esa-sceva/satcom-chunk-collection huggingface dataset:
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 4 Leakage Localization Based on BP Neural Network
### Back-Propagation Neural Network  
The back-propagation neural network is a type of multilayer network that trains the weights of the nonlinear differential function. This kind of algorithm of the neural network was first put forward in 1986 by the parallel distributed processing group [PERSON] et al. Currently, the BP-based multilayer perception is one of the most widely-used neural networks. It is suitable to process the massive amount of data in long-distance pipeline monitoring with good properties of fault-tolerance, self-organized learning and self-adaptivity. The typical back-propagation network consists of an input  
Figure 5: Hoop strain time history at the end node in different leak locations.  
layer, an output layer and at least one hidden layer, as shown in Figure 6. The BP neural network has great capabilities for nonlinear approximation and pattern recognition, which have been used in many fields across mathematics, medical science, finance and engineering.
# Emotion Classification Algorithm for Audiovisual Scenes Based on Low-Frequency Signals
## 3 Numerical Results
### Data Results and Analysis of Subjective Evaluation Experiments
#### 3.1.2 Brain Wave Data Statistics and Result Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotion** & **Lead Positions** & \(\theta\)**Rhythm** & \(\alpha\)**Rhythm** & \(\beta\)**Rhythm** \\ \hline \multirow{4}{*}{Happiness} & AF3 & 1.17 & 1.04 & 0.94 \\  & AF4 & 1.15 & 1.05 & 0.91 \\  & F3 & 1.17 & 1.09 & 0.94 \\  & F4 & 1.18 & 1.07 & 0.90 \\ \hline \multirow{4}{*}{Sadness} & AF3 & 1.05 & 1.08 & 1.21 \\  & AF4 & 1.11 & 1.03 & 1.18 \\ \cline{1-1}  & F3 & 1.10 & 0.94 & 1.21 \\ \cline{1-1}  & F4 & 1.06 & 1.03 & 1.13 \\ \hline \multirow{4}{*}{Shock} & AF3 & 0.87 & 0.99 & 1.22 \\ \cline{1-1}  & AF4 & 0.84 & 1.02 & 1.25 \\ \cline{1-1}  & F3 & 0.85 & 0.97 & 1.19 \\ \cline{1-1}  & F4 & 0.85 & 0.96 & 1.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of the average relative power spectral density ratio results between the LFS added and the original video signal for four lead positions. by three LFS signals known to trigger happy emotions were consistent with the results in Table 3, but there was no apparent pattern among these three signals. However, when the known emotions were sadness and shock, the effects of LFS signals on EEG energy showed a consistent pattern. For the signals S4, S5, and S6, the strength of \(\theta\) rhythm energy induced after adding them to video signals that clearly elicited sadness emotions was in the order of S6 > S5 > S4, while the relationship between the LFS signals and the increase in \(\alpha\) and \(\beta\) rhythm values was opposite, i.e., S6 < S5 < S4. This indicates that the addition of LFS signals overall stimulates the energy of emotion-related brain functional areas in terms of EEG waves, but the effects of different LFS signals on different rhythm energies are different. In the observed EEG results of known shock, it was found that the signals S7, S8, and S9 showed a relatively consistent relationship with the three rhythm energies. Although they all suppressed \(\theta\) rhythm energy, the suppression intensity was in the order of S7 > S8 > S9, and the activation effects on \(\alpha\) rhythm were in the same order. Similarly, they all had an activating effect on \(\beta\) rhythm energy, but the degree of activation was in the opposite order, i.e., S7 < S8 < S9 (Table 6).
