esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Validation of the Proposed Method
### Validation of Fault Diagnosis Method and Inference  
The training and test sample datasets consisted of 37.2 k data, respectively. Validation used the six fault types listed in Table S1. We adopted a 10-Fold approach in training which took nine parts as the training set and one part as the test set. The Gaussian-linearized transformer model was trained for 100 iterations. In the training process, the batch size was 50 with a learning rate of 0.01; the latter was set to the dynamic learning rate. With the number of training epochs increasing, the learning rates decreased by 0.0005 every 20 epochs, so as to make the model find the global optimum faster. The results are shown in Table 1. The test accuracy reached 99.75% when the test time and training time were 0.01 and 480 s, respectively.  
To evaluate the performance of the proposed method, other traditional methods were selected to compare prediction accuracy.  
The selected methods were CNN+RF, Traditional Transformer_Encoder (Transformer_En), and the Gaussian-linearized transformer (GLTrans.). The comparison results are shown in Table 1. It shows that CNN+RF had the lowest training time, 300 s and low accuracy, 96%, respectively, while Transformer_En required 600 epochs and reached 98% accuracy.
# Efficient GPU Implementation of the McMurchie-Davidson Method for Shell-Based ERI Computations
## 5 Implementations in Graphics Processing Unit (GPU)
### Sorting of ERIs  
In order to further enhance the computational efficiency of the proposed algorithms, this section describes the mechanisms to ensure effective parallelism of the implemented algorithms. BTM and STM are designed to perform distinct ERI computations as thread-based mappings. One of the key performance considerations in thread-based implementations lies in the ability to avoid the _warp divergence_ phenomenon [46], which occurs when threads within a _warp_ (a group of 32 threads) take different computation paths, causing the serial execution of computation paths and thus the inefficient use of GPU resources. As such, an effective strategy to minimize the occurrence of the _warp divergence_ lies in the notion of ordering and grouping similar ERI calculations within a warp to ensure threads follow identical computation paths, thus improving the overall effective parallelism.  
Ordering similar ERIs to improve GPU resource utilization is a commonly used strategy in ERI computations. In this study, we also adopt this approach to reduce warp  
Figure 9: Parallel thread assignment of CUDA blocks and CUDA threads to the shell-based ERI computation in SBM.  
divergence and enhance computational efficiency. While this technique itself is not novel, it is an essential step to ensure that the proposed GPU implementation operates efficiently.
## 4 Discussion
### Impact of Trust on Dispatchers' Behavioral Responses  
Trust in the ATS system significantly influences dispatchers' reliance behavior during routine monitoring, supporting Hypothesis 4a. This aligns with studies showing that operators' trust in automation systems increases their reliance on the system, particularly when the interface effectively displays system status [41]. Furthermore, reliance behavior during routine monitoring positively affects reliance behavior during emergency responses, supporting Hypothesis 4c. This suggests that daily interactions with the ATS system shape operators' confidence and behavior in high-stakes scenarios [57]. Additionally, the capability redundancy of the ATS system significantly impacts reliance behavior in both routine and emergency scenarios, supporting Hypotheses 5a,b. This finding corroborates traditional research emphasizing the role of system reliability in shaping operator dependence [66; 67].  
However, Hypothesis 4b, which proposed that trust directly affects reliance behavior during emergency responses, was not supported. This may be because, in emergency scenarios, dispatchers prioritize situational awareness and immediate action over their trust in the system. Interestingly, reliance behavior during emergencies is indirectly influenced by trust and capability redundancy through routine monitoring, as evidenced by the marginally significant mediation effects of ACR \(\Rightarrow\) RBR \(\Rightarrow\) RBE (\(p\) = 0.059) and DTA \(\Rightarrow\) RBR \(\Rightarrow\) RBE (\(p\) = 0.064). This indicates that daily reliance behavior serves as a bridge between trust, capability redundancy, and emergency responses. The lack of a direct effect of trust on emergency reliance may also reflect the unique demands of subway dispatching, where operators must balance trust in automation with their own expertise and situational judgment [68; 69].
