esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Protocols for Synchrotron X-Ray Experiments
### HAXPES
#### 2.3.3 Calculation of d-Band Center and Peak Properties  
According to the d-band model proposed by [PERSON] et al., the d-band center reflects the energy center of the valence d-band density of states at surface sites and correlates with catalytic activity [17; 18]. The d-band center is calculated using the following equation by integrating the intensity over the range of 0-8 eV:  
\[\text{d-}\textit{band center}=\frac{\int_{0\text{ eV}}^{8\text{ eV}}\left(\text{Binding energy}(E)\times\text{Intensity}(E)\right)\text{d}E}{\int_{0\text{ eV}}^{8\text{ eV}}\text{Intensity}(E)\text{d}E} \tag{5}\]  
The correlation between the Pt particle size, measured via SAXS at BL40B2 at SPring-8, and the d-band center values is explored. Additionally, the features of the shoulder peak near the Fermi edge are quantified and linked to variations in the catalyst structure and particle size.  
Finally, the Doniach-Sunjic function is applied to Pt 4f, Pt 3d, and C 1s spectra to account for asymmetry in spectral line shapes during signal fitting. This approach enables precise determination of the binding energy peaks and FWHM, facilitating accurate spectral characterization. Insights into the electronic structures are further enhanced by calculating the d-band centers from the valence band spectra.  
Although these analyses employ advanced techniques, their reliance on manual oversight for curve fitting remains a limitation. Streamlining these processes through automation would reduce the manual workload and enhance the efficiency and reproducibility of HAXPES studies, enabling broader applications in catalyst research.
## 3 Dynamic Analysis
### Three-Dimensional Numerical Model
#### 3.1.2 Train Loads  
In the coupling calculation of the train-track system, the iteration convergence of the two subsystems is required, which is extremely complicated and time-consuming [32]. Therefore, in this work, the train loads were calculated first and then applied to the rail supporting nodes of the sleeper beam elements (Figure 5a).  
To derive the train loads, a series of moving axle loads can be obtained according to the train geometry shown in Figure 6a. Accordingly, the successive axle loads can be expressed as a periodic function \(f(t)\) with a period of \(t_{4}\)[30]:  
\[f(t)=\left\{\begin{array}{ll}F&0\leq t\leq t_{1}\\ 0&t_{1}\leq t\leq t_{2}\\ F&t_{2}\leq t\leq t_{3}\\ 0&t_{3}\leq t\leq t_{4}\end{array}\right. \tag{5}\]  
For the sake of simplicity, \(f(t)\) is supposed as a sufficiently smooth function, which can then be transformed using the Fourier function, which is composed of a series of sines and cosines.  
\[f(t)=a_{0}+\sum_{n=1}^{\infty}\left[a_{n}\cos\left(\frac{2n\pi t}{T}\right)+b _{n}\sin\left(\frac{2n\pi t}{T}\right)\right] \tag{6}\]  
where \(a_{0}\), \(a_{n}\), and \(b_{n}\) are the Fourier series coefficients. Therefore, the train loads can be calculated if \(a_{0}\), \(a_{n}\), and \(b_{n}\) are determined. It should be noted that the principal frequencies in Equation (6) range from 0 to \(\infty\). However, according to [PERSON] et al. [33], considering the frequency range from 0 to 25 Hz is sufficient to accurately calculate the train loads. The adoption of this range of frequency is dependent on the previous study, which has  
Figure 6: Schematic of (**a**) train wagons, (**b**) normal force variation of wagon wheels, and (**c**) dynamic load of a train with an axle load of 275 kN and speed of 60 km/h.  
shown that the dominant frequencies were found within 10 Hz [25]. Therefore, in this study, only the first 46 terms of Fourier series that have a significant energy were considered, and loads with frequencies greater than 25 Hz were ignored. Figure 6c shows the dynamic load of a train with a wagon length of 11.094 m, an axle load of 275 kN, and a speed of 60 km/h.  
