esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Flow Characteristics and Effects of Turbulence Models for the Butterfly Valve
## 5 Sensitivity Analysis
### Sensitivity Result of Two-Equation \(k-\varepsilon\) Model  
Figure 6 presents the extreme values of sensitivity coefficients with respect to the constants for the \(k-\varepsilon\) turbulence model. The flow properties were obtained at the 5 x/Y, developing region and 10 x/Y, developed region. Individual sensitivity coefficients had different shapes for individual flow properties, and varied with disc opening cases. The resulting sensitivity coefficients had both positive and negative values, where positive values mean increasing functions of the constant and negative values mean decreasing functions [37; 54].  
Figure 6: _Cont._  
The notable observation in the sensitivity analysis was that the most sensitive flow property was the turbulence dissipation rate and this property was affected mainly by constants \(\mathrm{C}_{\varepsilon 1}\) and \(\mathrm{C}_{\varepsilon 2}\). Constants \(\mathrm{C}_{\mu}\), \(\mathrm{\sigma}_{k}\) and \(\mathrm{\sigma}_{\varepsilon}\) hardly affected flow properties. Moreover, the constants affected flow properties differently depending on the disc opening percentage and locations. At the location of \(5\,\mathrm{x}/\mathrm{Y}\), developing region, larger sensitivity values were observed with decreasing disc opening percentage. The sensitivity of flow properties was influenced mainly on the value of constant of \(\mathrm{C}_{\varepsilon 1}\). However, at the developed region of \(10\,\mathrm{x}/\mathrm{Y}\), sensitivity value increased with increasing disc opening percentage. The sensitivity of flow properties was mainly influenced by constant, \(\mathrm{C}_{\varepsilon 2}\) except for \(80\%\) disc opening cases.  
From the sensitivity analysis results for the \(k-\varepsilon\) turbulence model, it was shown that sensitivity coefficients were mainly related with the flow behavior of the valve with different opening percentages. The sensitivity coefficient value increased with small opening case in the developing region, and with large opening in the developed region. From the result, the \(k-\varepsilon\) turbulence model would be not suitable for the area of the large pressure drop and velocity increase. This is due to the relatively smaller effective flow region which resulted in a large pressure drop and velocity increase, leading to increasing turbulence.  
Figure 6: Extreme value of the sensitivity coefficients with respect to the constants for the \(k-\varepsilon\) turbulence model: (**a**) 20% opening, (**b**) 50% opening, (**c**) 80% opening case.
## 4 MADDPG-Based Integrated Space-Air-Sea Network System Cost Minimization Algorithm
### Partially Observable Markov Decision Process (POMDP) Strategy
#### 4.1.2 Action Space  
To minimize the total delay and energy consumption of the M-IoT devices, the intelligent devices need to determine their task partitioning and corresponding transmission power \(a_{i}(t)=\left\{a_{ij}(t),D_{i}^{l}(t),D_{i}^{u}(t),D_{\mathrm{i}}^{s}(t)\right\}\), while the intelligent UAVs need to determine the association control and resource allocation strategy of the MEC server \(a_{i}(t)=\left\{P_{ij}^{u},f_{ij}^{u}\right\}\). Therefore, the joint action space for the intelligent agents is \(a(t)=\left\{a_{ij}(t),D_{i}^{l}(t),D_{i}^{u}(t),D_{\mathrm{i}}^{s}(t),P_{ij}^ {u},f_{ij}^{u}\right\}\).
