esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-GeV Laser Wakefield Electron Acceleration with PW Lasers
## 3 Multi-GeV LWFA with PW-Class Lasers
### LWFA with Texas PW Laser  
The PW laser at the University of Texas at Austin was developed by implementing the hybrid OPCPA scheme with Nd:glass laser amplifiers and had a pulse duration of 140 fs and pulse energy of 140 J [16]. The laser has been used for various laser-plasma experiments such as electron acceleration, ion acceleration, and neutron generation. Especially, the electron energy of 2 GeV was successfully demonstrated [11]. The experiment was performed by focusing the PW laser pulses with a spherical mirror having an f-number of 47 onto a 7-cm-long helium gas cell. The accelerated electron beam was dispersed by a 6.7-cm-long dipole magnet having a field strength of 1.1 T. Fiducial arrays made of tungsten wires were inserted between the magnet and the detection screens to measure the electron energy correctly. The beam cross-section at the focus was not optimal: the intensity profile was asymmetric and had several spots.  
The experimental results showed that the PW laser pulses produced electron beams with energy over 2 GeV, as shown in Table 1. According to the energy formula (1), the electron energy of 2 GeV is expected for the PW laser's power and a plasma density of \(5\times 10^{17}\) electrons/cm\({}^{-3}\). The electron energy was lower at lower plasma densities, which is opposite to the prediction from (1). This behavior can be attributed to the poor focal spot. A low-quality focal spot with internal structures may be beneficial to induce self-injection in such a low-density plasma but deteriorates the laser propagation and electron acceleration. It was pointed out that the spatial shaping of the PW laser pulse is necessary to enhance the electron energy.
## 4 Discussion
### Comparison Between S-Curve Based Model and Time Series Analysis-Moving Average Model  
A case is investigated using a simple time series analysis-moving average method to predict the eroded soil area in Equation (7) [24]. This method in Equation (7) uses the average of the previous three values as the prediction value.  
\[\mathit{s_{t}}\,\text{'}=\frac{\mathit{s_{t-1}}+\mathit{s_{t-2}}+\mathit{s_{t-3 }}}{3},t\geq 4 \tag{7}\]  
where \(\mathit{s_{t}}\,\text{'}\) is the output object of the prediction model at time \(t\). \(\mathit{s_{t-1}}\), \(\mathit{s_{t-2}}\), and \(\mathit{s_{t-3}}\)are the observation data at time \(t\)-1, \(t\)-2, and \(t\)-3, respectively. The result is as shown in Figure 15.  
Figure 14: Dimensionless plots of soil erosion area: (**a**) Soil erosion under different rainfall intensity conditions; (**b**) soil erosion under different rainfall intensity conditions (fitting curve); (**c**) soil erosion under different material conditions; and (**d**) soil erosion under different material conditions (fitting curve).  
Firstly, we did relative error analyses for the S-curve prediction model and moving averages model, as shown in Figure 16 (for the case of sample1 under 25 mm/h rainfall). The average relative error of all the experiments is 3%-9.7% for the S-curve prediction model. For the moving averages model, the experimental average relative error is 5.7%-12.1%. The S-curve prediction model includes information on the S-curve trend, and can thus describe the whole process, especially the changes of phase. On the other hand, the moving averages model, whose prediction depends on the previous data, cannot simulate the sudden change of Phase 1 to Phase 2. Thus, in the second stage, error exists all the time until the final stage, reducing the increase of area. It has to be pointed out that the prediction accuracy of the model still depends on the number of predictions. The greater the number of predictions from the model, the lower its accuracy is, while the cost of accurate prediction is a smaller number of forecasts. Secondly, the moving averages model is like a \"black box\". Its use only involves giving the input and getting the output without any clear physical meaning and process. In our study, the S-growth curve model framework needs the rainfall intensity and cumulative rainfall (which can be obtained from the rainfall intensity and time) as parameters. It can also describe the three phases of eroded soil area growth from the physical angle. In addition, the model considers different soil materials affecting the erosion area.  
