esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Driving Stability Analysis on Single-Factor
#### 4.1.3 Effects of Various Driving Conditions on LTR  
The influence of different factors on LTR is shown in Figure 10. The load will be transferred from the inside wheels to the outside wheels, while the vehicle is traveling in a curve segment, so the LTR firstly increases over time and sharply fluctuates up and down while driving across rutting. It is concluded that the LTRs are generally less than 0.4 in this section, which implies that the risk of roll-over is low. During initial 0-2 s, the vehicle velocity increases from 0 to constant speed, so the LTR changes in a transitional phase. In the range of 2-15 s, the LTR gradually increases, due to the effect of lateral wind, which is consistent with the previous findings of [PERSON] et al. [61]. In the range of 15-20 s, the LTR significantly increases and then rapidly decreases, due to crossing rutting along with the effect of lateral wind. Afterward, the lateral wind gradually dissipates, so the LTR decreases and remains unchanged.  
Figure 10: \(Cont.\)  
Figure 9: Change of SA over time under various driving conditions: (**a**) Vehicle speed; (**b**) WHR of the rutting; (**c**) Radius of circular curve; (**d**) Superelevation; (**e**) Crosswind angle; (**f**) Wind speed; (**g**) Friction coefficient between road & tire.  
The effects of vehicle speed, radius of circular curve, superelevation, crosswind angle, and crosswind speed on LTR can be recognized as \"quantitative changes but consistent with dynamic mode\", that is, vehicles are driven with similar modes in the above cases, but only varies in exact values. Note that the sudden change in LTR occurs when the WHR of rutting is 5 in Figure 10b, which is consistent with the sudden change in LA and SA, sharply fluctuating up and down.
## Materials and Methods
### Quality Control and Quality Assurance  
Quality control and quality assurance (QA/QC) experiments, including a blank sample test, recovery test, repeatability test, and limits of detection (LOD) experiment were performed to evaluate the feasibility of the method. In addition, 5 L ultrapure water served as the water sample for the blank test, while the water samples for the recovery test and repeatability test were made by spiking 50 \(\upmu\)L 20 mg L\({}^{-1}\) working solution into 100 mL ultrapure water. The LOD experiment was undertaken following the USA Environmental Protection Agency method.
## 3 Signal Demodulation
### PLS-Based Detection Method  
Signal detection plays an important role in the low-BER acoustic communications. One of the most frequently used acoustic signal detection methods is the matched filter (see Equation (2)). The underwater sound channels and ambient noise usually distort the message signals seriously,  
Figure 3: Overall flowchart of demodulation.  
Figure 2: Correlation of chaos phase modulation signals. (**a**) Autocorrelation; (**b**) Cross-correlation.  
resulting in marked degradation in signal detection performance. Figure 4a plots the autocorrelation between the original chaos phase modulation signal and its observation at the receiver simulated by using the sound channel response estimated from the trail data. We can see that the main-to-side lobe ratio goes from 4.1\(\times\) (see Figure 2) down to lower than 1.94\(\times\). Whereas, the maximum cross-correlation increases from 0.217 up to 0.42, see Figure 4b.  
The simulations above demonstrate that the distinguishability of the received chaos signals is reduced significantly during the underwater propagation. For the purpose of high accuracy performance, we use a partial-least square (PLS) regression-based classifier as the detector array in Figure 3.  
PLS is a wide class of methods for modeling relations between sets of observed variables by means of latent variables. It comprises regression and classification tasks, as well as dimension reduction techniques and modeling tools. Projection of the observed data to its latent structure by means of PLS was developed by [PERSON] and coworkers [48; 49; 50]. The underlying assumption of all PLS methods is that the observed data is generated by a system or process which is driven by a small number of latent (not directly observed or measured) variables. Its goal is to maximize the covariance between the two parts of a paired dataset even though those two parts are in different spaces. This method is essentially a machine learning one, which necessitates the training and testing processes, simultaneously.
