esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### MCS-PSO Optimization Results  
The MCS-PSO method is applied to solve the reliability optimization problem for 3-area LFC systems. In this case study, each MCS run uses a sample size of 1000, and a total of 50 MCS replications are conducted. In this study, 50 particles, each with 6 dimensions, are used, and 60 iterations are performed. Given that reliability values fall between 0 and 1, both the maximum velocity and maximum position are set to 1. The cognition learning factor and social learning factor are both set to 0.1 to align with the reliability range [0, 1]. The inertia weight is chosen as 0.6, based on the effectiveness of values between 0.9 and 0.4 in previous experiments. The system reliability curves, comparing scenarios with and without optimization (refer to Table 6), are illustrated in Figure 7. The results indicate that optimized control strategies substantially enhance the reliability of LFC systems. Optimal control strategies achieve near-maximum reliability (close to 1) in all values of \(T_{0}\). Compared to earlier designs, the proposed method demonstrates superior performance and increased reliability, effectively mitigating the impact of component degradation. Table 9 lists the optimal parameters of each PI controller for total runtime \(T_{0}\in 0,1,\ldots,11\) as determined by applying the MCS-PSO method to the time-varying model of the aging 3-area LFC system under cyber-attacks. Additionally, the table presents the fitness values for each optimal control strategy and the baseline strategy without optimization, across different \(T_{0}\) values. The observed trend shows that as \(T_{0}\) increases, the fitness value increases due to a decrease in reliability resulting from the degradation process. A smaller fitness value indicates better performance and reliability. The results indicate that the proposed controller outperforms the traditional controller in terms of efficacy. Specifically, in areas 2 and 3, the parameters \(K_{P_{1}}\) and \(K_{P_{3}}\) initially exhibit an increasing trend, which subsequently reverses into a decreasing trend, suggesting an adjustment to the system deviations. Conversely, in area 1, \(K_{P_{2}}\) demonstrates an initial decreasing trend, followed by an increasing trend, reflecting a dynamic response to maintain control stability and effectiveness. This behavior is attributed to the interconnected nature of the three areas, which allows them to cooperate and compensate for the degradation process. \(K_{I_{1}}\), \(K_{I_{1}}\) and \(K_{I_{1}}\) only exhibit minor variations across \(T_{0}\), as they are less critical in determining the power output compared to \(K_{P_{1}}\), \(K_{P_{2}}\) and \(K_{P_{3}}\) (proportional parameters). The simulation results further indicate that the MCS-PSO method significantly improves the reliability of multi-area LFC systems. This improvement is evident, as the fitness value is closely linked to the system's reliability.
# Hybrid Strategy Improved Beetle Antennae Search Algorithm and Application
## 4 Simulation Experiment and Analysis
### Convergence Analysis of Algorithms  
To further verify the convergence performance of the HSBAS algorithm, Figure 4 gives the convergence curves of HSBAS, BAS, and PSO, GA algorithms for some representative standard test functions in Table 2. Setting the population size of PSO and GA algorithms to 30, and the maximum number of function evaluations to 1000, the performance of each intelligent algorithm for single-peak test function, multi-peak test function, and fixed-dimension multi-peak test function can be reflected intuitively.  
According to Figure 4, when dealing with low-dimensional functions, HSBAS algorithm retains the advantage of BAS algorithm in convergence speed, entering the convergence stage in less than 40 steps and obtaining better optimization results; when dealing with ordinary high-dimensional functions, HSBAS algorithm also shows a fast convergence speed and enters the convergence stage in about 30 steps; when dealing with multi-peak functions, HSBAS algorithm enters the convergence stage in less than 500 steps owing to the need to jump out of the local optimum several times. However, it facilitates better jumping out of the local optimum and obtaining better objective function values. Comparing the performance of PSO and GA algorithms, the PSO algorithm achieves good optimization results and convergence speed in most functions. However, it enters the convergence stage more slowly and has worse optimization results when dealing with multi-peak functions. In a comprehensive comparison, the HSBAS algorithm performs well on both low-dimensional and high-dimensional functions, with moderate convergence speed and good optimization results, especially in jumping out of the local optimum.  
Figure 4: Convergence curves of HSBAS, BAS, PSO, and GA algorithms on representative standard test functions. (**a**) \(f_{1}\); (**b**) \(f_{3}\); (**c**) \(f_{6}\); (**d**) \(f_{7}\); (**e**) \(f_{9}\); (**f**) \(f_{11}\).