## 3 Results and Discussion  
### Structural Analysis and Morphological Study  
XRD powder patterns of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Eu\({}_{x}\)Sm\({}_{x}\)Fe\({}_{1.5-2\mathrm{x}}\)O\({}_{4}\) (0.01 \(\leq\) x \(\leq\) 0.05) NPs are offered in Figure 1. It is clear that all peaks belonged to a single phase of Mn-Zn spinel ferrite and no other peaks for the extra phase could be observed. This is evidence that the substituted ions were merged successfully into the spinel lattice. The lattice parameters and crystallite size were estimated by full proof refinement [26; 27; 28]. The lattice parameter \"a\" was 8.463, 8.434, 8.428, 8.413, and 8.408, respectively. It is obvious that the lattice parameters decreased when the ratio of Sm and Eu ions increased because of the substitutions of some Fe ions by larger ionic radii ions. The average of the crystallite sizes is in the range 7-12 nm. Figure 2 presents the SEM images of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Eu\({}_{x}\)Sm\({}_{x}\)Fe\({}_{1.5-2\mathrm{x}}\)O\({}_{4}\) (x = 0.01, 0.03, and 0.05). All samples exhibited an agglomerated spherical particle. EDX was used to approve the stoichiometric composition spinel ferrite. The TEM of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Eu\({}_{x}\)Sm\({}_{x}\)Fe\({}_{1.5-2\mathrm{x}}\)O\({}_{4}\) (x = 0.03 and 0.05) spinel ferrites displayed a spherical particle, as seen in Figure 3. It is well known that the complex 3d-metal oxides easily allow oxygen excess and/or deficit. Oxygen nonstoichiometry greatly affects the magnetic and magnetoelectric properties of complex oxides. Oxygen nonstoichiometry changes the oxidation degree of 3d-metalls and magnetic parameters, such as the total magnetic moment and Curie point. The intensity of the exchange interactions decreases with the oxygen vacancy concentration increase. Exchange near the oxygen vacancies is negative according to Goodenough-Kanamori empirical rules [29; 30].  
Figure 1: XRD powder patterns of Mn\({}_{0.5}\)Zn\({}_{0.5}\)Eu\({}_{x}\)Sm\({}_{x}\)Fe\({}_{1.5-2\times}\)O\({}_{4}\) (0.01 \(\leq\) x \(\leq\) 0.05) NPs.
## 4 Optimization of the IWOLA + FFT Algorithm  
### Architecture of the IWOLA + WOLA Algorithm  
Based on the analysis results, the IWOLA + FFT algorithm can be used to perform ultra-large FFT calculations, with a nearly identical channel performance with respect to the ultra-large FFT, thus achieving spectral analysis with an ultra-fine frequency resolution. For improved channel characteristics, this paper proposes an IWOLA + WOLA algorithm by substituting the second-stage FFT with the WOLA filter bank to optimize the IWOLA + FFT algorithm. The overall framework of the IWOLA + WOLA algorithm is shown in Figure 13.  
Figure 11: Comparison of the spectral leakage.  
Figure 12: Transient response of the IWOLA filter bank.  
The three upper modules of the IWOLA + WOLA algorithm in Figure 13 are the same as those of the IWOLA + FFT algorithm. The WOLA filter bank outputs are the time-frequency spectrum for each subchannel ranging from 0-K/2. Therefore, the output data of the WOLA filter bank should be accumulated for linear frequency modulation (LFM) signals if the LFM signal is modulated within a long time-period. Otherwise the bandwidth of the LFM signals may be reduced after processing by the IWOLA + WOLA algorithm. In addition, the data accumulation for each sub-channel can improve the SNR. Before the output, the data of each sub-channel from the module of the power calculation and accumulation should then be re-ordered, given that the input data into the succeeding WOLA filter bank are complex. The re-ordering method is presented in Table 2. Furthermore, the re-ordered data should be partially selected to represent the entire sub-channel due to the NMDFB, as shown in Table 3. The number of selected data is K1/2. The selected data can then be combined into one group according to the order of the channel numbering.
## 3 The Proposed HSDW-MI Algorithm
### Complexity Analysis of the Algorithm  
The time complexity and space complexity of the proposed HSDW-MI are analyzed as follows.  
1. Time complexity: Let \(N\) samples in the data stream be divided into \(D\) data chunks, then the number of samples in each data chunk is \(N/D\). The time required for adaptive spectral clustering to cluster the classes in the data chunk is \(T_{sp}\). The time to calculate the safety factor of the samples is \(T_{sf}\). The time to create a new classifier is \(T_{new}\) and the time to predict the classifier in the ensemble \(E\) containing \(m\) classifiers is \(O(N\times mlogN)\). Therefore, the time required to process the data stream containing \(N\) samples is \(O((T_{sp}+(T_{sf}\times N/D)+T_{new})\times D+O(N\times mlogN)\).
2. Space complexity: The sample storage pool of the HSDW-MI algorithm is determined based on the chunk size and the number of classes, so the space consumed is a constant, and its space complexity is \(O\left(1\right)\).
