esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Research has focused on tracking laser-driven, ultrafast spin transport either using the transverse magneto-optical Kerr effect [25; 26] or by exploiting the short wavelength of the XUV radiation in time-resolved small angle X-ray scattering experiments [27; 28; 29; 30] as well as via ultrafast coherent imaging experiments [31]. Furthermore, the great potential of probing magnetization with resonant XUV radiation was convincingly demonstrated in a series of experiments, which showed that transient spectra can provide fingerprint signatures of microscopic processes responsible for ultrafast demagnetization, in particular differentiating Heisenberg type delocalized magnon or Stoner type spin-flip excitations [32; 33; 34]. In this contribution, we present two complementary experiments dedicated to an element-specific view of femtomagnetism based on ultrafast XUV spectroscopy of multi-component magnetic systemsperformed at the FEL facility, FLASH, DESY, Hamburg and at a HHG beamline at the Max Born Insitute, Berlin. At the FEL facility, we exploit the _dispersive_ (real) part of the magneto-optical functions in a resonant Faraday rotation geometry to study the ultrafast response of an FeGd alloy and reveal decoupled dynamics of the two sublattices Gd and Fe. In the experiment using HHG radiation, we compare the ultrafast response of a Co film and a CoPt alloy by measuring the _absorptive_ (imaginary) part of the magneto-optical functions in an MCD experiment. Here, we were able to demonstrate that OISTR between Pt and Co atoms in the two-component CoPt alloy dominates the early time dynamics and leads to a faster and more efficient demagnetization.
## 3 Results
### Analysis of Spatial and Temporal Changes in Snow Cover and NDVI
#### 3.1.2 Variations in NDVI  
As shown in Figure 5, the NDVI on the whole plateau surface exhibited a sizeable non-significant increase, with a multi-year rate of change of 0.0005/a between 1981 and 2020. In particular, the NDVI exhibited a fluctuating increasing trend between the 1980s  
Figure 4: Multi-year spatial and temporal variation of snow depth on the QTP: (**a**) spatial distribution of trend changes; (**b**) significance of trend; (**c**) time series of changes.  
and 2000s, but a significant increase occurred after 2000, where the mean value fluctuated around 0.13 to 0.14, and it remained at this level in subsequent years. The average value increased significantly, but the fluctuations decreased significantly. Spatially, the multi-year NDVI increased by 78.83% over the total area of the whole plateau, but the increasing trend was not significant. Significant increases occurred in western Sichuan, northwestern Yunnan, western Gansu, the \"Sanjiangyuan\" region, and part of southeastern Tibet, with multi-year rates of change greater than 0.001/a and greater than 0.003/a in some areas, although these areas only accounted for 3.17% of the total, where these changes may have been related to the implementation of grazing bans and other ecological protection policies in these areas. The increases in the NDVI were slower in the western and northern parts of the plateau, where the trend was between 0 and 0.001 per year. These changes occurred in 55.79% of the total area of the plateau, and this was the main trend in the NDVI on the plateau. The rates of increase in the NDVI in the Qaidam Basin, Tanggula Mountains, and some human activities and development areas in the \"Yijiang Lianghe\" (Brahmaputra, Nianchu, and Lhasa Rivers) basin were less than 0.
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 4 Experimental Validation
### Visualisation of Feature Extraction  
The one-dimensional signal with a length of 1024 after noise reduction is converted into a 32 \(\times\) 32 two-dimensional grey-scale map for feature extraction by inputting it into CNN. In order to have a deeper understanding of how CNN extracts and distinguishes fault features, this paper uses the t-SNE algorithm to visualise the feature extraction results of CNN.  
The t-SNE is a stream-learning algorithm for the visualisation of high-dimensional data. It preserves the local structure in the original data by converting the similarity between data points into a probability distribution and finding a similar probability distribution in the low-dimensional space. The t-SNE algorithm projects the high-dimensional feature space into a two- or three-dimensional space for intuitive analysis and visualisation. Figure 7 shows the CNN input layer data visualisation results and Figure 8 shows the CNN output layer data visualisation results.  
Figure 8: Output layer visualisation.  
Figure 7: Input layer visualisation.  
It can be seen from Figures 7 and 8 that in the input layer, the original data distribution is very scattered and mixed with each other; in the output layer, the data points of different categories are tightly clustered together to form an obvious cluster structure, and the boundaries between different categories become very obvious. It can be learnt that the CNN successfully transforms the original data into feature representations that are easy to classify, thus achieving the basic classification effect.
