esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
Taking Daoxu Town Quarry slope as an example, Tables 4 and 5 tabulate the values for _RMR_ and _CSMR_ estimations, respectively. A value of 30.8 was obtained by Equations (13) and (14) of the slope, and therefore the slope is classified to grade III, which is in the range from 25 to 50 in _CSMR_. This result revealed the unstable state of the slope, however, given the actual collapsed state, a grade IV should be estimated to fit the reality. It is considered that this discrepancy was yielded due to the incomplete and imprecise information being used in the estimation, which again suggested the necessity of the introduction of probabilistic methods to slope stability assessments.  
Three rock slope samples (T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\)) from group A were firstly selected to determine the weight w for the attributes, and w\({}_{1}\), w\({}_{2}\), and w\({}_{3}\) for the subattributes. As shown in Table 1, there are three indices for attribute or subattribute, therefore, the weight vector has a form of \(W\) = (x\({}_{1}\), x\({}_{2}\), x\({}_{3}\)), where x\({}_{1}\) + x\({}_{2}\) + x\({}_{3}\) = 1. A simple algorithm based on MATLAB was developed, which tested different combinations of weight values to identify the suitable vector under that the calculated stability grades of T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\) agree with their actual values (III, IV and II) as tabulated in Table 6.  
The calculated weight vectors are as follows.  
1. For attributes, \(W\) = (0.6, 0.3, 0.1).
2. For subattributes, \(W_{1}\) = (0.3, 0.5, 0.2), \(W_{2}\) = (0.2, 0.2, 0.6), and \(W_{3}\) = (0.2, 0.4, 0.4).  
This set of weight vectors was employed to estimate the stability of slopes of group B via Equation (12). Note that in calculation, all the single values tabulated in Table 6 should be expressed as neutrosophic number. For example, a single value 0.3 is transformed into [0.3, 0.3], and its neutrosophic number is \(N=0.3+0\times I\).  
The calculated possibilities \(D_{W}\) of each grade for the slopes in group B are tabulated in Table 8. The maximum value of \(D_{W}\) for each slope represents its calculated stability grade. Comparison between the calculated and the actual grades shows that the stability grades of slopes T\({}_{2}\), T\({}_{7}\), and T\({}_{8}\) are underestimated using the current set of weight vector. The accuracy is 70% when the weight vectors are determined based on the data of three slopes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(H\) & \(h\) & \(\mathbf{T_{1}}\) & \(\mathbf{T_{2}}\) & \(\mathbf{T_{3}}\) & \(\mathbf{T_{4}}\) & \(\mathbf{T_{5}}\) & \(\mathbf  
Repeating this procedure, it was found that when six samples were used for the determination of weight vectors, the accuracy increased up to 90%. Finally, when eight samples were used for the determination, the accuracy became 100%. The determined weight vectors are  
1. For attributes, \(W=\) (0.6, 0.2, 0.2);  
2. For subattributes, \(W_{1}=\) (0.3, 0.5, 0.2), \(W_{2}=\) (0.2, 0.2, 0.6), and \(W_{3}=\) (0.2, 0.7, 0.1).
## 4 Numerical Examples
### Example 1  
The first scenario under investigation is a two-dimensional homogeneous isotropic stationary heat conduction problem [39], as shown in Figure 2. For a two-dimensional domain, \(\Omega\), enclosed by an elliptical-shaped section, the governing equation of the heat conduction problem is expressed as follows:  
\[\
abla^{2}u(\mathbf{x})=0\text{ in }\Omega \tag{26}\]  
The two-dimensional object boundary under consideration is defined as  
\[\Gamma_{1}=\Big{\{}\left(x_{1},y_{1}\right)\lvert{x_{1}}^{2}/{a^{2}}+{y_{1}} ^{2}/{b^{2}}=1\Big{\}},\ a=2,\ b=1 \tag{27}\]  
\[\Gamma_{2}=\big{\{}\left(x_{2},y_{2}\right)\lvert x_{2}=0,\ 0\leq y_{2}\leq 1\big{\}} \tag{28}\]  
\[\Gamma_{3}=\big{\{}\left(x_{3},y_{3}\right)\lvert 0\leq x_{3}\leq 2,\ y_{3}=0 \big{\}} \tag{29}\]  
Figure 1: The profile of the composite materials for the analysis.  
