esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Examples
### Stationary Wind Fields
#### 5.1.1 Non-Ergodic Stationary Wind Fields  
In the non-ergodic stationary wind field, the time and frequency parameters are selected as: upper cutoff frequency \(\omega_{u}=4\pi\) rad/s; frequency interval \(\Delta\omega=0.006\) rad/s; number of frequency divisions \(N=2048\); time duration 1024 s; time interval \(\Delta t=0.25\) s; number of time divisions \(M=4096\).  
Figure 3 shows the simulated sample time histories at Points 1, 21, and 41, based on the above two phase results under the same random phase. It can be seen that the two results at Point 1 are identical and have no difference, while the results at Points 21 and 41 have an evident distinction and the difference becomes larger and larger with the increase in the distance. This is because the sample at Point \(j\) is only related to the \(j\)th row of the decomposed coherence matrix, as shown in Equation (10). The larger the simulation point number, the more complex phases involved in the simulation, and the greater the influence of the phase on the simulation results. Therefore, using the classical formula to calculate the phase will have a certain impact on the simulated samples, and this impact will be greater for the later simulation points.  
Figure 2: Phases obtained by two schemes.  
In order to further study the influence of the phase on correlation function, 1000 samples are generated to estimate the ensemble auto-/cross- correlation function by Equation (23). Figure 4 shows a typical autocorrelation function \(R_{21,21}(\tau)\) of Point 21 and a typical cross-correlation function \(R_{1,21}(\tau)\) between Points 1 and 21, where the targets are calculated by Equation (22). Comparing the results based on two phase schemes and the corresponding target, it can be found that two estimated autocorrelation functions are completely coincident, which shows that the phase has no effect on the autocorrelation function. However, the estimated cross-correlation functions are totally different, and the result obtained by the classical phase formula seriously deviated from the target. Therefore, the influence of the phase on the cross-correlation function is considerably great and the classical phase formula will cause the obvious simulation error.  
Figure 3: Simulated nonergodic stationary wind fluctuation samples: (**a**) Point 1; (**b**) Point 21; (**c**) Point 41.
## 5 Discussion  
### Comparison of RSSI Distributions Measured on the Surface and Inside an Underground Tunnel  
To facilitate the comparison of RSSI distributions of BLE signals received on the surface outside the underground tunnel, RSSIs were received for five minutes at distances of 10 m to 20 m away from the Bluetooth beacons in an open area of the Pukyong National University in Busan, Korea. Other experimental conditions were maintained as constants. Table 5 summarizes the RSSI distribution characteristics observed on surface. Observed mean RSSI values at distances of 10 m and 20 m measured \(-\)85.1 dBm and \(-\)95.4 dBm, respectively. When compared with RSSI distributions measured inside the underground tunnel (Table 3), results corresponding to surface measurements did not demonstrate a large difference. This implies that the findings reported in previous studies on Bluetooth-beacon-based PWSs with regard to their use in civil and construction industries [17; 18; 19] are equally applicable in mining-industry applications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Type of Alert**} & **Primary/Caution Alert** & **Secondary/Warning Alert** \\  & **(Caution Buffer Zone)** & **(Warning Buffer Zone)** \\ \hline
**No. of trials** & 50 & 50 \\
**No. of true positives** & 50 & 50 \\
**No. of true negatives** & 43 & 45 \\
**No. of false positives** & 7 & 5 \\
**No. of false negatives** & 0 & 0 \\
**Accuracy (\%)** & 93 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy assessment of alerts issued by BBUNPROWS application.  
Figure 6: Conceptual view of process for assessing the accuracy of the proposed BBUNPROWS application when employed in Yeonhwa underground tunnel.
# Study on the Influence of Sapphire Crystal Orientation on Its Chemical Mechanical Polishing
## 4 Conclusions  
1. Nanoindentation experiments were performed on sapphire with different crystal orientations by nanoindentation technology, and the hardness and elastic modulus increased in sequence according to the C-plane, M-plane, A-plane, R-plane.