###### Abstract  
In this study, a multi-walled carbon nanotube (MWCNT) was synthesized and used as an adsorbent for the removal of Ismate violet 2R dye from contaminated water. The morphology and structure of the synthesized adsorbent were examined via the Brunauer-Emmett-Teller (BET) surface area, X-ray powder diffraction (XRD) analysis, infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and Raman spectroscopy. The effects of an MWCNT on the removal of IV2R were examined via a batch method using different factors such as pH, agitation time, adsorbent dosage, temperature, and initial dye concentration. The results showed that, at the acidic pH 4, 0.08 g of an MWCNT with 10 mg L\({}^{-1}\) at 120 min realized the favorable removal of IV2R dye using an MWCNT. Under these operation conditions, the maximum elimination efficiency for real wastewater reached 88.2%. This process benefits from the ability to remove a large amount of dye (approximately 85.9%) in as short as 10 min using 0.005 g of MWCNTs. Moreover, the investigational isotherm data were examined by different models. The equations of error functions were used in the isotherm model to show the most appropriate isotherm model. The highest adsorption capacity for the removal of the dye was 76.92 mg g\({}^{-1}\) for the MWCNT. Moreover, the regression data indicated that the adsorption kinetics were appropriate with a pseudo-second order and an R\({}^{2}\) of 0.999. The thermodynamic study showed that the removal of IV2R is an endothermic, spontaneous, and chemisorption process. The MWCNT compound appears to be a new, promising adsorbent in water treatment, with 91.71% regeneration after three cycles.  
adsorption; dye; MWCNT; kinetics; isotherm models; thermodynamic +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Baseline Case Study of the Solar ORC System Performance
#### 3.1.4 Condensation and Evaporation Temperature Glides  
Figures 9 and 10 present the effect of the temperature glides on the working fluid mass fraction. The figures show that the trends for every mixture's evaporation and condensation temperature glide are identical. In general, the temperature glides for all mixtures have a local maximum and are 0 at  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Working Fluid Mixture** & **Mixture Composition (Mass Fraction)** & **Optimal Net Work Output (kW)** \\ \hline R245 fa/R152a & 0.9/0.1 & 9.07 \\ R245 fa/propane & 0.4/0.6 & 7.86 \\ R245 fa/hexane & 0.8/0.2 & 6.55 \\ R245 fa/heptane & 0.8/0.2 & 6.20 \\ Butane/propane & 0.8/0.2 & 5.70 \\ Butane/hexane & 0.8/0.2 & 4.03 \\ Butane/heptane & 0.8/0.2 & 4.06 \\ Isobutane/propane & 0.8/0.2 & 5.70 \\ Isobutane/hexane & 0.8/0.2 & 3.84 \\ Isobutane/heptane & 0.6/0.4 & 2.88 \\ Pentane/hexane & 0.8/0.2 & 5.36 \\ Isopentane/hexane & 0.2/0.8 & 4.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal net work output for working fluid mixtures.  
Figure 8: Volumet ratio vs. mass fraction for all mixtures.  
pure fluids. The mixtures R245/fa with hexane and heptane have a higher temperature glide compared to other mixtures, whereas the R245 fa/heptane mixture has the highest evaporation and temperature glides. Within the mixtures operating at standard conditions, i.e., at hot and cold source temperatures of 85 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, respectively, R245 fa/propane reaches the maximum condensation and evaporation temperature glides of 33 \({}^{\circ}\)C and 27.42 \({}^{\circ}\)C at a ratio of 0.8/0.2, followed by butane/propane at a ratio of 0.6/0.4 (Figures 11 and 12). Non-isothermal condensation and evaporation in mixtures can improve regeneration when both the condensation and evaporation temperature glides are both near to the cold and hot source temperature increases. In such a situation, heat transfer irreversibility in the condenser and evaporator is minimised. However, temperature glides in the condensation and evaporation should be checked systematically because a sizeable glide can trigger a pinch point, which hinders efficient heat recovery.  
Figure 10: Evaporation temperature glide vs. mass fraction for all mixtures.  
Figure 9: Condensation temperature glide vs. mass fraction for all mixtures.
