esa-sceva/satcom-chunk-collection huggingface dataset:
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 6 Conclusions
\begin{table}
\begin{tabular}{l l} \hline
**Area** & **Research Gaps** \\ \hline  & \(\bullet\) & Improve fault-tolerant capability without increasing size. \\  & \(\bullet\) & Avoid common mode current for fail-safe operation. \\  & \(\bullet\) & Improve power density. \\  & \(\bullet\) & Reduce ripple content of output current to improve battery health. \\  & \(\bullet\) & Improve reliability by avoiding single-element failure. \\  & \(\bullet\) & Solve unshared ground issues in multi-port converters. \\  & \(\bullet\) & Increase power levels for converters (\(>\)100 kW). \\  & \(\bullet\) & Increase output voltage range (\(>\)300 V). \\  & \(\bullet\) & Increase high-voltage conversion ratio. \\  & \(\bullet\) & Improve efficiency and power density. \\  & \(\bullet\) & Design modular converters. \\  & \(\bullet\) & Improve the design of input and output EMI filters. \\ \hline  & \(\bullet\) & Improve the specific impulse and durability of high-thrust systems. \\  & \(\bullet\) & Improve the efficiency and reliability of low-thrust systems. \\ Adoption of Solar EP/Nuclear EP & \(\bullet\) & Increase power levels (\(>\)100 kW). \\  & \(\bullet\) & Increase understanding of the reliability and lifetime of propulsion systems at higher power levels. \\  & \(\bullet\) & Reduce specific mass. \\ \hline  & \(\bullet\) & Develop electronic components capable of withstanding high radiation and temperatures in deep space. \\ Deep Space Exploration & \(\bullet\) & Develop high-power propulsion systems. \\  & \(\bullet\) & Develop new materials and technologies to enhance the efficiency and durability of solar panels in deep space. \\ \hline  & \(\bullet\) & Develop higher-rating space-grade SiC and GaN semiconductors \\  & \(\bullet\) & Design PCB layouts and packaging techniques with low parasitics for WBG devices. \\  & \(\bullet\) & Design gate drivers for switches. \\ Electronic Components for Space Applications & \(\bullet\) & Study the reliability of WBG devices under cryogenic and radiation-hardened environments. \\  & \(\bullet\) & Develop EMI mitigation methods. \\  & \(\bullet\) & Develop high-current/high-energy-density capacitors. \\  & \(\bullet\) & Develop low-loss magnetic materials that can withstand high temperatures. \\ \hline  & \(\bullet\) & Develop low-cost solar cells with a long lifetime. \\ Solar-Based Power Satellites & \(\bullet\) & Investigate Perovskite solar cells. \\  & \(\bullet\) & Develop large-scale microwave/laser-based wireless energy transfer methods. \\  & \(\bullet\) & Develop kilometer-scale space solar power plants. \\ \hline \end{tabular}
\end{table} 
Table 10: Research gaps in various space applications.
## 2 Materials and Methods
### Study Design  
An overview of the study design is displayed in Figure 1. This controlled clinical trial involved a cross-sectional assessment evaluating 20 persons with CNSLBP and 20 HCs. Persons with CNSLBP were recruited first. HCs were matched for age, sex, and body mass index (BMI) [40]. At least seven days before performing study assessments, each participant performed a standardized maximal cardiorespiratory exercise test (CPET) on a bike ergometer, used to design the individual HIIT/MICT protocols. Evaluation of CNSLBP consisted of two sessions, with 7-14 days in between. Each session was carried out at the same time of the day to optimize the analysis of inflammatory markers [41,42] and was designed identically, with exception of the performed HIIT/MICT protocol. To avoid the learning effect, each participant underwent the HIIT/MICT protocol in a random sequence. The evaluation for HCs consisted of one session. The design and content of this session was identical to that for CNSLBP with the exception of HCs only performing a HIIT protocol and not completing the questionnaires only applicable to CNSLBP (i.e., MODI, BPI, FABQ). All assessments were carried out at REVAL Research Center, Hasselt University (Hasselt, Belgium). The study protocol was approved by the medical ethical committee of Jessa Hospital (Hasselt, Belgium) and registered at clinicaltrials.gov (nr: NCT04902196).  
Figure 1: Study design. Abbreviations: HCs: healthy controls; CNSLBP: chronic nonspecific low back pain; CPET: cardiorespiratory exercise test; HIIT: high-intensity interval training; MICT: moderate-intensity continuous training.
