esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Stress Analysis
prevent initial ability inside the model (Figure 4, detail _b_); (3) the central body and the cover were constrained along their borders with a rigid \"tie\" constraint, in order to effectively reproduce the structural continuity provided by metal welding (Figure 4, detail _c_); (4) the gap between the guide and the central body was equal to 0.1 mm along its main borders (Figure 4, detail _d_), while the gap between the pin and its housing was set equal to 0.05 mm (Figure 4, detail _e_), according to plate design. Considering the full experimental setup detailed in Figure 5, the following components were modelled and integrated inside the previous model (_FE1_ model):  
* A standard femur (Sawbones(r), Europe AB, Malmoe, Sweden), whose geometry was modified in order to reproduce a central fracture with the classical \"V shape\" cutting having 20 mm base;
* Four metallic rollers were modelled and positioned in order to reproduce the exact loading setup of the four-point bending test (prescribed by ASTM F382 regulation);
* The plate-bone connections were modelled with rigid kinematic joints (_RBE2_) connected to each other with specific flexible elements (circular beams whose minimum section has a diameter equal to 3.5 mm) in order to reproduce the screws contribution inside the model. For all plate components the AISI 316 LVM steel was assigned as constitutive material, having an elastic modulus equal to 187.5 GPa and a Poisson's ratio equal to 0.3, and considering a linear and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Element Type** & **Number of Elements** & **Element Size (mm)** & **Material Type** \\ \hline Main Body & 3D Tetra4 & 415,456 & 0.3–0.8 & AISI 316 LVM \\ Guide & 3D Tetra4 & 134,410 & 0.3–0.8 & AISI 316 LVM \\ Cover & 3D Tetra4 & 267,582 & 0.4–0.5 & AISI 316 LVM \\ Pin & 3D Tetra4 & 46,147 & 0.2–0.3 & AISI 316 LVM \\ Inclined Plane & 3D Tetra4 & 33,209 & 0.4–0.5 & AISI 316 LVM \\ Bone & 3D Tetra4 & 230,396 & 1.8–2.2 & Sawbones® \\ Rollers & 2D Shell & 21,336 & 0.8–1.2 & Stainless steel \\ Screws & 1D Beam & \(4\times 8\) & \(7\times 3.5\) & Stainless steel \\ \hline \hline  & **Total n\({}^{\circ}\) of** & **1,148,568** & & \\  & **Elements:** & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details about finite element mesh of the whole bone-plate system. Figure 5: _FE2_ model with respective loads and boundary constraints; detail of one screw modelling section. isotropic behavior; the respective non-structural Sawbones(r) (0.6 GPa of elastic modulus and 0.3 of Poisson's ratio) was assigned to the bone, and a standard Stainless steel (210 GPa of elastic modulus and 0.3 of Poisson's ratio) to the beams and the rollers. Due to the multiple contacts involved, a static non-linear simulation was carried out with an initial step size of 0.01.
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 3 Materials and Methods
### Adaptive Cost Aggregation Module
#### 3.3.1 Adaptive Cost Aggregation Operation  
Applying softmax to the cost volume \(\mathbf{C}\) yields the parallax attention map \(\mathbf{M}\in\mathbb{R}^{H\times W\times W}\). Assuming \(\mathbf{Q}\) derives from the left features, and \(\mathbf{K}\) derives from the right features, the point \((i,j,k)\) on \(\mathbf{M}\) indicates that the disparity \(d\) between point \((i,j)\) in the left image and point \((i,k)\) in the right image equals \(k-j\), and their registration confidence is \(\mathbf{M}(i,j,k)\). We employ the standard deviation of the candidate parallax as a metric to quantify the degree of the unimodal distribution. For the point \((i,j)\) in the left image, its candidate disparities are the disparities between it and the set of points \(\{(i,k)|k\geq j\}\) in the right image. The degree of unimodality and the standard deviation of the candidate parallax, i.e., the unimodal coefficient \(U\), exhibit a negative correlation. As depicted in Figure 3, a higher degree of unimodal distribution indicates more accurate registration, signifying the pixel in the cost matrix is of lower value and contributes less to the optimization process. Therefore, it is imperative to attenuate the influence of low-value cost information while reinforcing the impact of high-value information. Mathematically, the unimodal distribution coefficient \(U\) is defined as follows:  
\[\begin{split} U=&\sqrt{\sum_{\forall d\geq 0}(d-d)^{2} \times\sigma(c_{d})},\\ \tilde{d}=&\sum_{\forall d\geq 0}d\times\sigma(c_{d}),\end{split} \tag{5}\]  
where \(d\) denotes the candidate disparity between one point on the left epipolar line and points on the corresponding right epipolar line. \(\sigma(\cdot)\) denotes the softmax operation.  