Figure 16: Relative error analyses of S-curve prediction model and moving averages model (Sample 1-25 mm/h).  
Figure 15: Prediction of moving averages method (Sample 1-25 mm/h) (\(n=174\)).
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Alpha-Particle/Helium-Ion Irradiation
Upon the He\({}^{2+}\) particle irradiation at different doses, a new PL peak appeared at 1.78 eV and the integrated intensity of this new peak increased with the irradiation dose. The new peak became stronger and broader after thermal annealing at 500 \({}^{\circ}\)C, which thermally introduced sulfur vacancies. The integrated intensity of the main PL peak at 1.90 eV increased three-fold while the PL peak position shifted to a higher energy by 20 meV after the irradiation. Calculations estimated that approximately one defect per 100 unit cells was generated under the \(8\times 10^{13}\) ions/cm\({}^{2}\) irradiation dose. [PERSON] et al. [91] employed 30 keV He\({}^{2+}\) beams to irradiate MoS\({}_{2}\) mono-layers on SiO\({}_{2}\)/Si substrates under various doses of \(10^{12}\)-\(10^{16}\) ions/cm\({}^{2}\) in a scanning helium-ion microscope. Helium-ion bombardments affected the intrinsic vibrational, luminescent, and valleytronic properties of the atomically thin MoS\({}_{2}\) sheets. [PERSON] et al. [92] grew MoS\({}_{2}\) mono-layers on SiO\({}_{2}\) substrates by a CVD technique and irradiated them with He\({}^{2+}\) at an energy of 30 keV and an angle of incidence of 0\({}^{\circ}\). These irradiation doses ranged from \(1\times 10^{13}\) ions/cm\({}^{2}\) to \(1\times 10^{17}\) ions/cm\({}^{2}\). With increasing He\({}^{2+}\) ion doses, the two characteristic Raman peaks quenched and broadened, reflecting the growing disorder with increasing irradiation. A new peak appeared at 227 cm\({}^{-1}\) after the irradiation and its intensity increased with increasing doses. Helium-ion beams were also employed to mill MoS\({}_{2}\) layers [121]. Some regions of MoS\({}_{2}\) few-layers were thinned to mono-layers under 30 keV He\({}^{2+}\) sub-nanometer ion beams. Figure 12: HRTEM image of (**a**) pristine and (**b**) He\({}^{2+}\)-irradiated (\((1.00\pm 0.02)\times 10^{18}\) He\({}^{+}\)/cm\({}^{2}\)) freestanding MoS\({}_{2}\) few-layers [89]. (**c**) Stoichiometry of an MoS\({}_{2}\) flake under different He\({}^{2+}\) doses as calculated from EDX analysis. (**d**) HRTEM images of crystalline (left panel) and amorphous (middle and right panels) MoS\({}_{2}\) nanoribbons fabricated from He\({}^{2+}\) milling. He\({}^{2+}\) energy: 30 keV. Reprinted with permission from Reference [89]. Copyright © 2015 American Chemical Society.
## 4 Discussion  
This study showed that atmospheric processes in the urban environment have a complex structure reflected in the precise numerical values of air parameters. The model demonstrated that air speed varies from \(\approx\)0.3 m/s to \(\approx\)5.8 m/s, with an average value of about 2.1 m/s and a standard deviation of 1.2 m/s. The vertical profile revealed a gradual increase in speed from the surface level (about 1.7 m/s) to the upper layers of the lower atmosphere (up to 2.5-3 m/s).  
Temperatures range from \(\approx\)12.0 \({}^{\circ}\)C to \(\approx\)19.5 \({}^{\circ}\)C, with a mean of about 16.1 \({}^{\circ}\)C and a standard deviation of 1.1 \({}^{\circ}\)C. Horizontal slices showed that the temperature field is characterized by a high value at the surface level (about 16.8 \({}^{\circ}\)C) followed by a decrease to about 15.5 \({}^{\circ}\)C at an altitude of 500 m.  
