esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Comparison for Results from the 10-Unit System with Different Cost Function Characteristics
#### 5.1.1 Case 1: 10-Unit Systems with MFs  
The suggested IALO method was tested on four sub-cases of load demands consisting of 2400, 2500, 2600, and 2700 MW. For each case, 100 trial runs were executed to obtain results and comparisons. In order to investigate the search potential, we compared the proposed method with other methods based on the obtained results (including the best cost, minimum cost, and average cost) and the number of fitness evaluations \(N_{\textit{fs}}\) (which was calculated by using Equation (46) with the presence of the population size and the number of iterations). The comparison of obtained results indicated the search performance of the methods, meanwhile the comparison of \(N_{\textit{fs}}\) indicated the search speed of methods. For better comparison of search performance, we converted better cost into improvement in %, which was calculated by using Equation (48). Basically, methods have a better chance to reach better results if they are run with larger population sizes and more iterations. We wanted to reflect the real performance of the proposed method and so we compared the obtained results; however, \(N_{\textit{fs}}\) was always compared to assure a fair comparison between the proposed method and other methods. In Table 1, a comparison is shown for the same system with four different load cases. We ignored the comparisons of the mean and maximum costs because there were four comparison cases. Furthermore, the proposed method was run by using smaller \(N_{\textit{fs}}\) than that of other methods. Therefore, if the proposed method can reach the same best cost as the other methods, the proposed method is more effective because it is faster. Observing the best cost indicates that the suggested method is better than the ALO method in terms of the best cost for all of load demand cases. In comparison with the ALO, the highest improvement level was 0.931%, corresponding to the case of a 2600 MW load demand, and the lowest level of improvement was 0.330% for a 2700 MW load demand. As compared to the other methods, the IALO method can reach the same result as other methods, excluding CSA [27], which suffers from a slightly higher cost than the proposed method. For example, in the case of a load demand of 2600 MW, the best fuel cost of the IALO was 574.381 $/h. It is smaller than the 574.410 ($/h) value for the CSA, being tantamount to 0.005%. Although, the proposed method cannot reach better cost values than the other remaining methods, the proposed method uses smaller \(N_{\textit{fs}}\) than all other methods excluding EALHN [9], which is not a population-based method. In fact, the proposed method produced about 750 fitness evaluations while the other methods produced from 2000 to 20,000 fitness evaluations. The search speed of the suggested method was 2.6-26 times faster than those of the other methods. The analysis of the best cost and search speed indicates that the application of the suggested method to solve a 10-unit system with multi-fuel options is absolutely effective.
# Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms
## 4 Approximations
### IIR Approximations
#### 4.2.1 Deriche Form
 \tag{42}\]  
An example of the approximation with the first-order Deriche-style filter is provided in Figure 5. Same expressions as (39)-(42) corresponding to the second-order filter are provided below:  
\[ \begin{split} y^{+}[k]=b_{0}^{+}\,x[k]+b_{1}^{+}\,x[k-1]-a_{1}^{+ }\,y^{+}[k-1]-a_{2}^{+}\,y^{+}[k-2],\\ y^{-}[k]=b_{1}^{-}\,x[k+1]+b_{2}^{-}\,x[k+2]-a_{1}^{-}\,y^{-}[k+1]-a_ {2}^{-}\,y^{-}[k+2],\end{split}  \tag{43}\]  
