esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### Data Preprocessing  
#### 3.2.1 Data Denoising  
The raw data collected from the sensors might contain unwanted system measurement noise or unexpected disturbances caused by the individual's active movements during the experiments. This noise can compromise the valuable information embedded in the signal. Hence, it was crucial to mitigate the impact of noise to extract meaningful insights from the signal for subsequent analysis. Commonly utilized filtration techniques include mean, low-pass, Wavelet, and Gaussian filters [37; 38]. In our study, we opted for an  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Number of Subjects** & **Sensors** & **Positions** & **Activities** \\ \hline \multirow{4}{*}{Opportunity} & \multirow{2}{*}{12} & Accelerometer & Left lower arm, & Open door 1, Open door 2, Close door 1, Close door 2, \\  & & Accelerometer & Left upper arm, & Open and close fridge, Open and close dishwasher, \\  & & Gyroscope & Right upper arm, & Open drawer 1, Close drawer 1, Open drawer 2, Close drawer 2, \\  & & Magnetometer & Right lower arm, & Open drawer 3, Close drawer 3, Clean table, Drink from cup, \\  & & Back of the torso & Toggle switch \\ \hline \multirow{3}{*}{PAMAP2} & \multirow{3}{*}{9} & Accelerometer & Wrist, & \multirow{3}{*}{Lying, Sitting, Standing, Walking, Running, Cycling, Nordic walking,} \\  & & Gyroscope & Chest, & \\ \cline{1-1}  & & Magnetometer & Ankle & \\ \hline \multirow{3}{*}{REALWORLD16} & \multirow{3}{*}{15} & Accelerometer & Head, Chest, & \multirow{3}{*}{Climbing stairs (down and up), Jumping, Lying, Standing, Sitting,} \\ \cline{1-1}  & & Gyroscope & Upper arm, Forearm & \\ \cline{1-1}  & & Magnetometer & Waist, Thigh, Shin & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the three multi-position HAR datasets used in this work.  
average smoothing filter for signal denoising. This entailed applying the filter across all three dimensions along the accelerometer and gyroscope sensors.
## 6 Summary and Conclusions  
The guiding principle of bi-directional static test loads is to run separate measurements of the forces transferred by the pile base and pile shaft without the need of constructing an expensive retaining structure. The results obtained were approximated and analysed with the Brinch-Hansen 80% method. This allowed us to assess the bearing capacities and to determine \(Q\)-\(s\) curves of shafts and bases  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pile**} & \(R_{b}\) & \(R_{s}\) & \(R_{e,e}\) & \(R_{c}\) & \(R_{c,e}\)/\(R_{c}\) \\ \cline{2-6}  & **kN** & **kN** & **kN** & **kN** & - \\ \hline
1 & 1.61 & 2.16 & 3.77 & 4.69 & 0.80 \\
2 & 1.56 & 2.11 & 3.67 & 5.22 & 0.70 \\
3 & 2.54 & 3.08 & 5.60 & 6.07 & 0.92 \\
4 & 1.80 & 2.82 & 4.38 & 5.32 & 0.82 \\
5 & 3.22 & 4.62 & 7.78 & 8.66 & 0.89 \\
6 & 3.29 & 5.66 & 8.10 & 9.49 & 0.85 \\ \hline \hline \end{tabular} \(R_{k}\): pile base bearing capacity from bi-directional test. \(R_{c}\): total pile bearing capacity. \(R_{c,e}\): equivalent bearing capacity of total pile.  
\end{table}
Table 4: Test results.  
separately for each pile. Moreover, bi-directional test results were used to draw the equivalent \(Q\)-\(s\) curves according to the Osterberg method and to determine the total capacity of each pile.  
The results showed that the proposed method can be suitable for determining the pile capacity of the pile, which was also confirmed by field tests and numerical simulations [26]. However, in true scale pile tests, results can be difficult to predict due to different layered soil conditions.  