2. Polishing experiment results on sapphire with different crystal planes showed that the material removal rate of the A-, C-, M- and R-planes were 3.95, 5.93, 4.16, and 2.47 nm/min. The C-plane sapphire could obtain better surface quality in a shorter processing time.
3. The calculation of the adjacent atomic structures of different crystal orientations indicated that the C-plane was the easiest crystal plane to achieve material removal, and the R-plane was the most difficult to process which was consistent with the previous polishing experiment results. Moreover, the research results have great guiding significance for the engineering application of sapphire processing with different crystal planes.  
Conceptualization, L.C. and J.Y.; methodology, J.Y. and Y.M.; software, L.C., L.G. and T.H.; investigation, X.Z.; data curation, L.C. and X.Z.; resources, W.H.; writing--original draft preparation, L.C., writing--review and editing, Y.M., L.C., L.G. and W.H.; funding acquisition, J.Y. 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 number 51575492.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
## 2 Methodology
### Probability Distributions
#### 2.1.6 Weibull (W3)  
The distribution represents a particular case of the generalized extreme value distribution when the shape parameter is positive. It is also known as the Type III extreme value distribution [7,11,12,16,18,20,34].  
\[f(x)=\frac{\alpha}{\beta}\cdot\left(\frac{x-\gamma}{\beta}\right)^{\alpha-1} \cdot e^{-\left(\frac{x-\gamma}{\beta}\right)^{\alpha}} \tag{22}\]  
\[F(x)=e^{-\left(\frac{x-\gamma}{\beta}\right)^{\alpha}} \tag{23}\]  
\[x(p)=\gamma+\beta\cdot\left(-\ln(p)\right)^{1/\alpha} \tag{24}\]  
where \(\alpha,\beta,\gamma\) are the shape, the scale, and the position parameters, respectively; \(\alpha,\beta>0\); \(x\) can take any values in the range \(\gamma<x<\infty\).
## 3 Results and Discussion  
The results of the continuous measurements and subsequently the overall operation conditions are described in Section 3.1. Afterwards the impedance readings of the different devices are compared in Section 3.2. Followed by Section 3.3 where the impedance development at different frequencies measured by \(\mu\)EIS is discussed. For the visualization of the results battery 2-014 is chosen as one representative example, while the most important indicators are then given for all batteries. Section 3.4 describes the results of impedance spectra fitting and concludes with the comparison of the different implications gained from the measurements. Finally, Section 3.5 closes with a suggestion for a monitoring in the field.
## 2 Background
### Network Model  
There is a typical UASNs model as shown in Figure 1. There are three types of nodes in the model: mobile nodes, anchors and surface buoys [21]. Surface buoys are deployed on the water surface and often equipped with GPS to get their absolute locations with the help of GPS antenna arrays and GPS satellites. Anchors and mobile nodes communicate with each other using acoustic signals. Each anchor is vertically connected with one surface buoy by a cable. Anchors can obtain the two-dimensional coordinates with the help of the surface buoys. The main role of the anchors is helping the mobile nodes to finish self-localization.  
Figure 1: Underwater acoustic sensor networks structure.