# Research on Vibration Reduction in the Transmission System of Plastic Centrifugal Pump
## 4 Experimental Study on the Relationship between Pump Shaft Speed and Deflection
\begin{table}
\begin{tabular}{c c c} \hline
**Order** & \multicolumn{2}{c}{**Formula**} \\ \hline
1 & \(f(x)=0.01584x-15.41\) \\
2 & \(f(x)=1.294\times 10^{-5}x^{2}-0.04109x+32.46\) \\
3 & \(f(x)=7.563\times 10^{-9}x^{3}-3.698\times 10^{-5}x^{2}+0.05322x-13.96\) \\
4 & \(f(x)=-1.62\times 10^{-12}x^{4}+2.182\times 10^{-8}x^{3}-7.929\times 10^{-5}x^{2}+ 0.1014x-30.6\) \\
5 & \(f(x)=-5.505\times 10^{-15}x^{5}+5.893\times 10^{-11}x^{4}-2.238\times 10^{-7}x^{3}+ 0.3694\times 10^{-3}x^{2}-0.2562x+64.74\) \\
6 & \(f(x)=-4.966\times 10^{-18}x^{6}+6.004\times 10^{-14}x^{5}-2.786\times 10^{-10}x^{4}+ 6.313\times 10^{-7}x^{3}-0.7336\times 10^{-3}x^{2}\) \\  & \(+0.4142x-81.2\) \\ \hline \end{tabular}
\end{table} 
Table 4: Mathematical expressions of polynomials by order. Figure 17: First- to sixth-order polynomial fitting comparison. The Root Mean Square Error (RMSE) is calculated using Equation (29). \[RMSE=\sqrt{\frac{SSE}{n}}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y})^{2}} \tag{29}\]  
Table 5 summarizes the Sum of Squares due to Error (SSE), R-squared, and RMSE parameters for each polynomial fit compared to the experimental values. According to these data, the sixth-order polynomial shows the best evaluation.
# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## Vibration-Based Damage Detection (VDD)
### Time-Domain (TD) Methods
#### 3.2.2 Auto-Regressive Moving Average (ARMA) Methods
Using modal parameters as damage features, the variation of the extracted parameters for the reference and actual state could be considered for the condition assessment of a structure. The obtained results for modal parameters are more robust when compared to other TD methods. However, higher scatterings in damping ratios are observed and the reported error is much more than other TD methods, such as SSI.
## 3 Results and Discussion
### Effects of Rain Height on Rain-Induced Attenuation
The ITU curves overestimate rain attenuation at the north and northeastern stations (Polokwane and Pretoria) and moderately overestimate it at the central and central-western stations (Upington and Bloemfontein), but underestimate it at the southern coastal stations (Durban and Cape Town) due to the frequent occurrence of convective rain events. Figure 12: **(a–f)**. Comparison of rain height effects on rain-induced attenuation at a Ku-Band frequency for **(a)** Polokwane, **(b)** Pretoria, **(c)** Nelspruit, **(d)** Upington, **(e)** Durban, and **(f)** Cape Town.
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 5 Clinical Applications of Ultrasound Elastography
### Kidneys
#### 5.2.2 Technical Limitations in Renal Elastography  
The presence of an outer fibrous coating in the kidneys, resembling Glisson's capsule in the liver, introduces challenges in obtaining consistent data using ultrasound elastography. This is mostly due to the influence of blood, vascular movement, and urine pressure on kidney elasticity measurements. The aforementioned are the principal constraints of ultrasonic elastography. The potential exists for the recorded scans to exhibit inadequate precision due to the involuntary motion of the internal organs and vasculature, which may readily introduce distortions into the images. The native kidneys are constrained by their anatomical placement within the retroperitoneal space. Therefore, results show that USE can provide promising results in the ruling out of some pathologies and the detection of fibrosis since it is a non-invasive, affordable, and time-conserving alternative to renal biopsy, but it does not do a good job in differentiating between fibrosis stages or grading them; therefore, further research needs to be conducted regarding renal elastography to say that we can fully depend on it in the future [97; 98].