## 2 Related Work
### Encoder-Decoder Architecture  
The encoding and decoding network structure is commonly employed in computer vision tasks such as object detection and semantic segmentation [39; 40]. The encoder acquires semantic features by reducing image resolution and noise through the downsampling process, while the decoder restores image resolution through upsampling operations. The FCN network, based on an improved encoding and decoding structure [41], as well as the SegNet [20] network employed in the remote sensing image water body task, utilize the encoding and decoding structure to integrate feature information at various levels, ultimately generating semantic segmentation results based on predicted categories.  
However, the integration of feature information in decoder sampling may potentially impact the model's learning capability in both FCN and SegNet architectures. Moreover, conventional convolutions with limited receptive fields fail to capture multi-scale contextual information, thereby diminishing the accuracy of output image features.  
To address the issue of low image recovery accuracy, we propose a novel feature extraction module that is integrated throughout the model to optimize upsampling operations. This new module utilizes multi-scale feature fusion for feature recovery instead of solely focusing on image resolution restoration like traditional transpose convolution methods. By expanding convolution and enhancing global information understanding, multi-scale feature fusion mitigates the limitations imposed by limited receptive domain convolution during upsampling.
## 4 Discussion  
The proposed algorithm, in addition to the tasks of measuring the thickness of a non-conductive coating on a non-magnetic conductive base metal, can be used in the following tasks:  
* Measurement of the thickness of a non-conductive coating on a magnetic and non-magnetic conductive base metal and non-metal base;
* Simultaneous measurement of the thickness of layers of a non-conductive coating with a sublayer of a conductive non-magnetic coating on a conductive magnetic base metal, for example, a paint coating applied to a steel base with a zinc underlayer;
* Measurement of the thickness of non-magnetic conductive sheet with suppression of its specific electrical conductivity;
* Measurement of the specific electrical conductivity of the base metal with suppression of the influence of the lift-off or the thickness of the paint coating in a wide range of the base metal specific electrical conductivity values.  
Conceptualization, P.S.; methodology, M.S.; software, M.S.; validation, I.S. and N.A.; formal analysis, N.A.; investigation, P.S.; resources, M.S.; data curation, P.S.; writing--original draft preparation, P.S.; writing--review and editing, N.A.; visualization, P.S.; supervision, I.S.; project administration, I.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.  
Figure 9: Dependence of the deviation of the measured value \(d\) on the value \(\sigma\).
# Application of Three-Dimensional Porous Aerogel as Adsorbent for Removal of Textile Dyes from Water
## 3 Results
### Modeling Results
#### 3.2.2 Kinetic Analysis  
The kinetic models were fitted for dyes, and the obtained results were compared. Solutions of dyes were selected with the close initial concentration of total organic carbon as follows: Congo red--C\({}_{\rm o}\) = 5.517 mg/L; Naphthol green B--C\({}_{\rm o}\) = 4.112 mg/L; Rhodamine B--C\({}_{\rm o}\) = 7.014 mg/L; and Methylene blue--C\({}_{\rm o}\) = 3.008 mg/L.  
The results of the kinetic study of Congo red are presented in Tables 4 and 5. Values of the coefficient of determination (R\({}^{2}\)) and the average relative error (ARE) were also  
Figure 16: Equilibrium analysis for Methylene blue.  
Figure 15: The linearized form of the Freundlich isotherm for Methylene blue.  
In this case, the situation is very similar to the case of Naphthol green B. Considering the higher values of the determination coefficient and smaller values of the average relative error, pseudo-second-order models (both linear and nonlinear) would be more suitable for Rhodamine B.  
The results of the kinetic study of Methylene blue are presented in Tables 10 and 11. The line of the pseudo-first-order and pseudo-second-order kinetic models with the obtained constants are presented in Figure 20.  