Concentrations of pollutants vary from \(\approx\)5 \(\upmu\)g/m\({}^{3}\) to \(\approx\)100 \(\upmu\)g/m\({}^{3}\), with a mean of about 45 \(\upmu\)g/m\({}^{3}\) and a standard deviation of about 20 \(\upmu\)g/m\({}^{3}\). The distribution of pollutants shows an exponential decrease with altitude, with the central region characterized by higher values (mean about 50 \(\upmu\)g/m\({}^{3}\)) and the periphery significantly lower (\(\approx\)30 \(\upmu\)g/m\({}^{3}\)).  
Compared to studies like [9] and [7], which focus on AI-driven temporal forecasting of air quality indices, our model provides detailed spatial insight into pollution dynamics--especially in the vertical direction. Unlike simplified Gaussian plume models or 2D statistical interpolations, our approach resolves full Navier-Stokes dynamics, accounting for terrain-induced wind shear, temperature inversion, and surface friction effects.  
Moreover, while chemical transport models such as CMAQ handle pollutant chemistry well, they often lack the fine-grained spatial resolution and computational tractability for local zoning applications. Our method balances physical fidelity and computational efficiency, enabling practical urban-scale simulations over hourly timescales and capturing fine-scale pollution hotspots and vertical decay profiles relevant for public health and architectural design.  
Vertical stratification of the atmosphere shows that with increasing altitude, air velocity increases while temperature and pollutant concentrations decrease. This indicates effective vertical mixing of air, which helps reduce pollution at high levels.  
The horizontal distribution shows that the central part of the region has higher concentrations of pollutants, lower wind speed, and slightly higher temperatures compared to the periphery. This indicates the presence of local sources of pollution and insufficient ventilation in the central region.
# Experimental Simulation of Lightning Current Discharge on Rocks
## 2 Materials and Methods
### Experimental Hardware Setup  
The experimental simulation of the lightning current discharges was conducted in the High Voltage Laboratory at Drexel University's Center for Electric Power Engineering (CEPE). The controlled experimental environment described here allowed for non-destructive, repeatable testing. Data were collected using typical electronic lab equipment.  
A high voltage alternating current (AC) power source was connected across a set of electrodes aligned on either side of the target rock specimen. Target rock specimens were placed on a conductive brass base below a tungsten welding tip (Figure 1).  
The AC equipment used in this experiment was rated for up to 15 kVA and 150 kVRMS. A non-conducting vessel was designed for safety and to contain any fragments of the material that may be blown apart during the test. Figure 1 shows a circuit diagram of the electrical setup used in the experiments. The experiment was conducted at atmospheric temperature and pressure. Sample A0 was used to determine the fixed distance between the electrodes for all experiments. With sample A0 on the base electrode, the applied voltage was gradually increased using an autotransformer until an either (i) an arc passed through the sample (i.e., a \"fault\" occurred), or (ii) the upper voltage rating of 150 kV was reached. In the latter case, the laboratory was de-energized, and the distance between the electrodes was decreased (step length = 10 cm). The final distance between electrodes was fixed at 30 cm. Accounting for sample thicknesses, the vertical distance between the welding tip and the target specimens was fixed at 28.9 cm for samples A1-A4 and 26.5 cm for samples B1 and B2.  
Voltage and current waveforms were sensed on the primary (low voltage) side of a 440:150,000-V single-phase transformer (assumed ideal with no losses) and referred to the secondary (high voltage) side of the transformer (Figure 2).  
Figure 1: Circuit diagram for high voltage electrical arc discharge experiments. The 60 Hz AC voltage source magnitude was increased until an arc formed. This tripped the circuit breakers, which trip at 28 A (referred to the portion of the circuit show). The 0.44:150 kV transformer was assumed to be ideal. Measurements were taken on the primary side of the transformer.  