## 4 Performance Analysis
### Connection Outage Probability (COP)  
This metric characterizes the attainable reliability performance of the proposed system. It describes a situation, whereby the eavesdropper is unable to decode the confidential message of terrestrial users. This usually occurs when the end-to-end instantaneous SNR of the concerned system falls below a predefined threshold value \(\gamma_{th}\) and can be formulated as [46]:  
\[COP=P_{r}[\gamma\leq\gamma_{th}]. \tag{21}\]  
Thus, by substituting (20) into (21), the COP of the concerned system under different detection schemes can be obtained as:\[\begin{array}{c}\text{COP}=\frac{\xi^{2}\tau^{\alpha+\beta-2}}{2\pi^{r-1}(\Gamma( \alpha)\Gamma(\alpha))}C_{r+1,3r+1}^{3r,1}\binom{(\alpha\beta)^{r}}{r^{2}\mu_{r} }\gamma_{th}\bigg{|}\begin{array}{c}1,\,\psi_{1}\\ \psi_{2},0\end{array}\bigg{)}+\omega\sum_{q_{1}=0}^{m_{h}-1}\cdots\sum_{q_{N _{h}}=0}^{m_{h}-1}\Xi(N_{u})\Delta_{H}^{-\lambda}\\ H_{1,03,1;3,2}^{0,11,20,3}\left(\begin{array}{c}(1,-1,\frac{1}{r})\\ -\end{array}\bigg{|}\begin{array}{c}(1-\lambda,1)(0,1)(1,1)\\ (0,1)\end{array}\bigg{|}\begin{array}{c}(1-\xi^{2},1)(1-\alpha,\,1)(1-\beta, 1)\\ (-\xi^{2},1)\Big{(}0,\,\,\frac{1}{r}\Big{)}\end{array}\bigg{|}\begin{array}{c} \frac{1}{\Delta_{H}\Gamma},\frac{\mu^{1/r}}{\gamma_{th}^{1/r}M}\end{array} \right).\end{array} \tag{22}\]
## 2 Results  
### Effects of ABA and NAA on Berry Development and Ripening  
#### 2.1.1 Evolution of Sugar and Acidity  
Two ripening parameters of grape berry, total soluble solids (TSS) and tittatable acidity (TA), were compared between the treatments and the control at five phenological stages. There was no significant difference in TSS and TA at E-L 33 stage before the treatments were conducted. After ABA spraying in 2015 and 2016, the initiation of ripening (E-L 35 stage) of grape berries was advanced about one week and the subsequent ripening process was also accelerated by both ABA800 and ABA1000 treatments (Figure 1). Both ABA1000 and ABA800-treated berries accumulated TSS faster than the control. As a result, these berries achieved technological maturity (E-L 38 stage) about 17 days ahead of the control in 2015 and 25 days earlier than the control in 2016 (Figure 1). In contrast, NAA-treated berries reached technological maturity about 25 days later than the control group owing to the slower speed of TSS accumulation. At the same time, the ABA application markedly promoted the TA decrease, whereas NAA suppressed it. It was interesting to see that the influence of NAA mainly laid in delaying the onset of grape berry ripening (from E-L33 to E-L 35) and extending the berry coloration process (from E-L35 to E-L 36)
## 2 Methodology
### Theoretical Basis
#### 2.1.3 Anisotropic Flat Index (AFX)  
The AFX has an expression derived from the kernel-driven BRDF model and is especially appropriate for the description of the MODIS anisotropic reflectance [19]. For the convenience of calculation, the AFX equation was simplified by [PERSON] et al. [24,25]:  
\[AFX(\lambda)=\frac{\alpha_{WSA}(\lambda)}{f_{iso}(\lambda)}=1+\frac{f_{z0l}( \lambda)}{f_{iso}(\lambda)}\times 0.189184-\frac{f_{geo}(\lambda)}{f_{iso}( \lambda)}\times 1.377622 \tag{12}\]  
It can be considered that the AFX index is the normalization of the volume scattering coefficient and the geometric optical scattering coefficient to the isotropic scattering coefficient. When the volume scattering effect is dominant, \(AFX>1\); when the geometric optical scattering effect is dominant, \(AFX<1\); and when the two scattering effects are close, \(AFX\approx 1\), the surface is approximately Lambertian.  
