esa-sceva/satcom-chunk-collection huggingface dataset:
# Large-Scale Fabrication of Graded Convex Structure for Superhydrophobic Coating Inspired by Nature
## 2 Experimental Section
### Measurements and Characterizations  
A scanning electron microscope (Hitachi S4800, Shanghai, China) was used to study the morphology of coating surfaces; an atomic-force microscope (BRUKER Dimension Icon, Karlsruhe, Germany) was used to measure the surface roughness. The contact angles (CAs) and sliding angles (SAs) were measured using the appropriate measuring equipment (SL200, Shanghai Fangrui Instrument Co., Ltd., Shanghai, China). The size distribution of the PDVB-NPs was measured through a dynamic-light-scattering instrument (DynaPro NanoStar, Santa Barbara, CA, USA). The chemical structure of PDVB was characterized using a Fourier Infrared (FT-IR) Spectrometer (Nicolet Magna-IR 750, Madison, WI, USA). The spectra wavenumbers for the measurements were in the range of 4000\(-\)500 cm\({}^{-1}\).
# A Comprehensive Digital Model Approach for Adaptive Manufacturing Systems
## 1 Introduction
### Methodological Framework  
The methodology presented in this article provides a systematic approach to building a digital model of an adaptive manufacturing system. It outlines the steps involved in data integration, entity definition, event identification, and performance evaluation. The methodology also emphasizes the importance of validating the model against real-world scenarios to ensure its accuracy and relevance.  
A crucial aspect of the methodology is the system for evaluating adaptability, which forms the cornerstone of successful adaptive manufacturing. This system involves assessing various performance metrics in the context of adaptability, including response time to market changes, production flexibility, resource utilization, and customer satisfaction. The challenge here is not only defining these metrics but also integrating them into the digital model to provide real-time feedback on the system's adaptability performance. Striking a balance between accurate representation and computational efficiency in this evaluation system is essential.  
This approach differs from that of other authors in terms of perceiving the adaptive manufacturing system and its development with a digital model. While some authors emphasize real-time adaptability and simulation-based optimization, others highlight the concept of competency islands and their autonomous collaboration. Despite these variations, the common thread is the recognition of the significance of digital modeling and simulation for achieving efficient and adaptive manufacturing processes. This unique perspective stems from a blend of professional backgrounds and experiences in the realms of manufacturing and informatics.  
The novelty of the current study resides in its contribution to adaptive manufacturing systems by introducing the ability to dynamically modulate both system capacity and object count in real-time simulation environments. This feature necessitates the incorporation of system dynamics, a dimension heretofore unaddressed by traditional simulation frameworks which are typically constrained to static states or predefined system parameters. While existing digital twin technologies do offer emulation capabilities, they are largely designed to function within the bounds of static operational settings. In contrast, our proposed model is uniquely equipped to adapt to fluctuations in object dynamics and system capacities. Furthermore, the scholarly merit of this work is augmented by its applicability to 'competency islands'--sophisticated modular systems capable of reconfiguration and scalability--thereby extending its relevance beyond conventional manufacturing setups.
## 2 Surface Reconstruction from Point Cloud
### Triangular Irregular Network (TIN)  
TIN generation is a way to obtain surface reconstruction. Triangulation may be performed in two or in three dimensions, in accordance with the geometry of the input data. TIN utilizes the original sample points to create many non-overlapping triangles that cover the entire region according to a set of rules. The surface is described (approximately) with these triangles [15]. The computer graphics community tends to call this polygonal model \"_mesh_\". A mesh contains vertices, edges and faces and its easiest representation is a single face. For triangular meshes, an indexed face list consists of an array of vertices each having three coordinates, and an array of faces each having three indices in the vertex array [16]. The criterion for triangulation division is often used to construct the non-overlapping triangles based on the discrete sampling points. Delaunay is the most common triangulation algorithm.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 3 Technologies and Methods for Rockburst Research
