esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions
The results demonstrate that \(x_{1}\) (the number of the bus stop j), \(x_{2}\) (the dwell time of bus i\(-\)1 at stop j), \(x_{4}\) (the time of bus i travel from stop j\(-\)1 to stop j), \(x_{5}\) (the time of bus i travel from stop j\(-\)1 to stop j), \(x_{6}\) (the historical average travel time between stop j\(-\)1 to stop j), and \(x_{7}\) (the headway of bus i at stop j\(-\)1) are significant factors contributing to the increased bus bunching phenomenon. Bus bunching is transitive, and bus stops away from the departure place are more likely to be affected; thus, spatial attributes of bus stops should be emphasized in relevant prediction models. Moreover, when bus bunching occurs at an early stage along the routes, possible bus bunching points and bus stop-skipping schemes are taken on the basis of the spatial characteristics. Compared to the bus dwell time at the stop, the results showed that the bus travel time between stops was a more significant factor of bus bunching. As a result, detailed factors which may have an impact on the bus travel time, such as the influence of signalized intersections, bus lane planning, etc., should be taken into account in models. As the reliability of future prediction and dispatch applications greatly depends on the selected inputs, significant factors play an important role in improving the accuracy or efficiency of bus arrival/travel time prediction, bus bunching point estimation, bus dispatching strategies, etc. In addition, there are some areas that need to be improved. The eGA-DT model has the advantage of fitting non-linear relationships, but it also tends to overfit and converge slowly under different data sets. Therefore, methods related to data preprocessing, cross-validation, and random forest could be further studied to mitigate the problem. Moreover, multi-source data, such as bus passenger data, mobile signal data, traffic flow data, etc., also greatly help to reflect the characteristic of headway. In addition, this paper mainly focused on the analysis of the characteristics of a single bus line, and the interaction among multiple bus routes along the same corridor could be taken into account in future research. Conceptualization, M.Y., B.X. and G.X. ; data curation, M.Y. and G.X. ; formal analysis, M.Y. ; funding acquisition, B.X. ; investigation, M.Y. and G.X. ; methodology, M.Y. and B.X. ; project administration, B.X. ; resources, B.X. ; software, M.Y. and G.X. ; supervision, B.X. ; validation, B.X. and G.X. ; visualization, G.X. ; writing--original draft, M.Y. ; writing--review & editing, B.X. All authors have read and agreed to the published version of the manuscript. This research was supported by National Natural Science Foundation of China (71974043) and the Science and Technology Innovation Committee of Shenzhen under Grant JCYJ20210324133202006. The data presented in this study are available on request from the first author and corresponding author. The authors declare no conflict of interest.
## 4 Discussion  
Due to the interference of weather conditions (such as cloud and fog) and the revisit time of the satellite itself, the time interval of available image sequence in certain regions are considerably long, sometimes even for years. It creates a major challenge to the dynamic monitoring of forests based on long-term remote sensing sequence images in these regions. The results of this paper show that the fusion of Landsat and HJ-1 A/B images has the potential to improve forest monitoring in Fuling, a mountainous area with many cloudy weathers. This may provide ideas for other areas with similar conditions. HJ-1 A/B is currently in extended service. However, the Sentinel-2 launched in 2015, including multi-spectral bands with spatial resolutions of 10 m, 20 m and 60 m, can be used as substitutions for (or supplements to) future time series.  
The optical satellite images used in this study are susceptible to cloud cover, which limits the improvement of the time series. When monitoring rapid changes is need, SAR data can be considered as it is not affected by cloud cover.