\[h_{2}(x)=\Big{(}\gamma_{1}\,\cos\Bigl{(}\frac{\omega\,x}{\sigma}\Bigr{)}+ \gamma_{2}\,\sin\Bigl{(}\frac{\omega\,x}{\sigma}\Bigr{)}\Bigr{)}\cdot e^{- \frac{bx}{\sigma}},\quad\gamma_{1},\,\gamma_{2},\,\omega,\,b\in\mathbb{R}, \tag{44}\]  
\[\gamma_{1}=0.9629,\,\,\gamma_{2}=1.942,\,\,\omega=0.8448,\,\,b=1.26, \tag{45}\]  
\[b_{0}^{+}=\gamma_{1},\quad b_{1}^{+}=\Big{(}-\gamma_{1}\,\cos\Bigl{(}\frac{ \omega}{\sigma}\Bigr{)}+\gamma_{2}\,\sin\Bigl{(}\frac{\omega}{\sigma}\Bigr{)} \Bigr{)}\cdot e^{-\frac{b}{\sigma}},\]  
\[a_{1}^{+}=-2\,\,\cos\Bigl{(}\frac{\omega}{\sigma}\Bigr{)}\cdot e^{-\frac{b}{ \sigma}},\quad a_{2}^{+}=e^{-\frac{2b}{\sigma}},\]  
\[b_{1}^{-}=b_{1}^{+}-a_{1}^{+}b_{0}^{+},\quad b_{2}^{-}=-a_{2}^{+}b_{0}^{+}, \tag{46}\]  
\[a_{1}^{-}=a_{1}^{+},\quad a_{2}^{-}=a_{2}^{+},\]  
\[C=\frac{1+a_{1}^{+}+a_{2}^{+}}{b_{0}^{+}+b_{1}^{+}+b_{1}^{-}+b_{2}^{-}}.\]  
Note that values of \(\gamma_{1},\,\gamma_{2},\,\omega,\) and \(b\) have already been reported in [15], and our result of minimizing the error of the form (34) exactly agrees with them. Figure 5: Approximation of Gaussian with \(\sigma=10\) by the first-order and the second-order Deriche filters. These low-order filters require a small fixed number of arithmetic operations. Applying the first-order Deriche-style filter requires 3 additions and 4 multiplications per item, and the second-order filter 7 additions and 8 multiplications.
## 3 Evaluation of PM\({}_{10}\) Generated in Cutting Operation through Chamber Experiment (Level 2)
### Experimental Results and Analysis
#### 3.2.3 SEM Analysis  
The characteristics of PM generated from the asphalt concrete specimen-cutting activities were analyzed using SEM and EDS. Figure 10a,b show SEM images of PM generated from the asphalt concrete specimen cutting-activities at 200\(\times\) and 1000\(\times\) magnifications. Furthermore, Figure 10c,d show the EDS elemental map which is the surface analysis outcomes for each element and the EDS spectrum, respectively.  
In the SEM image analysis, individual particles of various shapes and sizes ranging from several to tens of \(\upmu\)m appeared in the PM. In addition, as a result of analysis with increased magnification, it was found that the tens of \(\upmu\)m particles were formed due to the combination of several \(\upmu\)m particles.  
Particles with typical characteristics among the observed images were classified into three types, and the analysis results are presented in Table 8. In the case of Type-1, C and O components were found to constitute 65.8% of the total. This is a carbon compound, which is a major component of the asphalt binder, and it can be seen that Type-1 is PM mainly containing the asphalt binder. Type 2 was composed of large quantities of O and Si which accounted for 77.5% of the total. These are quartz particles and are considered to be the aggregate part of the asphalt concrete specimen [36]. Unlike the above two types, Type 3 was composed of C, O, Al, Si, Mg, and various amounts of Na, S, and K. Accordingly, this type can be composed of Al-Si-O, Ca-Al-Si-O, Fe-Al-Si-O, K-Al-Si-O, Ti-Al-Si-O, and other compounds. They are considered to be aluminosilicate minerals that can be found in nature [37; 38]. Furthermore, if the contents of Ca in the above components are slightly  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test ID** & **Collected PM (g)** & **Silt (g)** & **Cut Area (m\({}^{2}\))** & **sL (g/m\({}^{2}\))** \\ \hline
1 (dry) & 200 & 35.19 & 0.04 & 879.79 \\
2 (dry) & 323 & 64.31 & 0.056 & 1148.39 \\
3 (dry) & 442 & 96.97 & 0.08 & 1212.14 \\
4 (wet) & 560 & 133.08 & 0.08 & 1663.44 \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of silt loading values associated with cutting activities.  
increased, these components could be detected as CaCO\({}_{3}\) composed of Ca-C-O, Ca-S-O, Ca-Si-Fe-Mg-O, and other compounds [39].  