###### Abstract  
Many security detectors do not have the ability to output individual luggage package images and are not compatible with deep learning algorithms. In this paper, a luggage package extraction of X-ray images based on the ES-MBD (Edge Sensitive Multi-channel Background Difference Algorithm) method is proposed, which is aiming at the problem that background difference binarization is insensitive to texture features and edge detection binarization is insensitive to smooth areas. In this method, X-ray luggage package images from complex original video images are used as a key target, the RGB three-channel background difference is calculated from the original X-ray image, the edge detection of the grayscale map is performed using the Sobel operator optimized by local gradient enhancement, and the morphological expansion process is performed on the combined results to obtain the complete wrapping target. The Suzuki algorithm is used to detect the outline of the binarized package image, match the package frame area and determine the key target. The ES-MBD method solves the problem of information loss in the traditional binarization method, and retains the information of insensitive regions while reducing noise. Through experimental comparison, the accuracy of ES-MBD binarization method reaches 97.3%, the recall rate reaches 96.5%, and ES-MBD method has obvious advantages in key target extraction of X-ray images.  
image processing; deep learning; X-ray contraband; edge detection; binarization method
## 1 Introduction
The obtained results suggested that the differential modulation of the identified metabolites (apparent from altered metabolic profiles in response to the four PGPR strains) was dependent on bacteria-derived stimuli and that the plants adjusted their adaptive responses based on the perceived signal(s). As such, the aim of this investigation was to fill the gap in our understanding of how these strains promote growth and induce resistance in crop plants. This was achieved by profiling the secreted VOCs and comparing them to PGPR strains that were reported to have these effects on plants. Due to their volatile nature, VOCs are appropriately analyzed by gas chromatography for separation coupled to mass spectrometry for detection and identification (GC-MS). The non-destructive headspace (HS) sampling strategy is a sensitive extraction method for detecting and analyzing natural volatile compounds, and various methods (either dynamic or static) have been developed. In this regard, procedures such as stir bar sorptive extraction, in-tube extraction and solid phase micro-extraction (SPME) fibers have been applied for HS analysis [31; 32; 33]. As a solvent-free extraction method, SPME integrates the sample preparation stages (sampling, extraction, concentration and sample introduction) into a single step [34; 35]. This significantly decreases preparation time and provides samples ready for analysis. Different needle-coating chemicals such as polydimethylsiloxane, carboxen, divinylbenzene, polyacrylamide and benzenesulfonic acid have been developed as sorptive phases for different analyte classes [35; 36]. Coatings with different thickness and polarities also broaden fiber selection [36]. Notwithstanding all the advantages offered by SPME, the chemical base coating of the stationary phase can limit sensitivity or favor certain classes of analytes based on their size and/or polarity [37; 38].
## 2 Materials and Methods
### Influence of Temperature on Parameters  
The temperature employed during extraction plays a critical role in obtaining extract from tomato pomace. It holds significant sway over the resultant extract's quality and composition. Elevated temperatures aid in cell disintegration, thereby facilitating the release of active constituents from tomato pomace. This can also be observed in our samples; the samples with the highest amount of lycopene (3.48 mg/100 g), DPPH activity (77.95%), and polyphenols (29.19 mg/L) can be observed in Table 5 at the highest temperature, namely at 50 \({}^{\circ}\)C. Nonetheless, excessively high temperatures can trigger the degradation of heat-sensitive substances, including vitamins and aromatic compounds. Thus, it is imperative to carefully select the optimal extraction temperature to maximize both yield and extract quality while safeguarding valuable components from deterioration. Research indicates a broad spectrum of temperatures can be utilized for extracting substances from tomato pomace, contingent upon the specific target compounds and objectives of the extraction process [9]. However, the samples with flavones showed the best extraction efficiency at 30 \({}^{\circ}\)C, reaching a level of 48.65 mg/L. Moreover, temperature significantly influenced (\(p<0.001\)) lycopene extraction.
