esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 SSM-GA Optimization for Energy Management
### SSM and Fitness Function  
In an SSM, each state which includes one or more components (state variables) is a timing sequence. The current state is coherent to the previous state. In essence, the target of GA is to find the best decision set over the optimal horizon while its optimal objective is included in its fitness function.  
Figure 6: Flowchart for the GA optimization.  
In the system, energy balance equation can be expressed as Equation (30).  
\[p_{dir}(k)+p_{fc}(k)=p_{load}(k) \tag{30}\]  
where \(p_{grid}(t)\), \(p_{load}\), \(p_{fc}\) refer to the power from the wind turbine, load, and fuel cell, respectively.  
In order to structure a state-space representation, two state variables and one output variable are defined as follows.  
\[x_{1}(k)=ESOC_{S}(k) \tag{31}\]  
\[x_{2}(k)=y(k)=p_{dir}(k) \tag{32}\]  
The state space model can be presented as  
\[x_{1}(k)=x_{1}(k-1)+\xi_{1}\cdot x_{2}(k)+\phi_{1} \tag{33}\]  
\[y(k)=x_{2}(k) \tag{34}\]  
Besides, there is an energy balance equation for calculating the wind power fed into the grid.  
\[p_{dir}(k)+p_{H_{2}O}(k)+p_{grid}(k)=p_{wind}(k)\] (35a) or \[p_{grid}(k)=p_{wind}(k)-p_{dir}(k)-p_{H_{2}O}(k) \tag{35b}\]
## 5 FSK Modulation and Demodulation
### FSK Modulation for Physiological Implants  
To monitor the various physiological parameters inside the human body, the transmitter unit sends the sensor data outside the human body as explained by [PERSON] _et al._[64]. This paper proposed a new low-power (FSK) modulator for biomedical sensors. The new circuit integrates the modulation functionality into the oscillator itself by using the data signal to control the oscillation frequency, and the circuit can generate a tunable carrier frequency for different data signals. The author compared his work with another works such as [PERSON] _et al._[65], which provided one silicon chip which combines single-channel and multi channel with lower dissipation. [PERSON] _et al._[66] developed a prototype integrated circuit for wireless neural recording from 100-channel microelectrode. [PERSON] _et al._[67] proposed a system consisting of a FSK generator block and data generator used for low-voltage and low-power operation. However, [PERSON] _et al._[64] developed a FSK modulator for biomedical sensor circuits that has better properties for physiological implants.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 3 The Taylor Method
### Synthesizing Antenna Arrays Using Taylor One-Parameter Distribution
#### 3.1.4 Apply Amplitude Weights  
The amplitude weights \(w_{n}\) are applied to the elements of an antenna array by multiplying each element's magnitude by its corresponding weight. The weighted amplitude \(w_{n}\) of the \(n\)th element is calculated as follows:  
* Apply the calculated amplitude weighting coefficients as the weights for the elements of the antenna array.
* These weights determine the relative strength of each element and thus shape the radiation pattern.
## 2 Methods
### Studies Selection  
A study is retained for the literature review if it meets the following criteria: the paper (i) utilizes AI/ML method(s) to investigate student mental health; (ii) appraises the performance of the AI/ML technique(s) used; (iii) is published in a peer-reviewed outlet; and (iv) is written in the English language. However, an article is excluded from our corpus if (i) its main target group is not university students; (ii) it examines physical disorders other than mental health such as epilepsy or HIV; (iii) it focuses on suicide or self-harm; or (iv) the full text of the manuscript is not available (e.g., conference abstracts only).  
Our decision to exclude gray literature like policy reports and unpublished theses and to focus on peer-reviewed articles aligns with established guidelines in SLRs [18] and ensures that our corpus comprises rigorously validated studies. While this approach may limit the scope of our findings, it safeguards their relevance and validity. In the context of our SLR, that is a new, interdisciplinary, and technically complex research area such as AI/ML-enabled SMH, we believe that prioritizing quality and reliability over comprehensiveness is essential
## 4 Discussion
### Data Collection and Analysis of a New Design  
The integration of sensors and physiological monitoring devices into the V-Cockpit system provides an innovative approach to assessing user experience and safety. Traditional methods often rely on subjective feedback and limited testing, which can miss subtle but critical issues [6; 18; 19]. The real-time physiological feedback captured in the VR environment allows for the early detection and correction of potential problems.  