Figure 10: Scanning electron microscopy (SEM) image and energy dispersive X-ray spectroscopy (EDS) mapping analysis of PM. (**a**) SEM image of PM (200\(\times\)), (**b**) SEM image of PM (1000\(\times\)), (**c**) EDS elemental mapping, and (**d**) spectrum obtained by EDS.  
Figure 9: Particle size distribution of deposited PM; Test IDs (**a**) 1, (**b**) 2, (**c**) 3, and (**d**) 4.  
The result of SEM and EDS analyses showed that three types of PM were generated during the cutting process of the asphalt concrete specimen. It was found that PM produced included mainly type 2 and type 3 PM. This is because the composition of the asphalt concrete specimen is generally 95% aggregate and 5% asphalt binder.
## 3 Results
### Carcass Characteristics  
For carcass characteristics, the inclusion of macadamia oil and vitamin E in the supplement of grazing lambs did not affect hot carcass weight, cold carcass weight, and hot and  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{4}{c}{**Experimental Diets**} & \multirow{2}{*}{**SEM**} & \multirow{2}{*}{**\(p\)-Value 1**} \\ \cline{2-2} \cline{5-6}  & **Control** & & & & \\ \hline Dry matter intake, g/day & 878.18  & 809.42  & 807.22  & 17.56 & 0.0239 \\ Supplement intake, g/day & 344.40 & 350.32 & 354.41  & 0.003 & 0.1242 \\ Pasture intake, g/day & 533.78  & 459.10  & 453.14  & 17.99 & 0.0077 \\ Metabolic energy intake, & 1.92 & 2.00 & 1.98 & 0.04 & 0.2786 \\ Mcl/kg & 16.36  & 42.93  & 42.94  & 0.90 & 0.0001 \\ c9 C16:1 intake, g/day & 0.10  & 6.35  & 6.37  & 0.17 & 0.0001 \\ C18:0 intake, g/day & 0.28  & 3.77  & 3.78  & 0.09 & 0.0001 \\ c9 C18:1 intake, g/day & 1.84  & 13.08  & 13.00  & 0.31 & 0.0001 \\ C18:2 n6 intake, g/day & 5.34  & 7.45  & 7.44  & 0.13 & 0.0001 \\ C18:3 n3 intake, g/day & 3.28  & 2.79  & 2.70  & 0.08 & 0.0001 \\ Average daily gain, g/day & 118.27 & 136.44 & 129.77 & 5.22 & 0.1737 \\ slaughter weight, kg & 32.63 & 32.16 & 32.74 & 0.89 & 0.8887 \\ Feed efficiency & 0.13  & 0.17  & 0.17  & 0.007 & 0.0007 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of macadamia oil and vitamin E supplementation on grazing lambs’ growth performance and carcass characteristics.  
cold carcass yield (\(p>0.05\)). Lambs supplemented with macadamia oil, with or without vitamin E, had on average 0.20 mm thicker subcutaneous fat compared to lambs under the Control treatment (\(p=0.0403\); Table 6).
# _Applied_
## 5 Feature Selection for both ML Approaches  
Tools for feature extraction such as _librosa_ offer a large variety of features that can be extracted from audio files. In reality, however, a large set of features do not necessarily lead to an accurate prediction. In order to find the importance of each audio attribute, algorithms for feature selection are utilized [40]. The WEKA data mining tool [41] is a useful tool for the evaluation of the audio features. Instead of using all the attributes of the features in the audio classification, only relevant attributes are involved. A lower number of features reduces the processing time as well as increases the performance of the data mining task. Therefore, features and attributes selection is performed before applying data mining tasks such as classification, clustering and outlier analysis. The WEKA features selection uses the ranker algorithm InformationGain, which gives the set of the most relevant features per dataset based on their information gain or entropy with respect to other attributes. This entropy increases with the relevance of the attribute, which can then be used to select the most relevant feature set [7]. The ultimate objective is to achieve the highest accuracy while using the smallest number of features as possible. For this evaluation, only the Spectral features supported by _librosa_[24] are considered. The features are: MFCCs, RMS, CHROMA, MEL, SC, SB, SCT, SRP, ZCR, and TZ.