### Theoretical Research  
Many rockburst hypotheses have been suggested based on rockburst testing and field study, serving as the foundation for the development of prediction and preventive measures [62]. The current theoretical research focuses on analyzing the mechanisms and categories of rockburst through various aspects, such as strength, energy, defect, instability, and catastrophe theory. Bifurcation, dissipative structure, and chaos theory [24; 63; 64; 65; 66] are used to investigate the deformation and stability of rock mechanics systems. The most prevalent theories include strength, energy, stiffness, instability, and blasting reliability. The strength hypothesis is based on material strength in classical mechanics, but it does not specify how rockburst occurs. The energy hypothesis explains the rockburst failure process from an energy standpoint, but not the failure circumstances of an equilibrium state. The stiffness hypothesis states that the mine structure's stiffness must be larger than the mine load for rockburst to occur, but it does not comprehensively understand the division and stiffness of the mine structure and load systems. The strength theory is a classic criterion for material failure, stating that when stress exceeds a material's strength limit, it begins to fail. [PERSON] and [PERSON] [25] first used this theory for rockburst prediction and it is commonly used in the form of stress-to-strength ratios, such as [PERSON]'s [67], [PERSON] [68], [PERSON]'s [69], and [PERSON]'s [32] criteria, based on which a series of rockburst strength criteria has been proposed, as summarized in Table 4. These standards can be divided into two categories: max tangential s to uniaxial strength and initial max primary stress to uniaxial strength. A comparison of the rockburst proneness grade divisions of these two groups is shown in Figure 3a. The concentrated local stress in the surrounding rock may exceed its long-term strength without causing rockburst. The strength theory can only show whether a rock mass has failed, but not whether the failure is stable or unstable [70]. [PERSON] [76] proposed the energy hypothesis for rockburst in 1963, stating that it happened when the energy released by the surrounding rock system was higher than the energy used during mechanical equilibrium state failure [24]; it was seen as a violent energy release mechanism. [PERSON] [77; 78] and [PERSON] [79] examined the balance between
## 3 Performance Simulation and Analysis  
### CCM Transmission Efficiency  
Combined with the data in Tables 1 and 4, the CCM TEF of Kashgar G/S, Beijing G/S and Sanya G/S can be obtained, as shown in Table 5. It can be seen that:  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**MCS**} & \multicolumn{3}{c}{**Link Availability/\%**} & \multicolumn{3}{c}{**Link Unavailability/\%**} & \multicolumn{3}{c}{**Outage Period/d**} \\ \cline{2-10}  & **Kashgar** & **Beijing** & **Sanya** & **Kashgar** & **Beijing** & **Sanya** & **Kashgar** & **Beijing** & **Sanya** \\ \hline
1 & 99.985 & 99.216 & 93.599 & 0.015 & 0.784 & 6.401 & 0.05 & 2.86 & 23.36 \\
3 & 99.982 & 98.986 & 92.751 & 0.018 & 1.014 & 7.249 & 0.07 & 3.7
###### Abstract  
Interferometric synthetic aperture radar (InSAR) technology has been widely employed in the rapid monitoring of earthquakes and associated geological hazards. With the continued advancement of InSAR technology, the growing volume of satellite-acquired data has opened new avenues for applying deep learning (DL) techniques to the analysis of earthquake-induced surface deformation. Although DL holds great promise for processing InSAR data, its development progress has been significantly constrained by the absence of large-scale, accurately annotated datasets related to earthquake-induced deformation. To address this limitation, we propose an automated method for constructing deep learning training datasets by integrating the Global Centroid Moment Tensor (GCMT) earthquake catalog with Sentinel-1 InSAR observations. This approach reduces the inefficiencies and manual labor typically involved in InSAR data preparation, thereby significantly enhancing the efficiency and automation of constructing deep learning datasets for coseismic deformation. Using this method, we developed and publicly released a large-scale training dataset consisting of coseismic InSAR samples. The dataset contained 353 Sentinel-1 interferograms corresponding to 62 global earthquakes that occurred between 2015 and 2024. Following standardized preprocessing and data augmentation (DA), a large number of image samples were generated for model training. Multidimensional analyses of the dataset confirmed its high quality and strong representativeness, making it a valuable asset for deep learning research on coseismic deformation. The dataset construction process followed a standardized and reproducible workflow, ensuring objectivity and consistency throughout data generation. As additional coseismic InSAR observations become available, the dataset can be continuously expanded, evolving into a comprehensive, high-quality, and diverse training resource. It serves as a solid foundation for advancing deep learning applications in the field of InSAR-based coseismic deformation analysis.  
