esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relationship between Soil Properties and Soil Enzyme Activities in CK Lands  
The Pearson's correlation coefficients and linear fit results revealed an overall significantly positive correlation between soil enzyme activities and soil properties (\(p<0.01\), Figure 4). The results showed that soil enzyme activities were largely affected by DOC (Pearson's R: 0.41-0.76, \(p<0.01\)) and DON (Pearson's R: 0.44-0.72, \(p<0.01\)). Particularly, soil catalase and sucrose were highly related with DOC and DON, while soil alkaline phosphatase was more closely related with AP; however, SWC showed lower effects.  
RDA showed that soil properties were significantly correlated with soil enzyme activities (together explaining more than 70% of the total variation) (Figure 5). Specifically, DOC and DON showed a closer relationship with soil enzyme activities in all seasons; in contrast, AP only showed significantly high effects on soil enzyme activities in the summer (June and August), and SWC showed a minimal relationship in all seasons. These results were also confirmed by the correlation analysis between soil enzyme activities and soil properties for each season (Table S3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Enzyme** & **Growing Season** & **Stand Age** & **Depth** \\ \hline
**Total** & 0.18 * & 0.30 ** & 0.28 ** \\
**Catalase** & 0.25 ** & 0.58 ** & 0.23 * \\
**Sucrase** & 0.16 * & 0.40 ** & 0.32 ** \\
**Urease** & 0.09 NS & 0.34 ** & 0.48 ** \\
**Alkaline phosphatase** & 0.17 * & 0.40 ** & 0.32 ** \\ \hline \end{tabular}
* significant in \(p<0.05\); ** significant in \(p<0.01\); NS means no significance.  
\end{table}
Table 4: The R value from the analysis of similarities (ANOSIM).  
Figure 4: Linear fitting and Pearson’s correlation coefficients (Pearson’s R) between soil properties and soil enzyme activities across all treatments. For each subfigure, \(n\) (144) = four seasons \(\times\) four land-use types \(\times\) three soil depths \(\times\) three replications. The line is the linear fitting line, and the shaded bands delineate the 95% confidence interval of the regressions. ** and * correspond to the statistically significant levels of \(p\) < 0.01 and \(p\) < 0.05, respectively. DOC, DON, AP, and SWC represent dissolved organic carbon, dissolved organic nitrogen, available phosphorus, and soil water content, respectively.  
Figure 5: The results of redundancy analysis (RDA) to identify the relationships between the soil enzyme activities (black arrows) and soil properties (red arrows) in different months (marked in each part of the figure) across all land-use types and soil depths. For each subfigure, \(n\) (36) = four land-use types \(\times\) three soil depth \(\times\) three replications. The percentages on the x-axis and y-axis reflect the response rate of the soil properties to the changes in soil enzyme activities in different dimensions. DOC, DON, AP, and SWC represent dissolved organic carbon, dissolved organic nitrogen, available phosphorus, and soil water content, respectively.
# A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes
## 3 Non-Destructive Detection Method for Steel Wire Ropes
### Electromagnetic Detection Method
#### 3.1.1 Mechanism and Model  
For electromagnetic detection mechanism and theoretical model, many studies have been carried out by researchers at home and abroad.  
For instance, [PERSON] et al. [12] used the finite element analysis method to optimize the pole shoe length, and the optimized results help make the magnetic flux distribution in the measured parts as uniform as possible, which was more conducive to magnetic leakage detection. In [13], [PERSON] et al. performed finite element analysis for the magnetic flux leakage (MFL) distribution of WR typical defects based on strong magnetic detection method and obtained the relationship between MLF signal and detection distance, damage depth, damage axle width and internal wire breakage. In [14], [PERSON] et al. paid attention to oil and gas pipelines, and analyzed the influence of line pressure, bending and residual stresses on MFL signal in MFL detection process through experiment and finite element simulation. To study the effects of probes and probe lift-off on the defect leakage magnetic field, [PERSON] [15] and [PERSON] et al. [16] respectively carried out research and proposed that the smaller the lift-off value of magnetic sensitive probes, the better.  
