esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Scientometric Analysis Insight: Role of Co-Citation Reference and Keyword Co-Occurrence Analysis
#### 4.2.2 Modeling the Impacts of Climate Change on Forest WUE
Large-scale spatial vegetation indices for photosynthesis and transpiration rates are retrieved through satellite-based observations, such as the normalized difference vegetation index (NDVI) and solar-induced fluorescence (SIF) [105]. The retrieved satellite-based observation data can be assimilated into process-based models to improve their accuracy and spatial mapping [106; 107]. For example, [PERSON] and [PERSON] (2019) utilized satellite-based SIF data to assess global patterns of WUE in forests due to climate change. They observed an overall increase in WUE in forests, mainly due to rising atmospheric CO\({}_{2}\). Elevated CO\({}_{2}\) concentrations can enhance photosynthesis, leading to higher carbon uptake per unit of water consumption [108]. Another study employed remote sensing data alongside terrestrial biosphere models to investigate the WUE on the Tibetan Plateau. This study concluded climate change as the major factor influencing WUE in forests, with rising global temperature levels being a critical contributor to this change [109]. Remote sensing has the advantage of covering huge and unreachable forest areas, but it may experience some constraints due to cloud cover and sensor limitations. Modeling the impacts of climate change on WUE in forests involves a variety of techniques and approaches, ranging from empirical models to complex process-based and DGVM models. Each modeling approach has its own advantages and disadvantages. Therefore, Future research should focus on integrating these techniques, improving model accuracy, and increasing input data precision in order to better predict the climate change impacts on forest ecosystems.
## 4 Results
### Qualitative Data Perspective Analysis
Based on the statistical summary of qualitative data and the observations from the observing teacher, the difficulties encountered during the implementation of virtual reality strategies can be summarized as follows:  
1. Overview of Virtual Reality Course Implementation:  
The virtual reality teaching strategies received high levels of agreement in all aspects. From \"problem identification\", \"data collection\", \"problem analysis\", \"creative generation\", \"solution evaluation and selection\", to \"implementation and evaluation\", all phases recorded agreement coefficients close to or exceeding 0.89, reflecting the participants' strong affirmation of the teaching strategies. Particularly during the data collection and problem analysis phases, participants recognized the value of utilizing multiple data sources and conducting thorough problem analysis. The observing teacher recommended placing greater emphasis on students' adaptation processes to virtual reality in the experimental group's curriculum. Specific recommendations include enhancing technical guidance during the early implementation of the course, expanding information sources, observing students' interactive behaviors in detail, and encouraging broader student participation during the creative generation phase. 2. Implementation Challenges of Virtual Reality Courses  
During the implementation of the virtual reality (VR) curriculum, students in the experimental group encountered several technical challenges. These included unfamiliarity with virtual software technologies, difficulties in operating VR equipment, insufficient availability of devices, and psychological pressures along with physiological sensations of dizziness experienced by some students. These physical and mental issues have, to some extent, affected the learning motivation and outcomes of certain students.
Furthermore, it can also be utilized for identifying the potential source areas impacting a fixed region through backward calculations. This aspect also provides insights for simulating the movement of unpowered airborne balloons. Preliminary research suggests that most airborne balloons found in China exhibit isotropic behavior by remaining stable along isogeneity surfaces, with minimal changes in the vertical direction, whereas their horizontal movement is predominantly influenced by wind fields, resembling particle diffusion patterns. Despite the widespread application of FLEXPART, research on the numerical prediction and traceability of airborne balloon trajectories using this model is still at an early stage. In this study, the Lagrangian diffusion model FLEXPART was employed in conjunction with the high-resolution meteorological model WRF (Weather Research and ForecastingModel) to simulate both forward and backward trajectories of airborne balloons. Sensitivity tests on the radius (volume) and density of the airborne balloons were conducted, and the simulation results were compared and validated against observational data from an actual airborne balloon. This approach not only enables simultaneous prediction of forward and backward trajectories of airborne balloons, addressing concerns regarding trajectory traceability, but also represents a pioneering exploration of FLEXPART in the prediction of airborne balloons, thereby further expanding its potential scope.
