esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Results and Discussion  
In this section, we present Monte-Carlo simulation results to verify our analysis for the proposed system as explained in the previous section for both TS and PS protocol. The simulation parameters are given in Table 1. We use MATLAB to run the Monte-Carlo simulation by averaging over \(10^{5}\) random realizations of Rayleigh block fading channels \(h_{IoT_{R}}\), \(h_{senc}\), \(h_{IoT_{Trec}}\) and get the simulation results. In Figures 5 and 6, the outage probability of the source user and IoT relay user are plotted against the transmit SNR at different time switching factor \(\alpha\) = 0.3, 0.  
Considering, source user and IoT relay user as two user in the system for our proposed system, in Figure 7, we plotted the sum-throughput against the transmit SNR at time switching \(\alpha\) = 0.3, 0.5, & 0.7 for TS and different power splitting factor \(\varepsilon\) = 0.3, 0.5, & 0.7 for PS. It can be observed that sum-throughput is a increasing function with respect to increase in transmit SNR and \(\alpha\) for TS. Also, it is observed that sum-throughput is a increasing function with respect to increase in \(\delta\) and \(\varepsilon\) for PS. Moreover, sum-throughput is higher for PS as compared to TS with the same varying amount of \(\alpha\) and \(\varepsilon\) respectively for transmit SNR greater than 10 dB. At transmit SNR less than 6 dB, TS outperforms the PS protocol.  
Next, we wanted to verify our analysis for the proposed system at different time switching factor \(\alpha\) and power splitting factor \(\varepsilon\) for both TS and PS protocol. We plotted the sum-throughput against  
Figure 6: Outage Probability of IoT Relay User.  
Figure 7: Sum-throughput of proposed system.
## 1 Introduction  
The interplay between recovery and fatigue significantly influences athletic performance, a topic that has captivated the sports science community for decades. Achieving an optimal balance between stress--encompassing training, competition demands, and other life stressors--and recovery is crucial for athletes to maintain peak performance levels [1]. Recent research primarily explores how nutritional interventions can enhance athletic performance [2]. Sports nutrition professionals invest considerable effort in reviewing the literature, identifying the most effective products, and determining methods to provide essential nutrients [3]. Moreover, athletes' macronutrient needs vary based on their sport, the season, and the time of year, necessitating dietary adjustments or supplementation, especially for those who exclude certain food groups, such as vegetarians, to prevent nutritional deficiencies [4].  
Furthermore, research on glutamine supplementation in professional basketball players has demonstrated its potential to reduce markers of muscle injury following eccentric muscle strain [5]. In rowing, athletes who were supplemented with a combination of 0.04 g/kg/day of creatine monohydrate and 3 g/day of \(\beta\)-hydroxy \(\beta\)-methylbutyrate during a 10-week training regimen experienced a synergistic improvement in aerobic capacity [6]. Athletes frequently complement their diets with supplements to boost performance.  
In recent years, maca (_Lepidium meyenii_) has gained recognition among athletes as a natural supplement due to its beneficial properties. Maca thrives at altitudes ranging from 2800 to 5000 m above sea level, with Peru being a prominent producer [7]. The primary varieties of maca are yellow, red, and black [8]. Previous research indicates that spray-dried extracts of red and black maca enhance mood, energy, and overall health status [9]. Moreover, comparative studies of the three maca ecotypes--yellow, red, and black--suggest that black maca may particularly improve stamina [10].  
Many athletes use supplements to boost their performance, but these often contain artificial chemicals that can cause side effects. In contrast, maca is a natural and safe alternative that has demonstrated performance-enhancing effects in animal studies [11, 12]. Despite the focus on athletes in prior research, there remains a significant gap in understanding the benefits of maca. Thus, the aim of this study is to investigate the impact of black maca consumption on isokinetic muscle function and inflammatory markers in athletes and to expand this research to include non-athletes.
