esa-sceva/satcom-chunk-collection huggingface dataset:
# Diversity of Physical Processes: Challenges and Opportunities for Space Electric Propulsion
## 1 Introduction  
Current space exploration plans include the deployment of multiple satellite constellations made up of hundreds to several thousands of small orbital assets [1; 2; 3; 4], each capable of being highly active by changing their attitude and orbital parameters. As a result, there is a strong need for advanced space electric propulsion platforms to provide such capabilities [5; 6; 7]. Furthermore, recently, there has been an increased interest in deep space missions aimed, for instance, at asteroid capture or voyages to Mars. These missions are feasible only with highly efficient means of space electric propulsion that allow for overall spacecraft mass reduction while increasing spacecraft manoeuvrability [8; 9; 10]. Miniaturized satellites that occupy orbits around the Earth [11; 12] also require highly efficient electric propulsion systems, capable of operating on orbit without refuelling [13; 14]. Advanced materials and nanofabrication that drive progress in the development of these on-orbit assets, and space technology in general, [15; 16; 17] will also play an important role in the emergence of permanent stations on the Moon and Mars, which are closer to their realisation than ever [18; 19; 20]. These lighter, more durable systems, possibly featuring self-healing [21; 22] and self-restoration abilities [15], will be less costly to deliver to and maintain at these distant outputs, with the outpots themselves serving as hubs from which smaller space assets can be managed and deployed.  
The aim of this Opinion article is not to provide a comprehensive review of the current state of the art for modern space propulsion systems, though interested readers are directed to the most recent reviews where electric propulsion systems, including their operating principles for various types of propulsion platforms and their key characteristics, are discussed in significant depth [5; 23; 24; 25; 26]. Instead, the core objective of this article is to emphasize the importance of such features of space electric propulsion systems as:  
* The wide diversity of physical processes and effects involved in space electric propulsion technologies, and
* The amenability of these systems to miniaturization and implementation of cutting-edge materials for the advancement of gridded ion thrusters, Hall-type thrusters, magnetoplasmadynamic (MPD) accelerators, Rotamak-type platforms, electrospray systems and other promising propulsion platforms [27; 28; 29].  
The improvement in the performance of these systems relies on the ongoing development of the specific sub-systems, e.g., highly efficient cathodes [30; 31; 32], as well as on the use of complex modelling approaches [33] and unique test equipment [34] to probe the physical processes that govern their performance.  
Thus, the ultimate aim of this article is to introduce the reader to some of the challenges and opportunities for the development of future space electric propulsion systems in the context of the diversity and complexity of physical processes that underpin their operation (Figure 1).
## 3 Results
### Inorganic Pigment Analysis
#### 3.1.2 Raman Spectroscopy Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\#** & **Layer Structure** & **Characteristic Element** & **Characteristic Raman Peak** & **Possible Substance** \\ \hline  & Red layer-1 & Pb, Au & 150 vs., 203, 395, 514, 628 & Litharge + plattnerite \\
1 & Red layer-2 & Ca, S & 1001 vs., 1108, 620, 483 & Gypsum \\  & Red layer-3 & Pb, Au & (Same as Red layer1) & Litharge + plattnerite \\  & Red layer over foil & Si, Fe & 456 & Hematite? \\
2 & Gold foil & Au, Ag (little) & Gold \\
2 & Red layer base & & 348 vs., 305, 1577, 1383 & Carbon-based material \\  & Purple-white layer & Ca, S & 1003 vs., 1284, 1570 & Gypsum + dye \\  & Gold foil & Au & & Gold \\
3 & Dark-red resin & Pb (little) & 126 vs., 261, 1324, 1570 & Minimum + carbon-based \\  & Orange layer & Pb & 70, 126 vs., 267, 537,1570 & Minimum \\  & White layer & Ca & 1010 vs., 1116, 1180, & Gypsum \\  & Red layer-1 & Pb, As & 70, 126 vs., 265, 529. & Minimum + carbon-based \\
4 & Light-red layer & Ca, As & 1011 vs., 1122, 1605 & Gypsum + dye \\  & Red layer-2 & Ca Fe & 1011 vs., 412, 293 & Gypsum + hematite \\  & White layer & Ca & 1107, 817, 1295 & Gypsum? \\  & Gold and red layer & Ca, S, Au & 1000, 814, 1365, 1572, 1758 & Gypsum + dye + carbon-based material \\  & Gold layer & Au & & Gold \\