In order to apply the magnetic dipole to the three-dimensional magnetic field modeling of MFL, [PERSON] et al. [17] first presented a three-dimensional (3D) magnetic leakage model of ferromagnetic materials surface fracture based on magnetic charge theory. Based on [17], an improved 3D MFL model for numerical analysis is proposed by [PERSON] et al. [18], which can be used to detect surface fracture of ferromagnetic materials under saturated magnetization. To further explain the formation principle of MFL, [PERSON] et al. [19] explained the mechanism of MFL in engineering from the perspective of magnetic refraction, and found that the signal component in defect detection was different from that in traditional MFL analysis, which was produced by protruding defects. Considering the effect of tensile stress on the strength of the MLF of a steel WR defect, [PERSON] et al. [20] loaded a variable force onto reproduced samples of typical defective WRs relying on an inspection platform. The experimental results showed that the peak-to-peak values of the MFL signal from all defective WRs increased with tensile stress, in an approximately linear relationship.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 5 Analysis
### Elevation Difference  
From the location of the UAV, we can calculate the range of each image covering the ground, and the highest and lowest elevations in that range are obtained to calculate the elevation difference. The amount of elevation difference for each image is shown in Figure 13. From Figure 13a, the grid-type maximum elevation difference is 175-200 m, and 65% of the images have an elevation difference of less than 150 m. From Figure 13b, the contour-type maximum elevation difference is 125-150 m, and the elevation difference is less than 150 m for 100% of the images.
## 4 Friction-Induced Vibration Behavior under Startup Processes
### Effect of Young's Modulus on Frictional Vibration Behavior  
This section analyzes the effect of the Young's modulus on the frictional vibration behavior during the startup process. The initial Young's modulus is set as E\({}_{0}\) (E\({}_{0}\) = 13.71 MPa). E\({}_{0}\) is multiplied by different coefficients to obtain five Young's moduli: 0.5 E\({}_{0}\), 0.75 E\({}_{0}\), E\({}_{0}\), 1.25 E\({}_{0}\), and 1.5 E\({}_{0}\). The obtained Young's modulus is substituted into finite element software for analysis.  
Figure 19 shows the time-domain and frequency-domain diagrams of the frictional vibration during the startup process under five Young's moduli ranging from 0.5 E\({}_{0}\) to 1.5 E\({}_{0}\). From 0.5 E\({}_{0}\) to 1.5 E\({}_{0}\), the peak frequency increases from 1575.7 Hz to 2093.7 Hz, respectively, an increase of 32.9%, as shown in Figure 20. Similarly, in engineering design, choosing low-modulus materials to reduce the vibration frequency can also lead to a decrease in the load-bearing capacity. Therefore, a comprehensive consideration should be given between the vibration behavior and the load-bearing capacity to select the most suitable material.  
Figure 18: Peak frequency and downforce displacement of startup process under different COFs.  
The change in the Young's modulus mentioned earlier for the low-speed operating conditions indirectly affects the frictional vibration behavior, and the same is true during startup. During the startup process, the time-varying rotational speed and friction coefficient intensify the frictional vibration. As a result, the frictional vibration frequency increases with the increase in the Young's modulus. In addition, under the same Young's modulus, the friction-induced vibration frequency during the startup process is higher than that under low-speed operating conditions.
# Model-Based and Model-Free Point Prediction Algorithms for Locally Stationary Random Fields
## 6 Model-Free vs. Model-Based Inference: Empirical Comparisons
### Real-Life Example: CIFAR Images  
The CIFAR-10 dataset [32] is used as a real-life example to compare the Model-Based and Model-Free prediction algorithms discussed before. The original CIFAR-10 dataset consists of 60,000 32 by 32 color images in 10 classes, with 6000 images per class. We pick 100 images from the class \"dog\" where the original images have 3 RGB (red, green, blue) channels with discrete pixel values. We pick the R (red) channel of each image, and standardize these to generate a new real-valued dataset. Our final transformed dataset has 100 32 by 32 random fields. The NSHP limits are set from \((32,32)\) to \((16,16)\), this defines the region \(E_{LL}\) as shown in Figure 2. The rest of the image is considered occluded and their pixel values are not available for prediction. Sample images used for prediction are shown in Figure 4. Let \(t_{1}=16,t_{2}=16\) where point prediction is performed. Bandwidths for all Model-Based, Model-Free, and baseline predictors are calculated using cross-validation as described in Section 5.  
The results for point prediction using mean square error (MSE) over all MB and MF methods are shown in Table 2. From this table, it can be seen that MF-LLH and MF-LLM are the best point predictors. The superior performance of the Model-Free estimators as compared to their Model-Free counterparts can be attributed to the fact that the CIFAR-10 image data is not compatible with the additive model as given by Equation (1). It can also be seen that unlike the synthetic 2D AR dataset the two best predictors MF-LLH and MF-LLM are closer in performance which is owing to the lack of a linear trend at the point where prediction is performed.  