### Industry 4.0 IIoT, Cloud, and Edge Computing for Next-Generation Offshore Wind Farms
Figure 4 illustrates the distributed architecture of the traditional networks against the centralized architecture of the software-defined networks. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Service** & **Communication Direction** & **Priority** & **Data Rate** & **Latency** & **Reliability** & **Packet Loss Rate** \\ \hline Protection traffic & WTG \(\rightarrow\) vPAC & 1 & 76,816 bytes/s & 4 ms & 99.999\% & \textless{}10^{-9}\) \\ \hline Analogue measurements & WTG \(\rightarrow\) vPAC/ECP & 2 & 225,544 bytes/s & 16 ms & 99.999\% & \textless{}10^{-6}\) \\ \hline Status information & WTG \(\rightarrow\) ECP & 2 & 58 bytes/s & 16 ms & 99.999\% & \textless{}10^{-6}\) \\ \hline Reporting and logging & WTG \(\rightarrow\) ECP & 3 & 15 KB every 10 min & 1 s & 99.999\% & \textless{}10^{-6}\) \\ \hline Video surveillance & WTG \(\rightarrow\) ECP & 4 & 250 kb/s-1.5 Mb/s & 1 s & 99\% & No specific requirement \\ \hline Control traffic & vPAC \(\rightarrow\) WTG & 1 & 20 kb/per turbine & 16 ms & 99.999\% & \textless{}10^{-9}\) \\ \hline Data polling & ECP/vPAC \(\rightarrow\) WTG & 2 & 2 KB every second & 16 ms & 99.999\% & \textless{}10^{-8}\) \\ \hline Internet connection & Internet \(\rightarrow\) WTG/ECP/vPAC & 3 & 1 GB every two months & 60 min & 99\% & No specific requirement \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Offshore wind farm data communication parameters between the wind turbine generators (WTG), the offshore hub-mounted pico datacenter components, and the Internet [20].
# Generating Bessel-Gaussian Beams with Controlled Axial Intensity Distribution
## 3 Experimental Implementation of the Bessel-Gaussian Beam  
### Experimental Setup  
The possibility of implementing the previously proposed method has been confirmed experimentally. The source was a diode-pumped CW single-frequency laser (Cobolt AB, Solna, Sweden) with a wavelength of \(\lambda=659.6\pm 0.3\) nm, which also had a spatial single-mode transverse field structure (TEM\({}_{00}\)) with a quality factor \(M^{2}<1.1\). An afocal optical system was used to expand the laser beam to a diameter of 4 mm (at \(1/e^{2}\) width). Then, a quasi-parallel laser beam illuminated the mask and the axicon. The axicon AX2510-A (Thorlabs, Newton, NJ, USA) had the following parameters: aperture diameter 25.4 mm, apex angle \(2\alpha_{0}=160^{\circ}\). The mask (BMSTU, Moscow, Russia) was installed directly, close to the front surface of the axicon. The mask we used was glass plane-parallel plates with a circular aperture 25.4 mm in diameter, on one of the surfaces of which a sufficiently thick layer of chromium (\(\tau<0.01\)) was applied. Figure 1 shows the masks used in the research. The parameters of the mask (clear and inner apertures) were designed the same as in modeling and mentioned in the caption of Figure 2. To register the axial intensity distribution after the axicon, an afocal optical system with 3\({}^{\mathrm{x}}\) magnification was placed in front of a sCMOS camera CS2100M-USB (Thorlabs, Newton, NJ, USA) on the optical axis.
## 4 Results
### Mooring Line Tension  
The drift process is highly related to the time-series tension variation. In Mode A, the stage two time-series tension showed that the horizontal distance from the platform fairlead to the mooring anchor point decreased and then increased during the drift process, as shown by the curve in line #2 tension (Figure 13, blue line). On the contrary, Modes B and C did not have obvious tension variety as the surge drift was less than in the Mode A cases, although in some cases the platform position changed only slightly after line#1 broke (for example, with the incident wind at 180\({}^{\circ}\)).  
