esa-sceva/satcom-chunk-collection huggingface dataset:
# A Numerical Analysis on Lateral Resistance of Pile-Bucket Foundation for Offshore Wind Turbines
## 2 Finite Element Model
### Modeling of Pile Bucket Foundation  
The steel (Q345C) of the foundation is modeled as an ideal elastoplastic material. It has a yield strength of 325 MPa, Young's modulus \(E\) = 2.1 \(\times\) 10\({}^{8}\) kPa, and Poisson's ratio (\(v\)) 0.25. The pile diameter (\(d\)) is chosen as 6 m, which is frequently used in practical engineering. The pile length (\(l\)) is 80 m (the depth embedded into the soil is 60 m). The thickness of the pile wall (\(\delta\)) is around 1% of the pile diameter; thus, it is 6 cm. Due to the bucket performed as a reinforcement structure, in order to save material and improve its economic benefits, the thickness of the bucket wall (\(\delta\)) is 2 cm. The physical parameters are summarized in Table 1.  
The diameter (\(D\)) and height (\(h\)) of the bucket are shown in Table 2. The width of the soil in the model is far greater than the diameter of the pile and bucket (length \(\times\) width \(\times\) depth = 150 \(\times\) 150 \(\times\) 80 m). Thus, the influence of the boundary can be neglected. The Mohr-Coulomb constitutive model (M-C model) is used. The physical parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Yield** & **Pile** & **Pile Length** & **Thickness of** & **Young’s** & **Poisson’s** \\
**Strength** & **Diameter** & **Pile Wall** & **Modulus** & **Ratio** \\ \hline \(f_{y}\)/MPa & \(d\)/m & \(l\)/m & \(\delta\)/cm & \(E\)/MPa & \(v\) \\
325 & 6 & 80 & 2 & \(2.1\times 10^{8}\) & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of the foundation.  
Figure 2: Location of the offshore windfarm.
###### Abstract  
The impacts of anthropogenic land use and land cover (LULC) changes on the spatiotemporal distribution of precipitation in megacities have been highlighted in studies on urban climate change. In this study, we conducted a quantitative analysis of urban growth on the impact on precipitation in Shanghai, China. We considered four periods of LULC data in 1979, 1990, 2000 and 2010, in addition to the long-term (1979-2010) trend of daily precipitation. The results indicate that the trend in precipitation exhibit different characteristics for urban (Ur), outskirt of urban (OUr) and outer suburb (OS) regions. Most Ur regions had an upward trend in annual and extreme precipitation during 1979-2010, while annual precipitation for the OUr and OS regions exhibited a decreasing trend. From 1979 to 2010, the areas of fastest expansion were located in the OUr region. The OS region, far away from the central area, had a relatively lower rate of change. In addition, OUr regions with rapid LULC changes exhibited higher increasing trends in annual and daily extreme precipitation, which is critical for the identification of frequent precipitation areas and the reliable projection of further changes.  
Keywords:land use and land cover; precipitation trends; precipitation extremes; built-up area; Shanghai +
Footnote †: journal: Applied Sciences
## 3 Results
### Photoluminescence (PL) Spectroscopy  
Figure 2 shows the PL spectra obtained from three specific regions on the CdTe surface, characterized by RMS roughness values of 307 nm (green line), 467 nm (blue line), and 602 nm (red line). These regions were chosen for detailed analysis because of the significant differences in their respective PL emission spectra.  
PL spectra exhibited a peak centered at 1.5 eV, consistent with the material's bandgap energy. Notably, the peak position remained invariant across different surface roughness values. Conversely, PL intensity was significantly influenced by surface morphology. The spectrum acquired from the region with an RMS roughness of 307 nm displayed the highest signal intensity. To facilitate comparison, the spectra obtained for RMS values of 467 nm and 602 nm were normalized by factors of 1.67 and 22.8, respectively, relative to the 307 nm spectrum. Furthermore, a clear correlation was observed between spectral linewidth, quantified by the full width at half maximum (FWHM), and surface roughness.  
As shown in Figure 2, increasing surface roughness led to a broader photoluminescence emission spectrum. The measured FWHM values for RMS roughness of 307 nm, 467 nm, and 602 nm were 38.42 meV, 44.98 meV, and 69.35 meV, respectively. This observed correlation between FWHM and surface roughness in CdTe is graphically illustrated in the inset of Figure 2. To the best of our knowledge, the correlation between the FWHM of the PL spectra and the RMS roughness of the CdTe surface is a novel finding.  