5 & Blue layer & Na, Al, Si & 532 vs., 1081, 1632 & Lapis lazurite \\  & Blue layer-2 & Ca & 1573 vs., 1367, 1309, 1 & Gypsum + indigo \\  & White layer & Ca, & 997 vs., 1124, 402, 480 & Gypsum \\  & Gold layer & Au, Ag (little) & Gold \\
6 & Red layer & - & 1555, 1369 & Carbon-based material \\  & White layer & Ca & 1010 vs., 616, 493, 413 & Gypsum \\  & Particle on the gold foil & Pb, Ca, Si, Al & 152, 543 vs., 473, 1088 & Minimum \\
7 & Gold layer & Au & & Gold \\  & Orange layer & Pb & 134, 538 vs., 474, 1077 & Minimum \\  & Red layer on gold foil & Pb, Al, Au & 1587, 1364 & Carbon-based material \\
8 & Gold foil & Au, Ag & & Gold + silver \\  & Dark red layer & Pb & 1570, 1328, 355,273, 133 vs. & Minimum + carbon-based \\  & Orange layer & Pb & 122 vs., 151, 226,314,389, 545 & Minimum \\  & Orange layer on gold & Ca, Pb, Au & 999 vs., 1108 & Gypsum \\
9 & Gold foil & Au & & Gold \\  & Dark-red layer & Pb & 534, 461, 1076 & Minimum + dye \\  & White layer & Ca & 1005, 567, 427, 180 & Gypsum \\  & Gold foil & Au & & Gold \\
10 & Dark-red layer & Pb (little) & 77, 141 vs., 332, 549 & Minimum \\  & Orange layer & Pb & 124 vs., 153, 229,319, 392, & Minimum \\  & White layer & Ca & 414, 470, 606, 666, 1006, 1117 & Gypsum \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of elemental and Raman analysis results.
# Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification
## 5 Results
### A Preliminary Comparative Analysis  
This subsection is devoted to introducing a performance comparison considering several contributions from the SoTA. In recent years, numerous additional studies have attempted to address the classification problems of race [4] and ethnicity [46] by means of DL approaches, e.g., CNNs. Table 1 presents the results provided by several methods of the SoTA dealing with the tackled problem. Note that it was not feasible to carry out a direct numerical comparison against all methods due to several of them were trained and assessed on not accessible private datasets.  
In [4], the authors developed and made available a large-scale library of over 175,000 photos of faces of celebrities from multiple ethnicities and races, which were used for training and testing. The method was compared against four cutting-edge CNNs on the topic. Another dataset called VNFaces [6] was used to compare the accuracy of RR-CNN and RR-VGG methods, in which accuracy of 88.64 and 88.87 percent, respectively, was achieved.  
Previous studies have used datasets consisting mostly of Western celebrities [8] as well (WD, RBAL, BD) [11] and obtained accuracy rates of 91 and 96 percent. It should be noted that GPU was the hardware platform used in all the previous studies. Our proposal provides a new extension of the SoTA by using FPGA, being one of the first successful attempts making use of this specific hardware platform for addressing the real-time classification of human races. The reported results in Table 1 demonstrated the higher performance achieved by our method with an accuracy rate of 96.9 percent.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Dataset**} & **Number of** & \multirow{2}{*}{**Groups**} & **Accuracy** & **Hardware Platform** \\ \cline{4-5}  & & & & & **Platform** \\ \hline
[4] & Internet images & over 175,000 & Asian, Afro, Caucasian, and Indian & 96.64\% & GPU \\
[6] & VNFaces & 6100 & Vietnamese and others & 88.87\% & Not mention \\
[8] & Celeb-A & over 200,000 & predominately Western celebrities & 91\% & Not mention \\
[23] & Private & 22 & WD, RBAL and BD & 96\% & GPU \\ Our & Private & 3141 & Chinese, Pakistani, and Russian & 96.9 & GPU and FPGA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison results.  
Figure 5: The schematic of a convolutional block within an FPGA.
# Durability of Fabric-Reinforced Cementitious Matrix (FRCM) Composites: A Review
## 4 Durability of Composite
### Durability of the Composite-Substrate Bond Capacity  
Being employed as externally bonded reinforcements, FRCM can debond at the composite-substrate interface. However, limited number of studies dedicated to investigating the durability of the composite-substrate bond are available in the literature. They are summarized in this section and details of the tests included in the figures below are provided in Table 11.  