The early detection of issues ensures that user comfort and safety are prioritized. This proactive approach can prevent costly redesigns and modifications later in the development process, enhancing the overall quality and usability of the final product. The ability to monitor user involvement, focus, and distraction provides an additional layer of insight, ensuring that the infotainment system meets high standards of user satisfaction and safety.  
The collaborative potential of the VR environment is another significant advantage. Multiple stakeholders can interact with the virtual prototype, providing real-time feedback and suggestions. This collaborative approach not only improves the quality of the design but also streamlines the review and approval process, further accelerating the development timeline.
## 4 Experimental Setup
### Experiments Results
#### 4.1.4 Ablation Experiments  
To verify the proposed methods in SA-MPCMOEA, this paper conducted ablation experiments for verification. The verification methods were divided into four algorithms: the SA-MPCMOEA algorithm proposed in this paper; SA-MPCMOEA/MP without multi-preference modeling; SA-MPCMOEA/MP, which uses tournament selection [34] to generate offsprings; SA-MPCMOEA/FS without fill sampling strategy, which uses non-dominated sorting to select non-dominated candidate solutions for real function evaluation; SA-MPCMOEA/DA without dynamic assessment strategy, which uses a fixed \(w_{\text{max}}\) of 20.  
The four methods were run ten times independently on the CF test suit using the same random seeds. As shown in Table 4, among the 24 problems, SA-MPCMOEA achieved the optimal value or a value close to the optimal value.  
In the experiments, SA-MPCMOEA/MP performed the worst because the control of search direction in ECMOPs has an important impact on the algorithm's performance, generating offspring without considering the current evolutionary needs of the population, which leads to a waste of computational resources.  
In the experiments, the performance of SA-MPCMOEA is better than that of SA-MPCMOEA/FS in 18 problems and is similar in 6 problems. The fill sampling strategy is responsible for selecting candidate solutions for real function evaluation. A good fill sampling strategy needs to select candidate solutions that can optimize the surrogate model and guide the population search. The fill sampling strategy based on optimization requirements effectively selects solutions with the greatest potential for evaluation, avoiding the population from getting trapped in local optima.  
In the experiments, SA-MPCMOEA outperformed SA-MPCMOEA/DA in 16 problems, and the performance was similar in 6 problems. In surrogate-assisted evolutionary algorithms, a larger number of surrogate model evaluations means generating more candidate solutions to explore the decision space. However, as the number of surrogate model evaluations increases, the error of the approximate objective values obtained by evaluating the newly generated candidate solutions through the surrogate model also increases, which may mislead the population to evolve in the wrong direction.
# Differential Evolution and Fuzzy-Logic-Based Predictive Algorithm for V2G Charging Stations
## 2 Materials and Methods  
As presented in Figure 1, the considered V2G system is composed of multiple converters, both DC-DC and DC-AC. The proposed algorithm consists of a differential evolution component, featuring a 2-hour receding-horizon time window, and a fuzzy logic component that adapts certain parameters online to attain better performance. The final goal of the algorithm is to determine each converter power flow as ultimately specified by their currents. To operate the V2G system optimally, forecasts of the solar irradiance and the energy tariff are exploited, both being fully time-varying, which adds to the complexity of the overall task.
