esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
The proposed LO's performance is evaluated using 23 standard test functions in this section. Minimization optimization problems with various complexity and dimensional search spaces are used in these test cases. These test functions are divided into three categories, including unimodal, multimodal, and fixed-dimension multimodal functions. The primary features of the three categories are presented in Tables 2-4. The tables show the function name, the function mathematical model, the range of the search space's boundaries, the functions dimension (\(n\)), and the functions' optimum solution \(f(x^{*})\). The programming language MATLAB version 9.12.0 is used to conduct the experiments, and the code is available in the \"Lemurs-Optimizer\" GitHub, available online: [[https://github.com/ammarabbasi/Lemurs-Optimizer](https://github.com/ammarabbasi/Lemurs-Optimizer)]([https://github.com/ammarabbasi/Lemurs-Optimizer](https://github.com/ammarabbasi/Lemurs-Optimizer)), accessed on 28 September 2022.  
As mentioned previously, the test functions used in this section are divided into unimodal, multimodal, and fixed-dimension multimodal functions. The multimodal and fixed-dimension multimodal categories are similar but differ from each other to define the number of decision variables. The fixed-dimensional test functions provide various search spaces compared with multimodal test functions. However, tuning the decision variables cannot be done using the fixed-dimensional test functions' mathematical model. In this evaluation section, the unimodal functions are utilized to investigate the proposed LO exploitation ability, whereas the multimodal functions are utilized to examine and evaluate the exploration side of the LO [37].
## 4 Comparative Analysis
#### 4.1.2 When Top-1 Nearest Neighbor Is Used  
The Figure 6 shows the comparison between traditional CF algorithms (CF_TMC, CF_TWA, and CF_TZS) and CF algorithms using items' categorical attributes (CF_UIMC, CF_UIWA and CF_UIZS), based on MAE value using Top-1 nearest neighbor at datasets ML\({}_{1}\) and ML\({}_{2}\). In the graph CF algorithms using items' categorical attributes attain low prediction errors than traditional CF algorithms in all traditional prediction approaches. Therefore, CF_UIMC, CF_UIWA, and CF_UIZS provide more accurate recommendation results than CF_TMC, CF_TWA, and CF_TZS respectively.  
Figure 5: RMSE Value at Dataset2.  
The Figure 7 depicts the comparison between traditional CF algorithms and CF algorithms using items' categorical attributes, based on MAE value at various datasets ML\({}_{3}\), ML\({}_{4}\) and ML\({}_{5}\). We can notice that CF algorithms using items' categorical attributes provide less prediction error in all traditional prediction approaches (i.e., MC, WA, and ZS). Therefore, CF_UIMC, CF_UIIWA and CF_UIZS outperform CF_TMC, CF_TWA and CF_TZS respectively.  
Figure 6: MAE Value at Dataset1.  
The Figure 8 shows the comparison between traditional CF algorithms and CF algorithms using items' categorical attributes, based on RMSE value at Top-1 nearest neighbor. As shown in the graph, CF algorithms using items' categorical attributes have comparatively low RMSE values than traditional CF algorithms. Therefore, the comparative results of the above graph uphold the fact that CF_UIMC, CF_UIWA, and CF_UIZS are better CF algorithms than CF_TMC, CF_TWA, and CF_TIZS respectively.  
Figure 7: MAE Value at Dataset2.  
The Figure 9 represents the comparison between traditional CF algorithms and CF algorithms using items' categorical attributes, based on RMSE value at various datasets ML\({}_{3}\), ML\({}_{4}\) and ML\({}_{5}\). The CF algorithms using items' categorical attributes have a minimal prediction error than traditional CF algorithms for all traditional prediction approaches (i.e., MC, WA, and ZS) Therefore, on the basis of RMSE, CF_UIMC, CF_UIWA and CF_UIZS are more preferable algorithms than CF_TMC, CF_TWA, and CF_TZS respectively.  
Figure 8: RMSE Value at Dataset1.  
From Figure 2-9, we can easily observe that UIBC achieves comparatively lower MAE and RMSE than BC for all k values. Therefore, we can claim that SeM using items' categorical attributes enhances the recommendation accuracy under all traditional prediction approaches.  
