. Using the introduced sensitivity matrix, the machine state can be classified (Figure 11 (last row)).  
In principle, the time behavior of the machine states indicates the same structural behavior, with a run-in phase (at the beginning of the measurement), an interim phase of permanent wear, and a final destruction phase. Constant wear progression phases were used to divide (and therefore to classify) the time periods with steady state behavior. Based on the experiments, the following can be observed:  
* The probability of sudden changes increases as the system is used longer.
* Sudden changes are often connected to other sudden changes.
* The periods of reliable use alternate with periods where strong changes in operation occur.
* After some periods with changes, the system is not capable of stable problem-free usage.  
Figure 11: Establishing diagnosis-oriented data filtering and condition evaluation using sensitivity matrix based on two different test runs (Z15 and Z20); first row: time-behavior of smoothed _AS_ gradient (blue) and LM-value (black); second row: time behavior of trend value for the complete test run; last row: evaluated machine state (approach C).
## 4 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Input Image**} & \multicolumn{2}{c}{**Elemental Image Size (Pixel)**} & \multicolumn{2}{c}{**Size Error (Pixel)**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Average of Position** \\ **Error (Pixel)** \\ \end{tabular} } \\ \cline{2-7}  & **Ground Truth** & **Existing** & **Proposed** & **Existing** & **Proposed** & **Existing** & **Proposed** \\ \hline Diagonal pattern & 187.43 & 181 & 187.72 & \(-6.43\) & +0.29 & 35.87 & 1.62 \\ \hline Car and bear & 217.38 & 216 & 217.03 & \(-1.38\) & \(-0.35\) & 7.37 & 1.87 \\ \hline Ruler & 223.2 & 222 & 223.54 & \(-1.20\) & +0.34 & 10.10 & 2.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results in terms of accuracy. Figure 6: Experimental images: (**a**) diagonal pattern; (**b**) car and bear; (**c**) ruler, raw EIA images of: (**d**) diagonal pattern; (**e**) car and bear; (**f**) ruler. Based on the experimental results, we verified that the proposed method can be applied to the extraction of an elemental image array, and it improved the existing method significantly by suppressing errors in the results of the existing method. Moreover, it is confirmed that the proposed lens array area detection works well. As shown in Figure 9, we reconstructed 3D images for integral image display to show the effectiveness of the misaligned size of elemental images. The standard computational integral imaging reconstruction (CIIR), which is used in [9; 16; 23], is employed to show integral imaging. The method reconstructs 2D images according to depth based on back projection. Figure 9 shows the reconstructed images with different elemental image sizes. These resulting images were focused on the bear object. It is seen that the bear object is well focused with the correct elemental image size, as shown in Figure 9a, whereas imaging for the object fails with the misaligned elemental image size, as shown in Figure 9b. Figure 7: Resulting images through the existing method and proposed method of (**a**) diagonal pattern; (**b**) car and bear; (**c**) ruler.
## 3 Results  
In order to estimate the velocities of fast-moving landslides over the whole region, a Matlab code based on the Heim method [27] was created. Through the code, two tables with the velocity values were obtained: one with the maximum velocity value and the other one with the point velocity values for each landslide polygon. These tables can be easily imported into a GIS environment and used for studies with different purposes.  
An example of the results can be seen in Figures 5 and 6, where the velocity distribution of a shallow landslide and of a rockfall are reported, respectively. The shallow landslide shows a velocity distribution ranging from 0 m/s to 21 m/s, while that of the rockfall is from 0 m/s to 35 m/s. Over the whole region, the obtained velocity values are characterised by an average maximum value of about 19 m/s for shallow landslides and about 28 m/s for rockfalls. Therefore, the distribution of the velocity values resulting from the Matlab code based on the Heim method [27] is consistent with the values acquired from the monitoring activities that are available in the literature. These results were confirmed by several works carried out on the Alpine territory [11; 39; 40; 41; 42; 43], which found that the average rockfall velocity values on forested slopes range between 15 and 25 m/s, and in non-forested areas, they reach a maximum value of 30 m/s. The results for shallow landslides are also consistent with the velocity values found in the literature. As stated by [PERSON] [44] and [PERSON] [45], fast-moving flows can travel over long distances with velocities between 2 and 20 m/s.  
