esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Test Data Analysis  
### Analysis of Displacement Vector  
The test photos before and after the slope models were processed by using image measurement technology, and cumulative displacement vector diagrams of the three slope models were obtained, as shown in Figure 7e,f. The boundary, as the development position of the toppling-bending zone, between the toppling zone and the un-toppling zone of the slope can be identified clearly from the variable in the displacement direction in the vector diagram. The part below the toppling-bending zone could be regarded as an un-toppling zone, the direction of the displacement vector of the rock mass in this zone was generally downward, and the rock mass was dominated by settlement deformation under gravity. The phenomenon of toppling deformation was not obvious; the part above the toppling-bending zone was the toppling zone of the slope, and the relative displacement was larger. The resultant vector was deflected downward in the oblique direction and nearly parallel to the toppling-bending zone due to the displacement in the direction of the free face outside the slope under gravity.  
The differences between the displacement vector diagrams of the three slope models were as follows: as the slope angle became steeper, the location of the toppling-bending zone inside the slope became deeper, and the zone of toppling continued to expand. The direction of the resultant vector of the toppling zone changed from moderate to steep. During the toppling deformation process, the models with slope angles of 55\({}^{\circ}\) and 65\({}^{\circ}\) were dominated by horizontal tension, and the toppling deformation body presented good integrity; therefore, the horizontal displacement was more significant than the vertical displacement. The resultant displacement vector was directed gently downward and presented a certain degree of integrity. In contrast, in the model with a slope angle of 75\({}^{\circ}\), there were numerous interlayer shear failures in addition to horizontal tension during the deformation process. The toppling deformation was intense enough that part of the rock mass broke away from the parent rock and collapsed downwards, causing the displacement vector near the free face to be slightly messy. The vertical displacement of the toppling zone was generally larger than the horizontal displacement, and the resultant vector appeared to be directed steeply downward.
# Springback Prediction of Free Bending Based on Experimental Method
## 2 Free-Bending Technology
The tube blank is continuously sent out by the feeding mechanism, and an arc section with bending radius \(R\) is generated within the guide distance. The \(U\) of the arc section and feeding length \(L_{2}\) can be calculated by Equations (5) and (6):  
\[U=R-\sqrt{R^{2}-A^{2}} \tag{5}\]  
\[L_{2}=\frac{\pi\theta R}{180^{\circ}}-\frac{\pi R\mathrm{arcsin}\frac{A}{R}}{ 180^{\circ}} \tag{6}\]  
(3) Second transition section  
Figure 3b shows the second transition section of the bending die from \(U\) back to the initial equilibrium position. In the second transition section, the center of the bending die returns to the equilibrium position at a uniform speed from the processing position. Similar to the first transition section, the tube is in a state of dynamic combination and transition from a straight line and an arc section, the straight section is first generated at the outlet of the guide mechanism and gradually spreads to the whole guide distance. At the same time, the bending radius of the newly generated arc section gradually increases to infinity until the bending die reaches the equilibrium position, and the tube section sent out from  
Figure 3: Diagram of the transition sections: (**a**) first transition section; and (**b**) second transition section. the bending die is completely a straight section. The feeding length \(L_{3}\) in second transition section can be calculated by Equation (7):  
\[L_{3}=A \tag{7}\]  
Set the feeding time of this section as \(t_{3}\), the feeding distance at time \(t_{3}\) is \(v_{z}t_{3}\). The relationship between the offset \(U\) of the bending die, the bending radius \(R\) and the feeding time \(t_{3}\) can be calculated by Equation (8):  
Figure 2:0  
To sum up, the value of the \(U\) is related to the tube bending radius \(R\), the guide distance \(A\) and the feeding length \(L\). The guide distance \(A\) in the bending die follow-up tube bender is a fixed mechanical distance, and the corresponding bending radius \(R\) can be formed by adjusting \(U\) for a circular arc tube with the arc length \(L\). Therefore, the relationship between \(U\) and \(R\) is defined as a \(U\)-\(R\) relationship. Establishing a reasonable and accurate \(U\)-\(R\) relationship is the key to ensure the forming accuracy of free bending. In the free-bending process, these factors, such as the gap between the bending die and the tube, the friction between the tube and the guide mechanism, the friction between the bending die and the spherical bearing and so on, which will affect the accuracy of the \(U\)-\(R\) relationship, and different materials have different \(U\)-\(R\) relationship during the process. The \(U\)-\(R\) experiment was carried out, considering the influence of these factors.
