esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment
### Experimental Setup  
The schematic diagram of femtosecond laser damage to optical thin film is displayed in Figure 2. The whole experimental setup consists of a pulse laser, a half-wave plate, a Gran laser polarizer, a focusing mirror, an energy meter, a CCD camera, a microscope, an experimental sample, and a three-dimensional translation stage. The excitation source was an ultrafast Ti:sapphire femtosecond laser system (Coherent (Santa Clara, CA, USA), Libra-HE) with a center wavelength of 800 nm, a pulse width of 50 fs, and a repetition frequency of 1 kHz. The laser system was modified to the single-shot mode by sending a command ('man:trig') to the serial port (RS232) of the synchronization and delay generator. The laser energy was measured by an energy meter (Coherent, LabMax-TOP laser energy meter + EnergyMax-RS J-10 MB-LE Energy Sensor). The energy attenuation system was composed of a computer-controlled rotating half-wave plate (HWP) and a Glan-laser polarizer (Glan); the fluctuation of pulse energy was less than 0.5% within 8 h. A quarter-wave plate (Q) was used to adjust the polarization of the femtosecond laser. For circularly polarized pulsed beam, the angle of the quarter-wave plate was 45\({}^{\circ}\) + n \(\times\) 90\({}^{\circ}\); for linearly polarized pulsed beam, the angle of the quarter-wave plate was n \(\times\) 90\({}^{\circ}\). The n represented an integral multiple of 90\({}^{\circ}\). Next, a plano-convex lens (L, focal length of 25 cm, diameter of 25 mm) was used to focus the pulse beam vertically onto the sample surface. The diameter of the focused spot at the sample surface was approximately 30 \(\upmu\)m. In addition, the sample to be measured was placed on a computer-controlled three-dimensional translation stage (Thorlabs, PT3/M-Z8, with an accuracy of \(\pm\)1.5 \(\upmu\)m), and the minimum movement step of the translation stage was 1.0 \(\upmu\)m. During the experiment, the translation stage was used to complete the positioning of the sample and control the movement of the sample test point. The damage morphology was monitored by a microscope and a CCD  
Figure 1: (**a**) Ta\({}_{2}\)O\({}_{5}\)/SiO\({}_{2}\) multilayer film sample; (**b**) reflectivity curve of the film; (**c**) electric field distribution of the film at 800 nm.  
camera for real-time observation, and a more precise microscope (Olympus, BX51) was used to observe the damage morphology of the film after the experiment. All measurements were performed in an atmospheric environment, room temperature (22 \({}^{\circ}\)C), and 40% relative humidity.
# 1D CNN Based Detection and Localisation of Defective Droppers in Railway Catenary
## 4 Fault Classification through CNNs
### Result Analysis  
(1) Dataset size and splitThe data of a span-length contact force obtained from the simulation were prepared for classification. The details of the dataset are listed in Table 4.  
In addition, in order to demonstrate how the data are processed by the integrated 1D CNN, the data flow diagram is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset Details** & & & & & **Quantity** & & & & \\ \hline Size of dataset & & & & & 2173 & & & & \\ Damage degree labels & & 482 & & & 672 & & & 1019 & \\  & & (high risk) & & & (low risk) & & & (healthy) & \\ Dropper position labels & 234 & 225 & 249 & 200 & 200 & 292 & 245 & 281 & 247 \\  & (D1) & (D2) & (D3) & (D4) & (D5) & (D6) & (D7) & (D8) & (D9) \\ Data used for K-fold & & & & & 1996 & & & & \\ Data used for testing & & & & & 177 & & & \\ Features of each sample & & & & & 1200 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of the dataset.  
Figure 5: Structure of the proposed integrated 1 CNN model.  
As can be seen from Figure 6, the dataset was divided into two parts prior to the classification process. A total of 90% of the data was allocated to create a new dataset called the K-fold dataset, which was used for training and validating the integrated 1D CNNs model. For each sub-model, 75% of the corresponding K-fold dataset was utilised for training, and 25% was reserved for validation.  
The remaining 10% of the data, which was not utilised in any training or validation processes, was subsequently employed to test the performance of the integrated 1D CNNs model, which consisted of the two fine-tuned sub-models.  
The application of Z-score [38] was used to normalise the dataset. It is notable that the datasets for validation and testing (validation and testing dataset) were normalised based on the training dataset. Hence, during the whole process of classification, the testing dataset for the integrated 1D CNN model was not used for training and validation of the sub-models. This avoids data leakage and proves the authenticity of the validation results.  