# Effect of Non-Homogeneous Soil Characteristics on Substation Grounding-Grid Performances: A Review
## 5 Conclusions
Besides, the study on the impact of reflection factor, K, on the soil apparent resistivity shows that when the soil resistivity in the bottom layer is higher than the top layer (K > 0), the apparent resistivity reduces for \(\rho_{12}<\rho_{2}\) and increases for \(\rho_{23}<\rho_{1}\) and vice versa for (K < 0). Vertical two-layer soil structures are more difficult to compute and analyze than horizontal two-layer soil structures. As a result, a comprehensive study of grounding performance in vertical two-layer soil structures has yet to be performed. The only analysis in vertical soil layer was conducted to examine the effects of varying electrode spacing 'a', and the distance from two-layer soil interface 'd' and the angle '\(\beta\)' between a perpendicular line to the soil border and the line where four electrodes are positioned on the apparent resistivity. The analysis concluded that the apparent resistivity increases as the distance 'a' and the angle '\(\beta\)' increases while the that the apparent resistivity reduces as the distance 'd' increases. Relatively few detailed studies have been published on the influence of multilayer soil, particularly on the effect of the grounding-grid design parameters in vertical soil structure. Therefore, further works on the effect of multilayer soil layers and vertical soil layers on grounding system performances with various design parameters such as the grounding grid dimensions, number, and position of vertical electrodes attached to the main grounding grid, need to be conducted. The outcomes of these analyses could serve as an important guide on the substation grounding grid design to be appropriate and safer for the public and personnel. N.P., writing--original draft preparation; M.O., writing--review and editing; A.M.A., supervision; M.Z.A.A.K., supervision. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Higher Education (MOHE) Malaysia through the Fundamental Research Grant Scheme (FRGS) 2020 under Grant FRGS/1/2020/TK0/UNITEN/02/13. Not applicable. The authors would like to thank the Ministry of Higher Education (MOHE) Malaysia through Fundamental Research Grant Scheme (FRGS) 2020 under FRGS/1/2020/TK0/UNITEN/02/13 and BOLD Publication Fund. The authors declare no conflict of interest.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 4 Experimental Studies
### Decreasing Analysis  
In cases where the improvement in accuracy after generating GAN samples was less than 5%, the study reduced the number of original samples. This reduction helped investigate whether the original high accuracy was the reason GAN-generated samples had little effect. Reducing the number of initial samples lowered the overall accuracy, which allowed the study to evaluate whether the GAN's contribution was more noticeable with fewer original samples.  
Table 9 shows the results for cases with an improvement of less than 5%. In these cases, reducing the number of original samples significantly increased the contribution of GAN-generated samples, confirming that the high initial accuracy limited the effect of GAN generation in previous tests.
# Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach
## 3 Methodology
### Video Data Processing-Object Detection and Tracking
#### 3.2.2 Object Tracking  
Several object tracking methodologies were assessed, including the use of Kernelized Correlation Filter (KCF) [28; 29], Multiple Instance Learning (MIL) tracking [30; 31], and Deep Simple Online Realtime Tracking (DeepSORT) [32]. In addition, several methods were assessed on how to best identify the location of the tracked objects and visualize them on a dynamic mapping interface. For the presented application, DeepSORT turned out to be the best solution. It was selected due to its robustness in handling occlusions and maintaining consistent vehicle identities across frames. Given that Move Over law compliance analysis required tracking vehicles over multiple frames, ensuring high tracking accuracy was critical. Other tracking methods, KCF and MIL, were both considered, but during the initial research they showed inferior performance in maintaining vehicle identities across longer sequences.
## 3 Results  
A test dataset was collected every minute for 108 h (i.e., 6500 samples). The collected data had been pre-processed and the estimated time-delay (explained in Section 2.1) had been conducted and then applied to the trained model. Figure 8 shows the test dataset of input parameters. The objectives were the prediction of upcoming coarse return for 1, 5, 10, and 15 min later. Table 2 presents the MAE and average deviation (MAE/average coarse return) between the actual coarse return and its future predictions. These two comparison methods were chosen due to their extensive applications in the comparison domain. Based on the presented results, the coarse return for the next minute was predicted with 95% overall accuracy, and then for the prediction for every single minute ahead, the accuracy dropped by 1%, for example, for a quarter-hour ahead forecast, its accuracy was about 80%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Parameters Number** \\ \hline Hidden layer 1 (Dense) & (None, 30) & 870 \\ ELU 1 & (None, 30) & 0 \\ Hidden layer 2 (Dense) & (None, 90) & 2790 \\ ELU 2 & (None, 90) & 0 \\ Hidden layer 3 (Dense) & (None, 80) & 7280 \\ ELU 3 & (None, 80) & 0 \\ Hidden layer 4 (Dense) & (None, 70) & 5670 \\ ELU 4 & (None, 70) & 0 \\ Dropout 1 & (None, 70) & 0 \\ Output (Dense) & (None, 4) & 284 \\ \hline \hline \end{tabular}
\end{table}
Table 1: FCDNN layer types, output shapes, and parameters number.  