# A Predictive Reliability Model to Assess the Performance of Photovoltaic Systems
### The Operational Conditions  
These include the atmospheric conditions and the field orientation parameters. The atmospheric conditions include the ambient temperature \(T_{A}\), the velocity of wind \(V_{w}\) and the global horizontal solar radiation, or shortly, the global horizontal irradiation. While the atmospheric conditions are determined by the location of the system on Earth, the parameters of field orientation can be adjusted for optimum performance. There are two parameters for the field orientation: tilt angle of the PV plane \(\beta\) and its azimuth angle \(\alpha_{z}\). These two parameters affect the amount of solar energy collected by the system. According to [7], the three parameters zenithal angle \(\theta_{z}\), azimuth angle \(\alpha_{z}\) and altitude define the solar coordinates. The zenith angle \(\theta_{z}\) is the angle of incidence of a beam from the sun on a horizontal plane, which is the angle between the vertical direction and the line to the sun.  
The azimuth angle \(\alpha_{z}\) is defined as the angular displacement on the horizontal plan taken from south of the projection of the beam radiation [7]. This angle is given by:  
\[\alpha_{z}\ =\arcsin\ (cos(\delta)sin(H)/cos(h))\]  
where,  
* \(h\) is the solar altitude angle which is the complement of \(\theta_{z}\);
* \(H\) is the hour angle, defined as the angular displacement that the local meridian makes by a rate of \(15^{\circ}\) per hour--either east or west due to the rotation of the Earth. According to [7], \(H\) is negative in the morning and positive in the afternoon and given by: \[H\ =\ 15^{\circ}\ (RTS-12)\]
* \(RTS\) is the real time system in hours and is counted from 0 to 24 h;
* \(\delta\) is the declination angle. In [8], \(\delta\) is defined as the angle that the rays make with the equator plane of the Earth for a specified day number \(n\) and is given by: \[\delta\ =\ 23.45\ sin\ \bigg{[}360\ *\ \frac{284\ +\ n}{365.25}\bigg{]}\]
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 3 Description of the GeoHyTE Platform
### Computational Architecture
#### 3.2.1 Overview  
Data input and execution in GeoHyTE are controlled from six sequential GUIs, with free forward and backward navigation, complemented by the Matlab-native pdetool interface for the generation of non-regular meshes and a detailed visualisation interface.  
An overview of the GUI structure is presented in Figure 2. GUI 1 handles the visualisation and interpretation of the experimental input signal, including the definition of the tentative arrival time and correlation window. The finite element model of the bender element experiment is defined through GUIs 2 to 5. GUI 6 is the runtime interface. It remains open during the execution of GeoHyTE and hosts the cross-correlation plots and the current estimates for the arrival time of the shear wave as well as for the shear modulus.  
The next sections illustrate the data input, execution, and output of GeoHyTE using one of the International Parallel Test benchmarks as an example.
# Assessing the Efficacy of Seawater Batteries Using NASICON Solid Electrolyte
## 4 Discussion
### Testing the Performance of Seawater Batteries Containing the NASICON Membrane
The total operating time was approximately 100 h, as each cycle lasted for 2 h. From the potential curves, at a current of 50 \(\mu\)A per charge, it was observed that the discharge/charge potential was 2.18 V and 3.06 V with a potential difference of 0.88 V while for applying a current of 100 \(\mu\)A was 2.73 V, respectively, 3.27 V with a potential difference of 0.54 V. The voltage efficiency during charge and discharge was 71% (50/100 \(\mu\)A) and 83.5% (100/100 \(\mu\)A) after around 100 h of operation. It is found that at a constant value of the discharge current and at the constant temperature of the electrolyte, the discharge voltage decreases over time due to the increase of the potential, electromotive voltage, and resistance of the electrodes and electrolytes. When the value of the charge current is constant as temperature, the charge voltage increases over time due to the increase of the potential electromotive energy. The power of the battery when applying a current of 50 \(\mu\)A per discharge and 100 \(\mu\)A per charge was 0.153 mW and 0.2177 mW, respectively, while for a current of 100 \(\mu\)A per charge, the discharge and the load were  
Figure 11: XRD measurements on NAS 6%-1200 at different pH levels (**a**) 60 days; (**b**) 90 days. 0.273 mW and 0.327 mW, respectively. The energy obtained for a current of 100 mA was 32.2 mW per charge and 22.98 mW per discharge at a current of 50 \(\mu\)A, while for a current of 100 \(\mu\)A, it was 45.98 mW per charge loading and 37.82 mW per unloading (Table 2). The applied current is responsible for increasing the accumulated energy, as shown by the analysis of energy curves. Using a current of 50/100 \(\mu\)A (discharge/charging) was obtained a capacity of 0.10 mAh per charge and 0.19 mAh per discharge while a current of 100 \(\mu\)A was obtained capacity of 0.033 mAh per charge and 0.04 mAh per discharge.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 5 Limitations of Current AGB Maps and Future Improvements
### Data Sources for Forest AGB Mapping  
Although relationships between retrieved forest parameters and biomass have been demonstrated, their correlations vary in regions and are sometimes poor [119]. A full exploration of the relationships between forest biomass and its related parameters at different spatial resolutions as well as the associated factors influencing these relationships should be performed. The detailed relationships could be further utilized to improve the accuracy of AGB mapping.  