# Privacy-Preserved Image Protection Supporting Different Access Rights
It used cloud storage and computing power while it did not let the cloud server know the content. They used an elliptic curve cryptosystem (ECC) to build keys among users, registration authorities and cloud servers, and proposed a biometric-based authentication system to make authentication much stricter. Registration authorities authenticate not only users but the cloud servers to make the whole process more secure. In this paper, we present a privacy-preserved method that supports different access rights in a single image for privacy protection based on the difficulty of solving factoring problems and discrete logarithm problems. We use an access policy to decide who can decrypt the cipher blocks of the image to satisfy every privacy requirement in the original image. The privacy-preserved images with the access policy and authentication information are uploaded to the cloud server. When exploring the image, users follow the authentication information to confirm the blocks that are accessible in their permission to get a decryptionkey of the blocks to obtain the original image. With the designed strategy, the image management is executed by cloud servers such that cloud servers are not aware of the private information contained in the protected image. To make this paper self-contained, in Section 2 we introduce the basic concepts of RSA public-key cryptography [28] and ElGamal public-key cryptography [29]. Section 3 explains the proposed scheme consisting of image protection method and image management method. Experimental results and their security analysis appear in Section 4. We also demonstrate how to perform different access rights in a single image for privacy protection. Finally, the paper is concluded in Section 5.
## 2 Basic SSA and Its Improvement
### Improved Sparrow Algorithm  
#### 2.2.1 Introduction of Golden Sine Strategy  
The horizontal coordinates in Figure 1 represent producers sorted by fitness values, while the vertical coordinates represent the coefficient of variation of each producer's position. As illustrated in Figure 1, the larger the producer's ordinal number, the smaller the coefficient of variation of the position, which increases the probability of the producer falling into the local optimum. In summary, Equation (1) does not carry out a global search.  
To improve the problem of the weak global search ability of the producers, the golden sine algorithm (Gold-SA) was introduced [23]. This algorithm traverses the whole search space by the relationship between the unit circle and the sine function on the one hand, and on the other hand, it gradually moves from the boundary of each dimension to the middle by the number of golden divisions until the best position for each dimension is found. Thus, in the paper, the golden sine algorithm is used to improve the global search ability of the producers in SSA. The equation of the discoverer position update after the introduction of the golden sine algorithm strategy is shown in Equation (4).  
\[X=\left\{\begin{array}{ll}X_{b,j}^{t}\cdot|\sin(r_{1})|+r_{2}\cdot\sin(r_{1}) \cdot|x_{1}\cdot X-x_{2}\cdot X|&\text{if }R_{2}<ST\\ X_{i,j}^{t}+Q\cdot L&\text{if }R_{2}\geq ST\end{array}\right. \tag{4}\]  
where \(r_{1}\) and \(r_{2}\) are the random numbers within \([0,\,2\pi]\) and \([0,\,\pi]\), respectively. \(X_{b,j}^{t}\) represents the position of the \(j\)-th dimension of the optimal individual in the \(t\)-th iteration. The initial value of \(x_{1}\) is \(a\cdot(1-\tau)+b\cdot\tau\), and the initial value of \(x_{2}\) is \(a\cdot\tau+b\cdot(1-\tau)\), where \(\tau\) is the golden mean and the initial value of \(a\) and \(b\) is \(-\pi\) and \(\pi\), respectively. \(x_{1}\), \(x_{2}\), \(a\), and \(b\) dynamically are updated as shown in Algorithm 1.  
