esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### GCNs  
A GCN is a feature extraction method for a graph structure and has many advantages over a CNN. Namely, traditional CNNs depend on a large number of training data. In addition, they can only extract numerical information of samples and cannot effectively use the structural information of the samples. As a result, there is insufficient use of the sample information, and the diagnostic accuracy of CNNs is not ideal. The GCN can take full advantage of both structural and numerical information and reduce the dependence on samples, thereby enabling good performance in the case of a small number of samples or sample imbalance. Graph construction and forward transfer are important components of the GCN. The forward transfer process of the GCN is similar to the convolution operation, that is, for a central node, information of its neighbors and itself is considered as its characteristic information. The expression is as follows:  
\[h_{i}^{k}=\partial\Big{(}w_{k-1}\sum_{u\in N_{i}}\frac{h_{i}^{k-1}}{N}+B_{k-1} h_{i}^{k-1}\Big{)} \tag{6}\]  
where \(h_{i}^{k}\) represents the characteristics of \(v_{i}\) in the k-layer neural network, \(N_{i}\) represents the neighbors of \(v_{i}\), \(N\) is the number of neighbors of \(v_{i}\), \(w_{k-1}\) and \(B_{k-1}\) are the learnable weight parameters of the neural network, and \(\partial\) stands for activation function.  
The mathematical principle of the GCN is mainly to explore the properties of the graph with the help of the eigenvalues and eigenvectors of the Laplacian matrix [26]. After a series of optimizations, such as Chebyshev polynomial approximation, the final graph convolution expression can be expressed as follows:  
\[h^{k}=ReLU\big{(}\hat{A}h^{k-1}w_{k-1}\big{)} \tag{7}\] \[\widetilde{A}=A+I_{N}\] (8) \[\hat{A}=\tilde{D}^{-\frac{1}{2}}\widehat{A}\tilde{D}^{-\frac{1}{2}}\] (9) \[\widetilde{D}_{ii}=\sum_{j}\widetilde{A}_{ij} \tag{10}\]  
where \(A\) is the adjacency matrix, \(h^{k}\) is the feature matrix of nodes in the k-th layer of the GCN, \(w_{k-1}\) is the trainable weight matrix of the network, \(\widetilde{A}\) is the node self-connection added to the adjacency matrix, \(\tilde{D}\) is the degree matrix of \(\widetilde{A}\), \(I_{N}\) is the identity matrix, \(\hat{A}\) is the matrix after normalizing the adjacency matrix, and \(ReLU\) is the activation function.
## 2 Wavelet Transform Algorithm Transmittance
### Algorithm Transmittance Impact on Error Signals
If these realizations are uncorrelated with each other, or the autocorrelation mentioned is small, and the assumption of the same power spectral density of these signals in the frequency range \(\hat{f}\in[0;\frac{1}{2}f_{s}]\) holds, the variance of the random error signal at the algorithm's output can be described by the following equation:  
\[\sigma_{X,r,i}^{2}=a_{i,0}^{2}\sigma_{x,r}^{2}+a_{i,1}^{2}\sigma_{x,r}^{2}+ \ldots+a_{i,N-1}^{2}\sigma_{x,r}^{2}=\sigma_{x,r}^{2}\sum_{j=0}^{N-1}a_{i,j}^{ 2}. \tag{6}\]If the specified conditions are not satisfied for the analyzed signal, its variance should be calculated using Equation (4). In this scenario, it is also feasible to calculate the average variance of the analyzed random error signal:  
\[\sigma_{X,r,i}^{2}=\frac{1}{\pi}\int_{0}^{\pi}\sigma_{x,r}^{2}\bigg{(}\frac{ \omega_{n}}{T_{s}}\bigg{)}\bigg{|}H_{i}\Big{(}\mathrm{e}^{\mathrm{j}\omega_{n} }\Big{)}\bigg{|}^{2}\mathrm{d}\omega_{n}, \tag{7}\]  
where \(\omega_{n}=\omega T_{s}\) is the normalized pulsation [24]. Note that Equations (6) and (7) are equivalent when the \(e_{x,r}(i)\) signal has a constant power spectral density (in case where \(\sigma_{x,r}^{2}(\omega)=\mathrm{const}\)), which occurs, i.e., in case of white noise or in case of a quantization error signal [27; 28; 29; 30]. In the case of non-constant power spectral density, only Equations (4) and (7) can be used to calculate the analyzed signal variance correctly.