The smallest differences in adsorption amount were found for the pseudo-second-order kinetic model constructed by using a nonlinear fit. The highest value for the determination coefficient was 0.86, and the average relative error was 1.41%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{\(\mathbf{q_{e,exp}}\)} & \multicolumn{3}{c}{**Pseudo-First-Order Model (Linear Form)**} & \multicolumn{3}{c}{**Pseudo-First-Order Model (Nonlinear Form)**} \\ \cline{2-9}  & \(\mathbf{q_{e,calc}}\) & \(\mathbf{k_{1}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{ARE}\) & \(\mathbf{q_{e,calc}}\) & \(\mathbf{k_{1}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{ARE}\) \\ \hline
0.94 & 0.104 & 0.038 & \textless{}0 & 92.9\% & 0.906 & 0.533 & 0.60 & 1.41\% \\ \hline \end{tabular}
\end{table}
Table 10: Constants for the pseudo-first-order kinetic models for Methylene blue.  
Figure 19: The kinetic experimental data with the pseudo-first-order and the pseudo-second-order kinetic models using the obtained constants (Tables 8 and 9).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{\(\mathbf{q_{e,exp}}\)} & \multicolumn{3}{c}{**Pseudo-Second-Order Model (Linear Form)**} & \multicolumn{3}{c}{**Pseudo-Second-Order Model (Nonlinear Form)**} \\ \cline{2-9}  & \(\mathbf{q_{e,calc}}\) & \(\mathbf{k_{2}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{ARE}\) & \(\mathbf{q_{e,calc}}\) & \(\mathbf{k_{2}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{ARE}\) \\ \hline
0.94 & 0.938 & 1.208 & 0.43 & 1.24\% & 0.924 & 2.187 & 0.86 & 0.84\% \\ \hline \end{tabular}
\end{table}
Table 11: Constants for the pseudo-second-order kinetic models for Methylene blue.
## 3 Analytical Models of Lattice Structures Built with Polygon Hollow Shapes
### Analytical Models for Predicting the Behaviour of Selected Polygon Hollow Structures  
Analytical models predicting the structural behaviour of lattice structures are available in the literature for in-plane and out-of-plane loads [18; 19; 20; 21; 22; 23; 24; 25; 26]. The in-plane and out-of-plane loads for the analytical models derived for polygon hollow structures based on plates/beams are depicted in Figure 3.  
Analytical models for polygon structures, such as hexagonal hollow shapes built with plates or beams, have been developed using tensile or compressive loads, which leads to respective tensile or compressive deformation behaviour [18; 19]. These two deformation behaviours have been investigated in numerous studies, for three mutually orthogonal directions [18]. Figure 4 illustrates the three mutually orthogonal directions used in studying the deformation behaviour of polygonal structures, in this case a hexagonal hollow structure.  
Figure 3: Shows (a) in-plane and out-of-plane loading of a plate and (b) out-of-plane bending on the left and in-plane bending on the right and (c) planes of walls before and after bending.  
Currently, the analytical models for the deformation behaviour of polygonal structures that are available in the literature only describe the bending stiffness of these hollow structures with respect to their elastic moduli [17; 18; 20] and predict their load-bearing capacity with reference to the yield stress of their wall-material [18; 20]. Polygonal structures are particularly useful in applications for high-energy absorption [2; 5; 7; 8; 13; 14; 15; 16; 17; 21; 22; 23; 24; 25]. Given the fact that there are no specific models for the absorption mechanisms of energy for polygon hollow structures, this poses a challenge in accurately predicting their related behaviour. Presently, the analytical models that are available in the literature for use in predicting the absorption mechanisms of energy for polygon hollow structures are predicated on general mathematical equations that describe the strain energies caused by bending, tensile, and shear loading [7; 17; 18; 19]. A supposition is being investigated by the authors in ongoing work that the two-stage stress-strain curve typically used for predicting the behaviour of cellular/lattice structures is not accurate as it does not represent all the deformation mechanisms taking place in a lattice structure. This presents a clear limitation with regard to having specific analytical models for describing the energy-absorption behaviour of polygon structures. Therefore, further investigation is required to address these gaps in knowledge with a view to building more accurate analytical models to describe the deformation behaviour of polygon hollow structures. For particularly porous materials such as polygon hollow structures, the Gibson-Ashby model is typically employed to relate their stiffness and yield strength to density [20].
## 4 Materials and Methods
### siRNA  
ALDH2 siRNA and siRNA NC were designed by RiboBio (Guangzhou, China). NPC cells were seeded into 6-well plates at \(2.5\times 10^{5}\) cells/well and incubated overnight to reach 60-70% confluence for transfection. Transfections were performed using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's protocol. The target sequences used in the siRNA were as follows: siLDH2-1: 5'-GGAGACTTCTCAGCTACA-3'; siALDH2-2: 5'-GAGCCAACAATTCCACGTA-3'; siALDH2-3: 5'-GCAGCAACCTCAAGAGAGT-3'.