To check the range and the average of the tension in each case, Figure 21 shows the boxplot of the tension on line #2 in the first and third stages, respectively. On each box the central mark indicates the average and the bottom and top edges of the box indicate the 25 th and 75 th percentiles, respectively. The two extended 'whiskers' indicate the maximum and minimum values.  
In stage one the average fairlead tension of line #2 grew with an increase in the wind direction. However, changes in wave direction had no impact on the mean tension in line #2. The same trend was apparent for surge drift (Section 4.1).  
Notably, the remaining mooring lines induced extreme tension after the mooring line disconnect with certain load cases exceeding the minimum breaking load, as seen in Figure 21, potentially leading to a series of mooring line failures with very serious consequences. It is suggested that designers select a strong enough grade of chain to endure the worst-case situation.  
The stage one trend of anchor force (Figure 22) is similar to trend of fairlead force. The stage three negative tension cases explain that the pull directions are opposite from those in stage one. On the other hand, the maximum vertical force on the anchor is small, which means there may not be a lifting anchor issue in this condition; however, more study in needed.  
Figure 21: Boxplot of mooring line#2 fairlead tension in stages 1 and 3.
## 5 Conclusions  
Conclusively, in the context of the rapid development of the environmental IoT, this study proposed a general method for predicting the timely environmental noise via the application of the LSTM network in a large data volume scenario and compared it with the performance of the classical model on the same data set, verifying the feasibility and effectiveness of the LSTM neural network in predicting environmental noise. However, due to the limitation of the duration of data collection time, this prediction has no way to verify the predictive ability of the LSTM network on the daily/monthlynoise level. Nonetheless, as the time interval increases, the average noise level is more stable, and the randomness declines. Under the condition that the training samples are sufficient, the performance of the prediction model should be better. Furthermore, from this study, it is believed that the LSTM network can be applied simply to other noise data sets on predicting environmental noise, but different data sets may need to be re-adjusted. The shortcoming of this study is that the LSTM network structure used is relatively simple, and in the future, a more in-depth, broader and more powerful optimized LSTM model can be designed to improve accuracy. At the same time, the LSTM does not provide insight into the physical meaning of their parameters [62], in addition to time, more variables should be considered.  
China's past environmental noise monitoring method is mostly based on the statistical average. The monitoring data can only represent the overall average level of the specific time and place, which lacks understanding and application of environmental noise characteristics and acoustic laws, and in addition, lacks analysis of causes and effects. Establishing the connection between environmental noise and position, time, traffic, population, and other factors, replacing part of the monitoring work with prediction is the development direction of environmental noise evaluation. The monitoring and evaluation methods should be optimized from this perspective to make the monitoring results more in-depth and representative.  
[PERSON], [PERSON], and [PERSON] conceived the general idea of the paper. [PERSON] and [PERSON] designed the methodology and the model. All authors analyzed and discussed the results. [PERSON] and [PERSON] wrote this paper. All authors have read and agreed to the published version of the manuscript.  
This study was funded by The National Key Research and Development Program of China (No. 2016 YFC0503605).  
The authors declare no conflict of interest.
# A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint +
## 2 Methodology  
The methodology employed in this work and depicted in Figure 1 adheres to three fundamental steps commonly found in SBO: (i) dataset definition and generation; (ii) surrogate model training, validation, and testing; and (iii) the optimization process. All these tasks were performed using available open-source Python codes and specially developed Python scripts. For illustrative purposes, the methodology is applied for a cruise speed of 30 m/s, but it is applicable to other speeds.  