Figure 9: RMSE Value at Dataset2.  
Comparison of the Proposed Recommendation Approach and CF Algorithm Using Items' Categorical Attributes  
However, ICA contributes a considerable impact on the accuracy of the prediction of CF. In this section, we explain the importance of'most' similar neighbor in a prediction approach for the sparse dataset. The following Figures 10-17 represent the comparative results of the proposed recommendation approach and CF using ICA.
## 1 Introduction
### Contributions and Paper Organization  
Subsequent to the variation in FCL from WTGs due to the stochastic nature of wind speed, wind direction, and metrological conditions in wind farm-dominated distribution networks, the relay settings can be disturbed. Therefore, the relay settings must be updated adaptively in line with the variation in FCL. In this paper, a robust adaptive overcurrent relay coordination scheme for a WTG-integrated distributed system based on forecasting WD is presented to deal with the variation in FCL. It forecasts the wind speed and direction and calculates the FCL in advance. Furthermore, the seasonal autoregression integrated moving average (SARIMA) and adaptive neural network-based fuzzy inference system (ANFIS) are adopted for forecasting the periodic and nonperiodic WD, respectively. On the basis of the predicted FCL, the ORC problem is formulated as an NLP problem and is tackled by hybrid Harris hawks optimization and linear programming (HHO-LP). The main purpose of employing HH--LP is to achieve a remarkable reduction in the total operating time of relays by optimizing the relay settings to reduce the level of equipment damage and nuisance-related WTG disconnection, and to improve the reliability.  
The key contributions of this paper are as follows:  
* The delay in updating the relay settings and the coordination with other relays can cause the malfunctioning of OCRs. A considerable delay time is evaded when updating the relay settings by predicting the wind speed and FCL variation in advance.
* The hybrid ANFIS-SARIMA is devised for predicting periodic and nonperiodic wind series.
* An efficient optimization model HHO-LP is established for the existing constraints.
* A significant reduction in the overall tripping time of relays is achieved.
* The is no record of miscoordination or limit violation.  
The rest of the paper is organized as follows: Section 2 describes the problem formulation and objective functions for relay coordination. Section 3 addresses the proposed methodology. The investigated test systems and the obtained results are reflected in Section 4. Finally, Section 5 concludes the manuscript.
## 3 Results and Discussion
### Effects of the Convergent Length on Cavitation Characteristics
#### 3.3.2 Analysis of Cavitation Collapse Strength  
The formation and collapse of cavities often lead to changes in the local turbulent kinetic energy [14; 20]. Figure 15 depicts the profile of turbulent kinetic energy distribution within the Venturi tube under varying inlet pressures. The plot effectively captures the discernible alterations in turbulent kinetic energy resulting from the inception of cavitation at the throat region and the subsequent bubble collapse in the divergence segment. In contrast to Venturi tubes with varying divergence lengths, alterations in the convergence length of a Venturi tube exert an influence on the turbulent kinetic energy at both the throat and diffuser sections. Comparing the profiles of the turbulent kinetic energy in Figure 15a-c, it is evident that the effect of the convergence length on the turbulence kinetic energy at the throat is consistent, regardless of the pressure conditions. Clearly, the peak value of the turbulent kinetic energy at the throat increases with the increasing convergence length. The turbulent kinetic energy distribution at the divergence section of a Venturi tube varies differently under varying pressure conditions. Specifically, at an inlet pressure of 0.4 MPa, it is apparent from Figure 15a that Venturi tubes with longer convergence lengths exhibit higher peak turbulent kinetic energy and stronger bubble collapse intensity at the divergence section. Conversely, at an inlet pressure of 1.2 MPa, the peak turbulent kinetic energy at the divergence section of Venturi tubes with different convergence lengths is similar, and the intensity of the bubble collapse is comparable as well. These observations suggest that the variations in the turbulent kinetic energy at the divergence section of the Venturi tube are more sensitive to pressure conditions than the convergence length.  