# A Phase Error Correction System for Bioimpedance Measurement Circuits
## 5 Performance Overview  
### Noise  
At low frequencies, operational amplifier noise spectral density rises to 3 dB/octave. The power spectral density in this region is inversely proportional to frequency; therefore, the voltage noise spectral density is inversely proportional to the square root of the frequency and is commonly referred to as \(\frac{1}{f}\) noise [32; 33]. Since the output of a synchronous demodulator is a DC signal, it is prone to excessive flicker (\(\frac{1}{f}\)) noise, as provided by Equation (15).  
\[\frac{1}{f}=efnorm\sqrt{\ln\left(\frac{f_{H}}{f_{L}}\right)} \tag{15}\]  
where, \(efnorm\) is the normalised value of noise voltage spectral density in the \(\frac{1}{f}\) region; \(f_{H}\) is the upper cut-off frequency; \(f_{L}\) is the lower cut-off frequency corresponding to 0.1 Hz.  
To avoid an increase in flicker noise due to compensation, the output of the phase synchroniser circuit was high-pass filtered at a cut-off frequency of fc = 16 Mhz, which is over five decades more than the signal bandwidth to avoid affecting both the amplitude and phase of demodulator signals. To further verify this effect, noise analysis was performed on Lt spice for SD systems with and without compensation. The outcome remained the same in both cases, as illustrated in Figure 14.
# Shallow Subsurface Wavefield Data Interpolation Method Based on Transfer Learning
## 3 Results
### Evaluation Indicators
#### 3.3.1 Structural Similarity Index Measure  
The structural similarity index measure (SSIM) provides an assessment of perceptual differences between two images, capturing the visual impact of changes in luminance, contrast, and structural patterns, as defined by Equation (13). The SSIM replicates the efficiency of the human visual perception system in identifying structural information within network outputs and serves as an intuitive method for quantifying such outputs. The SSIM metric also takes into account the strong neighborhood dependencies that carry significant structural information, which play a critical role in evaluating the fidelity of network outputs. In this context, \(u\) represents the signal map of the complete gather reconstructed by the network, while \(v\) corresponds to the gather map of the actual wavefield acquisition. The local SSIM index measures three components between these datasets:  
Figure 9: Two of the signaling effects: (**a**) the fifth signal; (**b**) the 121 st signal.  
local luminance, local contrast, and structural similarity. The cumulative effect of these components is expressed in the SSIM formula, as follows:  
\[SSIM=[l(u,v)]^{\alpha}\cdot[c(u,v)]^{\beta}\cdot[s(u,v)]^{\gamma} \tag{13}\]  
Here, \(\alpha>0\), \(\beta>0\), \(\gamma>0\) is a parameter used to adjust the relative importance of the luminance comparison function, contrast comparison function, and structural comparison function. The specific formulation of _SSIM_ is provided in Equation (14):  
\[SSIM=\frac{(2\mu_{u}\mu_{v}+C_{1})(2\sigma_{uv}+C_{2})}{(\mu_{u}^{2}+\mu_{v}^{ 2}+C_{1})(\sigma_{u}^{2}+\sigma_{v}^{2}+C_{2})} \tag{14}\]  
In this context, \(\mu_{u}\) and \(\mu_{v}\) represents the average of \(u\) and \(v\), while \(\sigma_{uv}\) denotes the covariance. The constants \(C_{1}\) and \(C_{2}\) are values close to 0. When the _SSIM_ result approaches 1, it indicates a better interpolation result by the network.
# A Multi-Physics Modeling-Based Vibration Prediction Method for Switched Reluctance Motors
## 1 Introduction
With the aid of a piecewise first-order linear function, [PERSON] et al. [27] proposed an efficient vibration calculation method. However, the saturation effect is not taken into consideration. In-depth analysis shows that the above vibration prediction methods only analyzed the vibration characteristics of the stator and ignored the influence of other components on the system vibration. To predict its vibration more accurately, it is necessary to consider the influence of more components including stator, rotor, housing winding, end cover, and bearing on the system modal. Consequently, this paper proposes a novel vibration prediction method for SRM based on multi-physics modeling. It includes three core segments: control algorithm integration, electromagnetic field dynamic analysis, electromagnetic force calculation, and modal analysis. The highlight of the proposed method is that it allows modification of system inputs, such as geometry dimensions, material parameters, or control strategies, at each step of the simulation process. The vibration data under different control strategies is obtained in real time, and data dynamic interaction between the three segments can be also achieved. Therefore, from the perspective of reducing vibration, it can be used to optimize the structural design and the control strategy of the switched reluctance motor. In addition, in the mechanical module, it not only includes the influence of the stator, but also fully considers the influence of rotor, end cover, bearing, and other components of the motor on the system modal. The remainder of this paper is organized as follows. In Section 2, the operational principle and the origin of vibration of the SRM are presented. Section 3 develops the multi-physics modeling-based vibration prediction method. In Section 4, the implementation of the prediction model is presented, and experiments are conducted to validate the proposed method. Finally, the conclusions are drawn in Section 5.