In addition, comparing the performance of MB-LL versus its corresponding baseline LL and that of MF-LLH and MF-LLM versus their corresponding baselines LLH and LLM, respectively, show that the baseline estimators underperform as they do not take into account the spatial dependence present in the data either by estimating the coefficients of the 2D AR model (Equation (6) as in the Model-Based case) or by estimating the autocovariance matrix \(\hat{\Gamma}_{n}^{AR}\) (Model-Free case).  
Figure 3: Linear trend for NSHP where prediction is performed \((50,50)\). Here the axes labeled \(x\) and \(y\) denote the coordinates of the random field and the axis labeled \(z\) denotes the corresponding value of the random field at those coordinates.
## 3 Research Methods and Process
### Numerical Simulation Test (Flac\({}^{3\text{D}}\)6.0)
#### 3.1.1 Numerical Model Construction  
Taking the buried depth of \(2^{-2}\) coal seam in the study area, the macroscopic layered structure of the overburden, the thickness of the rock and soil layer, and the coal seam as the geological prototype, the numerical model is constructed in FLAC\({}^{3\text{D}}\) software. Firstly, the geometric size parameters of the numerical model are designed. The total thickness of the model is 315 m, of which the thickness of the floor is 10 m, the thickness of the \(2^{-2}\) coal seam is 5 m, the total thickness of the bedrock is 250 m, and the total thickness of the loose layer is 50 m (including the thickness of the red soil layer is 20 m, and the thickness of the sand layer is 30 m). The width of the model is 300 m and the length is 1000 m. It is used to simulate the width and annual advance distance of the \(2^{-2}\) coal seam mining face in the study area. Secondly, according to the characteristics of the number of strata, lithology, and interbedded structure of the overlying bedrock section of the \(2^{-2}\) coal seam in the study area, the bedrock layered structure is designed as the upper and lower sections. The upper section is the \"sandy mudstone-fine sandstone\" interbedded structure, which is fixedly divided into 4 groups of cycles, and the lower section is the \"siltstone-fine sandstone\" interbedded structure. The number of cycles is set according to the geological prototype and research needs. Thirdly, the constraints of each boundary of the numerical model are set, specifically: the left and right boundaries are set as single constraint boundaries, and \(u=0,\ v\
eq 0,\ w\
eq 0\) is taken. The front and rear boundaries are set as single constraint boundaries, taking \(u\
eq 0,\ v=0,\ w\
eq 0\). The bottom boundary is set as the fully constrained boundary, taking \(u=0,\ v=0,\ w=0\). The upper boundary is set as a free boundary and is not constrained, as shown in Figure 3.
# Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control
## 3 An Active Noise Control System with Dynamically Adapted Reconstruction and Anti-Aliasing Filters
### Analysis of Computational Complexity  
The computational complexity is jointly determined by the extraction ratio \(Q\), the filter order \(N_{h}\), the period of dynamic matching, and the number of Fourier transform points. Let \(Q=8\) and \(N_{h}=6\); \(\hat{G}(z)\) and \(W(z)\) have 256 orders. Each of the filter libraries for \(T_{xd}(z)\), \(T_{ud}(z)\), and \(T_{ed}(z)\) is composed of 20 filters. In the process of power spectrum estimation, a 1024-point Fourier transform is employed. Considering the acoustic feedback of the secondary path, the average numbers of multiplication operations with different channel numbers are shown in Table 1 for a comparison with the single-rate method. The order of the FIR filter selected in [PERSON]'s method is 40, while sixth-order direct Type I IIfilters are selected in this study. The update period of the secondary path is 1000 times for the period with the low sampling rate.  
As the process of the dynamic matching algorithm is only required at long intervals, the amount of computation introduced in each sampling interval is limited. In the case with one reference microphone, one secondary sound source, and one error microphone, the multiplication operations of [PERSON]'s method and the dynamic matching method compared with single-rate methods are increased by 12% and 27%, respectively. As the number of channels increases, the proportion of the increase multiplications continuously decreases. In the case with two reference microphones, two secondary sound sources, and two error microphones, the increased multiplication operations of [PERSON]'s method and the proposed method are about 4% and 9%. In practical applications, virtual microphone technology [34; 35] is often used in low-sampling-rate regions, so the proportion of computational complexity introduced by the proposed method will be further reduced.
