esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Formulation of the Discrete Element Model
#### 2.1.3 The Computation of the Forces and Moments for Granular Flows  
After the breakage of the bonds, contacting particles interact according to the granular flow model. Moreover, parallel bonds (6-9) act in parallel with the granular portion of the force-displacement behavior, when the depth of the overlap between the particles is more than zero. Thus, the implemented granular flow model is outlined as follows. In the case of granular flows, a model for the normal contact force depends on the contact geometry and the properties of the particle's material. In general, the normal contact force \(\mathbf{F}^{c}_{ij,n}\) can be expressed as the sum of the elastic (\(\mathbf{F}_{ij,n,elastic}\)) and viscous components. The elastic counterpart of the normal contact force is computed according to the formula as follows:  
\[\mathbf{F}_{ij,n,elastic}=\frac{k^{p}_{i,n}k^{p}_{j,n}}{k^{p}_{i,n}+k^{p}_{j,n}} \delta_{ij,n}\mathbf{n}_{ij}, \tag{12}\]  
where \(\mathbf{n}_{ij}\) is the unit normal vector, \(\delta_{ij,n}\) is the depth of the overlap between the particles \(i\) and \(j\) in the normal direction, and \(k^{p}_{i,n}\) and \(k^{p}_{j,n}\) are the normal stiffnesses of the contacting particles \(i\) and \(j\), respectively.  
The computation of the tangential contact force is more complex, since the phenomena of tangential deformation, as well as static and dynamic friction, have to be considered. The most popular approach comprises the evolution of the tangential contact force \(\mathbf{F}^{c}_{ij,t}\) divided into the parts of static friction \(\mathbf{F}_{ij,t,static}\) and dynamic friction \(\mathbf{F}_{ij,t,dynamic}\):  
\[\mathbf{F}^{c}_{ij,t}=-\mathbf{t}_{ij}\;min\Big{(}\big{|}\mathbf{F}_{ij,t,static}\big{|}, \big{|}\mathbf{F}_{ij,t,dynamic}\big{|}\Big{)}, \tag{13}\]The dynamic friction force is directly proportional to the normal component of the contact force:  
\[\mathbf{F}_{ij,t,dynamic}=-\mu\left|\mathbf{F}_{ij,n}\right|\mathbf{t}_{ij}, \tag{14}\]  
where \(\mathbf{t}_{ij}\) is the unit vector of the tangential contact direction and \(\mu\) is the friction coefficient. The static friction force \(\mathbf{F}_{ij,t,static}\) can be calculated by summing up the elastic counterpart \(\mathbf{F}_{ij,t,elastic}\) and the viscous damping counterpart. The elastic counterpart of static friction can be obtained as follows:  
\[\mathbf{F}_{ij,t,elastic}=-\frac{k_{i,t}^{p}k_{j,t}^{p}}{k_{i,t}^{p}+k_{j,t}^{p}} \left|\delta_{ij,t}\right|\mathbf{t}_{ij}, \tag{15}\]  
where \(\left|\delta_{ij,t}\right|\) is the length of the tangential displacement, while \(k_{i,t}^{p}\) and \(k_{j,t}^{p}\) are the tangential stiffnesses of the contacting particles \(i\) and \(j\), respectively.  
Finally, the moment of the interacting particles (3) is computed as follows:  
\[\mathbf{M}_{ij}^{c}=\mathbf{d}_{cij}\times\mathbf{F}_{ij}, \tag{16}\]  
where \(\mathbf{d}_{cij}\) is the vector pointing from the particle's center to the contact center. The details and implementation of the outlined granular flow model can be found in the refs. [9; 30; 31].
# Train-Induced Vibration Analysis and Isolation Trench Measures in Metro Depot Structures
## 3 Propagation Law of Train-Induced Vibration in Metro Depot
### Vibration Assessment of the Over-Track Building  
The analyzed over-track building is a nine-story residential structure. According to the \"Urban Regional Environmental Vibration Standard\" in China, the Z-vibration level limits for residential and educational areas are 70 dB during the daytime and 67 dB at night. The standard floor slabs numbered 1, 2, 14, and 15 in this building correspond to bedroom slabs. Table 7 presents the Z-vibration levels of bedroom slabs from the 1 st to the 9 th floor. It can be observed that, except for slabs 1 and 15 on the 8 th and 9 th floors, where the Z-vibration levels exceed the nighttime limit, the Z-vibration levels of bedroom slabs on all other floors are below both the daytime and nighttime limits.  
