esa-sceva/satcom-chunk-collection huggingface dataset:
## Results  
Time Series of VIs (GCC, CamNDVI, CamNIRv, CamRVI), GPP, and Their Relationship with Meteorological Conditions  
The seasonal variation of VIs and GPP, as well as their correlations, are shown in Figure 3 and Table 3, respectively. In general, all VIs have distinct seasonal variations and are consistent with the temporal variability of GPP (Figure 3 and Table 3). At four sites, all VIs have significant and good correlation with GPP (Pearson's correlation coefficient: \(r\geq\)0.85; Table 3). The determination coefficients (\(R^{2}\)) of linear regression between VIs and GPP also range from 0.72 to 0.87, in particular, with slightly higher \(R^{2}\) for CamNDVI and CamRVI. However, VIs have distinct discrepancies on variation range. For instance, the GCC of our sites ranges 0.32-0.42, while the CamRVI could vary between 1 and 7.  
Distinct interannual variability of VIs and GPP are also observed at the four sites (Figure 3). The time series (GCC, CamNDVI, CamNIRv, CamRVI, and GPP) have an obvious \"two-humped\" shape in the hydrological year 2014 (Hydro-2014: 1 August 2014-31 July 2015) and hydrological year 2016 (Hydro-2016: 1 August 2016-31 July 2017), while the shape is not clear in the time series for the hydrological year 2015 (Hydro-2015: 1 August 2015-31 July 2016; Figure 3). The absence of the two-humped shape in Hydro-2015 coincides with the warmer mean air temperature (Ta) observed in winter 2015 (9.4 \(\pm\) 2.4 \({}^{\circ}\)C) and lower Ta in spring 2016 (13.5 \(\pm\) 3.5 \({}^{\circ}\)C) compared to Hydro-2014 (6.4 \(\pm\) 3.3 \({}^{\circ}\)C and 16.3 \(\pm\) 4.3 \({}^{\circ}\)C, respectively) and Hydro-2016 (8.0 \(\pm\) 3.4 \({}^{\circ}\)C and 16.4 \(\pm\) 4.6 \({}^{\circ}\)C, respectively; Table A1 and Figure 4). The onset of growing season in each hydrological year clearly followed the onset of the rainy season, confirming that autumn phenology in these ecosystems is driven by precipitation (Figure 3). Larger precipitation (281.7 mm) in the spring of Hydro-2015 also leads to higher GPP compared to Hydro-2014 (94.9 mm) and Hydro-2016 (94.0 mm; Figure 4 and Table A1), both characterized by less than half of the precipitation compared to Hydro-2015.  
Figure 3: Time series of PhenoCam-based green chromatic coordinate (GCC), normalized difference vegetation index (CamNDVI), near-infrared reflectance of vegetation index (CamNIRv), ratio vegetation index (CamRVI), maximum gross primary productivity (GPP), and meteorological variables (temperature and precipitation) for four Mediterranean tree–grass ecosystems (FLUXNET IDs are ES-LM1, ES-LM2, ES-LMa, and ES-Abr, respectively). Daily mean temperature (Ta) and total precipitation (Prec) of the Majadas de Tiétar (Maj) and La Albuera (Ala) are shown in the bottom panel.