## 4 Conclusions  
The present study demonstrates the applicability and viability of lignocellulosic bio-waste as a sustainable thermal insulation alternative to mineral-wood. After one-week of dynamic thermal loading, the mineral wool showed a 5% higher U-value compared to steady-state conditions, while short-cut rapeseed showed that a 12% lower U-value and short-cut woodchips reached 0.3% higher U-value. Compared to straw bales, the thermal conductivity and the heat capacity of the insulated wall remained homogeneous across the metering area, providing low thermal energy losses. Moreover, a modified guarded hot box method was successfully used for realistic simulations of thermal behavior of building envelopes and for the determination of insulation panels' thermal transmittance (U-values) under steady and unsteady state thermal conditions.  
From the human wellbeing point of view, lignocellulosic bio-waste materials, especially rapeseed, emitted a similar amount of VOCs comparing to mineral wool. Therefore, it can be considered quite harmless for interiors' occupants.  
Furthermore, the bio-based sandwich panel allows for complicated structural shapes (elements) engineering. It can be installed easily with loose-fill insulation, giving the architects more freedom in designing sustainable building envelopes, using only a 1.5% thicker layer of rapeseed than mineral wool, while reaching the same thermal resistance.  
The work presented in this paper is a collaborative development by all of the authors.  
This work was supported by the Faculty of Forestry and Wood Sciences of the Czech University of Life Sciences Prague (Internal Grant Agency, Project No. A20/16: Thermal insulation properties of wood-based sandwich panel for use as structural insulated walls), and by grant \"EVA 4.0\", No. CZ.02.1.01/0.0/0.0/16_019/0000803 financed by the OP RDE (Operational Programme Research, Development and Education.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Insulation Sample** & \
\begin{tabular}{c} **Short-Cut Rapeseed** \\ **(Brassica Napus)** \\ \end{tabular} &
\begin{tabular}{c} **Short-Cut** \\ **Woodchips** \\ \end{tabular} & **Mineral Wool** \\ \hline
**COMPOUND** & – & – & – \\ Pentanal & – & 30 & – \\ Hexanal & 63 & 180 & – \\ Heptanal & – & 11 & – \\ Octanal & – & 15 & – \\ Nonanal & 14 & 25 & – \\ alpha-Pinene & 9 & 155 & – \\ beta-Pinene & – & 69 & – \\
3-Carene & – & 9 & – \\ Decane, 3,6-dimethyl- & – & 14 & – \\ Dodecane & 12 & – & – \\ Pentadecane & – & – & 11 \\
2-t-Butyl-4-methyl-5-oxo- [1,3]dioxolane-4-carboxylic acid & 7 & – & – \\ Hydrazine, (1,1-dimethyl)- & – & – & 66 \\ Nonane, 5-(2-methylpropyl)- & – & – & 14 \\ _Note:_ selected mass areas were divided by \(10^{4}\) & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 7: Detected VOCs from specific thermal insulation materials—intensities of monitored VOC emissions; intensities detector response for unique mass.
## Results
### Comparison with Yield Statistics  
Annual yield statistics for different crop types were compared to the satellite-detected vegetation stress situations. The depicted crops (Figure 9) are winter wheat (A), rye and winter cereal (B), silage maize (C), and potatoes (D). The harvests are drawn as decitonne per hectare to prevent influence of changing cultivation quantities. Annual crop yields below one standard deviation from the mean are displayed in orange, which is the case for all four crops in 2003 and 2018. Apart from these two years, 2006, 2007, 2011, and 2013 reveal yields below one standard deviation from the mean for some of the crops.  
Figure 9: Yield statistics of Germany for the years 2000–2018 from the federal statistics office for four different crops [31]: winter wheat (**A**), rye and winter cereal (**B**), silage maize (**C**), and potatoes (**D**). The dashed line shows the mean yield and the dotted lines one standard deviation from the mean for the period of interest. Bars in orange are below the standard deviation. Be aware that the origin of the y-axis is non-zero in all four plots.