Figure 1: Atomic force microscopy (AFM) images of cadmium telluride (CdTe) regions with various root-mean-square (RMS) values: (**a**) 602 nm, (**b**) 522 nm, (**c**) 467 nm, (**d**) 390 nm, (**e**) 307 nm, and (**f**) 223 nm.
# Sand Production Characteristics of Hydrate Reservoirs in the South China Sea
## 2 Research Status Worldwide
### Numerical Simulation Study of Sand Extraction from Gas Hydrate in the Sea Area
COMSOL Multiphysics was also used to simulate the gas-water production and mud-sand spalling behaviour of hydrate-bearing sediments under different buckling scenarios at a laboratory scale [29], and the results showed that the contribution of sand spalling behaviour to the expansion of the pore fluid transport space during hydrate disintegration was only 0-13.49%, and the minimum contribution of hydrate disintegration behaviour was 86.51%. The existing numerical simulation of hydrate mining out of sand is still in the exploratory stage due to the lack of clarity on the mechanical fracturing mechanism of hydrate mud chalk reservoirs. It is easy to ignore the change of fluid-solid transport channels triggered by inhomogeneous strata under different hydrate saturation levels, as well as the carrying effect of high-yield gas flow on mud and sand, thus restricting the reliability of the simulation results in guiding the engineering practice.
# A Detailed One-Dimensional Hydrodynamic and Kinetic Model for Sorption Enhanced Gasification
## 2 SEG Process Description and Model Development
### Sorbent Deactivation  
If limestone is subjected to several calcination-carbonation cycles, its CO\({}_{2}\) sorption capacity is reduced, due to sintering phenomena on the particle surface [43]. For limestone, Grasa and Abanades [44] described the decay of the CO\({}_{2}\) carrying capacity \(X_{N}\) as  
\[X_{N}=\frac{1}{\frac{1}{1-X_{r}}+kN}+X_{r}. \tag{34}\]  
Equation (34) depends on the number of calcination-carbonation cycles \(N\) and uses the empirical constants \(k\) = 0.52 and \(X_{r}\) = 0.075. However, in a fluidized bed system, particles have different residence times, which leads to a distribution of the average CO\({}_{2}\) carrying capacity. According to the references [45; 46; 47], an average carrying capacity \(X_{ave}\) is calculated by a population balance:  
\[X_{ave}=\sum_{N=1}^{\infty}\frac{F_{0}}{F_{R}}\Big{(}1-\frac{F_{0}}{F_{R}}\Big{)} ^{N-1}\cdot X_{N} \tag{35}\]  
In the current study, Equation (35) was integrated into the model to calculate the average CO\({}_{2}\) carrying capacity of the particle system with regard to the carbonation reaction (Table 1, Reaction 4). Therein, \(F_{R}\) is the molar-based flow of \(\dot{M}_{RegOut}\), describing the circulation flow of CaO from the regenerator into the gasifier. To compensate mass losses, mostly due to attrition, the reactor inventory was maintained by an input flow of raw limestone. In Equation (35), this input flow \(F_{0}\) was considered on a molar basis. As, in practice, the measured material flow of fresh limestone (by dosing units) contains particles which are small enough to be directly discharged, especially when feeding into the regenerator with fluidization velocities up to 5.5 m/s [11], it was assumed that \(F_{0}\) only represents the effective material flow that remains longer in the system. Fresh limestone and purge material are not considered in the mass and energy balance calculations, as their flow rates are very low compared to the circulating CaO mass flow. If this should be taken into account, detailed information is needed regarding the size of the particles and their degree of calcination when they are directly discharged from the regenerator due to their hydrodynamic properties.
# High-Precision Tracking of Free-Space Optical Communication System on Mobile Platforms
## 1 Introduction
The system captures the light spot through the coarse sighting structure electro-optical pod and realizes alignment and tracking through the internal precision structure precise targeting load. On the moving platform, the system collects the spot miss in real time through the internal detector and compensates the miss by controlling the coarse sight structure and the precision sight structure so as to realize the high-precision alignment. The system also adopts a miniature tubular folding optical path design, which effectively reduces the volume of the equipment and ensures the tracking speed and tracking range.
## Appendix E Hybrid-DSPEB Model
### Hybrid H-DSPEB Preliminary Results  
In this section, we present our preliminary results using H-DSPEB and we evaluate its performance in a comparison to the results of DSPEB submodels and S2 Shores presented in previous sections.  
