esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The actual methomyl concentrations in the 0 (control), 0.2, 2, 20 and 200 \(\upmu\)g L\({}^{-1}\) groups were 0, 0.23, 2.12, 21.50, 182.0 \(\upmu\)g L\({}^{-1}\) respectively, just after the fish were exposed, and 0, 0.21, 1.92, 18.52, 179.0 \(\upmu\)g L\({}^{-1}\) respectively, after exposure for 24 h. And the results were discussed in relation to the nominal concentrations.  
The results of effect of chronic exposure to pesticide methomyl on antioxidant defense system in testis of tilapia (_Oreochromis niloticus_) and recovery pattern were showed in Figure 1 and Table S1. No significant changes (\(p\) > 0.05) were observed in the antioxidant enzyme activities and contents in testis of tilapia exposed to 0.2 \(\upmu\)g L\({}^{-1}\) methomyl, whereas the testis of fish exposed to concentrations of 2, 20 or 200 \(\upmu\)g L\({}^{-1}\) showed significant changes (\(p\) < 0.05): GPx and GSSG significantly increased, and GSH significantly decreased (\(p\) < 0.05). Moreover, dose-response relationships were found between methomyl and GPx, GSSG or GSH. Specifically, GPx and GSSG increased with increasing concentrations of methomyl; GSH decreased with an increase in methomyl concentration. CAT, SOD, GRand GST were induced in testis of tilapia exposed to 2 and 20 \(\upmu\)g L\({}^{-1}\); however, they were inhibited in testis of fish exposed to 200 \(\upmu\)g L\({}^{-1}\).  
Figure 1: _Cont._  
When the tilapia were transferred to methomyl-free water to recover for 18 days, SOD, GR, GST, CAT, GPx, GSSG and GSH in testis of fish in each group recovered to some extent. There were no significant differences in SOD, GR, GST, CAT and GPx activities, nor in GSSG and GSH contents, in fish exposed to 2 ug L\({}^{-1}\) and 20 ug L\({}^{-1}\) compared with the controls. Conversely, there were significant changes (\(p<0.05\)) in the 200 ug L\({}^{-1}\) treatment group compared with the control group.
## 2 Materials and Methods
### Data Processing Flow  
A basic flow diagram of the training and evaluation using the various datasets is shown in Figure 2. The details involved in each of the processing steps will be described in more detail below. The datasets, with spatial and temporal resolutions listed in Table 1, may be grouped into several distinct subsets: (1) reflectances used to compute VIs (MCD43); (2) radiation, TOA short-wave (SW\({}_{\text{TOA}}\)); (3) FLUXNET 2015 eddy covariance GPP estimates, half used for training and the other half for evaluation; (4) SIF (used to delineate highly productive regions) or downscaled SIF (SIF\({}^{\star}\) used as a GPP proxy); (5) independent satellite data-driven GPP estimates used as benchmarks (VPM and FLUXCOM-RS). Further details on other datasets examined are given in Appendix A.  
Figure 2: Flow of data used in training and evaluating simple satellite-based GPP models. SIF, solar-induced fluorescence; GOME-2, Global Ozone Monitoring Experiment 2; VPM, Vegetation Photosynthesis Model; LUE, light-use efficiency; FLUXCOM-RS, machine learning upscaling of flux data using remote sensing (RS) data.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 5 K-Means Clustering Results and Discussion
### Finding the Ideal Number of Clusters
#### 5.2.3 Third Method: Clusters with at Least 50% Attack Data as Attack Clusters  
In the third method, clusters were labeled as attack clusters only if 50% or more of the data points within the cluster were attacks. Table 4 presents the results for the clusters with at least 50% attack data. Since a seed of 1 and 2 were used in the previous runs respectively, a seed of 3 was used in this set of runs. At 350 clusters the results were very close to that of 300 clusters, but performance did not improve significantly, hence the runs were stopped at 350. Again, though all runs were done for all clusters between 2 and 350, only results for 2, 10, 50, 100, 200, 300, and 350 clusters are presented.  
