esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Numerical modeling of 20 configurations of a four-bladed Darrieus wind turbine (NACA 0021 profile) showed that rotor geometry has a significant impact on the aerodynamic efficiency of the plant. For each diameter and chord length combination, the torque (\(\rm C_{m}\)) and aerodynamic drag (\(\rm C_{d}\)) coefficients were calculated in steady state. Based on these data, the signal-to-noise ratio values were determined using the Taguchi method.  
It was found, as a result of calculations. that the configuration with a rotor diameter of 3 m and a chord length of 0.4 m provides the best aerodynamic indicators: \(\rm S/N_{LB}=-15.77\), \(\rm S/N_{SB}=14.14\) and the ratio \(\rm C_{m}/C_{D}=0.948\). This indicates a high efficiency of energy conversion at a relatively low level of resistance. The selected configuration was also tested for different values of the speed coefficient \(\lambda\) (from 0.2 to 2.8), which allowed us to plot the dependence \(\rm C_{p}(\lambda)\). The maximum power \(\rm C_{p}=0.35\) was achieved at \(\lambda=2.2\), which is 64% higher than the mode at \(\lambda=0.2\).  
The obtained results demonstrate that the combination of the Taguchi method with CFD modeling allows to effectively solve the problems of optimizing the aerodynamic characteristics of the wind turbine. In the future, based on the optimized configuration, it is planned to develop and manufacture a prototype of the installation for experimental verification.  
Conceptualization, methodology, validation, formal analysis, investigation, and visualization; investigation and software, N.K.; investigation and resources, Z.B.; formal analysis, investigation, and visualization, R.M.; conceptualization and methodology, A.Y.; review and editing, funding acquisition, and supervision, M.I.; investigation and resources, A.K.; methodology, writing, review, and editing, Z.S.; investigation, B.B.; methodology, B.A. All authors have read and agreed to the published version of the manuscript.  
Figure 17: Dependence of power coefficient \(\rm C_{p}\) on tip speed ratio \(\lambda\) for an optimized four-bladed wind turbine.  
**Funding:** This work was carried out within the framework of the competition for grant funding for scientific and (or) scientific and technical projects for 2024-2026, conducted by the Ministry of Science and Higher Education of the Republic of Kazakhstan. Project topic: AP23487726 Planning and manufacturing of a semi-industrial wind turbine with a capacity of 5 kW of a unique design.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** We declare that all the data in this article are authentic, valid, and available for use upon reasonable request. The data and materials in the current study are available from the corresponding author, [PERSON] ([EMAIL_ADDRESS]). Data are contained within the article.  
**Conflicts of Interest:** The authors declare that they have no financial or personal interests that could influence the research conducted or the results presented in this article.
## 4 Experiments and Results
### Results Analysis
#### 4.3.2 Attack Detection Rate  
In this section, the effectiveness of the proposed solution for detecting and isolating wormhole threats during routing is discussed. Figure 9 illustrates the comparison between the proposed SLF-RPL mechanism and the PCC-RPL. The SLF-RPL was found to have a higher detection ratio compared to the PCC-RPL. Initially, both systems detected multiple harmful nodes, but as the trust became fully implemented, the number of malicious nodes significantly decreased. At 10 min, the detection rate for the SLF-RPL was 0.56, while for the PCC-RPL, it was 0.45. At time thirty, the detection rates for the SLF-RPL and the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & **Deployed** & **Attacker** & **Controller** & **Border** & **Runtime in** \\  & **Nodes** & **Nodes** & **Nodes** & **Router** & **Minutes** \\ \hline Ex 1 & 30 & 3 & 1 & 1 & 30 \\ \hline Ex 2 & 60 & 6 & 1 & 2 & 60 \\ \hline Ex 3 & 90 & 9 & 1 & 3 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Simulation Parameters**} & **Value** \\ \hline Simulation Tool & Contiki/Cooja 3.2 \\ Routing Protocol & RPL \\ Coverage Area & 100 m \(\times\) 100 m \\ Simulation Time & 30, 60 and 90 min \\ Simulation Scenarios & 03 \\ \# of Nodes in Topology & 30, 60, 90 \\ Attacker Nodes with Ratio & 3, 6, and 9 \\ Legitimate vs. Malicious Nodes Ratio & 1:10 \\ RX Ratio & 40\% to 100\% \\ TX Ratio & 100\% \\ TX Range 50 m & 60 m \\ Inference Range & 65 m \\ Initial Node Energy & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Environment.  
