esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupling and Trapping of Light in Thin-Film Solar Cells Using Modulated Interface Textures
## 2 Materials and Methods
### Solar Cells  
After the characterization of the ZnO:Al front contact material hydrogenated amorphous and microcrystalline silicon (a-Si:H/uc-Si:H) tandem solar cells were prepared on these ZnO:Al front contacts. The layer stack is provided in Figure 1a. a-Si:H has a bandgap of around 1.7 eV and is used in the top cell, while uc-Si:H with an indirect bandgap similar to crystalline silicon (~1.1 eV) is used as the bottom cell absorber. All cells were stacked as p-i-n/p-i-n tandem cells without the application of an intermediate reflector between the top and bottom cells. The p-i-n refers to the doping p-type (as hole contact), intrinsic (absorber), n-type (electron selective contact) and all layers consist of a-Si:H or uc-Si:H [52]. The tandem solar cells of test series 1 and 3 were deposited using a single-chamber plasma-enhanced, chemical vapor deposition (PECVD) process in a vacuum deposition system by von Ardenne Anlagentechnik, Dresden, Germany. Details regarding the single-chamber deposition process and the deposition system, in general, can be found in [53]. All samples of test series 1 were co-deposited and yielded a total absorber thickness of ca. 1.9 \(\upmu\)m (ca. 320 nm i-a-Si:H top cell, ca. 1.56 \(\upmu\)m i-uc-Si:H bottom cell, ca. 80 nm doped layers). The thickness values given here are only approximate values for each test series because the solar cell thicknesses may vary slightly from substrate to substrate even within the same silicon deposition [54]. All samples of test series 3 were likewise co-deposited, however, with a reduced bottom cell thickness, resulting in a total absorber thickness of ca. 1.6 \(\upmu\)m (ca. 320 nm i-a-Si:H top cell, ca. 1.25 \(\upmu\)m i-uc-Si:H bottom cell, ca. 80 nm doped layers). The absorber layers of test series 2, on the other hand, were co-deposited in a large area PECVD system by Materials Research Group, Wheat Ridge, Colorado, USA. The total absorber thickness of test series 2 was ca. 1.8 \(\upmu\)m (ca. 330 nm i-a-Si:H top cell, ca. 1.44 \(\upmu\)m i-uc-Si:H bottom cell, ca. 80 nm doped layers). All tandem solar cells feature a ZnO:Al/Ag back contact that was deposited via sputter deposition in the previously mentioned sputter coater. The cell area used for evaluations is 10 \(\times\) 10 mm\({}^{2}\) and was defined by a laser scribing process of the P3 line [55]. The IV-characteristics of all solar cells were measured under illumination with an AM1.5g spectrum at standard test conditions using a class AAA sun simulator. Additionally, the external quantum efficiency (EQE) was determined using a spectral response measurement based on 22 supporting points in the wavelength range between 350 nm and 1100 nm. The wavelength selection was performed using band pass filters and the sun simulator spectrum. From these measurements the short-circuit current densities of the top and bottom cells were calculated using the AM1.5g solar spectrum. The crystalline volume fraction of the microcrystalline silicon films was determined by RAMAN scattering [56].
# Parameter Determination of a Minimal Model for Brake Squeal
## 2 Materials and Methods
### Response Surface Optimization Methodology for Model Parameters  
#### 2.3.1 Initialization of Parameters  
The natural frequency of the minimal model is mainly affected by the mass and stiffness. First of all, initial values are chosen as \(m_{1}=1.2\) kg, \(m_{2}=1\) kg, \(\theta=0.15\) rad, \(\mu=0.4\). Assuming \(k_{2}=2.1{\times}10^{9}\) N/m, relative error between the coalescence frequency of the minimal model and the optimization objective, which is 7168.6 Hz, are calculated. Figure 6 gives the evolution of error versus \(k_{1}\). It can be determined that when \(k_{1}=2.27{\times}10^{9}\) N/m, the minimum frequency error is only about 0.01%.  
Figure 6: Evolution of frequency error versus stiffness \(k_{1}\).  
The Hopf bifurcation point of the minimal model is mainly affected by its slope angle \(\theta\). When the kinetic parameters are set as same as the above-mentioned values, as shown in Figure 7, the value of the bifurcation point \(\mu_{0}\) decreases with the increase of \(\theta\). It can be determined that when \(\theta\) = 0.0065 rad, the corresponding bifurcation point is \(\mu_{0}=0.4\), which is consistent with the finite element model result.  