_(2) K-fold cross-validation:_ Cross-validation measures generalised error through the existing dataset [39]. K-fold as a common cross-validation method is widely used to estimate prediction accuracy [40]. Compared with the typically practiced cross-validation method, the results from the K-fold method are closer to real performance [41].  
In K-fold validation, the dataset is divided into k subsets of equal size for training and validation. In this paper, we chose 10 folds for cross-validation. Compared with other  
Figure 6: Data flow diagram of the integrated CNN model.  
cross-validation methods, better performance with 10 folds has been proven in terms of bias and variance [42].
## 3 Theoretical Formulation and Numerical Modeling  
### Governing Equations  
The governing equation of unsteady incompressible viscous flow on an arbitrary control volume, \(U\), for a moving boundary, \(A\), can be written as follows:  
\[\frac{d}{dt}\int\limits_{U}QdU+\int\limits_{A}FdA=\int\limits_{A}DdA+\int \limits_{U}SdU \tag{2}\]  
Figure 1: Schematic diagram of the structure of the cylinder with two symmetric strips: (**a**) physical model; (**b**) the specific distribution of a pair of strips; and (**c**) strips of different shapes.  
where \(U\) is the control volume in space that varies in size and shape with time. Moreover, \(dU\) is the moving boundary of the control volume. The conserved variables (\(Q\)), the convective fluxes (\(F\)), the diffusion fluxes (\(D\)) and the source term (\(S\)) are given by the following:  
\[Q=\left[\begin{array}{c}1\\ \rho\\ \rho\mathrm{u}\end{array}\right],F=\left[\begin{array}{c}-u_{b}\\ \rho(u-u_{b})\\ \rho u(u-u_{b})\end{array}\right],D=\left[\begin{array}{c}0\\ 0\\ \mu\mathrm{V}^{2}u\end{array}\right],S=\left[\begin{array}{c}0\\ 0\\ -\
abla p\end{array}\right] \tag{3}\]  
where \(u\) is the velocity of the fluid, \(u_{b}\) is the velocity of the moving mesh, \(\rho\) is the density of the fluid, \(\mu\) is the dynamic viscosity of the fluid and p is the pressure of the fluid.
## 3 Results and Discussion
However, there is little data in the available literature on the proportion of individual flavonoids in black garlic, and it lacks information on the comparison of the content of selected catechins or flavonoids in organic and conventional black garlic. According to this study, the level of catechins in both white and black garlic was at a similar level and amounted to 18.46 \(\pm\) 0.73 mg/100 g d.m. (total for G and EG) and 17.90 \(\pm\) 0.72 mg/100 g d.m. (total for BG and EBG), respectively. Both white and black garlic were dominated by catechins (9.54 \(\pm\) 0.07 mg/100 g d.m. on average in G and EG and 10.30 \(\pm\) 0.47 mg/100 g d.m. in BG and EBG), and epicatechin was present at approximately 2.5-fold lower concentrations (in all garlic variants studied). The content of catechins and epicatechins was found to be significantly (\(p\) < 0.05) higher in organic garlic, both white and processed black. The only catechin reported to be higher in conventional garlic than in organic garlic, both fresh (unprocessed) and processed (black), was epigallocatechin gallusate. There was also a significant (\(p\) < 0.05) decrease in the content of this phenolic compound during heat treatment of garlic, by 28.17% in BG and 32.92% in EBG, respectively. As for flavonoids, significant (\(p\) < 0.05) differences were found in both white and black garlic. White organic garlic was characterised by a significantly higher content of flavonoids (24.15 mg/100 g d.m.) than conventional garlic (19.81 mg/100 g d.m.). Organic black garlic EBG had a significantly higher content of these compounds (49.68 mg/100 g d.m.) than conventional black garlic BG (40.41 mg/100 g d.m.). The heat treatment processes applied resulted in a more than two-fold increase in the content of flavonoids, both in BG and EBG. The main flavon in both white (organic and conventional) and black garlic was quercetin, which accounted for approx. 65% (G), 61% (EG), 72.32% (BG), and 68.13% (EBG) of all flavonoids. There was also a wide variation in the content of kaempfer. Fresh organic garlic contained more than 1.5 more (6.01 \(\pm\) 0.03 mg/100 g d.m.) kaempfer than fresh conventional garlic (3.67 \(\pm\) 0.02 mg/100 g d.m.). This trend was also maintained during heat treatment of garlic, because organic black garlic was characterised by a significantly higher content of kaempfer (12.12 \(\pm\) 0.50 mg/100 g d.m.) than conventional black garlic (7.66 \(\pm\) 0.05 mg/100 g d.m.). The heat treatment processes applied contributed to an approx. 2-fold increase in kaempfer content in black garlic compared to unprocessed white garlic. Miricetin accounted for the smallest proportion of total flavonoids. On average, it was shown to be 3.27 \(\pm\) 0.02 mg/100 g d.m. in white garlic without any significant differences in G and EG. The heat treatment processes applied resulted in a small but significant (\(p\) < 0.05) increase in the amount of this flavonoid in black garlic (3.61 \(\pm\) 0.13 mg/100 g d.m. on average in BG and EBG), with its highest content found in EBG (3.71 \(\pm\) 0.05 mg/100 g d.m. ).