Measurement equipment included: (i) Tektronix DPO 3014 oscilloscope (Tektronix, Beaverton, Oregon, USA); (ii) Tektronix TCP 303 current probe; (iii) Tektronix TCPA 300 current amplifier; and (iv) Tektronix P5200A differential voltage probe.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 4 Discussion
Regarding the comparison with the Copernicus Global Land Cover and the WorldCover by ESA, the results indicate reasonable agreement between the three land covers, albeit with a somewhat low Kappa coefficient attributed to some discrepancies. These discrepancies may arise from the higher resolution of our product, allowing for the differentiation of various urban cores within cities and the recognition of different vegetation types in between, while the other products represent cities as a single compact urban area, as can be noticed in Figure 12. Furthermore, the non-exact correspondence of the classes used in the different land covers certainly has an effect on the inaccuracy of the comparison. Finally, it is noteworthy that both the Copernicus Global Land Cover and the ESA WorldCover utilize products from throughout the year to derive the final land cover. In contrast, our study has focused solely on the summer of each year. This distinction in temporal scope may contribute to differences observed between the land covers, as seasonal variations can affect land cover characteristics. Considering these differences, we believe that the results obtained are reliable, and we are satisfied with the accuracy achieved.
## 3 Materials
### Field Data  
A continuous LAI surface observation experiment was performed in the experimental area during the maize-growing season from 2013 to 2014. For the LAI measurements, LAINet was used in 2013, and the LICOR LAI-2000 Plant Canopy Analyzer was used in 2014. LAINet is an automatic device that obtains multi-angle transmittance of the vegetation canopy during the day following the sun's movement, based on wireless sensor network (WSN) technology, and then calculates the crop LAI from multi-angle transmittance using a vegetation gap probability model. one advantage of LAINet is its ability to provide continuous observations of the vegetation growth process. When the LAI is greater than 3.5, the measured LAINet values may be more accurate than the LAI-2000 values. For the design, the algorithm and validation of LAINet were fully described and tested in previous studies [40; 41]. There are four ground samples of measurement during the maize growing season for each year, and the sample size is 30 \(\times\) 30 m. We selected five points in each sample to perform LAI measurements and took the average to represent the LAI result of the sample. The spatial distribution of the five measurement points consists of four corner points and a center point.  
In 2013, the value of the observed sample time series from Julian days 185 to 233 was calculated [42] (Figure 4a). Since the weather changes may cause errors in the measurement results of the LAI, the mean value in the 8-day interval was taken as the LAI value of the sample. In Figure 4a, the dotted black line indicates the LAI timing sequence variations, and the vertical line represents the standard deviation of measurement results for every sampling point. Figure 4a shows a significant growth trend from Julian days 185 to 210; subsequently, the LAI curve gradually becomes flat and stable with a maximum of approximately 3.5.  
In 2014, through observations from Julian days 185 to 247 in a single area, the LAI tendency of maize was obtained. From Julian days 185 to 210, the LAI rapidly increased; subsequently, the maximum LAI value was attained, and the LAI gradually became stable at a maximum value near 3; finally, after day 240, the LAI began to decline.
# Deepwater 3D Measurements with a Novel Sensor System
## 4 Discussion
[39] uses a color camera with a fisheye lens and is suitable for the extensive recording of the seabed at a depth of up to 6000 m. It can cover a much larger area than our system in a shorter time. It also enables 3D capture, although with significantly lower resolution and accuracy.
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis was carried out in multiple stages using Statistica v. 12 software (Tibco StatSoft Inc, Tulsa, OK, USA). The Shapiro-Wilk test was applied to assess the normality of the distribution of the spatiotemporal parameters and extreme values.  
In the first stage, ANOVA and Tukey HSD post-hoc tests were utilized to determine whether significant differences existed between the means of the spatiotemporal parameters for three groups of data: the perturbed limb, the reactive limb, and the treadmill gait without perturbations. Additionally, the partial eta-squared (\(\eta_{p}^{2}\)) was reported as a measure of the effect size (Table 1), representing the proportion of variance in the dependent variable explained by the independent variable, while controlling for other factors in the model [28].  