Analysis for the Third Method:
* Balanced Labeling Approach
* The 50% threshold ensured that only clusters predominantly composed of attack data were labeled as attacks, reducing false positives compared to the first method.
* Optimal Performance at 300 Clusters
* Though the computational time increased significantly at 300 clusters, this method achieved 98.64% precision and 98.61% recall. These best results are bolded in Table 4.
* Increasing clusters to 350 resulted in a slight performance drop, confirming that 300 clusters were the ideal choice for this method.
* Summary of the Third Method
* The second and third methods produced nearly identical results across all statistical measures.
* In terms of computation time, the computation time increased as the number of clusters increased. Also, in general, the third method had the highest average computation time of all three methods.
## 4 Implementation Results of a Fully-Connected ANN and CNN
### Sample Classification Based on Selected Indicators  
All samples are classified according to four input indicators, as shown in Figure 9. The samples only contain three indicators: maintenance time, bridge technical condition, and highway grade.  
Figure 7: The calculation results of VIM of the main influencing factors.  
In Figure 9, the maintenance cost samples are divided into three categories according to the highway grade. According to the technical conditions of the bridge, each category was divided into four sub-categories and the samples were sorted by maintenance time. The sample values increased significantly as the technical conditions of the bridge worsened. As the highway grade worsened, the sample values also tended to increase, and they gradually increased with maintenance time. These results demonstrate the feasibility of predicting the maintenance costs based on the classification of these indicators.
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
## 3 Control Strategy of the Converter
### Complete Control Strategy  
The complete control scheme of the converter is given in Figure 17. The control system includes the input voltage loop and the output current loop. In normal, the maximum power point tracking (MPPT) controller outputs the reference of the input voltage \(V_{inner}\). The result of the voltage PI regulator \(G_{\text{P}}\)(s) is the reference of the output current \(I_{inner}\). The feedback signal \(v_{ic}\) participates in the current closed-loop control.  
Figure 16: Loop gain with PI compensation.  
Figure 17: Complete control scheme of the converter.  
In the process of LVRT, as the depth of the grid voltage drop increases, the output current rises. When the output current reaches the maximum current, the converter will switch from MPPT mode to constant current mode. There will be a threshold time for the LVRT. The converter will confirm the grid fault and shut down if the voltage recovery time exceeds the threshold time. When the grid voltage recovers, the converter will return to the MPPT mode.
# Perceived Usefulness of a Mandatory Information System
## 6 Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dependent** & \multicolumn{2}{l}{**Independent Constructs**} \\ \hline  & \multicolumn{2}{l}{**Perceived Ease**} & \multicolumn{2}{l}{**Supervisor Influence**} & \multicolumn{2}{l}{**Perceived Usefulness**} \\ \hline Standardized Total Effect (STE) & & & \\ \hline \multirow{4}{*}{Perceived Usefulness} & \(\beta=0.64\)\({}^{**}\) & \(\beta=0.29\)\({}^{*}\) & \\  & BSE = 0.11 & BSE = 0.11; & \\  & 95\% BC: & 95\% BC: & - \\  & (0.41; 0.87) & (0.07; 0.48) & \\  & \(\mathrm{H_{1}}\) & & \\ \hline \multirow{4}{*}{Symbolic Adoption} & \(\beta=0.70\)\({}^{*}\) & \(\beta=0.20\)\({}^{*}\) & \(\beta=0.85\)\({}^{*}\) \\  & BSE = 0.11 & BSE = 0.11 & BSE = 0.16 \\ \cline{1-1}  & 95\% BC: & 95\% BC: & 95\% BC: \\  & (0.43; 0.88) & (0.00; 0.44) & (0.54; 0.99) \\ \cline{1-1}  & \(\mathrm{H_{4}}\) & & \(\mathrm{H_{5}}\) & \(\mathrm{H_{2}}\) \\ \hline Standardized Direct Effect (SDE) & & & \\ \hline \multirow{4}{*}{Perceived Usefulness} & \(\beta=0.64\)\({}^{**}\) & \(\beta=0.26\) & \\  & BSE = 0.11 & BSE = 0.11 & \\  & 95\% BC: & 95\% BC: & - \\  & (0.41; 0.87) & (0.07; 0.48) & \\  & \(\mathrm{H_{1}}\) & & \\ \hline \multirow{4}{*}{Symbolic Adoption} & \(\beta=0.16\) & \(\beta=-0.04\) & \(\beta=0.85\)\({}^{*}\) \\  & BSE = 0.17 & BSE = 0.09 & BSE = 0.16 \\ \cline{1-1}  & 95\% BC: & 95\% BC: & 95\% BC: \\ \cline{1-1}  & \(\mathrm{(-0.18;\,0.47)}\) & \(\mathrm{(-0.26;\,0.13)}\) & (0.54; 0.99) \\ \cline{1-1}  & \(\mathrm{H_{4}}\) & & \(\mathrm{H_{5}}\) & \(\mathrm{H_{2}}\) \\ \hline Standardized Indirect Effect (SIE) & & & \\ \hline \multirow{4}{*}{Symbolic Adoption} & \(\beta=0.54\)\({}^{**}\) & \(\beta=0.24\)\({}^{*}\) & \\  & BSE = 0.15 & BSE = 0.11 & \\ \cline{1-1}  & 95\% BC: & 95\% BC: & - \\ \cline{1-1}  & (0.32; 0.98) & (0.07; 0.52) & \\ \cline{1-1}  & \(\mathrm{H_{4}}\) & & \(\mathrm{H_{5}}\) & \\ \hline \hline \end{tabular} Note: \({}^{*}\)\(p<0.05\); \({}^{**}\)\(p<0.01\); TTBS: Two Tailed Bootstrap Significance; BSE: Bootstrap Standard Error; 95% BC: 95% Bootstrap Confidence (LB: Lower Bound; UB: Upper Bound); \(H_{i}\): Belonging to the specific research hypothesis in the present study.  
\end{table} 
Table 2: Standardized path coefficients for final model. Bootstrap Resampling: 1000 bootstrap samples were generated from the original data set to estimate the model parameters multiple times, ensuring robust standard errors and confidence intervals. Estimation Method Comparison: Amos was used to fit the model to each bootstrap sample using different discrepancy criteria (CADF, CML, CGLS, and CULS). This comparison helped identify the most suitable estimation method for the data and model. The results from these estimation methods were good enough considering the small sample size, validating the robustness of the model, and ensuring the reliability of the findings.
## 3 Results
### Test Results
#### 3.2.2 Trapezoid-Shaped Field  
The geodetic coordinates of the trapezoid-shaped field boundary points for the test were entered clockwise from the northeast corner. The lengths of the four-line segments of the outer boundary of the field are approximately 34 m, 102 m, 21 m, and 103 m, clockwise from the first input point, respectively, and the area is approximately 2845 m\({}^{2}\). Figure 7a shows the result of generating the inner boundary of the field with the outer boundary of the field. The length of the four-line segments of the generated inner boundary are approximately 28 m, 96 m, 15 m, and 96 m. The generated MBR of the internal boundary to generate candidate sets consisting of line segments within the MBR is shown in Figure 7b.  
The results of the generated working line segments in the inner-field boundary are shown in Figure 8a. A total of 74 working line segments in the inner-field boundary were generated in the north-south direction. The range of the lengths of the generated working line segments in the inner-field boundary is 15.4 m to 27.5 m, and the sum of the generated working line segments in the inner-field boundary is 1586.8 m. Figure 8b shows the result of the generated working line segments in the headland area. A total of four working line segments in the headland area were generated, since the shape of the field is a trapezoid. The lengths of the four-line segments of the generated working line segments in the headland area are approximately 26.0 m, 93.7 m, 13.4 m, and 94.5 m. The total area of the ridges created through the working line segments is 2177 m\({}^{2}\), so 77% of the field area is created as ridges.  
Figure 7: External and internal boundaries and MBR for the trapezoid-shaped field: (**a**) External and internal boundaries; (**b**) internal boundaries and MBR for internal boundaries.  