###### Abstract  
Phase mask optimization is one of the critical steps in designing a wavefront coding system to extend the depth of field (DoF). As a classical phase mask, a cubic phase mask was taken as an example. An improved particle swarm optimization (PSO) algorithm was applied to calculate the parameters of the cubic phase mask by introducing the modulation transfer function as the optimization criterion and a threshold as a constraint. The quality of the subsequent image restoration is guaranteed on the premise of the extended DoF. Finally, the improved PSO was proved to be faster, more efficient, and more accurate compared to the simulated annealing algorithm and the traditional PSO. The experimental results verify that the cubic phase mask optimized by the improved PSO can achieve DoF extension in the wavefront coding system. The improved PSO can also be applied to other phase masks of wavefront coding systems.  
wavefront coding; phase mask; particle swarm optimization; depth of field extension +
Footnote †: journal: Applied Sciences
## 1 Introduction
The derived AODs from Sentinel-2/MSI, Landsat 8 Operational Land Imager (OLI), and GF-1/WFVs are validated using ground measurements from 4 sites of the AErosol Robotic NETwork (AERONET) around the world. The validation indicates that this method is very good in arid areas for MHSR imagery.
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 4 Experimental Results and Analyses
### Evaluation Methods
#### 4.1.2 Precision and Recall  
Precision and Recall [62] are used to evaluate the performance of the proposed method. Both are therefore based on relevance. The precision (\(P\)) of the kth image is equivalent to the ratio of relevant images retrieved (\(N_{TP}\)) to the total number of images retrieved (\(N_{TR}\)):  
\[P(I_{k})=\frac{N_{TP}}{N_{TR}}\times 100\% \tag{39}\]  
Recall (\(R\)) is the ratio of relevant images retrieved (\(N_{TR}\)) to the number of relevant images in the database (\(N_{RI}\)):  
\[R(I_{k})=\frac{N_{TP}}{N_{RI}}\times 100\% \tag{40}\]  
The average precision (AP)/average Recall (AR) is obtained by taking the mean over the precision/recall values at each relevant image:  
\[AP=\frac{1}{N_{Q}}\sum_{k=1}^{N_{Q}}P(I_{k}) \tag{41}\]  
\[AR=\frac{1}{N_{Q}}\sum_{k=1}^{N_{Q}}R(I_{k}) \tag{42}\]  
where \(N_{Q}\) is the number of queries and \(I_{k}\) represents the \(k\)th image in the database.  
The rank-based retrieval system displays the appropriate set of the top k retrieved images. The \(P\) and \(R\) values of each group are displayed graphically with curves. The curves show the trade-off between \(P\) and \(R\) at different thresholds.
## 3 Method Description
### Phase Spectrum Smoothing Demodulation
Experimental results indicate that the selected parameters demonstrate high performance and good robustness of the algorithm under a variety of signal environments and interference conditions, proving the rationality of our parameter selection. The PSSD algorithm offers a more effective approach to demodulation in eLoran systems by addressing the limitations of traditional methods. Its frequency domain processing and carefully selected parameters enhance the signal's stability and accuracy, providing a promising solution for improved demodulation performance.
## 4 Model Development and Performance Metrics
For WKLM, 200 kernel parameters and \(C\) = 1 regularization coefficient is used based on trails for Fal at Tregony and 20,000 kernel parameters for Teifi at Glanteifi. The number of hidden neurons in two-layered WDNN are selected based on trials and error for best model performance. Therefore, first and second hidden layers consist 20 and 10 neurons respectively for both catchments. The optimal batch size is 10 and epochs are set to 100 for WDNN. To evaluate the model's performances, five statistical indicators have been used, namely (a) mean absolute error (MAE), (b) mean absolute percentage error (MAPE), (c) Nash-Sutcliffe efficiency (NSE), (d) coefficient of determination (R\({}^{2}\)), (e) RMSE and (f) variance account factor (VAF) which are formulated in Equations (21)-(26). The 'hydroGOF' R package [75] is used to compute the fitness values. \[\text{(a)}\ MAE=\frac{1}{q}\underset{i=1}{\overset{q}{\sum}}|Y_{C_{i}}-Y_{E_{ i}}| \tag{21}\]  
