esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion  
### Relationship between Shore Hardness and COSF  
The relationship between the shore hardness of slip specimens (A20, A25, A30, A35, A40, A45, A50, A55, A60, A65, A70), which have a flat pattern, and the COSF is shown in  
\begin{table}
\begin{tabular}{l c} \hline \hline
**States** & **Description** \\ \hline Dry (D) & The PV panel surface is kept so that there is no water contamination \\ Wet (W) & The PV panel surface is water contaminated with a water flow of 3 L per minute \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of working condition states of PV panel.  
Figure 8: Shore hardness measurement on slip specimen using durometer.  
Figure 7: Practical slip resistance test apparatus.  
Figure 9. In this particular scenario, a flat pattern is employed to conduct the study, aiming for an evaluation of the relationship between shore hardness and COST. The selection of the flat pattern is based on its suitability, which is a brilliant candidate to effectively disregard the influence of the outside patterns on slip resistance. The results indicate a significant correlation between the COSF and the shore hardness of slip specimens in both dry and wet working conditions. In the dry working condition, the COSF ranged from 0.72 for shore A20 to 0.28 for shore A70, while in the wet working condition, it ranged from 0.62 to 0.26. The differences in the COSF between dry and wet working conditions were significant as the shore hardness transitioned from A20 to A50. However, these differences became smaller as the shore hardness changed from A55 to A70. When the shore hardness was A70, an insignificant difference in the COSF was observed between dry and wet working conditions (i.e., 0.28 for dry and 0.26 for wet). This can be attributed to the high hardness of the specimens, which makes it difficult to expand and increase the contact area with the PV panel while reducing the level of water contamination. While slip specimens with lower shore hardness exhibit higher COSF features, materials with low shore hardness are not feasible for manufacturing the belt-based track due to their limited lifespan.  
Another observation regarding the COSF is that the values were approximately the same for shore A35 and A40 in both working conditions. However, shore hardness A40 is likely prone to offer a longer lifespan for the belt-based track. Building upon these initial test findings, additional trials will be undertaken to explore the geometric characteristics of slip specimens, focusing specifically on those with shore hardness A40.
## 3 Analysis of Test Data
### Dynamic Performance Analysis
#### 3.2.3 Average Strain Rate Analysis  
The failure difficulty of specimens can be reflected by the average strain rate.  
The variation in the average strain rate with water temperature is shown in Figure 10.  
Average strain rate first decreased but then increased with the change in water temperature, and an inflection point appeared at 45 \({}^{\circ}\)C. The correlation coefficients reached 0.9614 and 0.9750, respectively, by quadratic function fitting, indicating obvious correlation. This was related to the dynamic tensile strength. Under the influence of water bonding force and the Stefan effect, the dynamic tensile strength of the specimens increased, so the  
Figure 10: Relationship between average strain rate and water temperature.  
Figure 9: Relationship between dynamic tensile strength and water temperature.  
failure difficulty of the specimens increased and the average strain rate decreased accordingly. When the water temperature exceeded the inflection point, the damaging effects of temperature and water were more obvious, so the specimens were easier to destroy and the average strain rate increased accordingly. Due to the influence of its structure, the dynamic tensile strength of the annular sandstone specimen was low, and this specimen was more prone to failure. Therefore, the average strain rate of the annular sandstone specimen was lower than that of the intact sandstone specimen.  
\[\left.\begin{aligned} \dot{\epsilon}_{1}&=86.454-0.128t+0.001 \times 10^{-5}t^{2}\,\left(\mathrm{R}^{2}=0.9614\right)\\ \dot{\epsilon}_{2}&=84.258-0.157t+0.002\times 10^{-5}t^{2} \,\left(\mathrm{R}^{2}=0.9750\right)\end{aligned}\right\} \tag{6}\]  
where \(\dot{\epsilon}_{1}\) is the annular sandstone specimen's average strain rate; \(\dot{\epsilon}_{2}\) is the intact sandstone specimen's average strain rate.
# SDR-Enabled Multichannel Real-Time Measurement System for In Situ EMF Exposure Evaluation
## 3 Results  
### E-Field Sensor  
Figure 8 presents the variation of all S parameters measured for the sensor.  
The reflection coefficients (Sii) are higher at lower frequencies and the resonant frequencies are found mostly in the higher frequency range of the operating bandwidth. The \(S_{11}\) mean value is \(-9.06\) dB, with a standard deviation of 3.93 dB and a minimum value of  
Figure 8: Variation with frequency of the reflection and transmission coefficients of the sensor elements when the mutual coupling is present.  