Another limitation is that current AGB maps are retrieved mainly from optical data acquired by TM/ETM+ and MODIS sensors, which are known to saturate even at lower biomass and thus unable to acceptably capture high biomass values. The use of SAR and LiDAR data could help improve this problem to some degree, but currently, less than 1/3 of AGB maps are generated with a combination of field biomass, LiDAR data, optical and/or SAR data. The inclusion of SAR and LiDAR data should be fully considered for the future generation of biomass maps on a large scale [120]. Moreover, hyperspectral data look promising for improving the accuracy of biomass estimates, due to their capabilities of providing information on vegetation health and species composition [121; 122]. The use of hyperspectral data in combination of other sensors in future studies should be considered to improve biomass mapping on a large scale.
# Long-Term Stability of Er:YAG Laser Non-Surgical Periodontial Treatment
## 3 Results
One month after laser treatment of the pockets, the level of clinical attachment decreased from 4.71 mm to 3.34 mm. Between the first and third month, a clinical attachment gain of 0.28 mm is observed, which is also significant. In Er:YAG laser-treated pockets, there is some loss of clinical attachment between the third and sixth month, as well as between the sixth and twelfth month, but it is an extremely small difference (0.02 mm and 0.04 mm) with no significant expression. The average gain of a clinical attachment for a period of 1 year is 1.59 mm. In all post-treatment measurements, the reduction in pocket depth in the Er:YAG laser-treated group showed a statistically significant better improvement (\(p\) < 0.05) except for the third-month result (\(p\) = 0.253) (Table 4). The biggest difference of PPD between both groups is found in the first month, where the improvement in the laser group is about 0.20 mm more in comparison to the control group. The PPD reduction at 1 year is with 0.15 mm more in the laser group compared with the control group (\(p\) = 0.013). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**GR**} & \multicolumn{8}{c}{**Laser Group**} & \multicolumn{8}{c}{**Control Group**} \\ \cline{2-13}  & **N** & \(\overline{\pi}\) & **S\({}_{\mathbf{X}}\)** & **Mean Diff.** & **t** & \(p\)**-Value** & **N** & \(\overline{\pi}\) & **S\({}_{\mathbf{X}}\)** & **Mean Diff.** & **t** & \(p\)**-Value** \\ \hline GR—baseline & 572 & 0.14 & 0.49 & \multirow{2}{*}{0.04} & \multirow{2}{*}{2.279} & \multirow{2}{*}{_0.02_} & 527 & 0.09 & 0.41 & \multirow{2}{*}{0.12} & \multirow{2}{*}{6.340} & \multirow{2}{*}{_0.00_} \\ GR—1 month & 572 & 0.18 & 0.52 & & & & & & & & \\ \hline GR—1 month & 572 & 0.18 & 0.52 & \multirow{2}{*}{0.03} & \multirow{2}{*}{1.779} & \multirow{2}{*}{0.08} & 527 & 0.22 & 0.61 & \multirow{2}{*}{0.00} & \multirow{2}{*}{0.069} & \multirow{2}{*}{0.95} \\ GR—3 months & 572 & 0.22 & 0.59 & & & & & & & & & \\ \hline GR—3 months & 572 & 0.22 & 0.59 & \multirow{2}{*}{0.02} & \multirow{2}{*}{0.973} & \multirow{2}{*}{0.33} & 527 & 0.22 & 0.62 & \multirow{2}{*}{\(-\)0.01} & \multirow{2}{*}{0.507} & \multirow{2}{*}{0.61} \\ GR—6 months & 572 & 0.24 & 0.58 & & & & & & & & & \\ \hline GR—6 months & 572 & 0.24 & 0.58 & \multirow{2}{*}{0.01} & \multirow{2}{*}{0.240} & 527 & 0.21 & 0.65 & \multirow{2}{*}{0.01} & \multirow{2}{*}{0.620} & \multirow{2}{*}{0.54} \\ GR—12 months & 572 & 0.23 & 0.61 & & & & & & & & & \\ \hline GR—baseline & 572 & 0.14 & 0.49 & \multirow{2}{*}{0.10} & \multirow{2}{*}{4.325} & \multirow{2}{*}{_0.00_} & 527 & 0.09 & 0.41 & \multirow{2}{*}{0.13} & \multirow{2}{*}{4.959} & \multirow{2}{*}{_0.00_} \\ GR—12 months & 572 & 0.24 & 0.61 & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dynamic changes in gingival recession after treatment with Er:YAG laser (laser group) and hand instrumentation (control group).