## 6 Conclusions and Future Work  
Through simulation experiments, we compared the SA-MCSDN algorithm with the SA-FFCCPP, HS-PSO, and SA-MCSDN algorithms using the NS3 simulator and an Ubuntu 64-bit tool in the SDN environment. We judged the improvement compared to our previously proposed algorithm. In order to increase the convergence rate and reduce deployment latency and connection latency, the following metrics were considered: propagation delay, round-trip time (RTT), matrix of time session (TS), average delay, reliability, throughput, cost, and fitness value. The proposed SA-MCSDN approach provides an improved solution to extend control over the SDN environment to Linux virtual machines, in addition to providing sufficient flexibility to handle such environments. Additional studies on this topic are necessary.  
We found that the proposed SA-MCSDN algorithm achieves better results than our previously proposed algorithm in solving the problem of distributing control units and placing them in optimal and appropriate positions in terms of spatial location within the network in the control layer, as well as the time taken for implementation. This analysis is based on the following main criteria: the shortest distance and the method of its calculation, the decrease in the time taken for distribution, and the communication between devices of the infrastructure layer and the control units (S2 Cs), as well as between the control units themselves (C2 Cs) with consideration of fault. The selection and preparation of control units and the measures of performance shown in Table 2 indicate the level of effectiveness  
Figure 13: Comparison of fitness values.  
of the network, the increase in the expansion of the network, and its reliability. In future studies, we intend to emphasize and identify various drawbacks of a variety of approaches.  
Conceptualization, N.S.R., S.T.F.A.-J. and K.S.J.; methodology, N.S.R., S.T.F.A.-J. and K.S.J.; software, N.S.R., S.T.F.A.-J. and K.S.J.; validation, N.S.R., S.T.F.A.-J. and K.S.J.; formal analysis, N.S.R., S.T.F.A.-J. and K.S.J.; investigation, N.S.R., S.T.F.A.-J. and K.S.J.; resources, N.S.R., S.T.F.A.-J. and K.S.J.; data curation, N.S.R., S.T.F.A.-J. and K.S.J.; writing--original draft preparation, N.S.R., S.T.F.A.-J. and K.S.J.; writing--review and editing, N.S.R., S.T.F.A.-J. and K.S.J.; visualization, N.S.R., S.T.F.A.-J. and K.S.J.; supervision, N.S.R., S.T.F.A.-J. and K.S.J.; project administration, N.S.R., S.T.F.A.-J. and K.S.J.; funding acquisition, N.S.R., S.T.F.A.-J. and K.S.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
All data are presented in the main text.  
We would like to thank all individuals and organizations who provided support to complete this paper.  
The authors declare no conflict of interest.