The results of generating waypoints using working line segments based on the autonomous driving scenarios are shown in Figure 9. Waypoints for autonomous ridge-forming in the inner field were generated sequentially along the working line segments of the inner field that are closest in distance, starting near the first outer-field boundary point, as shown in Figure 9a. The total number of generated waypoints is 295, including 1 starting waypoint, 146 turning waypoints, 74 work-starting waypoints, and 74 work-ending waypoints, and the total length is 16,827.8 m. Figure 9b shows the results of the waypoints for returning to near the first outer-field boundary point. The waypoints were generated to reach near the first outer-field boundary point by the shortest distance within the headland area. Three waypoints consisting of one starting waypoint, one turning point, and one ending waypoint were generated. Figure 9c shows the result of the waypoints for autonomous ridge-forming in the headland. The total number of generated waypoints is 13, including 1 starting waypoint, 4 turning waypoints, 4 work-starting waypoints, and 4 work-ending waypoints, and the total length is 726.2 m.
## 3 Results
### Correlations Between TRS Parameters and MMSE Scores, Subject's Age  
TRS measurements revealed significant positive correlations between the MMSE score and SO\({}_{2}\) in the bilateral PFC (\(r=0.40\), \(p<0.01\)). In addition, \(C_{\textit{oxy-Hb}}\) (\(r=0.23\), \(p<0.01\)) and \(C_{\textit{t-Hb}}\) (\(r=0.14\), \(p<0.05\)) in the right PFC exhibited significant positive correlations with the MMSE scores. In contrast, \(C_{\textit{deoxy-Hb}}\) in the left PFC exhibited a significant negative correlation with the MMSE score (\(r=-0.19\), \(p<0.01\)). Moreover, SO\({}_{2}\) and \(C_{\textit{oxy-Hb}}\) in the bilateral PFC exhibited negative correlations with age.  
However, OPL in the PFC did not exhibit significant correlations with the MMSE score and patient's age (\(p>0.05\)). Table 3 summarizes the correlations between the MMSE scores, subject's ages, Hb concentrations and SO\({}_{2}\) at rest in the PFC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Age** & **MMSE** \\ \hline \multirow{4}{*}{Right PFC} & \(C_{\textit{oxy-Hb}}\) [\(\upmu\)M] & \(-0.196\)** & \(0.230\)** \\  & \(C_{\textit{deoxy-Hb}}\) [\(\upmu\)M] & \(0.029\) & \(-0.047\) \\  & \(C_{\textit{t-Hb}}\) [\(\upmu\)M] & \(-0.127\) & \(0.142\)* \\  & SO\({}_{2}\) [\%] & \(-0.270\)** & \(0.396\)** \\ \hline \multirow{4}{*}{Left PFC} & \(C_{\textit{oxy-Hb}}\) [\(\upmu\)M] & \(-0.189\)** & \(0.135\) \\  & \(C_{\textit{deoxy-Hb}}\) [\(\upmu\)M] & \(0.084\) & \(-0.191\)** \\ \cline{1-1}  & \(C_{\textit{t-Hb}}\) [\(\upmu\)M] & \(-0.107\) & \(0.022\) \\ \cline{1-1}  & SO\({}_{2}\) [\%] & \(-0.302\)** & \(0.398\)** \\ \hline \hline \end{tabular}  
* indicates \(p<0.05\). ** indicates \(p<0.01\).  
\end{table}
Table 3: Correlations between the MMSE score, subject’s ages, Hb concentrations and SO\({}_{2}\) at rest in the right and left PFC (\(n=202\)).