\[\text{(b)}\ MAPE=\frac{1}{q}\underset{i=1}{\overset{q}{\sum}}\left|\frac{Y_{E_ {i}}-Y_{C_{i}}}{Y_{E_{i}}}\right|\times 100 \tag{22}\]  
\[\text{(c)}\ NSE=\left(1-\frac{\underset{i=1}{\overset{q}{\sum}}\left(Y_{C_{i} }-Y_{E_{i}}\right)^{2}}{\underset{i=1}{\overset{q}{\sum}}\left(Y_{C_{i}}-Y_{C_ {i}}\right)^{2}}\right) \tag{23}\]\[\text{(d) }R^{2}=\left(\frac{\sum_{i=1}^{q}\left(Y_{E_{i}}-Y_{\overline{E}_{i}} \right)\left(Y_{O_{i}}-Y_{\overline{O}_{i}}\right)}{\sqrt{\sum_{i=1}^{q}\left(Y _{E_{i}}-Y_{\overline{E}_{i}}\right)^{2}\sum_{i=1}^{n}\left(Y_{O_{i}}-Y_{ \overline{O}_{i}}\right)^{2}}}\right)^{2} \tag{24}\] \[\text{(e) }RMSE=\sqrt{\frac{\sum\limits_{i=1}^{q}\left(Y_{E_{i}}-Y_{ O_{i}}\right)^{2}}{q}}\] (25) \[\text{(f) }VAF\left(\%\right)=\left(1-\frac{\text{var}(Y_{E_{i}}-Y_{ O_{i}})}{\text{var}(Y_{E_{i}})}\right)\times 100 \tag{26}\]  
where \(Y_{E_{i}}\) symbolized as the \(i^{\text{th}}\) estimated daily discharge using a model; \(Y_{O_{i}}\) is the \(i^{\text{th}}\) observed daily discharge; \(Y_{\overline{E}_{i}}\) is the average of the predicted daily discharge; \(Y_{\overline{O}_{i}}\) is the average of the observed daily discharge and \(q\) is the total number of observations. \[D_{L}=\log_{2}[N/(2K-1)] \tag{19}\]0 \(Cont.\)  
Furthermore, to ensure the trustworthiness and efficacy of eight models (ELM, KELM, PSO-ELM, EO-ELM, DNN, SVR, ANN and GBM), a quantitative assessment [76,77] is carried out.
# A Novel Multi-Objective Harmony Search Algorithm with Pitch Adjustment by Genotype
## 5 Experimentation and Results
### Analysis of Results  
#### 5.3.1 Problem P1  
Figure 2 shows the integrated PFs resulting from the solution of problem P1, as well as its real PF (convex front). The legend indicates the number of points that make up each front. Note that all the algorithms converged to the solution of the real PF. However, in the detail view, points appear belonging to the three algorithms (specially the MOHS3 algorithm) that do not belong to the real PF, that is, they are dominated by that front. In the search space, it can be observed that the PS has an abrupt change in behavior (Figure 3). The algorithms found most of the solutions in the left region of the set, while in the right side, a higher dispersion is observed, especially for the proposed algorithm MOHSg.  
Table 3 shows the statistical analysis of the behavior of the algorithms for P1, where the best values are indicated in bold type. As can be seen in the error rates, an average of 21.8% of the vectors obtained by MOHSg do not belong to the real PF, while for MOHS2 and MOHS3 they constitute 28% and 41%, respectively. Likewise, it can be observed that, in general, the PFs and PSs nearest to the real PF and PS were obtained by MOHS3, with the drawback that approximately half of its points are dominated by the real PF. The best distributed fronts correspond to the proposed MOHSg algorithm, followed by MOHS2 and MOHS3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Value**} \\ \cline{2-4}
**Parameter** & **MOHSg** & **MOHS2** & **MOHS3** \\ \hline \(HMCR\) & 0.95 & 0.70 & 0.85 \\ \hline \(PAR_{min}\) & \multirow{2}{*}{0.80} & \multirow{2}{*}{0.70} & 0.20 \\ \(PAR_{max}\) & & & 2.00 \\ \hline \(bw_{min}\) & & & 0.45 \\ \(bw_{max}\) & — & 1\% & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for problems P1 to P5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Value**} \\ \cline{2-4}
**Parameter** & **MOHSg** & **MOHS2** & **MOHS3** \\ \hline \(HMCR\) & 0.40 & 0.50 & 0.70 \\ \hline \(PAR_{min}\) & \multirow{2}{*}{0.60} & \multirow{2}{*}{0.10} & 0.80 \\ \(PAR_{max}\) & & & 2.00 \\ \hline \(bw_{min}\) & — & 1\% & 0.45 \\ \(bw_{max}\) & — & 1\% & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for problem P6.  