deep learning; interferometric synthetic aperture radar (InSAR); coseismic deformation; dataset construction; data augmentation 2015-2024). _Remote Sens._**2025**, 17, 1832. [[https://doi.org/10.3390/s17111832](https://doi.org/10.3390/s17111832)]([https://doi.org/10.3390/s17111832](https://doi.org/10.3390/s17111832))
## 2 Study Area and Materials
### Landsat Image Datasets
Landsat-8 (L8) images were employed for mapping in years 2016 and 2021, while Landsat-9 (L9) datasets were included for 2021. According to previous studies [35; 36], the MC algorithms can provide consistent phenological datasets and reduce cloud conditions over large coverage regions with the requirement of an atmospheric correction and accurate cloud masking [34; 37]. MC was applied to all spectral bands and the derived VIs were involved in this work. Composites were implemented for all years and for all time steps considered in this work. Specifically, Landsat data available from January to December of each year were collected and MC was subsequently computed in order to generate a single annual image for each VI considered for each of the five years (i.e., 2001, 2006, 2011, 2016 and 2021). In particular, MCs composites were generated by using the \"ee.Reducer.median()\" and mosaicked by the \"ee.Reducer.mosaic()\" function available on GEE. The resulting images were finally compared with high-resolution imagery available in Google Earth Pro in order to assess their output quality. Final number of annual image composites (i.e., spectral bands and derived VIs) and topographical indicators used for mapping agricultural fields/LC for each of the five base years are reported in Table 3.
## 3 Numerical Methods
### Validation Cases
#### 3.3.2 Flow Past a Transversely Oscillating Cylinder  
To examine the capability of the method for capturing moving boundary, we further test the flow past a transversely oscillating cylinder in this section. The computational domain size in the streamwise and vertical directions is 50D \(\times\) 30D, and the number of grid points is 512 \(\times\) 384. D represents the diameter of the cylinder. The centre of the cylinder is 12.5D and 15D from the inlet and top boundaries, respectively. The boundary conditions are set to be the same as those for the flow past a stationary sphere. The cylinder oscillates in the vertical direction, and the Reynolds number is fixed at \(Re=U_{\infty}D/\
u=185\). The motion of the cylinder center \([X_{c},\,Y_{c}]\) is prescribed as \(X_{c}=0\) and \(Y_{c}(t)=Y_{A}\cos(2\pi f_{e}t)\), where \(Y_{A}=0.2D\) and \(f_{e}\) represent the amplitude and frequency of the oscillation, respectively.  
In total, we ran six cases with different normalized oscillating frequencies \(f_{e}/f_{0}\): 0.8, 0.9, 1.0, 1.1, 1.12, and 1.2. Here \(f_{0}\) denotes the frequency of vortex shedding for the flow past a stationary cylinder at \(Re=185\). The Strouhal number based on \(f_{0}\) is found to be \(St=f_{0}D/U_{\infty}=0.194\). The contours of the spanwise vorticity for the six cases when the cylinder reaches its extreme upper position are shown in Figure 7. Solid and dashed lines represent positive and negative vorticity, respectively. The contour interval is \(0.75U_{\infty}/D\). For \(f_{e}/f_{0}\ \leq 1.0\), the size of the attached negative vortex denoted by \"A\" decreases as \(f_{e}/f_{0}\) increases (Figure 7a-c). At \(f_{e}/f_{0}=1.1\), a sudden change of the vortex pattern is observed (Figure 7d). The positive attached vortex denoted by \"B\" becomes dominant for \(f_{e}/f_{0}\geq 1.1\) (Figure 7d-f), and vortex A is detached. As \(f_{e}/f_{0}\) increases from 1.1 to 1.2, vortex B moves upwards, and a small negative attached vortex denoted by \"C\" is observed. The sudden change of the vortex pattern at \(f_{e}/f_{0}=1.1\) has been observed in literature using different numerical methods [(33; 34)].  
Figure 8 compares the mean drag coefficient \(\overline{C}_{d}\) and RMS (root mean square) of the lift coefficient fluctuation \(C_{l}^{\prime}\) with those obtained by [PERSON] et al. [33] and [PERSON] et al. [34]. Both \(\overline{C}_{d}\) and \(C_{l}^{\prime}\) obtained in the present study agree with those given by [PERSON] et al. [34]. Although the values of the present results of \(\overline{C}_{d}\) are slightly higher than those of [PERSON] et al. [33], the differences are smaller than 3% for all test cases. The results demonstrated in Figure 8 indicate that the present algorithm can capture the moving boundary accurately.