## 3 Modeling Methodology  
### Mesh Generation  
The Integrated Computer Engineering (ICEM) was used to divide the boiler model into the mesh system. Considering the influence of OFA arrangement on co-firing lean coal and semi-coke, two more layers of OFA nozzles were arranged above the OFA layer in practical boiler. When the boiler was divided to structured hexahedral grids, the primary combustion zone grids were refined. The mesh system of the wall-fired boiler employed in the present work is described in Figure 2. If the mesh is relatively sparse, calculation results may be inaccurate. If the mesh is too dense, the computing time can be excessively prolonged. When the change of calculated results is defined within the allowable deviation range of the practical application by continuing to refine grids, the number of grids can be determined. Three sets of mesh system with the grid number of 1102158, 1796184 and 2402552 were used to verify the grid independence here. The comparative results of the average temperature along the height of the furnace with three different grid numbers are described in Figure 3. The temperature variations along the furnace height obtained by 1796184 and 2402552 cells were almost the same. Hence, it can be considered the mesh system of 1796184 cells met the condition of grid independence. Based on both calculation accuracy and simulation efficiency, the mesh system included 1796184 cells [25; 26; 28].  
Figure 1: The detailed configuration of the wall-fired boiler. OFA: over fire air.
## 4 Simulation Results and Security Evaluation
### Entropy Analysis (Randomness Test)  
#### 4.8.1 Global Entropy Analysis  
[PERSON] et al. [28] have provided evidence supporting the use of information entropy as a measure to evaluate the uncertainty or randomness of a variable in an image. The calculation of information entropy is performed using Equation (27), which is an essential component in quantifying the uncertainty of a given system. In this equation, parameter B represents the probability of pixel X within the image under consideration. By incorporating Equation (27) into our analysis, we gain valuable insights into the information content and distribution of the image's pixels. A high value of entropy indicates a good and strong encryption mechanism when \(E\approx 8\). Table 5 shows the entropy values of both plain and encrypted images, while Table 6 provides a comparison with other studies. The results show that our proposed algorithm is suitable and less prone to revealing information, making it safe from brute force attacks.  
\[E=\sum_{i=1}^{256}p(i)\times\log\biggl{(}\frac{1}{p(i)}\biggr{)} \tag{27}\]  
Figure 14: Key sensitivity analysis in Lena encryption image.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Round** & **Small Change in Parameter Value** & **Average Correlation Coefficients** \\ \hline
1 & \(x_{0}=0.189+10^{-10}\), \(a=1.4\), \(b=0.3\) & \(-0.003030\) \\
2 & \(x_{0}=0.189-10^{-10}\), \(a=1.4\), \(b=0.3\) & \(-0.005816\) \\
3 & \(x_{0}=0.189\), \(a=1.4+10^{-10}\), \(b=0.3\) & \(-0.0000415\) \\
4 & \(x_{0}=0.189\),\(a=1.4\), \(b=0.3+10^{-10}\) & \(-0.002644\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Key sensitivity analysis for cipher Lena.
## 3 Results and Discussion
### Effect of the Isothermal Process on the RF-Deformed Blank
As shown in Figure 9g, when the ST increased to 630 \({}^{\circ}\)C, relatively small semi-solid spherical grains can be seen when the DT is 3 min. At this condition, the liquid film thickness and the grain dimension were basically the same as the microstructures in Figure 9e, where the ST was 620 \({}^{\circ}\)C and the DT was 6 min. However, some of the grains were still bonded together due to the low liquid fraction resulting from the short duration of time. When the duration time was extended to 6 min, the liquid fraction increased, and the liquid film uniformly distributed around the solid grains, as illustrated in Figure 9h. Moreover, the AGS was 41.48 \(\upmu\)m, and the ASF was 0.8, which was suitable for the SSFF process. However, when the DT extended to 9 min, a large number of as-cast dendritic crystals appeared near the solid grains, which is shown in Figure 9i. This generation of dendritic crystals was because of a rapid quench of the liquid phase that resulted from the melting of some solid grains with the prolonging of the DT at higher temperatures. In addition, the grain size became larger, and its spheroidization degree deteriorated. Although a large number of the liquid phases was produced under the action of high ST and long DT, the liquid-phase distribution was not uniform, resulting in the large solid grains gradually formed by the coarse solid grains that contacted each other [35; 57]. Namely, the merging and growth phenomenon of solid grains took place in this condition. Based on the analysis of the metallographic structure shown in Figures 8 and 9, the influence of process parameters, such as forging ratio, sustaining temperature, and duration time, on the microstructures of the semi-solid blank from Figures 8b-d and 9c,e-i can be summarized in Table 3. Because the microstructures shown in Figures 8a and 9a,b,d are not clear, thus, the AGSs and SFs of solid grains shown in Figures 8a and 9a,b,d are not calculated in this work. It can be concluded that the semi-solid microstructures shown in Figures 8d and 9f,h are more qualified. Therefore, the reasonable values of the operating parameters for preparing a 7075 aluminum alloy SSB are FR of 75%, ST of 620 \({}^{\circ}\)C, and DT of 9 min, or FR of 75%, ST of 630 \({}^{\circ}\)C, and DT of 6 min. Under these reasonable operating parameters, the 7075 aluminum alloy SSB can be prepared with an AGS of 41.48-42.57 \(\upmu\)m and an ASF of 0.80-0.81.