Figure 16 presents the trends in the variations of the two locations of turbulent kinetic energy peaks within the Venturi tube. In general, both peaks show an increase as the inlet pressure rises. The growth trend of the peak at the throat is relatively stable. Longer convergent sections in the Venturi tube lead to higher turbulent kinetic energy peaks. However, the impact of the convergent section length on the turbulent kinetic energy peak in the divergence section depends on the inlet pressure. As the inlet pressure increases, the effect of the convergent section length on the turbulent kinetic energy peak in the divergence section gradually diminishes. This indicates that the effect of the Venturi tube convergence section length on the downstream bubble collapse strength is limited, and this effect gradually weakens as the inlet pressure increases.  
Figure 14: Vapor volume fraction contour of Venturi tubes at various inlet pressures and convergence lengths: (**a**) 0.4 MPa, (**b**) 0.8 MPa, and (**c**) 1.2 MPa.
## 3 Results and Discussion
### Changes in pH, as Well as Lactic Acid, Reducing Sugar, and Uronic Acid Contents, During Pickling
#### 3.1.4 Uronic Acid Content  
The amount of uronic acid content in apples is related to the quantity of pectin, a cell wall component.  
Pectin can be demethylated by pectin methyl esterase, obtaining low esterification pectin, which in combination with Ca\({}^{2+}\) improves the texture of fruits and vegetables. Uronic acid content decreased continuously until the 35 th day of fermentation, independent on the type of salt used (Figure 6).  
These results support the variation of firmness values for the apple mesocarp tissues of all samples. Galacturonic acid, a monomer of pectin molecules, induces changes in textural parameters. Usually, pectin is water soluble, but the solubility depends on the degree esterification of galacturonic acid and the makeup of the side chain. Similar observations were also reported for pickled cucumber [41].  
Figure 5: Changes in reducing sugar content during apple fermentation.  
Table 1 shows that there were strong correlations (\(r>0.75\)) between the physicochemical parameters that were observed during 35 days of fermentation. Thus, independent of the salt used to prepare the brine, the pH values were very well correlated both with the level of reducing sugars (\(r=0.8245\ldots 0.9869\)) and uronic acids (\(r=0.7043\ldots 0.7361\)). These correlations could be explained by the fact that the microbial population in the samples fermented the sugars and the uronic acids (\(r=0.8023\ldots 0.9104\)) obtained by hydrolysis of the macromolecular carbohydrates from apples, starch, and pectin substances, inducing the decrease in pH [42].  
Replacing NaCl with the two types of salt, KCl and MgCl\({}_{2}\), did not influence the dynamics of the reactions of polysaccharide hydrolysis and fermentation, because both K\({}^{+}\) and Mg\({}^{2+}\) are mineral elements important in the nutrition of the native vegetal microbiome [43].
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 2 Materials and Methods
### Knuth Shuffle Algorithm  
The Knuth shuffle algorithm [12] can realize in-situ shuffling when the length of the array is known. It only needs one traversal to randomly shuffle the elements in the array, and its time complexity is \(O(n)\). Since there is no need to open up additional space, the space complexity is only \(O(1)\). The shuffle process is as follows:  
1. Enter data, numbered \(1\sim n\);
2. Randomly generate an integer \(k_{1}\) in the range \(1\sim n\), and exchange \(k_{1}\) and \(n\);
3. Randomly generate an integer \(k_{2}\) in the range \(1\sim n-1\), and exchange \(k_{2}\) and \(n-1\);
4. Randomly generate an integer \(k_{i}\) in the range \(1\sim n-i+1\), and exchange \(k_{i}\) and \(n-i+1\);
5. Continue until \(i=n-1\), the shuffle process is complete.  
Figure 1: Bifurcation diagram of Kent chaos.  
Figure 2: Bifurcation diagram of 2D logistic chaos (**a**) \(u_{1}\) and \(x\); (**b**) \(u_{1}\) and \(y\).  