# Variable Tap-Length Algorithm with Mixed Parameter
###### Abstract  
Convergence speed and steady-state performance are important factors to estimate the variable tap-length algorithm. The step-size of tap-length (ST) and the error width considerably impact the performance of the fractional variable tap-length algorithm. A mixed fractional variable tap-length algorithm that simultaneously applies time-varying ST and error width strategies is presented in this paper. The ST and the error width are analyzed in detail. This method uses the same variable to control both aspects, so their changes are relatively consistent. The parameters have strong adaptive ability at different stages. The proposed algorithm can solve some defects of the previous algorithms, including slow convergence rate, weak steady-state performance and robustness. A novel mean square deviation (MSD) gradient experiment is performed. Experimental results show good agreement with the theoretical analysis.  
adaptive algorithm; variable tap-length; convergence speed; steady-state performance; error width; step-size of tap-length +
Footnote †: journal: Applied Sciences
## 5 Global Spatial and Temporal Variability of Soil Salinity
### Africa  
On the African continent, the area of salinized soil in the Siwa Oases, Egypt, increased by 14.3% between 1987 and 2017. This rise in salinized land may be attributed to the expansion of agricultural areas. Most irrigated agricultural regions employ traditional irrigation methods that lack adequate drainage systems, and long-term mismanagement of irrigation practices has negatively impacted soil quality, contributing to the process of soil salinization ([PERSON], 2020). Conversely, the Tafilalet Plain in Morocco experienced a decrease of 17.02 km\({}^{2}\) in the total area of saline soils during the assessment conducted from 1984 to 2018 ([PERSON], 2020). Historical studies indicated that more than 5% of Moroccan soils have been affected by soil salinization to varying degrees.
# A New Type Bionic Foldable Wing Design for High Maneuverable Unmanned Aerial Vehicle
## 2 Preliminary Design of the Folding Wing Structure
### Dynamics Model of Fold-Wing Aircraft
#### 2.1.2 Translational Dynamics  
The momentum theorems for the remaining part of the aircraft and two foldable wings, respectively, are shown in Equation (2):  
\[\left\{\begin{array}{c}\mathbf{F_{af}}+\mathbf{F_{1f}}+\mathbf{F_{2f}}+ \mathbf{G_{f}}+\mathbf{T}=\mathrm{m_{f}}\frac{\mathrm{d}\mathbf{v_{f}}}{ \mathrm{d}\mathbf{t}}\\ \mathbf{F_{a1}}+\mathbf{F_{f1}}+\mathbf{G_{1}}=\mathrm{m_{1}}\frac{\mathrm{d} \mathbf{v_{i}}}{\mathrm{d}\mathbf{t}}\\ \mathbf{F_{a2}}+\mathbf{F_{f2}}+\mathbf{G_{2}}=\mathrm{m_{2}}\frac{\mathrm{d} \mathbf{v_{i}}}{\mathrm{d}\mathbf{t}}\end{array}\right. \tag{2}\]  
**Faf**--aerodynamic force on the remaining part of the aircraft; **Ff\({}_{\mathbf{f1}}\)**--the force exerted by the left foldable wing on the rest of the aircraft; **G\({}_{1}\)**--gravity of the left folding wing; **T**--engine thrust, etc. By combining Equations (1) and (2), we obtain Equation (3):  
\[\mathbf{F_{a}}+\mathbf{G}+\mathbf{T}=\mathrm{m}\frac{\mathrm{d}\mathbf{v_{0}}} {\mathrm{d}\mathbf{t}}+\sum_{i=1}^{2}\mathrm{m_{i}}\frac{\mathrm{d}\mathbf{v_ {pi}}}{\mathrm{d}\mathbf{t}} \tag{3}\]  
**m**--total mass; **G**--total gravity.  
Compared with the traditional 6-DOF equation, the additional force \(\Delta\)F caused by wing folding is added in this equation, as shown in Equation (4):  
\[\mathbf{F_{a}}+\mathbf{G}+\mathbf{T}=\mathrm{m}\bigg{(}\frac{\mathrm{d} \mathbf{v_{0}}}{\mathrm{d}\mathbf{t}}\bigg{)}_{\mathrm{b}}+\omega\times \mathbf{v_{0}}+\sum_{i=1}^{2}\mathrm{m_{i}}\bigg{[}\frac{\mathrm{d}^{2} \mathbf{\rho_{i}}}{\mathrm{d}\mathbf{t}^{2}}+2\omega\times\frac{\mathrm{d} \mathbf{\rho_{i}}}{\mathrm{d}\mathbf{t}}+\frac{\mathrm{d}\omega}{\mathrm{d} \mathbf{t}}\times\rho_{i}+\omega\times(\omega\times\rho_{i})\bigg{]} \tag{4}\]  
If the thrust of the engine is only in the \(\mathrm{x_{i}}\) direction, there is no component force in other directions. The expression of the combined external force acting on the system in the coordinate system \(\mathrm{Ox_{i}y_{i}z_{i}}\) is:  
\[\mathbf{F_{a}}+\mathbf{G}+\mathbf{T}=\left[\begin{array}{c}\mathrm{T}- \mathrm{mg}\sin\theta-\mathrm{D}\cos\alpha\cos\beta-\mathrm{Y}\cos\mathrm{a} \sin\beta+\mathrm{L}\sin\alpha\\ \mathrm{mg}\cos\theta\sin\varpi-\mathrm{D}\sin\beta+\mathrm{Y}\cos\beta\\ \mathrm{mg}\cos\theta\cos\varpi-\mathrm{D}\sin\alpha\cos\beta-\mathrm{Y}\sin \alpha\sin\beta-\mathrm{L}\cos\mathrm{a}\end{array}\right] \tag{5}\]  
D--resistance; Y--lateral force; L--lift;\(\alpha\)--angle of attack; \(\beta\)--sideslip angle.
