esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Table 1 presents the baseline characteristics of the participants in the LL-HV and HL-LV groups. The \(p\)-values confirm the absence of significant differences in age, body mass, height, and powerlifting experience between the groups (\(p>0.05\)). These findings validate the randomization process, ensuring that the groups were statistically similar at baseline and minimizing potential confounding factors. Table 2 summarizes the changes in MV for 80%, 85%, 90%, and 95% of pre-intervention 1 RM. For MV at 80% of 1 RM, the interaction effect demonstrated significant effects (\(F=5.6\); \(p=0.033\)), due to the greater values observed in the LL-HV group at post-intervention compared to pre-intervention. No significant interaction effects were observed for MV at other percentages of 1 RM (\(F\leq 3.3\); \(p\geq 0.084\)), suggesting that the changes over time were similar between groups. Figure 2 highlights the standardized differences for MV improvements across all submaximal loads, showing a notable advantage for the LL-HV group at 80% of 1 RM. For 1 RM, no significant interaction effect was observed (\(F=3.5\); \(p=0.072\)), suggesting similar maximal strength gains in both groups. Changes in Wilks coefficient and IPF-GL points also showed non-significant interaction effects (\(F\leq 3.8\); \(p\geq 0.063\)), with comparable improvements in relative strength for both protocols. Minimal changes in body mass were observed, with no significant group differences (\(F=0.6\); \(p=0.455\)). Those results revealed that the enhancements in performance levels were comparable for LL-HV and HL-LV (Table 3). Table 4 presents changes in arm and chest circumferences. A significant interaction effect was found for arm circumference (\(F=4.7\); \(p=0.040\)), favoring the LL-HV group. Although the interaction effect for chest circumference approached significance (\(F=3.9\); \(p=0.060\)), the improvements were comparable between groups. When evaluating the magnitude of differences between the groups, small effect sizes (ES = 0.20-0.56) were observed for most variables, favoring the LL-HV group for MV at 80% of 1 RM and arm circumference. A trivial effect size (ES = 0.17) was noted for chest circumference (Figure 3), further supporting the comparable efficacy of both protocols. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**\% 1 RM** & **Group** & **Time** & \begin{tabular}{c} **Mean \(\pm\) SD** \\ **(m/s)** \\ \end{tabular}  & \begin{tabular}{c} **\(\Delta\)** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **Hedges’ g** \\ **ES (95\% CI)** \\ \end{tabular}  & \begin{tabular}{c} **ANOVA** \\ **Time** \\ \end{tabular}  & \ \begin{tabular}{c} **\(\text{{}^{\prime}}\)** \\ **Group** \\ \end{tabular}  & \begin{tabular}{c} **Interaction** \\ **Interaction** \\ \end{tabular}  \\ \hline
80\% & LL-HV & \ \begin{tabular}{c} Pre \\ Post \\ Pre Post \\ Pre Post \\ Post \\ \end{tabular}
# Personalized-Template-Guided Intelligent Evolutionary Algorithm
## 3 Results
### Engineering Problem Experiment
#### 3.4.1 The Problem of Compression Spring Design  
This problem involves three parameters, including the diameter of spring wire \(d(x_{1})\), the average diameter of spring coil \(D(x_{2})\), and the number of active coils \(P(x_{3})\). The goal is to minimize the weight of a spring. When the spring is subjected to an external force, several factors are constrained: the maximum allowable deformation at its end or designated position, the ratio of shear force generated by the internal material due to the external force to the material's cross-sectional area, and the number of vibrations per unit time when subjected to a periodic external force. For specific definitions, see Equations (25)-(27), and the problem diagram is shown in Figure 7.  
Definition:  
\[X=[x_{1},x_{2},x_{3}]=[d,D,P] \tag{25}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **CEC2022** & **CEC2005** \\ \hline PTG & 1.0000 & 2.5217 \\ WOA & 6.5833 & 6.8478 \\ HHO & 4.7500 & 5.5435 \\ COA & 9.4167 & 4.0652 \\ TSA & 8.1667 & 8.7391 \\ RSA & 8.6667 & 6.8696 \\ FATA & 4.3333 & 4.8261 \\ MFO & 5.2500 & 6.9130 \\ ECO & 3.0833 & 5.5217 \\ HO & 3.7500 & 3.1522 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the Friedman rank test.  
Figure 6: Box plots (F1–F12) of optimization algorithms on CEC2022 benchmark functions.  