The problems that can appear in relation to the bi-directional method are similar to those in the Osterberg test: the bearing capacity of the whole pile is in fact determined by the capacity of the weaker pile element (shaft or base). Hence, it is necessary to perform an earlier assessment of pile capacity in a given soil. In general, the equivalent bearing capacity according to the Osterberg method appeared to be about 20% lower than the standard total capacity.  
Operating conditions of a pile base are also different depending on the type of test carried out. In the traditional compression test, loading of the shaft can increase the bearing capacity of the base. In the bi-directional test, pulling out the shaft can cause a reduction of stresses at the pile base level, and in consequence reduces the base bearing capacity.  
Conceptualization, J.R and M.B.; Funding acquisition, J.R.; Investigation, M.B.; Methodology, M.B. and J.R.; Supervision, W.B and J.R.; Visualization, M.B.; Writing--original draft, M.B.; Writing--review & editing, M.B., W.B. and J.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Experiments and Discussion
### Comparison of FPDD and Direct Template Matching  
The proposed machine-vision-based method was validated when \(\alpha=1^{\circ}\), \(2^{\circ}\),..., 360\({}^{\circ}\), and the result is shown in Figure 11. The displayed values are the errors between the measured values and actual values. We found that the measuring errors were in the range of [\(-1^{\circ}\), \(0.5^{\circ}\)], which was good enough to insert the drive rod into the drive bore. Moreover, the average time for each measurement was 0.868 s.  
Figure 11: The validation result of the proposed machine-vision-based measuring method.  
If we were to directly conduct template matching (cross-correlation criterion) using a single template, the drive bore at zero position (\(\alpha=0^{\circ}\)) for instance, a lot of mismatching would be found. Even if we use eight templates, as shown in Figure 12, and select the matching with the highest correlation, there are still some mismatching results, as shown in Figure 13. Moreover, the average time for each measurement by direct template matching was 7.981 s.
# Technological Aspects of Sintering Low-Quality Wolframite Concentrate with Potassium Carbonate
## 3 The Results and Discussion
### Thermodynamic Assessment of the Main Reactions
According to the results obtained, the processing of sintering products of the burnt concentrate with K\({}_{2}\)CO\({}_{3}\) ensures the transfer to a solution of 97.5% W, 98.2% Mo, 0.61% P, 100% S. Silicon, iron, manganese, copper, and aluminum remain almost completely in the cake. In turn, when using Na\({}_{2}\)CO\({}_{3}\), the indicators for the extraction of target metals into solution are slightly lower and amount to the following wt. %: 95.3 W and 95.3% Mo. In comparison with K\({}_{2}\)CO\({}_{3}\), there are high values of the degree of transition of phosphorus (up to 6.0%) and silicon (up to 9.2%) into solution, the presence of which increases the duration of purification operations from impurities. In addition, complete melting of the charge materials using Na\({}_{2}\)CO\({}_{3}\) leads to a greater wear of furnace equipment elements, as well as losses of target metals when extracting a dense solidified mass from the walls of a metal pallet. To avoid melting of products, it is necessary to reduce the proportion of low-melting components (Na\({}_{2}\)WO\({}_{4}\), Na\({}_{2}\)MoO\({}_{4}\)) in the resulting product by diluting the initial charge with a leaching cake, in the  
Figure 7: Sintering products of burnt concentrate with Na\({}_{2}\)CO\({}_{3}\) (**a**) and K\({}_{2}\)CO\({}_{3}\) (**b**) after sintering treatment at 1073 K for 2 h.  
amount necessary to reduce the total content of WO3 and MoO3 in the charge to 20-22%. However, such intensifying measures significantly reduce the process productivity. The extraction of tungsten and molybdenum from aqueous leaching solutions can be accomplished through established technological methods, such as the precipitation of CaWO4 and MoS3 when CaCl2 or Na2S is introduced to the solution [1]. The resulting products can be employed in the production of ferroalloys or processed through established techniques to yield suitable oxides for the isolation of pure metals. Cake containing  
Figure 8: Diffractograms of sintering products of burnt concentrate with Na2 CO3—Regions 1 (**a**) and 2 (**b**), as well as with K2 CO3 (**c**). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reagent**} & \multicolumn{8}{c}{**Content, \%**} \\ \cline{2-9}  & **W** & **Mo** & **Si** & **Fe** & **Mn** & **Al** & **P** & **Cu** \\ \hline K2 CO3 & 1.21 & 0.12 & 18.3 & 18.2 & 8.01 & 1.69 & 0.28 & 9.39 \\ Na2 CO3 & 1.89 & 0.26 & 13.2 & 23.9 & 6.56 & 1.39 & 0.21 & 7.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Composition of cases of aqueous leaching of sintering products of low-quality wolframite concentrate containing Na2 CO3 and K2 CO3. 18.2-23.9% Fe and 6.6-8.0% Mn, following purification from copper, can be employed as a feedstock for the production of ferrosilicomanganese [27].