# The Effect of 3D TVs on Eye Movement and Motor Performance
## 3 Results
### Eye Movement
#### 3.1.4 Time to First Fixation  
Overall, the environment influenced time to first fixation (F\({}_{1,11}\) = 4.965, \(p\)-value = 0.048). A longer time to first fixation occurred when a virtual target appeared on 3D TV displays (Table 6). Furthermore, the Tukey HSD test showed that time to first fixation on 2D TV screen displays differed from time to first fixation on a 3D TV. However, there was no significant difference between time to first fixation and depth (F\({}_{2,22}\) = 0.398, \(p\)-value = 0.677), index of difficulty (F\({}_{5,55}\) = 0.408, \(p\)-value = 0.841), interaction of environment and depth (F\({}_{2,22}\) = 0.392, \(p\)-value = 0.681), interaction of environment and index of difficulty (F\({}_{5,55}\) = 0.853, \(p\)-value = 0.518), interaction of depth and index of difficulty (F\({}_{10,110}\) = 1.365, \(p\)-value = 0.206), and interaction of environment, depth, and index of difficulty (F\({}_{10,110}\) = 0.906, \(p\)-value = 0.531). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Time to First Fixation**} \\ \hline  & **Level** & **Mean (s)** & **Group \({}^{\mathbf{a}}\)** & **SD** & **F\({}_{\mathbf{n,m}}\)** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Environment} & 2D Screen Displays & 0.076 & A & 0.060 & F\({}_{1,11}\) = 4.965 & \(p\) = 0.048 \\  & 3D Stereoscopic Displays & 0.105 & B & 0.083 & F\({}_{1,11}\) = 4.965 & \(p\) = 0.048 \\ \hline \multirow{3}{*}{Depth} & 210 cm & 0.085 & A & 0.068 & \multirow{3}{*}{F\({}_{2,22}\) = 0.398} & \multirow{3}{*}{\(p\) = 0.677} \\  & 190 cm & 0.093 & A & 0.070 & & \\  & 140 cm & 0.096 & A & 0.083 & & \\ \hline \multirow{3}{*}{ID} & 2.8 bits & 0.094 & A & 0.090 & \multirow{3}{*}{F\({}_{5,55}\) = 0.408} & \multirow{3}{*}{\(p\) = 0.841} \\  & 3.3 bits & 0.083 & A & 0.053 & & \\ \cline{1-1}  & 3.7 bits & 0.090 & A & 0.063 & & \\ \cline{1-1}  & 4.2 bits & 0.097 & A & 0.086 & & \\ \cline{1-1}  & 5.1 bits & 0.088 & A & 0.077 & & \\ \cline{1-1}  & 6.1 bits & 0.091 & A & 0.070 & & \\ \hline \multicolumn{2}{l}{Environment \({}^{*}\) Depth} & & & & \multicolumn{1}{c}{F\({}_{2,22}\) = 0.392} & \(p\) = 0.681 \\ \hline \multicolumn{2}{l}{Environment \({}^{*}\) ID} & & & & \multicolumn{1}{c}{F\({}_{5,55}\) = 0.853} & \(p\) = 0.518 \\ \hline \multicolumn{2}{l}{Depth \({}^{*}\) ID} & & & & \multicolumn{1}{c}{F\({}_{10,110}\) = 1.365} & \(p\) = 0.206 \\ \hline \multicolumn{2}{l}{Environment \({}^{*}\) Depth \({}^{*}\) ID} & & & & \multicolumn{1}{c}{F\({}_{10,110}\) = 0.906} & \(p\) = 0.531 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Means, SDs, ANOVA, and Tukey HSD test results of time to first fixation.
## 3 Background
### Graph Neural Network  
In recent years, GNN has gradually become prominent in the field of deep learning because of the flexibility and high efficiency they bring. GNN is a subclass of deep learning techniques that work with graph data. Unlike image or text data, graph data allow GNN models to take advantage of the inherent graph structure of many real-world non-Euclidean data, such as relationships in telephones, social networks, and molecules. A graph is created by nodes, and the connections between them are called edges. By effectively leveraging the correlation relationship among the components of the graph, the GNN model outperforms conventional DL models like CNN and RNN when handling non-Euclidean data. The objective of GNN is to learn an embedding state that encapsulates the information of the neighborhood for each node. This state is then utilized to generate the output. There are many different ways to perform deep learning on graphs, and the best approach for a particular problem depends on the data structure and desired output. Some types of tasks on GNN can be mentioned such as node classification, link prediction, and graph  
Figure 2: Diagram of the IDS model.  
classification. The most crucial concept of a graph neural network is the message-passing mechanism that is presented in Figure 3.  
