esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
## 4 Results and Discussion
### Non Uniform Irradiance Condition
Table 4 presents a summary of the exact values for extracted power (Pmpp), settling time (T\({}_{s}\)) required to reach the MPP, and tracking efficiency (\(\eta\)) for each of the simulated MPPT algorithms under PSC. Figure 8: PV output power under PSC case 2. Figure 7: PV output power under PSC case 1. Figure 9: PV output power under PSC case 3. **Case 1:** The first PV array (PV1) in this case is subjected to a different irradiance level compared to the other two arrays, resulting in a power voltage curve with two peaks and only one global peak at a power level of 31.37 KW, as shown in Figure 2b. The simulation results in Figure 7 demonstrate that the RSA-MPPT based algorithm achieved the global maximum power point (GMPP) with the highest efficiency of 99.94% among all the tested algorithms, followed by GWO with 99.71% and WOA with 98.15%. DOA obtained an efficiency of 97.45%, while the PSO algorithm had the least efficiency of about 94.23%. Furthermore, the RSA algorithm was the fastest in settling at the global peak in less than 10 ms, while the other algorithms took more time due to the fluctuations they exhibited. **Case 2:** only one of the PV arrays (PV3) is partially shaded while the other two arrays receive the same level of irradiance. However, the difference in irradiance level between the arrays is small compared to Case 1. The PV curve (Figure 2b) exhibits the same behavior as Case 1, with a global maximum power of 64.19 KW. Based on the simulation results presented in Figure 8, the RSA-MPPT algorithm outperformed the other algorithms. It achieved a convergence to the GMPP of 64.15 KW with 99.94% efficiency in just 16 ms. The WOA algorithm maintained a high efficiency rate of 99.32% in 25 ms, while the GWO method achieved a 99.07% efficiency rate in 20 ms. The DOA technique sustained an efficacy of about 98.61% in 35 ms, whereas the PSO algorithm attained a 93.35% efficiency in 20 ms. As seen in Figure 8, DOA, WOA, and PSO algorithms exhibited less fluctuation compared to the previous case of PSC. **Case 3:** In this PSC scenario, the three series-connected PV arrays are subject to different degrees of shading. The resulting P-V curve, as shown in Figure 2, exhibits a single global peak and two local peaks at power levels of 48.10 KW, 41.34 KW, and 23.58 KW, respectively. The overall results demonstrate that the proposed RSA algorithm exhibited superior performance and higher efficiency compared to the other examined methods. Figure 9 shows that RSA, GWO, DOA, WOA, and PSO techniques extracted output powers of 48.03 KW, 47.92 KW, 46.38 KW, 44.91 KW, and 44.55 KW, respectively. The RSA technique was not affected by the local peaks and achieved GMPP within 25 ms with the highest efficiency of approximately 99.85%. The GWO method maintained 99.63% efficiency in 40 ms. The DOA algorithm attained a 96.43% efficiency rate in 30 ms, while the WOA technique
# Probabilistic Models for the Shear Strength of RC Deep Beams
## 5 Discussion
### Comparison with Experimental Observations  
Figure 5 demonstrates the performance of the probabilistic models by comparison with each experimental data point. The test values were sorted according to an increasing order of the mean shear strengths calculated by the probabilistic models. The mean curve was plotted with a shaded area representing mean +/- SD interval. It covered approximately 70% of the probability distribution of the strength. As shown in Figure 5a, the curve of the mean shear strength mostly passed through the center of the experimental points. The mean +/- SD covered most of the experimental data throughout the whole range of strengths in the database. It noted that the proposed model without a deterministic model was reliable and consistent with the experimental data.  
Figure 4: Boxplots of the experiment-prediction ratios.  
Figure 5: _Cont_.  
By contrast, the deterministic models show relatively large scatter or biases. Most of the biases were observed on the conservative side, especially in GB 50010-10, CSA 23.3-04, CEB-FIP, and EC2. After being improved by the Bayesian-MCMC method, most modified probabilistic models showed good performance in bias and scatter. For example, the Mitchell and Collins, CEB-FIP, and ACI 318-14 have higher accuracy and lower discreteness (a narrower shaded area).
## 3 Results and Discussion
### Total Acidity  
The results of the total acidity of quince fruits are shown in Figure 2. Fresh quince fruits had the lowest total acidity per malic acid (0.84 \(\pm\) 0.03 g/100 g), while freeze-dried fruits had the highest values of this parameter (1.34 \(\pm\) 0.01 g/100 g). The total acidity of convection-dried quince at 40 \({}^{\circ}\)C (1.01 \(\pm\) 0.02 g/100 g) and 60 \({}^{\circ}\)C (1.09 \(\pm\) 0.06 g/100 g) were similar and not statistically significantly different.  