Figure 7: The loss changes and convergence during training.  
Noteworthy, the cement grinding process variables, as seen in Figure 3, had a complex, non-linear and unstable process causing plugging phenomena. The extracted features (6 inputs and single output) of the process control using the proposed techniques and the trained model was successfully validated and verified in practice and by the cement production engineers. The accuracy of the proposed method was also compared with the single-layer NN and LSTM, to demonstrate its superior performance against two other rival and relevant approaches.  
Figure 9 presents the actual coarse return and those predicted for the next 15 min in a single graph.
Future research efforts may benefit from fusing ML-driven magnitude estimation with predictive models for secondary disaster impacts, ultimately contributing to more comprehensive earthquake response strategies. The rapid evolution of ML and DL methodologies has demonstrated their potential for improving the accuracy and efficiency of earthquake magnitude estimation. However, despite these advancements, several challenges persist in fully integrating these techniques into operational real-time seismic monitoring systems. Issues such as data variability across different seismic networks, the need for extensive computational resources, and the generalization of models to diverse tectonic environments remain critical research areas. This systematic review analyzes literature on the application of machine learning and deep learning techniques to early earthquake magnitude estimation. It is a valuable tool for researchers and professionals in this field. Additionally, it provides a comprehensive overview of recent advancements, compares relevant models, and outlines key challenges. Moreover, it highlights potential future research directions to enhance earthquake magnitude estimation and its integration into real-time seismic monitoring. The remaining sections of the article are structured as follows: Section 2 describes the methods used in this work. Section 3 presents early earthquake magnitude estimation findings as a classification and regression task. Section 4 shows the trends and future work. Finally, in Section 5, the conclusions of this systematic review are presented.
## 3 Test System, Description, and Modeling
### Operating Point Calculation of the Grid-Connected Three-Phase VSC  
The obtained linearized state-space model needed steady-state values for the PCC voltage and current. These could be obtained by evaluating the nonlinear state-space model or conducting simulations, which were more accurate but more time-consuming. This subsection presents a simple algorithm to calculate the operating point condition based on the mathematical model and Figure 5, as shown in (3).  
\[\begin{array}{l}Inputs\to P,C_{f},L_{g1},L_{g2},V_{dc},V_{fd},V_{g}\\ X_{g}=\omega_{1}\big{(}L_{g1}+L_{g2}\big{)},X_{C}=\frac{1}{C_{f}\omega_{1}}, \delta=a\sin\left(\frac{X_{g}\cdot P}{V_{fd}V_{g}}\right),\\ Q_{g}=\frac{V_{g}}{X_{g}}\Big{(}V_{fd}\cos(\delta)-V_{g}\Big{)},S_{g}=\sqrt{Q _{g}+P^{2}},\big{|}I_{g}\big{|}=\frac{S_{g}}{V_{fd}}\\ Q_{L}=X_{g}.|I_{g}\big{|}^{2},Q_{C}=\frac{V_{fd}}{X_{C}},Q_{inv}=Q_{g}+Q_{L}-Q _{C}\\ I_{dc}=\frac{P}{V_{dc}},I_{fd}=\frac{P}{V_{fd}},I_{q}=-\frac{Q_{inv}}{V_{fd}}\\ Outputs\to I_{dc},I_{fd},I_{fd}\end{array} \tag{3}\]  
Figure 5: Power flow diagram in a grid-connected VSC.  