Numerous calculation cases show that the stiffness \(k_{1}\), \(k_{2}\) have the most significant effect on the real part of the model. As the stiffness getting greater, the real part of unstable mode becomes larger, and thus the squeal propensity increase. Variations of the stiffness also result in slight change of the coupling frequency and Hopf bifurcation point, which is mainly affected by angle \(\theta\). Therefore, the coupling frequency, bifurcation point and real part of the finite element mode is selected as targets. And multi-parameter and multi-objective optimization is performed to determine \(k_{1}\), \(k_{2}\) and \(\theta\).
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 2 Materials  
### Overview of ZY3-02 Satellite  
The ZY3-02 satellite was launched into a sun-synchronous orbit on 30 May 2016. It covers the global region every 59 days. The ZY3 satellite carries a multispectral imager (MUX) and three-line panchromatic cameras (TLC). The multispectral imagery provides repetitive acquisition of multispectral data with a high resolution of 5.8 m at ground sample distance (GSD), while the panchromatic imagery of 2.1 m resolution at GSD is mainly used for topographic purposes and is not discussed in this paper (Table 1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ZY3-02 Satellite** & **Technical Specification** \\ \hline Launch Schedule & May 2016 \\ \hline \multirow{6}{*}{Orbit} & Altitude: 506 km \\ \cline{2-3}  & Type: sun-synchronous, \\ \cline{2-3}  & Equator Crossing time: 10:30 AM \\ \cline{2-3}  & Cycle: 97 min \\ \hline Mission duration & 5 Years \\ \hline \multirow{2}{*}{Image Band of Remote Sensor} & \multirow{2}{*}{Panchromatic and multi-spectral (Four-band: blue, green, red, NIR)} \\ \cline{2-3}  & \\ \end{tabular}
\end{table}
Table 1: The technical specification of ZY3-02 Satellite.
## 1 Introduction
[35] used SBAS-InSAR (Small Baseline Subsets Interferometric Synthetic Aperture Radar) technique to refine the analysis of the swelling soil area of the South-North Water Diversion Project. It showed that the settlement of the canal section was highly correlated with rainfall, soil moisture, and other factors. In this paper, for the characteristics of high fill, deep excavation, and large scale of the South-North Water Diversion Project. The PS-InSAR technique was used to obtain the overall deformation results of the high-fill section of Wenzhuang Village in Ye County of the Middle Route of the South-North Water Diversion Project. At the same time, successfully inversion the spatial and temporal variation characteristics of the east and west banks of the canal. Through the comparison and verification of the second-order leveling data. The feasibility and effectiveness of the time-series InSAR technology for the safety monitoring of the high-fill canal of the South-North Water Diversion Project have been proved.
## 2 Three-Dimensional Printing Program of Artificial Vertebrae and Analysis of Influencing Factors
### Effect of 3D Printing Process Parameters on Bone Density
#### 2.2.4 Effect of Printing Speed on Specim Density  
To investigate the impact of printing speed on specimen density, five distinct printing speeds (in mm/s) are chosen: 30, 50, 70, 90, and 110. Throughout the experiment, all other process parameters are held constant to isolate the effect of printing speed. The baseline settings for the remaining molding parameters are provided in Table 4.  
Based on the measurement outcomes presented in Table 5, it can be concluded that the printing speed exerts a negligible impact on the density of the printed specimens. Any slight variations in density observed are attributable to inherent machine errors. Moreover, the effect of printing speed on printing duration is notably substantial.  
The impact of various process parameters, including the filling density, material flow rate, layer thickness, and printing speed, on bone density has been analyzed through a series of printing experiments. As a result, the primary influential parameters have been identified: the filling density, material flow rate, and layer thickness. This analysis provides a solid foundation for defining the critical factors that will be used in the predictive modeling of bone density.