The analytical solution may be found as  
\[u=\frac{(b^{2}-a^{2})xy}{a^{2}+b^{2}} \tag{30}\]  
For solving this example, the order of the particular non-singular basis function \(P\), is 15. The boundary condition is assumed to be the Dirichlet boundary condition using the analytical solution which is expressed as Equation (30) for the problem. The boundary and source points are uniformly distributed on the boundary for the hybrid boundary-type meshless method, as demonstrated in Figure 3. We compared the computed temperature of the inner collocation points in the computational domain with those from the analytical solution, as shown in Figure 4a. We obtained highly accurate numerical solutions in the order of \(10^{-16}\) for this problem. Figure 4b demonstrates the accuracy of the element-free Galerkin method (EFG) [39] and the improved interpolating element-free Galerkin (IIEFG) method [39]. It is found that the accuracy of the EFG and the IIEFG methods can only reach the order of \(10^{-16}\) for the same problem. Figure 5 shows the comparison of the computed results with the analytical solution of temperature along line AB. In addition, Figure 6 depicts a plot of the comparison of the computed temperature distribution with the analytical solution. From the figures, it can be seen that the numerical solution agrees very well with the analytical solution. This example shows that the proposed method may obtain more accurate results than the EFG and IIEFG methods.  
Figure 4: Comparison of computed results of the proposed method. (**a**) Comparison of results with the analytical solution and (**b**) comparison of results with the EFG and the IIEFG methods.  
Figure 5: Comparison of temperature results with the analytical solution along line AB.
## 4 Experimental Verification
### Analysis of Experimental Results
#### 4.2.3 Angular Positioning Deviation of the Rotary Table  
According to the calibration method of the angular positioning deviation of the rotary table based on the laser tracer multi-station measurement system proposed in this paper, the angular positioning deviations \(\Delta\theta_{j}\) of the rotary table were obtained according to Equations (32) and (33) and provided in Table 3. The angular positioning deviation of the rotary table at each indexing angle is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cordinates** & \(\mathbf{X}_{j}\) & \(\mathbf{Y}_{j}\) & \(\mathbf{Z}_{j}\) \\ \hline
1 & \(-64.9938\) & \(-6.6256\) & \(122.5133\) \\
2 & \(-53.0600\) & \(-38.1824\) & \(122.5136\) \\
3 & \(-26.9493\) & \(-59.5448\) & \(122.5134\) \\
4 & \(6.3444\) & \(-64.9883\) & \(122.5128\) \\
5 & \(37.8969\) & \(-53.0580\) & \(122.5120\) \\
6 & \(59.2589\) & \(-26.9510\) & \(122.5112\) \\
7 & \(64.7067\) & \(6.3400\) & \(122.5106\) \\
8 & \(52.7784\) & \(37.8971\) & \(122.5103\) \\
9 & \(26.6701\) & \(59.2624\) & \(122.5105\) \\
10 & \(-6.6235\) & \(64.7100\) & \(122.5110\) \\
11 & \(-38.1803\) & \(52.7804\) & \(122.5118\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimized coordinates of laser tracer stations (unit: mm).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Stations** & **P2** & **P3** & **P4** & **P5** & **P6** & **P7** & **P8** & **P9** & **P10** & **P11** \\ \hline Angular positioning deviation \(\Delta\theta_{j}\) /(unit: \({}^{\prime\prime}\)) & 5.9 & 0.7 & \(-0.4\) & \(-9.9\) & \(-9.2\) & \(-7.2\) & 1.0 & \(-0.1\) & 1.0 & 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Angular positioning deviation of the rotary table obtained by experiments.  
Figure 7: Schematic diagram of the fitting plane.  