## 2 Theory and Methodology
### Theory of Common-Receiver Reverse Time Migration
the response matrix of the subsurface medium is the inverse of the forward propagation matrix during reverse propagation:  
\[D^{\prime}(z_{1},z_{0})=X^{-1}(z_{1},z_{0})X(z_{1},z_{0})S(z_{0})R(z_{1})X(z_{2}, z_{1})=S(z_{0})R(z_{1})X(z_{2},z_{1}) \tag{5}\]  
Meanwhile, the seismic wavelet propagates from the receiver location, producing a wavefield, as shown below:  
\[D^{\prime}(z_{1},z_{2})=S(z_{0})X(z_{2},z_{1}) \tag{6}\]  
Substituting the source and receiver wavefields of CRRTM into the cross-correlation imaging condition in Equation (1) yields \(R(z_{1})\) at the reflection point, which represents the true reflectivity of the reflector. At the correct reflector, the source and receiver wavefields generated via CRRTM still satisfy the imaging condition, allowing for the accurate imaging of the subsurface structure. However, for artifacts caused by incorrect cross-correlation, changes in the propagation path alter the artifact positions generated via the erroneous cross-correlation of wavefields, which are subsequently suppressed during the stacking process.
Elicitors are stress stimuli capable of inducing similar defense responses in plants as induced by the pathogen infection [5; 7]. Elicitors induce plant resistance against pathogens by activating signals that enhance the production of secondary metabolites. Elicitors are of different types; chemical elicitors such as benzothiadiazole or methyl Jasmonate, physical elicitors such as light, salinity, or temperature, and elicitors of biological origin, such as oligosaccharides, yeast derivatives, or protein fragments [12; 13]. The use of elicitors as alternatives to agrochemicals in preventing grape diseases and infections also has a great impact on the quality components of grapes [14; 15]. Numerous studies intending to improve wine aroma quality have investigated the effects of different elicitors on the volatile compositions of grapes. However, their impact varies depending on several factors such as grape cultivar, type of elictor, and dose.  
Under this framework, the main focus of this review is to provide an overview of some of the commonly used elicitors in grapevines and their impact on the quality of grape-derived aroma compounds. The work is intended to serve as a reference for growers for the sustainable development of high-quality grapes using elicitors.
# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 1 Introduction
However, with the increasing scale and complexity of the network, many machine learning-based detection techniques are not effective enough for learning large-scale network traffic data. Therefore, in recent years, deep learning-based detection techniques have gained more attention due to their ability to perform better with large-scale and complex network traffic and their ability to learn representations of features from raw data, making them adaptable to different attack scenarios [14]. The proposal contributions can be summarized as follows:  
* Building an effective deep learning-based intrusion detection model in the cloud computing environment that covers a range of cyberattack scenarios. * Utilizing the CNN paradigm for effective intrusion detection on the CSE-CICIDS2018 dataset [12] for evaluation. * Implementing a multi-stage process to optimize detection accuracy and efficiency. * Integrating advanced preprocessing techniques and feature selection methods, such as the Pearson correlation coefficient matrix heatmap, ensuring that the most relevant features are used for training. * Selecting relevant features to reduce computational overhead and time complexity while enhancing the precision of the model. * Training and testing the model in a cloud environment, demonstrating practical and scalable deployment. * Addressing common issues such as data imbalance and feature redundancy, resulting in improved robustness and reliability. * Providing significant advantages in terms of accuracy, efficiency, and scalability, positioning it as a superior solution for modern intrusion detection in cybersecurity.
# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 3 Research Methodology
### Comparison and Selection of Deep Learning Algorithms
As shown in Figure 2, 600 image data points were used to validate the discrimination model generated using the VGG-16 algorithm. The \(x\)-axis represents the epoch, which indicates the number of times the entire training dataset was used for learning. The \(y\)-axis represents the measured values. Figure 2a illustrates the accuracy of the discrimination model. The model progressively learned from the training dataset through epochs and improved its performance. Setting an epoch value to 10 or higher can affect the training time and performance, potentially leading to overfitting. Therefore, the epoch values were appropriately adjusted. The average accuracy values were 0.988, with the highest recorded value of 0.998. Figure 2b presents the loss values for the VGG-16 algorithm. For validation, 600 images were used, and the epochs were plotted on the \(x\)-axis, whereas the \(y\)-axis shows the measured loss values for each epoch. Comparing epoch values up to 10, as higher epochs can impact the training time and performance, the average loss value was 0.026, with the lowest recorded value being 0.017. Based on these performance metrics, the VGG-16 algorithm was the optimal choice; therefore, it was applied to discriminate the OLED cell defects to ensure precise defect classification.
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 3 Results
### Relationships Between Crop Parameters  
To understand the relationship between the study's dependent variables, a correlation matrix was created. The results of the correlation matrix are presented in Figure 7.  
The % crop plants parameter presents a very strong negative correlation (\(-0.90\)) with the % weed plants parameter. However, the DM productivity (t ha\({}^{-1}\)) does not show good correlations with the % crop plants and % weed plants parameters.
# Seismic Response and Mitigation Analysis of a Subway Station in the Site with Weak Interlayers
## 2 Seismic Response of the Subway Station in the Site with Weak Interlayers
### Computational Result
The subway station structure is nearly destroyed under seismic activity with a 0.4 g acceleration, resulting in severe damage throughout the subway station structure. Particularly noteworthy is the pronounced damage to the station's side wall as the interlayer thickness increases. The computational results presented above underscore the adverse effect of the weak interlayer on subway station structure deformation and its exacerbation of structural seismic damage. Figure 5:0 Maximum displacement of the station lower column. (**a**) Chi-Chi earthquake. (**b**) Dzuce earthquake. (**c**) Manjil earthquake. Figure 5:1 Tensile damage of the station at different positions under a 0.05 g Chi-Chi earthquake. (**a**) \(\rm H=0\) m. (**b**) \(\rm H=1\) m. (**c**) \(\rm H=1.5\) m. (**d**) \(\rm H=2\) m. (**e**) \(\rm H=2.5\) m. (**f**) \(\rm H=3\) m.  
Figure 5:2 _Cont._  
Figure 5:3 Tensile damage of the station at different positions under a 0.2 g Chi-Chi earthquake. (a) H = 0 m. (b) H = 1 m. (c) H = 1.5 m. (d) H = 2 m. (e) H = 2.5 m. (f) H = 3 m.  
Figure 5:4 Cont. Figure 5:5 Tensile damage of the station at different positions under a 0.1 g Chi-Chi earthquake. (a) H = 0 m. (b) H = 1 m. (c) H = 1.5 m. (d) H = 2 m. (e) H = 2.5 m. (f) H = 3 m.  