Figure 17 shows the vibration levels in the 1/3 octave band for slabs 1, 2, 14, and 15 on the 1 st, 5 th, and 9 th floors of the over-track building. From these diagrams, it can be observed that high-frequency vibrations in the range of 63-80 Hz gradually decrease with increasing floor height, while vibrations at certain frequency points in the range of 5 to 31.5 Hz exhibit amplification as the floor height increases. The vibration levels of 1/3 octave band in this study are calculated using the weighting method and control standards specified in the Chinese standard \"Limits and Measurement Methods for Building Vibration and Secondary Noise Induced by Urban Rail Transit\". This standard sets the daytime vibration level limit in the 1/3 octave band at 65 dB and the nighttime limit at 62 dB for residential and educational areas. As shown in Figure 17, the maximum vibration levels in the 1/3 octave band for slabs 1, 2, 14, and 15 occur at 31.5 Hz and 40 Hz. At these frequencies, the vibration levels of bedroom slabs on certain floors exceed the daytime and nighttime limits.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Floor**} & \multicolumn{4}{c}{**Floor Slab Number**} \\ \cline{2-5}  & **1** & **2** & **14** & **15** \\ \hline
1 & 65.9 & 65.8 & 65.4 & 65.4 \\
2 & 64.1 & 65.3 & 63.9 & 64.5 \\
3 & 65.9 & 62.5 & 61.8 & 64.0 \\
4 & 63.6 & 60.4 & 61.2 & 63.2 \\
5 & 62.8 & 60.4 & 61.1 & 63.3 \\
6 & 63.8 & 61.1 & 62.3 & 64.9 \\
7 & 65.3 & 62.5 & 64.2 & 66.2 \\
8 & 67.1 & 64.5 & 65.7 & 67.2 \\
9 & 68.6 & 65.2 & 66.7 & 67.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Floor Z-vibration level (dB).  
Figure 16: First-order mode shape of the floor slab: (**a**) floor slab 1; (**b**) floor slab 3.
# A Comparative Study of Web Application Security Parameters: Current Trends and Future Directions
## 4 Comparison of Selected Web Assessment Tools
### Quality of Analyzing  
It is the responsibility of the analyzer to analyze the results that the fuzzer generates. It should detect vulnerabilities without generating false positives.  
This section presents the obtained results from different research reviews with our contributions using the following measures: vulnerabilities detected by each scanner, categorization of vulnerabilities based on their severity level, the classification of detected vulnerabilities, and accuracy of each scanner.  
Moreover, DVWA (Damn Vulnerable Web Application) is a vulnerable web application used as a test bench [20]. It is a PHP MySQL web application that is thoroughly vulnerable. It is developed to test tools and professionals' skills in a legal environment. According to the survey, there is no accurate measure for detecting vulnerabilities. Therefore, there is a need to discover more vulnerabilities by using scripts and plugins in open-source tools. Results from proprietary tools such as Acunetix, APPSCAN, Nessus, NetSparker, and HP WebInspect were collected from different research studies. The results collection process aimed at a state-of-the-art comparison of these proprietary tools' performances on OWASP and NIST defined vulnerabilities. Open-source tools such as OWASP-ZAP, Burp Suite, Nikto, Arachni [51], and Wapiti [48] are tested on the DVWA framework. As a result, this framework is vulnerable to the following attacks: Brute Force Attack, Command Injection Attack, CSRF, File Inclusion, File Upload, Insecure Captcha, SQL injection, SQL Injection blind, CSS stored, CSS reflected, CSS-DOM, CSP (Content Security Policy) bypass, and Weak Session ID's. Most of these attacks' vulnerabilities are defined by OWASP listed in Table 3[9]. Tables 4 and 5 classify and present the detection rate of each vulnerability for open-source and proprietary evaluated scanners. For example, as presented in Table 5, the detection rate of Acunetix is higher in the category of CSS, File Injection, Sensitive Data Exposure, CSRF, and Broken Authentication vulnerabilities. On the other hand, an open-source tool, OWASP-ZAP, in Table 4 has a lower detection rate in several categories such as CSS, File Injection, and Insecure Communication.