The GNN propagates information across the graph through a series of message-passing steps. This mechanism allows the GNN layer to update the hidden state of each node from its neighborhood nodes. This process is repeated, in parallel, for all nodes in the graph and thus, the hidden state of the graph is also aggregated and updated continuously through each GNN layer. In the GNN model, the message-passing mechanism takes place through two stages: aggregation and update. Initially, information on neighboring nodes is compiled and sent to the node that needs to be updated as a'message'. Then, this information is updated for that node along with the information statuses it is storing in the previous layer. After passing through multiple GNN layers, the resulting output is the final embedded representation of the graph's nodes. These embeddings are subsequently employed to address various tasks, including node classification, graph classification, and link prediction in different methods.  
Observing the structure of a graph, we notice that nodes with similar features or properties are often connected. Therefore, the GNN exploits this fact to learn how and why specific nodes connect while others do not. This is why the message-passing mechanism is widely regarded as the most critical strength of GNNs.
# Static Evaluation of a Midimew Connected Torus Network for Next Generation Supercomputers
## 5 Cost Effectiveness Analysis of MTNs
### Cost-Effective Factor  
Supercomputers provide fast computational power for various complex simulations. Therefore, speed, as a result of connecting large numbers of nodes, is critical. In addition, the level of efficiency while using these nodes contributed to the fast response. Therefore, speed and efficiency were indicated using CEF. In addition, the number of links used to connect the nodes depended on the topology configuration. These links had to be considered when analyzing system costs. The \(CEF\) was expressed as Equation (4), where \(\rho=\frac{cost\;of\;W_{L}}{cost\;of\;nodes},0\leq\rho\leq 1\); in our evaluation \(\rho=0.1\)  
\[CEF=\frac{1}{1+\rho\times G_{p}} \tag{4}\]  
The CEF of the MTN was almost equal to that for the six compared networks. However, the MTN's efficiency resulted in a good CEF.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Cost** & \begin{tabular}{c} **Cost Performance** \\ **Trade-Off Factor** \\ \end{tabular} & \begin{tabular}{c} **Cost Effective** \\ **Factor** \\ \end{tabular} & \begin{tabular}{c} **Time Cost** \\ **Effective Factor** \\ \end{tabular} & \
\begin{tabular}{c} **Packing** \\ **Density** \\ \end{tabular} &
\begin{tabular}{c} **Message** \\ **Traffic Density** \\ \end{tabular} \\ \hline \multicolumn{7}{c}{**256 Node**} \\ \hline
16 \(\times\) 16 Mesh & 120 & 0.2500 & 0.8421 & 1.67868 & 2.1333 & 5.69066 \\
16 \(\times\) 16 Torus & 64 & 0.5 & 0.8333 & 1.66125 & 4.0000 & 4.000 \\ TESH (2,2,0) & 84 & 0.309 & 0.8602 & 1.71466 & 3.0476 & 6.4430 \\ TTN (2,2,0) & 90 & 0.850 & 0.8247 & 1.64418 & 2.8444 & 3.5011 \\ TFBN (2,2,0) & 80 & 2.5 & 0.7619 & 1.51928 & 3.2000 & 1.84 \\ MMN (2,2,0) & 68 & 0.382 & 0.8602 & 1.71466 & 3.7647 & 5.5815 \\ MTNs (2,2,0) & 102 & 0.75 & 0.8247 & 1.64418 & 2.5098 & 3.2611 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Static network cost comparison.