# Simulation Study of Microwave Ablation of Porous Lung Tissue
## 4 Discussion
Although the porous model in this paper considers the vascular distribution and blood flow inside the lung tissue, it adopts the local thermal equilibrium equation to study, ignoring the temperature difference \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Microwave Ablation Power (W)**} & \multirow{2}{*}{**Maximum Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Ablation Zone (cm)**} & \multirow{2}{*}{**Ablation Volume (cm\({}^{3}\))**} \\ \cline{3-3} \cline{5-5}  & & & **Transverse Diameter (cm)** & & **Longitudinal Diameter (cm)** \\ \hline
10 & 87.3 & 0.92 & 1.88 & 2.28 \\
20 & 121.0 & 0.97 & 2.02 & 3.65 \\
30 & 142.0 & 1.40 & 2.11 & 4.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation temperature, ablation area and ablation volume at different ablation powers. between the lung tissue and the blood and the heat exchange existing between them during the actual ablation process. Therefore, in the subsequent study of the porous heat transfer model, the local thermal non-equilibrium equation should be used to consider the heat transfer process of lung tissue, which will be closer to the actual microwave ablation. To ensure that the tumor tissue is ablated and the risk of recurrence is reduced, in subsequent studies, we will consider the safety boundary of the ablation area in the simulation and will consider the difference in tumor porosity in the clinical application to better provide surgical planning.
## 3 Methodology  
### Evaluation Indicators for Satellite Precipitation Products  
The observed historical precipitation records at the five weather stations (Figure 1) in the Chindwin River basin were analyzed to evaluate the accuracy of IMERG and 3B42V7 products. The IMERG and 3B42V7 precipitation estimates at the grid boxes where the weather stations are located were compared with the corresponding gauge-based precipitation from 1 April 2014 to 31 December 2015 at multiple temporal scales.  
To comprehensively evaluate IMERG and 3B42V7 precipitation products, six statistical indices were adopted as follows. The Pearson correlation coefficient (CC) describes the agreement between satellite precipitation estimates and gauge observations (Equation (1)). A perfect positive fit is reflected by a CC value of 1, whereas a weak linear correlation is indicated by CC that is close to zero. The relative bias (BIAS) represents the systematic bias of satellite-based precipitation (Equation (2)). A positive BIAS indicates an overestimation of satellite precipitation, whereas a negative value implies an underestimation. The root mean square error (RMSE) measures the average absolute error of satellite precipitation (Equation (3)). The smaller RMSE is, the closer the satellite precipitation estimates are to the observations. CC, BIAS, and RMSE are expressed by  
\[CC=\frac{\sum_{i=1}^{n}\left(P_{i}^{o}-\overline{P}^{o}\right)\left(P_{i}^{s} -\overline{P}^{s}\right)}{\sqrt{\sum_{i=1}^{n}\left(P_{i}^{o}-\overline{P}^{ o}\right)^{2}}} \tag{1}\]  
\[BIAS=\frac{\sum_{i=1}^{n}\left(P_{i}^{s}-P_{i}^{o}\right)}{\sum_{i=1}^{n}P_{i }^{o}}\times 100\% \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(P_{i}^{s}-P_{i}^{o}\right)^{2}}{n}} \tag{3}\]  
where \(n\) is the sample size of the satellite or the gauge-based precipitation time series; \(P_{i}^{s}\) and \(P_{i}^{o}\) represent the satellite precipitation and gauge-based precipitation amounts at the \(i\)th time step (mm); and \(\overline{P}^{s}\) and \(\overline{P}^{o}\) are the mean values of satellite precipitation and gauge-based precipitation (mm). The probability of detection (POD), the false alarm ratio (FAR), and the critical success index (CSI) represent the contingency of satellite precipitation estimates. POD, which is also known as the hit rate, describes the fraction of precipitation events correctly detected by the satellite among all real precipitation events (Equation (4)). FAR denotes the fraction of false events among all the events detected by the satellite (Equation (5)). CSI represents the overall fraction of precipitation events correctly detected by the satellite (Equation (6)). POD, FAR, and CSI are expressed by  
\[POD=\frac{H}{H+M}\times 100\% \tag{4}\]  
\[FAR=\frac{F}{H+F}\times 100\% \tag{5}\]\[CSI=\frac{H}{H+M+F}\times 100\% \tag{6}\]  
where \(H\) denotes the number of real rain events correctly detected by the satellite; \(M\) represents the number of real rain events failed to be detected by the satellite; and \(F\) is the number of rain events detected by the satellite that do not occur actually. The perfect scores of POD, FAR, and CSI are 1, 0, and 1, respectively.