3. Influence of the strength of the weak interlayer  
A 2 m thick weak interlayer located in the middle of the subway station structure is assumed in this section. The aim is to investigate the impact of the interlayer's strength on the seismic response of the subway station structure by varying the interlayer's shear wave velocity. Figure 18 illustrates the maximum displacements of the station's side wall and middle columns under a 0.4 g earthquake. From this figure, it is evident that as the shear wave velocity increases, the maximum displacement of the structure's side wall and middle column decreases. In general, firmer soil layers correspond to higher shear wave velocities. Therefore, an increase in the stiffness of the weak interlayer can, to some extent, limit the deformation of the structure. Figure 19 displays the seismic tensile damage nephograms of the subway station structure under various shear wave velocities of the weak interlayer conditions. The seismic input used here is the Chi-Chi earthquake, with a magnitude of 0.4 g. From the figure, it becomes apparent that as the shear wave velocity of the weak interlayer increases, there is a noticeable reduction in damage to the station's side wall. However, the impact on other parts of the subway station structure remains relatively minor. This highlights the positive effect of increased stiffness in the weak interlayer on the subway station structure's seismic resistance. Figure 5:6 Tensile damage of the station at different positions under a 0.4 g Chi-Chi earthquake. (a) H = 0 m. (b) H = 1 m. (c) H = 1.5 m. (d) H = 2 m. (e) H = 2.5 m. (f) H = 3 m.
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## Appendix A Derivation of PLoS Expression for Cases 1 & 2: \(p_{1,2}\)  
In this Appendix, we derive the PLoS expression for the cases / event (1 or 2), denoted by \(p_{1,2}\).  
As explained in Section 2.3, this probability can be expressed as follows:  
\[p_{1,2} =\Pr\{h_{{}_{V}}<\min(h_{{}_{T_{x}}},h_{{}_{Rx}})\}\] \[=\int_{h_{\min}}^{h_{\max}}\int_{h_{\min}}^{s}f_{{}_{N_{V}}}(x)\, f_{y}(y)\;dx\;dy, \tag{1}\]  
Figure 7: Average throughput vs. transmission distance in an NLoS scenario.  
where \(y=\min(h_{T_{x}},h_{R_{x}})\) and \(f_{h_{y}}\) is the PDF of \(h_{v}\), which is assumed to be uniformly distributed, between the minimum vehicle's height, denoted by \(h_{\min}\), and the maximum vehicle's height, denoted by \(h_{\max}\).  
Accordingly, the expression of \(f_{h_{V}}\) is given by  
\[f_{h_{V}}=\frac{1}{h_{\max}-h_{\min}}. \tag{12}\]  
Consequently, the PDF expression of \(y\) can be derived, from the relevant cumulative distribution function (\(F\)), as follows:  
\[f_{y}(y) =\frac{\mathrm{d}F(y)}{\mathrm{d}y}\] \[=\frac{\mathrm{d}}{\mathrm{d}y}\Big{[}\mathrm{Pr}\big{\{}\min(h_ {T_{x}},h_{R_{x}})\leq y\big{\}}\Big{]}\] \[=\frac{\mathrm{d}}{\mathrm{d}y}\Big{[}1-\mathrm{Pr}\big{\{}h_{T _{x}}>y\big{\}}\;\mathrm{Pr}\big{\{}h_{R_{x}}>y\big{\}}\Big{]}. \tag{13}\]  
Now, by using the PDF expression in (12), the expression of \(f_{y}\) is given by  
\[f_{y}(y) =\frac{\mathrm{d}}{\mathrm{d}y}\Bigg{[}1-\int_{y}^{h_{\max}}f_{h _{V}}(x)\;dx\int_{y}^{h_{\max}}f_{h_{V}}(x)\;dx\Bigg{]}\] \[=\frac{\mathrm{d}}{\mathrm{d}y}\Bigg{[}1-\frac{h_{\max}^{2}+y^{2 }-2h_{\max}y}{\left(h_{\max}-h_{\min}\right)^{2}}\Bigg{]}\] \[=\frac{2(h_{\max}-y)}{\left(h_{\max}-h_{\min}\right)^{2}}. \tag{14}\]  
By using the expressions of \(f_{h_{V}}\) and \(f_{y}\), (11) can be rewritten as follows:  
\[p_{1,2}=\int_{h_{\min}}^{h_{\max}}\frac{2(h_{\max}-y)}{\left(h_{\max}-h_{\min} \right)^{2}}\;\left(\;\int_{h_{\min}}^{y}\frac{1}{h_{\max}-h_{\min}}dx\right)\;dy. \tag{15}\]  
