esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
A spatial coupling model of CWR on a suspension bridge was established, based on bridge-track interaction theory and the actual situation of a suspension bridge with a 1092 m main span in China. Three layout schemes of REJ were put forward according to practical engineering experience. The interaction between CWR and suspension bridge was studied. The installation location and range of REJ were optimized by static and dynamic analysis methods. The following conclusions are drawn:  
(1) The rail longitudinal force can be significantly reduced with REJ, which is a crucial structure of CWR. It is necessary to adopt REJ in the design of CWR on super-long-span suspension bridges.  
(2) For a super-long-span bridge, the bending load usually plays a primary role in rail strength calculation compared to expansion load. The maximum tension appears in the middle of the primary beam, while the maximum stress appears at the main tower.  
(3) The layout of REJ can reduce the CWR deformation under seismic load. The longer the distance between REJ and beam end is, the smaller the REJ expansion under seismic load is. Meanwhile, the longitudinal force of the side-span approach bridge fixed pier is increased.  
(4) The vibration of the track and bridge in the REJ area will increase significantly. When the laying position of REJ is close to the beam end, it is beneficial to reduce the structure vertical deformation, but to increase the vibration acceleration of rail, sleeper, bridge deck, and vehicle as well as the wheel unloading rate, affecting driving safety and comfort.  
(5) Due to the super long bridge span, the bridge expansion caused by temperature variation is significant. For the recommended scheme in this paper, the REJ expansion reaches more than 380 mm under expansion load. The factors affecting the REJ range selection from major to minor are the expansion load, seismic load, rail fracture, braking load, and bending load.  
Figure 17: Rail displacement: (**a**) Under expansion load; (**b**) Under braking load and rail fracture.  
**Author Contributions:** Conceptualization, G.Z. and X.C.; methodology, G.Z.; formal analysis, W.L.; investigation, T.W. ([PERSON]) and [PERSON] ([PERSON]); resources, X.C.; data curation, T.W. ([PERSON]) and W.L.; writing--original draft preparation, G.Z. and T.W. ([PERSON]); writing--review and editing, X.C., T.W. ([PERSON]) and T.W. ([PERSON]); supervision, G.Z. and X.C.; project administration, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51778050, 52178405), the Fundamental Research Funds for the Central Universities (No. 2018 JBZ003, No. 2020 JBZD013).  
Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
The authors declare no conflict of interest.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 5 Performance Analysis  
In this section, we analyze the performance of the proposed online resource allocation algorithm. This section is divided into three sections: Section 5.1 Required Battery Capacity, Section 5.2 Bounded Data Queues and Section 5.3 Optimality of the Proposed Algorithm. Section 5.1 shows the battery capacity that we need to support the operation of BAN system. Section 5.2 shows that the data queues are bounded. Section 5.3 shows the gap between user utility achieved by the proposed algorithm and the optimal utility.
## 4 Experiments
### Bubble Diagram Dataset Establishment  
Currently, there is no readily available dataset for residential bubble diagrams; thus, we developed a residential bubble diagram dataset based on the RPLAN dataset. Following the survey conducted by [PERSON] et al. [38], the RPLAN dataset is chosen [39] for its quality, quantity, labeling, and accessibility. The RPLAN dataset contains over 80,000 residential building floorplans, sufficiently meeting our data requirements.  
As shown in Figure 5, each floorplan image features four channels: the boundary mask channel, the inside mask channel, the wall mask channel, and the instance mask channel. We extracted structural information from the floorplan using various image-processing techniques, as shown in Figure 6. First, we identify room instances based on the pixel encoding of instance mask channels to construct room graph nodes. Next, the corners of each room are detected using the Harris corner detector [40]. Each room is then constructed by connecting the detected corner points into line segments, ensuring that each room instance is paired with the corresponding surrounding wall segments. The link relationships between room nodes are established by calculating the distances between parallel wall segments of room pairs. If the distance between two parallel wall segments is below a specified threshold, it indicates a wall link. Additionally, if door pixels are present between two rooms, a door link is established. Importantly, door links and wall links cannot coexist; when both conditions are satisfied, the door link takes precedence over the wall link. We traverse all room pairs to identify all potential wall and door links. Finally, the resulting bubble diagram is created and stored in a CSV file.  