## 3 Results and Discussion  
### Characterization of the Raman Spectra of Fruits  
Diverse peaks were identified in these fruits with different intensities (Figure 1). The core of the pineapple contains significantly higher amounts of fructose and glucose [33]. Meanwhile, the soursop contains carbohydrates, proteins, folate, calcium, phosphorus, iron, and vitamin C, and [34; 35; 36] reported that the spectrum of sucrose presents the characteristic bands of \(\alpha-\)glucose (847 cm\({}^{-1}\)) and \(\beta-\)fructose (868 cm\({}^{-1}\)). In this work, glucose was present with the highest intensity in cherimoya and pineapple, at 855.15 cm\({}^{-1}\) and 851.93 cm\({}^{-1}\), respectively, and with the lowest intensity in soursop (854.08 cm\({}^{-1}\)). [PERSON] and [PERSON] [36] identified other peaks related to sugars in carrots (1462, 1126, and 840 cm\({}^{-1}\)).  
The assignments of molecule vibration associated with the sugars in tropical fruits were made according to the published literature (Table 1). Glucose is one of the most common hexoses with aldehydes in its structure [37], containing a carbonyl group (C-O stretch) [38]. This sugar was found at 1131.22 cm\({}^{-1}\), 1134.44 cm\({}^{-1}\), and 1133.37 cm\({}^{-1}\) in cherimoya, soursop, and pineapple, respectively. Fructose, also known as 'fruit sugar', occurs in fruits and plants in a free state and as a building block of sucrose [37]. The Ramanbands at 1472-1454 cm\({}^{-1}\), corresponding to CH2 scissor vibrations [39], contain peaks at 1464.22 cm\({}^{-1}\), 1467.44 cm\({}^{-1}\), and 1464.22 cm\({}^{-1}\) in therimoya, soursop, and pineapple, respectively; nevertheless, the peak corresponding to fructose's \(\beta\)(CCO) vibration was absent in soursop. Although RS allowed the identification of peaks associated with sugars in the three tropical fruits, it was not possible to quantify them. Therefore, it is also suggested that other techniques be used to complement the study. One technique that could be useful to validate the results is gas chromatography-mass spectrometry (GC-MS) analysis and profiling with the HS-SPME technique, since it is an effective tool due to its ability to recover and characterize compounds as mentioned by [PERSON] et al. [40].
## 2 Results and Discussion
### Analysis of Volatiles in Cooked Sorghum Samples by Using GC\(\times\)GC-TOFMS
In total, 16 esters were identified in cooked sorghum, and they usually contributed to desirable odors, like fruity, floral and sweety. Terpenes are a kind of natural hydrocarbon that are widely found in plants. In the current study a total of 14 terpenoid compounds were detected, including mono- and poly-terpene hydrocarbons, alcohols, carbonyls and esters. A total of 14 acid compounds were identified in this study, of these identified acids, the homologous series of straight-chain monocarboxylic fatty acids from C4-C10, C14, and C16 were all detected, which contributed to acidic, rancid and cheesy odors. Some heterocycles were also detected in the samples, including furan, furanone and pyrrole. 2-Pentyfuran exists in the cooking process of sorghum and it is derived from lipid peroxidation. This compound has a typical butter odor at a dilute concentration, but at higher concentrations, it has the less pleasant aroma characteristic of soybeans [13]. Furfural (sweet, woody and almond) which has been identified as an aroma-active compound in some cereals, was only detected in cooked samples.
