esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Turbulence Effect  
In order to calculate the impact of atmospheric turbulence on the \(n\)-th received signal, the Malaga (\(M\)) distribution has been employed, which is described by the following probability density function (PDF) [12; 13; 32; 33]:  
\[f_{I_{n}}(I_{n})\ =\ A_{n}\sum_{k=1}^{\omega}C_{k,n}I_{n}^{\frac{\alpha_{n}+k}{ 2}-1}K_{a_{n}-k}\Big{(}2\sqrt{B_{n}I_{n}}\Big{)}\ =\ A_{n}\sum_{k=1}^{\omega}C_{k,n}I_{n}^{\frac{\alpha_{n}+k}{ 2}-1}C_{0,2}^{2,0}\Big{(}B_{n}I_{n}\Big{|}\frac{a_{n}-k}{2},\frac{k-a_{n}}{2} \Big{)} \tag{3}\]  
where provided that \(b_{n}\in\mathbb{N}\), then \(\omega_{n}^{(\mathbb{N})}=b_{n}\), \(A_{n}^{(\mathbb{N})}=\frac{2a_{n}^{\frac{\alpha_{n}}{2}}(b_{n}c_{n})^{b_{n}+ \frac{\alpha_{n}}{2}}}{\frac{\alpha_{n}+2}{\alpha_{n}+2}\frac{\alpha_{n}+2}{ \alpha_{n}+2}\Gamma(a_{n})(b_{n}c_{n}+\Omega_{n})^{b_{n}+\frac{\alpha_{n}}{2}}}\), \(B_{n}^{(\mathbb{N})}=\frac{a_{n}b_{n}}{b_{n}c_{n}+\Omega_{n}}\), \(C_{k,n}^{(\mathbb{N})}=\ \left(\begin{array}{c}b_{n}-1\\ k-1\end{array}\right)\frac{(b_{n}c_{n}+\Omega_{n})^{1-\frac{1}{2}}}{(k-1)!} \Big{(}\frac{\Omega_{n}}{c_{n}}\Big{)}^{k-1}\Big{(}\frac{\Omega_{n}}{b_{n}} \Big{)}^{\frac{k}{2}}\); while for \(b_{n}\in\mathbb{R}\), then \(\omega_{n}^{(\mathbb{R})}\rightarrow\infty\), \(A_{n}^{(\mathbb{R})}=\frac{\frac{\alpha_{n}}{2}\frac{\alpha_{n}}{(b_{n}c_{n}) ^{b_{n}}}}{\frac{\alpha_{n}+2}{\alpha_{n}+2}\Gamma(a_{n})(b_{n}c_{n}+\Omega_{n })^{b_{n}}}\), \(B_{n}^{(\mathbb{R})}=\frac{a_{n}}{c_{n}}\), \(C_{k,n}^{(\mathbb{R})}=\frac{(b_{n})_{k-1}(a_{n}c_{n})^{\frac{k}{2}}}{[(k-1)!] ^{2}\alpha_{n}^{k-1}(b_{n}c_{n}+\Omega_{n})^{k-1}}\) with \(\alpha_{n}=\frac{\alpha_{n}}{2}\).  
Figure 1: Schematic diagram of the system’s configuration with \(M\) DF relayed multi-hop and \(N\) OC receiver diversity for each individual link.  
\(c_{n}=2\beta_{0,n}(1-\rho_{n})\), \(\left(b_{n}\right)_{k-1}=\prod\limits_{p=1}^{k-1}(b_{n}-p+1)\) representing the Pochhammer symbol of descending factorial, \(\Gamma(\cdot)\) is the Gamma function, \(a_{n}>0\) denotes the effective number of large-scale cells of the scattering, \(\Omega_{n}\) corresponds to the average optical power of the coherent contributions of the line-of-sight (LOS) and the coupled LOS scattering terms, \(b_{0,n}\) depends on the total scatter components and terms, \(0\leq\rho_{n}\leq 1\) indicates the correlation between the two scattering components of the \(M\) model for the \(n\)-th link and the amount of scattering power coupled to the LOS component [12; 13], \(K_{\
u}(\cdot)\) is the second kind of modified \(\
u\)-order Bessel function, and \(G(\cdot)\) stands for the Meijer-G function [34].  
Also, this model is of great importance, since many particular statistical distributions, like negative exponential, log-normal, gamma, gamma-gamma (GG), K, homodyned-K, Rice-Nakagami, shadowed-Rician, or gamma-Rician, are accurately shown to be special cases of \(M\)-distribution for specific parameters' values; for instance, the GG distribution is derived for \(\rho=1\)[12; 13; 35; 36].