The train loads calculated using the method described above were applied to the sleepers with a shift in the loading time to simulate the passage of a train bogie. At a speed of 60 km/h, for example, the train requires 0.036 s to traverse a distance of 0.6 m; thus, 0.036 s is the loading interval in the time domain. This method of loading has been used in the previous study and demonstrated to be correct and reasonable [10].
## 2 Materials and Methods
### Prototype Development and Field Test  
To evaluate the functionality and practicability of the double-cutterbar structure, a prototype was fabricated with a 2.6 m wide header based on rice plant deformation and shearing properties.  
Figure 5: **(a) Rice stalk division and numbering; (b) shearing test rig. (1) Placement platform, (2) stalk, (3) blade, (4) blade holder, (5) texture analyzer**The prototype was mounted onto a walking chassis of World 4 LZ-3.0E combine harvester, as shown in Figure 6a,b, and its basic parameters are listed in Table 1.  
On 13-15 August 2019, the field test was carried out at the same place where the deformation and shearing test material were sampled in 2018. Three adjacent fields were used as repetitions of the tests and their areas were 0.65, 0.71, 0.51 ha, respectively. The rice growing procedure used was identical in the three fields in 2018 and 2019. The average heights of the rice in 2018 and 2019 were \(886\pm 33\) mm and \(875\pm 41\) mm (values presented as mean \(\pm\) standard deviation), respectively, and the required stubble heights were both 350 mm. Five parameters were selected as the performance indexes and their computing methods are shown as follow:  
1. Stubble rolling rate: \[R=\frac{\sum_{1}^{3}A_{ri}}{A_{all}}\cdot 100\%\] (10) where, \(R\) is stubble rolling rate, %; \(A_{ri}\) is stubble rolling area in each testing filed, ha; \(A_{all}\) is total area of the three fields, ha.
2. Panicle straw length: stop the harvester three times and collect 50 panicles from the header at each time. Measure and average the lengths of the stalk on panicle of all samples: \[L_{a}=\frac{\sum_{1}^{150}L_{i}}{150}\] (11) where, \(L_{a}\) is panicle average straw length, mm; \(L_{i}\) is panicle straw length on a single sample, mm.
3. Stubble height: randomly choose three spots in the test field after harvesting and select 50 stubbles at each spot. Measure and average the heights of all samples. \[H_{a}=\frac{\sum_{1}^{150}H_{i}}{150}\] (12) where, \(H_{a}\) is stubble average height, mm; \(H_{i}\) is stubble height of a single sample, mm.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value** \\ \hline Header width/mm & 2600 \\ Caterpillar width/mm & 350 \\ Machine power/kW & 51.5 \\ Reel speed/rpm & 45 \\ Auger speed/rpm & 200 \\ \hline \end{tabular}
\end{table}
Table 1: The basic parameters of the developed harvester.  
Figure 6: (a) Assembly and (b) field test of the double-cutterbar prototype.  
4. Feeding rate: \[Q=\frac{W_{v}}{t}\] (13) where, \(Q\) is feeding rate, kg\(\cdot\)s\({}^{-1}\); \(W_{v}\) is total weight of grain harvested and discharges from cleaning parts in a time of \(t\), kg.
5. Working efficiency: \[E_{w}=\frac{A_{all}}{T}\] (14) where, \(E_{w}\) is working efficiency, ha\(\cdot\)h\({}^{-1}\); \(T\) is total time consumed in harvesting of the three fields, h.