Minimization function:  
\[f(x)=(x_{3}+2)x_{2}x_{1}^{2} \tag{26}\]  
Set of constraint function:  
\[\begin{split} g_{1}(x)&=1-\frac{x_{2}^{3}x_{3}}{71785x_ {1}^{4}}\leq 0,\\ g_{2}(x)&=\frac{4x_{2}^{2}-x_{1}x_{2}}{12566(x_{2}x_ {1}^{3}-x_{1}^{4})}+\frac{1}{5108x_{1}^{2}}-1\leq 0,\\ g_{3}(x)&=1-\frac{140.45x_{1}}{x_{2}^{2}x_{3}}\leq 0,\\ g_{4}(x)&=\frac{x_{1}+x_{2}}{1.5}-1\leq 0,\\ 0.05&\leq x_{1}\leq 2,\quad 0.25\leq x_{2}\leq 1.3, \quad 2\leq x_{3}\leq 15\end{split} \tag{27}\]  
The solution results of PTG and other comparison algorithms are shown in Table 8. Thus, compared with other algorithms, PTG obtains the optimal value, and the mean and mean square error of 30 independent operations are the smallest, and the algorithm results are the most stable, and its performance in dealing with pressure vessel design is better than that of competitive algorithms.
## 2 Materials and Methods
### Evaluation Metrics and Methodology
#### 2.7.2 Evaluation Methodology  
The process of evaluating symbolic expressions is the same in the binary case and in the multiclass case. The process starts by randomly selecting hyperparameter values of the GPSC algorithm. Then the 5-fold cross-validation is performed on the train part of the dataset (70% of the dataset). After each fold, the obtained symbolic expressions were evaluated i.e., the ACC, AUC, precision, recall, and F1-score are determined. After the process of 5-fold cross-validation is completed the mean values of the aforementioned metrics are determined. The next step is to apply \"termination criteria\" which is basically the condition that states if all evaluation metric values are higher than 0.9 then the final training/testing process can occur. If one of the metric values is below 0.9 the process is repeated with the random selection of new hyperparameters.  
In the final stage after the obtained evaluation metric values passed the termination criteria test i.e., all mean values are greater than 0.99 the training and testing are performed with GPSC. The training process is performed using GPSC on 70% of the dataset with the same hyperparameters used in the 5-fold cross-validation process. After the training process is complete the evaluation metric values were obtained on the train and test dataset and mean values and standard deviation values were obtained.
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 5 Performance Evaluation
### Fairness Measurements
#### 5.2.1 Goodput Fairness  
Goodput fairness measures how fairly the scheduler allocates the bandwidth. Figure 5 shows the goodput fairness for LTE, URLLC and eMTC use-cases with the EPA, EVA and ETU channel. The overall results show that the JFI index of the FBMC waveform is slightly lower than that of the OFDM due to its complex nature. For all these cases, the proposed SRA and baseline approaches show full fairness (100%) for up to three users. In the case of the URLLC and eMTC use-cases, dynamic and greedy show similar performance for OFDM and FBMC waveforms, as shown in Figure 5d-i. In the case of LTE and URLLC, the JFI index remains more than 96%, and for eMTC, it is more than 95%, as shown in Figure 5. Dynamic-OFDM outperforms dynamic-FBMC by up to 0.02% (\(\approx\)2.5%), as shown in Figure 5a-c for LTE and in Figure 5d-f for URLLC. In the case of eMTC, dynamic-OFDM outperforms dynamic-FBMC by up to 0.01% (\(\approx\)1.5%), as shown in Figure 5g-i.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 4 Challenges and Development Trends in Reflector Antenna-Based SAR Technology  
### Emerging Techniques for Reflector Antenna-Based SAR  
To achieve HRWS imaging, some emerging techniques have been applied in current spaceborne SAR systems. These emerging techniques are also combined with reflector antennas, mainly including the following three: digital beamforming (DBF), staggered SAR, and SweepSAR. This section provides an in-depth analysis of these emerging techniques.