# Measurement and Prediction of Wind Fields at an Offshore Site by Scanning Doppler LiDAR and WRF
## 2 Measurement and Prediction Methods
### Wind Field Measurement by Scanning Doppler LiDAR
As stated earlier, in order to retrieve the velocity vector (\(V\)) from the LiDAR-measured line of site velocity (\(u_{r}\)), additional processing of the data is required. The method for the estimation of \(V\) from the PPI and RHI scan data is explained next. Assuming that the vertical component of wind speed w is very small, it was neglected in this study. \(u\) and \(v\) components can then be expressed as:  
\[u=V\sin\alpha,\quad v=V\cos\alpha, \tag{4}\]  
with \(\alpha\) being the wind direction. The LOS velocity \(u_{r}\) measured by the scanning LiDAR is the projection of \(V\) in the laser beam direction; therefore, V can be expressed as:  
\[V=\frac{u_{r}}{\cos\phi\cos(\alpha-\theta)}. \tag{5}\]  
Here, \(\theta\) is the azimuthal angle and \(\phi\) is the elevation angle. The wind direction at every point in a scan is assumed to be the same as that measured by the wind vane on the meteorological tower or the nacelle. With \(u_{r}\) measured by the LiDAR, \(\phi\), \(\alpha\), and \(\theta\) known, computation of V is then straightforward from Equation (5). Once V is known, it can be substituted into Equation (4) to compute \(u\) and v.  
An alternative approach to retrieve the velocity vector (\(V\)) from \(u_{r}\) can be to sweep the laser beam in a conical scan for a complete 360\({}^{\circ}\). The azimuth angle for which the magnitude of \(u_{r}\) is maximum can be considered as the wind direction. Figure 4 shows an example of \(u_{r}\) as a function of \(\theta\) collected using a PPI scan. Peaks can be observed around azimuth angles 30\({}^{\circ}\) and 210\({}^{\circ}\). Of the two peaks, the azimuth angle for which the magnitude of \(u_{r}\) is higher is used to estimate the wind direction. Note that, for the current LiDAR, \(u_{r}\) is positive when the wind blows away from the LiDAR and is negative otherwise. In order to avoid interference due to the meteorological tower and other installations on the platform, the region 90\({}^{\circ}\leq\theta\leq 180^{\circ}\) is not scanned in the current study. With the wind direction known, V can be computed from Equation (5). This essentially is a form of VAD approach (see Section 1). Wind speeds reconstructed using this approach are also presented in the next section. Figure 3: Schematic of two scan modes (**a**,**b**) used for wind field measurements.
## 2 Theoretical Background
### Evolutionary-Based Algorithm  
Genetic algorithms (GA) are used in this work since they can be easily adjusted to this particular problem with some advantages over other evolutionary-based algorithms. For instance, GA keep a population of potential solutions, whereas other techniques work with a single variable. Another benefit is their concept simplicity and their easiness of implementation [29]. GA are a set of elements based on [PERSON]'s theory of survival of the fittest. The following five features, applied in an iterative process, allow the GA to be a functional optimization search: (i) design variable coding; (ii) objective function and fitness value; (iii) selection mechanism and genetic operators; (iv) crossover; and (v) mutation [30]. It is worth understanding how the technique works. In natural genetics, a group of chromosomes (or individuals) make up the population to be evolved (converged); therefore, each individual is integrated by genes, which represent the design variables to be optimized (or searched for).  
According to [29], the next steps illustrate a general form in which GA are implemented:  
**Step 1:** Definition of general parameters, according to the problem to be solved, and generation of a random initial population (potential solutions).  
**Step 2:** Evaluation of the population by substituting the potential solution in an objective function that calculates the fitness value, which evaluates quantitatively how good every individual is.  
**Step 3:** Performance of an elitist selection of the best individuals according to the fitness value for reproduction purposes with the genetic operators: crossover and mutation.  
**Step 4:** Evaluation of the termination criteria for the iterative process, and if they are satisfied, then go to Step 8, where the best solutions are presented; otherwise, go to Step 5.  
**Step 5:** Generation of a new population by applying the crossover operation to the selected individuals in Step 3. This ensures evolution (convergence) of the possible solutions.  
**Step 6:** Generation of population diversity, and local trapping scope, by applying the mutation operation according to a mutation probability to avoid losing valuable information.  
**Step 7:** Replacement of the initial population with the new population obtained through Steps 5 and 6 and go to Step 2.  