The Knuth shuffle algorithm divides the array into two parts: the head and the tail, which store the unprocessed data and the processed data respectively. Each time, one datum is randomly selected from the unprocessed data and exchanged to the tail of the array without opening up additional storage space. This shuffle process is mainly divided into two processes: card selection and card exchange. Take step (4) as an example, card selection corresponds to the process of generating an integer \(k_{i}\), and card exchange corresponds to the process of exchanging \(k_{i}\) and \(n-i+1\). The algorithm in this paper redesigns the card selection and card exchange processes, and uses improved card selection as the selection strategy for bit-scrambled pixels. In the improved card exchange, the selected three pixels are bit scrambled each time.
###### Abstract  
The Folin-Ciocalteu assay is a widely used method for measuring the total phenolic content (TPC) in honey, but it can be affected by the presence of reducing sugars in honey, which can lead to interference and an over-estimation of its TPC. To optimize the Folin-Ciocalteu assay for honey analysis, the effect of pH on the assay was investigated. A number of pH scenarios were tested using different concentrations of Na\({}_{2}\)CO\({}_{3}\) (0.00%, 0.75%, 0.94%, and 7.50%) in order to minimize reducing sugar interference and maximize the reaction of phenolics in the assay. The modified TPC method was then validated in accordance with current International Council on Harmonisation (ICH) guidelines. The findings of this study demonstrate that the traditional Folin-Ciocalteu assay (using 7.50% aqueous Na\({}_{2}\)CO\({}_{3}\) solution, pH 10.8) leads to a significant overestimation of the TPC of honey due to the interference of reducing sugars. However, a pH of 7.9, achieved by using a 0.75% aqueous Na\({}_{2}\)CO\({}_{3}\) solution, provides suitable conditions to account for most of the phenolic compounds without interference from reducing sugars. This finding was further confirmed by testing various sugar solutions and artificial honey which yielded TPC values below the established limit of detection and quantification of the assay. However, a slight increase in pH, even by a moderate deviation (pH 8.9), leads to significant discrepancies in absorbance readings, indicating that pH control is crucial for the accurate analysis of TPC in honey.  
modified Folin-Ciocalteu assay; Folin-Ciocalteu (FC) assay; total phenolics content assay; TPC; _Eucalyptus marginata_ (Jarrah); _Leptospermum_ sp. (Manuka); honey; assay validation
## 5 Results
### Exergoeconomic Analysis  
The exergoeconomic analysis represents a unique combination of an exergetic analysis and an economic analysis. For all design cases, a ranking of components according to their cost influence is given in Table 6. For a comparison of the cases with different capacity, the cost rate associated with the investment and the exergy destruction (\(\dot{C}_{D,K}+\dot{Z}_{K}\)) is related to the exergy of product. In addition, an objective for improvement can be derived for each component by considering the exergoeconomic factor \(f_{k}\). High values of \(f_{k}\) suggest a decrease of the investment cost, while low values recommend an increase in the exergetic efficiency in order to reduce the cost rate associated with the exergy destruction. In particular, the combustion units, the air and syngas compressors, and the turbomachinery have a high cost impact. Single heat exchangers in the heat recovery unit (sub-system V) also have a significant influence on the expenses. Gas- and steam heated tubular reactors (R-05 in Case I, R-04 and R-08 in Case II, R-04, R05 and R07 in Case III) have comparatively small cost rates. In the following, the most important components of the cases are discussed. The direct fired steam reformer in Case I has the largest effect on the overall cost, because of the cost of exergy destruction within it. Large irreversibilities are related to the mixing of natural gas, purge gas and air as well as to the combustion reaction and the heat transfer to the tubes. Furthermore, the syngas reacts with steam in a highly irreversible catalytic reaction taking place within the tubes. The low value of the exergoeconomic factor indicates the dominant cost influence of the irreversibilities within this component. Mainly due to the high investment cost, the air compressors CM-01 and CM-02 are rated to positions two and three. The \(f\)-value is close to one, suggesting a decrease in investment cost by accepting larger irreversibilities. This also applies to the components T-01, T-02, CM-04 and CM-06, whereby their influence on the overall cost is of lower significance. In Case II the furnace R-07 