###### Abstract  
How to model shadow fading by applying the inverse gamma (IGA) distribution has recently gained widespread attention in wireless transmissions. However, the sum of \(\alpha\)-\(\mu\)/IGA variates, and its applications under independent and/or correlated scenarios, have yet to be addressed in open research works. Hence, this paper provides a systematic investigation of the \(\alpha\)-\(\mu\)/IGA model. First, we derive the expressions of the fundamental statistics of the univariate and bivariate \(\alpha\)-\(\mu\)/IGA models including the probability density function, cumulative distribution function, and moment generating function, and propose a mixture \(\alpha\)-\(\mu\) model to approximate the \(\alpha\)-\(\mu\)/IGA model. Then, according to the above statistical expressions, the statistical properties of the sum of \(\alpha\)-\(\mu\)/IGA variates are obtained and employed in the maximal ratio combining receivers. Third, the novel exact and approximated expressions of some performance metrics of interest are also derived, for instance, the average bit/symbol error probability, the outage probability, the average channel capacity, and the effective rate. Moreover, to prove the asymptotic properties of the performance metrics at the high signal-to-noise regions, some examples are performed. Finally, we explore numerical analysis and simulations to demonstrate the accuracy of the theoretical expressions under the different channel and system parameters. These results provide some significant insights into the reliability design and deployment of some conventional and emerging wireless communication applications.  
composite fading channel; performance analysis; \(\alpha\)-\(\mu\) fading model; inverse gamma distribution; mixture \(\alpha\)-\(\mu\) distribution +
Footnote †: journal: Applied  
1
## 4 Results  
### Validation of the Extracted ROD  
The ROD extracted from the measured SWE was used to validate the accuracy of that extracted from Sentinel-1 data (Figure 5). It shows that bare land has the highest accuracy among all land cover types. The correlation coefficient (R), mean absolute error (MAE), and root-mean-square error (RMSE) between the ROD of stations and that of Sentinel-1 are 0.50, 16 d, and 20 d, respectively. The accuracy of the tree cover is the lowest, with the R of 0.22, the MAE of 24 d, and the RMSE of 32 d. The accuracy for shrubland grassland was similar, falling between that of bare land and tree cover. The R for shrubland and grassland are 0.51 and 0.52, respectively. The MAE are both 18 d, and the RMSE are 21 d and 23 d, respectively.  
The above results confirm that vegetation significantly affects the accuracy of the ROD extracted from C-band SAR data. Especially in densely vegetated areas, the accuracy of the tree cover is the lowest with the MAE of 24 d. Therefore, it is necessary to improve the accuracy of the ROD.  
Figure 5: Validation of the ROD under different land cover types ((**a**) tree cover, (**b**) shrubland, (**c**) grassland, and (**d**) bare land) extracted from Sentinel-1 data.  
Using 2023 as an example, Figure 6 presents the spatial distribution of the ROD extracted by Sentinel-1 under four land cover types in the study area and the area proportions of different land cover types at different runoff onset dates. The ROD in the tree cover area mainly concentrates in middle and late March. In the subsequent period, the proportion of tree cover land decreases in the areas where the ROD occurs. The ROD of grassland and bare land is mainly concentrated in June.
## 1 Introduction
[33; 34] demonstrated the reliability of AE monitoring for damage sources, proposing a real-time health monitoring framework based on AE, which can effectively identify potential damage initiation/propagation locations in advanced sandwich composite structures. [PERSON] et al. [35] defined new AE parameters, load ratio, and static pressure ratio, to determine structural damage. It can be summarized from previous work that AE technology is excellent in the fields of crack propagation monitoring, damage monitoring and failure type judgment. Therefore, AE technique was used in this paper to monitor the crack propagation and failure types of BFRPC beams. In summary, although there are lots of laboratory studies about the mechanical properties of RPC material, the bending resistance and failure type of BFRPC beams under the effects of the notch and interfacial damage is not clear. In this paper, BF was used to replace SF for modifying RPC materials as BFRPC. The bending resistance of BFRPC beam under the conditions of notch and interfacial damage was analyzed, and the damage type of BFRPC beam was studied by AE. It will lay a foundation for the popularization and application of BFRPC in the actual engineering projects.
## 4 Case Study
### Results
#### Analytical-Model  
The Analytical Model (AM) provides representative levels of the states presented in Table 5. This makes it better to see the cause of abnormal situations A1, A2, A4, and A5, and at the same time, it is possible to follow the degradation levels that occur within the tolerable limits of the system represented by A3. Table 15 describes the parameters adjusted for AM where \(kd_{1}\) and \(Ld_{1}\) are the parameters of the spring used to simulate degradation.  