Figure 7: (a) DO-enabled measurement system, (b) DO screen during a measurement activity.  
\(-27.64\) at the resonant frequency of 4222 MHz. In general, the figure shows a sufficiently good matching for the specific application.  
Sij parameters are quite low (\(S_{12}\) mean value is \(-38.13\) dB, with a standard deviation of 3.78 dB and a minimum value of \(-53.12\) dB), indicating a small mutual coupling.  
The estimation of the mutual coupling error based on Equation (1) is shown in Figure 9. The \(S_{21}\) data obtained from measurement were first transformed from dB to a natural value to be inputted to Equation (1), then converted back to dB and plotted in Figure 9.  
The value is generally low, with a maximum error below 0.2 dB, confirming quite a small impact of the mutual coupling effects on the received signal.  
Figure 10 presents the \(S_{11}\) measurement results for the patch antenna in the absence of an external perturbation, respectively, with the sensor placed in the vicinity of the antenna at distances of 1, 2, and 3 cm, respectively.  
Patch antenna \(S_{11}\) variation can be observed mainly around the antenna resonant frequencies. As the antenna has several resonant frequencies, the highest \(S_{11}\) variation (approximately 3 dB) occurs at the lowest frequency, and smallest distance from the antenna.  
Figure 10: Patch antenna \(S_{11}\) parameter variation with frequency in different experimental scenarios.  
Figure 9: (**a**) Estimation of the error due to mutual coupling effect vs. frequency for all sensor elements. (**b**) Error variation boxplots.  
However, as the frequency increases, the \(S_{11}\) variation due to the sensor presence is reduced, and the mean \(S_{11}\) variation over the entire investigated frequency range was found to be 0.21 dB, 0.11 dB, and 0.08 dB for the three distances presented in the boxplots of Figure 10.  
The field perturbation is expected to be proportional to the sensor dimensions, especially if metallic structures are used. One must keep in mind that the proposed sensor was manufactured using commercially available E-field probes, but in future developments, we will not exclude custom probe manufacturing which will enable miniature design and \(S_{11}\) parameters optimization.
## 4 Results
### Simulation Results  
For the simulations, the parameter settings of the SI algorithms considered are summarized as follows: starting with the common parameters, the number of iterations, which was 15; the population size was 30; the variables were 54; and the simulation time was 10 s. The particular parameter settings are given in Table 2.  
SI algorithms presented in Table 2 were considered for comparison purposes because they have been employed previously in the state-of-the-art for neural network training, as reported in [39; 40; 41; 42; 43; 44; 45; 46; 47; 48]. In all simulations, the specifications of the test machine were an AMD Ryzen 9 4900H\({}^{\text{\textregistered}}\) (AMD Ryzen is a registered trademark of Advanced Micro Devices, Inc., Santa Clara, CA, USA ) CPU 3.0 GHz and 16 GB of RAM. Moreover, the experiments were performed in the MATLAB R2015a environment(r) (MATLAB is a registered trademark of MathWorks, Inc., Natick, MA, USA). For comparative purposes, we tested each SI algorithm 50 times independently. To qualify the results, we calculated statistical data of the mean, standard deviation (SD), and the best and worst results for different performance indices and the proposed objective function. The performance of the algorithms is reflected in a small mean value with a low standard distribution, implying a small difference between the best and worst results. Table 3 shows the performance measures. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Algorithm** & **Parameter Values** \\ \hline
[30] & ABC & Limit: 100, \(F_{l}\): 0.1, \(F_{u}\): 0.9, \(p\): 0.5 \\
[31] & ACO & \(\alpha\): 1.0, \(\beta\): 3.0, Evaporation Rate: 0.5 \\
[32] & ALO & Probabilistic Switch: 0.1, Random Walk Length: 1.5, Levy Flight \(a\): 1.0, \(b\): 1.0 \\
[33] & BA & \(A\): 0.25, \(r\): 0.5, \(\alpha\): 1.0, \(\gamma\): 0.1, \(f_{min}\): 0.0, \(f_{max}\): 2.0 \\
[34] & CS & Discover Rate \(p_{a}\): 0.25, Levy Flight \(a\): 0.1, \(b\): 0.9 \\
[35] & GWO & \(a_{0}\): 2.0 \\
[36] & MFO \(a\): 1.0, \(b\): 1.0 \\
[37] & PSO & \(\varphi_{1}\): 2.0, \(\varphi_{1}\): 2.0, \(w\): 0.7, \(v_{\max}\): 0.1 \\
[37] & APSO & \(\varphi_{1}\): 1.5, \(\varphi_{2}\): 1.5, \(w\): 0.7, \(v_{\max}\): 0.1, \(p_{a}\): 0.1, \(p_{r}\): 0.1 \\
[38] & WOA & \(a_{1}\): 2.0, \(a_{2}\): \(-1.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter settings for SI algorithms. Figure 4: Diagram of the 2-DOF robot manipulator. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**ABC**} & \multicolumn{4}{c}{**ACO**} & \multicolumn{4}{c}{**ACO**} \\ \hline  & **IAE** & **ISE** & **ITAE** & **ITSE** & **BOLZA** & \(F_{obj}\) & & **IAE** & **ISE** & **ITAE** & **ITSE** & **BOLZA** & \(F_{obj}\) \\ \hline  
vg. CPU time** & 196.3359 & 193.7482 & 196.1711 & 195.6669 & 194.9560 & 195.0981 & **Avg. CPU time** & 205.5427 & 204.3713 & 205.1566 & 204.0309 & 204.3103 & 203.5299 \\ \hline \end{tabular}
\end{table} 
Table 3: Simulation results performance.