## 6 Model Verification
### Model Validation  
Various statistical indicators were used in this study to verify the accuracy of the predictions. These indicators were RMSE, MAE, \(k\), \(k^{\prime}\), and \(R_{o}^{2}\). \(k\) and \(k^{\prime}\) are the regression slope through the origin, and \(R_{o}^{2}\) is the squared correlation coefficient. \(k\) is calculated by plotting experimental values against prediction values. Conversely, \(k^{\prime}\) is the line slope by plotting prediction against experimental values. It should be noted that \(k\) and \(k^{\prime}\) were used by [PERSON] and [PERSON] (2016), and \(R_{o}^{2}\) was introduced by [PERSON] et al. (2016). Table 9 displays the statistical indicators used for predicted parameters of this study. It should be noted that the used thresholds of the statistical indicators were proposed in Refs. ([PERSON] et al., 2016; [PERSON] et al., 2016). Based on the table, all the criteria were satisfied for all the predictions, except for \(\varepsilon_{c1}\) where one of the criteria, i.e., \(R_{m}\), was not satisfied. Figure 11 shows the box plot of \(f^{\prime}_{c1}\) and \(\varepsilon_{c1}\) of this study. These parameters were selected as representative of the results. As can be seen in the figure, there were only a few samples where their predictions were in out-layers.  
Figure 10: Obtained relevance factor for transition zone: (**a**) \(f^{\prime}_{c1}\) and (**b**) \(\varepsilon_{c1}\).
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 5 Conclusions  
This manuscript highlights the results of a complete experimental measurement campaign aimed at investigating the LV NB-PLC channel in the frequency range from 9 to 500 kHz in various sites (i.e., rural, urban etc.) located in France. In particular, the aim is to fully characterize the noise characteristics of the PLC channel in time-frequency representation.  
The time-frequency analysis shows the dominance of the cyclo-stationary noise. The short-time variation analysis shows impulsive noise due to a high number of loads connected to the network. Even if these loads may be switched on and off, the background noise is mostly constant over the period of a few hours. At the customer side, the noise variation is more important in comparison with the noise profile measured in the transformer substation.  
The performances of the NB-PLC transmission can be calculated from the measurement results of the transfer function, and the noise PSD. The analysis of the SNR-values shows a good transmission capacity in the PLC channel. The channel capacity corresponds to the maximum theoretical bit rate, which this channel can support in the five topologies chosen in this study, based on the measured noise profiles. The results point out that the channel capacity is better in the FCC band.  
[PERSON] and [PERSON] performed the measurement on the low-voltage network. [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the paper. [PERSON]-[PERSON] and [PERSON] supervised the research work.  
This research was funded by the digital applications laboratory of the HF COMPANY group and the \"ANRT\" grant number [2015/1262].  
This study was carried out in partnership with the French distributor of energy \"ENEDIS\" represented by [PERSON] (PLC expert, ENEDIS, France) and [PERSON] (PLC team manager, ENEDIS, France). The authors would like to thank the team from ENEDIS both for their assistance during the measurements and their support at the experimental sites located in the city of Ottmansheim, and in the city of Tours in France. Many thanks to [PERSON] (PLC expert, EDF R&D, France).  
The authors declare no conflict of interest.
# A Reaction Microscope for AMO Science at Shanghai Soft X-ray Free-Electron Laser Facility
## 3 Result and Discussion
### Single Ionization Experiment of Oxygen Molecules  
The \(2.3\times 10^{14}\) W/cm\({}^{2}\)-intensity laser interacted with the oxygen molecules, and photoelectrons and \(O_{2}^{+}\) ions were generated in the ionization process. During the experiment, the extraction electric field was set to 1.5 V/cm and the coil current was set to 4.0 A, which produced a magnetic field around 7 Gauss.  
Figure 4a shows that most of the ions are \(O_{2}^{+}\) from the gas jet. It can be seen in Figure 4b that electrons and \(O_{2}^{+}\) ions had a clear correlation in TOF, which represents the momentum conservation between those two particles in the z direction. After selecting the events within the coincidence line and calculating the corresponding momenta, a one-dimensional spectrum was generated by summing the electron momentum and the ion momentum in Figure 4c, and a Gaussian-like distribution was obtained. By fitting the spectrum, as is shown with the red curve, a good agreement with the experimental data was achieved. The full-width-at-half-maximum (FWHM) of the momentum sum spectrum was 0.11 a.u., and this represents the convoluted momentum resolution of the two detectors. Thus 0.11 a.u. was the upper limit of momentum resolution for both electrons and ions in this test. With the position resolution of 0.1 mm, the resolution in the x and y directions could be thus estimated to be 0.13 a.u.  