The analysis of the distribution shape of the BRDF archetype with angles and several typical BRDF shapes of vegetation surfaces are drawn in Figure 2, and the observation data come from the measurement data of the University of Maryland in the United States. The figure shows the curves of the BRDF of vegetation surfaces with an observation angle with a solar zenith of 45\({}^{\circ}\) on the principle plane (PP) and cross-principal plane (CPP). Figure 2a,b show the BRDF changes in the visible red band (0.58\(\sim\)0.68 \(\upmu\)m), and (Figure 2c,d) are the BRDF curves of the same surfaces in the near-infrared band (0.73\(\sim\)1.1 \(\upmu\)m). When the scattering characteristics of the surface are dominated by volume scattering, AFX is greater than 1, and the BRDF is bowl-shaped. When the shape of the BRDF tends to be flat, AFX tends to be 1, and the volume scattering and geometric optics scattering effects are close. The reflectivity is also affected by the wave band, because the vegetation surface is affected by chlorophyll, and the visible and near-infrared bands are quite different. To facilitate the research, the isotropic factor \(f_{iso}\) is normalized to 0.5, \(f_{i}=f_{i}/f_{iso}\), to eliminate the influence of the spectrum. The normalized BRDF shape and AFX value will not change.  
AFX is used as a characteristic parameter of BRDF, which is used to classify the shape of BRDF. The iterative self-organizing data analysis techniques algorithm (ISODATA) is used to classify AFX and to establish different types of BRDF archetype libraries, and the mean values of each class are used as BRDF archetype parameters. When the multiangle data space is insufficiently sampled, it provides prior knowledge for albedo inversion.
## 5 Conclusions
Firstly, the time-correlated error present in the measurements produced by the VT-based GNSS SDR can be reduced; secondly, the more accurate PVT results or state estimations in the prior epoch will be realized based on the first improvement, so that the innovation sequence used in the A-VTTCES-KF is able to adaptively enhance the performance of the priori process covariance matrix in the KF. It means that the positioning accuracy using the A-VTTCES-KF in the current epoch can be again increased. In a word, both bias in terms of the time-correlated error and the random noise error in terms of the KF process covariance matrix are promising to be suppressed by the proposed approach towards the VT loop architecture. These superiorities can overall improve the PVT performance produced by the VT-based GNSS receiver, as well as enhance its robustness ability in the challenging environments, e.g., weak, dynamic, or multipath-interfered cases. In our case, only the GM process is identified and quantified by the Allan deviation curve towards the time-correlated error modelling, but such error source may be accounted for using more than one models besides of the GM process in reality. An improved tool, GMWM, has recently been proposed to analyze the noise source distribution of the stochastic data [67]. Our research for the time-correlated error modelling in the VT loop may be further fulfilled in a more precision way with the GMWM in the future work. **Author Contributions:** Conceptualization, [PERSON] ([PERSON]) and J.L. ; methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]), J.L., C.Y. and B.X. ; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), J.L., C.Y., B.X., Y.L. ([PERSON]), L.-T.H. and N.E.-S; supervision, N.E.-S.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 31727901, and in part by the Chang Jiang Scholars Programme under Grant T2012122. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Interpretation of Models Through SHAP Values  
Figure 5 shows the feature importance obtained with SHAP from the best regression model, GB. The analysis identifies life quality and lifetime as the most influential features. Features such as instruction, benzene, and temperature also contribute to the predictions, although with lower SHAP values. The plot shows both the magnitude and direction of the impact for each feature, with higher SHAP values indicating a stronger influence on the predicted mortality rates. The x-axis represents the SHAP values, showing the range and direction of the impact a feature has on the model's predictions. Positive SHAP values indicate an increase in the prediction of a higher value of outcome, while negative values indicate an increase in the prediction of a lower value of outcome. Points are color-coded to reflect the feature's value for the observation. Red is used for high feature values and blue for low values, helping to show how the feature value correlates with its impact.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & \(\mathbf{r^{2}}\) & **MAE** & **RMSE** \\ \hline Gradient Boosting & 0.55 & 0.17 & 0.05 \\ \hline Random Forest & 0.48 & 0.19 & 0.05 \\ \hline XGBoost & 0.06 & 0.24 & 0.08 \\ \hline Linear regression & \(-0.05\) & 0.23 & 0.11 \\ \hline \end{tabular}
\end{table}
Table 1: Performance of different regression models.  