**Step 8:** Return the best solutions obtained.
###### Abstract
From a theoretical point of view, complex and huge systems can be represented with differential equations of a high order. In a high-order mathematical model, it is not possible to obtain an exact complex model, so unconsidered variables become vulnerable to attackers and lead to inaccurate detection. In CPS security modeling, data used in training for anomaly detection are acquired in a controlled environment set up or experiment [10]. For obtaining a reliable and safe CPS security model, machine learning models are deployed  
Figure 1: Applications of CPS. that work by finding correlations between input and output on large amounts of data. Machine learning algorithms help in generating a model that can explore the complex relationship in CPS components, handle different network protocols and complex cyber software, and enhance the security level. In this paper, the attacks that damage the CPS and manipulate the processes are discussed. We mainly focus on different security designs of CPSs. The main contributions of the article are:  
* CPS architecture is presented into three layers, namely the physical layer, network layer, and application layer. The layered architecture is used to provide more clarity in terms of functionality. Then, CPS attacks on each layer are discussed, mainly from the perspective of the physical system. * Key features, challenges, and attack handling methods using machine learning-based models are highlighted regarding different layers. * Keeping in view the nature of different attacks, a simple deep learning model is deployed for attack detection, especially the detection of malicious URLs and CPS attacks. For this purpose, multilayer perceptron (MLP) is adopted as it is not used in the existing literature. Despite existing hybrid and sophisticated models, the proposed MLP provides better and more robust results. * Finally, future research directions are outlined for CPS security research to handle CPS attacks in real-time networks. The rest of the paper is organized as follows: Section 2 provides the most recent related work of CPS. Section 3 provides the CPS architecture as a layered approach. Section 4 introduces different types of CPS attacks at each layer. Section 5 discusses securing methods for CPS. Section 6 presents the potential research directions for CPS security. Finally, we conclude the article and provide future directions in Section 7.
## 3 Fuzzy Kernel Nearest Feature Line Embedding (FKNFLE)
### Classification Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Number of**} & \multirow{2}{*}{**GCK-MLR (\(K^{\omega}\))**} & \multirow{2}{*}{**FKNFLE**} & \multirow{2}{*}{**KNFLE**} \\ \cline{2-2} \cline{5-6}  & **Train** & **Test** & & & \\ \hline Alfalfa & 3 & 51 & 47.06 \(\pm\)15.41 & 65.22 \(\pm\)15.32 & 56.52 \(\pm\)16.42 \\ Corn-no till & 71 & 1363 & 78.24 \(\pm\)3.01 & 70.66 \(\pm\)3.05 & 67.44 \(\pm\)3.03 \\ Corn-min till & 41 & 793 & 64.17 \(\pm\)3.01 & 67.71 \(\pm\)3.04 & 71.08 \(\pm\)3.05 \\ Corn & 11 & 223 & 48.211 \(\pm\)1.76 & 43.88 \(\pm\)11.54 & 47.68 \(\pm\)12.14 \\ Grass/pasture & 24 & 473 & 87.76 \(\pm\)2.27 & 84.47 \(\pm\)2.18 & 87.16 \(\pm\)2.58 \\ Grass/tree & 37 & 710 & 95.13 \(\pm\)1.40 & 96.58 \(\pm\)1.42 & 94.79 \(\pm\)1.32 \\ Grass/pasture-mowed & 3 & 23 & 53.04 \(\pm\)11.74 & 92.86 \(\pm\)11.88 & 92.82 \(\pm\)10.68 \\ Hay-windrowed & 24 & 465 & 98.84 \(\pm\)0.61 & 97.28 \(\pm\)0.59 & 98.12 \(\pm\)0.62 \\ Oats & 3 & 17 & 68.82 \(\pm\)17.33 & 65.10 \(\pm\)16.31 & 70.12 \(\pm\)15.35 \\ Soybeans-no till & 48 & 920 & 68.42 \(\pm\)5.22 & 70.27 \(\pm\)5.12 & 66.87 \(\pm\)5.42 \\ Soybeans-min till & 123 & 2245 & 82.56 \(\pm\)1.26 & 77.43 \(\pm\)1.31 & 73.93 \(\pm\)1.25 \\ Soybeans-clean till & 30 & 584 & 74.52 \(\pm\)5.35 & 62.56 \(\pm\)5.32 & 61.89 \(\pm\)5.52 \\ Wheat & 10 & 202 & 99.36 \(\pm\)0.52 & 91.71 \(\pm\)0.54 & 94.63 \(\pm\)0.51 \\ Woods & 64 & 1230 & 95.46 \(\pm\)1.53 & 96.60 \(\pm\)1.49 & 97.15 \(\pm\)1.54 \\ Bldg-grass-tree-drives & 19 & 361 & 50.75 \(\pm\)3.49 & 38.34 \(\pm\)3.18 & 48.19 \(\pm\)3.38 \\ Stone-steel towers & 4 & 91 & 62.09 \(\pm\)6.95 & 82.80 \(\pm\)6.89 & 83.87 \(\pm\)6.59 \\ Overall accuracy & & 80.16 \(\pm\)0.73 & 77.19 \(\pm\)0.71 & 76.43 \(\pm\)0.73 \\ Average accuracy & & 73.40 \(\pm\)1.26 & 75.22 \(\pm\)1.21 & 75.76 \(\pm\)1.