The experiment measurements are made in millimeters since the model is based on displacement in this work, and for that, a total of 690 simulations were conducted in a workbench to provide the AM  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline \(g\) & 9.806 & Considered gravity acceleration in m/s\({}^{2}\). \\ \(k_{1}\) & 575 & Spring 1 stiffness coefficient in N/m. \\ \(k_{2}\) & 575 & Spring 2 stiffness coefficient in N/m. \\ \(kd_{1}\) & 405 & Simulation spring 1 degradation stiffness coefficient in N/m. \\ \(\hat{d_{1}}\) & 8 & Spring 1 viscous estimated damping coefficient in N.s/m. \\ \(\hat{d_{2}}\) & 8 & Spring 2 viscous estimated damping coefficient in N.s/m. \\ \(m_{1}\) & 0.390 & Body 1 mass in kg. \\ \(m_{2}\) & 0.400 & Body 2 mass in kg. \\ \(m_{3}\) & 0.340 & Body 3 mass in kg. \\ \(L_{1}\) & 0.097 & Spring 1 length in m. \\ \(L_{2}\) & 0.097 & Spring 2 length in m. \\ \(Ld_{1}\) & 0.11 & Simulation spring 1 degradation length in m. \\ \hline \hline \end{tabular}
\end{table}
Table 15: Parameters and variables of the Analytical Model.  
with the first scenario feature space, which has position measurements. The left column in Figure 6 are signals from \(k_{1}\) parametrised spring while the right column is those with \(kd_{1}\) parametrised spring. It is possible to notice that the degradation levels simulated by \(k_{1}\) are apparent in that figure and, indeed, it does not happen all the time. Another case in Figure A4 presents two misclassified profiles.  
It is necessary to analyze the AM from two perspectives. First, Figure A5 shows the confusion matrix that summarizes state detection accuracy, and the second Figure A6 shows the confusion matrix indicating spring degradation classification regardless of any of the states. This analysis concludes that, alone, the applied AM can achieve accuracy of 96.10% on state detection and accuracy of 93.7% on state isolation in a scenario where displacement measures are provided.  
Figure 6: State signals profiles. **Left column** shows the spring stiffness coefficient without degradation while the **right column** shows the simulated degradation of the stiffness coefficient.
# Equivalent Modeling and Verification of a High-Steepness and Lightweight Elliptical Aluminum Mirror
## 6 Verification and Surface Accuracy Testing  
### Test of Surface Shape after SPDT  
After SPDT manufacturing, the mirror assembly was tested using computer-generated hologram (CGH) components. Figure 12 shows the test optical path. CGH can produce an oddly shaped wavefront without making expensive references or null optics [29]. The detection area of the primary mirror is oval, and a circular cat eye area is designed outside the oval detection area to achieve a high precision and fast alignment. The test results show that the low-frequency surface shape error is 0.34 \(\lambda\) (\(\lambda\) = 632.8 nm), and the high-frequency surface roughness error is Ra 6.172 nm after single - point machining, as shown in Figure 13. Periodic turning tool patterns can also be seen on the surface.
## 3 Results  
### Electrical Conductivity  
Figure 2 shows the electrical conductivities of MWNT/NS-incorporated cement composites as functions of the MWNT content on a logarithmic scale. In all the sample groups, which are denoted as NS0, NS1, NS3, and NS5, an exponential increase in the electrical conductivity occurred as the MWNT increased. This exponential increase, which is often called an \"S-curve,\" can be found in the electrical conductivity results of the conductive-filler-incorporated cement composites reported in the literature, and this was due to the conductive fillers being evenly distributed in the cement matrix [26; 34; 35]. This principle can be applied to the present work in the same manner. The exponential increase in the electrical conductivity represents an even distribution of MWNTs in the cement matrix. In addition to the electrical conductivity results, color examination by the naked eye further suggested the even distribution of MWNTs. The greater the MWNT content, the darker the composite samples examined became.  
In the relationship of the MWNT content and electrical conductivity, the percolation threshold, which is the particular range of MWNT contents where a prominent increase in the conductivity occurred, was in the range of 0-0.3%. This percolation threshold range can be found in previous studies in which the electrical characteristics of the carbon-nanotube-added cement composites were explored [26; 34]. The percolation threshold range and electrical conductivity levels in each group shown in Figure 2 showed marginal differences. Accordingly, it was inferred that NS, which was an electrically insulating material, did not produce adverse effects on the conductive MWNT networks.  
The effect of the silica fume incorporation on the electrical characteristics of MWNT-added cement composites has been explored in previous studies. [PERSON] et al., 2012 and [PERSON] et al., 2014 concluded that silica fume incorporation with an appropriate content yielded improvements in the distribution of MWNTs due to breaking of the electrostatic attraction of the MWNTs by means of silica fume particles, and this, in turn, led to an enhancement of the electrical conductivities of the composites [24; 25]. Silica fume with an average diameter greater than 5 \(\upmu\)m was used in these previous studies [24; 25]. However, in the present study, the diameters of the NS particles were 10-20 nm, which was close to the diameters of the MWNTs. This considerable difference in the diameters of the siliceous materials may have resulted in different outcomes, which means siliceous materials whose diameters were as small as the diameters of MWNTs were not likely to improve conductive MWNT networks.  