PCC-RPL were 0.78 and 0.65, respectively. During the simulation, SLF-RPL detected all the malicious nodes, while the PCC-RPL remained unsuccessful in detection.  
Furthermore, the average detection rate for different numbers of nodes (30, 60, and 90) is shown in Figure 10, demonstrating the effectiveness of the proposed solution (SLF-RPL).  
Overall, the proposed solution showed promising results in detecting and isolating wormhole threats during routing.
## 4 Conclusions  
In conclusion, we proposed a method to obtain the optimal focal position for MHSS using vector diffraction theory. The optimal focal position corresponded to minimal SA. We analyzed the micrograting size in the photopolymer for different wavelengths and NAs, based on vector diffraction theory, and compared the results defined by \(\lambda/(2\text{NA})\) in scalar diffraction theory. A TGI was used to locate the exact focus of two counter-propagating beams, within 1% diffraction efficiency. Our results indicated that this method could be used to obtain the appropriate micrograting size for areal and \(z\)-direction multiplexing in MHSSs.
# Computational Fluid Dynamics-Aided Simulation of Twisted Wind Flows in Boundary Layer Wind Tunnel
## 4 Development Stage
### Optimization of Guide Vane Configuration
#### 4.2.2 Spacing between Guide Vanes  
After determining \(d_{vane,wall}=750\) mm, the next step is to identify the optimal spacing between the two vanes. Figure 12 shows the distribution of the lateral wind speed at \(z=0.3\) m with \(d_{vane,wall}=750\) mm and the spacing between the vanes, denoted by \(d_{vane,vane}\) herein, ranging from 700 to 1000 mm with intervals of 100 mm. The figure shows that a so-called \"low-speed area\", where the mean lateral wind speed is below 0.7 m/s (colored in green), is formed in the downstream region of the vanes. The length of the low-speed area increases with \(d_{vane,vane}\) and eventually extends to the turntable after \(d_{vane,vane}\) reaches 900 mm. Obviously, the low-speed area is unfavorable to the generation of evident TWF within the model test region, and, therefore, the configuration with \(d_{vane,vane}=700\) mm, which corresponds to the smallest low-speed area, is selected for the following simulations.
# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 5 Objective Function  
Life cycle cost analysis evaluates the costs of covering all expenditures incurred during the activity period. The net present cost (NPC) is used as the charge of the system life cycle. The NPC includes initial installation costs, replacement costs, repairs, and maintenance of the equipment, the cost of the power failure, the cost of connecting to the grid. In NPC calculations, the costs are considered positive and earnings are considered negative.  
All costs and expenses are assessed at a fixed interest rate throughout the year. In this type of assessment, in order to influence the increase rate in calculations at the end of the process of analysis and review of the system, it should be applied to the NPC, by calculating the real interest rate caused by inflation, according to certain relationships. The real interest rate is the difference between the nominal interest rate and the inflation rate. The NPC value of the equipment can be calculated according to the equation  
\[NPC_{i}=N_{i}\times(CC_{i}+RC_{i}\times K_{i}+O\&MC_{i}\times PWA(ir,R)) \tag{17}\]  
In the above statement, \(N\) will equip the unit, or capacity (kW or kg), \(CC\) is initial investment cost ($/unit), \(RC\) stands for cost of each replacement annual maintenance cost ($/unit-year) equipping at \(R\) project lifetime (in this study is 20 years). The cost of the initial purchase of hydrogen is considered at the cost of the tank investment and represents real interest, which can be calculated in terms of the nominal interest (\(ir_{nominal}\)) and the annual inflation rate (\(ir\)) in accordance with Equation (18).  
