esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Synthesis of SLN/EO  
The lipid matrix for nanoparticle synthesis used was MM, considering the lipophilic nature of the main VOCs, LaDEO and CnEO. MM is appropriate for the development of stable colloidal nanoparticles, as shown by earlier studies from our group [24].  
A stable emulsion of EO in SLN was produced. TEM pictures revealed the existence of uniform and spherical nanoparticles (Figure 1). Compared to empty SLN, the incorporation of CnEO into the MM matrix appeared to result in smaller nanoparticles.  
The outcomes of the TEM images were confirmed by a dynamic light scattering (DLS) analysis (Figure 2). For nanoparticles dispersed in aqueous environments, DLS is a more suitable and less-invasive technique than TEM since it reflects the presence of agglomerates and aggregates in a more representative way (indicated by the PDI parameter). The mean diameters of SLN, SLN/LaDEO, and SLN/CnEO were 147, 151, and 141 nm, respectively. The PDI was \(\leq\)0.3 in all cases, indicating high compatibility for biomedical purposes [29]. Indeed, SLN/LaDEO and SLN/CnEO displayed a PDI \(\leq\) 0.2, whereas SLN presented a PDI of 0.24. These findings may be attributed to the good emulsion dispersion but also suggest that EO inclusion would promote a reorganization in the SLN structure [20; 21; 24]. This observation became even clearer when a population of SLN particles larger than 1000 nm vanished after being combined with EO (Figures 1 and 2A), highlighting the monodispersed nature of the SLN/EO formulations. A decrease in the Z-pot of SLN from \(-\)12 mV to \(-\)5/6 mV was found after the incorporation of EO, which further reinforces the idea that EO would be crucial for SLN restructuring.
# Modern Techniques for Flavonoid Extraction--To Optimize or Not to Optimize?
## 2 Flavonoids Classification and Sources  
Flavonoids are a diverse group of specialized metabolites derived from the general structure of a 15-carbon skeleton (Figure 1). Based on their structure, which can be distinguished by the pattern of the central heterocyclic pyran ring of the flavan core structure, flavonoids can be divided into six groups: Flavones, flavonoids, flavonols, flavan-3-ols, flavanonols, flavanones, isoflavones, and anthocyanins (Table 1).  
Flavonoids can occur in free form in plants, but are more commonly glycosylated, methylated, acetylated, prenylated, or polymerized [22]. Most commonly, flavonoid glycosides, O-glycosides, and, less commonly, C-glycosides are identified with a single oligosaccharide or, in some cases, a polysaccharide unit [23]. Glycosidation improves solubility, biodistribution, and metabolism by increasing transport across cell membranes [24]. Methylated flavonoids are rarer than free forms or flavonoid glycosides, and common types of methylation include C-methylation and O-methylation, which enhance flavonoid entry into cells and prevent degradation [22]. Some plants accumulate biflavonoids, flavonoid dimers consisting of two monoflavonoids through a direct link or a linear linker [25; 26], or even flavonoid trimers or tetramers [27].  
Flavonoids are distributed throughout the plant body, but the quality and quantity of specific flavonoids vary widely among different plants, plant organs, and even the same plants grown under different environmental conditions [28]. In general, the occurrence and amount of flavonoids in some plant samples are influenced by genetic and environmental factors, and some flavonoids may be specific to a particular plant genus, species, or even cultivar. Some examples of flavonoids detected in various plant samples are presented in Table 2.  
In recent years, modern analytical techniques have become available and some flavonoids that occur at lower concentrations can now be more easily detected. In addition, new extraction methods have also been optimized to achieve the most efficient extraction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flavones** & **Flavonols** & **Flavan-3-ols** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The structure and examples of flavonoids from different groups.