## 2 Methods
### Data Extraction and Evaluation  
The TIDieR-Rehab checklist [15] is an extension of the original TIDieR tool specifically developed for the planning, reporting, synthesis, and critique of rehabilitation intervention studies. The TIDieR-Rehab checklist [15] was used to evaluate intervention reporting quality according to 22 items across the following sections: _'Brief name'_, _'Why'_, _'Who'_, _'When'_, _'What'_, _'Who provided'_, _'How'_, _'Where'_, _'How much'_, _'How challenging'_, _'Regression/Progression'_, _'Personalisation'_, _'Protocol deviations'_, _'How well'_, and _'Harms'_. Two reviewers ([PERSON] and [PERSON]) undertook data extraction and evaluation. One reviewer ([PERSON]), a physiotherapist and early career academic, had comprehensive knowledge of the TIDieR-Rehab checklist from their contribution to its development. The other reviewer ([PERSON]) was a senior neurological physiotherapist and clinical educator with extensive experience in stroke rehabilitation but had no previous experience with the TIDieR-Rehab checklist. Neither reviewer had been involved in the earlier systematic review and meta-analysis [11]. Prior to data extraction, the two reviewers completed structured training and piloting to build foundational knowledge, refine the methods, and establish standards of reporting specific to robotic and conventional upper-limb stroke rehabilitation interventions. In this training process, they familiarized with seven articles that had been excluded [24; 25; 26; 27; 28; 29; 30], mapping intervention descriptions against the TIDieR-Rehab checklist to clarify reporting expectations, identify common reporting gaps, and develop a table of reference (refer to Supplementary File S3) containing intervention-specific guidance and examples to support evaluation. Then, they independently piloted data extraction and evaluation using the table of reference on these seven excluded articles [24; 25; 26; 27; 28; 29; 30] and a further three articles that were included in the review [31; 32; 33], resolving discrepancies and refining the table of reference iteratively with another author (GA). This process ensured a shared understanding of reporting standards and readiness for systematic data extraction and evaluation. Using the TIDieR-Rehab checklist and table of reference, the two reviewers independently evaluated the reporting quality of robotic and conventional upper-limb interventions within the included articles and their associated materials (e.g., Appendix, Supplementary Files, referenced protocols). Intervention information was extracted into
## 2 GNSS RO Event Simulation and Assessment Approaches
### Global Coverage Fraction and Uniformity of GNSS RO Events  
Firstly, the Earth's surface is spatially divided by a grid into uniform meshes. Since the GNSS RO event positions are recorded by longitude and latitude, the Earth's surface can be divided into uniform grids of M \(\times\) M (km\({}^{2}\)) with latitude and longitude (\(0^{\circ}\), \(0^{\circ}\)) as the reference origin. In this study, the size of the uniform grids was set as 400 \(\times\) 400 km\({}^{2}\).  
Secondly, according to the temporal order, the GNSS RO events are accumulated into corresponding grids sequentially by equal-area projection to obtain the GNSS RO spatiotemporal datasets. Specifically, it includes the following steps:  
\(\circ\) Time normalization:  
\[t=t_{ro}-t_{0} \tag{10}\]  
where \(t_{ro}\) is the RO event time and \(t_{0}\) is the start time of the analysis period.  
\(\circ\) The RO events are accumulated to corresponding grids by equal-area projection [59]:  
\[col=k\cdot\frac{a_{E}\varphi\cos\lambda_{0}}{\sqrt{1-{e_{E}}^{2}\sin^{2} \lambda_{0}}} \tag{11}\]  
\[row=k\cdot\frac{a_{E}Q(\lambda)\sqrt{1-{e_{E}}^{2}\sin^{2}\lambda_{0}}}{\cos \lambda_{0}} \tag{12}\]  
where \(col\) and \(row\) are column and row index numbers in the divided grid matrix, respectively, \(k\) is the scale factor, \(a_{E}\) is the equatorial radius of the Earth, \(e_{E}\) is the first numerical eccentricity of the ellipsoid, \(\varphi\) and \(\lambda\) are the longitude and latitude of the RO tangent point, respectively, \(\lambda_{0}\) is the scale reference latitude, and the expression of \(Q(\lambda)\) is  
\[Q(\lambda)=\left(1-{e_{E}}^{2}\right)\left[\frac{\sin\lambda}{\left(1-{e_{E}} ^{2}\sin^{2}\lambda\right)}-\frac{1}{2e_{E}}\ln\left(\frac{1-{e_{E}}\sin \lambda}{1+{e_{E}}\sin\lambda}\right)\right] \tag{13}\]  
\(\circ\) Calculation of the global coverage fraction (\(GCF\)) in time series:  
\[GCF=\left({S_{occ}}/{S_{tot}}\right)\times 100\% \tag{14}\]  
where \(GCF\) is the global coverage fraction of RO events, \(S_{occ}\) is the accumulated area of the grid cells visited by RO events, and \(S_{tot}\) is the total grid area, i.e., the entire Earth's surface in the given case of a global coverage fraction.  
\(\circ\) Calculation of the uniform coverage index (\(UCI\)) in time series:  
In order to quantitatively assess the global coverage uniformity of RO events, the \(UCI\) was defined as  
\[UCI(n)=\left({N_{\geq n}}/{N_{tot}}\right)\times 100\% \tag{15}\]  
where \(N_{\geq n}\) denotes the number of grids with GNSS RO event-visited-times greater than or equal to \(n\), and \(N_{tot}\) denotes the total number of grids. Therefore, \(UCI\) denotes the global coverage uniformity of occultation events when \(n\) is set as the average value of the number of RO events relative to \(N_{tot}\).