## 3 Method
### Algorithm Steps
#### 3.2.2 Coarse Calculation  
This section mainly involves boundary discretization and calculating the angle relationship between the sub-points and discrete points on the boundary within the vicinity of the VTW. The goal is to determine the start-end times of the VTW. The main steps include boundary discretization and angle calculation.  
Step 4: Boundary Discretization. As shown in Figure 9, the regional target A is discretized into red equidistant points \(N_{i}\)[25]. The approach involves obtaining the length \(R\) of the polygon based on the latitude and longitude of the polygon vertices. The distance between each discrete point is then determined as \(R/N_{i}\), and the latitude and longitude of each discrete point are calculated accordingly through linear interpolation [38]. The angle \(\beta_{i}\) between the payload and each discrete point is computed, and the minimum angle \(\beta_{min}\) among the discrete points is selected as the angle \(\beta_{t}\) between the payload and the polygon region at the current time. The formula for calculating the length of the polygon \(R\) is given by Formula (7), where \(r\) is the radius of the Earth and (\(lon_{Li}\), \(lat_{Li}\)) represents the coordinates of each vertex of the polygon.  
\[R=\sum_{i=1}^{n-1}r\cdot\arccos(\sin(lat_{Li})\cdot\sin(lat_{Li+1})+\cos( lat_{Li})\cdot\cos(lat_{Li+1})\cdot\cos(lon_{Li+1}-lon_{Li})) \tag{7}\]  
Step 5: Angle Calculation. Within the time interval \(\Delta t_{2}\), a large-step \(t_{d}\) is used to traverse and calculate the relationship between the angle \(\beta_{t}\) and the half-field angle \(\alpha\). If the angle \(\beta_{t}\) changes from greater than to less than \(\alpha\), it is considered the starting point \(T_{3s}\) of the VTW. Subsequently, if there is a process of transitioning from being less than to greater  
Figure 9: Schematic diagram of the discretization principle for regional targets.  
than and then back to being less than \(\alpha\), and if \(\beta_{t}\) changes from less than to greater than \(\alpha\), it is considered the endpoint \(T_{3e}\) of the VTW. This process determines the interval \(\Delta t_{3}\).
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 3 Description of the Communication System
### Transmitter System
#### 3.1.1 USB \(\leftrightarrow\) Serial Converter  
A commercial device used to emulate a serial port in the sending system's computer. The data from the serial converter are used as the modulating signal.  
Figure 4: Magnetic field measured with ELT-400 and simulated.
# Numerical Analysis of Cyclic Impact Damage Evolution of Rock Materials under Confining Pressure
###### Abstract  
To study the effect of cyclic impacts and confining pressure on the damage evolution of rock materials, numerical simulations of cyclic impact tests on rock materials under confining pressure were carried out by LS-DYNA using dynamic relaxation and full restart analysis. The static confining pressure was applied by dynamic relaxation, and cyclic impacts were realized by full restart analysis. As the crack generation and propagation result in the failure of elements in the finite element model, the damage variable defined by the crack density method was characterized by volume reduction. Numerical simulation indicates that both the confining pressure and amplitude of incident stress waves significantly affect the damage evolution of rock materials. High incident stress waves lead to severe damage, while large confining pressure results in minor damage. Under confining pressure, the damage to rock materials is alleviated due to the constraint effect on crack propagation. The number of cyclic impacts before macroscopic fracture increases as the confining pressure increases and decreases when the amplitude of the incident stress waves increases. The cumulative damage of rock materials under confining pressure progressively increases with the number of cyclic impacts, and the damage evolution exhibits three distinct stages: rapid rising, steady development, and sharp rising.  
c pacs: **Keywords:** cyclic impact; confining pressure; crack density; damage evolution
## 3 Field Investigations
### Horizontal-to-Vertical Spectral Ratio (HVSR) Survey  
An HVSR (Horizontal-to-Vertical Spectral Ratio) survey measures the seismic ambient noise or vibrations to calculate the average ratio of the horizontal-to-vertical Fourier amplitude spectra [33]. The surveys were conducted following the guidelines established by the Site Effects Assessment Using Ambient Excitations (SESAME) Project [34].  