Typically, a residential floorplan must include essential components, such as a bedroom, living room, kitchen, and bathroom. However, some floorplans in the RPLAN dataset lack these critical elements. To prevent the model from learning incorrect structural information and to enhance the reliability of the dataset, we filtered out non-compliant data, retaining only reasonable configurations [17]. The resulting bubble diagram dataset consists of 72,678 graphs, featuring nine node types and two link types, with a total of 494,240 nodes and 1,058,478 edges. The detailed data statistics are shown in Figure 7.
# Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm
## 4 Test Results and Analysis
It can be seen that NNT outperforms CNT in all statistics. The improvement of RMSE is 11.6 percent from CNT to NNT, which indicates that the accuracy of NNT is higher than that of the CNT. The statistical bias result, which is a frequently used measure of the systematic error, also shows that the model from the NNT is more stable than the CNT model. The Pearson correlation coefficient of the NNT shows a weaker correlation than the CNT. This may be because the PCC has a great cardinal number. Even if the CNT technique produces a poor PCC result (e.g., 90 percent), the improvement from the CNT to NNT technique cannot exceed 10 percent. Figure 8: The root-mean-square error (RMSE) of the common and new node-based tomographic approach (CNT and NNT) between DOY 121 and DOY 151 in 2015. Figure 10 presents the scatterplots of the calculated WVD value from the CNT and NNT tomographic techniques tomographic results (CNT/NNT), respectively. The standard deviation (SD) also present in Figure 10. The performances of the CNT and NNT technique can be evaluated directly by considering the correlation of the values with the baseline for the accuracy of the tomographic results (blue line). Values that are closer to this represent better tomographic results. The figure implies that the NNT technique (bottom panel) has higher accuracy in comparison with the CNT (top panel). Alongside, the statistics presented in Table 3, these figures represent a high degree of consistency in the CNT results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ApproachStatistics** & **RMSE (g/m\({}^{3}\))** & **Bias (g/m\({}^{3}\))** & **PCC (\%)** \\ \hline CNT & 1.11 & \(-\)0.145 & 96.4 \\ NNT & 0.981 & \(-\)0.061 & 98.7 \\ Improvement (\%) & 11.6 & 57.9 & 2.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of two approaches throughout the entire month of May. Figure 9: Water vapor profiles calculated from CNT (red line) and NNT (yellow line) are compared with water vapor data (WVD; blue points) at UTC 12 in four days—(**a**) DOY 127, (**b**) DOY 131, (**c**) DOY 141 and (**d**) DOY 139.
## 2 Feature Extraction Method for Axis Motion Trajectories
### Axis Trajectory Signal Preprocessing  
The positions and angles of the data acquisition equipment cannot be expected to remain constant, and the distribution and magnitude of axis trajectory signals in the two-dimensional plane vary under different working conditions. These variations inevitably affect the extraction of axis trajectory characteristics. To eliminate such influences, the axis trajectory data are preprocessed through centering and normalization.  
1. Centering  
The axis trajectory signal is synthesized from the vibration displacement signals collected in two perpendicular directions. Let the two sets of collected vibration displacement signals be \(X=[x(1),x(2),\cdots,x(N)]\) and \(Y=[y(1),y(2),\cdots,y(N)]\), where \(N\) is the length of the data sequence. The centering process is as follows:  
\[\left\{\begin{array}{l}x_{s}(i)=x(i)-\overline{X}\\ y_{s}(i)=y(i)-\overline{Y}\end{array}\right. \tag{1}\]  
where \(x\)\((i)\) and \(y\)\((i)\) are the original vibration displacement signal data and \(x_{s}(i)\) and \(y_{s}(i)\) are the centered data. Here, \(i=1,2,\cdots N\), \(\overline{X}\), and \(\overline{Y}\) represent the mean values of the data.  