According to Equation (34), the angular positioning deviation of the rotary table based on laser tracer multi-station measurement is calibrated as  
\[\overline{\sigma}_{\Delta\theta}=\pm\frac{1}{2}\sqrt{\frac{\sum\limits_{j=1}^{m-1 }\Delta\theta_{j}^{2}}{m(m-1)}}=\pm 0.9^{\prime\prime} \tag{37}\]  
2.4 Algorithm Error of the Calibration Method of The Angular Positioning Deviation of the Rotary Table Based on the Laser Tracer Multi-Station Measurement System  
According to Equation (36), with the above experimental data, the algorithm error of the laser tracer multi-station measurement system for calibrating the positioning accuracy of the rotary table was calculated as  
\[\Delta=\pm 0.4^{\prime\prime} \tag{38}\]
## 2 Materials and Methods  
### Fabrication  
The samples were prepared via DC/RF magnetron sputtering on thermally oxidized Si layer substrates at room temperature. CoFeB target with 99.9% purity, MgO target with 99.9% purity, Ta target with 99.5% purity, and W target with 99.5% purity were used as sputtering targets. The base pressure of the sputtering chamber was maintained below 5 \(\times\) 10\({}^{-8}\) Torr and the deposition rate was kept below 0.5 A/s. Deposition rateswere determined by measuring the film thickness using atomic force microscopy (AFM). During sputtering, the substrate was rotated to ensure uniform deposition of the films. The prepared samples were rectangular shapes measuring 4 \(\times\) 4 mm (Figure 1) and consisted of substrate/Ta(3)/CoFeB(7)/MgO(1)/Ta(3) or substrate/W(3)/CoFeB(7)/MgO(1)/W(3) configurations (thickness indicated in nm within parentheses). The composition of the CoFeB alloy sputtering target was Co:Fe:B = 20:60:20 (Co\({}_{20}\)Fe\({}_{60}\)B\({}_{20}\)); 3 nm thick Ta and 3 nm thick W served as the buffer layers and MgO(1)/Ta(3) and MgO(1)/W(3) served as the capping layers. The Ar working pressures of MgO, CoFeB, Ta, and W were 4, 1.5, 3, and 3 mTorr, respectively. Following post-annealing at 350 \({}^{\circ}\)C for 1 h under vacuum, the temperature-dependent magnetic properties of the samples were measured and analyzed to elucidate the effects of the Ta and W buffer and capping layers on the temperature dependence of the magnetic properties.
## 1 Introduction
Some of the modified models of the direct grey model DGM(2,1) in this work are the Markov direct grey model, or MDGM(2,1), the Fourier direct grey model, or FDGM(2,1), and the Fourier Markov direct grey model, or FMDGM(2,1). The effectiveness of the proposed models will be tested with the data of three case studies including the revenue of Thailand's medical tourism industry, the foreign direct investment (FDI) in Myanmar, and the total residential electricity consumption in Chong Qing. The precision of the proposed models will be evaluated through some statistical measurements. Then, the predicted accuracy of the modified models will be compared with that of the original DGM(2,1) model to assess the forecast performance. Following this introduction, the rest of this paper presents the mathematical functions of grey forecasting model DGM(2,1) and its modified models. The empirical results and conclusions are addressed in Sections 3 and 4.
## 4 Discussion
### Classification and Quantitative Evaluation of Pore Permeability
#### 4.1.3 Subcategory Classification  
Further data analysis on sandstone in categories I and II reveals that intracategory sandstone still has large differences with respect to the five classification indicators (PPACS). For example, the variation range of the intracategory permeability of category I sandstone is 0.70-102.17 mD, which indicates that the pore structure of these sandstones is still quite different. To accurately grout sandstone pores based on sandstone permeability classification, both the intercategory and intracategory differences should be fully taken into account; thus, it is necessary to further divide the two sandstone categories into more subcategories. Here, the cluster analysis method is introduced into this paper to classify pore structure subcategories.  
**For category I**, first, the sample database is established based on the data of grain size analysis, casting thin slice observations, mercury injection tests, and porosity and permeability tests of all Jurassic sandstone samples, and second, K-means cluster analysis is carried out on all samples based on the five indicator variables (PPACS). Category I is divided into two subcategories (I\({}_{1}\) and I\({}_{2}\)). The cluster analysis results are shown in Table 7.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Classification Indicators**} & \multicolumn{2}{c}{**Category I**} & \multicolumn{2}{c}{**Category II**} \\ \cline{2-5}  & **Range** & **Average** & **Range** & **Average** \\ \hline K (mD) & \textgreater{}0.7 & 23.83 & \textless{}0.7 & 0.09 \\ \(\Phi\) (\%) & \textgreater{}15 & 17.95 & \textless{}15 & 7.50 \\ \(\text{R}_{\text{ave}}\) (\(\mu\)m) & \textgreater{}0.7 & 2.37 & \textless{}0.7 & 0.20 \\ C (\%) & \textgreater{}2.5 & 4.56 & \textless{}2.5 & 1.46 \\ \(\text{S}_{\text{o}}\) & \textless{}1.4 & 1.07 & \textgreater{}1.4 & 1.73 \\ \hline \hline \end{tabular}  
* K—Permeability under a net effective pressure (mD); \(\Phi\)—Porosity under a net effective pressure (%); \(\text{R}_{\text{ave}}\)—Average pore throat radius (\(\mu\)m); C—Comprehensive diagenetic coefficient (%); \(\text{S}_{\text{o}}\)—Sorting coefficient (%).  