###### Abstract  
Transmission efficiency is a significant index of the transmission system. Even though much research has been carried out to calculate gear transmission efficiency, only a few of them studied spiral bevel gear due to its complexity. Moreover, spiral bevel gear does not have a \"standard surface\", which means more complex coupling relations between different parameters and makes efficiency optimal design more difficult. Therefore, an instantaneous transmission efficiency computing model of a spiral bevel gear was set up based on loaded tooth contact analysis and hybrid elasto-hydrodynamic lubrication theory. Then, the particle swarm optimization-gravitational search algorithm (PSOGSA) optimal model was constructed to obtain the best parameters that maximize the average transmission efficiency of spiral bevel gear's control parameters and machining parameters are optimized in sequence based on the proposed optimal model. The results showed that both optimal designs could help improve transmission efficiency, but the range of machining parameters is limited in a small interval because of the complex coupling relations. Therefore, the machining parameters optimization are conducted after control parameters optimization, which showed good results. Transmission efficiency was finally improved to 98.78%, which increased more than 4% at least. The proposed optimal model could also be applied into other gear design methods or even other fields.  
spiral bevel gear; elasto-hydrodynamic lubrication; transmission efficiency; hybrid PSOGSA algorithm; optimal design Article
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 5 Conclusions  
This paper proposes a novel MConv-TasNet for the space-based ADS-B signal separation with a single antenna. It can capture different scale temporal features from the overlapped ADS-B signal and improve the separation performance. Moreover, a large ADS-B signal dataset is created to verify our proposed method. Compared with other deep learning networks, our proposed MConv-TasNet performs better. Furthermore, MConv-TasNet is far superior to traditional methods, such as PASA and TDBSS. When the power difference, carrier frequency difference and relative time delay between overlapping signals are small, our proposed method can still separate the signals with high accuracy. Therefore, the ADS-B signal can be separated more accurately by taking advantage of deep learning methods that are good at mining signal features.  
Conceptualization, Y.B. and C.L.; methodology, Y.B. and C.L.; software, Y.B. and C.L.; investigation, Y.B. and C.L.; writing, Y.B. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research is partly funded by the National Natural Science Foundation of China (No. 62101512), Fundamental Research Program of Shanxi Province (20210302124031).  
Not applicable.  
Not applicable  
The data presented in this study are available on request 305 from the corresponding author  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Experiment III: The Availability of Transfer Learning Method Based on HAs  
Experiment III is designed to verify whether transfer learning between classifiers in a tree could improve the training performance of the classifier tree. Therefore, based on the results of the first two experiments, this experiment further modified the XLNet + HA + FC and XLNet + BiLSTM + HA + FC models. Therefore, when training the classifier tree to obtain each node in sequence, the current classifier can use the HA parameters of its parent node classifier (the model that has completed the training and has the best accuracy in the validation set) as the initial values of its own HA parameters. In Experiment III, we train all classifiers in the classifier tree using XLNet + HA + FC and XLNet + BiLSTM + HA + FC models. We then obtain 2 classifier trees and 54 classifiers for each classifier's P, R, and F1 values. Simultaneously, we directly reuse the training results of model XLNet + HA + FC in Experiment I and the results of model XLNet + BiLSTM + HA + FC in Experiment II. Comparing the results shown in Table 5, the transfer learning of the nodes in the classifier tree can improve the classifier classification effect from the perspective of P, R, and F1. The XLNet + BiLSTM + HA + FC + TL model has significant performance advantages in various metrics, with the average values of P, R, and F1 reaching 96.5%, 96.1%, and 96%, respectively. Note that 5 of the 27 classifiers in the classifier tree do not use transfer learning during the training. This is because (1) the classifier C00000 is the root node of the tree, and there is no parent node available to it for transfer learning and (2) the target categories of these four classifiers, C01000, C02000, C03000, and C04000, are not semantically related to the target categories of the root node classifier C00000, which results in the poor performance of the classifier using transfer learning. Therefore, these four classifiers do not use transfer
## 4 Discussions
### Quantitative Analysis Accuracy Using Matlab
#### 4.2.2 Color Space  
The accuracy of image binarization directly affects the distribution of pores, and has the most direct effect on quantitative analysis. The premise of image binarization is the perfect selection of color space and the control of parameters. There are four color space modes in the Color Thresholder (RGB, HSV, YCbCr, and L*a*b*). Different color space modes are suitable for different types of images. Take the common \"red-grey-white\" casting thin sections as an example (grey white is the interference color of tight sandstone, and red is the staining color of pores). Three conventional color space modes of RGB, HSV, and L*a*b* are used to carry out analysis on the same image, respectively (Figure 11). For color mode 'RGB', the three variables r, g, and b have a color difference due to the red color in the casting thin sections (r value is not 255), and cannot eliminate the red in the color space very well. Linear transformation controlled by variables in the color space leads to a large amount of noise in the dark minerals, and a matrix in the casting thin sections image. In the L*a*b* mode, 'L' represents Luminosity, 'a' represents the color range from magenta to green, and 'b' represents the color range from yellow to green in the blue range, although the parameter b can eliminate the blue in the image well. However, due to the linear transformation, some of the gray particles will also be affected, resulting in a large amount of noise in the particles. For the HSV mode, the 'H' color wheel can easily remove blue as a non-linear transformation in space, without affecting other colors. The most important element is that no noise was produced in this process. So, it is also best suited for tight sandstone casting thin sections.  