# Noble Metal-Based Heterogeneous Catalysts for Electrochemical Hydrogen Evolution Reaction
## 4 Noble Metal-Based Heterogeneous Catalysts
### Interface Engineering
#### 4.2.1 Metal/Metal Core-Shell Nanostructures  
Catalysts with core-shell structure are usually composed of core (one composition) and shell (another composition). For the bimetallic compounds of the core-shell structure, their formation is driven by the difference in the surface free enthalpy between the two metals [97,98]. Usually, the component with less enthalpy is going to be the shell. Because the shell has more exposed surface than the core, it needs higher energy. Among them, the precious metal components can exist in the shell to improve the atomic utilization. The noble metal shell can enhance the catalytic activity through electronic coupling with components in the core at the interface or through the strain effect. The common synthesis method of the core-shell structure is to preferentially grow the seed core, and then the noble metal shell grows on the core with chemical reduction or thermal decomposition. At present, numerous efforts have been made in the preparation of core-shell catalysts to achieve the controlled synthesis of composition, morphology, size and shell thickness. In addition to changing the composition, 0D, 1D and 2D core-shell structures have been developed by controlling the morphology. The 0D core-shell nanostructure mainly consists of nanoparticles and nanopolyhedra. [PERSON] et al. [99] studied Ru-Pt model catalysts with the same ligand effect but different surface geometry. The Ru@Pt core-shell structure with a strain interface has a higher activity than a strain-free alloy (RuPt). The enhancement of HER activity induced by strain in an alkaline electrolyte was larger than that in an acidic electrolyte, ascribing to a more significant interaction between the catalytic intermediates and OH- caused by compressive strain. Therefore, the basic HER activity can be improved by the strain at the core-shell interface. The 1D core-shell nanostructure mainly contains nanorods, nanowires and nanoribbons. [PERSON] and co-works [83] used the Au nanoribbons as templates, on which other noble metals such as Ag, Pd, and Pt with lattice mismatch less than 5% were epitaxially grown. They then obtained a series of core-shell nanoribbons, including Au@Ag, Au@PdAg (Figure 7a-d), Au@PtAg, and Au@PtPdAg. Among them, Au@PdAg nanoribbons show excellent HER electrocatalytic activity (\(\eta_{10}=26.2\) mV, Tafel slope = 30 mV dec\({}^{-1}\)) and durability (nearly no loss of catalytic activity after 10,000 cycles) (Figure 7e-g). Its
## 2 ARD Framework
### Processing Pipelines
#### 2.4.1 Sentinel-1 Product Processing  
Sentinel-1 products must be processed before they are analysis-ready [52]. The framework adopts a standard processing workflow for GRD products [53] and best practices for the preparation of Sentinel-1 SAR data for the data cube [54]. Informed by reported work, the processing pipeline for Sentinel-1 products implemented in the framework is shown in Figure 3. The Level-1 GRD product must be radiometrically calibrated so that acquisitions captured on different dates can be compared with consideration of the global incidence angle, converting the radar backscatter intensity to the normalised radar cross-section (\(\sigma_{0}\)). SAR data are impacted by'salt-and-pepper' speckle noise and, although speckle filtering is not a requirement for ARD [40], it is nevertheless performed on the calibrated normalised radar cross-section to minimise further post-processing. Speckle filtering is performed using a Lee-Sigma filter with a window size of 7 \(\times\) 7, \(\sigma\) of 0.9 and target window size of 3 \(\times\) 3 [55]. Range Doppler terrain correction is then executed using a Shuttle Radar Topography Mission Height (SRTM) 1 sec HGT Digital Elevation Model (DEM) [56] to remove geometric distortions such as foreshortening, layover and shadow. For illustration, the evolution of the VV band of SAR data through the different stages of the pipeline is shown in Figure 4. The final two steps in the Sentinel-1 processing pipeline are clipping to the required area and the band extractor operation, which selects the bands required in the final product. The cropping of the satellite image to the ROI is implemented in two steps; the subset operator in SNAP crops the tile to the bounds of the ROI to bring down the size of the raster file and reduce the processing time in SNAP, followed by cropping of the image to the exact cut-line of the ROI using the Python Geospatial Data Abstraction Library (GDAL) [57].  