## 3 Results  
In the proposed article, we have used an overlapping factor (\(L\) = 1, 2, 3, and 4), 64-QAM transmission technique, PHYDYAS filter, 64-sub-carriers, Rayleigh channel, and bandwidth of 10 MHz. This study aims to analyze the throughput and high spectral efficiency of FBMC waveform to regularize in 5G-based smart hospitals. The PSD of the FBMC signal for \(L\) = 1, 2, 3, and 4 are given in Figure 4. It is observed that there is a minimum sidelobe in the FBMC spectrum. Hence, there is no spectrum leakage problem, and spectrum efficiency is high. However, the best spectral performance is noticed for overlapping factor \(L\) = 4.  
The amplitude and phase response of FBMC and OFDM is given in Figure 5. It is observed that the magnitude and phase of the OFDM are approximately constant to normalized frequency, whereas the peak and phase of FBMC decrease with the increase in frequency. Hence, it is concluded that the OFDM is more prone to multipath fading as compared to the FBMC waveform.  
Figure 4: PSD of FBMC for L = 1, 2, 3, and 4.  
Figure 5: Amplitude and phase characteristics.  
The PSD of the FBMC and OFDM is analysed in Figure 6. The spectrum of the OFDM waveform remains constant, whereas FBMC decreases with the increase in frequency. Therefore, the transmission power requirement is less for the FBMC structure than the OFDM.  
The capacity performance of FBMC is better than that of OFDM, as given in Figure 7. According to [PERSON]'s theorem [29], the capacity of the system is directly proportional to bandwidth. In the FBMC structure, there is no use of CP, due to which \(11\%\) of the bandwidth is utilized, unlike the OFDM employed CP where \(11\%\) of the bandwidth is wasted.  
The throughput of the OFDM and FBMC structures is analyzed by estimating the BER performance, indicated in Figure 8. At BER of \(10^{-4}\), the SNR of the FBMC and OFDM is \(8\) dB and \(9.1\) dB, respectively. Hence, the FBMC accomplished a gain of \(1\) dB as compared to the OFDM framework.  
Figure 6: Noise PSD.  
Figure 7: Capacity.  
The PAPR performance of the waveform techniques is given in Figure 9. At the complementary cumulative distribution function (CCDF) of \(10^{-2}\), the PAPR of the FBMC structure is significantly lower compared to the OFDM. However, it is noted that, at CCDF of \(10^{-4}\), the PAPR performance of both waveform schemes is approximately the same.
# Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization
## 4 Experimental Results and Discussions
### Performance Evaluation
#### 4.3.1 Comparison with CFA Counterparts  
Our CFA enhances the GSO and the FA by incorporating the responsive strategies from the adaptive memory programming domain. Thus, it is important to validate our idea by comparing the performance of the CFA against its counterparts, the GSO and the FA. The results shown in the second to the fifth columns of Table 7 are the mean best function values obtained by the competing algorithms, and those in the last three columns correspond to the relative merit values. We observe that for the test functions with fewer than ten variables, the CFA has a unit merit or a merit value less than one with one order of magnitude, indicating that all the competing algorithms perform nearly equally well. For the test functions with ten or more variables, the merit value of the CFA in relation to the GSO and the FA becomes significantly less, implying a superior performance in favor of the CFA. The product of the merit values gives an overview of the comparative performance on all test function. It is seen at the bottom of Table 7 that the CFA has a merit product of \(7.32\times 10^{-43}\) and \(9.51\times 10^{-32}\) in relation to the GSO and the FA, respectively. We further compare the CFA to the better one of the best function values obtained by the GSO and the FA, giving a merit product of \(8.99\times 10^{-29}\) as shown in the last column of Table 7. The result suggests that the CFA significantly outperforms its counterparts over the benchmark dataset, and that the CFA also beats a hybrid which takes the better one of the best function values obtained by the two counterparts. We further verify the online performance advantage of our CFA against its counterparts by a statistical test suggested in Taillard [28]. As the best objective value produced by a metaheuristic approach is non-deterministic, we model the result obtained from multiple runs of method A (and method B) as a random variable \(X_{a}\) (\(X_{b}\)) and we want to testify the confidence regarding that \(X_{a}\) is less than \(X_{b}\).