## 6 Experimental Setup
### Test Environments  
The collection of experimental data has been carried out in three locations. Location A is a three-storey production plant employed for mineral processing. The environment is an open space cluttered with metal tanks, production machinery and a radio-controlled crane, while the three storeys are separated by metal grate flooring. A resident IEEE 802.11 WLAN covering the whole production plant was running at the time of the experiments.  
Location B is a small mechanical workshop with an abundance of metal cluttering and soldering tools. A total of fourteen IEEE 802.11 access points with overlapping spectrum allocation on Channels {1, 6, 7, 11} is detectable in this environment. In Figure 5, we show the position of three of the nearest access points, which we label with AP1, AP2 and AP3, while the remaining devices were placed outside the range of the map, or on the upper floors of the building.  
Location C is an office area, with nine IEEE 802.11 access points and residential MWO. We use this location to perform experiments on the classification of microwave interference, since neither industrial, nor residential MWO were present in the other two selected sites.
## 5 Performance Analysis
### Comparative Analysis of Results
To better evaluate model performance, we selected two days with significant traffic fluctuations from the PEMS04 and PEMS08 datasets and conducted experiments using the traffic flow data. The comparison curves between the real traffic flow and the model-predicted flow for these two days are shown in Figure 7. The daypart is represented by the x-axis, while traffic flow numbers are represented by the y-axis. From Figure 7 [50], it is evident that our forecast model performs exceptionally well, correctly capturing the variations in traffic flow trends throughout the day. Furthermore, with the accurate understanding of traffic flow patterns, administrators can optimize the traffic management system, improve traffic efficiency, reduce congestion, and provide city residents with a more convenient commuting experience. Figure 6: Performance comparison of different models on the PEMS04 (**above**) and PEMS08 (**below**) datasets. Figure 7: Comparison of real value and predicted traffic flow in one day (traffic flow prediction of PEMS04 for 24 h (**above**). Traffic flow prediction of PEMS08 for 24 h (**below**)).
## 4 Discussion
### Performance of Adaptive GNSS-RTK/LIO Fusion
When running on less urban dataset, the best threshold for the proposed method would be lower than 15\({}^{\circ}\). Nevertheless, the best precision achieved by the adaptive fusion method is still at meter-level due to the poor GNSS measurements provided by commercial level receivers in urban canyons. Deeper sensor fusion to correct the GNSS measurement model would lead to enhancement.
# A Highway Pavement Crack Identification Method Based on an Improved U-Net Model
## 6 Conclusions  
This paper proposed an improved U-Net segmentation model for highway pavement crack identification. The multi-scale feature prediction fusion and the improved parallel attention module were applied to the original U-Net network, and the bottleneck layer of the original U-Net network was improved to achieve higher accuracy and better segmentation results. Publicly available datasets (Crack500 and CFD) were used to validate the methodology. When detecting road pavement cracks, the improved U-Net improved in all four metrics compared to the original U-Net (when both models were trained with 100 epochs). Experimentally comparing the method of this paper with other competing methods proposed in the literature, the maximum scores using the Crack500 dataset were: precision (89.60%), recall (95.83%), mIOU (83.80%), and F1-score (92.61%). High values in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mIOU/\%** & **Precision/\%** & **F1-Score/\%** & **Recall/\%** \\ \hline Crack-UNet & 72.51 & 89.7 & 83.13 & 77.45 \\ DeepLabV3+ & 76.83 & 91.94 & 86.29 & 81.3 \\ RAO-UNet & 80.23 & 90.89 & 88.34 & 85.92 \\ Ours & 82.07 & 93.29 & 89.64 & 86.26 \\ \hline \hline \end{tabular}
\end{table}
Table 7: CFD dataset experimental results.  
Figure 20: CFD dataset segmentation effect diagram.  
precision (93.29%), mIOU (82.07%), recall (86.26%), and F1-score (89.64%) were also found using the CFD dataset. The results show that this method performed the best among the four methods. Thus, the improved network showed some advantages in identifying cracks in highway pavements and is an ideal tool for practical engineering.  
Methodology, Q.W., Z.S. and H.C.; writing--original draft, Q.W. and Z.S.; writing--review and editing, Q.W., H.C., Y.L. and L.Z.; funding acquisition, Q.W. All authors have read and agreed to the published version of the manuscript.  