###### Abstract
Experiments are time-consuming and should only be conducted when there are several variables or when exploring existing textiles. However, the use of three-dimensional (3D) models enables efficient analysis and prediction by facilitating the design of all types of fabric architectures [20; 21; 22; 23]. Woven composites are characterized by complex geometries and possess large variations in fiber volume fraction and mechanical behavior because of the type of weaving and interaction between fiber bundles. Therefore, the application and analysis of the structures of parts of woven composites are complicated. To solve the aforementioned problems, various studies have been conducted to model the microstructures of fiber bundles by setting the representative volume element (RVE), which is the unit structure of woven fibers, and to predict RVE characteristics. Thus, the need for a material property prediction method has emerged. Recent research has defined the curvature of woven fibers according to weaving patterns, including plain-woven, twill-woven, and theoretically calculated the fiber volume fraction and fundamental parameters of woven composites [24; 25; 26; 27]. Based on these analytical techniques, research has been conducted to predict the physical properties of diverse materials and the behavior of composite materials [28; 29; 30; 31]. If research on these fabrics continues, they could replace complex fiber-reinforced composites. Current fiber reinforcement mechanisms are uniaxial and require the simultaneous lamination of fiber-reinforced composites at different angles, which results in a complex and time-consuming process. To overcome these drawbacks, there is a need to develop biaxially isotropic composites. To compensate for the shortcomings of existing fiber-based composites, the development of woven isotropic composites will expand the range of applications of polymer composites. For further research on UHMWPE/PP woven composites, it is necessary to analyze their elastic properties using finite element analysis methods. In this study, theoretical simulations are used to estimate the elastic properties of composites with the aim of developing high-strength PP-based, UHMWPE-reinforced composites. By replicating plain and twill-woven textiles using UHMWPE fibers, textiles are manufactured, and composite 3D models are generated. Additionally, composites composed of textiles woven with UHMWPE fibers are incorporated into a PP matrix, secured with PP, and analyzed. Furthermore, the volume fractions of PP and UHMWPE in the composites are modified and compared. FEA is performed to analyze the properties of the composites containing textiles with different volume fractions of UHMWPE ranging from 45% to 75%.
## 4 Experimental Setup
### Model Configuration  
The experiments were performed on an Ubuntu 16.04 LST operating system and were implemented using the PyTorch deep learning framework. A total of 20% of the speech in each corpus is randomly selected as the test set, and the remaining speech is mixed. The model was trained with the batch size of 32 and iterations of 50, where Adaptive Moment Estimation (Adam) with learning rate of 0.01 was used as the optimizer. When using the GradNorm adaptative loss balancing method, the alpha hyperparameter was set to 0.16. When using the conventional experience-based weight adjusting method, the value of \(w_{1}\) is set in the range of 0.1 to 0.9. Note that \(w_{1}+w_{2}=1\). For the cross validation, ten-fold cross validation was used and the average of the ten-fold testing results was taken as the final performance of the model. The details of the model are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter Group**} & \multicolumn{2}{c}{**Low-Level**} & \multirow{2}{*}{**Functions**} & \multirow{2}{*}{**Amount**} \\  & & & (V): am, stddevNorm, 20 th, 50 th, and \\  & & & 80 th percentile, the range of 20 th to \\  & & & 80 th percentile, the mean and \\ Frequency & & & standard deviation of the slope of \\  & & & rising/falling signal parts \\  & & Jitter & (V): am, stddevNorm & 2 \\  & & Formant 1, 2 and 3 frequency & (V): am, stddevNorm & 6 \\  & & Formant 1, 2 and 3 bandwidth & (V): am, stddevNorm & 6 \\ \hline  & & Shimmer & (V): am, stddevNorm & 2 \\  & & am, stddevNorm, 20 th, 50 th, and & \\ Energy & & Loudness & & 80 th percentile, pctrange0-2, the \\  & & mean and standard deviation of the & 10 \\  & & slope of rising/falling signal parts & \\  & & Harmonics-to-Noise Ratio & (V): am, stddevNorm & 2 \\  & & (HNR) & (V): am, stddevNorm & 2 \\ \hline  & & Alpha Ratio & (V): am, stddevNorm & 3 \\  & & & (UV): am, stddevNorm & 3 \\  & & Hammarberg Index & (V): am, stddevNorm & 3 \\  & & & (UV): am & \\  & Spectral Slope 0-500 Hz and & (V): am, stddevNorm & 6 \\  & & 500-1500 Hz & (UV): am & \\  & & Formant 1, 2 and 3 relative & (V): am, stddevNorm & 6 \\ Spectral & & energy & (V): am, stddevNorm & 2 \\  & Harmonic difference H1-H2 & (V): am, stddevNorm & 2 \\  & Harmonic difference H1-A3 & (V): am, stddevNorm & 2 \\  & & MFCC 1–4 & (V): am, stddevNorm & 16 \\  & & & am, stddevNorm \\  & & Spectral flux & (V): am, stddevNorm & 5 \\  & & & (UV): am & \\ \hline \hline \end{tabular} Abbreviations: am: arithmetic mean; stddevNorm: coefficient of variation; pctrange0–2: the range of the 20 th to 80 th percentile; UV: functions are applied to the unvoiced segment; V: functions are applied to the voiced segment. No special markings mean that functions are applied in all segments.  
\end{table}
Table 2: Overview of LLDs and functions used in our experiment.
## 2 Preliminaries and Notations
### Definition (Keys)  
Keys are one of the most important components in cryptography and are used for different purposes, such as signing, encryption, and decryption. In the proposed protocol, various keys are used. We cannot explain all of them due to space limitations; however, remember to note that we use the notation \(PK_{name}\) for the public key and \(SK_{name}\) for the private key.  
To facilitate the sharing of a secret key between [PERSON] and [PERSON], the Diffie-Hellman (DH) key exchange protocol is employed. Throughout, for this protocol, we use the notation DH (\(PK_{Alice},PK_{Bob}\)) for the shared secret exchange between [PERSON] and [PERSON] using the [PERSON] and [PERSON] public keys \(PK_{Alice}\) and \(PK_{[PERSON]}\), respectively. The DH protocol utilized in the suggested scheme is known as X25519 and is based on the cyclic group Curve25519 over an elliptic curve.
###### Abstract  
We investigated whether the use of technological tools can effectively help in manipulating the increasing volume of audio data available through the use of long field recordings. We also explored whether we can address, by using these recordings and tools, audio data analysis, feature extraction and determine predominant patterns in the data. Similarly, we explored whether we can visualize feature clusters in the data and automatically detect sonic events. Our focus was primarily on enhancing the importance of natural-urban hybrid habitats within cities, which benefit communities in various ways, specifically through the natural soundscapes of these habitats that evoke memories and reinforce a sense of belonging for inhabitants. The loss of sonic heritage can be a precursor to the extinction of biodiversity within these habitats. By quantifying changes in the soundscape of these habitats over long periods of time, we can collect relevant information linked to this eventual loss. In this respect, we developed two approaches. The first was the comparison among habitats that progressively changed from natural to urban. The second was the optimization of the field recordings' labeling process. This was performed with labels corresponding to the annotations of classes of sonic events and their respective start and end times, including events temporarily superimposed on one another. We compared three habitats over time by using their sonic characteristics collected in field conditions. Comparisons of sonic similarity or dissimilarity among patches were made based on the Jaccard coefficient and uniform manifold approximation and projection (UMAP). Our SEDnet model achieves a F1-score of 0.79 with error rate 0.377 and with the area under PSD-ROC curve of 71.0. In terms of computational efficiency, the model is able to detect sound events from an audio file in a time of 14.49 s. With these results, we confirm the usefulness of the methods used in this work for the process of labeling field recordings.  