Firstly, the Goland wing [21] was selected to be the baseline model for this work. The reason for this choice was two-fold: (i) experimental data exist to validate the implementation; (ii) it has been already used to validate SHARPy [22], the open-source code chosen for the aeroelastic simulations and developed by Imperial College London. SHARPy provides a coupled aeroelastic solution considering the Unsteady Vortex Lattice Method (UVLM) [23] and Geometrically Exact Beam Model (GEBM) [24] for aerodynamic and structural analyses, respectively. Krylov and modal projections are used in SHARPy to linearize aerodynamic and structural subsystems, respectively. These are then used to estimate the flutter speed by means of the iterative p-k method at the nonlinear aeroelastic equilibrium. The flutter speed was predicted in the SHARPy implementation used for this work to be under 5% of the relative error for the Goland wing, in agreement with the value obtained by SHARPy developers [25]. To improve the accuracy of the drag estimation, the flat-plate theory [26] was implemented, offering an estimation of skin-friction drag multiplied by form (more adequate for small angles of attack) and interference factors.  
Figure 1: Implemented surrogate-based optimization methodology.  
Currently, there are some efforts being made by the SHARPy team [27; 28] to include viscous considerations in the UVLM, which are typically not of major importance when the focus is on aeroelastic phenomena.
# Aqueous-Organic Phase Transfer of Gold and Silver Nanoparticles Using Thiol-Modified Oleic Acid
## 1 Introduction  
Metallic nanoparticles are of great interest due to their physical and chemical properties, which makes them suitable for a broad range of applications. In this regard, gold nanoparticles are the most stable metallic particles, and they offer several features including size-related electronic, magnetic, and optical properties; biocompatibility; non-cytotoxic properties; multiple assembly types; and surface functionalization with a broad range of applications [1; 2]. Parallel silver nanoparticles present optical, electronic, and antibacterial properties that depend on their dimensions as well as their structure, form, size distribution, and chemical-physical environment. As such, they are suitable for an extensive range of applications [3; 4].  
Polar and non-polar environments could be used to synthesize metallic nanoparticles of different shapes and sizes. Each environment presents its unique advantages and disadvantages [5; 6; 7]. Nevertheless, in general, the synthesis of metallic nanoparticles in organic solvents is very sensitive and thus requires considerable effort to develop convenient size- and shape-controllable processes, while the synthesis of those in aqueous solutions has many well-defined preparation procedures [8]. In many cases, the transfer of gold nanoparticles between polar and non-polar environments is necessary to increase the applications of the particles, as well as to obtain advantages of the manufacturing process. The phase transfer of gold nanoparticles from polar to non-polar solvents has been studied using ionic liquids [8], phenolic hydroxyl groups [9], thiol ligands [10; 11], etc. There are various reports that presentmany particular methodologies that have been established for the metallic nanoparticles phase transfer from an aqueous phase to an organic medium or vice versa [12]. There are also reports presenting the ligands commonly used for surface modification in the phase transfer of metallic nanoparticles [13]. Oleic acid (OA) has been used as a ligand for phase transfer of iron oxide nanoparticles [14] and silver nanoparticles [15]. It has also has been used to synthesize gold nanoparticles coated with oleic acid and oleylamine [16]. In that case, the synthesis is carried in organic solvents, followed by precipitations and redispersions to obtain nanoparticles of 6.7 nm.  
This work aims to study the synthesis of a thiol ligand prepared through modification of oleic acid with 2-aminoethanethiol, and the metallic nanoparticle aqueous-organic phase transfer using this ligand, with a particular focus on gold and silver nanoparticles. However, the proposed strategies could be of general nature and work similarly to other metallic nanoparticles. This phase transfer method could be applied to a wide range of established nanoparticle aqueous synthesis.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
###### Abstract  
The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments.  