\[ir=\frac{(ir_{nominal}-f)}{(1+f)} \tag{18}\]_PWA_ and \(K\) are respectively the annual and constant payments current value, which are defined as  
\[PWA(ir,R)=\frac{\left(1+ir\right)^{R}-1}{ir\left(1+ir\right)^{R}} \tag{19}\]  
\[K_{i}=\sum_{n=1}^{y_{i}}\frac{1}{\left(1+ir\right)^{n\times L_{i}}} \tag{20}\]  
\(y\) and \(L\) are the number of replacements and useful life of the equipment, respectively. For each component of the system, the initial cost, in accordance with the cost in year zero, is the replacement cost according to the need to replace that component at the end of its lifetime and the cost of \(O\) & \(MC\) for each year of the project. The cost of replacing a component varies for several reasons with its initial cost. One of these reasons is that when replacing a component due to the expiration of its life-span, all the components of that component that have been expended during the initial installation do not need to be replaced.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 4 Methodology
### Rock-Fall Prediction Model  
Due to the randomness of a rock-fall occurrence, the function of the rock-fall event probability P = \(f_{x}(r,s,t)\) is uncertain; therefore, mapping the relationship between the rock-fall possibility P, the slope angle s, the rain-fall r, and the temperature variation \(t\) cannot be strictly described. To solve the uncertainty of the mapping function, the BP (backpack) artificial neural network can learn the relationships between the input and output of the corresponding procedure by analyzing sample data and adopting a model that gives the expected output value when given the input value [29].
# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
## 5 Experimental Results
### Copy-Move Forgery Detection
#### 5.4.1 Geometric Transformation Forgery Detection
To further test the superiority of the proposed method in terms of time complexity, we conducted tests on images in the Ardizzone and CoMoFoD datasets, and recorded the time required for the proposed method and the comparison algorithms to detect a single image. Table 5 presents the data. The proposed method required an average of 2.54 s for feature extraction on the Ardizzone dataset, and 61.58 s on average for feature matching and detection of mismatched pairs. On the CoMoFoD dataset, the average time for feature extraction was 2.24 s, and the time required for other detections was 51.99 s. The method proposed in [38] required a longer time for feature extraction due to the greater number of iterations and redundant feature points extracted, resulting in longer detection time. The average detection time was 122.12 s on the Ardizzone dataset and 99.25 s on the CoMoFoD dataset. The method proposed in [17] segmented the image equally and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Dataset** & **Precision** & **Recall** & \(F_{1}\) \\ \hline Ardizzone & \(\pm\)0.14 & \(\pm\)0.20 & \(\pm\)0.21 \\ CoMoFoD & \(\pm\)0.15 & \(\pm\)0.24 & \(\pm\)0.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Standard deviation of three evaluation indicators on the two datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(P\) **(\%)** & **R (\%)** & \(F_{1}\) **(\%)** \\ \hline [PERSON] et al. [17] & 89.52 & 91.23 & 90.37 \\ [PERSON] et al. [38] & 90.23 & 90.56 & 90.39 \\ Proposed method & 92.75 & 92.43 & 92.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of precision (\(P\)), recall (\(R\)), and \(F_{1}\) evaluation metrics for images in the Ardizzone dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(P\) **(\%)** & **R (\%)** & \(F_{1}\) **(\%)** \\ \hline [PERSON] et al. [17] & 89.52 & 91.23 & 90.37 \\ [PERSON] et al. [38] & 90.23 & 90.56 & 90.39 \\ Proposed method & 92.75 & 92.43 & 92.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of precision (\(P\)), recall (\(R\)), and \(F_{1}\) evaluation metrics for images in the CoMoFoD dataset. without overlapping, and used a segmentation algorithm to divide the entire image into superpixel blocks, which undoubtedly increased the computational complexity of the algorithm. Consequently, the average time required for this method was 184.23 s on the Ardizzone dataset and 145.23 s on the CoMoFoD dataset. In summary, as a result of combining SURF and A-KAZE, which has the advantage of two detectors complementing one another, the proposed method outperformed the two comparison algorithms in terms of time complexity.
# Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model
###### Abstract  
Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (_IoU_) and the Dice Coefficient (_DC_) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to 90.42% and 91.42% as calculated by _IoU_ and _DC_ metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation.  
deep learning (DL); unmanned aerial vehicle (UAV); photovoltaic (PV) systems; image-processing; image segmentation; semantic segmentation
## 2 Data Gathering  
For developing a predictive model, there is a need for a valid dataset. The dataset needs to include all the parameters, which are possible to be effective on the outputs as inputs, except those that are constant among all records. In the present research, a dataset including 234 patterns was collected from literature [62; 63; 64; 65; 66; 67; 68; 70; 71; 72; 73; 74; 75]. In all patterns, the curing condition were the same, and the used cement was ordinary Portland cement (OPC). Moreover, the superplasticizer (SP) in all mixes was Polycarboxylican, and the type of specimen in all recorded patterns were cube with the same size (100 mm by 100 mm). The inputs of the models in this study are the testing age (TA), the amount of OPC, sand (S), coarse aggregate (CA), fine aggregate (FA), the ratio of water to cement (W/C), superplasticizer to cement by percentage (SP/C), the ratio of recycled coarse aggregate to total coarse aggregate (CRA), and the ratio of recycled fine aggregate to total fine aggregate (FRA). The only output variable of the models is the CS. Figure 1 shows the histogram of the inputs and output variables. Furthermore, the anatomical statistics of the gathered dataset are represented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Min** & **Max** & **Mean** & **Standard Deviation** & **Skewness** & **Kurtosis** \\ \hline OPC (kg/m\({}^{3}\)) & 262.00 & 460.00 & 386.68 & 39.35 & –0.38 & –0.07 \\ W/C & 0.34 & 0.77 & 0.55 & 0.09 & –0.27 & –0.02 \\ CA (kg/m\({}^{3}\)) & 520.00 & 1295.00 & 877.55 & 222.53 & –0.13 & –1.66 \\ FA (kg/m\({}^{3}\)) & 0.00 & 364.00 & 154.98 & 165.06 & 0.16 & –1.93 \\ CRA & 0.00 & 1.00 & 0.47 & 0.40 & 0.19 & –1.53 \\ FRA & 0.00 & 1.00 & 0.21 & 0.35 & 1.42 & 0.45 \\ Sand (kg/m\({}^{3}\)) & 492.00 & 1065.00 & 781.96 & 144.13 & 0.24 & –1.41 \\ SP/C (\%) & 0.00 & 1.62 & 0.04 & 0.26 & 6.04 & 34.79 \\ Age (Days) & 3.00 & 28.00 & 13.56 & 10.74 & 0.57 & –1.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anatomical statistics of the inputs and output.  
Figure 1: Output and input variables’ histogram: (**a**) ages of samples, (**b**) the amount of OPC, (**c**) the amount of sand, (**d**) the amount of coarse aggregate, (**e**) the amount of fine aggregate, (**f**) the ratio of water to cement, (**g**) the ratio of super-plasticizer to cement by percentage, (**h**) the ratio of recycled coarse aggregate to total coarse aggregate, (**i**) the ratio of recycled fine aggregate to total fine aggregate, and (**j**) compressive strength.