Fruit acidity is a critical quality parameter and a key determinant of taste [25]. When the acidity is between 0.08 and 1.95%, the fruit can be classified as mild in flavour and generally more appreciated by consumers when fresh [26]. The literature data report that this parameter depends on several factors, including cultivar and genotype, growing conditions, agricultural practices, harvesting date (related to fruit maturity), and even extraction and analytical procedures [27; 28; 29]. Therefore, the data available in the literature on the acidity of fresh quince fruit are quite disparate. For example, in a study by [PERSON] et al. [27], the fruit's acidity ranged from 1.11% to 1.25%, depending on the crop's location. [PERSON] et al. [30] and [PERSON] et al. [25] report similar results, stating that quince fruit acidity is 1.20% and 1.33-1.35%, respectively. [PERSON] et al. [26] obtained analogous results for the acidity of fresh fruit compared to those obtained in the present study. They found acidity in 10 varieties of common quince, ranging from 0.81 to 1.00 g malic acid/100 g. Different results were obtained by [PERSON] et al. [31], stating that the total acidity of fresh quince fruit was 0.26 g/100 g. On the other hand, the researchers' conclusions agree with the results obtained in the present study and confirm that drying quince fruit increases its acidity, especially in the case of freeze drying. [PERSON] et al. [32] obtained similar results, finding an increase in the acidity of tunnel-dried quince (1.39%) compared to fresh fruit (1.18%).  
Figure 2: Total acidity of quince fruits. Fresh—fresh fruit; Freeze-Dried—freeze-dried fruit; Conv-40—convection-dried fruit at 40 \({}^{\circ}\)C; Conv-60—convection-dried fruit at 60 \({}^{\circ}\)C. Values with the same letter are not significantly different at \(p<\) 0.05 ([PERSON]’s post hoc test).
# A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection
## 4 Methodology  
Figure 2 shows the overall workflow of our method. In the offline scenario, we clustered the sensors into groups according to the correlation of each sensor's historical data, and the correlated sensors would be clustered into the same group. With the generated sensor structure, we monitored the data correlation within each cluster, and selected a set of representative features. A simple solution to our online feature selection is shown in Algorithm 1.  
Figure 2: Workflow of our method.  
According to the solution shown in Algorithm 1, we calculated the correlation for each pair of correlated features and selected that feature pair when the correlation is smaller than a predefined correlation threshold. Since Algorithm 1 needs the correlated feature set as input, we need to conduct feature clustering to generate the correlated feature sets according to the historical equipment sensor data. To cluster the correlated features together in our clustering method, we defined the similarity based on the feature correlations. We will discuss this in detail in Section 4.1.  
The real time calculation of feature correlations (line 4 in Algorithm 1) needs to be further discussed in practice. The calculation of feature correlation is based on a piece of data segment of the two features. Since we wanted to capture the time-lagged correlation, we needed to conduct curve alignment to infer the time lags. Since the feature correlation is dynamic, we needed to conduct curve alignment based on the real time feature data. The size of the data segment is very important to our online correlation calculation. If the data segment is too big, the computation cost of online alignment would be considerable. On the contrary, if the data segment is even smaller than the time lag, we will have insufficient data to calculate the feature correlation. We will further discuss this in Section 4.2.  
To enumerate all the feature pairs in the same feature set is also inefficient (line 3-8 in Algorithm 1). Considering a feature set \(a^{(1)},a^{(2)},\ldots,a^{(h)}\) including h features, if only one feature (e.g., \(a^{(1)}\)) gets abnormal, the correlations between \(a^{(1)}\) and \(a^{(j)}\)\(j=2,3,\ldots,h\) will be small. According to Algorithm 1, the selected features will include all the h features in the feature set. Obviously, it is not necessary to involve all features because of merely one feature's abnormal. As a result, we need a better representation for each feature set's correlation changes. We will further discuss this in Section 4.3.