Figure A5 presents two example results obtained using H-DSPEB in the French Guiana site. The pattern produced by H-DSPEB in both single-date and composite estimates suggests that the model learns to rely on the C-DSPEB submodel more than W-DSPEB. We presume this is due to the higher accuracy of C-DSPEB on the training and validation sets compared to W-DSPEB in French Guiana, as can be seen in Figure 7, which could be leading the H-DSPEB model towards the same minima as C-DSPEB. However, a comparison to the DSPEB submodels' results presented in Figure 8 shows that the hybrid model H-DSPEB does improve the accuracy estimation over the individual DSPEB submodels in cases where C-DSPEB is more accurate than W-DSPEB, suggesting that H-DSPEB does make use of the W-DSPEB submodel to perform the final (merged) approximation.
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 1 Introduction
Despite these developments, critical gaps persist in understanding ensemble learning's adaptability across varied geological contexts. This underscores the need for comprehensive investigations to optimize ensemble architectures for region-specific disaster risk assessments. Landslide susceptibility modeling requires rigorous execution of three interdependent phases, namely data acquisition, predictor variable selection, and model development, all of which collectively determine prediction accuracy [4; 31; 32]. Recent advances include explainable AI frameworks that integrate synthetic aperture radar time series, NDVI dynamics, and geoenvironmental parameters for enhanced landslide forecasting [33]. Despite the progress made, critical methodological gaps persist. Current approaches frequently prioritize statistical collinearity analysis during variable selection, yet this focus inadequately addresses model performance optimization. Such oversights in regard to predictor selection propagate uncertainty during subsequent modeling stages, potentially omitting key geospatial parameters and compromising map reliability [32]. Factor engineering emerges as a vital corrective strategy; a systematic framework for transforming raw data into optimized predictive features through creation, transformation, and strategic selection of relevant factors [34; 35; 36]. While successfully applied in general machine learning domains to boost model efficiency, its implementation in geohazard prediction remains nascent. Advancing factor engineering methodologies specifically for landslide susceptibility analysis could substantially improve both prediction accuracy and operational efficiency, enabling more robust disaster mitigation frameworks. This study introduces an integrated methodology, combining homogeneous and heterogeneous ensemble architectures with systematic factor engineering, for comprehensive landslide susceptibility assessments. The key innovations include: (1) the integration of advanced machine learning frameworks, including random forest, XGBoost, LightGBM, CatBoost, bagging, and stacking, to rigorously evaluate the relevant influencing factors through the use of multidimensional importance ranking; and (2) the utilization of factor engineering techniques to optimize model performance by identifying and prioritizing the most critical factors. The refined framework generates high-resolution susceptibility maps that improve spatial risk stratification, addressing critical gaps in regional landslide modeling, wherein previous efforts inadequately explored synergistic model combinations and factor engineering's performance impacts.
# Design and Shear Analysis of an Angled Morphing Wing Skin Module
## 4 Results
### Effect of the CFRP Reinforcement  
It was shown in Section 4.1 that the CFRP reinforcement plays a major role in preventing the skin from wrinkling when compressive stress occurs. Here we investigate its effects through a series of numerical models listed in Table 6, which have various Young's modulus \(E_{c}\), diameter \(d_{1}\), length \(L\), and interval \(l_{1}\) of the CFRP rods. All the other geometric and material parameters are identical to those of the physical specimen M50. The results also presented in Table 6 indicate that the \(\theta_{\rm max}\) and \(\theta_{\rm min}\) of models with the same value of \(E_{c}d_{1}{}^{4}/l_{1}L^{2}\) are also very close, and \(\Delta\theta\) increases with this value. The reason is that the wrinkling of the skin is always accompanied by the buckling of the CFRP rods. Along the direction of the CFRP rods placed, the cross-sectional bending stiffness per unit length can be calculated as \(E_{c}I/l_{1}\), where the area moment of inertia \(I=\pi d_{1}{}^{4}/64\). Since the buckling resistance of the rod is proportional to its bending stiffness and inversely proportional to the square of its length, increasing \(E_{c}d_{1}{}^{4}/l_{1}L^{2}\) would delay the buckling of the CFRP reinforcement and therefore improve the wrinkle-free deformation range of the skin, as seen from the relationship between the critical wrinkling angles and \(E_{c}d_{1}{}^{4}/l_{1}L^{2}\) in Figure 10d. It should be noted that the above analysis is valid only when the interval of the rods is small enough so that there is no local wrinkling between two CFRP rods. In addition, when \(L\) is very large, it would be difficult to achieve a large \(\Delta\theta\) by adjusting \(E_{c}\), \(d_{1}\), and \(l_{1}\) as  
Figure 10: (**a**) Material properties of the rubbers with different hardness; (**b**) effects of rubber hardness; (**c**) minimal principal stress at different hardness; (**d**) effects of CFRP reinforcement.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hardness/HS** & \(\mathbf{C_{10}}\) & \(\mathbf{C_{20}}\) & \(\mathbf{C_{30}}\) & \(\mathbf{D}\) \\ \hline
10 & 0.04467 & \(-\)0.005278 & 0.0007991 & 0 \\
20 & 0.10620 & \(-\)0.015063 & 0.0029714 & 0 \\
30 & 0.13025 & \(-\)0.018224 & 0.0035482 & 0 \\
40 & 0.17823 & \(-\)0.024546 & 0.0048051 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Material properties of the rubbers with different hardness.  