\end{table}
Table 6: Classification of the pore structure category of the deep-mine sandstone.
# Using Science and Technology to Unveil The Hidden Delicacy Terfezia arenaria, a Desert Truffle
## 3 Results and Discussion
### Showcasing T. arenaria's Nutritional Value
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & **Moisture** & **Ash** & **Proteins** & **Lipids** & **Carbohydrates** & **Fibre** & **Energy** & **References** \\  & **\% fw** & **g/100 g** & **g/100 g** & **g/100 g** & **g/100 g** & **g/100 g** & **g/100 g** & **kcal/100 g** & **References** \\ \hline \multirow{4}{*}{**P. ostreatus**} & _Terfezia arenaria_1 & 77 & 7.3 & 14 & 2.2 & 67 & 10 & 387 & This study \\  & & n.a & 4.3 & 23 & 5.1 & 77 & n.a & 394 & [23] \\  & _Terfezia claveryi_1 & 73 & 4.3 & 16 & 7.0 & 65 & 8 & n.a & [68] \\  & & 83 & 15.3 & 32 & 2.8 & 46 & n.a & 338 & [21] \\  & _Terfezia boutieri_1 & n.a & 12.9 & 17 & 6.4 & 60 & 4 & n.a & [24] \\  & & 78 & 4.5 & 26 & 8.0 & 62 & n.a & n.a & [20] \\  & _Terfezia dibensis_1 & 80 & 15.3 & 36 & 3.2 & 48 & n.a & 366 & [21] \\  & _Agaricus bisporus_1 & 91 & 12.7 & 19 & 2.0 & 67 & 10 & 360 & [69] \\  & & 90 & 9.4 & 25 & 2.3 & 64 & n.a & 374 & [70] \\  & & 91 & 7.8 & 18 & 2.6 & 71 & 14 & 382 & [69] \\  & _Pleurotus ostreatus_1 & n.a & 9.3 & 9 & 1.3 & 70 & 11 & n.a & [71] \\  & & 89 & 6.7 & 13 & 2.5 & 78 & n.a & 383 & [70] \\  & & n.a & 3.8 & 18 & 0.9 & 30 & 32 & 264 & [72] \\  & _Lentinula edodes_1 & 94 & 6.7 & 16 & 1.8 & 74 & 15 & 382 & [69] \\  & & 88 & 7.4 & 17 & 2.1 & 73 & n.a & 381 & [70] \\  & _Tiber melanosperm_1 & n.a & 0.0 & 22 & 2.3 & 75 & n.a & 411 & [23] \\  & _Pork_2 & & & 13 & 37.0 & 2.4 & n.a & 390 & [83] \\  & & 64 & 0.9 & 18 & 17.5 & n.a & n.a & 228 & [90] \\  & _Beef_2 & & & 20 & 4.2 & 2.0 & n.a & 126 & [83] \\  & & 63 & 0.8 & 18 & 19.4 & n.a & n.a & 243 & [90] \\  & _Chicken_2 & & & 19 & 1.3 & 9.4 & n.a & 167 & [83] \\  & & 75 & 1.0 & 18 & 7.2 & n.a & n.a & 133 & [90] \\ \hline \end{tabular}  
* a mushroom values presented for dry weight, except for moisture (% fresh weight). b
* meat values presented for fresh weight: n.a—data not available.  
\end{table} 
Table 1: Nutritional composition and energy values for _T. arenaria_ and other desert truffles, commercial mushrooms and meat (pork, beef and chicken). Values for _T. arenaria_ were determined in the present study while values for the other mushrooms, truffles and meat were determined in other studies. Figure 2: Fresh _T. arenaria_’s nutritional and mineral composition in relation to reference values reported for other fresh edible mushrooms and meat. Plot of the first two principal components of the PCA model built with common nutritional and mineral composition values for fresh _T. arenaria_ determined in this study, and fresh _A. bisporus_, _L. edodes_, _P. ostreatus_ and beef, pork and chicken meat from the literature.