## 2 Results and Discussion
### Sustained-Release Performance Analysis  
Figure 5a shows the sustained release properties of TTO microspheres at various monomer ratios. The sustained release curve shows a quick release trend at first, with a slower release thereafter. At the beginning of the first 25 h, the TTO release rate was fast, most likely caused by the volatilization of the TTO on the surface of the microspheres. As shown, a faster release rate could be observed using a larger proportion of BMA. The reason for this finding was due to BMA featuring flexible long chain lipophilic groups that may increase the storage space for hydrophobic molecules on the P(St-BMA) microspheres. Furthermore, the porosity of the three-dimensional network structure increased, and therefore the intermolecular forces and diffusion resistance were found to be reduced, ultimately leading to an increased TTO release rate. In addition, the larger the St ratio, the better the sustained release performance. Because St, exhibiting a rigid benzene ring structure, could increase the tightness of the three-dimensional network structure of the microspheres, the TTO release rate could be slowed down, improving the overall sustained release performance. However, this feature was not favorable to TTO absorption due to the increased tightness of the three-dimensional network structure.  
Figure 5b shows the sustained release properties of TTO microspheres at various amounts of the DVB crosslinking agent. The sustained release performance of the TTO microspheres improved as the amount of the DVB crosslinking agent increased. The amount of DVB determined the degree of cross-linking to determine the tightness of the three-dimensional network structure. With increasing tightness, intermolecular forces, and diffusion resistance, an impeding effect on the TTO release rate was observed, leading to the slowing down of the TTO release and the general improvement of the sustained-release properties.  
In order to further study the sustained release mechanism of the TTO microspheres, the data of different monomer ratios were fitted to the kinetic models of Zero-order [27], First-order [28], Higuchi [29], and Korsmeyer-Pappas [30], respectively. The corresponding results are shown in Table 2. The most consistent model for this sustained release behavior was the Korsmeyer-Pappas kinetic model. When the ratio of m\({}_{\text{St}}\):m\({}_{\text{BMA}}\) was 2:8, 4:6, 6:4, 8:2, and 10:0, the release indices \(n\) were 0.3418, 0.4446, 0.4191, 0.3275, and 0.4005, respectively. The indices \(n\) were less than 0.45, indicating that the TTO release was mainly based on Fickian diffusion [31; 32].  
Figure 5: (a) Sustained release properties of the TTO microspheres at different monomer ratios and (b) sustained release properties of TTO microspheres at various dosages of the DVB crosslinking agent.
## 3 Time-Dependent Reliability Analysis of an Aqueduct Based on Dual Power Transformation
### Direct Fourth-Moment Method  
After obtaining the first four moments of the performance function, the reliability index and failure probability can be calculated. It is noted that the first four moments can be estimated by using the point estimation method [39], high-order unscented transformation (HUT) method [40], etc. The reliability index using the first four moments (i.e., mean \(\mu_{G}\), standard deviation \(\sigma_{G}\), skewness \(\alpha_{3G}\), and kurtosis \(\alpha_{4G}\)) of the performance function can be expressed as follows [41]:  
\[\beta=\frac{\sqrt[3]{2}p}{\sqrt[3]{-q+\triangle}}+\frac{\sqrt[3]{-q+\triangle }}{\sqrt[3]{2}}+\frac{l_{1}}{3k_{2}} \tag{8}\]  
where  
\[\Delta=\sqrt{q^{2}+4p^{3}},\ p=\frac{3k_{1}k_{2}-l_{1}^{2}}{9k_{2}^{2}},\ q= \frac{2l_{1}^{3}-9k_{1}k_{2}l_{1}+27k_{2}^{2}(-l_{1}+\beta_{2M})}{27k_{2}^{3}}\]  
Here,  
\[l_{1}=\frac{\alpha_{3G}}{6(1+6l_{2})},\ k_{1}=\frac{1-3l_{2}}{(1+l_{1}^{2}-l_ {2}^{2})},\ k_{2}=\frac{l_{2}}{(1+l_{1}^{2}+12l_{2}^{2})},\]  
\[l_{2}=\frac{1}{36}(\sqrt{6\alpha_{4G}-8\alpha_{3G}^{2}-14}-2)\]  
where \(\beta_{2M}=\frac{\mu_{G}}{\sigma_{G}}\) is second-moment reliability index.  
Then, the failure probability, \(P_{f}\), is given as  
\[P_{f}=\Phi(-\beta) \tag{9}\]  
Since there are certain errors in calculating large reliability indexes by using the fourth-moment method [8; 42], a new method is proposed by converting the original performance function into a near-normal distribution. The proposed method is applied to estimate the large time-dependent reliability index of the aqueduct structures based on dual power transformation and HUT, which addresses the error issues associated with the fourth-moment method.