Then, it is concluded that for this test function, MOHS3 presented a poor performance because it produced the PFs with the lowest population and with the highest proportions dominated by the real PF. MOHSg had a slight superiority in both the error ratio and the uniform distribution with respect to MOHS2. However, this last algorithm surpassed the proposed one in terms of the proximity in relation to PS and real PF. Thus, MOHS2 and MOHSg presented a similar performance for this problem.
## 5 Discussion  
The novelty of the proposed energy-based discrete element modeling method is that, it is more applicable than the displacement-based discrete element modeling method because it does not need to determine the position of the potential sliding surface before DEM modeling.  
The proposed energy-based method is a generalized form of the displacement-based method. This is because it can be applied in the cases where the sliding surface is easy or difficult to be determined. The proposed energy-based method considers not only the displacement of each block but also the weight of each block. Moreover, the computational cost of the proposed energy-based method is approximately the same as that of the displacement-based method. In summary, compared with the displacement-based method, the proposed energy-based method does not need to determine the potential sliding surface before DEM modeling. Thus, the proposed energy-based method is much more applicable than the displacement-based method.  
The energy-based method is applied to investigate the mining-induced slope deformation in the Yanqianshan iron mine. In the application case, the potential sliding surface of the slope is difficult to be determined; in this case, the displacement-based method is difficult to be applied. The computational results of the Yanqianshan iron mine are sufficient to indicate when to terminate the iterative process. The computational results also indicate that the proposed energy-based method is applicable.  
As mentioned above, the energy-based method proposed in this paper could be employed to analyze the deformations and failures of both simple and complicated slopes without considering the shape and size of the slopes. However, the shortcomings of the proposed energy-based method are that it needs to investigate the displacements and weights of all rock blocks, rather than part of the rock blocks.  
In the future, the Python script language is planned to be used to integrate into the discrete element calculation software, where it enables the conducting of real-time calculations and automatic termination of the iterative process.
# Design of a 2 m Primary Mirror Assembly Considering Fatigue Characteristics
## 4 Dynamic Analysis of Three Flexible Supports
### Strength Analysis  
The input acceleration signal in strength analysis is usually in the form of sine or random vibration. Three stress and acceleration sampling points are set on the mirror surface, as shown in Figure 13. The coordinate system is the same as that in the finite element model. The corresponding magnification is calculated by comparing input and output acceleration values. The stress distribution is determined to evaluate the ability of the support structure to bear vibration. The test conditions of sine and random vibrations are listed in Tables 3 and 4, respectively. Strength analysis, especially the input PSD random vibration signal and the stress response, is the prerequisite for subsequent fatigue analysis.  