\begin{table}
\begin{tabular}{l l l} \hline \multicolumn{2}{c}{**Power System Parameters**} & \multicolumn{2}{c}{**Control Parameters**} \\ \hline Nominal power (\(P_{n}\)) & 10 [kW] & CC \\ \hline Nominal line voltage (\(v_{g}\)) & 400 [V] & \(k_{pc},k_{ic}\) & 9.425, 4.4 \(\times\) 10\({}^{3}\) \\ \hline Grid frequency (\(\omega\)) & 50 [Hz] & DVC \\ \hline Inverter-side inductor (\(L_{f}\)) & 2 [mH] & \(k_{pd},k_{id}\) & 0.088, 1.934 \\ \hline Grid-side inductor (\(L_{g1}\)) & 0.5 [mH] & AVC \\ \hline Filter capacitor (\(C_{f}\)) & 10 [\(\mu\)F] & \(k_{pu},k_{ia}\) & 0, 3.428 \\ \hline Grid inductor (\(L_{g2}\)) & 5-30 [mH] & HPF \\ \hline Grid SCR & 1.67–10 & \(k_{a},\omega_{a}\) & 1, 6.6 \(\times\) 10\({}^{3}\) \\ \hline DC-link capacitor (\(C_{dc}\)) & 1.5 [mF] & SRF-PLL \\ \hline DC-link voltage (\(v_{dc}\)) & 700 [V] & \(V_{fd},\xi_{PLL},\omega_{PLL}\) & 400, 0.7, 14\(\pi\) \\ \hline Sampling and switching frequencies & 10 [kHz] & \(k_{pp}=\xi_{PLL}.V_{fd}-1\) & 0.154 \\ \hline \(T_{d}\) & 150 [\(\mu\)s] & \(k_{pp}=\omega_{PLL}\).\(V_{fd}-1\) & 4.836 \\ \hline \end{tabular}
\end{table}
Table 2: System and control parameters of the main power system.  
In summary, the inverter power level and grid inductance (\(P\), \(L_{g2}\)) were two independent inputs that impacted the VSC operating point. Moreover, it was assumed that the system was stable and, hence, the control system could keep the DC-link voltage (\(V_{dc}\)) and AC magnitude of the PCC voltage (\(V_{fd}\)) at the desired values (here, \(V_{dc}\) = 700 V, \(V_{fd}\) = 400 V). Therefore, based on the mentioned parameters and assumptions, the PCC (\(I_{fd}\), \(I_{fg}\)) and DC source (\(I_{dc}\)) currents could be calculated. All parameters and signals were available to build the state-space model of the system (A1).
# Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free +
## Appendix A Relevant Range of the Resolution Parameter \(\alpha\)  
In this section, we make a rough estimation on the relevant range of the resolution parameter \(\alpha\) for each fixed scaling factor \(\beta\) in the community fitness function  
\[F_{\alpha}^{\beta}=\sum_{\mathcal{G}}\frac{\left(k_{in}^{\mathcal{G}}\right)^ {\beta}}{\left(k_{in}^{\mathcal{G}}+k_{out}^{\mathcal{G}}\right)^{\alpha}}\]  
It has been discussed in [42] that the resolution limit problem in community detection is due to two opposite tendencies (or _biases_): the tendency of merging small communities into larger ones, and the tendency of breaking large ones into smaller pieces. These tendencies/biases can often occur simultaneously. A strict deduction for a \"relevant\" resolution range, within which both biases can be avoided, is not straightforward--nor is it necessary for our purpose in this paper. In this section, we investigate each bias separately, and then give a rough estimation on the bounds of the relevant range for the resolution parameter \(\alpha\) in Formula (3) in the main text. It should be noted that our purpose is very simple: all we need is a rough range for \(\alpha\) to vary in. Therefore, we only investigate _necessary conditions_, rather than sufficient or necessary-and-sufficient conditions.