# Simulation of Lid-Driven Cavity Flow with Internal Circular Obstacles
## 1 Introduction
In fact, it requires massive numbers of grid cells (due to uniform resolution) causing'slow' overall solution times. In addition, high-Mach number flows in aerodynamics are still difficult for LBM, and a consistent thermo-hydrodynamic scheme is absent. Additionally, we also aimed in this study to adopt the LBM to observe a lid-driven cavity flow with and without an internal obstacle under \(Re\) number ranging from 100 to 5000. To identify the crucial parameters influencing the flow inside the cavity, our work was focused on observing the detailed flow inside a cavity under a uniform velocity \(U\) on the top lid of the cavity. To find the effects of the internal obstacles inside a cavity, four sizes of internal obstacles (\(R/L=1/16\), \(1/6\), \(1/4\), and \(2/5\)) have been considered. As reported, the flow tends to be stable in the case of a low \(Re\) (i.e., \(Re\) = 100 in our case) and unstable/complicated in the case of a relatively high \(Re\) (i.e., \(Re\) = 5000). Moreover, in this study, an attempt was made to compare our results with the existing data of lid-driven cavity flows, and attention is then focused on the effect of an flow induced around an internal obstacle in a lid-driven cavity. The rest of this paper is organized as follows. In Section 2, we describe the design of the numerical method used, and thus the lattice Boltzmann governing equation and the boundary condition of the lid-driven cavity are introduced. In Section 3, the results and discussions regarding the flow characteristics of the lid-driven cavity with and without an internal obstacle are presented. In Section 4, we present the core findings and conclusions of this study.
## 4 Evolutionary Algorithms in VANETs
### Broadcasting Algorithms  
To define the best conflict-free transmission system in a finite number of time slots, the evolutionary technique was used. Ref. [39] addresses the broadcast scheduling problem, which is the problem discussed above, using Binary Particle Swarm Optimization (BPSO). The literature also reveals that other well-known EAs, such as GA [40], have been employed to address the same problem [39; 40].
# Wavelength-Dependent Optical Nonlinear Absorption of Au-Ag Nanoparticles
## 2 Sample and Experiments  
The water-soluble dispersion of Au-Ag NPs was obtained from Xiamen Luman Tech Co. Ltd., Xiamen City, Fujian Province, China, with a concentration of 0.5 mg/mL and a linear transmittance of 42%. The microstructure of the Au-Ag NPs was observed using transmission electron microscope (TEM). The linear absorption spectra of the Au-Ag NPs were measured by UV-VIS spectrophotometer. The theoretical fit of liner absorption was conducted by the finite difference time domain (FDTD) method.  
The nonlinear absorption properties of the Au-Ag NPs were studied by OA Z-scan experiments using a 6 ns pulsed laser (Nd:YAG). In order to produce a tunable wavelength, an optical parametric oscillator (OPO) was also used. A 2 mm-thick cuvette was used to fill the dispersion of Au-Ag NPs.
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 2 Jumping Animals and Biomimetic Miniature Jumping Robots
### Invertebrates
#### 2.1.2 Locust Inspired Jumping Robots
Theoretical calculations show that the transmission ratio of the rope-driven mechanism is about 1/50, which is much higher than the transmission ratio of an eccentric cam, which is about 1/5\(\sim\)1/10. A higher transmission ratio also allows a small motor to drive a torsional spring of higher stiffness: more energy is stored but the process, of course, takes longer time. Even if this robot has a better jumping performance than adult locusts, it does not have the controllability over the take-off angle, velocity, and landing. In a second version, it solves the landing with a gliding wing [98] and can change its orientation during in-air phase. Thanks to its high take-off velocity, it can still jump 1.7 m, open the folded wing at the peak of the trajectory, and achieve a long gliding distance of 4.5 m. In the third and last version of this robot, [PERSON] et al. proposed a propelled-shaped tail mechanism that helps achieve orientation adjustment during repeated jumps [99]. [PERSON] et al., instead, designed a locust-inspired jumping robot [100], which uses a linear spring to mimic the extensor and a rope to mimic the flexor in locusts, in combination with a small DC motor, reduction gears and an eccentric cam. [PERSON] et al. [101; 102; 103] have proposed two generations of locust-inspired jumping robots and carry out systematic and in-depth research on steering, resetting, and path planning of swarm jumping robots. Their first generation of locust-inspired jumping robot uses a similar eccentric cam as the robot developed by [PERSON] et al. [88]. The addition of a support tail enables active resetting after the robot falls down and an intermittent adjustment of the take-off direction [104]. By changing the position of the counterweight fixed into the tail end, this robot can adjust the take-off angle within a small range. [PERSON] et al. endowed their robotic swarm with Zigbee network technology to investigate the possibility of applying this technology to remote home monitoring [105; 106]. Their second-generation of locust-inspired jumping robot can simultaneously perform crawling and jumping using a single motor, mainly simulating the multi-locomotion mode of locusts [102]. Finally, [PERSON] et al. [107; 108; 109; 110; 111; 83] conducted in-depth research on locust jumping, flapping, and land buffering mechanisms and developed a wheeled jumping robot using incomplete gear. By analyzing the recorded high-speed videos of locust landing, they established a mathematical model to study the land buffering mechanism of locusts, and they proposed a multibranch buffering leg model that provided new insight into the strategies for overcoming the limitations about landing performances of jumping robots [110]. Recently they designed a one degree of freedom (one DOF) jumping leg mechanism, which mimics the kinematic and dynamic stability during locusts' take-off phase [113]. Jumping stability is the key parameter to keep aerial stability after take-off.