After evaluating the integrations in (10), with respect to \(x\) and \(y\), and after some simplifications, the final expression of \(p_{1,2}\) is given by  
\[p_{1,2}=\frac{1}{3}. \tag{16}\]
## 3 Results
### Mitigation of Odor and Targeted Gases as a Function of UV Wavelength
#### 3.3.1 Greenhouse Gases  
Effect of PM Size and Concentration  
In the case of N\({}_{2}\)O mitigation, impressively, there was a statistically significant reduction, regardless of the different concentrations of suspended PM (Table 9). Additionally, the N\({}_{2}\)O percent reduction did not decrease even for the unfiltered worst-case scenario. CO\({}_{2}\) was still generated under all PM conditions (Table S10), similar to the results reported in Table 4. CH\({}_{4}\) did not significantly increase or decrease under the different airborne PM concentrations (Table S9).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Filtration** & **PM 1** & **PM 2.5** & **PM 4-10** & **PM 10** & **Total PM** & \\  & & \multicolumn{4}{c}{**Concentration (mg\(\cdot\)m\({}^{-3}\))**} & & **\% Reduction** (_p-Value_) \\ \hline Unfiltered & & & & & & \\ swine barn exhaust & 0.10 \(\pm\) 0.13 & 0.09 \(\pm\) 0.11 & 0.08 \(\pm\) 0.08 & 0.11 \(\pm\) 0.07 & 0.22 \(\pm\) 0.16 & - \\ MERV 8 & 0.05 \(\pm\) 0.02 & 0.03 \(\pm\) 0.01 & 0.02 \(\pm\) 0.01 & 0.02 \(\pm\) 0.01 & 0.06 \(\pm\) 0.03 & **77.1 (0.05)** \\ MERV 8 and 15 & 0.008 \(\pm\) 0.001 & 0.003 \(\pm\) 0.000 & 0.003 \(\pm\) 0.000 & 0.003 \(\pm\) 0.000 & 0.004 \(\pm\) 0.001 & **98.3 (0.01)** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of the MERV8 and MERV15 filtration. Values in the table are mean \(\pm\) S.D. Bold signifies statistical significance. The effluent was used for UV treatment in experiments described in Sections 3.1 and 3.2.  
Figure 2: Mitigation of O\({}_{3}\) concentration with different UV wavelengths irradiating gaseous emissions inside #2 chamber. Air flow = 0.28 m\({}^{3}\)\(\cdot\)s\({}^{-1}\), inlet air temperature (influent of chamber #2) = 28 \({}^{\circ}\)C, inlet air RH = 67%, outlet air temp. (influent of chamber #3) = 31 \({}^{\circ}\)C, outlet air RH = 61%.
# Fault Location and Restoration of Microgrids via Particle Swarm Optimization
## 3 Derivation of Fault Location Approach
### Solution Procedure of the Proposed Fault Location Algorithm  
Short-circuit faults may occur at the bus or in the line segment. Thus, \(Z_{Bus}\) can be modified on two conditions. If the fault occurs at the bus, then \(Z_{Bus}\) does not need to be modified. Otherwise, if the fault occurs in the line segment, a negative impedance in the line segment should be added to remove the original line segment. Then, a virtual bus that is assumed to be in the middle of the line segment, as well as two elements, should be added, with one element added as a new branch from the existing bus to the virtual bus, and with the other element added as a new link from the virtual bus to the other existing bus; the procedure is shown in Figure 2. Therefore, the dimension of the _BIBC_ matrix becomes \((n+1)\times(n+1)\), and the new \(Z_{Bus}\) can be derived by using Equation (3). The fault point is assumed at the virtual bus. In this study, PSO [28; 29] is used to search for the faulted bus, and the assumed faulted bus is modeled as a particle whose objective function is shown in Equation (16). The fault will be detected according to the minimal value of the voltage error in the **VEM**. The proposed fault location approach of the MG is shown in Figure 3.  
\[\text{F}\ =\ \min\ v_{i},\ v_{i}\in\textbf{VEM} \tag{16}\]Figure 3: Proposed fault location approach of the MG.  
Figure 2: \(Z_{\text{Bus}}\) modification procedure for adding a virtual bus.