Figure 4: Scatter plot of the predicted SMR values (y-axis) generated by the Gradient Boosting (GB) model against the observed SMR values (x-axis) for endocrine, nutritional, and metabolic diseases across 107 Italian provinces. The blue line represents the line of best fit, illustrating the correlation between the predicted and observed values.
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 2 Application of VSL and RM Control in HDV Traffic Flows
### Impact of VSL and RM on HDV Traffic Flows
#### 2.5.1 VSL Impact on Stable Traffic Flows  
According to [7], a stable traffic flow is characterized by the fact that the traffic flow density \(\rho\) (veh/km) is less than \(\rho_{c}\) (left green side in the approximated triangular fundamental diagram, Figure 3). In a stable flow, traffic runs smoothly without much interaction between vehicles. The impact of VSL on a stable flow is evident from the reduction in mean speed. After the flow adapted to the newly set VSL speed limit, it remained stable, but at a lower mean speed and higher density compared to VSL-free traffic flow. VSL application is characterized by a lower than \(\rho_{c}\) value resulting in an increased \(TT\) and a reduced LoS.  
The temporary decrease in vehicle flow due to the transition of traffic flow state after the start of VSL operation is due to the fact that the flow density is higher in the  
Figure 3: Impact of VSL on triangular fundamental flow–density traffic diagram [6].  
state under the effect of VSL compared to uncontrolled traffic flow (lower speeds mean less space between vehicles). Due to the change in flow density, the flow is temporarily reduced, and a new higher density is created (active VSL). This influence of VSL on traffic flow was determined in [2]. The application of VSL upstream of a bottleneck that is in a state close to the occurrence of congestion can temporarily reduce the flow of vehicles approaching the bottleneck area. This potentially relieves the bottleneck area and delays or even prevents congestion.
## 2 Materials and Methods
### Wavelet Transform  
The quantization of the fNIRS signal can be estimated using the Hilbert transform, Fourier transform or wavelet transform, which are mathematically equivalent when applied to spectral analysis [27]. However, the use of the wavelet transform provides an adjustable window, thus providing an intuitive visualization of the time-frequency domain as well the high resolution for the high- and low-frequency components [28]. This transforms the time series \(g(u)\) with a series of usually non-orthogonal basis functions generated from the mother wavelet. Its equation is as follows:  
\[W(s,t)=\frac{1}{\sqrt{s}}\cdot\int_{-\infty}^{\infty}\Psi\left(\frac{u-t}{s} \right)g(u)du \tag{1}\]  
where \(W(s,t)\) is the wavelet coefficient, \(\Psi\) is the Morlet wavelet, \(s\) is the scale factor, and the corresponding frequency is the Morlet wavelet, which is a complex sinusoidal curve modulated by a Gaussian function:  
\[\Psi(u)=\frac{1}{\sqrt[4]{\pi}}\cdot e^{-i2\pi u}\cdot e^{-\frac{u^{2}}{2}} \tag{2}\]  
where \(i=\sqrt{-1}\).  
Using WT, we can derive the wavelet amplitude (WA) of each Delta [HbO2] signal. WA is the average result of WT in the time domain. WA reflects the amplitude of fluctuations in the original signal at a certain frequency, serving as a power index that can be used to describe the intensity of activity in cortical areas. The quintessential hemodynamic response to mental activity provides the basis for fNIRS measurements [29]. When specific brain regions are activated, neurometabolism is supported by a local vascular response, leading to an influx of oxygen-rich blood into the active region and surrounding tissues. This impurity leads to an increase in [HbO2] and decrease in [dHb] in the active brain regions [30]. The functional congestion mechanism adjusts the distribution of cerebral blood flow (CBF) according to the functional activity of different brain areas [31]. Thus, when the activity in a cortical region increases or decreases, the blood flow to that region changes correspondingly. This change is reflected in the WA of the fNIRS signal [HbO2] during the game, which is used to characterize the difficulty of the activity in specific cortical regions. In the present study, brain activation represents the mission-induced cortical activity associated with the task.