# Attenuation of Lightning-Induced Effects on Overhead Distribution Systems in Urban Areas
## 2 Lightning Details, FEM Model, and LIGHT-PESTO Code
### Induced Voltage Computation  
The EM fields represented the input for the field-to-line coupling. The induced voltage computation was obtained by means of the LIGHT-PESTO code [14], which implements a discretized version of [PERSON]'s model by means of a Finite Difference Time Domain (FDTD) code interfaced with Simulink-Simscape Power Systems. In addition to the data provided for the EM fields' computation, the LIGHT-PESTO code needs the knowledge of the line geometry in terms of the conductor height, radius, and location, as well as the electrical connections of the lines at the beginning, at each pole, and at the end of the line.
## 5 Diagnostic Methods and Techniques
### Non-Invasive Imaging Modalities
#### 5.2.3 Cardiac Magnetic Resonance Imaging (MRI)  
Cardiac MRI is a diagnostic technique that utilizes strong magnetic fields and radio waves to generate cardiac images. This method is effective in distinguishing between fresh thrombi, which exhibit increased signal intensity, and older thrombi that display decreased signal intensity, making it more effective than TEE in thrombus detection [112; 113]. Additionally, cardiac MRI can measure flow velocity in the LAA and has shown good correlation with TEE [114]. However, it may overestimate the area of thrombi [115]. Its limitations include lower spatial resolution, the necessity for gadolinium-based contrast agents, and incompatibility with patients who have implanted cardiac devices [116].  
With the ongoing development of phase-contrast (PC) MRI, time-resolved 3D PC MRI with velocity encoding in three directions (known as 4D flow MRI) has become widely adopted [117]. This technique allows comprehensive measurement of 3D hemodynamics in the heart, providing global coverage throughout the entire cardiac cycle. The resulting data, which include 3D spatial information integrated with time and three velocity directions, enable the calculation of various derived hemodynamic parameters, such as peak and mean flow velocity, stagnation areas, WSS, kinetic energy, and pressure gradients [7; 78; 118; 119]. By analyzing LA stasis velocity and peak velocity, insights into the extent of blood stasis in the LAA of AF patients can be inferred [78; 120]. Despite its advantages, 4D flow MRI remains a hot topic in cardiovascular imaging; however, the technique is hindered by limited temporal and spatial resolution, which can lead to the loss of flow information [117; 121]. [PERSON] et al. [122] noted that, compared to 2D PC-MRI, the lower spatial resolution of 4D flow MRI tends to overestimate flow in smaller vessels due to partial volume effects.
## 3 Control Algorithm
### Solution of the Objective Function  
Since the actual data is heteroskedastic, this paper uses the weighted least squares method to solve the objective function and eliminate the influence of heteroscedasticity. By doing so, the solution parameter \(\beta\) satisfies the Markov estimation [26; 27]. Compared with the basic least squares method, the weighted least squares method adds a weighting factor \(\omega\) to the objective function, which is the reciprocal of the square of the load deviation at the corresponding point. The improved objective function and \(\omega\) are:  
\[\omega(j)=\frac{1}{\left(Y_{o}(j)-Y_{s}(j)\right)^{2}},\,j\in[1,\tau] \tag{12}\]  
\[\left\{\begin{  
Then, using Equation (14), the objective function is recast as:  
\[\begin{split} F=minW(Y_{o}-Y_{s})^{2}=min\Big{(}\sqrt{W}Y_{o}-\sqrt{ W}Y_{s}\Big{)}^{2}\\ =min(Y_{o}^{\prime}-Y_{s}^{\prime})^{2}=min(Y_{o}^{\prime}-C\cdot B )^{2}\end{split} \tag{15}\]  
\[Y_{s}=Y\cdot\left(\begin{array}{c}\beta_{1}\\ \beta_{2}\\ \vdots\\ \beta_{m}\end{array}\right)=Y\cdot B \tag{16}\]  
\[C=\sqrt{W}Y \tag{17}\]  
where \(Y_{o}^{\prime}\) and \(Y_{s}^{\prime}\) are, respectively, the target vector and fitting vector considering the weight coefficient \(W\). \(Y_{s}\) can be recast as Equation (16). In addition the load curve combination \(Y\) is treated as the coefficient matrix.  