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The comparison between algorithm GCK-MLR (\(K^{\omega}\)) and the proposed method for dataset IPS of 16 classes (in percent). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Number of Samples**} & \multirow{2}{*}{**GCK-MLR (\(K^{\omega}\))**} & \multirow{2}{*}{**FKNFLE**} & \multirow{2}{*}{**KNFLE**} \\ \cline{2-2} \cline{5-6}  & **Train** & **Test** & & & \\ \hline Asphalt & 548 & 6631 & 82.64 & 83.14 & 82.64 \\ Bare soil & 540 & 18,649 & 68.62 & 82.89 & 82.07 \\ Bitumen & 392 & 2099 & 75.04 & 81.75 & 79.32 \\ Bricks & 524 & 3064 & 97.00 & 93.21 & 92.95 \\ Gravel & 265 & 1345 & 99.41 & 99.93 & 99.93 \\ Meadows & 532 & 5029 & 93.88 & 80.47 & 79.48 \\ Metal Sheets & 375 & 1330 & 90.08 & 92.26 & 92.41 \\ Shadows & 514 & 3682 & 91.36 & 85.61 & 85.17 \\ Trees & 231 & 947 & 97.57 & 99.89 & 99.89 \\ Overall accuracy & & 80.34 & 85.76 & 84.04 \\ Average accuracy & & 88.40 & 88.79 & 88.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The comparison between algorithm GCK-MLR (\(K^{\omega}\)) and the proposed method for dataset Pavia University of nine classes (in percent).
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 5 Evaluation
### Evaluation Metrics  
Average packet delay: the average difference between the time when the consumer receives the packet at the new location and the time when the content producer sends the packet.  
Packet loss rate: the ratio of the total number of packets received by the consumer at the new and old locations to the total number of packets sent by the content producer.  
Handover delay: Ref. [36] defines handover delay as the time between the mobile entity leaving the previous subnet and receiving the first packet in the new subnet. We define handover delay as the difference between the time when the consumer receives the first packet at the new location and the time when the consumer receives the last packet at the old location.
# Relevance of Surface Electromyography Assessment and Sleep Impairment in Scoliosis: A Pilot Study
## 3 Results  
### Results of Clinical Assessment and Anthropometric Measurements of the Subjects  
Clinical assessment revealed an asymmetry in the shoulder level and muscle imbalance highlighted by the Adams test, which was positive for all 15 patients. By the end of the therapeutic protocol, only 6 patients (40%) still presented a positive Adam test.  
Anthropometrical parameters were assessed before starting the therapeutic protocol (T1 moment), as presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Patient Code** & **Gender** & **Age** & **Height [cm]** & **Weight [kg]** & **BMI [kg/m\({}^{2}\)]** \\ \hline P1 & F & 15 & 160 & 50 & 19.5 \\ P2 & F & 15 & 162 & 52 & 19.8 \\ P3 & F & 15 & 160 & 48 & 18.8 \\ P4 & F & 14 & 157 & 50 & 20.2 \\ P5 & M & 15 & 176 & 68 & 21.9 \\ P6 & F & 7 & 132 & 21 & 12 \\ P7 & M & 7 & 134 & 23 & 12.8 \\ P8 & F & 10 & 142 & 39 & 19.3 \\ \hline \end{tabular}
\end{table}
Table 1: Anthropometrical parameter values in T1 before starting the therapeutic protocol.  
Figure 4: Schroth posture. (**a**) Schroth posture 1; (**b**) Schroth posture 2.  
The mean value of the BMI was determined to be 18.3, which indicates a normal weight for all patients.  
The chest elasticity index, a parameter that highlights the behavior of chest dynamics, which can be affected in idiopathic S-type scoliosis, is presented from an evolutionary point of view in Table 2.  
Based on the above data, we may conclude that eight out of fifteen subjects (53.3%) had a higher chest elasticity index after completing the rehabilitation program; for one subject (6.66%) the chest elasticity index decreased, while the chest elasticity index did not change value for the remaining six (40%).