## 1 Introduction
[36] applied BME to predict PM2.5 concentration distributions, achieving a 21.89% error reduction compared to ordinary kriging. Machine learning also has certain applications in the field of spatial interpolation. [PERSON] et al. [37] introduced a leapfrog algorithm to optimize the variation function of kriging experimentation, proposing the Shuffled Frog Leaping Algorithm (SFLA)-kriging algorithm, integrating borehole data from an open-pit mine. [PERSON] et al. [38] [PERSON] a Particle Swarm Optimization (PSO) algorithm for variogram parameter estimation, while [PERSON] et al. [39] developed a PSO-kriging 3D geological modeling method based on a PSO-optimized kriging algorithm. [PERSON] and [PERSON] [40] advanced a k-nearest neighbor (KNN)-kriging hybrid method for 3D geological modeling. In contrast to geostatistical interpolation, machine learning operates without reliance on data trends or assumptions; however, overfitting remains a central challenge affecting the predictive precision of ma chine learning. Currently, its primary application in 3D geological modeling is reflected by optimizing the parameters of geostatistical interpolation algorithms. For high-precision 3D geological modeling of a coal face, the data sources for terrain elevation, strata, and coal seam thickness vary. Terrain elevation data sources are typically dense and widespread, whereas strata thickness data sources are sparse, exhibit inconsistent variations between strata, and demonstrate significant shifts in spatial data characteristics. Coal seam thickness data sources comprise not only \"hard\" data, such as those derived from drilling and tunnel realities, but also \"soft\" data, such as coal thickness estimations from channel wave exploration or other geophysical prospecting methods, which contain uncertainties. Effectively integrating these \"soft\" and \"hard\" datasets for interpolation presents a challenge. Currently, research on selecting scientifically robust interpolation methods for high-precision 3D geological modeling of a workforce, especially specific data sources, is limited. Similarly, studies focusing on 3D geological modeling of coal faces, addressing issues of missing strata and the integrated use of \"soft\" and \"hard\" data, are scarce. Focusing on the 25214 workforce in the Hongliulin coal mine, this study systematically evaluates the interpolation data sources for terrain elevation, strata, and coal seam thickness. The precision of nine interpolation methods, including kriging, IDW, RBF, and BME, was evaluated through cross-validation. By combining stratigraphic sequence analysis with the optimal interpolation method, a high-precision 3D geological model of the 25214 workforce was constructed. The objective of this research is to develop a high-precision three-dimensional geological model of the coal face, reflecting the actual geological characteristics, and to offer scientific support for intelligent coal mining operations.
## 3 Proposed Methodology
### Z-Number Based Fuzzy DEMATEL  
The following is an overview of the steps of Z-number based Fuzzy DEMATEL [65].  
Step 1: Create the Z-fuzzy initial relationship matrix (ZFIRM) for the Z-number fuzzy DEMATEL to evaluate the objectives and challenges. Linguistics phrases is used in the building of fuzzy relationship matrices for evaluating criteria and sub-criteria from the Tables 4 and 5.  
Step 2: Transform the Z-numbers comprising experts' preferences into fuzzy numbers using the Equations (3) and (5). Aggregate the experts' preferences. Using the geometric mean to combine the experts' preferences fuzzy initial relationship matrix (FIRM).  
Step 3: Compute the normalised matrix (\(\tilde{a}_{ij}\)) using the Equation (6) to obtain the normalized matrix (\(\tilde{a}\)). Given that \(\widetilde{a}_{ij}=(a1,\ a2,\ a3)\) and \(N=\frac{1}{\max}\sum_{j=1}^{n}u_{ij}\), then  
\[\widetilde{X}=N*\tilde{M} \tag{6}\]  
Step 4: To establish the total relation matrix (TRM) \(\widetilde{T}\) employing the Equation (7), where \(I\) is an Identity matrix.  