## 4 Control Design
### Evolutionary Algorithms
#### 4.2.1 Differential Evolution Algorithm  
\(\bullet\) Step 1: Generation of initial population  
In the solution space, \(M\) individuals are generated randomly and each individual \(x_{i}^{g}\) is composed of a \(N\)-dimensional vector.  
\[x_{i}^{g}=\Big{[}x_{i,1}^{g},\ x_{i,2}^{g},\ x_{i,3}^{g},\ \ldots\,\ x_{i,N}^{g}\Big{]}, \tag{26}\]  
where \(g\) stands for iteration counter, \(g=0\), \(1\), \(2\),..., \(G\); \(i\) denotes individual number, and \(i=1\), \(2\), \(3\),..., \(M\). Each individual represents a set of parameters in the control architecture, where \(n\) indicates the total number of the parameters. The \(j\)-th component vector of the \(i\)-th individual in the initial generation is as follows:  
\[x_{i,j}^{0}=b_{j-\text{min}}+\text{rand}_{i,j}(0,1)\big{(}b_{j-\text{max}}-b_{j -\text{min}}\big{)}, \tag{27}\]  
where \(j=1\), \(2\), \(3\),..., \(N\), \(b_{j-\text{min}}\) and \(b_{j-\text{min}}\) represent upper and lower bounds of the \(j\)-th component vector, respectively, and \(\text{rand}_{i,j}(0,1)\) means a random number between 0-1.  
\(\bullet\) Step 2: Mutation  
In the \(g\)-th iteration, three individuals \(x_{p1}^{g}\), \(x_{p2}^{g}\) and \(x_{p3}^{g}\) were randomly selected from the population, and \(i\
eq p1\
eq p2\
eq p3\). Then the mutation vectors are generated by the following process.  
\[h_{i,j}^{g+1}=x_{p1,j}^{g}+F\Big{(}x_{p2,j}^{g}-x_{p3,j}^{g}\Big{)}, \tag{28}\]  
where \(x_{p2,j}^{g}-x_{p3,j}^{g}\) is a differential component vector, and this difference operation is the key to the DE algorithm. \(p1\), \(p2\) and \(p3\) are random integers, \(F\) is known as a scaling factor.  
\(\bullet\) Step 3: Crossover  
Crossover is to increase population diversity, which can be expressed as follows  
\[v_{i,j}^{g+1}=\left\{\begin{array}{ll}h_{i,j}^{g+1},&\text{rand }l_{i,j}\leq CR\\ x_{i,j}^{g},&\text{rand }l_{i,j}>CR\end{array}\right., \tag{29}\]  
where rand \(l_{i,j}\) is a random number between 0-1, \(CR\) is a crossover rate, and \(CR\in[0,1]\).  
\(\bullet\) Step 4: SelectionIn order to determine whether \(x_{i}^{g}\) is the next generation member, the fitness function values of trial vector \(v_{i}^{g+1}\) and target vector \(x_{i}^{g}\) are compared.  
\[x_{i}^{g+1}=\left\{\begin{array}{ll}v_{i}^{g+1},&f(v_{i}^{g+1})<f(x_{i}^{g}) \\ x_{i}^{g},&f(v_{i}^{g+1})\geq f(x_{i}^{g})\end{array}\right., \tag{30}\]  
where \(f(x)\) is the fitness function. Therefore, under the condition that the number of individuals is constant, the population can obtain better performance or maintain the previous fitness value.  
* Step 5: Iteration and end condition  
Run the operation of steps 2-4 repeatedly until the maximum number of iterations is reached (\(g=G\)). Then we can get the best result.
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 3 System Control and Modelling with the Proposed Compensator
### Regulation of the LV-VSC  
#### 3.1.1 AC Voltage Regulation  
Because there are no additional passive LC filter elements in the LV-VSC, the added voltage, \(u_{\text{o,B}}\), is the output voltage of the LV-VSC and can be realized directly through a unit PCC harmonic voltage feedforward control as shown in Figure 8. The harmonic voltage components of \(u_{\text{pcc}}\), \(u_{\text{pcc,h}}\), are extracted through a notch filter with the center frequency \(\omega_{0}\) shown in Figure 8.
# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
## 4 Conclusions  
The impact of noise interference and variable load on fault diagnosis in a realistic industrial environment needs to be alleviated, so a rolling bearing fault diagnosis method based on CBAM_ResNet and ACON activation function is proposed in this paper. The experiments use the rolling bearing dataset of CWRU, results indicate that the proposed method demonstrates better noise immunity and generalization ability compared to other deep-learning diagnostic methods. In discussion, the effectiveness of the optimization in this paper is confirmed. It serves as a rolling bearing fault diagnosis method that can be applied in real industry. The main conclusions are as follows.  