Figure 2: Axis trajectory purification and denoising process. (**a**) Original axis trajectory; (**b**) the trend of the first 15 singular values, with the specific values of each singular value indicated by red \({}^{max}\) in the figure; (**c**) the axis trajectory after purification and denoising.  
Normalization  
Normalization is applied to scale the data to a specific range to avoid the impact of differences in data magnitude. In this paper, the axis trajectory data are mapped to the interval \([-1,1]\), as follows:  
\[\left\{\begin{array}{l}x_{n}(i)=\frac{x_{\mathrm{s}}(i)-\min(X_{\mathrm{s}})}{ \max(X_{\mathrm{s}})-\min(X_{\mathrm{s}})}\\ y_{n}(i)=\frac{y_{n}(i)-\min(Y_{\mathrm{s}})}{\max(Y_{\mathrm{s}})-\min(Y_{ \mathrm{s}})}\end{array}\right. \tag{2}\]  
where \(X_{\mathrm{s}}\) and \(Y_{\mathrm{s}}\) are the centered vibration displacement signals and \(x_{n}(i)\) and \(y_{n}(i)\) are the normalized vibration displacement signals.  
After centering and normalization, the original axis trajectory pattern is shifted towards the origin and mapped to the interval \([-1,1]\), as shown in Figure 3. The origin \(O\) is chosen as the sole fixed point for subsequent calculations in the extraction of axis trajectory signal features.
# Production of a Human Metabolite of Atorvastatinin by Bacterial CYP102A1 Peroxygenase
## 2 Materials and Methods
### LC-MS Analysis  
To characterize the major metabolite of atorvastatin produced by the CYP102A1, the reaction mixtures (1 mL) included 0.20 \(\mathrm{\SIUnitSymbolMicro u}\)M whole M371 enzyme and 200 \(\mathrm{\SIUnitSymbolMicro u}\)M atorvastatin in 100 mM potassium phosphate buffer (pH 7.4). The initial reaction was started by the NADPH-generating system at 37 \({}^{\circ}\)C for 30 min. The atorvastatin and metabolites were analyzed using Shimadzu LCMS-2010 EV system (Shimadzu, Kyoto, Japan). The metabolites and substrates were separated on a Shim-pack VP-ODS column (2.0 mm i.d. 250 mm; Shimadzu) using a mobile phase with acetonitrile-water-formic acid (70:30:0.1, \(v/v/v\)) at a flow rate of 0.16 mL/min. To identify the metabolites, electrospray ionization in positive mode recorded the mass spectra. The interface and detector voltages were 4.4 and 1.5 kV, respectively. The nebulization gas flow was set at 1.5 L/min. The interface, curve desolvation line, and heat block temperatures were 250 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, and 200 \({}^{\circ}\)C, respectively [23].
## 2 Research Methods and Principles  
### Graph-Based Modeling of Multivariate Time Series  
Compared with univariate forecasting, the core assumption of the multivariate time series forecasting task lies in the existence of complex interactive dependencies between variables, and the model forecasting not only focuses on the historical data evolution law of a single variable, but also emphasizes the analysis of the dynamic coupling effect between different variables. From the graph perspective, the variables can be abstracted as nodes in the graph structure, from which potential associations between time series can be mined according to the characteristics of graph neural network arrangement invariance, local connectivity, and combinability. However, there are two problems when applying graph neural networks to time series prediction scenarios: first, in most cases, multivariate time series data lack a displayed graph structure; second, predefined graph structures usually need to be optimized in parallel with model training.  
MTGNN can be targeted to solve the above multivariate time series prediction problem by dynamically and adaptively learning the temporal dependencies between nodes while optimizing the joint modeling of graphs and time series. MTGNN is mainly composed of three parts: graph learning layer, temporal convolution module, and graph convolution module, and its specific network structure is shown in Figure 2. The 1 \(\times\) 1 convolution kernel first compresses the channel dimensions and maps the input features to the latent space. Then, the temporal convolution module and graph convolution module are stacked alternately to capture temporal and spatial correlations, respectively. The graph learning layer adaptively learns the hidden adjacency matrix between nodes. Residual connections and skip connections are introduced into the model to mitigate the gradient vanishing problem. The output module generates predictions by projecting the fused features to the target dimension.  