We would like to point out that this research addresses a specific gap in the existing literature on American Options, and while there has been extensive research on American Options, there is a lack of focus on the analysis, emphasis, visualization, and comparison of the \"policies (set of exercise decisions)\" generated by different algorithms: (1) BOMP: Binomial Option Pricing Model, (2) LSM: Least Squares Monte Carlo, and (3) Reinforcement Learning. Our work is the first to concentrate on the final policy decisions generated by these algorithms, exploring how these policies differ and evolve during the training process (in the RL-based method). To implement this idea and conduct an analysis, we considered three distinct pricing models, encompassing both constant and calibratedstochastic volatility models. The results provide new insights into final policy behaviors under various market conditions (represented by different uncertainty models) and a comparison of their resulting values obtained from these algorithms. These findings offer a better understanding of decision-making processes in the context of American options. The main contributions of this work are threefold: (a) We explored and implemented three methods for pricing the American Option for both constant and stochastic volatility models of underlying uncertainty. (b) The RL method was studied to understand and shed light on how learning in RL contributes to updating \"Decisions\" at each batch. (c) We replicated Table 1 from the study by [7] utilizing the Least Squares Monte Carlo Method for pricing American Options, facilitating a comparative analysis with the outcomes derived from the RL approach. This paper is organized as follows: Section 2 presents the \"Problem Statement\". Section 3 discusses the modeling of the uncertainty of underlying price dynamics. Section 4 presents and briefly reviews the three methods for pricing the American Option. Section 5 presents the work results. This paper concludes with Section 6, \"Discussion\", and Section 7, \"Conclusions\".
## 4 Open Issues for Consideration
### Privacy and Security Issues  
In today's world, you either pay for the product or service you are using on the Internet or you (and your data) literally become the product, which brings up the following issue in terms of privacy [97]: current data collection techniques enable applications to collect and track many critical pieces of information such as tracking the user's physical movements, physiological or psychological responses, virtual and real interactions with their environments and more. It is a trade-off with the tailored, unique user experience that the users will receive for their privacy. There should be a balance between those goals, requiring critical attention to privacy issues these would cause for unaware users.  
Further, the achievement of such innovative technology (i.e., metaverse) has its own security threats as well, including the following [97].
## 3 Proposed 3-D Sound Image Reproduction Based on Spherical Harmonic Expansion
### Spherical Harmonic Expansion  
The SH function \(Y_{n}^{m}(\theta,\phi)\) is a solution of the 3-D wave equation in the spherical coordinate system shown in Figure 6[15]. It is defined as:  
\[Y_{n}^{m}(\theta,\phi)=\sqrt{\frac{(2n+1)}{4\pi}\frac{(n-m)!}{(n+m)!}}P_{n}^{m}( \cos\theta)e^{jm\phi}, \tag{5}\]  
where \(n(0\leq n\leq\infty)\) is the order of the SH function, \(m(-n\leq m\leq n)\) is the degree of the SH function, \(\theta(0\leq\theta\leq\pi)\) is the elevation angle, \(\phi(0\leq\phi\leq 2\pi)\) is the azimuth angle, and \(P_{n}^{m}(\cdot)\) is the Legendre function. Figure 7 shows the shapes of the SH function at the order of \(n=2\) and degree of \(m=2\) in Cartesian coordinates. The color of the SH function means phase \(\angle Y_{n}^{m}(\theta,\phi)\): yellow and blue show positive and negative values, respectively. This function can be applied to an orthogonal function expansion to determine the arrival direction of a plane wave. Therefore, any directivity pattern \(D(\theta,\phi)\) can be expanded using the SH function \(Y_{n}^{m}(\theta,\phi)\) and coefficient \(A_{nm}\) in the SH domain as:  
\[D(\theta,\phi)=\sum_{n=0}^{\infty}\sum_{m=-n}^{n}A_{nm}Y_{n}^{m}(\theta,\phi). \tag{6}\]  
Here, \(A_{nm}\) indicates the strength of each basic directivity. In the SH domain, it is possible to analyze what percentage of the desired directivity pattern \(D(\theta,\phi)\) includes basic directivity, such as monopole and dipole. From Equation (6), \(A_{nm}\) can be calculated as:  
\[A_{nm}=\int_{0}^{\pi}\int_{0}^{2\pi}D(\theta,\phi)Y_{n}^{m}(\theta,\phi)^{*} \sin\theta d\theta d\phi, \tag{7}\]  
Figure 4: Examples of Fourier series expansion and spherical harmonic expansion (SH) expansion.  
Figure 5: Difference between the position of the target sound image on each system.  
where \(*\) is the complex conjugate.