# A Novel Method for Modeling the Electromagnetic Characteristics of Switched Reluctance Motors
## 5 Conclusions  
An accurate method for modeling the electromagnetic characteristics of SRM is proposed in this paper. The flux-current (\(\psi-i\)) characteristics of five special positions are obtained by combining the measurement technique of the equilibrium position and the estimation method of flux-linkage in the middle position. The analytical model of flux-linkage that decouples the phase current and rotor position is built based on five-order Fourier series expansion. Once the coefficients of the Fourier series expansion are determined by five special flux-linkage curves, all the electromagnetic characteristics can be obtained easily. The comparative study shows that the calculated electromagnetic characteristics from the proposed method are in good agreement with the measured electromagnetic characteristics by the conventional rotor clamping method. Finally, the dynamic simulation model of the SRM prototype is constructed based on the calculated electromagnetic characteristics. The accuracy of the proposed method is further verified by the comparison between the simulation and experiment results.  
This work was supported in part by the National Natural Science Foundation of China (No. 51609033) and in part by the Fundamental Research Funds for the Central Universities (Nos. 3132017133 and 3132017130). The authors sincerely thank the reviewers for their valuable comments, which improved the quality of the paper.  
[PERSON] and [PERSON] proposed the modeling method and wrote the manuscript. [PERSON] carried out the simulation analysis for the method. [PERSON] and [PERSON] performed the experiments and edited the manuscript.  
The authors declare no conflict of interest.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 2 Materials and Methods
### Method
#### 2.3.3 Correlation Analysis  
The Pearson correlation coefficient is widely used to measure the degree of linear correlation between two variables. In this study, the Pearson correlation coefficient was used to evaluate the correlation between vegetation and meteorological data and to conduct a quantitative analysis of the relationship through MATLAB 2018b (MathWorks, USA MA). The calculation formula for the correlation coefficient is as follows:  
\[R_{xy}=\frac{\sum\limits_{i=1}^{n}{\left[{{{({x_{i}}-\overline{X})}{({y_{i}}- \overline{Y})}}}\right]}}{\sqrt{\frac{\sum\limits_{i=1}^{n}{{{({x_{i}}- \overline{X})}^{2}}\sum\limits_{i=1}^{n}{{{({y_{i}}-\overline{Y})}^{2}}}}}}} \tag{3}\]  
where \(\overline{X}\) and \(\overline{Y}\) represent the mean values of vegetation data (NDVI, LAI, or GPP) and climate data (P or Ta). \(R_{xy}\) is the correlation coefficient, indicating the degree of correlation between vegetation indices and climatic factors. A larger correlation coefficient indicates a stronger correlation between the two factors at that pixel.
## 5 Experimental Validation  
Comparative experiments are conducted with the traditional DCC method on the semi-physical simulation experimental platform shown in Figure 4. The experimental platform includes a host computer, three controllers, three PMSM drivers, three load PMSM drivers, three PMSMs, and three load PMSMs. The host computer sends control signals to the three controllers, and the controller applies control algorithms to the three PMSM drivers and three load PMSM drivers to drive the PMSM to rotate. The three controllers form a local area network with the host computer through a switch and transmit data through the user datagram protocol (UDP). Table 1 presents the PMSM parameters on the experimental platform. Table 2 presents the parameters of the traditional DCC method and the new control method proposed in this paper.  
Considering practical engineering applications, the new control method is compared with the traditional DCC method on the multi-motor speed control and load integration experimental platform. Comparative experiments are conducted on operations such as speed-up and speed-down as well as forward and reverse operations, sudden load, and sudden relief load, as well as on low speed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name and Symbol** & **Value** \\ \hline Phase resistance \(R_{s}/\Omega\) & 0.5 \\ dq-axis inductance \(L\)/H & 0.01 \\ Friction coefficient \(B/(\)N\(\cdot\)m\(\cdot\)s\()\) & 0.0043 \\ Moment of inertia \(J/(\)kg\(\cdot\)m\({}^{2}\)\()\) & 0.00194 \\ Flux linkage \(\psi_{f}/(\)V\(\cdot\)s\()\) & 0.1 \\ Polo pairs \(n_{p}\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of PMSM.  