Figure 2: Network structure of MTGNN.
## 5 Conclusions
Third, the GRACE TWSA signal integrates over large spatial footprints, limiting the model's ability to resolve fine-scale storage anomalies. To mitigate this, future studies could incorporate data from missions like GRACE-FO (Follow-On), which continues to provide large-scale water storage measurements, and SWOT (Surface Water and Ocean Topography), whichwill offer high-resolution surface water data that could enhance the understanding of fine-scale hydrological dynamics. Finally, the interpretability of deep learning models--despite the use of attention maps--still requires caution, and future study should incorporate uncertainty quantification and explainable AI techniques to enhance transparency. Attention mechanisms produced saliency maps that identified irrigation-driven stress periods--insights obscured in recurrent baselines--thus enabling more transparent dialogue with water authorities. Future study should include the following:  
1. Embed Bayesian uncertainty layers to quantify forecast confidence. 2. Assimilate forthcoming Sentinel-1C acquisitions for near-real-time updates. 3. Couple transformer outputs with hydro-economic optimization to directly inform adaptive groundwater extraction limits. Our findings point out that transformer-based architectures unlock the predictive potential of long eco-hydrological time series, offering a scalable and transferable blueprint for sustainable groundwater management in data-scarre heritage regions worldwide. Conceptualization, P.H. ; methodology, P.H. and F.P.A.L. ; validation, P.H., F.P.A.L. and Y.T. ; formal analysis, P.H. and Y.T. ; investigation, P.H., F.P.A.L. and Y.T. ; resources, P.H., S.F.F., T.Y., H.S., A.A. and R.S. ; data curation, Y.T. ; writing--original draft preparation, P.H., Y.T. and F.P.A.L. ; writing--review and editing, P.H., Y.T., F.P.A.L., A.O.P. and M.E.O.M. ; visualization, P.H., F.P.A.L., T.Y., S.F.F. and G.Z. ; supervision, S.F.F., A.A. and G.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are available on request from the first author due to institutional restrictions and data sharing agreements. The authors would like to thank the institutions and personnel who actively contributed and supported this research by providing the facilities and resources necessary for the successful completion of this study. The authors declare no conflicts of interest.
## 5 Results and Analyses
### Prediction Models
#### 5.3.7 Impact of Parameter Tuning for Random Forests  
In Table 17, the performance of AUC for RF with default parameters was compared with that of Mtry parameter tuning. Boldface values in the table highlight the best results among each dataset in each scenario, whereas AUC-T refers to the AUC for parameter tuning and AUC-D refers to the AUC for default parameters. The comparison of the results indicated that AUC-T outperformed AUC-D across all datasets in all scenarios, except mct dataset in the third scenario, which achieved the same result of AUC-D (0.99). AUC-T reached the optimal result (1.00) in the mcMMO dataset in the third scenario, and the average of AUC-T in all datasets in this scenario provided the highest prediction accuracy. Additionally, the grid search method provided different Mtry values for different datasets, which indicates that this method is an alternative to save time and effort instead of trying different parameters manually. The percentage of change between the average AUC-T and AUC-D across all datasets was 10.13%, 8.97%, 3.19%, and 2.20 in the first, second, third,  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Source**} & **Sum of** & **Degrees of** & **Mean** & **F** & **\(p\)-Value** & **Eta-Squared** \\  & **Squares** & **Freedom** & **Square** & **F** & **\(p\)-Value** & **Eta-Squared** \\ \hline
**Factor A** & 0.78 & 3.00 & 0.26 & 13.95 & 0.00 & 0.28 \\ \hline
**Error** & 2.02 & 108.00 & 0.02 & & & \\ \hline
**Total** & 2.80 & 111.00 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 16: Scenarios in the empirical study.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{l}{**Change-Proneness Dataset**} \\ \hline  & **antr4** & **junit** & **MapDB** & **mcMMO** & **mct** & **oryx** & **itin** \\ \hline ZeroR & 0.46 & 0.43 & 0.39 & 0.38 & 0.49 & 0.48 & 0.48 \\ \hline NB & 0.51 & 0.79 & 0.84 & **0.99** & 0.94 & 0.73 & 0.8 \\ \hline SVM & 0.51 & 0.65 & 0.82 & 0.91 & 0.81 & 0.52 & 0.59 \\ \hline KNN & 0.71 & 0.78 & 0.78 & 0.84 & 0.91 & 0.78 & 0.77 \\ \hline RF & **0.85** & **0.85** & **0.95** & 0.96 & **0.98** & **0.85** & **0.9** \\ \hline \hline \end{tabular}
\end{table}
Table 15: Scenarios in the empirical study.  