The second stage involved evaluating whether joint angles and torque curves differed between the treadmill gait and the perturbed and responsive limbs. This step employed ANOVA and non-parametric post-hoc tests using statistical parametric mapping (SPM) [30]. The SPM1D package ([[https://www.spm1d.org](https://www.spm1d.org)]([https://www.spm1d.org](https://www.spm1d.org)), accessed on 29 September 2024) was used for the analysis. For each SPM ANOVA, a statistical parametric map, SPM[F], was generated by calculating the univariate F-statistic across the gait curve [30]. When the SPM[F] exceeded the threshold for a 0.95 confidence level, post-hoc SPM[t] maps were created to compare individual pairs of variables. Significant differences between motion tasks were identified when the SPM[t] map crossed the critical threshold.  
All statistical analyses were performed using Matlab R2021a (MathWorks, Natick, MA, USA). A similar methodology has been reported in previous studies [31; 32].  
The third step involved comparing extreme values and ranges for the kinematic and kinetic parameters between the treadmill gait, the gait contacting the perturbation, and its response. This was resolved using an ANOVA and Tukey HSD post-hoc test as in the first step.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Value** & **Effect Size** \\ \hline
0.01 & Small/1\% of variance explained \\
0.06 & Moderate/6\% of variance explained \\
0.14 & Large/14\% of variance explained \\ \hline \hline \end{tabular}
\end{table}
Table 1: Interpretation of the partial eta-squared values [29].  
Figure 3: An example of the vertical component of ground reaction force (vGRF) for a single participant. (**A**) The vGRF for the perturbed limb and the responding limb during a trial in which perturbations occurred every ten seconds during the pre-swing phase; (**B**) the vGRF in the absence of perturbations; (**C**) the vGRF illustrating the presence of perturbations; and (**D**) the vGRF displaying the response to the perturbations.
# Characterization of Heat-Polymerized Monomer Formulations for Dental Infiltrated Ceramic Networks
## 2 Experimental  
### Materials  
The dental monomers BisGMA, UDMA, and TEGDMA and heat-initiator benzoyl peroxide (BPO) were used as provided by a dental manufacturer (VOCO, Germany). While volume percentage or mole fraction would be chemical convention, the study refers to them in weight percentage as this is the measure given in all experimental PICN studies. BPO was kept constant at 1 wt%. Different weight ratio mixtures of BisGMA:TEGDMA were 9:1 (89.43 vol%:10.57 vol%), 8:2 (79.00 vol%:21.00 vol%), 7:3 (68.70 vol%:31.30 vol%), 6:4 (58.5 vol%:41.48 vol%), 5:5 (48.47 vol%:51.53 vol%), 4:6 (38.54 vol%:61.46 vol%), 3:7  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Year** & **Rigid Network** & **Monomer/s** & **Initiator** & **Polymerization** & **Pressure (If** \\  & & & & & **Protocol** & **Applicable)** \\ \hline [PERSON] et al. [4] & 2013 & Feldspar & UDMA:TEGDMA & BPO & & \\ [PERSON] et al. [14] & 2013 & Alumina & Epoxy & & 40 \({}^{\circ}\)C 24 h & \\ [PERSON] et al. [15] & 2013 & Alumina & UDMA:TEGDMA 1:1 & 0.3 vol% BPO & 100 \({}^{\circ}\)C 2 h & 280 MPa \\ [PERSON] et al. [12] & 2013 & ZO2 & MMA & BPO & & \\ [PERSON] et al. [11] & 2014 & Yzr & PMMA & BPO & 85 \({}^{\circ}\)C, 20 h & \\ [PERSON] et al. [13] & 2014 & Albite glass & UDMA & 0.5 wt\% di-tert-amyl & 180 \({}^{\circ}\)C & 