## 3 Results and Discussion
### Use of SIF to delineate Highly Productive Agricultural Areas
Only the slopes between GPP and the product of FAPAR and PAR proxies were computed using the two different samples. Even though the results using SIF were positive, there is room for improvement. The use of a low-resolution static SIF dataset has obvious limitations. We do not account for the fact that only a fraction of our identified high productivity grid boxes was covered by highly productive crops. Improved SIF datasets are expected in the near future that will address some of these concerns as explained below. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**GPP Estimate** & \(\mathbf{r^{2}}\) & **Bias** & **RMSE** & **MEF** & \(\mathbf{\lambda}\) \\ \hline \(\text{NIR}_{V}\times\text{SW}_{\text{TOA}}\) & \(0.694\) & \(0.018\) & \(2.19\) & \(0.695\) & \(0.824\) \\ \(\text{NDVI}^{\prime}\times\text{SW}_{\text{TOA}}\) & \(0.693\) & \(-0.306\) & \(2.21\) & \(0.691\) & \(0.806\) \\ \(\text{NDVI}^{\prime}_{2}\times\text{SW}_{\text{TOA}}\) & \(0.718\) & \(-0.249\) & \(2.16\) & \(0.707\) & \(0.814\) \\ \(\text{7 band}\times\text{SW}_{\text{TOA}}\) & \(0.700\) & \(-0.262\) & \(2.20\) & \(0.696\) & \(0.818\) \\ \(\text{7 band}_{2}\times\text{SW}_{\text{TOA}}\) & \(0.725\) & \(-0.238\) & \(2.11\) & \(0.720\) & \(0.831\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical comparison between daily-averaged GPP estimates using MCD43D data at 0.00833\({}^{\circ}\) resolution from satellite data at 64 independent unique flux tower sites from FLUXNET 2015 with a total of 10,3671 individual collocated observations. Figure 5: Locations where SIF and NDVI data have been flagged as high productivity, requiring a different slope between GPP and NDVI. Blue diamonds represent the flux tower locations used to define the flagging criteria.
## 3 Results
### Validation of Specific Junctional Markers  
After culture time, we observed the expression and localization of important junctional proteins to validate the cell differentiation. The particularity of BMEC is their high localized expression of tight and adherent junctions at intercellular spaces. Claudins, occludin, and its associated protein zonula occludens-1 (ZO-1) constitute the major tight junctions at the BBB, sealing the paracellular space. As claudins and occludin are expressed to a less extent in hCMEC/D3 cells [43; 44], we first focused on ZO-1 immunodetection. Concerning adherent junctions, the b-catenin was preferred to VE-cadherin as its expression is highly dependent upon the culture conditions [45; 46]. Together, ZO-1 and \(\beta\)-catenin are mainly used as specific markers to validate BBB differentiation as there are key structural compo  
Figure 3: Phase contrast microscopic views of the static and dynamic (10 dyn\(\cdot\)cm\({}^{-2}\)) cultures of hCMEC/D3 cells after seven days in the barrier device; (**a,d**) images were taken from the center of the channels, while (**b,c**) were viewed at the edges. These images are representative of four independent experiments. Scale bars represents 1 mm.  
nents [34; 38; 42; 47]. Immunostaining procedures were performed at the end of the culture by incubating several antibodies after fixation of the cell monolayer. Precise visualization operated directly through the device (by crossing the underside of the membrane) with a spinning-disk confocal microscope. Results are shown in Figure 4.  
Nuclei staining are uniformly distributed in both conditions, demonstrating intact cell monolayers after seven days of culture. Phalloidin staining reveals elongation of the actin strains only in the dynamic condition. Both tight and adherent junctions are localized at the cell-to-cell boundaries, but also in the nucleus for ZO-1 (in accordance with previous staining [43]). The ZO-1 tight junction appears more continuous along the inter-endothelial contacts, while \(\beta\)-catenin adherent junction seems more homogenous and regular in the dynamic condition. We can note the presence of some punctuate signals in the \(\beta\)-catenin images which were also observed in well plate support (data not shown). In our opinion, this artefact provides from the antibody that nonspecifically aggregates due to dissolution problems.  
All images in dynamic condition show a more elongated cellular morphology, whereas it is not observed in static condition, as demonstrated above. These confocal images taken with high magnification confirm that in dynamic condition, while most cells align in the flow direction (as in actin fibers and \(\beta\)-catenin images), some cells in a few regions exhibit another orientation, sometimes perpendicular to the flow direction, as observed in ZO-1 staining. We confirmed the expression of these key BBB markers after seven days of culture. Our observations showed that the ZO-1 and \(\beta\)-catenin scaffolding proteins are more finely expressed in dynamic culture, which correlate with a more successful BMEC differentiation.