# The Role of Physical Fitness on FRAN CrossFit(r) Workout Performance
## 1 Introduction
In addition, a study on 22 (13 males and 9 females) healthy naive CrossFit(r) athletes found no significant correlation between the modified FRAN and percentage body fat (r = 0.26) [6], while a study on 95 adults (including males and females) showed significant correlations between the FRAN time-trial with lean mass (\(\rho\) = \(-\)0.37 to \(-\)0.64) as well as with fat mass (\(\rho\) = 0.33 to 0.60) [19]. The scarce research data on the relationship between body composition and performance in FRAN need further investigation. Muscle strength has a significant role in physical fitness in several sports, with stronger athletes having a greater advantage in sport-specific skills such as jumping, sprinting, and throwing [20; 21; 22]. A previous study on 15 male participants showed a significant correlation between the time-trial in FRAN and CrossFit(r) total (total is the sum of maximum strength in squat, strict shoulder press, and dead-lift) (r = \(-\)0.599), while 1 repetition maximum (1-RM) in back squat explained 42% of the variance in the FRAN time-trial [8]. Moreover, a study on 14 CrossFit(r) athletes (10 males and 4 females) found a significant correlation between CrossFit(r) total and FRAN time-trial (r = \(-\)0.65) [23] results, which is in line with a study on 17 CrossFit(r) athletes (12 males and 5 females), which also found significant correlations between the FRAN time-trial and 1-RM in squat (r = \(-\)0.644), 1-RM in dead-lift (r = \(-\)0.484), and CrossFit(r = \(-\)0.599) [4]. Similarly, a study on 22 novice CrossFit(r) athletes (13 males and 9 females) found significant correlations between performance in the modified FRAN time-trial and 1-RM in squat (r = \(-\)0.58), 1-RM in dead-lift (r = \(-\)0.57), and CrossFit(r) total (r = \(-\)0.61) [6]. The results from these studies suggest that 1-RM strength has a key role in the FRAN time-trial. However, Olympic lifts of the snatch and the clean and jerk [24] are regularly included in CrossFit(r) workouts without knowing if there is a possible connection between Olympic lifts and the FRAN time-trial. It would be interesting to investigate whether faster athletes in FRAN have greater 1-RM strength compared to slower athletes, as well as explore the relationship between the FRAN time-trial and 1-RM strength in Olympic lifts. Therefore, the purpose of this study was to investigate the role of physical fitness on the FRAN CrossFit(r) workout and to examine the correlation between the FRAN time-trial with CJ, muscle strength, power, and Olympic lifts (snatch and clean and jerk) in CrossFit(r)athletes. The hypothesis was that physical fitness will be significantly greater in faster athletes compared to their slower counterparts, while performance in CJ, strength, power, and Olympic lifts will be correlated with performance in the FRAN time-trial.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 5 Proposed Method
### Routing Using MOSOA
#### 5.2.3 Iterative Process  
The iterative process of the MOSOA-based CAR is as follows:  
1. At first, the seagulls are initialized with the routing path information, and the seagull has the information about the \(x\) and \(y\) coordinates of the next-hop CHs.
2. The best population among the entire seagulls is identified based on the fitness parameter expressed in Equation (22).
3. Equation (22) is used to avoid collision during the migration process. After accomplishing the collision avoidance, the remaining populations are updated based on the best population (i.e., optimal routing path).
4. Further, the positions of the seagulls are updated according to the exploitation phase. In this phase, the locations are updated by using the best solution from the MOSOA.
5. Finally, the developed MOSOA provides the optimal routing path from the source CH to the destination node.  
The data packet transmission is initialized once the routing path is identified using the MOSOA. The queue length is used to generate the collision-free path, which transmits the data over the network through the node that has less congestion. Next, the link quality is used to discover the value of the route based on the sent and received packets. The transmission distance over the WSN-IoT is minimized by considering the communication cost, and the residual energy to select the node with high remaining energy. Therefore, these fitness values are used to select the optimal collision-aware route for alert message transmission.
###### Abstract  
Due to the prevalent challenges of inadequate accuracy, standardized parameters, and suboptimal efficiency with regard to icing prediction, this study introduces an innovative online method for icing prediction based on _Robust STL-BTSF_ and IBWO-LSSVR. Firstly, this study adopts the Robust Seasonal Decomposition of Time Series and Bilinear Temporal-Spectral Fusion (_Robust STL-BTSF_) approach, which is demonstrably effective for short-term and limited sample data preprocessing. Subsequently, injecting a multi-faceted enhancement approach to the Beluga Whale Optimization algorithm (BWO), which integrates a nonlinear balancing factor, a population optimization strategy, a whale fall mechanism, and an ascendant elite learning scheme. Then, using the Improved BWO (IBWO) above to optimize the key hyperparameters of Least Squares Support Vector Regression (LSSVR), a superior offline predictive part is constructed based on this approach. In addition, an Incremental Online Learning algorithm (IOL) is imported. Integrating the two parts, the advanced online icing prediction model for transmission lines is built. Finally, simulations based on actual icing data unequivocally demonstrate that the proposed method markedly enhances both the accuracy and speed of predictions, thereby presenting a sophisticated solution for the icing prediction on the transmission lines.  
transmission line; online icing prediction; LSSVR; IBWO; data preprocessing +
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