Figure 4: Multi-motor speed control and load integration experimental platform.  
Figure 3: CSMC schematic diagram.
## 3 Results
### Response of Corn NPP to Climatic Factors
#### 3.3.2 Response of Corn NPP to Climatic Parameters
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**NPP Response to**} & \multicolumn{2}{c}{**NPP Response to**} & \multicolumn{2}{c}{**NPP Response to Solar**} \\ \cline{2-7}
**Relevance Level** & \multicolumn{2}{c}{**Temperature**} & \multicolumn{2}{c}{**Precipitation**} & \multicolumn{2}{c}{**Radiation**} \\ \cline{2-7}  & **Number of** & **Percentage** & **Number of** & **Percentage** & **Number of** & **Percentage** \\  & **Cells** & **(\%)** & **Cells** & **(\%)** & **Cells** & **(\%)** \\ \hline Significant negative correlation & - & - & - & - & - & - \\ Moderate negative correlation & - & - & - & - & 111 & 0.54 \\ Mild negative correlation & - & - & 16 & 0.08 & 10,380 & 50.80 \\ Mild positive correlation & 18,809 & 92.07 & 4867 & 23.82 & 9867 & 48.29 \\ Moderate positive correlation & 1589 & 7.78 & 7739 & 37.88 & 69 & 0.34 \\ Significant positive correlation & 34 & 0.15 & 7810 & 38.22 & 5 & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics on the geographical response of corn NPP to climatic parameters. Figure 8: Geographical response of corn NPP to climatic parameters. in the central region of the lower section and the southern region of the middle section, with scattered occurrences in the central and northeastern regions of the middle section. The geographical correlation coefficients between corn NPP and solar radiation ranged from \(-0.7860\) to \(0.8695\). Overall, the spatial relationship between corn NPP and solar radiation in the Fen River Basin was primarily characterized by mild negative and mild positive correlations. Specifically, the most prevalent correlation was mildly negative, accounting for \(50.80\%\) of the data. This mild negative correlation was predominantly observed in the western and northeastern areas of the upper section, upper and middle regions of the middle section, southern and northeastern regions of the middle section, and eastern portion of the lower section of the Fen River Basin. Conversely, mild positive correlations accounted for \(48.29\%\) and were mainly concentrated in the middle and lower sections of the Fen River Basin, with the exception of certain areas in the eastern region. Moderate negative, moderate positive, and significant positive correlations were rare, comprising \(0.54\%\), \(0.34\%\), and \(0.03\%\), respectively. These correlations were sporadically scattered across the western upper, central middle, and southern lower sections of the Fen River Basin.
# Late Fusion-Based Video Transformer for Facial Micro-Expression Recognition
## 5 Ablation Study
### Effect of the Fusion Location  
The proposed model extracts two features individually using grayscale information and optical flow, but it is unknown whether this yields improvement because there is no research on the transformer. Therefore, we compare the performance of EF and LF.  