denotes the cost between two pixels of which the disparity is \(d\). \(\hat{d}\) denotes the disparity regressed from the predicted cost matrix.  
We further utilize learnable parameters \(\alpha\) and \(\beta\) to normalize the unimodal distribution matrix \(\mathbf{U}\), resulting in the aggregation weight matrix \(\mathbf{W}\) of the cost. This is formulated as  
\[\mathbf{W}_{s}^{j}=\alpha_{s}\mathbf{U}_{s}^{j}+\beta_{s}, \tag{6}\]  
where \(s\in\{0,1,2\}\) denotes a different scale, and \(j\) denotes the number of APABs at the current scale. Finally, the aggregated cost is the weight summation of the cost matrix, which is formulated as follows:  
\[\mathbf{\hat{C}}=\sum_{s\in\{1,2,3\}}\sum_{i\in\{1,2,3,4\}}\mathbf{C}_{s}^{i} \mathbf{W}_{s}^{i}. \tag{7}\]
# A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems
## 2 System Models for RIS-Assisted Systems
### MISO Systems
#### 2.1.2 Problem Formulation  
In the problem formulated by the authors in [74], it is assumed that the transmitter is not able to acquire CSI. Therefore, the trouble to reach the maximum total rate is based on the channel statistics, i.e., the information angle and Rician factors, through the recursive linkage [78]. Following the mathematical calculations, the authors in [74] arrive at the solution of the following optimized problem to be a function of the distance-dependent path loss, the Rician factors and other channel statistics, and the attenuation effect has been averaged on a small scale, using the proposed PPO algorithm.  
\[\mathcal{P}1:max_{\Theta,f_{\text{k}}}\sum_{k=1}^{\mathbb{K}}\log_{2}\left(1+ \frac{\frac{p}{U}f_{k}^{H}C_{k}f_{k}}{\sigma_{2}+\sum_{i\
eq k}\frac{p}{U}f_{k} ^{H}C_{k}f_{k}}\right), \tag{6}\]  
\[\text{subject to }|\xi_{j}|^{2}=1,\text{j}=1,\ldots,\text{J},\]  
\[||f_{k}||_{2}^{2}=1,u=1,\ldots,\text{K},\]  
\(C_{u}\) (Theorem III.1. in [74]).
## 3 Characteristics of the Actual Laser Imaging Waveform
### Characteristics of Dynamic Laser Imaging Waveform  
In order to study the change law of imaging waveform when the measured object moved, the mobile platform was driven by the host computer to enable the object to move 0.1 mm horizontally at a speed of 20 mm/s, which was a tiny displacement to avoid the interference caused by the texture change of the object surface, and five groups real-time imaging waveforms were collected (similar to static experiments, two different objects were selected). On the basis of the result of the static waveform experiment, we controlled the peak intensity of the imaging waveform to be as close as possible to the ideal value (520) by giving a constant CMOS exposure time.  
The dynamic waveform change is shown in Figure 5. Although we tried to obtain a stable waveform by giving a constant exposure time, the imaging waveform was still distorted in the dynamic measurement process. Moreover, when the measured object was a black frosted reflector, the distortion of the imaging waveform changed more obviously than that of a white diffuse reflector, and the peak intensity oscillated violently. We considered that it was inevitable that the laser energy distribution on the CMOS was not uniform because of the superposition of light and the motion vector in the process of laser transmission. Therefore, it was necessary to develop a dynamic waveform adjustment strategy for dynamic waveform distortion.  