## 3 Master Control System and Motion Control System  
### Tasks and Functions of Master Control System  
The master control system of the extremely low speed calibration facility is the top-level central headquarter management control system used to harmonize the operation of the whole calibration facility. It is mainly composed of computers and control software, responsible for the following tasks: starting, running and stopping the whole facility, communicating with all subsystems, giving instructions to the subsystems and directly operating the auxiliary equipment, keeping watch on the operating status of the subsystems, etc. These subsystems and auxiliary equipment include carriage motion control system, enclosed chamber temperature and humidity control system, chamber pressure balancing system, chamber monitoring system, chamber lighting, chamber air stirring fan, limit switch, hot-wire probe calibration data acquisition system, etc. The core subsystems are the motion control system and the temperature and humidity control system. A master control computer (Figure 4) can achieve the above functions. The motion control system shares the same computer with the master control system, thus the computer can display the moving velocity and position of the carriage in real time. The master control computer sets up the communication with the temperature and humidity control system, sends control instructions, and acquires the control result data through the data interface (Ethernet or RS485) and displays the data. The master computer is also responsible for the man-machine interface management of the facility, the automatic failure monitoring and the safe operation of the facility such as the user data input, the storage and output of on-site data, and curve display of the facility.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 3 Method
### Signal Extraction  
The colour signals and the head orientation angles are tracked over a running time window of 8.53 s, which corresponds to 256 frames at 30 fps, or 512 frames at 60 fps. To counteract the impact of variations in frame rates of the input, all signals are resampled (using linear interpolation) to a fixed sampling rate of 30 or 60 Hz, whichever is closer to the frame rate of the source video. The choice of this window duration and sampling rate is based on the resulting signal length being a power of two, which is compatible with optimized fast Fourier transform operations. Subsequently, the three colour signals from R, G and B channels are combined into a single rPPG signal using the POS method [23]. This method filters out intensity variations by projecting the R, G and B signals on a plane orthogonal to an empirically determined normalized skin tone vector. The resulting 2-D signal is combined into a 1-D signal via a weighted sum with the weight determined by the ratio of standard deviations of the two signals. This ensures that the resulting rPPG signal contains the maximum amount of the pulsating component.
## 3 Results and Discussion
### Corrosion Behavior  
The potentiodynamic polarization measurements (Figure 19) demonstrate a positive correlation between the corrosion rate and the amount of B\({}_{4}\)C reinforcement. This phenomenon can be attributed to the formation of microgalvanic cells and enhanced surface interactions between B\({}_{4}\)C and the matrix. The corrosion rate was found to be highest in the 17-4 PH-12B\({}_{4}\)C specimen. The EIS analyses (Figure 20) demonstrated a decline in surface oxide film resistance with an increase in the B\({}_{4}\)C ratio. This decline was particularly evident in the 17-4 PH group. The findings of [PERSON] et al. [35] lend further support to these results.  
Throughout the production process, the morphology, particle size, and particle size distribution of the B\({}_{4}\)C particles used in composite alloys were kept constant, and only the reinforcement ratio was changed. Therefore, the effects of these factors on the corrosion behavior of B\({}_{4}\)C particles were neglected in the study.  
Upon the addition of B\({}_{4}\)C to the titanium matrix, a reaction occurs between the B\({}_{4}\)C particles and the titanium, resulting in the formation of boride (TiB) and carbide (TiC) phases. These ceramic-like phases act as reinforcement particles dispersed in the titanium matrix. The presence of boride and carbide phases has been observed to impede the integrity of a passive oxide layer, thereby compromising its stability and functionality. This phenomenon can result in the development of microgalvanic cells, which, in turn, can precipitate local corrosion, such as pitting corrosion. The passive layer is susceptible to damage from various ions, particularly in physiological environments. Despite its limited corrosion resistance, the Ti-5B\({}_{4}\)C alloy provides successful results in short- and medium-term implant applications due to its high hardness, strength, and wear resistance.
## 4 Discussion
### Model Interpretability Considerations  
Understanding how the PSCDR-BMPNet model arrives at its biomass predictions is crucial for building user trust and facilitating practical adoption in precision agriculture. While the current study primarily focused on predictive accuracy, future iterations should incorporate model interpretability techniques. For instance, methods like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) could be adapted to identify which points or learned features contribute most significantly to the biomass estimates. Given the use of the N2P Attention module, visualizing attention weights could also offer insights into the spatial regions of the point cloud that the model deems most important. Such analyses would not only demystify the \"black box\" nature of the deep learning model but also potentially guide further model refinement or data acquisition strategies.