## 4 Experimental Verification  
To verify the effectiveness of the proposed method, comparative experiments of the method in [14] and the proposed 2 DOF IMC ADO scheme are carried out by using an SRM platform. The platform is shown in Figure 5, consisting of an SRM, a power converter, a dSPACE DS-1103 controller board, an oscilloscope, etc. The detailed parameters of the test 1.5 kW 3-phase 12/8 SRM are listed in Table 1. The dSPACE DS-1103 controller board is employed to execute both control schemes. The asymmetric half-bridge converter is adopted to drive the SRM, and the PWM frequency is 10 kHz. A magnetic powder brake is coupled to the shaft of the SRM to simulate the load torque. The Hall effect sensors and incremental encoder are used to measure the phase current and rotor position, respectively. The parameters are used in the SRM driver as follows: \(L_{dc}=0.075\) H, \(L_{ac}=0.069\) H, \(R=0.9\)\(\Omega\), \(P=8,\lambda_{2}=0.003,\gamma=0.7\).
## 5 Discussion
### Network Congestion  
As pointed out by a few efforts [3; 14; 49], network congestion can become severe with a large number of VRUs when all VRU devices _actively_ participate in the V2P communication. To solve the network congestion issue, a few schemes, such as, Receive-only mode [3], contextual transmission [49], and clustering of VRUs [14] have been suggested.
\begin{table}
\begin{tabular}{c c c} \hline  & **Units** & **Definition** \\ \hline \(a_{g}(\lambda)\) & m\({}^{-1}\) & CDOM absorption coefficient \\ \(a_{d}(\lambda)\) & m\({}^{-1}\) & NAP absorption coefficient \\ \(a_{dg}(\lambda)\) & m\({}^{-1}\) & NAP and CDOM absorption coefficient \\ \(a_{p}(\lambda)\) & m\({}^{-1}\) & Particulate absorption coefficient \\ \(b_{lp}(\lambda)\) & m\({}^{-1}\) & Particle backscattering coefficient \\ \(b_{lp}(\lambda)\) & m\({}^{-1}\) & Total backscattering coefficient \\ CDOM & & Colored Dissolved Organic Matter \\ Chl & mg m\({}^{-3}\) & Chlorophyll concentration \\ DOC, DOM & \(\mu\)mol L\({}^{-1}\) & Dissolved Organic Carbon, -Material \\ \(E_{s}(\lambda)\) & W m\({}^{-2}\) nm\({}^{-1}\) & Downwelling surface irradiance \\ \(L_{\mu\mu}(\lambda)\) & W m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\) & Water leaving radiance \\ \(L_{\mu\mu}(\lambda)\) & W m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\) & Normalized water leaving radiance \\ POC & \(\mu\)mol L\({}^{-1}\) & Particulate Organic Carbon \\ \(R_{rs}(\lambda)\) & sr\({}^{-1}\) & Remote sensing reflectance \\ \hline \end{tabular}
\end{table} 
Table 1: Definition of terms, units, and abbreviations. CDOM absorption is a superposition of the spectral absorption by its varied chromophores, and increases roughly exponentially (or hyperbolically [9]) with decreasing wavelength in the visible (VIS) and ultraviolet (UV) spectral ranges, as described in the next section. CDOM tends to dominate the blue and UV spectrum in many coastal and estuarine environments (e.g., [7; 10; 11; 12]), and is the most important factor controlling UV and blue light penetration even in the open ocean [13] despite its generally lower concentration and distance from land. Within the visible spectrum, \(a_{g}(\lambda)\) reduces the photosynthetically available radiation supporting phytoplankton and macrophytic growth, and generates heat in the surface layer of the water column, thus affecting mixing [14]. In the UV, CDOM causes surface heating as well, but also acts to protectively shade aquatic organisms, thus reducing the amount of damaging high frequency radiation reaching vulnerable cell structures. From the passive remote sensing perspective, CDOM reduces the amount of blue light available for reflection out of the water column, and can therefore have a significant impact on ocean color algorithms, for example increasing uncertainty in blue-green band-ratio algorithms designed to estimate chlorophyll-\(a\) concentration (Chl) from its absorption peak near 443 nm [13; 15]. These types of Chl algorithms assume covariance in Chl, CDOM, and other water column constituents (i.e., the \"Case 1 waters\" assumption [16]). By contrast, semi-analytical algorithms (SAAs) that invert the ocean color signal to retrieve individual component absorption spectra (particles, CDOM, water) are stymied by the presence of non-algal particulates (NAPs), which have a similar spectral shape to CDOM. As a result, these approaches tend to retrieve only the sum of these two elements [17] (and references therein).