Figure 4: Analysis of the relationship between peak intensity and centroid position of imaging waveform. (**a**) Relationship between centroid position and peak intensity of imaging waveform for different objects; (**b**) Fitting results of relationship between centroid position and peak Intensity.  
Figure 5: Dynamic imaging waveform. (**a**) Imaging waveform of white diffuse reflector; (**b**) Imaging waveform of black frosted reflector.  
Before developing the dynamic waveform adjustment algorithm, we quantitatively compared the static and dynamic waveforms, as shown in Table 1. The peak intensity fluctuation of dynamic waveforms was two to seven times that of static waveforms, and the maximum centroid position shift was 24 times that of static waveforms. The peak intensity and centroid position drift of dynamic waveform were taken as the important indexes of dynamic waveform adjustment.  
In summary, based on the analysis of actual imaging waveforms of laser on the CMOS, the characteristics of static and dynamic imaging waveforms were significantly different. Therefore, we needed to fully consider the difference between the two measurement scenarios to develop the control algorithm.
## 3 Results and Discussion
### Characterization of Non-Volatile Metabolites of FAA Tea at Four Harvest Times
#### 3.3.3 Characterization of Differential Non-Volatile Metabolites in FAA Tea
Notably, our non-targeted metabolomics approach identified 154 non-volatile differential metabolites, among which kynure acid, 5-methoxyindoleacetate, 4-quinolinescarboxylic acid, fructose-1P, and gamma-aminobutyric acid were the most significant. It is important to note that key substances and key differential substances are not identical. Key substances typically refer to compounds with high abundance or significant bioactivity, while key differential substances are those that show the most significant changes under different conditions or treatments. For example, previous studies have highlighted the antioxidant and anti-inflammatory activities of flavonoids and polysaccharides in FAA, which are considered key substances due to their biological significance [40]. In contrast, our study focused on differential metabolites that vary significantly across harvest times, providing new insights into the dynamic metabolic profile of FAA tea. The potential presence of kynure acid in FAA is intriguing given its known biological activities, such as neuroprotection and antioxidant effects [43; 44]. However, as the identification of kynure acid is currently based on LC-MS/MS data and multivariate statistical analysis, further confirmation through co-elution experiments and NMR analysis is required in future studies [42]. This limitation highlights the need for more comprehensive structural verification to fully validate our findings.
# Characterization of the Migration of Soil Particles in Lateritic Soils under the Effect of Rainfall
## 6 Conclusions  
In this paper, based on the parameters used to characterize the fine particle content of the soil body obtained from the soil particle scale distribution formula, the distribution map of the fine particle content of the slope body under different rainfall conditions was drawn, and the fine particle dynamic model was established. In addition, the fine particle migration pattern of laterite slopes was investigated by combining the changes in pore water pressure and water content at different locations of the slopes under rainfall conditions. The following main conclusions were obtained:  
1. Under rainfall conditions, the erosion process of laterite slopes can be roughly divided into three stages: the \"mud film stage\", the fine-groove erosion stage, and the slope \"coarsening\" stage.
2. In the process of rainfall infiltration, the migration and aggregation of fine particles are influenced by the randomness and inhomogeneity of the pore distribution of the soil body, thus showing a strong randomness. As a result, the phenomenon of aggregation and loss of fine particles within the slope appears to be obviously random.
3. The failure of the slope typically initiates at the toe. Influenced by rainfall erosion, the internal migration of fine particles within the slope ultimately aggregates at the toe of the slope, resulting in an increase in pore water pressure and moisture content at the toe. This, in turn, leads to the slope's failure.
4. In the early stage of rainfall, the fine particles in the slope are mainly infiltrated in the vertical direction, and the fine particles are mostly aggregated in the form of bands. Afterward, with the continuous infiltration of rainfall, the seepage gradually becomes both vertically downward and slopes downward. Therefore, the fine particles always gather at the foot of the slope, resulting in a higher water content at the foot of theslope and an increase in pore water pressure, leading to the destruction of the slope from the foot of the slope.  