## 3 Results
### Results of Gap Acceptance
#### 3.1.2 Gap Acceptance Under Partial eHMI Penetration Condition  
AVs with eHMI  
Table 2 shows that gap size and vehicle speed also exerted statistically significant main effects on participants' gap acceptance. Similarly, the likelihood of crossing increased with larger temporal gaps and higher vehicle speeds. Regarding eHMI information content, the single-level medium configuration yielded a significant lower likelihood of crossing compared to the three-level low-medium-high condition. Notably, the interactions between gap size and eHMI information content conditions, including the single-level medium, two-level low-medium, and two-level medium-high configurations, were statistically significant and associated with negative coefficients. The interaction between gap size and the single-level high eHMI condition was marginal. As illustrated in Figure 6, these negative interactions suggest that participants exhibited reduced sensitivity to variations in temporal gap under these conditions. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Predictions** & **Estimate** & **Std. Error** & **z Value** & **Pr (\(\mathbf{\text{<}}\mathbf{\text{1}}\))** \\ \hline (Intercept) & \(-1.724\) & \(0.422\) & \(-4.091\) & \(-0.001\) \\ eHMI\({}^{\prime}\) Low & \(0.241\) & \(0.202\) & \(1.191\) & \(0.234\) \\ eHMI\({}^{\prime}\) Medium & \(0.385\) & \(0.193\) & \(1.991\) & \(0.046\) \\ eHMI\({}^{\prime}\) High & \(0.504\) & \(0.195\) & \(2.591\) & \(0.010\) \\ eHMI Low-medium & \(0.913\) & \(0.198\) & \(4.601\) & \(-0.001\) \\ eHMI\({}^{\prime}\) Medium-high & \(0.768\) & \(0.194\) & \(3.955\) & \(-0.001\) \\ Speed 36 km/h & \(1.936\) & \(0.123\) & \(15.744\) & \(-0.001\) \\ Gap size & \(4.793\) & \(0.307\) & \(15.625\) & \(-0.001\) \\ eHMI\({}^{\prime}\) Low: Gap size & \(-0.086\) & \(0.382\) & \(-0.225\) & \(0.822\) \\ eHMI\({}^{\prime}\) Medium: Gap size & \(-0.858\) & \(0.345\) & \(-2.487\) & \(0.013\) \\ eHMI\({}^{\prime}\) High: Gap size & \(-0.707\) & \(0.351\) & \(-2.013\) & \(0.044\) \\ eHMI\({}^{\prime}\) Low-medium: Gap size & \(-0.368\) & \(0.367\) & \(-1.002\) & \(0.316\) \\ eHMI\({}^{\prime}\) Medium-high: Gap size & \(-0.744\) & \(0.349\) & \(-2.130\) & \(0.033\) \\ Speed 36 km/h & Gap size & \(-0.035\) & \(0.207\) & \(-0.167\) & \(0.867\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: GLMM results for gap acceptance under full eHMI penetration conditions. Figure 5: The predicted outcome of gap acceptance in probability and logit space under full eHMI penetration conditions. TVs Without eHMI  
Table 3 shows that gap size, vehicle speed, and their interaction exerted statistically significant effects on pedestrians' gap acceptance. Specifically, the likelihood of crossing increased with larger temporal gaps and higher vehicle speeds. Moreover, at higher speeds, participants' gap acceptance rose more sharply with increasing gap size, indicating heightened sensitivity to temporal gap variations under faster traffic conditions, as illustrated in Figure 7.
## 3 Experimental Procedures
### Experimental Results
#### 3.3.3 Experimental Results Verification  
To verify the identification of the influence of the reflected blast wave on a blast-induced crack, theoretical analyses considering the specimen configuration and the wave speed in PMMA are carried out. The sketches of the reflected blast wave propagation path at the joint are shown in Figure 8.  
Figure 8: Sketch of reflected blast wave propagation path at the joint: (**a**) reflected wave created at the vertical joint; (**b**) reflected wave created at the oblique joint.  
Figure 6: Specim with a vertical unfilled joint: (**a**) without wave incidence; (**b**) opposite incidence of a dilatational wave; (**c**) opposite incidence of a shear wave.  
Figure 7: Specim with oblique unfilled joint: (**a**) oblique incidence of a dilatational wave; (**b**) oblique incidence of a shear wave.  