and fourth scenarios, respectively. This indicates that tuning the Mtry parameter in RF had a positive influence in each scenario. However, this influence was higher in the original datasets (e.g., without FS or sampling in the first scenario) and lower in the edited datasets (e.g., with FS and sampling in the fourth scenario). Additionally, there is a good agreement between the findings in this section and those in the previous section, in which the third scenario achieved considerable performance.
# Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia
## 3 Results
### Road Traffic Crash Hot Spots in Slovenia  
By considering the whole sample (2006-2018) of road traffic crashes in Slovenia (major injury, death), regardless of weather conditions, four significant hot spots emerged (Figure 2a). The largest consisted of municipalities belonging to the Mediterranean region, along the A1 and A3 highways and regional roads connecting the town of Kozina and the Starod border crossing (via Croatia) and the towns of Postojna and Ilirska Bistrica. Two significant road traffic crash hot spots were detected in the hilly parts of the Pannonian Basin region. One comprised three municipalities stretching across the Haloze Hills, and the other one, six municipalities in the Slovenian Hills. In rainy weather conditions, three spatial road traffic crash clusters were detected (Figure 2b). The largest one covered seven municipalities that extend across parts of the Mediterranean and the Dinaric Alps regions. This road crash hot spot was evident in three out of four weather conditions being considered (rain, snow, and wind) (Figure 2b-d). Under snowfall, significant road traffic crash hot spots were identified in municipalities with high relief energy (Phorje (Drava valley), Haloze, Slovenian Hills, Sava Hills, Javorniki Hills and Sneznik Plateau) in different parts of Slovenia. The wind road traffic crash hotspot footprint resulted in only one significant high value cluster. Here, the analysis identified nine municipalities exposed to the turbulent, katabatic, north-eastern Bora wind, which is present in all seasons and reaches the highest speed beneath the high karst plateau. In fog, a large part of the Pannonia Basin region was/is a road traffic crash hot spot (Figure 2e).
## 4 Energy Management Systems
### Rule-based Energy Management Systems
#### 4.1.1 Rate Limited Power (RLP)  
In this study, a highly effective rate limited power (RLP) energy management system was implemented as the benchmark. This EMS was designed to seamlessly integrate two crucial inputs--the load power \(P_{Load}\) and the SOC of the battery. The FC power \(P_{fc}\) was precisely aligned with the load power, but with a rate limiter in place to prevent significant changes in power delivered from the FC. The rate limiter was ingeniously crafted to restrict the rate of FC power increase or decrease to no more than 0.4% of the total delivered power per second, thereby preventing any sudden spikes in FC power. The battery power was determined with precision, calculated as the difference between \(P_{Load}\) and \(P_{fc}\). When the battery SOC exceeded 60%, the FC power was smoothly reduced to keep the SOC within a desirable range and minimize fuel consumption, thus preserving its longevity. Figure 16a illustrates this control system integrated into the Simulink, delivering an output that ranges from 0 to 1, symbolizing the fraction of the maximum FC power.
## 4 Experiments and Comparisons  
### Experimental Hardware Configuration  
We set up a 64-bit Linux operating system with a CUDA 8 toolkit, Cudnn 5.0, PyCharm, Pytorch, and Matlab v2016a as our experiment's software environment. The hardware environment of the computer included an NVIDIA GeForce GTX 1060 GPU, 16 GB RAM, an Intel Core i7-3770 CPU running at 3.4 GHz, and 1 TB of hard disk storage space.