Figure 10: Porosity measurement comparison chart with two experimental methods. (**a**) the left figure shows the porosity difference of different experimental methods; (**b**) the right figure shows the error of different experimental methods.
## 3 Results and Discussion  
### Crystal Structure  
At room temperature, the crystal structure of perovskite CsGeI\({}_{3}\) belongs to the triangular crystal system and the space group is R-3m, as shown in Figure 1. The lattice constant of CsGeI\({}_{3}\) was found by Findit, which was geometrically optimized before calculation. The data are shown in Table 1. It can be seen from Table 1 that the optimized lattice constant and cell angle both increased to a certain extent compared with before, but the increased amplitude was very small. When the hydrostatic pressure of \(-0.5\) GPa to \(0.5\) GPa is applied to CsGeI\({}_{3}\), the space group does not change.  
As can be seen from Figure 2, the energy, lattice constant, volume, cell angle, bond length, and strain of the crystal are significantly changed after the pressure was applied. Figure 2a shows that the energy increases slightly after the pressure is applied, indicating that the stability of the crystal decreases. Figure 2b shows that the lattice length decreases (increases) as the pressure (tension) increases. Figure 2c shows that the volume decreases (increases) with the increase of pressure (tension). Figure 2d shows that the overall trend of cell angle increases from \(87.5^{\circ}\)to about \(90^{\circ}\)with the increase of pressure. Figure 2e shows the variation of the bond length of Ge-I with the pressure. It can be seen from Figure 2e that the bond length becomes shorter (becomes longer) with the increase of pressure (tension). Figure 2f shows the pressure-strain curve. With the increase of pressure (tension), the strain gradually decreases (increases). Where (b), (c), and (f) have similar changes, the lattice constant, volume, and strain satisfy the linear equation after the applied pressure as follows:  
\[\text{V}=\text{a}^{3},\text{ a}=\text{a}_{0}(1+\varepsilon) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **a = b = c (Å)** & \(\alpha=\beta=\gamma\) (\({}^{\circ}\)) & **V (Å\({}^{3}\))** & **Space Group** \\ \hline Findit & 5.98 & 88.61 & 213.98 & R-3m \\ GO & 6.08 & 88.76 & 224.55 & R-3m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lattice parameters of CsGeI\({}_{3}\) with Findit compared with geometry optimization (GO).  
Figure 1: The crystal of CsGeI\({}_{3}\)  
where V is the crystal volume, a is the lattice constant after applied pressure, a\({}_{0}\) is the lattice constant without applied pressure, and \(\varepsilon\) is the strain after applied pressure.  
The structural parameters of the crystal changed obviously after applied pressure, which will affect the electronic structure of the crystal.