## 4 Results
### Fitting Distribution to the Upload Time  
In order to give an approximation to the upload time, we used two techniques, service rate and a distribution function. Service rate can be calculated as the ratio of the available link capacity and the average packet size [37]. This provides an average service rate but the exact values always change. In order to add more space to the upload time, we introduced a distribution function that describes network traffic well. In a recent study [29], it was proved that the Weibull distribution can accurately describe inter-arrival time and it is also found which parameter values can be used for adding features of a given network type. In the PDF of the Weibull distribution, the most important parameter is the shape parameter (\(k\)). In case of ISP Core Network traffic, \(k\) can be set to 0.9. This is the highest possible shape value if we consider network packets. If a DSL or Ethernet Access Networks are available, the \(k\) parameter should be lower, about 0.81. Wireless Hotspot Access Networks can be simulated with the lowest \(k\) value (0.75). The scale parameter (\(\lambda\)) can be approximated based on the Superposition theorem [38]. Superposition theorem states that in any linear, bilateral network in which multiple sources can be found, the resulting response is the sum of the responses from each source separately. [PERSON] showed that this superposition  
\begin{table}
\begin{tabular}{c c} \hline
**Configuration** & **Average Algorithm Execution Time per Windows** \\ \hline CPU: 200 MHz & \textgreater{}10 s \\ Memory: 128 MB & \textgreater{}10 s \\ \hline CPU: 400 MHz & \textasci{}10 s \\ Memory: 256 MB & \textasci{}10 s \\ \hline CPU: 800 MHz & \textasci{}10 s \\ Memory: 512 MB & \textasci{}10 s \\ \hline CPU: 1.4 GHz & \textasci{}10 s \\ Memory: 1024 MB & \textasci{}10 s \\ \hline CPU: 2.8 GHz & \textasci{}10 s \\ Memory: 2048 MB & \textasci{}10 s \\ \hline CPU: 4.8 GHz & \textasci{}10 s \\ Memory: 4096 MB & \textasci{}10 s \\ \hline \end{tabular}
\end{table}
Table 2: Average execution time of AF detection algorithm using different computer configurations.  
converges asymptotically to the inter-arrival process with Weibull distribution [39]. Based on that research, the rules listed in Equations (7) and (8) are valid.  
\[k\to 1-\alpha \tag{7}\]  
\[\lambda\to C\sin\frac{\pi\alpha}{\pi(1-\alpha)},\textit{ where }0<C<\infty \tag{8}\]  
Since the shape parameter is well-defined for the different types of networks, the scale parameter can be calculated using the correlations. In Figure 6 it is clearly visible how different network types determine the shape and scale parameters. We calculated the mean of the distributions in order to see the expected value for inter-arrival time in seconds. If the average inter-arrival time is calculated from the bandwidth and the average packet size, the shape and scale parameters can be further tuned. In our use case, a mobile device is used to transfer data, so the shape parameter was set to 0.75.
# A Miniature Flapping Mechanism Using an Origami-Based Spherical Six-Bar Pattern
## 4 Experimental Results  
To check whether the proposed transmission and the flapping mechanism properly works or not, experimental tests have been done. By using the high-speed camera, the flapping angle, frequency, and wing rotation angle are observed. In addition, the thrust is measured using a load cell to investigate the performance of the flapping mechanism.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 2 Background
### Secondary Phenomena (Aftershocks and Beyond)
#### 2.2.2 Slope Stability  
Landslides and rock falls, triggered by factors such as earthquakes or heavy rainfall, are driven by gravity and can lead to catastrophic consequences, particularly in populated areas, by causing significant damage and loss of life yearly [28; 29; 30; 31]. These downslope movements of material are classified based on their nature into slides, where an intact mass of material moves cohesively; flows, which occur when materials, usually with high water content, move similarly to viscous fluid; and creep, characterized by the very slow, often imperceptible movement of material. Additionally, rock falls occur when fractured rock masses on steep slopes break apart, causing rock blocks to detach and fall. The specific type of movement is determined by the interaction between the material's mechanical characteristics and the external forces acting on it, such as gravity and seismic activities. The work of [PERSON] et al. [32] emphasizes the importance of recognizing factors such as location, human activity, and the frequency of such events to mitigate their impact. Effective mitigation strategies involve several key actions: first, avoiding construction in areas known to be high-risk; second, implementing strict land-use policies to manage development; third, enhancing public awareness about the risks associated with landslides; and, finally, obtaining professional engineering evaluations to assess and mitigate potential hazards accurately. The authors suggest construction practices that minimize risk, such as avoiding steep slopes, stabilizing slopes through groundwater management, and using retaining structures. Furthermore, in the Israeli context, the Geological Survey of Israel [33] uses slope stability sensitivity maps to assess hazard risk levels (see Figure 1 on the rightbottom), focusing on potential slope failures and emphasizing the complex relationship between geological phenomena and the need for targeted mitigation efforts. Additionally, studies such as those by [34,35] have explored specific failure mechanisms, including the collapse of concrete separation walls due to soil pressure.