## 3 Results and Discussion
### Construction and Evaluation of Models
Therefore, the SG-LS-SVR model based on the dual-band spectral was applied to detect the tea polyphenol content of fresh tea leaves with the best model prediction performance, which the validation set was R\({}_{\text{P}}\) of 0.893, RMSEP of 0.897% and RPD of 2.230, indicating that the device was capable of collecting visible/short-wave and long-wave NIR spectral data simultaneously with a satisfactory result. This was in accordance with the conclusion of [18; 20; 43] that modeling based on dual-band spectral data was better than single-band. Figure 7a,b show the correlation plots of the SG-LS-SVR detection of tea polyphenol content and the comparison between the predicted and measured values of the prediction set samples, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Waveband Range** & **Parameters** & **R\({}_{\text{c}}\)** & **RMSEC/\%** & **R\({}_{\text{p}}\)** & **RMSEP/\%** & **RPD** \\ \hline Single-band & 5593.054 & & & & & \\ (400–1050 nm) & 29,796.119 & 0.957 & 0.912 & 0.875 & 1.155 & 2.067 \\ Single-band & 1377.945 & & & & & \\ (1000–1650 nm) & 29,311.147 & & & & & \\ Dual-band & 1893.681 & & & & & \\ (400–1650 nm) & 25,494.311 & & 0.976 & 0.679 & 0.893 & 0.897 & 2.230 \\ \hline \end{tabular} Note: Parameters are the kernel width \(\gamma\) and the regularization parameter \(\delta^{2}\). R\({}_{\text{c}}\) and R\({}_{\text{p}}\) represent the correlation coefficient of calibration and prediction set, respectively. RMSEC and RMSEP represent the root mean square error of calibration and prediction set, respectively. RPD is residual predictive deviation.  
\end{table} 
Table 2: Predictive performances of the LS-SVR model for tea polyphenol content detection in different waveband ranges. Figure 6: SG smoothing pretreatment spectral of dual-band. (**a**) Spectral curve before data fusion, and (**b**) spectral curve after data fusion. Different colors denote the raw spectral reflectance curves of different samples. There were 40 samples of each tea variety, with a total number of 80 samples, i.e., 80 samples and 80 spectral curves.
## 2 Materials and Methods
### Analytical Methods  
Total solids (TS) and volatile solids (VS) content were determined by standard methods [40]. A bench-top pH meter FE20 (Mettler Toledo, Greifensee, Switzerland) was used to measure pH. The content of fats, carbohydrates, proteins and fibers, as well as the elemental composition (C, H, N), was determined as described previously [40].  
The biogas composition was measured using gas probes and a gas meter (Figures 2 and 3). An additional gas chromatography (GC) analysis was performed to check the accuracy of the sensors every 2-3 days. To do this, biogas samples were taken using a 50 mL syringe from the sampling ports of the digesters and injected manually into a GC (Crystal 5000.2, Chromatek, Russia). CH\({}_{4}\) and H\({}_{2}\) were separated on Zeolite NaX 60/80 mesh and CO\({}_{2}\) on Hayesep Q 80/100 mesh. Helium and argon were carrier gases through steel columns (length--3 m, internal diameter--2 mm) with NaX and Hayesep Q at flow rates of 20 and 25 mL/min, respectively. The temperatures of the injector, oven and thermal conductivity detector were set to 45 \({}^{\circ}\)C, 60 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, respectively. The gases were calibrated using an external standard method based on a standard curve of detector response to known gas concentrations [41].  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Sewage Sludge** & **Feedstuff for Pigs** \\ \hline pH & 6.7 & - \\ TS, \% & 5.29 & 91.0 \\ VS/TS, \% & 65.34 & 89.5 \\ Crude fat, \%TS & 2.3 & 2.2 \\ Crude protein, \%TS & 20.7 & 13 \\ Carbohydrates, \%TS & 32.0 & 40.2 \\ Fiber, \% TS & 8.05 & 8 \\ N, \%TS & 4.3 & 2.76 \\ C, \%TS & 34 & 43 \\ H, \%TS & 5.1 & 6.3 \\ C/N & 7.9 & 15.6 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of cosubstrates.
# Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks
## 4 BER in the Case of MRC Combiners  
MRC gives the greatest diversity profit, but these combiners are very expensive with respect to practical realization. MRC receivers can have two, three, or more inputs. If the noise power is the same at all inputs, then the square of the signal at the output is equal to the sum of the squares of the signal at the inputs. The distribution that the signal has at the input of the MRC receiver is the same as at the output of the MRC receiver, and the average power increases in proportion to the number of inputs [12; 20].  
The MRC receiver with \(L\) inputs can be represented as in Figure 2, where \(\gamma_{1}\), \(\gamma_{2}\), \(\ldots\), \(\gamma_{L}\) are the SNR of signals at the input and \(\gamma\) is the SNR of the signal at the output.
## 6 Results and Discussion
### Classification Performance under a Noisy Environment  
A noisy condition is a situation that typically occurs in a practical distribution system. Therefore, the performance of the proposed classification system under a noisy environment was tested and compared to the conventional SSA, as well as the well-known methods, which are the conventional particle swarm optimization (PSO) and the Genetic algorithm (GA), as feature selection algorithms. The expression of these comparative algorithms was adopted from references [33, 44, 64]. The key parameters of PSO were set as follows; population size = 10, max inertia weight = 0.9, min inertia weight = 0.4, acceleration factor = 0.9, maximum number of iterations = 40, maximum needed number of runs = 20. The key parameters of GA were set as follows; population size = 10, max number of generations = 40, number of binary bits per design variable =10, crossover rate = 0.9, mutation rate = 0.1, transition probability = 0.01, maximum number of runs need to be = 20. Meanwhile, the key parameters of SSA were set similarly to the its adaptive. White noise with a signal-to-noise ratio of 10 to 50 dB were identically added to all power quality signal types. From Table 7, it is seen that the classification system based on the adaptive SSA optimal feature selection is qualitatively more resistant to noise than a system without an optimal feature selection, using conventional SSA, using PSO and using GA as optimal feature selection. Therefore, the proposed system could be efficiently operated in a noisy environment.
# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
## 2 Materials and Methods
### Hyperspectral Imaging System and Image Acquisition  
In this study, the hyperspectral imaging system (GaiaSorter, Zolix Instruments Co., Ltd., Beijing, China) was used to collect spectral and image information [12]. The spectral resolution was 5 nm in 895-1700 nm (with 254 wavelengths), the wavelength interval was 3.19 nm, and the resolution of each image was 320 \(\times\) 256 pixel. The core components of the system included the hyperspectral imager, uniform light sources, electronically controlled mobile platforms, computers, and control software. The spectral camera was Zolix's \"image-\(\lambda\)-N17E\". The light source consisted of four 250 W bromine tungsten lamps, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Defect Types** & **No. of Samples** & **Calibration Set** & **Prediction Set** \\ \hline Rust spot & 92 & 69 & 23 \\ Crack & 84 & 63 & 21 \\ Insect damage & 84 & 63 & 21 \\ Intact & 160 & 120 & 40 \\ Total & 420 & 315 & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The sample set results were divided by the SPXY algorithm.  
Figure 1: The intact and defective samples of _Census humilis_ fruits: (**a**) Intact sample; (**b**) Rust spot sample; (**c**) Insect damage sample; (**d**) Crack sample.  
Figure 2: Experiment platform of hyperspectral imaging system.  
During the process of image acquisition, it was necessary to set a reasonable exposure time and the moving speed of the mobile platform to avoid information oversaturation and image distortion [13]. According to experimental experience, we set the exposure time to 0.13 s, the platform moving speed to 8.0 mm/s, and the distance between the sample surface and the lens to 280 mm. To illuminate the target sample, the illumination unit was fixed above the sample from both sides, at an angle of 45\({}^{\circ}\)and a height of 220 mm, to reduce the effects of shadow. To accurately collect the spectral data from each region, the target region is oriented towards the lens when collecting the spectra of different samples.