1. To diagnose the location and degree of bearing faults, a fault diagnosis model based on CBAM_ResNet and ACON activation function is proposed. It achieves 100%  
Figure 16: The visualization of fault diagnosis results (0\(\rightarrow\)1): (**a**) MSCNN; (**b**) WDCNN; (**c**) Proposed model; (**d**) Proposed model without using Meta ACON-C; (**e**) Proposed model without embedding the CBAM; (**f**) Proposed model without embedding the CBAM and without using Meta ACON-C.  
diagnostic accuracy for experimental data without considering noise interference and variable loads.
2. After considering five different noise interferences from 2 to 10 dB, in increments of 2, the diagnostic accuracy using our method is 97.68%, which is 7.1%, 6.24%, and 2.78% higher, respectively, compared with the other three published methods.
3. Six groups of experiments are set up with a test set of 10 dB noise and variable load domain, and the diagnostic accuracy of our method is 93.93%. It exceeds the comparison methods by 3.18%, 4.68%, and 3.32%, respectively.  
This paper has explored the fault diagnosis performance of the proposed model in a realistic industrial environment with noise interference and variable loads, but fault diagnosis methods should not be limited to a single device. Therefore, we will endeavor to research fault diagnosis methods that have the ability to cope with real industrial production environments and across devices.  
Methodology, J.P. and H.Q.; Software, H.Q. and J.L.; Formal analysis, H.Q. and J.L.; Writing--original draft preparation, H.Q.; Writing--review and editing, J.P. and H.Q.; Supervision, J.P. and F.H.; Resources, J.P. and F.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Thanks to Case Western Reserve University for making the data publicly available: [[https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures](https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures)]([https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures](https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures)) (accessed on 28 May 2023).  
The authors declare no conflict of interest.
# Static Structures in Leaky Mode Waveguides +
## 1 Introduction
The purpose of the grating is to break the leaky mode light into different orders, each propagating at a different angle. These angles are determined by the laser-induced grating period. Depending on laser parameters, we have generated gratings with half a dozen or more orders. Our analysis assumes five such orders. Each of these diffracted orders will form a high-order image point that appears at a different location and is visible from a different angle than the original image point. We propose that these 'copies' of the original image point and others of different depths, can be superimposed over time to form a single image point, as illustrated in Figure 4a. Instead of scanning with just one point to form an image, now we scan with multiple points. We pulse only when one of these points is in a location that corresponds to a point location and a view in the target image (Figure 4b). Over time, as these spatial and angular ray bundles accumulate, a wide view angle for every point in the image is achieved (Figure 4c). The success of this approach depends upon several of the unique features of leaky mode devices: high refresh rate, smoothly varying chirp functions, fast moving surface acoustic waves and translating leaky mode light. However, these attributes are not sufficient to make the approach successful if the view zones for each diffracted order cannot be adequately separated at the viewer's eye. If, for example, the viewer saw two overlapped view zones, they would see two points simultaneously and the display designer could not 'write' one point without also writing the other. Figure 5a shows a multi-order, HPO, hologram rendered in grayscale resist and reproduced with 633 nm light. Here, multiple images are created, one for each order of diffraction and each with a corresponding view zone (Figure 5b). Notice the overlap between the second- and third-order view zones. If the images appeared at different depths (which they do in this case), the viewer would see two images in the overlap position instead of one. Therefore, for our proposed multi-order method we must be able to eliminate as much as possible the overlap between adjacent view zone orders and achieve what we will call a 'no-overlap condition'. Below, we will use vector analysis to identify parameters for a 'no overlap condition', for a near-eye geometry. Figure 4: The multi-order leaky mode concept: (**a**) multiple orders combined to create a large view zone (or, alternatively, field of view). (**b**) Orders will travel across the display in front of the viewer. (**c**) The illumination is pulsed as orders pass by the viewer so multiple views are stitched into a wide view zone.
## 5 Case Study and Discussion
### Parameter Configuration  
The parameters are established with consideration of the actual operating costs, local economic factors, and results of a sensitivity analysis.  
In alignment with the average load rate of the No. 10 bus line, the cancellation cost (\(C^{Q}\)) is set at 2000, whereas the deadhead cost (\(C^{D}_{i,j}\)) is determined by multiplying the deadheading time by a factor of 10.  