## 3 Results
### GIS Interface
#### 3.2.2 Winter  
Day 1 of January was selected to carry out this scenario. Figure 8 shows the spatial distribution of pressure throughout the study area. The cream colour indicated the lowest observed values and burgundy indicated the highest values, which varied between 0.67 to 4.07 bar, respectively. Areas including Al Rahmanya 1, Al Rahmanya 3, and Al Barashi pressure values were indicative of normal pressure patterns across that specific sector of the network, while Al Sabka, Al Ghuwair, Industrial Area 4, Industrial Area 6, Butina, Al Qadsia, Al Nassreya, Al Ghaphia, Al Maysaloon, and Al Fayah were displayed in burgundy, which reflected the highest pressure drops (below 1.3 bar) in the system for the analysed day. Hence, the dark burgundy areas at the western and eastern ends were selected (depicted in brick red) by the automation model to carry out further analysis.  
The IDW created for the flow data utilized the same colour patterns, with burgundy indicating the areas with the highest net flow values (target) and cream specifying areas with the lowest net flow. The net flow values within the study area fluctuated from 30 to 6500 m\({}^{3}\)/day. Based on the daily averages calculated by the model for each area, the values above the average were selected to be indicative of the leak. The suspected leak area (shown in brick red) was found to extend from Al Rahmanya 1 to Al Ghuwair, including Al Sabka and Al Qadsiya. The remaining areas showed a relatively regular distribution of net flows within the network. The chosen areas based on pressure and flow data were overlaid to indicate the most vulnerable areas of the network; however, they were further intersected to determine the common areas where both flow and pressure drops were observed. Finally, two chlorine monitoring points, 0.2 mg/L, and 0.14 mg/L, respectively, were identified to be lower than the minimum acceptable value. As shown in the last step of the flowchart, the automation model selected the part of the network where a leak was  
Figure 8: Analysis maps for January.  
identified based on the three parameters (Figure 8). This network section extended from Al Sabka through Al Ghafia to Al Ghuwair including a pipe length of around 480.0 km.
## 4 Simulation Results
### Performance Comparison of Proposed Work with other Forecasting Strategies
Thus, this work implements the hybridization of the FF and PSO algorithms to overcome the disadvantages offered by each optimization algorithm. The result of this hybridization has improved the performance of the WT-ANFIS on cloudy days and partially cloudy days. This is proved by Figures 11 and 12, where the dark orange bar (WT-ANFIS-HFPSO) has provided the lowest MAE, RMSE and NRMSE values compared to the other forecasting strategies. In summary, it is obvious that the application of the WT has improved the accuracy of the forecasting model for every condition of the day. However, the applicability degree of a single optimization algorithm (FF and PSO) to a forecasting model in every condition of the day is not fixed. For sunny days, the employment of the FF and PSO did not increase the performance of the WT-ANFIS. For partially cloudy days and cloudy days, the PSO algorithm improved the performance of the WT-ANFIS. However, the FF algorithm has not shown the same trend of results with the PSO, as it tends to deteriorate the precision of the WT-ANFIS model on both conditions of the day. Hence, both algorithms have been hybridized. Specifically speaking, the height differences of bars for sunny days, partially cloudy days and cloudy days have revealed the superiority of the proposed model, the WT-ANFIS-HFPSO, to forecast hourly solar power data.