Based on the geometrical relations, for the medium with a vertical unfilled joint, the time for the reflected blast waves encountered the blast-induced crack tip can be calculated through the following equation:  
\[t_{i}\ =\ \frac{2d_{1}-L_{i}(t)}{C_{i}}\ \ \ \ \ \ (i\ =\ p,s) \tag{9}\]  
And for the medium containing an oblique unfilled joint with 45\({}^{\circ}\), the time for the reflected blast waves encountered the blast-induced crack tip can be obtained using the following equation:  
\[t_{i}\ =\ \frac{1}{C_{i}}\sqrt{d_{1}^{2}+\left(d_{1}-L_{i}(t)\right)^{2}}\ \ \ \ \ \ \ \ \ (i\ =\ p,s) \tag{10}\]  
where \(t_{p}\) and \(t_{s}\) are the arrival time for the reflected dilatational wave and shear wave encountered the dynamic crack, respectively. \(d_{1}\) is the distance between borehole and the middle point of the unfilled joint; the \(L_{p}(t)\) and \(L_{s}(t)\) are the length of the main crack when it encountered the reflected dilatational and shear wave, respectively; \(C_{p}\) and \(C_{i}\) are the dilatational and shear wave speed of the PMMA, respectively.  
Table 1 shows the comparison of the arrival time for the reflected waves encountered the blast-induced crack between the theoretical and experimental results. The errors of arrival time for the reflected waves between the theoretical and experimental analysis are less than 3 us, indicating that the above identification of a reflected dilatational wave and a reflected shear wave using caustic images are reasonable. The errors between the theoretical and experimental results are partly induced by the calculation of the main crack and are partly due to the recording rate of the high-speed camera.
## 1 Introduction
### Nickel Oxide  
In recent years, studies on nickel oxide nanostructured materials have been reported extensively because of their wide-ranging potential applications.  
A recent report suggests that the crystallite size plays a major role on how nano-structured nickel oxide shells behave when reduced to solid nickel nanoparticles [13]. Nickel oxide shells were placed in a hydrogen gas environment at 350 \({}^{\circ}\)C, and the smallest shells reported had a diameter of approximately 12 nm; they were formed in a single nucleation site of pure nickel which then proceeded to expand to replace all of the starting NiO. Slightly larger starting shells (with a diameter of approximately 25 nm) formed multiple nucleation sites around the shell which grew and eventually merged into a single nickel nanoparticle. Much larger shells (of a diameter of approximately 96 nm) formed many nucleation sites about the shell. The conclusion was that the size of the nickel oxide shells influences the way reduction occurs, since the size of the nanoparticles prior to oxidation appears to determine both the structure of the NiO nanoparticles and the distribution of the nucleation sites of metallic nickel during reduction [13].  
Nickel oxide, which is an antiferromagnetic material, has been reported to display ferromagnetic characteristics as nanoparticles, probably due to the nickel vacancies, with the magnetic moment increasing with decreasing particle size [14; 15]. Below a size of about 10 nm, the Ni\({}^{2+}\)-O\({}^{2-}\)-Ni\({}^{2+}\) super exchange interaction breaks down (possibly due to the presence of more nickel vacancies) and the nanoparticles exhibit paramagnetic behavior.
## 3 Performance Analysis Methods
### Expressions for Outage Probability  
The outage probability is a key measure of the reliability and stability of a communication system. An outage occurs when the SNR corresponding to all communication links falls below a certain threshold SNR [47].  