urban wetlands; feature visualization; soundscape; sonic event detection +
Footnote †: journal: Applied Sciences
## 4 Statistical Analyses of Numerical Simulation Results
### Simplification of Calculation Conditions for Long-Term Prediction  
Since the 3D time-domain nonlinear numerical simulation is a time-consuming process, it is desirable to carry out as few simulations as possible. The contribution of short-term loads to the long-term extreme loads differs significantly for different short-term conditions. It has been turned out that the long-term extreme value estimated by using results with limited number of short-term condition is reasonable and the difference between the predicted extreme values is ignorable small within 1%. The simplification scheme of numerical simulation conditions is discussed and analyzed as follows. We assume the incident waves to be long-crested in this calculation. The fundamental work for long-term prediction is to select appropriate wave environments in which the ship will operate during its lifetime. Wave environment in long-term is usually described by wave scatter diagrams and the North Atlantic open sea states are chosen in this study. As seen from Table 2, the number of combinations of significant wave heights and characteristic periods is 197. In addition, the wave heading angles ranging from 0\({}^{\circ}\) to 180\({}^{\circ}\) are divided into 7 representative values with the same interval of 30\({}^{\circ}\). The probabilities of heading angles 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), 150\({}^{\circ}\) and 180\({}^{\circ}\) are set as 1/12, 1/6, 1/6, 1/6, 1/6, and 1/12, respectively. So there are 1379 short-term wave environment states in total, which could be very large. In fact, whipping is most pronounced in head or near head waves at high seas and the extreme load usually occurs in severe head or bow oblique waves. For simplification, only 44 wave conditions that with significant wave height higher than 12 m are involved in the long-term prediction. In addition, only head wave 0\({}^{\circ}\) and bow oblique wave 30\({}^{\circ}\) and 60\({}^{\circ}\) are involved in the long-term prediction. Ship motions and wave loads depend not only on sea states and wave heading angles but also on the forward speed among other things. The influence of ship forward speed on wave loads could be obvious since the whipping loads are sensitive to the change of speed. In fact, the ship forward speed can be involuntary reduced because of added resistance in waves. In addition, in severe sea states the ship's master usually reduces the speed in order to avoid excessive ship motions, accelerations, slamming and green water on deck. In this study, the selected speeds in relation with the significant wave heights are listed in Table 3, where \(Vs\) is the design service speed and it is selected as 24 knots in this study. Moreover, the influence of loading condition on the extreme value in long-term prediction is not considered since the involved ship in this study is a warship and her weight distribution change is small. Figure 17: Flowchart of the wave loads prediction and ultimate strength assessment.
## 4 EFTs Classification Scheme  
EFTs were identified following a multi-step approach (Figure 2). First, the time series of LST, Albedo and NDVI were pre-processed and filtered through a Savitzky-Golay [65] filter to smooth out noise caused mainly by cloud contamination, atmospheric variability, and bi-directional effects (Section 4.1). Second, different metrics related to vegetation phenology and ecosystem functioning (Section 4.2) were derived from these series and their contribution to discriminate EFTs was explored. Third, a SOM procedure was applied to extract and visually display the topological structure of high-dimensional input data (Section 4.3). Finally, the _k-means_ method was carried out based on the SOM prototypes, with the aim of partitioning the input data into EFTs (Section 4.4). A more detailed description of the methodology follows.
# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 3 Bidimensional Empirical Mode Decomposition  
Multi-source image fusion is to synthesize the same target scene information obtained by different sensors or the same sensor at different times. As a single image suffers from some defects, such as scene information incomplete, the gray scale distribution range and brightness differences are large, as illustrated in Figure 1. Source image A is a picture with a clear foreground and a fuzzy background, and source image B is a picture with a fuzzy foreground and a clear background. Fused image C exhibits both a clear foreground and a clear background. When image fusion is carried out, the feature information of multiple images is optimized and combined to obtain a final fused image (fused image C), which contains rich and complete scene information, is more in line with human visual experience, is more convenient for machine recognition, and improves computing efficiency.  