Finally, a new parameter relationship can be obtained, as shown in Equations (18) and (19):  
\[\sqrt{W}Y_{o}=\sqrt{W}Y\cdot B+\gamma \tag{18}\]  
\[Y_{o}^{\prime}=Y_{s}^{\prime}+\gamma=C\cdot B+\gamma \tag{19}\]  
The process of solving the objective function with weight coefficients is shown in Figure 5. At the beginning, \(\beta\) needs to be initialized. In order to improve the convergence rate of the solution process, the initial \(\beta\) is set as a weighted least squares solution with inequality constraints and the weight coefficient is initialized to 1. It can be seen that the load curves are respectively multiplied with the corresponding \(\beta\), and the fitting curve \(Y_{s}\) is obtained by superposition. Next, \(Y_{s}\) is compared with the target curve \(Y_{o}^{\prime}\) to obtain the load deviation and the weight coefficient matrix \(W\). Then, update the matrix \(C\) and the target vector \(Y_{o}^{\prime}\) according to \(W\). Finally, we calculate the least squares solution of the objective function \(F\) with constraints and determine whether the end condition is satisfied. If not, we update \(\beta\) using the value obtained by this cycle calculation and repeat the above process.  
Figure 5: Objective function solving module.
## 2 Centralized Voltage Optimization Problem: Recalls  
Let's start from the centralized VOP formulation. It is a linearized optimal power flow (OPF) that: (_i._) minimizes the sum of the squared distances of the squared nodal voltages from their reference values; (_ii._) is subjected to the linearized PF equations and to operating limits of both nodal voltages and DERs; and (_iii._) determines the set-points of active and reactive controllers of DERs. Assigned an initial operating point, it is possible to rewrite the centralized VOP in a matrix form as follows:  
\[\min_{\mathbf{x}}\frac{1}{2}\mathbf{y}^{\mathrm{T}}\mathbf{y}+\mathbf{y}_{ \mathrm{ref}}^{\mathrm{T}}\mathbf{y} \tag{1}\]  
subject to  
\[\mathbf{y} =\mathbf{\Gamma}\mathbf{x}\] \[\mathbf{y}_{\min} \leq\mathbf{y}\leq\mathbf{y}_{\max}\] \[\mathbf{x}_{\min} \leq\mathbf{x}\leq\mathbf{x}_{\max}\]with  
\[\mathbf{y} =\mathbf{\Gamma}_{P}\,\mathbf{x}_{P}\,+\,\mathbf{\Gamma}_{Q}\, \mathbf{x}_{Q} \tag{2}\] \[\mathbf{\Gamma}^{\mathrm{T}} =\left(\mathbf{\Gamma}_{P}^{\mathrm{T}}\,\,\mathbf{\Gamma}_{Q}^{ \mathrm{T}}\right)\] \[\mathbf{x}^{\mathrm{T}} =\left(\mathbf{x}_{P}^{\mathrm{T}}\,\,\mathbf{\Gamma}_{Q}^{ \mathrm{T}}\right)\]  
where \(\mathbf{y},\mathbf{y}_{ref}\) and \(\mathbf{x}\) are the vectors of the variations, respectively, of the squared amplitude of the nodal voltages, of the squared nodal voltage reference values, and of the set-points of the active and reactive powers for the DERs controllers; \([\mathbf{y}_{min},\,\mathbf{y}_{max}]\) and \([\mathbf{x}_{min},\,\mathbf{x}_{max}]\) are the admissible ranges of variation of, respectively, \(\mathbf{y}\) and \(\mathbf{x}\). Equation (2) refers to the linearized DistFlow equations of radially-operated distribution systems presented in [21]; in particular, \(\mathbf{\Gamma}\) is the sensitivity matrix that provides the closed-form analytical expressions of the sensitivity coefficients that linearly relate the variations of the squared nodal voltages to the variations of the powers injected by all the DERs connected to the grid.  
The VOP (1) is a strictly-convex constrained quadratic programming problem providing the global minimum.