An electron-TOF-radius correlation is presented in Figure 4d, where the radius is the distance between electron impact position and the origin position on the detection plane. The period of the electron cyclotron motion was 49 ns and the corresponding magnetic field was 7.2 Gs. Taking into account the detection area of the electron detector with a diameter of 80 mm, the electron-momentum detection range could be calculated to be within 1.13 a.u. In subsequent tests, it was measured that the device could provide a stable magnetic field of 17 Gs, which corresponds to a momentum detection range of 2.81 a.u. and an energy detection range of 108 eV for electrons.
# Buckling Analysis of CNTRC Curved Sandwich Nanobeams in Thermal Environment
## 4 Analytical Solution  
In this section, the equilibrium equations are solved analytically using the [PERSON] method for simply-supported (SS), clamped-clamped (CC) and clamped-hinged (CS) boundary conditions. The following displacement functions are thus assumed:  
\[\left\{\begin{array}{c}u_{0}\\ w_{0}\\ \varphi_{x}\\ \varphi_{z}\end{array}\right\}=\sum_{m=1}^{\infty}\left\{\begin{array}{c}U _{m}\frac{\partial X_{m}}{\partial x}\\ W_{m}\frac{\partial X_{m}}{\partial x}\\ \psi_{zm}X_{m}\end{array}\right\} \tag{32}\]  
with \(U_{m}\), \(W_{m}\), \(\psi_{zm}\) and \(\psi_{zm}\) being arbitrary parameters. The functions \(X_{m}(x)\) that satisfy the selected boundary conditions are defined as  
* For SS beam \[X_{m}=\sin(\beta x),\ \ \beta=\frac{m\pi}{L}\] (33)
* For CC beam \[X_{m}=1-\cos(\beta x)\ \,\beta=\frac{2m\pi}{L}\] (34)
* For CS beam \[X_{m}=\sin(\beta x)[\cos(\beta x)-1],\ \ \beta=\frac{m\pi}{L}\] (35)  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & \(P_{0}\) & \(P_{-1}\) & \(P_{1}\) & \(P_{2}\) & \(P_{3}\) & \(P_{4}\) \\ \hline \(E_{11}^{cnt}\) [\(\mathrm{TPa}\)] & 6.5653 & 0 & \(-8.9437\times 10^{-4}\) & \(1.9182\times 10^{-6}\) & \(-1.8198\times 10^{-9}\) & \(6.0043\times 10^{-13}\) \\ \hline \(E_{22}^{cnt}\) [\(\mathrm{TPa}\)] & 8.2271 & 0 & \(-8.9024\times 10^{-4}\) & \(1.9066\times 10^{-6}\) & \(-1.8063\times 10^{-9}\) & \(5.9486\times 10^{-13}\) \\ \hline \(G_{12}^{cnt}\) [\(\mathrm{TPa}\)] & 1.1056 & 0 & \(5.6727\times 10^{-3}\) & \(-1.4815\times 10^{-5}\) & \(1.6402\times 10^{-8}\) & \(-6.5007\times 10^{-12}\) \\ \hline \(\alpha_{11}[10^{-6}/^{\circ}\mathbb{C}]\) & \(-1.1279\) & 0 & \(-2.0340\times 10^{-2}\) & \(2.5672\times 10^{-5}\) & \(-1.0186\times 10^{-8}\) & \(5.9455\times 10^{-14}\) \\ \hline \(\alpha_{22}[10^{-6}/^{\circ}\mathbb{C}]\) & 5.4359 & 0 & \(-1.7906\times 10^{-4}\) & \(4.6367\times 10^{-8}\) & \(1.2424\times 10^{-11}\) & \(-5.3290\times 10^{-14}\) \\ \hline \(\
u_{12}^{cnt}\) & 0.175 & 0 & 0 & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 3: Temperature-dependent coefficients of CNT material properties [22].  
The accuracy of the proposed theoretical solution is explored in the next section, within a large systematic investigation aimed at determining the sensitivity of the buckling response. The proposed model is limited to uniform cross-sectional curved FG-CNTRC nanobeams with SS, SC, and CC boundary conditions and linear variation of temperature across the beam thickness; a further expansion should include more complicated cross-sectional geometries and thermal variations.