# Varietal Authentication of Extra Virgin Olive Oils by Triacylglycerlycerols and Volatiles Analysis
## Results and Discussion
### Triacylglycercerol Fraction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**FA**} & \multicolumn{4}{c}{**TAG**} \\ \cline{2-5}  & **Dolce Agogia** & **Frantoio** & **Leccino** & **Moraiolo** \\ \hline C16:0 & 12.3 \(\pm\) 0.6 & 12.4 \(\pm\) 0.8 & 13.2 \(\pm\) 0.2 & 13.1 \(\pm\) 0.8 \\ C16:1 (\(n\)-9 + \(n\)-7) & 0.9 \(\pm\) 0.0 & 0.9 \(\pm\) 0.1 & 0.9 \(\pm\) 0.1 & 0.8 \(\pm\) 0.0 \\ C18:0 & 2.1 \(\pm\) 0.1 & 1.8 \(\pm\) 0.4 & 1.9 \(\pm\) 0.5 & 1.8 \(\pm\) 0.2 \\ C18:1 (\(n\)-9 + \(n\)-7) & 78.0 \(\pm\) 1.9 & 77.2 \(\pm\) 2.0 & 76.2 \(\pm\) 2.2 & 76.5 \(\pm\) 2.0 \\ C18:2 \(n\)-6 & 6.0 \(\pm\) 0.7 & 7.1 \(\pm\) 0.9 & 7.0 \(\pm\) 0.8 & 6.9 \(\pm\) 1.1 \\ C18:3 \(n\)-3 & 0.6 \(\pm\) 0.0 & 0.6 \(\pm\) 0.1 & 0.8 \(\pm\) 0.1 & 0.9 \(\pm\) 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Total fatty acid (FA) percent composition of triacylglycercerol (TAG) fraction of monovarietal extra virgin olive oil (EVOO) samples (% mol, mean values \(\pm\) SD, \(n=3\)). \(\alpha\)-linolenic acid in the _sn_-1 position was found in Dolce Agogia variety compared to Leccino and Moraiolo (\(p<0.05\)). A minor incorporation of linoleic acid in the _sn_-2 position of TAG of Dolce Agogia variety was observed compared to Frantoio and Leccino (8.9 vs. 10.5/10.8). Moreover, lower percent content of palmitic acid in _sn_-2 position has been highlighted in Dolce Agogia variety, compared to Frantoio (\(p<0.05\)). The data of the intrapositional compositions of the TAG could also be used to obtain all the TAG molecular species, including the isomeric and enantiomeric species in order to evaluate the differences in the content of individual species among the different cultivars. It is, however, important to emphasize that the procedure for TAG stereospecific analysis has the disadvantage of being laborious, time-consuming, and difficult to automate. In this study, the little differences highlighted from stereospecific analysis data were better revealed applying a chemometric procedure as LDA, reported below.
## 2 Materials and Methods  
### Chemicals and Reagents  
Primers were synthesized from Macrogen (Seoul, Republic of Korea). _p_-nitrophenyl acetate (_p_NP-C\({}_{2}\)), linaly acetate, \(\alpha\)-terpinyl acetate, tert-butyl acetate, and glyceryl tributyrate were purchased from Sigma-Aldrich (St. Louis, MO, USA). B-PER(tm) Bacterial Protein Extraction Reagent was from ThermoFisher Scientific (Waltham, MA, USA). Ni\({}^{2+}\) affinity resin was obtained from Qiagen (Hilden, Germany). A QuikChange II kit was purchased from Agilent (Santa Clara, CA, USA). The 96-well PCR plate (Code 781368) was purchased from Avygen (Corning, NY, USA). All other reagents were of analytical grade, unless otherwise stated.  
Figure 1: Substrate binding pockets and interaction of PbAcE and substrates. (**a**) Surface representation of chain A of PbAcE (PDB ID. 6 AGQ). Acetyl group binding site (S0) and alcohol group binding site (S1) are indicated with arrows. The catalytic triad (Ser185-His303-Asp274) is colored in magenta, while residues chosen for site saturation mutagenesis are in lime. (**b–e**) Light representation of the interaction of _tert_-butyl acetate (**b**), \(\alpha\)-terpinyl acetate (**c**), linaly acetate (**d**), and glyceryl tributyrate (**e**) with PbAcE. The residues having non-bonded contacts are represented by arcs, while hydrogen bonds are shown with green dotted lines. (**f**) Representation of top 10 models of covalently docked glyceryl tributyrate. PbAcE in surface representation with SSM residues in lime color. Glyceryl tributyrate represented in stick.