For the proposed V2I-VLC system, the average received electrical SNR from the \(j^{th}\) Tx can be expressed by (21). Define it as follows:  
\[\text{SNR}_{j}=\frac{\left(\eta P_{t,j}h_{j}\right)^{2}}{\sigma_{o}\,^{2}} \triangleq R_{j}h_{j}^{2}. \tag{23}\]  
According to the definition of outage probability, and the threshold data transmission rate \(C_{th}\), the threshold SNR can be calculated from (21) and (22) as follows:  
\[\text{SNR}_{th}=\frac{\pi}{e}\left(e^{\frac{2C_{th}\ln 2}{B}}-1\right). \tag{24}\]  
Consequently, the outage probability of the proposed V2I-VLC system can be expressed as follows:  
\[P_{out}=\prod\limits_{j=1}^{2}\int_{0}^{\sqrt{\frac{\text{SNR}_{th}}{R_{j}}}} \frac{f_{h_{j}}\left(h_{j}\right)dh_{j}}, \tag{25}\]  
where \(R_{j}\) is shown in (23). Substituting (2) and (3) into (25) and applying the change of variables method for simplification, the closed-form expression for the outage probability is obtained as follows:  
\[P_{out}=\prod\limits_{j=1}^{2}Q\left(-\frac{\ln\sqrt{\text{SNR}_{th}}\sigma_{ o}}{2\sigma_{y}}\right), \tag{26}\]  
where \(Q(\cdot)\) is the Gaussian Q-function that represents the probability that the value of a Gaussian normal random variable is greater than the standard deviation of \(x\), and defined as \(Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{\frac{-t^{2}}{2}}dt\).
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 2 The Construction of RC-NB-LDPC Codes
### Design and Implementation of the RC-NB-LDPC Code
#### 2.3.1 Matrix of RC-NB-LDPC Codes  
The fast-coding method uses the special structure of the QC-LDPC code for encoding, so the check matrix and the generator matrix need to be set. The structure of the check matrix is shown in Figure 3. The base matrix size is \(4\times 16\), and the check matrix is divided into \(64\) blocks, and the size of each block matrix is \(48\times 48\). The numbers in the above matrix represent the element coefficients of the current matrix, and the matrix with a coefficient of \(0\) is the all-zero matrix. The numbers in the below matrix represent the circulant coefficients of the current matrix, and a coefficient of \(0\) indicates that the current matrix is not shifted.  
Since the check matrix of the NB-QC-LDPC code has a QC structure, the generator matrix also has a QC structure. The generation matrix is as shown in Figure 4. The right side of the matrix is a unit matrix of size \(576\times 576\), and each \(G_{i,j}\) on the left side is a circulant of size \(48\times 48\). Therefore, the entire matrix can be stored by simply storing the first row of each \(G_{i,j}\) matrix, greatly reducing resource usage. The source symbol is multiplied by the matrix of its corresponding code rate to obtain the coding result. Since the right side of the matrix is a unit matrix, it is only necessary to calculate the result of multiplying the source symbol by the \(G_{i,j}\), and then stitching the source symbol to obtain the final coding result. The source symbol and \(G_{i,j}\) are encoded to obtain a check sequence, wherein \(G_{i,j}\) is divided into four columns, which can be encoded simultaneously by four channels, so that the coding efficiency can be improved four-fold.
## 3 Numerical Experiment
### Single Vertical Geological Interface Model
When the surrounding rock conditions are the same, the \(P\)-wave reflection coefficient between surrounding rock and water is larger, and the reflection coefficient of the \(S\)-wave is much larger than that of the soft soil medium. Therefore, the energy of reflected \(P\)-/\(S\)-waves increases greatly. From the perspective of the waveform, the \(P\)-wave can pass through the cave, and the second reflected \(P\)-wave is formed at the interface behind the karst cave. The energy of \(PP2\) is stronger than that of \(PP1\). However, since the propagation of the \(P\)-wave inside the karst cave, the first arrival time of \(PP2\) is later than that of \(SS1\). The \(S\)-wave cannot propagate across the water, so the second reflected \(S\)-wave \(SS2\) will not be formed. However, there is an in-phase axis can be observed after \(SS1\) from  