is ranked to position number one, having the highest relative cost impact among all components. This is due to the destruction (to a large extent) of the costly exergy stream of the purge gas from the synthesis loop (stream 12) and the costly exergy stream from the air compression unit. Downstream of R-07, the high cost impacts of the medium pressure evaporator (MP-EVA) and the economizer (MP-ECO) are accordingly caused by the supply of costly exergy (flue gas) in conjunction with the high irreversibilities due to heat transfer. The high specific cost of the flue gas (stream 9 in Table 5) also results from the investment cost of the air compression unit (CM-02), which is rated to position number three. The component has an exergoeconomic factor of \(f_{k}=0.83\), which recommends a reduction of the investment cost by accepting higher exergy
# WAMS-Based Online Disturbance Estimation in Interconnected Power Systems Using Disturbance Observer
## 4 Power System Under Investigation  
In real power systems, areas are interlinked to form one overall power system. An area can represent a single power plant with a number of generators. These several power plants are then joined to form a multi-area power system. In some cases, an area can represent a country and a number of countries can be joined to form a multi-area power system. As an example, to analyze the advantages of the proposed method, a four-area power system with non-reheated turbines is used in our simulations. The power system has been used in some research papers dealing with load frequency control [5,6]. In our case, we are applying it to estimate a disturbance in the event of a generation loss. As seen in Figure 4 Areas 1, 2 and 3 are interconnected with each other, while Area 4 is only connected with Area 1. Areas 1, 2, 3 and 4 are identical systems with non-reheated turbines. Figure 3 shows the general structure of each area. The data for the power system is given in Table 1.
# Frequency Selective Surfaces: A Review
## 2 Periodic Structures
Perfect conducting surfaces (PCSs) create destructive interference when excited by antennas, horizontally polarized and closely-spaced. Conversely, perfect magnetic conductors (PMC) provide constructive interference due to magnetically induced surface currents. PMCs are naturally non-existent, however; artificial magnetic conductors (AMCs) are artificially produced PMCs, and from EM perspective, these are dual to PCSs. AMCs are based on periodically engineered structures that act as a sheet of magnetic currents having magnetic polarisability. FSSs are usually designed by periodic metallic arrays of elements on a dielectric substrate and exhibit transmission and reflection characteristics at a certain resonant frequency. Mushroom structures are the class of FSSs that is designed to resonate at low frequencies as the distance between metal array element and a metal plane creates an inductive effect at microwave regime. Similarly, perfect absorbing surfaces are thin layer structures which absorb nearly all the incident power of EM wave at a particular frequency and angle of incidence at microwave to optical operating frequency ranges. In conclusion, it is worth mentioning that if the periodicity of the array elements (Inter-element spacing) is not very small when compared to the wavelength in free space, it may not be possible for periodic structures to reach a stable resonance with varying angles of incidence [2].
## 2 Methods and Technologies
### Clinical Evaluation Tools  
#### 2.5.1 sEMG  
Surface electromyography (sEMG) data were measured by Trigno wireless EMG (Delsys, Boston, MA, USA), bipolar electrodes (Ag/AgCl), a pre-gelled diameter of 20 mm, and the inter-electrode distance was around 3 cm. The essential raw EMG signals were sampled at 1000 Hz and were handled into a root mean square (RMS) with a window of 50 ms. A bandpass filter of 20-450 Hz was used together with notch filters at 60 Hz. Every muscle was measured by RMS of a 5 s for normalization of sEMG data collected from the maximal voluntary isometric contraction (MVIC) for the muscles at the manual muscle test-position, followed by [PERSON] and [PERSON] (2005) [14].  
Collected raw data were presented by 60 Hz bandwidth according to the phase definitions for drinking tasks modified for the trial. Single sEMG makers were attached to upper trapezius, deltoid middle part, biceps brachii, triceps brachii, extensor carpi radialis longus.  
Figure 4: (**a**) speed bumps, braille beam block, general-purpose road curb; (**b**) ramps of similar standards to those installed in Korean public institutions and out-doors; (**c**) various types of flooring panels with different materials and roughness; and (**d**) general curve reflecting road conditions.