synthetic aperture radar; backscattering; InSAR; PolSAR; snow classification; wet snow; cryosphere; data fusion; machine learning +
Footnote †: journal: Remote Sensing
## 3 Methods
### The Calculation of Pupil Distance and Pupil Height  
#### 3.5.1 Calculation Method of Pupil Distance  
The pupil distance can be calculated based on the 68 facial landmarks. Specifically, we select four points (38, 39, 41, and 42) around the left eye and four points (44, 45, 47, and 48) around the right eye. The coordinates of these points are used to calculate the center coordinates of the left and right pupils. Suppose the coordinates of these four points are \((x_{1},y_{1}),(x_{2},y_{2}),(x_{3},y_{3}),(x_{4},y_{4})\), the geometric center coordinates \((x_{c},y_{c})\) can be obtained by computing their average. The calculation formula is as follows:  
\[\begin{array}{l}x_{c}=\frac{x_{1}+x_{2}+x_{3}+x_{4}}{4}\\ y_{c}=\frac{y_{1}+y_{2}+y_{3}+y_{4}}{4}\end{array}, \tag{7}\]  
thus the coordinates of the center of the left pupil \((x_{lc},y_{lc})\) and the center of the right pupil \((x_{rc},y_{rc})\) can be obtained. In two-dimensional space, the distance formula between two points can be used to calculate the interpupillary distance.  
\[PD=\sqrt{(x_{lc}-x_{rc})^{2}+(y_{lc}-y_{rc})^{2}}. \tag{8}\]
# Investigation on Vibration Influence Law of Double-Shield TBM Tunnel Construction
## 5 Research on Vibration Influence Range of Double-Shield TBM Construction
### The Longitudinal Vibration Trend of Double-Shield TBM Construction
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Buried**} & \multirow{2}{*}{**Direction**} & \multicolumn{8}{c}{**Distance between Cutter-Head and Monitoring Section**} \\ \cline{3-10}  & & \(-\)**60 m** & \(-\)**50 m** & \(-\)**40 m** & \(-\)**30 m** & \(-\)**20 m** & \(-\)**10 m** & **0 m** & **10 m** \\ \hline \multirow{3}{*}{\(0\) m} & X & 17.74 & 20.98 & 26.21 & 68.22 & 87.92 & 91.07 & 96.51 & 16.08 \\  & Y & 17.38 & 22.66 & 24.20 & 55.25 & 66.33 & 106.08 & 110.79 & 13.85 \\  & Z & 16.14 & 17.05 & 18.81 & 28.50 & 53.59 & 59.62 & 103.30 & 19.76 \\ \hline \multirow{3}{*}{\(10\) m} & X & 12.03 & 20.52 & 18.40 & 45.42 & 106.33 & 133.34 & 167.80 & 37.29 \\  & Y & 21.62 & 24.55 & 25.46 & 87.22 & 52.69 & 72.69 & 122.97 & 37.81 \\  & Z & 13.46 & 9.71 & 9.24 & 32.08 & 29.34 & 58.71 & 172.68 & 26.29 \\ \hline \multirow{3}{*}{\(20\) m} & X & 9.19 & 11.17 & 10.77 & 24.92 & 28.61 & 87.16 & 133.54 & 18.40 \\  & Y & 6.50 & 10.88 & 2.38 & 4.52 & 18.32 & 39.52 & 81.16 & 13.40 \\ \cline{1-1}  & Z & 9.91 & 11.98 & 8.72 & 20.68 & 26.16 & 42.04 & 207.60 & 16.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Peak accelerations of sampling hole Z-4 (unit: mm/s\({}^{2}\)). and monitoring section is from 60 to 40 m. When this distance is less than 40 m, the increase in peak acceleration is more obvious. Comparing the measuring points at 0 and 10 m depths, the peak acceleration at a depth of 20 m is smaller at \(-\)40--20 m. It can be seen that the peak acceleration decreases with the increase in burial depth when the cutter-head is \(-\)40--20 m away from the monitoring  
Figure 10: Longitudinal influence trend with different buried depths. (**a**) 0 m depth; (**b**) 10 m depth; (**c**) 20 m depth. section. When the distance is less than 10 m, the peak acceleration changes the most in the Z-direction, and the deeper the measuring point is buried, the greater the increase in the peak acceleration in the Z-direction.