## 3 Results
### Distribution and Sick Leave Days of Shoulder MSDs and Factors Associated with Shoulder MSDs
The logistic regression model was tested by the G value  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Total**} & \multirow{2}{*}{**Shoulder MSDs**} & \multicolumn{2}{c}{**Sick Leave Days**} & \multicolumn{2}{c}{**Logistic Regression on Shoulder**} \\ \cline{2-9}  & \(N\) & \(n\) & \% & \(n/N\) & **Mean** & **SD** & **OR** & **[95\% CI]** & \(p\) \\ \hline \multirow{4}{*}{Occupation Welder (Ref) Filter} & 985 & 298 & 100\% & 30.3\% & 330.9 & 158.1 & & \\  & 183 & 55 & 18.5\% & 30.1\% & 348.9 & 167.4 & & \\  & 189 & 74 & 24.8\% & 39.2\% & 311.1 & 129.0 & & \\  & 259 & 73 & 24.5\% & 28.2\% & 347.1 & 201.5 & & \\  & 100 & 35 & 11.7\% & 35.0\% & 300.5 & 103.4 & & \\  & 43 & 13 & 4.4\% & 30.2\% & 332.2 & 151.2 & & \\  & 54 & 9 & 3.0\% & 16.7\% & 412.7 & 79.0 & & \\  & 157 & 39 & 13.1\% & 24.8\% & 320.5 & 155.6 & & \\  & & \(\chi^{2}=15.595\), \(p=0.016^{*}\) & & & & & & \\  & & & & & & & 0.001 \({}^{*}\) \\  & 238 & 29 & 9.7\% & 12.2\% & 325.1 & 298.0 & & \\  & 40-49 & 267 & 61 & 20.5\% & 22.8\% & 293.8 & 123.4 & 1.786 & 1.008 & 3.166 & 0.047 \\  & 50-59 & 289 & 118 & 39.6\% & 40.8\% & 357.0 & 158.7 & 3.149 & 1.824 & 5.437 & 0.001 \({}^{*}\) \\  & 191 & 90 & 30.2\% & 47.1\% & 323.6 & 100.9 & 3.438 & 1.939 & 6.096 & 0.001 \({}^{*}\) \\  & & \(\chi^{2}=84.840\), \(p<0.001\) \({}^{*}\) & & & & & & & 0.001 \({}^{*}\) \\  & & & & & & & & & \\  & 209 & 47 & 15.8\% & 22.5\% & 308.5 & 160.2 & & & \\  & 380 & 0 & 0\% & 0\% & & & & & \\  & Rupture & 396 & 251 & 84.2\% & 63.4\% & 335.1 & 157.7 & 5.301 & 3.579 & 7.851 & 0.001 \({}^{*}\) \\  & & & & & & & & & \\of the model coefficient (\(\chi^{2}=489.584\), \(p<0.001\)). The explanatory power of the dependent variable was the Nagelkerke value (0.554), and the fitness test for the variable was the Hosmer and Lemeshow value (\(\chi^{2}=4.495\), \(p=0.721>0.05\)). Therefore, it was found that the model and explanatory power were suitable. In terms of age, the likelihood that workers \(\geq\)60 years old (3.438 times, 95% CI = (1.939, 6.096)), 50-59 years (3.149 times, 95% CI = (1.824, 5.437)), or 40-49 years (1.786 times, 95% CI = (1.008, 3.166)) will be confirmed as having a shoulder MSD is higher than that of workers \(<40\) years. In terms of MSD type, the possibility that a rupture will be confirmed as a shoulder MSD is 5.301 times (95% CI = (3.579, 7.851)) higher than that of a sprain.
# Detection of Transmission State of Multiple Wireless Sources: A Statistical Mechanics Approach
## Appendix A Network Implementation of the MP Algorithm  
Along with the performance of the message-passing detection algorithm, it is important to describe how this algorithm can be implemented in an SU network. Despite the distributed nature of belief propagation methods, their implementation in a particular network structure is not necessarily obvious [57]. In this section, we provide a simple protocol for distributing information in SU networks in the context of the message-passing algorithm.  