they are limited by the material, skin thickness, and fabrication. Therefore, in the practical application, the length of the rods should be limited.
## 3 Methods
### Path Model
#### 3.3.1 Model Validation and Estimation of Model Parameters  
The goal of path modeling is to statistically validate the internal structure of a model such as the one depicted in Figure 5 through estimation of the model parameters \(\gamma\) and \(\beta\) and the variance of residues \(\zeta_{i}\).  
To achieve this goal, the system of equations in Equation (1), translating the causal relationships between outputs, inputs, and residues depicted in Figure 5, is first established [40; 42],  
\[\begin{pmatrix}y_{1}\\ y_{2}\end{pmatrix}=\begin{pmatrix}\gamma_{11}&\cdots&\gamma_{16}\\ \gamma_{21}&\cdots&\gamma_{26}\end{pmatrix}\begin{pmatrix}x_{1}\\ \ldots\\ x_{6}\end{pmatrix}+\begin{pmatrix}0&\beta_{12}\\ \beta_{21}&0\end{pmatrix}\begin{pmatrix}y_{1}\\ y_{2}\end{pmatrix}+\begin{pmatrix}\zeta_{1}\\ \zeta_{2}\end{pmatrix} \tag{1}\]  
\[\Leftrightarrow y=\Gamma x+By+Z\ \ (\text{in vector form}).\]  
Next, expressions for the variances of the input and output variables and the covariances between them are derived, defining a system of equations in which the coefficients of the matrices \(\Gamma\) and \(B\) and the variances of the input variables \(V(x)\) and residues \(V(Z)\) are the model parameters requiring estimation. The solution of the system of equations uses the maximum likelihood procedure from [81], and the path model fit is assessed by comparing the resulting estimate and sample covariance matrices.  
To assess the quality of the path model, the chi-square (\(\chi^{2}\)) test is typically used under the null hypothesis that the model fits the data (in which case \(\chi^{2}=0\)). In this paper, the chi-square test was complemented with the Comparative Fit Index (CFI), Goodness of Fit Index (GFI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Squared Residual (SRMR), and Akaike Information Criterion (AIC). The standardized residual covariance matrix was also checked to confirm that coefficients were lower than 2 in absolute value.  
Even if it is unsuitable for deterministic evaluation, a path models that fits the sample data is useful for confirming hypothesized model structures, validating cause-effect relationships, distinguishing relevant direct and indirect (mediated) effects, and identifying significant drivers, all of which can provide meaningful insights into a system's structure and general behavior [82].
## 3 Experiments for Jason-3 Attitude Modes and Receiver PCVs Calibration
### Effect of Jason-3 Attitude Modes on Receiver PCO and POD
#### 3.2.1 Residuals Comparison  
In Figure 10, the residuals RMS of Jason-3 POD with and without PCVs map are approximately 4-6 and 6-8 mm, respectively. The mean residuals RMS with PCVs map are reduced from 6.5 to  
Figure 8: Comparison of orbital accuracies (RMS) on R direction (**a**) and 3D position (**b**) based on different attitude modes (using combined attitude in green, using event file in blue and using quaternions in red).  
Figure 9: Receiver PCVs map from ionosphere-free combination for Jason-3 satellite.  
4.6 mm; the internal accuracy is improved by 1.9 mm. These results are consistent with the noise level of the ionosphere-free combination of the carrier phase, which indicates that models and methods are reasonable for Jason-3 satellite.