## 2 Finite Element Model of Kagome Structure with Piezoelectric Shunt  
### Truss Core Kagome Sandwich Panel Structure  
The truss core Kagome sandwich panel [16] which is a kind of high-authority morphing structure, consists of a solid face sheet, a tetrahedral core and a planar Kagome truss as the back-plane (see Figure 1). The character of this structure is that there is strong coupling between the out-of-plane deformation of the face sheet and the in-plane deformation of the truss back-plane. The benefit of this character is that morphing of the solid face sheet can be realized by replacing some rods in the back-plane planar truss with in-plane tension-compression actuators. While for panel-like structures, constrained layer damping [17] is usually used for passive damping and a piezoelectric patch [18] is often utilized for active damping, the character of a Kagome structure can be exploitedfor passive/active controls by replacing a small portion of the rods in the planar Kagome truss with in-plane dampers [19] or actuators [20], which will not destroy the planeness of the face sheet. In this study, the rods in the planar truss are replaced by piezoelectric transducers to convert the mechanical energy to electric energy, which is dissipated by the resistance.  
The materials and parameters of the Kagome structure are listed in Table 1. There are a total of 546 truss rods in the planer Kagome truss. The boundary condition is clamped-clamped. The total weight of this structure is 5.8 kg.  
The finite element model of the Kagome structure is shown in Figure 2. The face sheet is discretized using 414 bending plate elements, while the 1110 core-trusses and planar Kagome trusses are modeled with Euler-Bernoulli beam elements. The first six modal shapes are sketched in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Face Sheet**} & \multicolumn{2}{c}{**Truss Rods**} \\ \hline
**Material** & **Al alloy** & **Material** & **Steel** \\ \hline Young’s modulus & 73.1 GPa & Young’s modulus & 193 GPa \\ Density & 2700 kg/m\({}^{3}\) & Density & 8030 kg/m\({}^{3}\) \\ Length & 0.97 m & Rod Length & 51 mm \\ Width & 0.87 m & Section & Circle \\ Thickness & 1.53 mm & Radius & 1.275 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters, size of face sheet and truss rods.  
Figure 1: Schematic representation of the Kagome structure. The solid face sheet (upper) is shown in dark grey, the truss core (middle) in grey and the planar Kagome truss (bottom) as one face plane in black.  
Figure 2: Sketch of finite element model of (**a**) a Kagome structure, where the red arrow represents the observation point in Section 5; (**b**) the core truss; (**c**) the planar Kagome truss.
## 3 Results and Discussions
### Ablation Tests with Data Augmentation  
Here the focus of the tests is extended beyond the basic algorithms by studying the effects of the additional data augmentation techniques (Groups A to E in Section 2.3). Experiments were conducted by introducing multiple combinations of the transforms on the input data to Baseline 2 in order to reveal the effectiveness of the different groups. Based on our previous tests and observation of the results, we found that settings for the inference algorithm, such as those for NMS, also played a significant part in the average precision level, apart from the neural network and the pre-processing steps. Therefore, the different NMS settings I and II will be considered, where I denotes the default setting of \(\mathit{thres}=0.65\), and II denotes \(\mathit{thres}=0.4\). The results from the test set augmented with various sets of transforms are shown in Table 2.  
The combined augmentation sets that have been employed all lead to a fairly large boost in the average precision score. Tested with the default threshold value, the increase in mAP relative to Baseline 2 ranges from 0.8% to 1.4%. Switching the NMS setting from I to II gives an additional improvement in the mAP and F1 scores in each of the cases and raises both precision and recall in most scenarios. A lower IOU threshold in the NMS process tends to produce better results in these cases since most of the targets in our dataset are rather sparsely distributed, and this adjustment may eliminate more redundant boxes generated from the trained model and TTA. Therefore, results obtained with setting II will be considered for the following discussion unless specified otherwise.  
The augmentation Groups D and E prove to be a slightly more effective combination compared to the set of A, B, and C. Incorporating the distortion transforms of C into the set (D, E) gives no more increase in the average precision. This may result from the counteraction between the different sets. In other words, the compound impact of two or more augmentation techniques from the general perspective is not known in advance, which makes verification through experiments a simple and effective practice in order to reveal the effects. The complete set of A, B, C, D, and E achieves the best mAP of 51.6% among the cases that we have tested for the augmentation methods.