## 6 FPSE Method Development  
Unlike solid phase microextraction and similar sorbent based microextraction techniques, method development in fabric phase sorptive extraction is simple and straight forward. Figure 1 presents a graphical schematic of a typical FPSE workflow.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Name of the SorbeSorbent Coating**} & **Polarity of the Sorbent** \\ \hline \multicolumn{3}{c}{**Neutral Sorbents**} \\ \hline
1. & Sol–gel poly(dimethylsiloxane) & Nonpolar \\
2. & Sol–gel poly(dimethyldiphenylsiloxane) & Nonpolar \\
3. & Sol–gel methyl & Nonpolar \\
4. & Sol–gel C4 & Nonpolar \\
5. & Sol–gel C8 & Nonpolar \\
6. & Sol–gel C12 & Nonpolar \\
7. & Sol–gel C18 & Nonpolar \\
8. & Sol–gel Graphene & Nonpolar \\
9. & Sol–gel Multi Wall Carbon Nanotubes & Nonpolar \\
10. & Sol–gel Single Wall Carbon Nanotubes & Nonpolar \\
11. & Sol–gel Activated Carbon & Nonpolar \\
12. & Sol–gel poly(tetrahydrofuran) & Medium polar \\
13. & Sol–gel poly(ethylene glycol)-poly(propylene glycol)-poly(ethylene glycol) & Medium polar \\
14. & Sol–gel poly(propylene glycol)-poly(ethylene glycol)-poly(propylene glycol) & Medium polar \\
15. & Sol–gel propyl methacrylate & Medium polar \\
16. & Sol–gel poly(caprolactone)-poly(dimethylsiloxane)-poly(caprolactone & Medium polar \\
17. & Sol–gel poly(caprolactone)-poly(tetrahydrofuran)-poly(caprolactone) & Medium polar \\
18. & Sol–gel poly(caprolactone diol) & Medium polar \\
19. & Sol–gel poly(caprolactone triol) & Medium polar \\
20. & Sol–gel Silica & Polar \\
21. & Sol–gel Sucrose & Polar \\
22. & Sol–gel Sucralose & Polar \\
23. & Sol–gel Chitosan & Polar \\
24. & Sol–gel Carbowax 20M & Polar \\
25. & Sol–gel poly(ethylene glycol), 300 & Polar \\
26. & Sol–gel poly(ethylene glycol), 10,000 & Polar \\ \hline \multicolumn{3}{c}{**Mixed Mode Sorbents**} \\ \hline
27. & Sol–gel cation exchanger, C18 & Ion exchanger/nonpolar \\
28. & Sol–gel anion exchanger, C18 & Ion exchanger/nonpolar \\
29. & Sol–gel Zwitterionic cation exchanger, anion exchanger, C18 & Dual ion exchanger/nonpolar \\ \hline \multicolumn{3}{c}{**Ion Exchanger Sorbents**} \\ \hline
30. & Sol–gel cation exchanger & Ion exchanger \\
31. & Sol–gel anion exchanger & Ion exchanger \\
32. & Sol–gel Zwitterionic anion and cation exchanger & Ion exchanger \\ \hline \end{tabular}
\end{table}
Table 1: List of major fabric phase sorptive extraction (FPSE) sorbents.  
FPSE method development exercises can be carried out using a conventional one-factor-at-a-time (One FAT) approach or using a chemometric design of experiment approach. The later approach is the more scientific and green approach, as it provides deep insight about the overall extraction process and sheds light as to whether different factors interact with each other or not. A screening design can be carried out to select factors with the most influence on the overall extraction efficiency. Subsequently, a response surface model (RSM) design can be employed to find the optimum levels of the most influential factors.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 5: Analyze Results  
Now EEDMI scores are analyzed to validate if adding nanogrids to an existing microgrid improves resilience. Three outcomes may occur comparing EEDMI scores including:  
\(EDMI=0\): The most ideal scenario from a military energy resilience perspective is when a specific system architecture returns a result of \(EEDMI=0\). This means that for all postulated threats, no load shedding occurs and there is no impact to the national security mission.  