Conceptualization, D.C.; methodology, D.C.; validation, D.C. and Y.L.; formal analysis, D.C. and Y.L.; investigation, D.C., T.Y. and Q.L.; resources, F.A.; data curation, D.C.; writing--original draft preparation, D.C. and Y.L.; writing--review and editing, D.C., F.A., T.Y. and Y.L.; visualization, T.Y. and Y.L.; supervision, F.A., T.Y., Q.L. and Y.L.; project administration, F.A.; funding acquisition, F.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, Grant No. 42267020.  
Not applicable.  
The data presented in this study can be obtained by contacting the corresponding author. Due to privacy restrictions, these data are not publicly accessible.  
Author [PERSON] was employed by the company Power China Kunming Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Experimental Investigation
### Experimental Design  
Based on the observed seismic damage to slope soil bodies across fault lines and related experiments by the research team on the rupture of overlying soil layers during bedrock dislocation, preliminary experiments were conducted on sand slopes during bedrock fault slip. The experiments were aimed at observing and identifying the trace positions of rupture zones in the soil slopes, as shown in Figure 4. Following this analysis, physical model tests were conducted on slopes with rupture zones appearing at the crest and toe of the slope, respectively, as illustrated in Figure 5.  
Figure 4: Pre-test site image of a sand slope.  
In the experiment, the displacement of the ground surface and slope surface was monitored using displacement sensors and laser displacement sensors, respectively. Soil pressure sensors were employed to monitor changes in the soil pressure within the sand overburden layer and inside the slope. Microseismic sensors were used to monitor the dynamic responses of the bedrock base, slope surface, and internal structure of the slope. Table 2 presents the parameters of these sensors. The purpose of arranging these sensors was to comprehensively analyze the dynamic evolution process of the soil rupture in the slope and its overlying layer.  
At the crest and toe of the slope, three displacement sensors (D1-D3, D9-D11) were installed at intervals of 20 cm. Five laser displacement sensors (J4-J8) were evenly distributed along the slope surface. Soil pressure sensors (T1-T11) were arranged longitudinally and transversely along the inferred main rupture zone and within the slope. A pair of microseismic sensors (A1, A2) was placed at the center of the steel plates, simulating two blocks of bedrock. Microseismic sensors (A3, A4) were positioned directly above A1 and A2 on the slope surface. Both longitudinal and transverse microseismic sensors (A5-A11 or A5-A12) were positioned directly above A1 and A2 on the slope surface. The displacement sensors (A5-A11 or A5-A12) were positioned directly above A1 and A2 on the slope surface.  
were also arranged along the inferred main rupture zone and on the slope surface. Figure 5 shows the sensor placement locations. Before each formal experiment begins, we rigorously calibrate and validate all sensors to ensure the reliability and accuracy of sensor data.
## 2 State of the Art and Theoretical Framework
### Description of the Systems Used in the Industry  
The device used to perform clogging tests at constant pressure consists of several components described in Figure 2. They replicate operational filtration conditions in a controlled laboratory environment. The equipment consists of a 1 L pressure tank (a) that holds the wine sample. This tank is connected to a pressure regulator valve (b), adjustable between 0 and 5 bar, ensuring that the pressure remains constant during the test. A gas shut-off valve (c) controls the supply of inert gas, such as nitrogen or compressed air (d), which pressurizes the system [16]. Additionally, the device is equipped with a pressure relief valve (e) to ensure system safety by preventing the pressure from exceeding set limits. A top connection (f) facilitates the opening of the tank to introduce the wine sample. At the bottom of the tank, a connection (g) allows for the installation of the filtration medium, which is held by a 25 mm filter holder (h). To regulate the start and end of the test, a ball valve (i) is used to control the flow of the wine sample. The system includes a stopwatch (j) to measure the filtration times, and a graduated cylinder (k) to measure the volume of filtered wine. These components enable precise, controlled testing, providing reliable data on clogging potential under standardized conditions.  
Figure 1: Relationship between time and filtered volume, highlighting the values used for the calculation of Vmax, FI, and FMI.
## 5 Results and Analysis
### Sensor Data  
The features listed in Table 3 extracted from the multimodal sensor data are presented in this section.  