Subsequently, a sensitivity analysis encompassing the number of backup vehicles (\(N\)), reassignment penalty (\(C^{P}\)), and minute change cost of intervals (\(C^{H}\)) is conducted. It is anticipated that sustaining such a scheme continuously could be challenging if a specific aspect is significantly compromised. Based on the findings detailed in Table 2, we perform the following parameter adjustments: the number of backup vehicles (\(N\)) is set at 0, the reassignment penalty (\(C^{P}\)) is fixed at 500, and the minute change cost for the departure interval (\(C^{H}\)) is established at 10.  
Taking into account the actual operational context, we configure the parameters as follows: the maximum delay (\(d_{max}\)) is limited to 10 min, the minimum rest time (\(i^{idle}_{min}\)) is set at 3 min, and the maximum departure interval (\(h_{max}\)) is established as 20 min. The adjustment parameter (\(\epsilon\)) for the soft constraint is defined as 5 min, and the learning rate (\(\gamma\)) for the recombination rules is set to 0.05. With these parameter settings, the computational cost of the sub-stages is approximately 15 s.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(N\)} & \multirow{2}{*}{\(C^{P}\)} & \multirow{2}{*}{\(C^{H}\)} & \multicolumn{3}{c}{**Timetable**} & \multicolumn{3}{c}{**Bus Schedule**} \\ \cline{3-10}  & & & **CT** & \(z_{Q}\) & \(z_{H}\) & **RR** & \(z_{P}\) & \(z_{C}\) \\ \hline
0 & 50 & 10 & 0 & 0 & 2778.26 & 18.26\% & 1050 & 0 \\  & & 20 & 0 & 0 & 5441.77 & 17.39\% & 1000 & 0 \\  & 500 & 10 & 0 & 0 & 2368.42 & 12.17\% & 7000 & 950 \\  & & 20 & 0 & 0 & 4601.84 & 13.91\% & 8000 & 950 \\
1 & 50 & 10 & 0 & 0 & 2698.59 & 21.74\% & 1250 & 0 \\  & & 20 & 0 & 0 & 4833.06 & 13.91\% & 1050 & 0 \\  & 500 & 10 & 0 & 0 & 2553.23 & 15.65\% & 9000 & 1250 \\  & & 20 & 0 & 0 & 4381.10 & 15.65\% & 9000 & 1250 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of sensitivity experiments.  
Figure 4: Schedules in the breakdown scenario.
# STGAT: Spatial-Temporal Graph Attention Neural Network for Stock Prediction
## 4 Experiments
### Experimental Results
#### 4.3.1 Predictive Performance of STGAT

Table 4: Comparison of predictive performance of different models on A-share dataset (divided using method II). The bold indicates the best performance among all models. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **MAE** & **DA\%** & **RE** & **RMSE** & **R2** & **Accuracy** \\ \hline GCN & 3.6217 & 51.42\% & 0.2701 & 5.6336 & 0.7765 & 0.5406 \\ GRU & 2.0720 & 53.12\% & 0.1290 & 3.9610 & 0.8895 & 0.6034 \\ LSTM & 2.8745 & 51.11\% & 0.1707 & 5.2883 & 0.8030 & 0.5768 \\ MLP & 2.3909 & 51.47\% & 0.1662 & 4.1556 & 0.8784 & 0.5702 \\ RNN & 2.2594 & 52.70\% & 0.1362 & 4.2614 & 0.8721 & 0.5941 \\ TCN & 2.4904 & 51.13\% & 0.1737 & 4.3004 & 0.8697 & 0.6156 \\ Transformer & 3.0561 & 53.21\% & 0.1878 & 5.2780 & 0.8018 & 0.5845 \\ STGAT & **1.5064** & **53.85\%** & **0.0946** & **2.6312** & **0.9460** & **0.6445** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of predictive performance of different models on A-share dataset (divided using method I). The bold indicates the best performance among all models.
## 3 Results and Discussion
### Impact of Vegetation Cover on Microwave-Based Soil Moisture Estimation
[84] estimated SM in the USA across different land covers (cropland, pasture, grassland, shrub, forest, barren, wetland, and developed) and found that ML models performed well in barren land but showed decreased performance in crops, grasslands, and forest areas. Compared to barren land, the RMSE for SSM increased by approximately 69% in cropland, 73% in grassland, and 85% in forest areas, indicating a substantial decline in model performance under denservegetation cover. [PERSON] and [PERSON] [121] estimated SM in China using Sentinel-1 and Sentinel-2 and found that low performance of ML models was observed in forest and croplands. To improve the performance of ML models, it is recommended to account for the vegetation effect on SAR signals. [PERSON] et al. [135] conducted a study on SM estimation in the Tibetan Plateau, where the vegetation effect was removed using the Water Cloud Model (WCM). The results showed that removing the vegetation effect increased R\({}^{2}\) between SM and SAR backscattering coefficients from 0.38 to 0.50. This enhanced correlation between backscattering coefficients and SM contributes to reducing the prediction error of ML models. To minimize the impact of vegetation on SAR signals and enhance the performance of ML models, future studies could integrate empirical and semi-empirical models (Figure 1) alongside ML techniques. Additionally, L-band SAR (e.g., NASA's NISAR, ALOS-2 PALSAR) offers a promising alternative, as it has a longer wavelength and greater penetration capability through vegetation cover compared to C-band SAR (e.g., Sentinel-1 and RADARSAT) [212].