## 2 Related Works  
### Social Interactions  
Pedestrian trajectories are greatly influenced by nearby pedestrians, especially in congested areas, making the modeling of pedestrian social interactions crucial in forecasting pedestrian trajectories. One technique is to utilize a grid-based algorithm, such as social pooling [6], which builds a grid around the domain space of the target pedestrian and calculates the social hidden-state tensor in each cell using a process similar to average pooling. Many other works have also adopted this method [14; 17; 18; 20; 33], including the improved circular occupancy map [16] and angular grid [33]. Another non-grid method [24; 26] embeds the relative position of other pedestrians with respect to the target pedestrian, similar to transformer position encoding [34; 35], and then concatenates the relative position embeddings with the LSTM hidden states. The Multi-Layer Perceptron (MLP) and pooling layers then process the resulting vector to obtain the social vector. Attention-based approaches [22; 23; 36; 37] acquire the influence weights of surrounding pedestrians on the target pedestrian in real time, while spatio-temporal graphs [11; 21; 25] consider each pedestrian as a node and encode spatio-temporal features using spatial-edge RNNs and temporal-edge RNNs. More complex, cross-spatio-temporal correlation features are learned and adaptively integrated into the spatial and temporal features [38]. [PERSON] et al. [26] summarized the design choices of social interaction modules and demonstrated equivalence between graph-based and LSTM-based pipelines. However, most of these methods only consider simple motion features as the factors influencing social interactions.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 5 Justification of EOSSA
### The Case of Multiple Roots  
In the case of multiple signal roots, Algorithm 2 formally fails because the matrix \(\mathbf{T}\) of eigendecomposition does not exist (the matrix \(\mathbf{M}\) is not diagonalizable and therefore the Jordan decomposition should be constructed; however, it is very unstable procedure). For stability of the algorithm in the case of close roots, the following option was proposed: at step 3 of Algorithm 2 the matrix \(\mathbf{T}\) is constructed from the eigenvectors of the matrix \(\mathbf{M}\) as columns. In the degenerate case of multiple roots of the characteristic polynomial, the eigenvector corresponding to the eigenvalue is one for each eigenvalue, but numerically we obtain a system of almost identical but linearly independent eigenvectors.  
The second way of finding the basis in step 5 described in Section 5.3 leads to the matrix \(\overline{\mathbf{T}}\) being block-diagonal with orthogonal blocks. Thereby, its numerical inversion is stable and is constructed by transposing the blocks.  
It turns out that Algorithm 2 works approximately for the case of multiple roots using the second way of finding the basis. The explanation is that numerically the roots are not exactly equal, and orthogonalization of blocks of the matrix \(\mathbf{T}\) makes the computation stable even in the case of very close roots. Thus, in further numerical investigations, we will use the second way of finding the basis from Section 5.3.