A selection of the features (seven out of 53) extracted from the sensor data are presented in Figures 2 and 3. They were selected as the fundamental features in order to represent the different groups of participants: those who were affected only by hip, only by pelvis, only by femur, and all participants. From Figures 2 and 3, it can be seen that the highest variation in the sensor data was among the group of participants with an injured hip. Figures 2c and 3b show that while the average magnitude of acceleration (\(l2\)-mean) and total motion events (motion-sum) were decreasing for participants with an injured hip, they remained more or less consistent for the rest of the participants. Figures 2a and 3a also show variations for total step count (increasing) and distance traveled from home (decreasing) for the pelvis fracture group, while they had minimal changes for the rest of the participants. We cover the results from all the participants (lower plot) in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline
**Timeline** & **Variable** & **Mean (SD)** \\ \hline Week 6 & TUG (seconds) & 18.64 (10.13) \\  & 30 s chair stand & 11.13 (2.80) \\  & SIS & 25.38 (2.60) \\  & Oxford Hip Score & 34.71 (7.69) \\  & Oxford Knee Score & 36.67 (8.33) \\ \hline Week 8 & TUG (seconds) & 17.2 (7.90) \\  & 30 s chair stand & 12.13 (2.93) \\  & SIS & 23.88 (3.55) \\  & Oxford Hip Score & 33.14 (8.18) \\  & Oxford Knee Score & 34.71 (8.48) \\ \hline \end{tabular}
\end{table}
Table 5: _Cont_.  
Figure 2: Line plot of 3 daily statistical features affected by fracture (top plot) and affected by all fractures (lower plot): (**a**) step-sum (total step count); (**b**) heart rate-mean (average heart rate from the smartwatch); and (**c**) \(l2\)-mean (average magnitude of acceleration). The gray line on each sub-figure indicates the trend line for the line plot.
## 5 Computational Results
### Kinetics of CO Production  
The CO* adsorption energies were compared with the energy barriers of desulfurization reactions to understand the origin of the experimentally observed CO production during the desulfurization process. The desulfurization reactions are predicted to be less favorable than CO desorption. The CO* adsorption energies ranged from 34 to 100 kJ, which are much lower energy requirements than the energy to activate the desulfurization kinetics (-200 kJ/mol). The high stabilities of CO* on the defect surface and the  
Figure 12: DFT evaluated COS oxidation reactions toward CO\({}_{2}\) on CuO(111) with and without the oxygen vacancies and a surface sulfur atom. The black, red, and blue lines are the reactions on pristine CuO(111), oxygen vacant employed CuO(111), and both surface sulfur atom and oxygen vacancy introduced CuO(111), respectively. TS stands for transition state.  
surface with both defects and surface sulfur do not have sufficient adsorption energies to remain on the surface compared to the energy barriers for desulfurization. As a result, CO* would experience desorption rather than CO2 formation, which corresponds well to the experimental observation of CO production during the desulfurization reactions. Such behavior would be more pronounced when the surface defect and surface sulfur atom are present because the initial step of desulfurization generating CO* has barrierless exothermic kinetics. Such favorable kinetics would accelerate the generation of CO* on the surface, which leads to facile CO desorption. The CO* production kinetics would occur more frequently at low temperatures because the reaction rates are more clearly distinguished at low temperatures. A future experimental and computational study will conduct an in-depth study of the CO generation kinetics on CuO surfaces to confirm the catalytic activity of CuO for CO production.
## 4 Discussion
Due to the high dimensional and complex nature of both the ASD spectral data and the GF5 AHSI image, the PCA-CNN model also suffers from a certain degree of overfitting, especially for the larger particle size loan particles, showing a certain lack of fault tolerance, resulting in a slight decrease in the predictive ability of the model. This requires further optimization for the model to be applied in intelligent detection and dynamic monitoring of soil texture. Currently, PCA-CNN has only been validated on a small number of samples from a limited number of areas due to the limited dataset available, which restricts its generalizability. It is a data-deficient model that needs to be further explored by incorporating more soil samples, especially in areas with varying soil conditions. Furthermore, this method is limited to bare soil, and more research is needed on the effects of some external factors, such as vegetation cover and soil moisture [48]. Although the results obtained from hyperspectral images are still incompatible with laboratory spectra, PCA-CNN provides an alternative approach to mapping soil texture content using laboratory spectra and hyperspectral data.