# Examining the Amount of Particulate Matter (PM) Emissions in Urban Areas
## 3 Measurement Results
### Railway Crossing  
We chose the location near the railway crossing due to the large accumulation of stationary vehicles during the passage of the train through the railway crossing. Vehicles are forced to stop and wait for the train to leave the station when the no entry signal activates. We carried out measurements at this location for 12 h because of the high all-day traffic intensity levels.  
Figure 5 shows similar results for the railway crossing as those in Figure 4. There was a high intensity of vehicles in the monitored section in the morning hours. For this reason, we monitored the increased amounts of PM\({}_{10}\) and PM\({}_{2.5}\) emissions. The highest number of vehicles passed through the location in the morning hours between 7:30 and 7:45 (411 vehicles). This corresponds to the amount of emissions produced, which also reached the highest levels at that time. The graph shows the high dependence between the produced emissions and the number of vehicles that passed the measuring section. As the number of vehicles decreases at the end of the morning rush hour, emission values also decrease. During the afternoon rush hour, which starts at 3:15 p.m., we observe a further increase in emissions, though this increase is not as intense as that during the morning peak.  
Figure 5: Particulate matter concentration at selected location—railway crossing.  
Table 4 shows the hourly concentrations of particulate matter at the measuring point. Table 4 and the graph (Figure 5) show the increase in emissions, especially in the morning and afternoon hours. This increase is mainly attributed to the increased passage of vehicles through the measuring point. In the morning peak, the particulate matter emissions values were at 62 \(\upmu\)g/m\({}^{3}\) however, this does not mean that the overall air quality can be assessed as deteriorated from the PM\({}_{10}\) emissions concentration point of view. On the other hand, after 10 a.m., the intensity of vehicle traffic decreases, and the emission values also decrease. This difference represents up to a 28% drop in PM\({}_{10}\) particulate matter emissions in the air.
## 3 Results
### Binary Logit Model  
#### 3.3.1 Binary Logit Model Development and Evaluation  
Complementary to the classification tree it was considered appropriate to develop a binary logit model. The main aim of the development of the binary logit model is to validate the results of the classification tree, as well as to quantify the impact of the significant predictors.  
The development of the binary logit model was also conducted in R programming language, through the aod package [29]. It should be mentioned that the base alternative (value 0) is the one in which the advanced app would have an impact on potential and existing users. As a first step, all the missing values were excluded from the analysis and as a result, the database, which was finally used consisted of 197 observations. Also, in cases where the first level of each variable had limited responses, the relevel () function was used to change the reference category. In all cases either the first or the last level was used as a reference category to facilitate the interpretation of the results. The independent variable selection was made with the objective to minimize the Akaike information criterion (AIC) value, which penalizes the complexity of the model [30]. Table 1 presents the coefficients of the binary logit model, the standard error, the z-value (also known as Wald statistic) and the p-values. It can be seen that only the \"user\" and \"age\" variables are statistically significant predictors of the dependent variable.  
Figure 2: Classifying travelers based on their intention to shift to the bike-sharing system due to a sophisticated mobile app.  
The evaluation of the final model is being achieved through the following metrics:  
* Akaike information criterion (AIC) value: the selected model achieves the lowest AIC value, which is 161.37.
* Pseudo R2: [PERSON]'s R2 suggests that the selected model can explain about 20% of the variance of the dependent variable.
* Prediction accuracy: the accuracy of the model has been found to be equivalent to 78.68% (true positive rate = 95.68%, true negative rate = 25.71%).