## 4 Conclusions  
The influence of various laser-power outputs, beam diameter and beam moving speed on the compressive strength, ultrasonic characteristics and crack distributions of the granite specimens were investigated in this paper. The compositions of original and irradiated granite specimens were also quantitatively reported. The results obtained in this study can be used to assess the cracking efficiency of hard granite rock by using laser irradiation, and  
Figure 12: Interior-crack images of vertical section at (**a**) axis and (**b**) 3/4 diameter plane.  
Figure 13: Interior-crack length and width at (**a**) starting point and (**b**) axial plane.  
therefore provide some useful guidance in the practice. The key findings are summarized as follows:  
1. The uniaxial compressive strength of the irradiated graphite is reduced by the higher laser power, smaller beam diameter as well as slower moving speed. The XRD and XRF results indicate that a change from crystalline to amorphous states for the irradiated specimen occurs.
2. Both the ultrasonic-wave velocity and amplitude of the first wave imposed on the irradiated specimens increase with the decreasing laser-power output, increasing the diameter or moving speed of the laser beam. The waveform is also observed to be significantly flattened by the same changes in the irradiation parameters. This suggests that the thermal damage to irradiated specimens caused by laser irradiation can be qualitatively assessed by ultrasonic testing, which is a non-destructive technology.
3. The crack angles and ratio of cracked areas at both ends increase when the laser power increases as well as when the diameter or the moving speed of the laser beam decreases. It is clearly observed that a U-shaped grooving kerf with a depth of about 12 mm is generated, which also matches the beam's movement. A considerable number of cracks are generated around the grooving kerf. Deep cracks are found at the front, middle and back of the irradiated specimen, and the deepest cracks reach almost 90% of the specimen's diameter.  
Methodology, Y.W.; investigation, L.K.; data curation, D.Y. and L.S.; writing--original draft preparation, D.Y. and Z.C.; writing--review and editing, J.D. and Y.W.; visualization, D.Y. and L.S.; supervision, Y.W. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (No. 51978653, No. 42107156), Higher Education Discipline Innovation Project (No. B14021), and the Fundamental Research Funds for the Central Universities (No. 2020 ZDPYMS18).  
Data available on request due to the privacy restrictions.  
The authors declare no conflict of interest.
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 3 Results and Discussions
### Evaluation of Network Coverage Optimization Model
The combination of high CR and low OR in the selected deployment demonstrates the potential of PSO in balancing coverage and efficiency. This balance is particularly important in resource-constrained environments, where maximizing sensing performance and minimizing redundancy directly impact the network's operational longevity and cost-effectiveness. Future work could explore the incorporation of additional constraints, such as obstacle avoidance or dynamic environmental changes, to further enhance the practicality and adaptability of the optimization approach.
## 2 Results and Discussion
### General workflow
#### 2.1.1 Data acquisition (LC-MS)  
It is known that LC-MS tuning is important to achieve better sensitivity, ionization efficiency, etc. [34]. Untargeted metabolic profiling requires full scan event efficiency of which depends on a single general tune file used. Five different compounds were used to determine the best tuning used for profiling: sucrose, rutin, naringin, indoleacetic acid and chlorogenic acid. Data mining with the use of comprehensive statistics allowed convincingly discriminate data obtained with the use of different tune files (data not shown). Small molecules were the most sensitive to the different tune files while larger sized polar lipids were less sensitive to described alterations. It was found that the sucrose tune file was the most appropriate one for the reason of its better metabolites' response in both positive and negative ESI modes [34], so the sucrose tune file was used in the MS methods applied to the current study. Data acquisition on LTQ linear ion trap MS (ThermoFinnigan, San Jose, CA) was performed in full scan featuring constant positive and negative modes switching. This allowed monitor positive and negative ions within a single LC-MS run.  
Figure 1: Small Molecule Analysis Workflow at UC Davis Metabolomics Core.