## 2 Domestic Electric Vehicle Charging Loads Modelling
### Domestic Users' Electric Vehicle Charging Loads Modelling  
Without an optimal electric vehicle charging strategy, electric vehicles are normally charged shortly after finishing their last trips, and stop charging when batteries are fully charged. Therefore, the charging loads of electric vehicles at time \(t\) can be written as:  
\[P_{ev}(t)=NP_{c}p_{t} \tag{3}\]  
where \(N\) is the number of electric vehicle, \(P_{c}\) is the electrical charging power of a single electric vehicle (in kW), and \(p_{t}\) is the probability of an electric vehicle under charging condition at time \(t\), which can be expressed as:  
\[p_{t}=\int_{0}^{T_{max}}\int_{0}^{t-t_{ev}}f_{tev}f_{end}dtdt_{ev}+\int_{0}^{T_ {max}}\int_{0}^{t+24-t_{ev}}f_{tev}f_{end}dtdt_{ev} \tag{4}\]  
where \(f_{end}\) and \(f_{tev}\) are the probability density functions of domestic users' daily return time from the last trip and domestic users' electric vehicle charging period, respectively and \(T_{max}\) is the upper limit of charging period \(t_{ev}\). In Equation (4), charging period \(f_{tev}\) can be expressed as:  
\[t_{ev}=\frac{ksc}{P_{c}} \tag{5}\]  
where \(k\) is a seasonal coefficient, \(s\) is the daily travelling distance, and \(c\) is electric vehicle energy consumption per mile in spring. In this paper, the Nissan Leaf, whose battery capacity is 24 kWh and rated charging power is 3.3 kW [21], is used as an example to model total electrical demand of electric vehicles. As mentioned in Section 1, the total daily charging loads vary greatly according to the number of electric vehicles within a distributed power network, and the Monte Carlo Simulation is an efficient tool to model electrical characteristics of power systems. Therefore, the Monte Carlo method is used to model total electrical demand of electric vehicles in this paper. Figure 4 is an example of the Monte Carlo Simulation results of the average daily charging loads of 2000 electric vehicles in different seasons.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Standardized Charging Loads** & **Spring** & **Summer** & **Autumn** & **Winter** \\ \hline Standardized Maximum Charging Loads & \(1\) & 1.37 & 1.07 & 1.23 \\ Standardized Average Charging Loads & \(1\) & 1.30 & 1.03 & 1.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Standardized charging loads in different seasons.  
Figure 3: Average daily load curves of 2000 electric vehicles in different seasons [20].  
In this section, key factors that can affect domestic users' charging behavior have been studied, and after analyzing these factors, the Monte Carlo Simulation is used to model daily charging loads curves of electric vehicles in different seasons. In the next section, an electric vehicle optimal charging control strategy is proposed to regulate charging loads and to reduce power system losses.
# Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses
## 2 State of the Art of Horizontal Fragmentation Methods
The executing time of the approach, even considering massive data, is brief; thus, it can be efficiently applied to distribute data for parallel resolving issues with enormous computational complexity. On the other hand, [25] established that improving OLAP query efficiency in a distributed system of the kind of Hadoop and Spark is a challenge since the star join operation is the most costly operation involved in these queries and the one requiring significant communication cost. A popular technique that reduces the network flow of the star join operation is to co-partition several DW relations on their join key. Nevertheless, this operation still needs multiple MapReduce cycles in established DW partitioning schemes. For that, the authors proposed two approaches called \"FKey\" and \"NewKey\" which have their basis in a data-mining technique to lead their physical design. The partitioning and distribution scheme assists the query optimizer to obtain a significant query-processing plan, to perform the star join execution in one Spark phase minus the shuffle stage. The authors performed experiments on a computer cluster to evaluate their approach using the TPC-DS benchmark. Finally, [PERSON] et al. [26] proposed a dynamic method to optimize group-by operation. This proposal applies partitioning and bucketing approaches to distribute a big DW among the nodes of a Hadoop cluster. This new schema aids the system omit accessing irrelevant data blocks, executes a star join operation in a single Spark phase excluding a shuffle stage, and accelerates group-by aggregation. Experimental findings demonstrated that the technique overcomes related methods considering OLAP query response time. Table 1 establishes the main characteristics of each horizontal fragmentation method described before. Although several DW fragmentation methods are based on data mining, they use clustering or association techniques. This paper introduces a horizontal fragmentation technique, called FTree, which uses decision trees to select the horizontal fragmentation scheme of data warehouses.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 4 UWSNs: Communication Models
### Attenuation Model  
Underwater acoustic signals experience power level degradation. Attenuation is calculated for distance \"\(R\)\" in meters and frequency \"\(f\)\" in KHz and is given as follows:  
\[A(R,f)=R^{k}\;a_{u}(f)^{R} \tag{1}\]  
\(a_{u}(f)\) is measured in dB/km where as k differs with geometry of propagation. It is 2 and 1 for spherical and cylindrical spreading respectively [38; 39; 40]. The absorption coefficient \(a\) is calculated using [PERSON]'s empirical formula:  
Figure 2: Network architecture.  
\[10 loga(f)=0.11\frac{f^{2}}{1+f^{2}}+22\frac{f^{2}}{4100+f^{2}}+2.75\ \times\ 10^{-4}f^{2}+0.003 \tag{2}\]