* Receiver Operating Characteristic (ROC) curve: Figure 3 plots the sensitivity (true positive rate) against the specificity (false positive rate). The area under the curve (AUC) is an index of accuracy since it shows the ability of the model to distinguish the two classes. The greater this value (maximum AUC value equals 1) the better the prediction power of the model. The AUC value of the selected model is equal to 0.7864, indicating that there is approximately a 79% chance that the model will achieve to distinguish between positive class and negative class.
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 3 Polyphenols: Properties and Significance in Food
### Application of Polyphenols in Food Technology
#### 3.3.5 Oxidation Inhibition in Fats and Oils  
Polyphenols play a crucial role in stabilising fats and oils by inhibiting oxidative degradation, a process that compromises flavour, aroma, and nutritional quality. Their application is particularly valuable in high-fat products and frying oils, where oxidative stability is a persistent challenge [115; 116; 117; 118]. The antioxidant activity of polyphenols in fatsand oils is attributed to their ability to scavenge free radicals, chelate metal ions, and inhibit lipid peroxidation. By donating hydrogen atoms, polyphenols neutralise reactive oxygen species, thereby interrupting the chain reactions that lead to the formation of harmful secondary oxidation products. The incorporation of polyphenol-rich extracts from plant by-products, such as grape seeds, olive leaves, and olive mill wastewater, has proven effective in reducing oxidation rates. For instance, grape polyphenols enhance the stability of frying oils by delaying the onset of rancidity, which extends the usability and sensory quality of the oils [119]. Similarly, olive-derived polyphenols help maintain the freshness and nutritional value of products like olive oil and margarine during storage. In addition to edible oils, polyphenols are also utilised in high-fat foods such as mayonnaise, butter, and spreads, where they act as natural stabilisers to prevent oxidative spoilage and extend shelf life. Emerging technologies have enhanced the effectiveness of polyphenols in fat stabilisation. Encapsulation methods, such as liposomal delivery systems and spray drying, protect polyphenols from degradation and ensure their gradual release into lipid matrices. As sustainability becomes a priority in the food industry, the use of polyphenols derived from agricultural by-products offers an eco-friendly solution for oxidation inhibition. Ongoing research aims to optimise extraction techniques and explore novel polyphenol sources, further broadening their applications in fat and oil stabilisation [120].
# Natural Frequency Prediction Method for 6R Machining Industrial Robot
## 2 Materials and Methods
### Prediction Model Construction and Optimization
#### 2.2.1 PLS Method Fitting Process  
The principle of PLS method in multivariate regression fitting is to extract principal components \(\mathbf{t}\) and \(\mathbf{u}\) respectively from independent variables \(\mathbf{X}\) and dependent variables \(\mathbf{Y}\) while retaining the most correlation, which can be mathematically described as  
\[\text{Cov}(\mathbf{t},\mathbf{u})\ =\ \sqrt{\text{Var}(\mathbf{t})\text{Var}(\mathbf{u})}\text{r}( \mathbf{t},\mathbf{u})\rightarrow\text{max}, \tag{4}\]  
The process of regression equation fitting by PLS method [27] is briefly introduced as follows.  
1. Data standardization.
2. First principal component calculation.
3. Principal component validation. Current principal component \(\mathbf{t}_{\text{h}}\) is validated by following indexes: \[R_{\text{Y}}^{2}(\text{cum})\ =\ \sum_{\text{h = 1}}^{\text{m}}\text{r}^{2}(\mathbf{Y},\mathbf{t}_{\text{h}}),\] (5) \[Q_{\text{h}}^{2}\ =\ 1-\frac{S_{SPE,\text{h}}}{S_{SE,\text{h}}},\] (6) \[Q_{\text{h}}^{2}(\text{cum})\ =\ 1-\prod_{\text{k = 1}}^{\text{h}}\frac{S_{SPE,\text{k}}}{S_{SE,\text{k}}},\] (7)  
where \(S_{SPE,\text{h}}\) is the square sum of prediction error of \(\mathbf{Y}_{\text{h-1}}\), \(S_{SE,\text{h}}\) is square sum of deviation of \(\mathbf{Y}_{\text{h-1}}\).  