## 4 Materials and Methods
### Mutagenesis and NR2E3 cDNA Plasmid Construction  
The _NR2E3_ cDNA was isolated from the Clontech Human Retina QUICK-Clone cDNA pool (TaKaRa Bio Europe, Saint Germain en Laye, France) while using specific primers (Supplementary Table S1), and the amplicon was cloned into the pGEM-T Easy vector system (Promega), according to the manufacturer's instructions. Mutagenesis was performed using the QuickChange Site-Directed Mutagenesis Kit (Agilent Technologies, Montpellier, France) to introduce the c.166G>A (G56R) and c.173_174 delAC (G56R-CRISPR2) mutations. The WT and mutated NR2E3 sequences were verified by Sanger sequencing prior to sub-cloning into the pcDNA3 expression system (Invitrogen; kindly provided by Dr. [PERSON], INM). For sub-cloning, the pGEM-T Easy constructs and pcDNA3 plasmid were both digested by the restriction enzyme _Eco_RI. The pcDNA3 plasmid was dephosphorylated-lated using the FastAP thermosensitive alkaline phosphatase (Thermo Fisher Scientific), and both the vector and inserts were purified using the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel, Hoerdt, France) prior to ligation and subsequent transformation in One Shot TOP10 chemically competent _E. coli_ (Thermo Fisher Scientific, Villebon sur Yvette, France).
## 5 Concluding Remarks  
In this paper, the free damping vibration problem of piezoelectric cantilever beams was solved using the biparametric perturbation method, and its theoretical solution was obtained. In addition, the related experiments were carried out, and the experimental results were compared with the theoretical results. The following main conclusions can be drawn.  
1. The theoretical solution given in this paper can be degraded to the existing vibration solution of the general cantilever beam, and the theoretical results are in good agreement with the experimental results. These indicate that the analytical solution given in this paper is correct, and the biparametric perturbation method used in this paper is effective.
2. From Equation (11), it can be seen that the piezoelectric properties of the piezoelectric materials will increase the elastic modulus, which is usually known as the piezoelectric stiffening effect peculiar to piezoelectric materials and structures. As we all know, the greater the elastic modulus, the higher the vibration frequency. Thus, the piezoelectric properties will increase the vibration frequency of the piezoelectric cantilever beams.  
Figure 5: The time–displacement curves under the initial displacement of 0.750 mm.  
Figure 6: The time–displacement curves under the initial displacement of 1.342 mm.  
* From the perturbation expansion, it is easy to find that the zero-order solution is the solution of the free vibration of the classical cantilever beam, without the piezoelectric properties and damping. The influences of the piezoelectric properties and the damping are reflected in the first-order and second-order perturbation solutions. The analytical characteristic and structural form of the perturbation solution are beneficial to the parameter analyses of the studied problem.  
The method proposed in this study may be extended to multi-physical fields like electric, magnetic, or thermal fields, in addition to the traditional mechanical field, and these electric, magnetic, or thermal parameters may be selected as the perturbation parameters. Therefore, the biparametric perturbation, even the multi-parametric perturbation, method, especially based on perturbation parameters with certain physical meanings, agree with, to a certain extent, the basic idea of homogenization. Moreover, the analytical results proposed in this study can provide a theoretical basis and reference for the analysis and design of sensors and actuators based on the piezoelectric effect.  
Conceptualization, X.-T.H. and J.-Y.S.; funding acquisition, X.-T.H. and J.-Y.S.; methodology, X.-T.H. and Z.-X.Y.; data curation, Z.-X.Y. and D.-D.P.; writing (original draft preparation), X.-T.H. and Z.-X.Y.; writing (review and editing), D.-D.P. and J.-Y.S. All authors have read and agreed to the published version of the manuscript.  
This project is supported by the National Natural Science Foundation of China (grant no. 11572061 and 11772072).  
The authors declare no conflict of interest.
# A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation
## 2 Previous Works
### Model Compression  
There are mainly four categories in models that compress NNs, namely, parameter pruning and quantization, low-rank factorization, compact convolutional filters and knowledge distillation (KD) [3]. Parameter pruning and quantization tries to decrease redundant parameters or downgrade the data precision without losing model performance. Low-rank factorization extracts informative parameters by matrix decomposition. Compact convolutional filters reduce the size of parameters by redesigning special convolutional filters. KD teaches the light NNs (the student) with the knowledge \"squeezed\" from the heavy NNs (the teacher).  
Among them, pruning is efficient but may hurt the performance or requires configuration of the sensitivities of the layers manually; low-rank factorization and compact convolutional filters demand prior knowledge, while KD does not need to change the structure severely and can achieve robust performance end-to-end [16].  
Three schemes are preferred in distillation: offline distillation that requires a pretrained heavy model, online distillation and self-distillation [17] that can train heavy and light models simultaneously. Self-distillation has the advantage of training the same network architecture for the heavy and the light model at the same time, which makes it practical in applications.