## 3 Experiments and Analyses
### Experiment Setup
#### 3.1.3 \(Q^{AB/F}\)  
The \(Q^{AB/F}\) metric is a gradient-based quality index to measure how well the edge information of source images conducted to the fused image [34]. It is calculated by:  
\[Q^{AB/F}=\frac{\sum_{i,j}\left(Q^{AF}(i,j)w^{A}(i,j)+Q^{BF}(i,j)w^{B}(i,j) \right)}{\sum_{i,j}\left(w^{A}(i,j)+w^{B}(i,j)\right)}\enspace, \tag{16}\]  
where \(Q^{AF}=Q^{AF}_{g}Q^{AF}_{0}\), \(Q^{AF}_{g}\) and \(Q^{AF}_{0}\) are the edge strength and orientation preservation values at location (i,j). \(Q^{BF}\) can be computed similarly to \(Q^{AF}\). \(w_{A}(i,j)\) and \(w_{B}(i,j)\) are the importance weights of \(Q^{AF}\) and \(Q^{BF}\) respectively.
## 4 Calibration Method for Micromechanical Parameters of Discrete Element Model and Case Application
### Case Verification of Moderately Consolidated Sandstone  
This study makes red sandstone from the Xinjiang Hutubi Gas Storage into standard cores with a diameter of 50 mm and a height of 100 mm, and conducts uniaxial compression tests on these cores to validate the accuracy of the micromechanical parameter calibration method.  
Based on the parameters obtained from experiments, the required parameters are calculated using the micromechanical parameter calibration method described above. The model dimensions are set to a height of 100 mm and a diameter of 50 mm. Adopting a radius ratio of \(R_{max}/R_{min}=2.0\), a particle density of 2.14 g/cm\({}^{3}\), and selecting a minimum particle radius of 0.2 mm, particles are randomly generated according to a porosity of 21.6%, resulting in a final particle count of 13,599.  
This study conducted three groups of numerical simulations of uniaxial tests on moderately consolidated sandstone. Based on the rock mechanical parameters obtained from the tests, a numerical model consistent with the experimental results was eventually established. The calculated model parameters are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Serial No.**} & \multirow{2}{*}{\(\begin{array}{c}\overline{R}_{max}\\ \overline{R}_{min}\end{array}\)} & \multirow{2}{*}{\(\begin{array}{c}\overline{E}\\ \overline{\mathbf{(GPa)}}\end{array}\)} & \multirow{2}{*}{\(\begin{array}{c}\overline{c}\\ \overline{\mathbf{(MPa)}}\end{array}\)} & \multirow{2}{*}{\(\begin{array}{c}\overline{c}\\ \overline{\mathbf{(MPa)}}\end{array}\)} & \multirow{2}{*}{\(\begin{array}{c}\overline{\mathbf{(\overline{\sigma}_{c}})}\\ \overline{\mathbf{(MPa)}}\end{array}\)} & \multirow{2}{*}{\(\begin{array}{c}\overline{\mathbf{(\overline{\sigma}_{c}})}\\ \overline{\mathbf{(MPa)}}\end{array}\)} & \multirow{2}{*}{\(\  
As illustrated in Figure 8, the stress-strain curves from laboratory tests and numerical simulations align closely in the elastic stage, and their fracture morphologies are also highly consistent. Due to microcrack development and heterogeneity in real cores, minor fluctuations appear in the experimental curve during axial loading. In contrast, the model's pre-compression cementation process leads to a denser particle arrangement, resulting in smaller fluctuations. The simulated fracture morphology matches well with that of the failed real core. Table 4 shows that the macroscopic mechanical parameters from experiments and simulations differ by less than 10%, confirming that the constructed model and calibration method are sufficient for experimental requirements.
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 2 Optical Devices
### Light Detectors
As such devices are typically made of glass, they have low mechanical stability; however, new miniature PMT designs (with metal housings, built-in power supply, and current-voltage converters included) make their use in instrumentation increasingly common in cases where the cost of the equipment is not a limitation. PMTs are widely used in many applications, including in industry, medicine, and academic research, where high-sensitivity photodetectors are required. Typical applications are in flow cytometers, blood inspection, hygiene monitors, portable survey meters, environmental measurement, and semiconductor wafer inspection systems. In summary, to measure optical radiation, the first consideration is the sensitivity of the detector in the band of interest, defined as the electric current generated by the optical power (A/W). Of course, each detector has its own peculiarities; for example, some detectors require cooling, for others it is necessary to modulate the radiation or they require a high voltage, while others can be damaged if subjected to excessive radiation or their sensitive window is touched. Each of these peculiarities must be evaluated when selecting the best option. Hamamatsu Photonics provides a guide for the selection of light detectors [23]. Figure 2 shows the wavelength ranges covered by the discussed light sources and detectors. Table 2 summarizes the main characteristics of the light detectors considered for chemical sensing. Figure 2: Wavelength ranges covered by various light sources and light detectors.