HVSR surveys were conducted at all ReMi sites in both GMMA and Leyte Province. Ideally, these surveys were performed at the center of the ReMi array; if this was not feasible, they were relocated only a few meters to ensure representativeness. Key factors considered in site selection included avoiding soft ground (e.g., mud or saturated soil), steep slopes that hinder proper sensor leveling, proximity to structures (e.g., buildings and trees), areas with underground structures, strong winds, rain, and other sources of noise. Whenever possible, surveys were conducted in quiet environments. In Leyte Province, HVSR surveys were also conducted at additional sites around the ReMi locations to maximize the genetic algorithm's utility. This approach allowed for broader spatial coverage and the derivation of Vs30 values.  
The HVSR survey used the OYO McSEIS MT-NEO single-station three-component microtremor (3 CMT) unit. This unit was oriented north and positioned on level ground using a bubble sensor. All connection cables and an external GPS signal were ensured before recording commenced. The recording duration was determined by site conditions, including the area's geology and the \"quietness\" of the vicinity. Quietness refers to the absence of transients such as footsteps, passing heavy vehicles, and industrial machinery, which can affect the quality of the recording. A minimum recording duration of 20 min was maintained, but the total duration varied depending on site conditions, as discussed.  
Raw recording files were extracted using OYO SeisImager software PickWin version 5.12.1.2 and converted into a three-column ASCII format for processing in Geopsy version 2.10 software. Thirty-second windows representing ambient noise were selected to produce the HVSR curve, with the peak amplitude period considered the predominant period for the site.
## 3 Results and Discussion
### Non-Cancerogenic and Cancerogenic Risks  
Building on the research by [PERSON] et al., this study examines the impact of 11 heavy metal elements: Cd, As, Hg, Pb, Cu, Zn, Ni, Mn, V, Co, and Cr [38; 39; 40; 41; 42]. Figure 11a illustrates the spatial distribution of the _HI_ within the study area. It is generally accepted that an _HI_ value less than 1.0 indicates no significant carcinogenic risk, although the risk level increases with higher _HI_ values. Spatial analysis reveals that the majority of _HI_ values range from 0 to 0.8, leading to the conclusion that there is no significant carcinogenic risk in the study area as a whole. However, two regions, located in the central-southern and central-western parts, exhibit _HI_ values greater than 1, which is consistent with the distribution of _RI_ values (Figure 6). Additionally, the _TLCR_ values for Cd, As, and Pb were calculated, and inverse distance weighting interpolation was applied to map their spatial distribution across the region (Figure 11b). The results indicate a maximum _TLCR_ value of 0.00023, suggesting that the carcinogenic risk in the study area is generally at a moderate level.
# Contrast Enhancement for Topographic Imaging in Confocal Laser Scanning Microscopy
## 2 Technique  
We call the investigated technique Pinhole Displacement Method (PDM). The procedure is illustrated in Figure 1.  
Figure 1: Schematic figure of the pinhole displacement, with L2 lens in front of the pinhole and detector of the confocal microscope.  
For this method, the distance along the optical axis between the lens focusing light through the pinhole and the pinhole is changed. This results in an axial shift of the focal spot related to the pinhole plane and a detection of a decreased intensity signal at the detector. Due to practical reasons the lens and not the pinhole itself is shifted which is also easier to implement in most CLSM systems. In an infinity corrected CLSM system a shift of the pinhole in the range of a few micrometers is equivalent to a shift of the lens.