How to extract the feature information from images pairs quickly and accurately is the key technology in image fusion processing. BEMD usually decomposes the input image pairs into multiple BIMPs from a high-frequency to a low-frequency layer and a residual component. The low-frequency layers express the color information and the high-frequency layers express the texture and contour information.  
In addition, the BEMD method has a boundary effect when processing the four edges of a 2D image. Symmetric padding is used to expand the image size, ensuring that the processed image is consistent with the original image size.  
The decomposition process of the image signal according to the BEMD algorithm [8] can be represented by the flow chart shown in Figure 2.  
Figure 1: Multi-focus color image fusion. (**a**) Source image A, (**b**) source image B, (**c**) fused image. Image A is a picture with a clear foreground and a fuzzy background, and image B is a picture with a fuzzy foreground and a clear background. Fused image C exhibits both a clear foreground and a clear background.  
The decomposed BIMF components are summed and subtracted from the original plot to achieve the residual Rm(x,y) between the two.  
\[R_{m}(x,y)=f(x,y)-BIMF_{m} \tag{5}\]  
Input image F(x,y) can be represented by BEMD decomposition as  
\[f(x,y)\sum_{i=1}^{m}BIMF_{1}(x,y)+R_{m}(x,y) \tag{6}\]  
The image is taken as the original input image and, after m cycles to meet the termination conditions, m BIMF components and residual images satisfying the conditions are obtained.
# Caroenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies
## 3 Salt-Stress Treatment for Caroenoid Production from Microalgae
### Microalgal Responses to Salt Stress
#### 3.1.2 Downstream Signaling  
Salinity-induced signals may then have effects on the gene expression of microalgae cells, which is related to salt concentrations [79]. Low salt concentration can promote the growth of some microalgae, such as cultures of _Scenedesmus sp._[80], _Botryococcus braunii_[81] and _H. pluvialis_[79]. Treatment of _H. pluvialis_ with low-dose NaCl (12.5 mg/L) showed a promoting effect on biomass concentration (28% higher) and productivity (from 0.15 d\({}^{-1}\)-0.22 d\({}^{-1}\)). These effects were related to upregulation of growth-related genes, such as _rbcL_, _rbcS_ and nitrate reductase gene (_NR_).  
Under high-dose salt stress, both ionic and osmotic homeostasis need to be maintained. As for the halotolerant microalga _D. salina_, osmoregulation under osmotic stresses can be divided into two mechanisms. One is to maintain intracellular Na\({}^{+}\) and K\({}^{+}\) concentrations by the plasma membrane electron transport (redox) system, as Na\({}^{+}\)-ATPase and K\({}^{+}\) carriers, and the other is to regulate glycerol concentrations inside to maintain the water potential inside and outside of cells [82]. Accumulation of glycerol as a soluble substance is a strategy for microalgae to keep osmotic homeostasis under salt stress, which has been demonstrated in _C. reinhardtii_[83] and _C. zofingiensis_[15]. Salt stress can also upregulate the genes related to starch catabolism (like pyruvate kinase, PK) and downregulate the genes for gluconeogenesis (like phosphoenolpyruvate carboxykinase, PEPCK), providing more building blocks for storage lipids (fatty acids and triacylglycercerol, TAGs) and carotenoids in _C. zofingiensis_[15]. Salt-induced acetyl-coenzyme A carboxylase (ACCase) expression for fatty acid synthesis was also demonstrated in _Chlamydomonas_ sp. [84], _Chlorella_ sp. [85] and _Nitzschia_ sp. [86].  
Omics approaches like genomic, transcriptomic and metabolomic are also applied to reveal the changes occurring under salt stress. By analyzing differential expressed genes (DEGs) under stress, transcription factors (TFs), such as myeloblastosis (MYB), WRKY and basic helix-loop-helix (bHLH), are demonstrated to play important roles in regulating gene expressions under salinity [68]. Responses of microalgae to both low-dose and high-dose salinity are shown in Table 3.