\(R_{\text{Y}}^{2}(\text{cum})\) indicates the ability of principal component to explain the variability of \(\mathbf{Y}_{\text{h-1}}\), and its upper limit value is 1. \(Q_{\text{h}}^{2}\) represents the contribution of \(\mathbf{t}_{\text{h}}\) to the model. When it is more than (1-0.95)\({}^{2}\) = 0.0975, \(\mathbf{t}_{\text{h}}\) has significant effect on prediction model. Meanwhile, \(Q_{\text{h}}^{2}(\text{cum})\) increases, meaning that the comprehensive significance in explaining \(\mathbf{Y}\) is enhanced.  
4. Subsequent principal components calculation. The residual matrixes from Step c. are continued to compute new principal components, until \(\mathbf{t}_{\text{Q+1}}\) deduces \(Q_{\text{h}}^{2}(\text{cum})\). The principal components before \(\mathbf{t}_{\text{Q+1}}\) are validated ones.
5. Regression equation \[\mathbf{Y}_{0}\ =\ \mathbf{t}_{1}\mathbf{r}_{1}^{\text{T}}+\mathbf{t}_{2}\mathbf{r}_{2}^{\text{T}}+ \ldots+\mathbf{t}_{\text{Q}}\mathbf{r}_{\text{Q}}^{\text{T}}+\mathbf{Y}_{\text{Q}},\] (8)
6. Data reduction. The final regression equation for prediction can be obtained through the reverse process of data standardization.
## 3 Methods
### Pre-Processing
#### 3.4.3 Data Reduction  
HSI image data are a collection of spectral bands spans over visible and infrared regions. The data contain useful information but sometimes it also accommodates noise and redundancy along with sparseness. Such factors make the data problematic as render, it is almost impossible to extract any useful information. Thus, to utilize HSI data to its full capacity, we must preprocess it by reducing the number of dimensions so that the data can become appropriate by only containing useful information. Furthermore, while reducing the dimensions of hypercube, the integrity of data must be maintained by preserving the objects and features. In this study, we have used Principal Component Analysis (PCA) for data reduction. PCA removes the correlation inherent and reduces data through orthogonal projection and truncation of the excessive transformed features [43].
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.3 The Indicator to Measure Urban Network  
This indicator promotes connectivity between roads of different hierarchies on a more human scale, reducing distances, and facilitating circulation for pedestrians and cycling in daily travel. It has been promoted by SBToolPT Urban, BREEAM C, and LEED ND, and addressed in SDGs. Therefore, the study has recognized this indicator as a prioritized environmental concern. To calculate this indicator, the SBToolPT Urban Guide indicates two calculation parameters: the Real Intersections Index and the Connectivity Promotion Index, the result is the Urban Network Index obtained by the weighting of the two parameters. The first parameter is obtained by the ratio between the number of intersections and the urban area studied. This calculation can be done through the BIM model and the use of Dynamo's routine. The second parameter is calculated through the verification checklist. The criteria established by the list are measurable and can be obtained through the information model. In this case, the Dynamo routine can attribute the points for each criterion. Thus, this indicator is adaptable in CIM.
## 4 Results: Path Loss  
### Lossless to Lossy Medium: Air to Muscle  
In this section, the results of the configuration from Section 2.1 are discussed, which consists of a simple two-layer model of free space and muscle tissue with the Tx and Rx dipole antennas at 2.45 GHz. Figure 6 shows the interface between free space and muscle tissue at 0 m. It can be seen that PL increases with increasing distance \(d\), where \(d\) is the separation between the Tx placed in free space (at 1 m from the muscle tissue layer) and the Rx placed in the muscle medium. A very good agreement is observed between the PL model and the simulations using the MoM (FEKO) in the muscle tissue medium with a maximum deviation of only 1.4 dB and an average deviation of 1 dB. Thus, the PL model can be used instead of carrying out simulations for a two-layered model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \(\mathbf{S_{11}}\) (\(\mathbf{dB}\)) & \(\mathbf{S_{22}}\) (\(\mathbf{dB}\)) & \(\mathbf{G_{t}}\) (\(\mathbf{dBi}\)) & \(\mathbf{G_{r}}\) (\(\mathbf{dBi}\)) \\ \hline \(Muscle\,tissue\) & \(-\)11.57 & \(-\)11.57 & 7.5 & 7.5 \\ \(Air\,to\,Muscle\) & \(-\)10.45 & \(-\)11.57 & 2.14 & 7.5 \\ \(Air\,to\,Layers\) & \(-\)10.45 & \(S_{22_{Med}}\)(\(See\,Table\) 3) & 0.11 & \(G_{r_{Med}}\)(\(See\,Table\) 3) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values of the Friis transmission formula.