Figure 4: Graphical representation of a generic landslide based on the Heim Energy Line method; the parameters obtained with a specific Matlab routine are indicated.
## 3 Ransomware Dynamic Analysis Studies Utilizing both Machine Learning and Deep Learning
### 1 Ransomware Hybrid Analysis Studies Utilizing Machine Learning  
Many ransomware detection studies utilized the machine learning technologies to carry out the hybrid analysis. Some of the studies are explained below.  
A pre-encryption ransomware detection system PEDA was proposed in [79]. This system was able to detect the ransomware before the encryption process starts. This study carried out hybrid analysis by using the signature comparison in first phase and analyzing the ransomware samples in second phase using cuckoo. However, samples were run for 30 s to capture the features from the subjected samples.  
An AI based ransomware detection framework referred as AIRaD was proposed in [103]. This tool was able to detect ransomware by combining both static and dynamic analysis. A multi-level analysis was performed on assembly, DLL, and function calls. Dynamic analysis was performed with use of sandbox and PIN tool while reverse engineering was performed using Ghidra tools.  
A multi-tier analytical model able to detect the ransomware attacks was proposed in [105]. It was a hybrid detection model utilized hybrid analysis as well as hybrid machine learning algorithms. Both static and dynamic traits were collected from semi realistic and realistic environment to detect the ransomware. Statistic and behavioral analysis were performed to detect the ransomware.  
A pre-encryption ransomware detection system was proposed in [106], which utilized hybrid analysis approach. The system worked in two stages supporting two analyses. First stage performed static analysis supporting pre-execution while second stage performed dynamic analysis performing pre-encryption. This system also generated signature repository by dynamically analyzing the crypto ransomware.  
I. Ransomware Hybrid Analysis Studies Utilizing Deep Learning  
During the survey we did not find any study which utilized hybrid analysis along with deep learning methods.  
I. Ransomware Hybrid Analysis Studies Utilizing Both Machine Learning and Deep Learning  
Some of ransomware detection studies ripe off the advantages of both, machine learning and deep learning technologies to carry out more accurate analysis. Some of the studies are presented below.  
A ransomware detection study performing feature analysis was presented in [80]. Two different datasets were generated to carry out static and dynamic analysis for the better detection. This study also utilized the machine and deep learning to perform the feature engineering. This study collected information from API calls and registry operations to make the decision. The study selected 300 dynamic features to calculate the accuracy.  
A ransomware detection study [107] utilized both static and dynamic analysis to early detect the crypto-ransomware. To analyze the behavior, file content entropy and I/O activities were monitored to extract the features. The fast bare-metal sandbox system was used to capture the time series samples execution for five minutes.
## 3 Content Acquisition Process and Caching Algorithm Optimization
### Content Acquisition Process
#### 3.2.2. Caching optimization for number k of content chunks
```
Initialization: All vehicles in coverage area of \(R_{m}\) constitute the set \(SET_{m}\), \(Max=\phi\), \(Pre=\phi\), \(NV\) 1. predict behavior of all vehicles in set \(SET_{m}\), make vehicles with prediction lane \(i+I\) become CMs of cluster \(C_{m,i+1}\), and put prediction probability into set \(Pre\) 2. for k = 1 to \(\min\left\{C_{m,i+1}^{num},NV\right\}\)do 3. use k to calculate \(P_{i+1}^{trans}(k)\) in (8) 4. choose elements with \(tp\) k values from \(Pre\) to constitute \(\left\{P_{i+1,1}^{pre},P_{i+1,2}^{pre},\dots,P_{i+1,k}^{pre}\right\}\) 5. use \(\left\{P_{i+1,1}^{pre},P_{i+1,2}^{pre},\dots,P_{i+1,k}^{pre}\right\}\) and \(P_{i+1}^{num}(k)\) to calculate \(P_{i+1}^{num}(k)\) in (9), and put \(P_{i+1}^{num}(k)\) into set \(Max\) 6. end For 7. make \(k\) corresponding to maximum value in \(Max\) become the number of content chunks
```  
**Algorithm 2** Caching Algorithm for Optimizing the Number of Content Chunks