Figure 3: Processing pipeline for Sentinel-1. The suggested framework implements this pipeline using the Graph Processing Tool (GPT) of the ESA SNAP toolbox.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 5 Results
### Explosions
#### 5.3.1 Baumgarten Natural Gas Hub Technical Failure  
A dangerous incident in Central Europe was a sudden gas release together with a serious fire in Baumgarten, Austria. On 12 December 2017, a technical failure in the natural gas hub effectively resulted in an explosion, which could be detected with a seismic array [34]. In order to investigate this precisely timed accident from LEO altitudes the box size has to be at least \(\pm 3.5\) deg in lat/long to have a minimum of two satellites in the time span \(\pm 1\) day around the event. There are small magnetic field variations in the traces, see Figure 7 (shown for _Swarm_ A VFM after wavelet analysis), in particular an enhancement in the Y-component, but the time- and distance-gap between the satellite traces and the location is too large to assign these features to this event.
## 3 Results and Discussion
It is possible to notice that, as supposed, pore size range and pore fraction are equal for every cumulative curve, yet they have different pore size distributions. The remarkable capability of the model to reproduce experimental data is confirmed by the calculated specific surface (Equation (8)), which is equal to 73 \(\pm\) 7 m\({}^{2}\)/g vs. about 73 \(\pm\) 1 m\({}^{2}\)/g, obtained by BET. Figure 4: SEM-SBF 3D reconstruction of cross-section profile of NP Al; scale bar: 20 \(\upmu\)m.  
Figure 5: Representative scheme of IFU model working structure. The capability of the fractal model to describe porous structures allows using it for predicting elastic properties [25]. Based on Equation (10), the Young's modulus is estimated to be about 18 \(\pm\) 2 GPa. As mentioned above, the first experimental estimation of elastic behavior for NP Al is reported in this paper. Mechanical tests allow estimation of the value of the nanoindentation Young's modulus which is equal to 18.3 \(\pm\) 1.4 GPa, which is very close to that predicted by the model. \begin{table}
\begin{tabular}{c c c c} \hline  & & **E1-1** & **E1-2** & **E1-3** \\ \hline \multirow{5}{*}{Unit A} & D\({}_{\text{f}}\) & 1.89 & 1.89 & 1.77 \\  & n & 1 & 1 & 1 \\  & R\({}_{\text{max}}\) (\(\mu\)m) & 16.5 & 16.5 & 16.5 \\  & Iteration & 10 & 10 & 10 \\  & R\({}_{\text{min}}\) (\(\mu\)m) & 0.001 & 0.001 & 0.001 \\  & Solid forever & 0 & 1 & 2 \\ \hline \multirow{5}{*}{Unit B} & D\({}_{\text{f}}\) & 1.77 & 3 & 1.89 \\  & n & 261 & 375 & 422 \\ \cline{1-1}  & R\({}_{\text{max}}\) (\(\mu\)m) & 1.83 & 1.83 & 1.83 \\ \cline{1-1}  & Iteration & 9 & 9 & 9 \\ \cline{1-1}  & R\({}_{\text{min}}\) (\(\mu\)m) & 0.2 & 0.2 & 0.2 \\ \cline{1-1}  & Solid forever & 5 & 0 & 2 \\ \hline \end{tabular}
\end{table} 
Table 2: Input data of fractal models for complete porous microstructure of NP Al. Figure 6: (a) Comparison between porosimetric curves obtained by IA and Fractal modeling; (b) model pore cumulative curves for typical NP Al, E1-1 (\(\bullet\)), E1-2 (\(-\)), E1-3 (\(-\)), whose input data are reported in Table 2.