We assume that at any given step of the iteration, the \(i\)th SU has local measurement and channel information \(\{y_{i},\mathrm{G}_{iv}\}\), as well as the messages \(h_{v\to i}\), for all neighboring sources \(v\) available. From this, it can calculate the messages \(\widehat{h}_{i\to v}\) using (24). It can then transmit each of the \(\widehat{h}_{i\to v}\) to a designated SU connected with source \(v\). This designated SU can then sum over all received \(\widehat{h}_{j\to v}\) for \(j\in\mathcal{N}(v)\) and then broadcast the resulting sum \(\sum\widehat{h}_{j\to v}\) to all SUs connected to \(v\). In the next step, each SU \(i\) can obtain the revised value of \(h_{v\to i}\) by subtracting its own \(\widehat{h}_{i\to v}\), as seen in (17).  
The choice of the above designated SU for each source should be made to minimize the total network communication energy consumption. In general, this is not trivial to determine, so we propose a suboptimal criterion, namely the nearest SU neighboring each source. This choice makes sense because this SU has the larger probability of including in its communication range all the other SUs the source is connected to. Additionally, assuming a semi-static topology, these two nodes of the bipartite graph will probably have a high-quality communication link between them (see Figure 2).  
In Figure 11, we compared the average SU energy consumed for the case where the chosen SU is the nearest neighbor of the source, i.e., the SU that has the minimum Euclidean distance from the source, with the case where the SU is the true SU, which minimizes the total energy consumed in the network, which is expressed for simplicity as  
\[E_{0}=\min_{j}\left\{E_{j}\right\}=\min_{j}\left\{\sum_{i=1}^{k}d_{ij}^{ \gamma}\right\} \tag{11}\]  
where \(E_{j}\) is the energy for every SU \(j\) for the case where the signal is degraded due to path loss. It can be readily seen that these two different schemes behave very similarly and, especially for a large number of nodes, they tend to consume the same amount of power.
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
## 6 Conclusions  
Aiming at the problem of low accuracy and efficiency of existing fault diagnosis methods for HV cable grounding systems, this paper proposes an intelligent fault diagnosis method for HV cable metal sheath grounding system based on LSTM.  
Figure 15: Confusion matrix of classification results of different algorithms. (**a**) DT algorithm; (**b**) DA algorithm; (**c**) NBC algorithm; (**d**) KNN algorithm; (**e**) SVM algorithm; (**f**) LSTM algorithm.  
1. Established an HV cable metal sheath grounding system model, analyzed four types of fault sheath currents, constructed 14 feature vectors with amplitude ratio and phase difference.
2. Building a fault database with 18 grounding system operating states by varying fault time, grounding resistance, cable lay spacing, and cable minor section length for different fault types.
3. By comparing the simulation results of different algorithms, the identification accuracy of the fault diagnosis of HV cable grounding system based on LSTM is the highest, and its accuracy rate reaches 100%.
4. The sheath current samples used for fault diagnosis in high-voltage cable grounding systems are in error with the actual sheath currents and further field verification is required.  
Conceptualization, Q.W. and X.Y.; methodology, X.Y.; software, X.Y.; validation, Q.W. and X.Y.; formal analysis, X.Y.; investigation, Q.W.; data curation, X.Y.; writing--original draft preparation, X.Y.; writing--review and editing, Q.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fund Project of Beijing Education Commission: Key Technology Research on Intelligent Operation and Maintenance of Big Data for Power Distribution under Grant (110052972027/067); Collaborative Innovation Center of Key Power Energy-Saving Technologies in Beijing under Grant (PXM2018_014212_000015\(4\)4); Technology project of State Grid Shanxi Electric Power Company: Research and Application of Cable electrification diagnosis Technology based on Harmonic method (5205C02000 GL).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Physical Layer Design
### OOK Modulator  
The proposed modulator is shown in Figure 6. The NMOS M6 works essentially as a pass transistor. The Base Band signal (BB) is fed into the gate terminal of M6. The BB signal when HIGH turns on the pass transistor allowing the carrier to pass to the drain of M6 from its source. Similar to the transformer X1 coupling the VCO to the modulator, another 1:1.5 transformer, X2 is designed to obtain single ended output from the modulator and for impedance matching of the modulator output with the input of the PA. NMOS M5 is used to drain the residual charge on the transformer when BB is LOW.