\[\tilde{a}=\begin{bmatrix}0&u_{12}&\cdots&a_{1n}\\ a_{21}&\cdots&\cdots&a_{2n}\\ \vdots&\vdots&\cdots&\cdots\\ a_{n1}&\cdots&\cdots&0\end{bmatrix} \tag{7}\]  
\[\tilde{T}=(I-\tilde{X})^{-1}\] \[\tilde{T}=\begin{bmatrix}\tilde{t}_{11}&\cdots&\cdots&\tilde{t}_{ 1n}\\ \vdots&\vdots&\cdot&\cdot&\vdots\\ \vdots&\cdot&\vdots&\vdots\\ \tilde{t}_{n1}&\cdots&\cdots&\tilde{t}_{nn}\end{bmatrix}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Linguistic Definition** & **Crisp Number** & **Triangular Fuzzy Number** \\ \hline No Influence (NI) & 0 & (0, 0, 0.25) \\ Low Influence (LI) & 1 & (0, 0.25, 0.50) \\ Medium Influence (MI) & 2 & (0.25, 0.50, 0.75) \\ High influence (HI) & 3 & (0.50, 0.75, 1.0) \\ Very High influence (VHI) & 4 & (0.75, 1, 1) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Triangular fuzzy membership linguistic terms for restriction part [66].  
Step 5: Determine the \(R\) and \(C\) values to compute the rows and columns respectively using Equations (8) and (9).  
\[R=\left[R_{i}\right]_{n\
eq 1}=\left[\sum_{j=1}^{n}t_{ij}\right]_{n\
eq 1},\,C= \left[C_{j}\right]_{n\
eq 1}=\left[\sum_{i=1}^{n}t_{ij}\right]_{n\
eq 1} \tag{8}\]  
\[T=\left[t_{ij}\right],\,i,j=1,2,\ldots n \tag{9}\]  
Step 6: Employ the \(R\) and \(C\) values to obtain the casual relationship.  
Step 7: Attaining alpha threshold value for the total-relation Matrix  
Step 8: Determine the \(t_{i}^{average}\) to compute the weight using the Equation (10).  
\[t_{i}^{average}=\frac{1}{2}\,\left(R_{i}+C_{j}\right)+\left(R_{i}-C_{j}\right) =\sum_{j=1}^{n}t_{i,\,j} \tag{10}\]  
Step 9: Estimate the local weights using the Equation (11).  
\[w_{i}=\frac{t_{i}^{average}}{\sum_{j=1}^{n}t_{i}^{average}} \tag{11}\]  
Step 10: Determine the global weights by converting the local weights of the criteria and sub-criteria into global weights. The local weights of the sub-criterion and the criterion are multiplied by their corresponding values to obtain the global weights.
# QoE Modeling on Split Features with Distributed Deep Learning
## 4 Effect of Video Content on QoE
### SHAP Sensitivity Analysis  
We use the TreeExplainer by SHAP [35] to apply sensitivity analysis and observe the features that are important for the decision of the trained ML model. In Figure 4, the effect of each input feature on model prediction is quantified from most to least important (descending order from top to bottom on the \(y\)-axis). Red regions indicate that the absolute value of the effect of the feature on the model prediction is high, while blue regions indicate the contrary. The dots being on the positive side of the \(x\)-axis indicate that the effect is positive, a higher feature value contribute to a higher MOS, and vice-versa if they are located on the negative side. The SHAP diagram indicates that the average bitrate (_meanBitrate_) is the most important metric in the model prediction, contributing to raising the MOS value  
\begin{table}
\begin{tabular}{c c c} \hline
**Split Index** & **Feature** & **Description** \\ \hline
0 & TI & Video temporal complexity \\ \hline
1 & SI & Video spatial complexity \\ \hline
2 & FPS & Played frames per second \\ \hline
3 & lastbitrate & Last playout bitrate \\ \hline
4 & nstalls & Number of stalls \\ \hline
5 & stallTimeInitialTotal & Initial stalling/buffering duration \\ \hline
6 & stallTimeIntermediateTotal & Total stalling duration \\ \hline
7 & bitrateTrend & Slope of the video playout bitrate \\ \hline
8 & meanBitrate & Average playout bitrate \\ \hline  & MOS (target variable) & Mean Opinion Score \\ \hline \end{tabular}
\end{table}
Table 1: Descriptions of the extracted features for the QoE models, where the Mean Opinion Score (MOS) is the target variable of the model.  
as expected. Similarly, the total video stall duration, _stallTimelntermediateTotal_, has a high influence on the negative side, which implies that it tends to lower the expected MOS value. The _TI_ and _SI_ features are considered more important than other features such as the number of stalls, _nStalls_, and the initial stalling time, _stallTimelnitialTotal_.