# Application of a Novel High-Order WENO Scheme in LES Simulations
## 3 Application Cases
### Supersonic Compression Ramp
Notably, the results of LES-G2 and LES-G3 exhibit a high degree of consistency with the DNS results in terms of the streamwise distribution of wall pressure. By determining the location where the mean friction coefficient \(C_{j}\) equals zero, LES-G1 predicts a separation point at \(x_{sep}=-2.75\delta\) and a reattachment point at \(x_{ran}=0.84\delta\). In contrast, LES-G2 and LES-G3 predict an average separation point within the corner flow at \(x_{supp}=-2.92\delta\) and a reattachment point near \(x_{ran}=1.05\delta\). The separation region predicted by LES-G1 is slightly smaller than the DNS results, while those predicted by LES-G2 and LES-G3 are more closely aligned with the DNS data. Overall, the pressure and friction coefficient distributions computed by LES-G2 and LES-G3 exhibit better agreement with the experimental values and DNS results. Consequently, the LES-G2 grid is adopted for subsequent studies. Figure 17 provides a visualization of the instantaneous flow structure within the principal domain. This figure delineates the quasi-ordered structure of the turbulent boundary layer through the application of the \(Q\)-criterion, with coloring based on streamwise velocity. The semi-transparent gray surface represents the dimensionless pressure iso-surface (\(Q=1.5({L_{\infty}}/{\delta})^{2}\)), showing the three-dimensional topology of the separation shockwave. This figure reveals pronounced three-dimensional features in the interaction region. Large vortex structures interacting with the shockwave's root experience fragmentation at the ramp due to increased turbulent fluctuations. Concurrently, deformation of the shockwave occurs at its root, with surface wrinkling becoming evident in the spanwise direction. The shockwave maintains typical two-dimensional structural characteristics when located away from the interaction region. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Principal Domain Size** & **Grid Resolution** & **Cells** \\  & \({L_{\pi}}\times{L_{y}}\times{L_{z}}/{\delta^{3}}\) & \(\Delta{x^{\star}}\times\Delta{y^{\star}}\times\Delta{z^{\star}}\) & \(N_{\pi}\times N_{y}\times N_{z}\) \\ \hline DNS [51] & \((9+7)\times 2.0\times 8.2\) & \((3.4\)–\(7.2)\times 4.1\times 0.2\) & \(1024\times 160\times 128\) \\ LES-G1 & & \((6.1\)–\(11.0)\times 7.2\times 0.46\) & \(392\times 89\times 112\) \\ LES-G2 & \((7.73+7.73)\times 2.15\times 5.23\) & \((4.5\)–\(8.2)\times 5.3\times 0.46\) & \(529\times 120\times 112\) \\ LES-G3 & & \((3.4\)–\(6.1)\times 4.0\times 0.46\) & \(708\times 161\times 112\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Grid details and comparison for the principal domain.
## 2 System Model Description
### Dynamic Programming  
The DP algorithm calculates the minimum cost between state points using a backward iteration process. It then determines the optimal path through a forward-solving approach. This method transforms the multi-stage decision-making process into a single-stage optimization, thereby substantially enhancing computational efficiency.  
Specifically, starting from the last moment T of the driving cycle, the algorithm calculates the possible costs associated with each different SOC at that moment. The cost value depends on the input variables at that time and the minimum cost is determined, as summarized in Equation (15).  
\[L_{T,opt}(\text{SOC}_{N})=\text{min}L_{T}(P_{bat,i}) \tag{15}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline M\({}_{bat}\) & 147 & USD/kWh \\ M\({}_{fc}\) & 411 & USD/kW \\ M\({}_{\text{H}_{2}}\) & 5.88 & USD/kg \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for price.  
Once the costs for all SOC levels at the current time are computed, the same method is employed to calculate the costs at the previous moment \(L_{T-1,opt}\). It is important to note that there exists a transition relationship between the SOC values at these two time points, which reflects how the battery's SOC varies with changes in the input variables, as described by Equation (4). At this stage, the cost function must also incorporate the terminal cost, as summarized in Equation (16).  
\[L_{T-1,opt}(\text{SOC}_{N})=\min(L_{T-1}(P_{bat,i})+L_{T,opt}(SOC_{next})) \tag{16}\]  
After iterating through all time points, the algorithm will yield an optimal input variable matrix and a cost matrix for each state point at every moment. By inputting the initial SOC, the minimum total cost at that time can be computed. Thus, the optimization problem of the algorithm can be expressed as follows:  
\[\min J=\sum_{t=1}^{T}L(x(t),u(t)) \tag{17}\]
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
## 2 Experimental Method  
This study evaluated two types of thin film force-sensitive resistor (FSR) sensors: including Flexiforce A201-1 (Tekscan, Norwood, MA, USA) and Interlink FSR-402 (Interlink Electronics, Irvine, CA, USA), as depicted in Figure 1. Specifications for these sensors, including linearity and repeatability, as provided by the manufacturers, are summarized in Table 1. The sensors' sensing areas were computed from these specified values.