The analysis results showed that the maximum stress responses of sine vibration of 95, 114, and 126 MPa in the \(x\)-, \(y\)-, and \(z\)-axes, respectively, are at the position of clover grooves. The acceleration response amplification in the \(x\)-, \(y\)-, and \(z\)-axes is 23.93, 23.41, and 32 times, respectively. The maximum stress responses of random vibration of 185,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Direction**} & \multicolumn{2}{c}{\(x\)**-axis**} & \multicolumn{2}{c}{\(y\)**-axis**} & \multicolumn{2}{c}{\(z\)**-axis**} \\  & **Frequency (Hz)** & **Magnitude** & **Frequency (Hz)** & **Magnitude** & **Frequency (Hz)** & **Magnitude** \\ \hline \multirow{8}{*}{**Parameters**} & 10–20 & +6 dB/oct & 10–20 & +6 dB/oct & 10–20 & +6 dB/oct \\  & 20–125 & 0.075 g\({}^{2}\)/Hz & 20–105 & 0.0315 g\({}^{2}\)/Hz & 20–105 & 0.0315 g\({}^{2}\)/Hz \\  & 125–185 & 0.01 g\({}^{2}\)/Hz & 105–165 & 0.004 g\({}^{2}\)/Hz & 105–165 & 0.004 g\({}^{2}\)/Hz \\  & 185–200 & 0.075 g\({}^{2}\)/Hz & 165–200 & 0.0315 g\({}^{2}\)/Hz & 165–200 & 0.0315 g\({}^{2}\)/Hz \\  & 200–2000 & –3 dB/oct & 200–2000 & –3 dB/oct & 200–2000 & –3 dB/oct \\ \cline{1-1}  & \multirow{2}{*}{6.69 g} & & 4.33 g & & 4.33 g \\ \cline{1-1}  & & & 2 min & & \\ \hline \end{tabular}
\end{table}
Table 4: Random vibration test conditions.  
Figure 13: Location of sampling points.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Direction**} & \multicolumn{2}{c}{\(x\)**-axis**} & \multicolumn{2}{c}{\(y\)**-axis**} & \multicolumn{2}{c}{\(z\)**-axis**} \\  & **Frequency (Hz)** & **Magnitude** & **Frequency (Hz)** & **Magnitude** & **Frequency (Hz)** & **Magnitude** \\ \hline \multirow{8}{*}{**Parameters**} & 5–10 & 13.8 mm & 4–9 & 1.48 g & 4–9 & 1.48 g \\  & 10–15 & 5.5 g & 9–31 & 7.5 g & 9–31 & 7.5 g \\  & 15–31 & 7.5 g & 31–80 & 4 g & 31–80 & 4 g \\  & 31–80 & 5.5 g & 80–100 & 2.5 & 80–100 & 2.5 g \\  & 80–100 & 2.8 g &253, and 296 MPa are also at the position of clover grooves, and their respective total RMS acceleration response amplification is 1.34, 1.69, and 1.82 times, respectively.
# Numerical Simulation of Rock Mass Structure Effect on Tunnel Smooth Blasting Quality: A Case Study
## 2 Material and Methods
### Mathematical Model and Numerical Mode  
#### 2.4.1 Surrounding Rock Mathematical Model  
The Johnson-Holmquist-Cook (JHC) material constitutive model was first proposed by [PERSON] et al. [30; 31] in 1993. It was a rate-dependent material constitutive model that can better describe the material in large strain and high strain; dynamic response under high stress conditions was initially used to study the problem of concrete penetration and then gradually applied to the study of the dynamic response of rocks [32; 33; 34]. Therefore, the JHC concrete constitutive model was selected to simulate surrounding rock in the present paper. The parameters for surrounding rock model calculation included static compressive strength\(f_{C}\); density \(\rho_{0}\); strength parameters \(A\), \(B\), \(C\), \(N\), \(S\)max, and \(G\); damage parameters \(D_{1}\), \(D_{2}\), and _EFMIN_; and pressure parameters \(P_{C}\), \(\mu_{C}\), \(P_{1}\), \(\mu_{C}\), \(K_{1}\), \(K_{2}\), \(K_{3}\), and \(T\). The above 19 parameters, together with the reference strain rate \(\dot{\epsilon}_{0}\) and failure type \(f_{s}\), constituted all 21 calculation parameters of the JHC model. The density, elastic modulus, compressive strength, and other properties of surrounding rock were obtained through field tests. Referring to the original literature [31] and the JHC parameter calculation method for surrounding rock, the remaining parameters were approximately calculated, the numerical simulation results were inversed with the field monitoring results, and the JHC model parameters were adjusted to make the calculation results consistent with the field blasting results. The material parameters of the JHC model were determined, as shown in Table 1.