Figure 5: Wavefield of \(v_{r}\) component of the karst cave with soft soil at different time. (**a**) 3D wavefield snapshot of the karst cave model filled with soft soil at different times. Planes of (**b**) \(P\)-wavefield and (**c**) \(S\)-wavefield in \(ro\theta\) under the karst cave model filled with soft soil at different times. the seismic profile, but the energy is much smaller than \(SS1\). Comparing the snapshot of the wavefield, it can be concluded that this event axis is the multiple waves generated by the \(S\)-wave. Figure 6: Three-component seismic profile of the karst cave filled with (**a**) soft soil, (**b**) water and (**c**) air. Figure 6c is the seismic profile of a karst cave filled with air. Because the \(S\)-wave velocity in the air is the same as that in the water, the energy intensity and first arrival time of the \(S\)-wave reflection are the same as that in Figure 6b. The main differences lie in the reflection of \(P\)-waves: the \(P\)-velocity in the air (acoustic wave) is much lower than that in other media. Therefore, it can be approximated that \(P\)-waves cannot pass through the cavity to form \(PP2\). This is why only the in-phase axis of \(PP1\) can be observed in Figure 6c. As for the energy of \(P\)-wave reflection, from a numerical point of view, although the maximum energy of the \(P\)-wave is reduced, it is the energy of \(PP1\), which is close to that of \(PP2\) enhanced by the focusing effect of karst cavity filled with soft soil. It can be determined that the energy of the reflected wave produced by the cavity is the strongest, which also explains the strong interference of the tunnel body to the seismic wavefield. Since the caves and tunnels are strong reflectors at this time, the energy of reflected waves is enhanced, and the energy of multiple waves is also greatly enhanced. Therefore, the multiple waves generated by \(P/S\)-waves can be observed in the seismic profile of Figure 6c.
## 3 Results
### WUE Driving Factor Determination  
As shown in Figure 7, the precipitation and temperature patterns in the study area changed greatly in the 10 years before and after 2010. After 2010, both precipitation and air temperature on the Loess Plateau increased significantly [28] (Figure 7b,e). The precipitation change rate increased from 1.29 mm/a to 3.89 mm/a, and the temperature increased from 0.03 \({}^{\circ}\)C/a to 0.13 \({}^{\circ}\)C/a. From 2011 to 2020, the trend of precipitation in the increased WUE changed from decreasing to increasing. The average change of precipitation in this region from 2000 to 2010 was \(-0.09\) mm/a, whereas, from 2011 to 2020, it was 0.27 mm/a (Figure 7f). Similar to the change in precipitation, the air temperature in the area where WUE increased on the Loess Plateau showed a significant upward trend, from \(-0.01\)\({}^{\circ}\)C/a to 0.38 \({}^{\circ}\)C/a. From 2001 to 2020, the air temperature in the reduced WUE area showed a decreasing trend.  
In order to test the dependence of WUE on potential driving factors, the correlation distributions between WUE and temperature and precipitation on the Loess Plateau from 2001 to 2010 and 2011 to 2020 were drawn. According to correlation distribution map at the 0.05 significance level, the regions where the precipitation passed the significance test from 2001 to 2010 accounted for 11.42% of the Loess Plateau, and nearly all of them displayed a negative association. (Figure 8a,b). Eastern Inner Mongolia and northern Shaanxi were the areas with the highest connection. Only 4.63% of the areas with temperature passed the significance test, showing an insignificant negative correlation as a whole. From 2011 to  
Figure 7: **(a)** Variation trend of precipitation on the Loess Plateau from 2001 to 2010. **(b)** Variation of precipitation on the Loess Plateau from 2011 to 2020. **(c)** Statistical graph of precipitation on the Loess Plateau. **(d)** Variation of temperature on the Loess Plateau from 2001 to 2010. **(e)** Trend map of temperature changes in the plateau from 2011 to 2020. **(f)** Statistical map of temperature changes in the Loess Plateau.  
2020, 12.65% of the precipitation areas passed the significance test, and most of the areas that passed the test were positively correlated; the area with the strongest correlation was in the central area where WUE showed an increasing trend, with 17.98% of the areas passing the significant correlation. The correlation was generally positive, mainly distributed in Inner Mongolia in the northeast and parts of Gansu in the southwest.