```
/* \(F\) represents the current fitness value, \(G\) represents the global optimal value, \(random_{1}\) and \(random_{2}\) represent random numbers between \([0,1]\)*/ Input: \(x_{1}\)+\(a\cdot(1-\tau)+b\cdot\tau\); \(x_{2}\)+\(a\cdot\tau+b\cdot(1-\tau)\); \(a\)+\(-\pi\); \(b\)+\(\pi\); Output: \(x_{1}\), \(x_{2}\)
1: if (\(F<G\)) then
2:\(b\gets x_{2};x_{2}\gets x_{1};x_{1}\gets a\cdot\tau+b\cdot(1-\tau)\);
3: else
4:\(a\gets x_{1};x_{1}\gets x_{2};x_{2}\gets a\cdot(1-\tau)+b\cdot\tau\);
5: end if
6: if (\(x_{1}=x_{2}\)) then
7:\(a\gets random_{1};b\gets random_{2}\);
8:\(x_{1}\gets a\cdot\tau+b\cdot(1-\tau)\); \(x_{2}\gets a\cdot(1-\tau)+b\cdot\tau\);
9: end if
```  
**Algorithm 1**Pseudo-code for partial parameter update of Gold-SA
## 2 Materials and Methods
### Cross-Validation and Generalized Cross-Validation  
The preference for smoothing parameter \(\lambda\) is crucial for selecting the best fit and smoothest curve. When \(\lambda\) increases, stress is imposed on the smoothness of the curve but less on the fitting of the curve, whereas when \(\lambda\) decreases, stress is imposed on the fitting of the curve but less on the smoothness. When \(\lambda=0\), it indicates that a least squares fit is applied. The smoothing parameter \(\lambda\) can be chosen either by automatic or subjective techniques. There are two methods of selecting \(\lambda\) automatically, cross-validation (CV) and generalized cross-validation (GCV), as initiated by [PERSON] and [PERSON] (2007). These methods allow the data to choose the value of \(\lambda\). The principle of CV is denoted by  
\[\text{CV}(\lambda)=\sum_{i=1}^{N}\left[y_{i}-\alpha_{\lambda}^{(-i)}-\int x_{ i}(t)\beta_{\lambda}^{(-i)}dt\right]^{2}, \tag{7}\]  
where \(\alpha_{\lambda}^{(-i)}\) and \(\beta_{\lambda}^{(-i)}\) are estimated regression parameters approximated without the \(i^{\text{th}}\) observation ([PERSON], 2007). GCV, a simpler method, was developed after CV, as it is more reliable and data undersmoothing is unlikely to take place ([PERSON], 2007). The principle of GCV is expressed as  
\[\text{GCV}(\lambda)=\left(\frac{n}{n-df(\lambda)}\right)\left(\frac{\text{SSE }}{n-df(\lambda)}\right), \tag{8}\]  
where \(df\) is the degrees of freedom for spline smoothing \(n\) times ([PERSON], 2007).  
However, it is suggested that an automatic method such as CV be used as a guide rather than a fixed rule prior to using a subjective method to select \(\lambda\)([PERSON], 2007). Nevertheless, the credibility of these two methods was tested. Both CV and GCV methods were analyzed using the _glmnet_ package in R, and the codes are provided in Appendix A.2. R Code A2 and Appendix A.3. R Code A3, respectively. The subjective technique used referred to the first and second derivatives, velocity and acceleration. The rule was to find an optimal \(\lambda\) and spline basis that smoothed the derivatives while maintaining similar trends or original traits of the derivatives obtained from the graph of the functional data object created (Figure 2b) and the smallest value of \(\lambda\) that preserved the original pattern of the curve (Figure 2c).