# Spatial Grid-Based Position Calculation Method for Satellite-Ground Communication Links
## 4 Discussion
Because the GeoSOT grid has two-dimensional and three-dimensional consistency, it can provide effective support for ground, satellite-ground and inter-satellite computations, and is conducive to the construction of a unified and integrated computing network between space and Earth. This paper is also the first to apply the grid system to the calculation of satellite-ground position, and there are still many problems in the application that deserve in-depth study. For example, the influence of weather and atmosphere on signal transmission, the satellite selection strategy and signal transmission routing, the scheduling of on-board resources and the optimization of on-board algorithm storage will be further studied in the future. Figure 13: Effect of grid hierarchy on boundary representation.
# Conditional Dependencies and Position Fixing
## 4 Random Data Evaluation
Figure 3: Interpretation of histogram uncertainty with respect to bin heights: (**a**) better-shaped histogram; (**b**) worse-shaped histogram. Given a set of histograms with various bin widths, we can calculate the relative deflection and result uncertainty using the following expression:  
\[\Delta_{Wi}=\frac{S_{Wi}-S_{Wini}}{S_{Wini}} \tag{2}\] \[\Theta_{Wi}=\Theta_{Ws}+\Delta_{Wi}*\Theta_{Ws}.\]  
where \(g_{Wini}\) is the minimum bin width for the histograms involved, and the denominator of Equation (2) is assumed to be greater than zero (see Figure 4 for illustration); \(g_{Wi}\) is the bin width measure for the \(i\)-th histogram; \(\Theta_{Ws}\) is the subjectively valued minimal uncertainty regarding the bin width measure for the histograms involved. Given the independent uncertainties related to the diversity of the bin heights and widths, one can obtain the resulting histogram uncertainty (\(\Theta_{Ri}\)) from Equation (3):  
\[\Theta_{Ri}=\Theta_{Wi}+\Theta_{Hi}-\Theta_{Wi}*\Theta_{Hi}. \tag{3}\]  
The presented equations engage the features of two structures. In the case of the exploitation of data pools that contain multiple sets, the initial reference histogram is necessary to create the required hierarchy. The histogram may take the shape of a logical structure that enables the obtainment of the proper relations within the considered pool. The sorted uncertainty list (\(|\Theta_{Ri}\leq\Theta_{Ri+1}|\)) indicates a hierarchy in the available evidence, and it needs to be upgraded to obtain the correct solution. The first item on the list contains the smallest uncertainty, and its evaluation requires recalling the elements of a hypothetical structure. The initial structure (\(HM^{ref}\)) with a set of attributes (\(|g_{H}^{ref}\), \(g_{W}^{ref}\), \(\Theta_{H}^{ref}\), \(\Theta_{W}^{ref}\)) is created to obtain the required ranking. The components in the logical ideal structure (\(HM^{ref}\)) are as follows:  
\(g_{H}^{ref}=\underset{i}{\text{max}}\ g_{Hi}\): the best bin height measure for a given set of histograms;  
\(g_{W}^{ref}=\underset{i}{\text{min}}\ g_{Wi}\): the best bin width measure for a given set of histograms;  
\(\Theta_{H}^{ref}\): the default reference bin height for the basic uncertainty measure;  
\(\Theta_{W}^{ref}\): the default reference bin width for the basic uncertainty measure. The proposed ideal histogram is a logical structure that is upgraded for a particular data pool. The reference bin height and width measures for a given set of histograms are selected. The histogram is a collection of the best options established based on the considered dataset. Because there is no histogram without uncertainty, a default value is assigned. Hereafter, the included examples exploit 0.05 as the basic bin height uncertainty, and 0.1 as the minimal width uncertainty.
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 2 Materials and Methods
### Multi-Drought Indices
#### 2.2.3. Hydrological Drought Indices  
Hydrological drought refers to a decrease in the amount of surface or underground water due to a lack of precipitation. The frequency and severity of drought are defined based on the size of the basin unit. In general, the time of occurrence of meteorological and agricultural drought has some time delay, and it takes more time for precipitation shortages to appear in hydrological systems, such as soil moisture, river flow, underground water, and reservoir water levels. Therefore, the various fields of water use have different water supply sources; hence, the timing of drought damage is different.  
In this study, the streamflow drought index (SDI, [25]), which evaluates drought using river flow rates, the standardized reservoir supply index (SRSI, [26]), which is based on dam water storage, and the water budget-based drought index (WBDI, [27]), which evaluates hydrological drought using recent satellite data, were used. Table 4 presents the classification of drought conditions according to the range of each hydrological drought index.