## 5 Conclusions  
This research shows that the meteorological drought in the Shendong mining area is characterized by a high frequency, short duration, and high intensity, while the ecological drought is characterized by a low frequency, long duration, and low intensity. The study area is located at the junction of semi-arid and semi-humid areas. The inter-monthly and inter-annual variability in temperature and precipitation is significant and severe drought conditions can develop rapidly. Conversely, while the frequency and intensity of ecological drought in the study area are relatively low, the prolonged duration of individual drought events significantly affects the ecosystems of the region.  
Increased vegetation coverage correlates with a decreased likelihood of meteorological and ecological drought incidence and transition. The traits of climatic and ecological drought on agricultural land resemble those of grasslands with moderate coverage, and surface vegetation cover can significantly mitigate the frequency of drought to some degree. Regions with increased vegetation density exhibit greater moisture retention, enhanced drought resilience, and a reduced duration of drought conditions.  
The frequency of drought and diachronic alterations are more intricate in the drought characteristics of regions undergoing land use change. The drought intensity is largely steady, with high meteorological drought intensity and low ecological drought intensity. The drought characteristics associated with the reciprocal transformation between cultivated land and grassland exhibit considerable consistency. The decline in surface vegetation significantly influences meteorological and ecological drought in the short term.  
The transition of meteorological drought to ecological drought is affected by climate, altitude, and soil type, among others. The predominant factors vary across distinct regions of the study area. The contribution degree of climate to the transition of meteorological drought to ecological drought was only about 55%. Various soil types, including semi-fixed aeolian sand, significantly influenced this transition due to their loose structure and inadequate water retention capacity. There were also significant differences in the transition efficiency at different altitudes.  
The prevalence of meteorological drought near many major mines in the mining region is considerable. The transition frequency from meteorological drought to ecological drought exceeds 60%. The meteorological drought intensity at Jitu well and Daliuta well is greater than in other mining sites. Therefore, drought-tolerant plants can be selected to carry out guided vegetation restoration in the Shendong mining area. Microbial reclamation technology can be adopted, soil improvement work can be carried out, and so on.
## 4 Evaluation of Prototypes with Edge Damping
### Measurement of the Surface Acceleration and Sound Field Radiation
Both variants have a higher mean value and a lower standard deviation, which leads to a more homogeneous directivity response. The variant BIF has better results than the variant BOF and a larger impact can be achieved by attaching the damping directly to the moving surface of the panel. In the frequency range of 100 Hz-1.0 kHz and above coincidence (7.5 kHz-20 kHz), the impact of damping is low.
# Passive Intermodulation at Contacts of Rough Conductors
## 5 Conclusions  
The main mechanisms of dissipative losses and sources of passive nonlinearities in contacts and joints of conductors with rough surfaces are reviewed. The discussed physical mechanisms are cast in the three main groups of (i) electrical, (ii) thermal and (iii) mechanical effects. It is shown that all these mechanisms are intrinsically linked but their time scales differ significantly. Namely, the electromagnetic interactions at MIM junctions are very fast and can follow the pace of the RF signals. The thermal processes are much slower being limited by the speed of heat flow in the conductor contacts. As well as this, the mechanical deformations develop even slower. It is emphasised that roughness of the contact surfaces considerably affects losses and nonlinearity in contact joints, especially at RF frequencies. The main sources of the passive nonlinearities at the contacts of rough surfaces have been discussed in the context of their effect on the RF performance of the joints of good conductors.  
An important outcome of this study is a demonstration that types of electrical, thermal and mechanical contact nonlinearities are very different in spite of being linked intrinsically. Namely, the MIM nonlinearity is of the exponential type, whereas the electrothermal nonlinearity is of Kerr type. The nonlinearities of mechanical deformations are of the mixed type described by the combination of the exponential and algebraic dependencies. This implies that the analysis of PIM in contact joints of good conductors requires multiphysics models taking into account multiple sources of nonlinearities.  
Conceptualisation, A.D., Y.H. and A.S.; writing--original draft preparation, A.D.; writing--review and editing, Y.H. and A.S.; supervision, A.S.; project administration, Y.H. All authors have read and agreed to the published version of the manuscript.  
Not disclosed.  
The authors declare no conflict of interest.