\(EEDMI_{\text{Proposed System}}<EEDMI_{\text{Baseline}}\): In this case, the proposed nanogrid system design is superior to the baseline microgrid because the proposed design's \(EEDMI\) is less than the baseline microgrid.  
\(EEDMI_{\text{Proposed System}}\geq EEDMI_{\text{Baseline}}\): If the \(EEDMI\) of the proposed nanogrid system design is greater than or equal to the baseline, it is worse than the baseline design with respect to energy resilience. Such an outcome may indicate undersized generation and storage capacity within the nanogrid(s). In this case, it may be worthwhile to double-check the sizing carried out in Step 1.4 and the designs produced in Step 2.2.  
The proposed nanogrid designs that produce \(EEDMI\) values below the baseline nanogrid can then be rank-ordered to understand which designs may be preferential to others. While the \(EEDMI\) metric is useful in understanding which designs are more resilient and are more likely to protect critical loads from threats to energy security that could impact national security missions, resilience is only one of several important requirements that military installations often use when focusing on improving energy infrastructure.
# A Fast Parallel Random Forest Algorithm Based on Spark
## 1 Introduction
(2020) calculated the information gain of various features on MapReduce computing framework by introducing a feature weighting system and improving existing data analysis with evaluation metrics. [PERSON] et al. (2011) combined data-parallel and task-parallel optimization methods to reduce communication costs between data and workload imbalances. [PERSON] (2018) grew all trees in parallel with a breadth-first node approach to reduce the number of data scans. In addition, Apache Spark Mlib (2018) also provides a standard Spark-MLRF algorithm and is widely accepted by many researchers. In general, there are lots of iterations in a RF algorithm, and the output of the previous job is the input of the next job. For the MapReduce computing framework, each job stores the intermediate results to disk during iterations, resulting in a large number of disk I/O operations and data storage requirements, leading to low running efficiency. Instead, the new generation big data technology Spark can cache data in memory and is more efficient in realizing the RF algorithm for large-scale data. However, in calculating the optimal split point of continuous features, the standard Spark-MLRF takes a random sampling strategy to achieve faster computation but a lower classification performance. In addition, when constructing a training subset, Spark-MLRF also provides each decision tree with a sampled data set consistent with the original data set size, resulting in a large storage load and data communication. Aiming at the above existing problems, we propose an optimized parallel random forest algorithm based on Spark. The main contributions are as follows: (1) A new Gini coefficient definition is proposed to calculate the feature information and reduce the impact of feature redundancy on classification accuracy. (2) An approximate equal-frequency binning method is proposed to optimize the number of candidate split points of continuous features and effectively reduce the number of Gini coefficient calculations. (3) A parallel decision tree training method based on the forest sampling index (FSI) table is proposed. We achieve less storage load and data communication by establishing the index table for the entire random forest. The rest of this paper is organized as follows. Section 2 briefly introduces the principle of the random forest algorithm and Spark technical framework. Section 3 improves the calculation of CART tree splitting information and proposes an approximate equal-frequency binning algorithm. Section 4 presents a parallel implementation of the proposed Random forest algorithm. Section 5 shows the related experimental results to evaluate the classification accuracy and parallel performance of the proposed algorithm. Section 6 concludes the work and presents an outlook for future research.
# Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone
## 4 Results
### Test 3  
Figure 15 shows the RMS results for the Galileo + GPS combination in Test 3, using the smartphone and a high-grade Septentrio PolaRx5 receiver. Three pillars (5, 9, 11) were surveyed with a duration of 30 min by the two devices simultaneously. Table 11 contains a summary of the statistics obtained.  
From the results of Test 3, we see that the errors obtained for the smartphone solutions are similar to the other solutions in Tests 1 and 2, although the biggest RMS errors are reaching the 30 cm in the CEUT station, respectively, in the N and U components. Comparing the two devices, we see that the geodetic receiver had a better performance, especially in the two smallest baselines, GAIA and VIGO, with values below 7 cm, while the mobile device shows for these stations values under 12 cm.