## 3 Results and Discussion
### Application to Real Matrices  
In order to demonstrate the reliability of the proposed methodology for real matrices, BA\(\mathrm{\SIUnitSymbolMicro}\)E(PS-DVB)-\(\mathrm{\SIUnitSymbolMicro}\)LD/HPLC-DAD was applied to monitor both hydrophilic UV filters in tap, estuarine, and sea water, as well as wastewater samples. For such purpose and in order to suppress possible matrix interferences, SAM was used for quantitative purposes, by spiking the samples with four working standards, ranging from 0.4 to 8.0 \(\mathrm{\SIUnitSymbolMicro}\)\(\mathrm{L}^{-1}\) for PBS and 2.0 to 40.0 \(\mathrm{\SIUnitSymbolMicro}\)\(\mathrm{L}^{-1}\) for BZ4. Blank assays were also performed without spiking to ensure maximum control of the analytical methodology. As expected, in the water matrices studied, any of the analytes were detected (\(<\) LODs). As already reported in the literature [14], significant matrix effects were observed during the study of the real matrices, although good selectivity and linearity were achieved, with determination coefficients higher than 0.9940 (BZ4, wastewater sample), obtained from the regression plots (Table 3). Even so, the methodology proposed showed to be influenced by the substantial matrix effects observed, in particular, for the more complex samples, i.e., sea and estuarine water, as well as wastewater. As the UV filters involved are extremely hydrophilic, the higher is the complexity of the matrix, the lower is the average extraction efficiency. For instance, the sea and the estuarine samples studied presented efficiencies which were three-quarters of that obtained for ultra-pure water, and the wastewater presented about one-third. Figure 3 depicts chromatogram profiles obtained from assays performed on spiked (4.0 \(\mathrm{\SIUnitSymbolMicro}\)\(\mathrm{L}^{-1}\) for PBS and 20.0 \(\mathrm{L}^{-1}\) for BZ4) ultra-pure (a), tap (b), sea (c), and estuarine (d) waters, as well as wastewater (e) samples, in which very good selectivity is noticed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**UV Filter** & **Tap Water** & **Sea Water** & **Estuarine Water** & **Wastewater** \\ \hline \multicolumn{5}{c}{\(r^{2}\)} \\ \hline PBS & 0.9999 & 0.9998 & 0.9975 & 0.9942 \\ \hline BZ4 & 0.9998 & 0.9998 & 0.9994 & 0.9940 \\ \hline \multicolumn{5}{c}{**Slope**} \\ \hline PBS & 15.06 & 10.21 & 9.25 & 5.25 \\ \hline BZ4 & 4.28 & 2.69 & 2.47 & 1.10 \\ \hline \multicolumn{5}{c}{**Content** (\(\mathrm{mg\ g^{-1}}\))} \\ \hline PBS & \(<\)LOD & \(<\)LOD & \(<\)LOD & \(<\)LOD \\ \hline BZ4 & \(<\)LOD & \(<\)LOD & \(<\)LOD & \(<\)LOD \\ \hline \hline \end{tabular}
\end{table}
Table 3: Determination coefficients (\(r^{2}\)), slopes, and contents obtained for the two UV filters by BA\(\mathrm{\SIUnitSymbolMicro}\)E(PS-DVB)-\(\mathrm{\SIUnitSymbolMicro}\)LD/HPLC-DAD in several real matrices, under optimized experimental conditions.
# Numerical and Experimental Study of a Large-Scale Natural Circulation Helium Loop
## 3 Mathematical Model
### Solution Algorithm
The simulation of heat loss through the pipe wall was based on the temperature of the environment and the heat flux coefficient of air. The primary sources of the energy input and output were the heater and cooler models. Both of them included an enhanced energy equation in which the energy fluxes and heat accumulation are modeled. The energy output--through the cooler--was moderated by the H\({}_{2}\)O mass flow rate and temperature, which were set as boundary conditions. The energy input--through the heater--was also set as a boundary condition. Thus, the value was known (electric power input). The minor loss coefficients were set as constants (according to results in [18]). The overall model also covered helium mass leakage, which was set as a boundary condition. Its value was based on an approximation of experimental data [18]. The initial conditions were evaluated from data measured along the loop for each scenario. The measured values were averaged, and the resulting value was set as an initial value. The initial variable values (\(T\), \(p\), and \(\dot{m}\)) were constant along the loop. The method's suitability for thermosyphon simulation was confirmed by the authors of [19]. The system of Equations (1)-(4) is nonlinear by nature and, therefore, was converted into a linear system that could be described by sparse matrixes. Thus, the solution could be obtained with a rapid direct technique (e.g., LU factorization) [20; 21]. The numerical implementation of the complex model was performed by using the C/C++ programming language.