[PERSON] received funding for this work through the Key Science and Technology Program of Henan Province (number 222102210084) and the Key Science and Technology Project of Henan Province University (number 23A413007).  
The datasets used in the experiment are publicly available online at: [[https://aistudio.baidu.com/aistudio/datasetdetail/74646](https://aistudio.baidu.com/aistudio/datasetdetail/74646)]([https://aistudio.baidu.com/aistudio/datasetdetail/74646](https://aistudio.baidu.com/aistudio/datasetdetail/74646)), accessed on 17 March 2023, and at: [[https://github.com/cuilimeng/CrackForest-dataset](https://github.com/cuilimeng/CrackForest-dataset)]([https://github.com/cuilimeng/CrackForest-dataset](https://github.com/cuilimeng/CrackForest-dataset)), accessed on 20 March 2023.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Image Acquisition  
A fast gradient echo MR imaging sequence has been developed at the University of California, San Diego (UCSD) to spatially capture lung water density in CF subjects and healthy controls [26]. CF subjects with stable disease were recruited from the UCSD Adult CF clinic. Healthy adults without CF were recruited from the local population in San Diego. All subjects provided written informed consent approved by the Human Research Protections Program at UCSD (#110368).  
MR density image data were acquired in an ACR accredited 1.5T GE EXCITE HDx Twinspeed MR imaging scanner (General Electric, Milwaukee, WI, USA) with imaging parameters kept within the established FDA guidelines for routine clinical MR examinations. Before being placed supine in the scanner, each subject was trained in breathing maneuvers to reach total lung capacity (TLC, full inspiration). The 3D maps of lung water density were collected with 15 mm thick sagittal slices (with no gap) encompassing the entire lung (15-18 2D sagittal slices) at TLC. Quantitative measurements of lung density were calculated as fractional lung density (FLD) with a range of values from 0 (100% air) to 1000 (100% fluid) on a voxel-by-voxel basis.  
Quantitative measures of lung water density are based on data acquired with a multi-echo fast gradient echo (mGRE) sequence that acquires multiple single echo acquisitions within a single breath-hold. The sequence collects 12 images alternating between two echo times (TE = 1.0 and 1.8 ms) within a breath-hold. After averaging data at each echo time, data were fit on a voxel-by-voxel basis with a single exponential decay function to determine the local decay constant (T\({}_{2}\)') and lung water content by back-extrapolating signal to an echo time of zero. Absolute water content was obtained with the inclusion of a phantom whose signal represents 100% water. Since the phantom does not have decay constants equal to that in the lung, a correction factor was empirically determined for the static sequence parameters (TR = 10 ms, T\({}_{2}\) decay effects negligible), permitting the mean phantom signal to be used as a reference for absolute calibration of water content.
## 2 Materials and Methods  
### Study Area and Sampling Sites  
In this study, the presence of genes encoding resistance to fluoroquinolones and sulfonamides was analyzed in two WWTPs with similar treatment systems and processing capacities. Two wastewater treatment plants (WWTPs) in Poland were analyzed in the study. The WWTPs are located in different Polish regions: Warmia and Mazury (WM-WWTP) and Silesia (S-WWTP). Treated effluents are evacuated to the Lyna River and the Gostynia River, respectively. Samples of river water collected upstream and downstream from effluent discharge points were examined to determine the influence of wastewater processing technology on rivers receiving treated wastewater. The analyzed WWTPs are characterized by similar treatment technologies, but they differ in the type of inflowing wastewater. Both WWTPs have a similar daily average processing capacity and employ a mechanical-biological treatment system based on the activated sludge. S-WWTP has an average influent flow rate of 32,000 m\({}^{3}\)/day, and it receives municipal sewage from the city of Tychy, hospital wastewater and industrial sewage. Industrial wastewater (25% of the receiving wastewater) is supplied mainly by a brewery where sewage is pre-cleaned in the methane fermentation process. The brewery is the largest and the most important industrial facility in the S-WWMT area. S-WWTP deploys mechanical-biological treatment methods and operates sequencing C-TECH reactors, activated sludge chambers (a system of chambers of various oxygen conditions: anaerobic, anoxic and aerobic), a secondary settling tank and an anoxic chamber. S-WWTP uses supplementary chemical phosphorus removal [40]. WM-WW TP operates a mechanical-biological treatment system with an elevated removal of nutrients (MB-ERN) with the following sections of the wastewater treatment process: a pre-denitrification chamber, a phosphorus removal tank, nitrification/denitrification chambers and secondary settling tanks. It processes municipal sewage from the city of Olsztyn, industrial wastewater and sewage from three hospitals. Industrial wastewater (20% of the receiving wastewater) is supplied by animal industry wastewater. According to the information obtained from the administration of the WM-WWTP, the plant has an average processing capacity of 35,000 m\({}^{3}\)/day. The prevalence of bacterial infections, the amount of antimicrobial drugs and the number of hospitalized patients differ across seasons. These parameters can influence the results noted in each sampling season. Therefore, samples were collected in the summer (June) and fall (November) of 2018. Samples of river water, wastewater and sludge from various stages of treatment were analyzed (n = 36). The types of samples collected in each WWTP are presented in Table 1.  