Figure 2: Electrical conductivity (logarithmic scale) variations with the MWNT content.
## 3 Results
### Meta-Analysis
#### 3.1.3 Time Required for Scanning and Clinical Impression  
The meta-analysis conducted with two clinical studies allowed us to evaluate the procedure time for the rehabilitations performed by intraoral scanning and conventional methods considering the implant level (total of scanned/impression dental implants). There was a significant difference between the groups, presenting a mean difference (MD) of \(-9.18\) min (95% CI: \(-11.93\) to \(-6.43\), \(p\) < 0.00001, Figure 4). The Chi\({}^{2}\) of heterogeneity was 9.34 (\(p\) = 0.002, I\({}^{2}\) = 89%), Figure 4.  
The meta-analysis conducted with two clinical studies allowed us to evaluate the procedure time for the group of rehabilitations made by the intraoral scanning method and the conventional group considering the level of patients treated (total of scanned/impression patients). There was a significant difference between the groups, presenting an MD of \(-9.02\) min (95% CI: \(-11.73\) to \(-6.3\), \(p\) < 0.00001, Figure 5). The Chi\({}^{2}\) of heterogeneity was 2.08 (\(p\) = 0.15, I\({}^{2}\) = 52%), Figure 5.  
The Chi\({}^{2}\) of heterogeneity was 0.02 (\(p\) = 0.88, I\({}^{2}\) = 0%), Figure 6.  
Figure 4: Meta-analysis for clinical execution time (min), implant level [4,6].  
Figure 5: Meta-analysis for clinical execution time (min), patient level [4,6].  
Figure 6: Number of retakes for scanning and impression, implant level [4,6].  
The meta-analysis conducted with two clinical studies allowed us to evaluate the number of times that rescanning or new impressions were necessary at the patient level. There was a significant difference between the groups, presenting RR of 3.17 (95% CI: 1.32 to 7.62, \(p\) = 0.010, Figure 7). The Chi\({}^{2}\) of heterogeneity was 0.03 (\(p\) = 0.87, I\({}^{2}\) = 0%), Figure 7.
## 5 Simulation and Results
### Soft Decision Fusion with Equal Gain Combining (EGC)  
Soft decision fusion with equal gain combining (SH-EGC) is a soft fusion rule, which has a good detection performance and is easy to apply [29]. It has been studied and widely used in cooperative spectrum sensing systems. Here, we compare our method SH-DFS with SH-EGC. Let N be 30, M be 4, \(P_{e}\) be 0.05, and SNR be 2 dB for all local sensors.  
Figure 18 shows the comparison of the detection performances of SH-DFS and SH-EGC. We found that SH-DFS (\(b=3\), 4) had a good performance as well as SH-EGC without quantization, and the little difference between them could be ignored. Although the largest number of quantization bits was decided by the system in practice, the two-bit quantization or the three-bit quantization used in the proposed method could usually be accepted, which helps the system achieve a probability of detection of about 0.8 and 0.9 when the probability of false alarm was fixed at 0.1.
## 3 Methodology
### Evaluation of Hand Detection Model  
Before integrating the hand detection model with the KCF tracker, the trained hand detection model is evaluated based on the average recall (AR) and mean average precision (mAP) for different intersection over union (IoU) threshold. Mean average precision is the mean of average precision evaluated in overall detection classes and/or all IoU thresholds, while average precision is the area under the curve of the precision-recall graph. On the other hand, average recall is the measure of assertiveness or the confidence level of the detector model in detecting a given class [31]. In this work, COCO detection evaluation metrics are used to evaluate the model. In the context of COCO evaluation metrics, the average precision equals mean average precision, standardly evaluated for 10 IoU thresholds from 0.5 to 0.95 with a step size of 0.05 (denoted as IoU (0.5:0.05:0.95)) [32]. Generally, mAP evaluates the accuracy of the detector model by comparing the predicted bounding boxes over the ground truth bounding boxes based on a given IoU threshold. For example, when evaluated at IoU = 0.50, only the predicted bounding boxes with the overlapped area with respect to the ground truth bounding boxes exceeding the threshold value is considered as true positive (TP). The more TP obtained at the given IoU threshold, the greater the mAP value, and thus the more accurate the model.