## 3 Device Structure and Operation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Transduction Mechanism** & **Frequency (Hz)** & **Bias Voltage (V)** & **Gap Width (nm)** & **Q** & **\(\mathbf{R_{m}}\) (Ohm)** \\ \hline [28] & Capacitive & \(9.65\times 10^{7}\) & 7 & 100 & 3600 & \(1.75\times 10^{4}\) \\ \hline [29] & Capacitive & \(3.4\times 10^{4}\) & 2.5 & 1000 & 60,000 & \(4\times 10^{4}\) \\ \hline [30] & Capacitive & \(1.07\times 10^{8}\) & N.A. & 70 & 12,400 & \(1.13\times 10^{6}\) \\ \hline [31] & Capacitive & \(2.27\times 10^{6}\) & 3.6 & 159 & 1000 & \(3.95\times 10^{5}\) \\ \hline [32] & Capacitive & \(51.33\times 10^{6}\) & 4 & 70 & 8200 & \(1.778\times 10^{6}\) (*) \\ \hline [33] & Piezolectric & \(132.94\times 10^{6}\) & N.A. & N.A. & 1099 & 246 \\ \hline [34] & Piezoelectric & \(4.96\times 10^{8}\) & N.A. & N.A. & 3800 & 600 \\ \hline [35] & Piezolectric & \(1.97\times 10^{9}\) & N.A. & N.A. & 3410 & 340 \\ \hline [36] & Piezolectric & \(8.82\times 10^{8}\) & N.A. & N.A. & 220 & 98.76 \\ \hline [37] & Hybrid & \(101.65\times 10^{6}\) & 20 & 150 & 14,000 & \(2.411\times 10^{3}\) (*) \\ \hline [38] & Hybrid & \(14.63\times 10^{6}\) & \(\sim\)70–141 & 2500 & 1952 & \(1.2\times 10^{3}\) \\ \hline [39] & Hybrid & \(167\times 10^{6}\) & N.A. & 200 & 4536 & \(4.94\times 10^{3}\) \\ \hline This Work & Hybrid & \(20\times 10^{6}\) & 12 & 84 & 28,677 & \(199.43\times 10^{3}\) \\ \hline \hline \end{tabular}  
* Calculated from the [37] based on reported insertion loss by using equations in [40].  
\end{table} 
Table 1: Comparison of the performance metrics of various MEMS resonators, including devices with capacitive, piezoelectric, and hybrid (capacitive/piezoelectric) transduction mechanisms.
# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 4 Evaluation Results and Discussion
### Analysis and Discussion of the Hybrid Method  
The APT of an airport is deemed to be effectively predicted when there is at least one of five proposed models can be used to predict the APT effectively for each airport under arbitrary criteria and the hybrid five-model method is applicable. Approximately 180 of 203 tested airports can be effectively predicted and the total predictable proportion is about 88% under Criterion 1. It reveals that the hybrid method combining the time series model, the causal model, the artificial intelligent model, the market share model and the analogy-based model is applicable to most airports. The unpredictable airports of the five-model hybrid method can generally be divided into two categories. One is because there are insufficient historical data for modelling. The other one is because the APT of the airports changes abruptly owing to expansion, relocation or other kinds of external forces such as earthquakes. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Criterion 1**} & \multicolumn{2}{c}{**Criterion 2**} & \multicolumn{2}{c}{**Criterion 3**} & \multicolumn{2}{c}{**Criterion 4**} \\ \cline{2-9}  & \multicolumn{1}{c}{**Number of**} & \multirow{2}{*}{**Percentage**} & \multicolumn{1}{c}{**Number of**} & \multirow{2}{*}{**Percentage**} & \multicolumn{1}{c}{**Number of**} & \multirow{2}{*}{**Percentage**} & \multicolumn{1}{c}{**Number of**} & \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Good**} \\  & \multicolumn{1}{c}{**Models**} & & & & & & & \\ \hline Summation & \multirow{2}{*}{16} & \multirow{2}{*}{8\%} & \multirow{2}{*}{17} & \multirow{2}{*}{8\%} & \multirow{2}{*}{20} & \multirow{2}{*}{10\%} & \multirow{2}{*}{18} & \multirow{2}{*}{9\%} \\ of all levels & & & & & & &  
For different levels of airport, all airports with APT of more than 10 million can be effectively predicted under arbitrary criteria with the five-model hybrid method. Regardless of the Zhangjiajie airport (DYG), all the airports with APT between 2 million and 10 million can be effectively predicted under Criterion 1. Under Criterion 2, all the airports with APT between 2 million and 10 million can be effectively predicted. For the airports with APT less than 2 million, 85% of the airports can be effectively predicted. By comparing the number of good models for each airport shown in Figure 8, there are usually more than three good models for the airports with APT of more than 10 million under Criterion 3. For the airports with APT between 2 million and 10 million, there is at least one good model. The applicability of all the models is promoted with the increasing APT. The applicability of all the models on different levels of airports is shown in Table 18. It reveals that large airports with high APT usually have high prediction accuracy with the hybrid evaluating method. Airports with low APT are hard to be predicted with the promoted hybrid evaluating method.