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 2 Modeling of the Proposed System
### Wind Turbine  
Every month, NASA supplies data on wind speed, which has been utilized as input data for this study (NASA, 2020). The following mathematical formulas are employed to calculate the wind turbine output \(\mathrm{WT_{P}(t)}\) based on a comprehensive literature review [2; 31].  
\[\mathrm{WT_{p}(t)=\left\{\begin{matrix}0,&\mathrm{V(t)}<V_{cut}^{in}\ \mathrm{or}\ \mathrm{V(t)}>V_{cut}^{off}\\ \mathrm{N_{WT}\times WT_{rat}^{P}\times\ _{WT}\left(\frac{V^{2}(t)-V_{cut}^{out}}{V_{rat}^{in}-V_{cut}^{out}} \right),&\mathrm{V_{cut}^{in}<V(t)<V_{rat}}\\ \mathrm{N_{WT}\times WT_{rat}^{P}\times\ _{WT}\ _{WT}}\ \mathrm{V_{rat}\times\ _{V (t)}<V_{cut}^{off}}\end{matrix}\right\} \tag{2}\]  
In which, \(\mathrm{V(t)}\), \(V_{cut}^{in}\), \(V_{cut}^{off}\), and \(\mathrm{V_{rat}}\) are WT speed at time t, WT speeds cut-in, WT speeds cut-off wind speed, and rated speed respectively. \(\mathrm{N_{WT}}\) denotes the number of WTs modules, \(\mathrm{\eta_{WT}}\) is the WT efficiency, and \(\mathrm{WT_{rat}^{P}}\) is the rated power of the WT (kW).  
Wind speed increases with height above ground level, and the wind turbine hub's height has also a major impact on wind speed, which affects power generation, according to the below power law equation [13]:  
\[\left(\frac{\mathrm{V_{n}}}{\mathrm{V_{ref}}}\right)=\left(\frac{\mathrm{H_{n }}}{\mathrm{H_{ref}}}\right)^{\epsilon_{wt}} \tag{3}\]  
where, \(\mathrm{V_{n}}\) represents the WT speed (m/s) at the new height \(\mathrm{H_{n}}\) (m), \(\mathrm{V_{ref}}\) is the WT speed (m/s) at the original turbine hub height \(\mathrm{H_{ref}}\) (m), and \(\mathrm{\varepsilon_{wt}}\) denotes the WT friction coefficient.  
According to the International Electro technical Committee (IEC), the value of the coefficient of friction in the case of normal wind conditions is 0.20 and in the case of intensive wind conditions is 0.11. The technical specifications of the selected WT modeling are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Model type & & PV-MLT260 HC \\ PV panel cost (\(\mathrm{C_{PV}}\)) & 14,854 & \$/m\({}^{2}\) \\ \(\mathrm{\gamma_{T}}\) & 0.0037 & - \\ \(\mathrm{\eta_{PV}}\) & 15 & \% \\ \(\mathrm{T_{STC}}\) & 25 & \({}^{\circ}\)C \\ \(\mathrm{PV_{rat}^{P}}\) & 1 & kW \\ Length & 1625 & mm \\ Width & 1019 & mm \\ Thickness & 46 & mm \\ lifetime of PV system (\(\mathrm{PV_{S}}\)) & 20 & year \\ PV replacement cost (\(\mathrm{C_{rep}^{PV}}\)) & 13,885 & \$ \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main parameters of the selected photovoltaic model [30].
# Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
## 4 Tunnel Geology Prediction Performance  
### Basic Neural Network  
The training set accuracy curve and validation set accuracy curve of the basic neural network crossed at approximately 2000 epochs, at 88.7% accuracy. Figure 5 shows the learning curve of the basic neural network. Since no large gap was observed between the training accuracy and validation accuracy curves, no overfitting is interpreted as having occurred, making the neural network model quite effective for geology prediction.