300 MPa \\ [PERSON] et al. [7] & 2016 & Feldspar (potassium, sodium, calcium) & BisGMA:TEGDMA 1:1 & BPO & 70 \({}^{\circ}\)C 8 h, & \\ [PERSON] et al. [17] & 2017 & Sodium aluminium & BisGMA:TEGDMA 1:1 & 1 wt\% BPO & 70 \({}^{\circ}\)C 16 h & \\ [PERSON] and [PERSON] [40] & 2017 & Zirconia & UDMA:TEGDMA 1:1 & & 180 \({}^{\circ}\)C & 300 MPa \\ [PERSON] et al. [10] & 2017 & 3Y-TZP & BisGMA:TEGDMA 1:1 & BPO & 70 \({}^{\circ}\)C 10 h & \\ [PERSON] et al. [5] & 2017 & Sodium aluminium & BisGMA:TEGDMA 1:1 & 1 wt\% BPO & 70 \({}^{\circ}\)C 8 h, & \\ [PERSON] et al. [9] & 2018 & Albite glass & UDMA & 0.5 wt\% di-tert-amyl & 180 \({}^{\circ}\)C & 300 MPa \\ [PERSON] et al. [3] & 2018 & Alumina & UDMA:TEGDMA 1:1 & 1 vol\% BPO & 50 \({}^{\circ}\)C 2 h, 60 \({}^{\circ}\)C & \\  & & & & & 90 \({}^{\circ}\)C 12 h & \\ [PERSON] et al. [8] & 2019 & Potassium & BisGMA:TEGDMA 1:1 & 1 wt\% BPO & 70 \({}^{\circ}\)C 8 h, 110 & \\ [PERSON] et al. [16] & 2019 & Silicon nitride & MMA & 1 wt\% BPO & & \\ [PERSON] et al. [6] & 2020 & Sidium aluminium & & & & \\  & & silicate & BisGMA:TEGDMA 1:1 & 1 wt\% BPO & 70 \({}^{\circ}\)C 8 h, 110 & \\  & & nanohydroxyapatite & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature review on relevant monomer formulations for resin infiltration of porous ceramic networks.  
(28.73 vol%:71.27 vol%), 2:8 (19.04 vol%:80.96 vol%), and 1:9 (9.46 vol%:90.54 vol%). Weight ratios mixtures of UDMA:TEGDMA were 9:1 (89.85 vol%:10.15 vol%), 8:2 (79.74 vol%:20.26 vol%), 7:3 (69.66 vol%:30.34 vol%), 6:4 (59.61 vol%:40.39 vol%), 5:5 (49.59 vol%:50.41 vol%), 4:6 (39.61 vol%:60.39 vol%), 3:7 (29.66 vol%:70.34 vol%), 2:8 (19.74 vol%:80.26 vol%), and 1:9 (9.85 vol%:90.15 vol%).
## 4 Experimental Results of the Proposed Systems
### Results of ANN Classifier Based on Fusion of Features  
This section summarizes the results of the performance of the ANN classifier based on the two X-ray datasets for early diagnosis of tuberculosis based on a set of hybrid features extracted by several techniques. This technique aims to extract the features using CNN models (ResNet-50 and GoogLeNet) and combine them with the shape and texture features extracted by the GLCM, DWT and LBP algorithms, then feed them into an ANN classifier. It is worth noting that both the ResNet-50 and GoogLeNet models produced 2048 deep features; the high dimensionality was reduced by the PCA algorithm, which reduced the dimensions of the deep features to 512 features and stored them in feature  
Figure 8: Confusion matrix for the classification of the second dataset of tuberculosis using the hybrid approaches. (**a**) ResNet-50 + SVM. (**b**) GoogLeNet + SVM.  
Figure 7: Confusion matrix for the classification of the first dataset of tuberculosis using the hybrid approaches. (**a**) ResNet-50 + SVM. (**b**) GoogLeNet + SVM.  
vectors. The features extracted by the GLCM, DWT and LBP algorithms were combined to produce 228 features and stored in feature vectors. Finally, the features of CNN models were combined with the features of the GLCM, DWT and LBP algorithms, which produced 740 representative features. Thus, the size of the first dataset was 662 \(\times\) 740, while the size of the second dataset was 4200 \(\times\) 740, and these were fed into an ANN classifier to classify them with high precision. This section summarizes a set of tools for evaluating the performance of the ANN classifier based on the two tuberculosis datasets.