## 2 Feasibility on ODEON-Based Auditory Scene Simulation in Closed Spaces
### Binaural Signal Collection in Real Rooms  
In two ordinary rooms with different spatial sizes (Figure 1), the original signal (the sound of Windows 7 system startup, lasting \(\sim\)3 s) is played with a spherical sound source and the binaural signals are collected with a dummy head. The structural parameters of the two rooms are shown in Table 2, where L, W, and H represent the length, width, and height of the closed space, respectively, S is the internal surface area, and V is the volume. The midpoint of the short side of the room's bottom is selected as the coordinate origin to establish a Cartesian coordinate system, in which the vertex coordinates of the windows in the rooms are Room A (2.45, \(-\)3.25, 1.00), (2.45, \(-\)3.25, 2.40), (0.75, \(-\)3.25, 2.40), (0.75, \(-\)3.25, 1.00); Room B (5.70, 0.50, 1.00), (5.70, 0.50, 2.40), (5.70, \(-\)1.00, 2.40), (5.70, \(-\)1.00, 1.00).  
In Room A, the spherical sound sources are placed at two different positions denoted as P\({}_{\rm A1}\) (3.60, 0, 1.50) and P\({}_{\rm A2}\) (1.60, \(-\)2.25, 1.50), and the corresponding two dummy heads are set at receiving points R\({}_{\rm A1}\) (1.60, 1.55, 1.13) and R\({}_{\rm A2}\) (5.70, \(-\)1.55, 1.13). In Room B, the position coordinates of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equipment** & **Model** & **Brand** & **Number** \\ \hline Spherical sound source & 4292 & B\&K, Denmark & 1 \\ Dual channel sound card & M-Track & M-AUDIO & 1 \\ Microphone & Mic M N201 & Shengwang, Beijing & 2 \\ Power amplifier & 2716C & B\&K, Denmark & 1 \\ Dummy head & 4100-D & B\&K, Denmark & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental apparatus for binaural signal collection in the rooms.  
sound sources and the receiving points are P\({}_{\text{B1}}\) (2.00, 0, 1.50), P\({}_{\text{B2}}\) (4.00, \(-\)0.30, 1.50), R\({}_{\text{B1}}\) (1.00, \(-\)1.00, 1.13), and R\({}_{\text{B2}}\) (5.00, 1.00, 1.13). The binaural signals from the two sound sources to the two receiving points in the room A are collected and recorded as P\({}_{\text{A1}}\)R\({}_{\text{A1}}\), P\({}_{\text{A1}}\)R\({}_{\text{A2}}\), P\({}_{\text{A2}}\)R\({}_{\text{A1}}\), and P\({}_{\text{A2}}\)R\({}_{\text{A2}}\). Additionally, the binaural signals in the room B are recorded as P\({}_{\text{B1}}\)R\({}_{\text{B1}}\), P\({}_{\text{B1}}\)R\({}_{\text{B2}}\), P\({}_{\text{B2}}\)R\({}_{\text{B1}}\), and P\({}_{\text{B2}}\)R\({}_{\text{B2}}\), respectively. The room windows remain closed during the signal collection. The positions of P\({}_{\text{A1}}\), P\({}_{\text{A2}}\), R\({}_{\text{A1}}\), R\({}_{\text{A2}}\), P\({}_{\text{B1}}\), P\({}_{\text{B2}}\), R\({}_{\text{B1}}\) and R\({}_{\text{B2}}\) are indicated in Figure 2.