From Table 6, it is difficult to determine which is better. However, when using LF, it needs one more backbone, which requires about twice as much additional operation as EF. In addition, except for Swin, the highest performance of each model comes from EF, so EF can be considered better. However, since the highest performance among all models comes  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **Video Length** & **Preprocess** & **UF1** & **UAR** & **Acc. (\%)** \\ \hline \multirow{8}{*}{3 DResNext [27]} & \multirow{4}{*}{8} & RGB + OF & 0.5954 & 0.5938 & 62.80 \\  & & GRAY + OF & 0.6087 & 0.6082 & 64.63 \\  & & OF & 0.5955 & 0.5939 & 62.80 \\ \cline{2-6}  & & \multirow{4}{*}{16} & RGB + OF & 0.6476 & 0.6540 & 65.24 \\  & & GRAY + OF & 0.6888 & 0.6773 & 71.34 \\  & & OF & **0.6924** & 0.6790 & **71.95** \\ \cline{2-6}  & & \multirow{4}{*}{32} & RGB + OF & 0.6130 & 0.6107 & 60.98 \\  & & GRAY + OF & 0.6487 & 0.6620 & 64.63 \\  & & OF & 0.6907 & **0.6983** & 69.51 \\ \hline \multirow{8}{*}{Video Swin [20]} & \multirow{4}{*}{16} & RGB + OF & 0.6481 & 0.6647 & 64.63 \\  & & GRAY + OF & **0.6977** & **0.6958** & **69.51** \\ \cline{1-1}  & & OF & 0.6678 & 0.6947 & 66.46 \\ \cline{1-1} \cline{2-6}  & & \multirow{4}{*}{32} & RGB + OF & 0.6301 & 0.6416 & 62.80 \\  & & GRAY + OF & 0.6226 & 0.6168 & 62.80 \\  & & OF & 0.6528 & 0.6471 & 66.46 \\ \cline{1-1} \cline{2-6}  & & \multirow{4}{*}{32} & RGB + OF & 0.6203 & 0.6231 & 62.20 \\  & & GRAY + OF & 0.6130 & 0.6177 & 62.20 \\  & & OF & 0.5762 & 0.5838 & 57.93 \\ \hline \multirow{8}{*}{TimeSFormer [28]} & \multirow{4}{*}{8} & RGB + OF & 0.6271 & 0.6440 & 62.20 \\  & & GRAY + OF & 0.6632 & 0.6834 & 65.85 \\ \cline{1-1}  & & OF & 0.6723 & 0.6750 & 67.07 \\ \cline{1-1} \cline{2-6}  & & RGB + OF & **0.7038** & **0.6953** & **71.34** \\ \cline{1-1} \cline{2-6}  & & \multirow{4}{*}{16} & RGB + OF & 0.6492 & 0.6572 & 64.63 \\ \cline{1-1}  & & GRAY + OF & 0.6392 & 0.6465 & 63.41 \\ \cline{1-1}  & & OF & 0.6465 & 0.6476 & 64.63 \\ \hline \end{tabular} Note: **Bold** show the best performance of each model in each metric.  
\end{table}
Table 5: Comparison based on the number of frames.  
from LF, it is still difficult to determine superiority and inferiority. As a result, it is still an open problem.
## 2 Methodology
### Position Servo Loop Model  
In this paper, the position servo loop receives the lifting points' information from the position and orientation measurement system. The EMPC controller outputs the desired movement increment \(\Delta v_{ux}\) of the crane in the \(X\) direction and the desired movement increment \(\Delta v_{uy}\) of the trolley in the \(Y\) direction. In this section, the subscript \(C\) denotes the crane and the subscript \(T\) denotes the trolley.  
Under the fixed coordinates OXY on the ground, the kinematic equation of the trolley and crane in \(X\) and \(Y\) directions is as follows:  
\[u(k)=\begin{pmatrix}\dot{X}_{u}(k)\\ \dot{Y}_{u}(k)\end{pmatrix}=\begin{pmatrix}v_{ux}(k)\\ v_{uy}(k)\end{pmatrix} \tag{11}\]  
where \(X_{u}\) and \(Y_{u}\) are the position of the crane and trolley in the coordinate system OXY, respectively.  