Three grab samples of around 160 mL of wastewater or river water were individually collected and combined into composite samples in sterile 500 mL bottles. Sludge samples were collected into sterile urine containers. The samples were transported to the laboratory on the day of collection and stored in 4 \({}^{\circ}\)C for further analysis.
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Path Traversal
#### 4.4.1 Depth-First Search
[93] introduces a distinct approach through DFS-tree path decomposition, yielding \(O(\sqrt{mn\log n})\) space. Note that all mentioned algorithms specifically address undirected graph scenarios. Regarding the fully dynamic DFS-Tree maintenance algorithms in directed graph, Ref. [90] first proposes efficient algorithms from the perspective of practical implementation. According to Lemma 1, the DFS-Tree maintenance problem is essentially to eliminate the forward-cross edges. To efficiently check the edge type in constant time, Ref. [90] attaches the discovery timestamp and finish timestamp of each vertex during the DFS-Tress search. Based on the attached timestamps, the proposed algorithms first identify a candidate time interval such that the DFS-subtree induced by the vertices whose attached timestamps in  
Figure 7: An example of DFS-Tree [155]. the candidate time interval may be changed and the other part of the DFS-Tree remains unchanged. By performing the DFS following the vertices whose attached timestamps in the candidate time interval, the correct DFS-Tree can be obtained. Ref. [90] further explores opportunities by refining the candidate time interval and transforming the graph search to tree search to improve the efficiency. Although the algorithms proposed in [90] do not have tight theoretical guarantees, their practical performance is impressive as verified by the experiments on large datasets. **Algorithm analysis. ** We analyze DFS-Tree algorithm over dynamic graphs. As shown in Table 7, all the algorithms' worst-time complexity are bounded by a polynomial in \(n\) or \(m\), where \(n\) is the number of vertices over graph, and \(m\) is the number of edges over graph. The DFS-Tree maintenance problem is well studied when the graph is updated in batch. However, the streaming BFS-tree maintenance needs further investigation.
## 6 Discussion
### Critical Analysis of Previous Studies  
Recent advancements in decision-making and control frameworks provide valuable insights into managing uncertainties and optimizing system performance across various domains. Studies such as [38; 39] proposed fuzzy control models for supply chain systems with lead times, demonstrating their ability to handle nonlinear dynamics and uncertain demand patterns. These methods highlight the potential for adaptive strategies to address fluctuations--a challenge that also resonates with EV infrastructure integration, particularly in balancing energy supply and demand.  
Further contributions, such as the mitigation of the bullwhip effect in closed-loop supply chains using fuzzy robust control [40] and adaptive inventory control models using fuzzy neural networks [41], emphasize robust methodologies to manage disruptions. These adaptive approaches could inspire similar strategies in EV charging networks, especially for ensuring system stability amid renewable energy variability.  
While these studies predominantly address supply chain management, this research extends their principles to energy infrastructure. By integrating PROMETHEE with fuzzy robust control frameworks, this study bridges gaps in the existing literature, addressing energy distribution challenges, grid stability, and infrastructure scalability for EVs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variant** & **Positive Flow \(\phi^{+}\)** & **Negative Flow \(\phi^{-}\)** & **Net Flow \(\phi\)** &
\begin{tabular}{c} **User Convenience** \\ **(Normalized)** \\ \end{tabular} \\ \hline
22 kW & 0.4 & 0.6 & \(-\)0.2 & 3 \\
50 kW & 0.5 & 0.5 & 0 & 2 \\
150 kW & 0.7 & 0.3 & 0.4 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Flow evaluation of variants and user convenience.