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
Therefore, learning an efficient short text representation scheme suitable for clustering is critical to the success of STC. In essence, the major drawback of the standard STC techniques is that they cannot adequately handle the sparseness of words in the documents. Compared with long texts containing rich contexts, distinguishing the clusters of short documents with few words occurring in the training set is more challenging. Generally, most models primarily focus on learning representation from local co-occurrences of words [21]. Understanding how a model works is critical for using and developing text clustering methods. STC generally contains different steps that can be applied, as shown in Figure 1.  
1. Pre-processing: It is the first step to take in STC. The data must be cleaned by removing unnecessary characters, words, symbols, and digits. Then, text representation methods can be applied. Pre-processing plays an essential role in building an efficient clustering system because short text data (original text) are unsuitable to be used directly for clustering. Figure 1: Components for text-data clustering. 1. Representation: Documents and texts are collections of unstructured data. These unstructured data need to be transformed into a structured feature space to use mathematical modelling during clustering. The standard techniques of text representation can be divided into the representation-based corpus and representation-based external knowledge methods. 2. Dimensionality reduction: Texts or documents, often after being represented by traditional techniques, become high-dimensional. Data-clustering procedures may be slowed down by extensive processing time and storage complexity. Dimensionality reduction is a standard method for dealing with this kind of issue. Many academics employ dimensionality reduction to lessen their application time and memory complexity rather than risk a performance drop. Dimensionality reduction may be more effective than developing inexpensive representation. 3. Similarity measure: It is the fundamental entity in the clustering algorithm. It makes it easier to measure similar entities, group the entities and elements that are most similar and determine the shortest distance between related entities. In other words, distance and similarity have an inverse relationship, so they are used interchangeably. The vector representation of the data items is typically used to compute similarity/distance measures. 4. Clustering techniques: The crucial part of any text clustering system is selecting the best algorithm. We cannot choose the best model for a text clustering system without a deep conceptual understanding of each approach. The goal of clustering algorithms is to generate internally coherent clusters that are obviously distinct from one another. 5. Evaluation: It is the final step of STC. Understanding how the model works is necessary before applying or creating text clustering techniques. Several models are available to evaluate STC.
## 3 Dynamic Properties Analysis  
### Effect of Structure on Dynamic Properties  
For the dynamic properties of the sensor, the transfer process of water molecules in the sensitive layer can be described by [PERSON]'s second law [39], as shown in the following equation,  
\[\frac{\partial c}{\partial t}=\frac{D\partial^{2}c}{\partial x^{2}} \tag{16}\]  
where \(c\) denotes concentration of water vapor, \(t\) denotes time, \(D\) denotes the diffusion coefficient, and \(x\) denotes the diffusion distance. In the three-dimensional model, it can be rewritten as the following equation,  
\[\frac{\partial c(x,y,z,t)}{\partial t}=\frac{D_{x}\partial^{2}c(x,y,z,t)}{ \partial x^{2}}+\frac{D_{y}\partial^{2}c(x,y,z,t)}{\partial y^{2}}+\frac{D_{z} \partial^{2}c(x,y,z,t)}{\partial z^{2}} \tag{17}\]  
Equation (17) implies that in the process of gas diffusion, the response time of the sensitive layer is affected by the distance and the diffusion coefficient. The final distance of the gas diffusion process is the thickness of the sensitive layer, so the thickness is closely related to the response time.  
As shown in Figure 6, when the temperature is 298 K and \(D=11.01\)\(\times\)\(10^{-10}\) m\({}^{2}\)/s, the relationship between the response time and the thickness can be obtained through simulation analysis.  
It can be seen from Figure 6 that with the increase of sensitive layer thickness, the response time increases, and the growth rate gradually increases. The thickness of the sensitive layer has a significant effect on the dynamic properties. Therefore, controlling the thickness of the sensitive layer can effectively improve the dynamic properties of the sensor, and, by adjusting the amount of dispensing, the sensitive layer thickness can be controlled.  
Figure 6: Relationship between thickness and response time.