In Figure 3, \(n_{xref}\) (rpm) is the reference speed of the motor that drives the crane to move in the \(X\) direction, and \(n_{yref}\) (rpm) is the reference speed of the motor that drives the trolley to move in the \(Y\) direction. The relationship between \(n_{ref}\) and \(u(k)\) is as follows:  
\[n_{ref}(k+1)=\begin{pmatrix}n_{xref}(k+1)\\ n_{yref}(k+1)\end{pmatrix}=\begin{pmatrix}\frac{60\sigma_{xy}}{2\pi r_{w}}v_{ux }(k)\\ \frac{60\sigma_{xy}}{2\pi r_{w}}v_{uy}(k)\end{pmatrix} \tag{12}\]  
When designing the position loop servo controller, the position servos of the \(X\) and \(Y\) directions are designed independently. In engineering, the system control generally adopts the incremental control method, and the incremental control is to output the increment \(\Delta v\) of the control variable \(v\) every period:  
\[v_{ux}(k)=v_{ux}(k-1)+\Delta v_{ux}(k)=v_{ux}(k-1)+u_{C}(k)T_{s} \tag{13}\]\[v_{uy}(k)=v_{uy}(k-1)+\Delta v_{uy}(k)=v_{ux}(k-1)+u_{T}(k)T_{s} \tag{14}\]  
The reference signal can be regarded as a state variable:  
\[X_{u}(k+1)=X_{u}(k)+v_{ux}(k)T_{s} \tag{15}\]  
\[Y_{u}(k+1)=Y_{u}(k)+v_{uy}(k)T_{s} \tag{16}\]  
The state-space models for the moving crane system and the moving trolley system are as follows:  
\[x_{C}(k+1)=A_{C}x_{C}(k)+B_{C}u_{C}(k) \tag{17}\]  
\[y_{C}(k)=Cx_{C}(k)\]  
\[x_{T}(k+1)=Ax_{T}(k)+Bu_{T}(k)\]  
\[y_{T}(k)=C_{T}x_{T}(k) \tag{18}\]  
where  
\[\begin{bmatrix}-40\\ -5\\ -10\\ -3.5\end{bmatrix}\leq x_{C}(k)=\begin{bmatrix}X_{u}(k)\\ v_{ux}(k-1)\\ \end{bmatrix}\leq\begin{bmatrix}40\\ 5\\ 10\\ 3.5\end{bmatrix}\]  
\[A_{C}=A_{T}=\begin{bmatrix}1&T_{s}\\ 0&1\end{bmatrix},\ B_{C}=B_{T}=\begin{bmatrix}T_{s}2\\ T_{s}\end{bmatrix},\]  
\[C_{C}=C_{T}=\begin{bmatrix}1&0\end{bmatrix}.\]  
The deviation in the crane and trolley's position in the \(X\) and \(Y\) directions from the desired trajectory converges to zero through the proposed control scheme. To implement Equations (17) and (18) in an explicit MPC scheme, a zero-order hold method with a sampling time of 0.005 s is utilized in this paper.
## 4 Numerical Results
Figure 3: The SEP of the considered SM-MIMO-FD relay system with and without optimal power allocation when BPSK modulation is used, \(N_{\text{rR}}=N_{\text{D}}=2,3,4;\tilde{\Omega}=-10\) dB. Figure 2: The SEP of the considered SM-MIMO-FD relay system with and without optimal power allocation versus the SNR for different modulation schemes, \(N_{\text{rR}}=N_{\text{D}}=4\). Figures 3 and 4 can be served as the guideline to determine when the optimal power allocation should be used. Specifically, for \(\text{SNR}<20\) dB, we use \(P_{\text{R}}^{*}\) to improve the system performance. For higher SNR, that is \(\text{SNR}>20\) dB, we use \(P_{\text{R}}=P_{\text{S}}\) to reduce the signal processing complexity at the FD relay. The advantage of the system with optimization is that it requires only \(\text{SNR}\geq 8\) dB to achieve a reliable voice transmission.
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 4 Simulation and Analysis
### Test in Hardware Platform
#### 4.2.3 Test of DCN Channel  
We used the network manager to configure an LFIB to the corresponding intermediate node and observed the results. The configuration screen for the LFIB on the network manager is shown in Figure 21.  
As shown in Figure 21, it could be seen that this LFIB is configured to node 2. According to the hardware platform architecture (Figure 17), we know that node 1 connects to node 2 through port 2. So, we monitored port 2 of node 1 and captured the results after co  
Figure 20: LSDB in One of the Nodes.  
Figure 19: Local resource collected by the link management protocol in all nodes.  
Figure 22 shows the sequence diagram of ten continuous AOS frames captured by port 2 of node 1. For the convenience of display, it mainly shows part of each AOS frame including the whole DCN field. As can be seen, the management message configured above has already been divided into ten segments and plugged into the DCN fields of ten continuous AOS frames sent by port 2 of node 1. The key values which are configured in the LFIB in the network manager can also be seen. It is clear that DCN channels has already been implemented to transmit management and control messages.