Sulfate attack can reduce the adhesion between the composite and substrate degrading the composite-substrate interface [33; 36]. Salt crystallization within the interface can also reduce the bond capacity and shift the failure from the matrix-fabric interface to the composite-substrate interface [33; 43]. Figure 11 shows the effect of sulfate and chloride attack on the load-carrying capacity of FRCM-masonry joints subjected to direct-shear tests. [PERSON] et al. [33] exposed FRCM-masonry joints with an AR-glass textile and a lime-based matrix to 10 wet-dry cycles in saline solution of sodium chloride (3.5 wt. % of NaCl) at 60 \({}^{\circ}\)C. After conditioning, the specimens failed at the composite-substrate interface and the load-carrying capacity was reduced. [PERSON] et al. [36] studied an SRG composite made of a lime-based matrix and steel cords externally bonded to masonry blocks. They found that four wet-dry cycles in a saline solution containing 8 wt. % of sodium sulfate decahydrate (Na\({}_{2}\)SO\({}_{4}\)-10H\({}_{2}\)O) followed by drying at 60 \({}^{\circ}\)C did not have a significant effect on the load-carrying capacity. In addition, all the specimens developed interlaminar failure characterized by fiber slippage from the internal layer of matrix, which remained bonded to the substrate. However, when 2 wt. % of sodium chloride (NaCl) was added to the same solution and the number of cycles increased to 6, the load-carrying capacity decreased.  
The results analyzed showed that the reduction in the bond capacity and shifting of the failure mode to the composite-substrate interface require a high salt accumulation and crystallization within the interface, which may depend on the porosity of the matrix and on the radius of the pores. For instance, a matrix with pores radius bigger than the radius of substrate pores will allow the solution to pass through it. Consequently, the effect of sulfate and saline attack on the interface may not be as significant as its effect on the fibers and matrix [36]. However, this hypothesis needs to be confirmed by using matrices with different porosity and pore sizes.  
Figure 10: Effect of the matrix alkalinity on the (**a**) maximum pull-out force and (**b**) ultimate tensile strength of the FRCM coupons tested by [PERSON] et al. [27; 47].
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 4 Radar and Remote Sensing Applications
### Monitoring of Human Body in Bio-Medical Applications  
Electromagnetic waves originated from UWB radars allow us to observe the intrinsic complex structure of the human body, and can, for instance, be employed to detect the movement of the heart wall [110]. The advanced technology of UWB radar systems allows today to monitor and assess many aspects of the human body: chest movements assessment, chest imaging, heart imaging, respiratory monitoring, non-invasive biomedical study of flexible tissues (e.g., brain), non-invasive study of metabolic processes, etc. [111].  
The respiration rate of a healthy person is almost 12 breaths per minute [112]. Thus, the difference in breathing rate from maximum to minimum levels reveals the variation in human health condition and allows indicating whether a medical deliberation may be required, i.e., the evaluation of the respiration rate can help doctors in assessing the actual health condition. In addition, the normal heartbeat for a typical adult person lies between 60 and 100 ppm [113]. If the heartbeat rate varies from the typical limits, this may indicate the possible presence of diseases [114]. Thus, the assessment of the heartbeat rate can also help to visualize human health condition. [PERSON] et al. investigated both the human respiration and the heart rate, demonstrating that the radar can be used to measure even minor distances between the inhale and exhale processes of the human body during respiration [115].
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 5 Experimental Setup
### Speech Quality Measures  
To evaluate the speech enhancement performance, the objective quality metric PESQ (perceptual evaluation of speech quality) [35] (from 0.5 to 4.5) is employed. STOI, short-time objective intelligibility [36], is used for objective intelligibility evaluation, where the intelligibility score is presented as a percentage. There are also results for quality metrics related to the MOS index. CBAK and COVL [37] are composite objective quality measures that provide a MOS from 1-5, where the first targets the background-noise intrusiveness, and the second target the overall signal quality. In all mentioned quality metrics, larger values indicate better speech quality.  
Additionally, for evaluating the speech estimator, the level of noise reduction was analyzed by means of the SNR output level using the WADA algorithm [38]. The distortion  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Kernel Size** & **Dilation** & **Input Dim.** & **Output Dim.** \\ \hline Block 1 (\(\times\)4 WRN) & 7 & 3 & 512 & 256 \\ \hline Block 2 (\(\times\)4 WRN) & 5 & 3 & 512 + 256 & 512 \\ \hline Block 3 (\(\times\)4 WRN) & 3 & 2 & 512 + 512 & 1024 \\ \hline Block 4 (\(\times\)4 WRN) & 3 & 2 & 512 + 1024 & 2048 \\ \hline Block 5 (\(\times\)4 WRN) & 3 & 2 & 512 + 2048 & 2048 \\ \hline Output (Linear Layer) & - & - & 512 + 2048 & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the network dimensions of the architecture used for estimating \(G_{MMSE}\). The dimension corresponds to the number of channels in the sequence of the processed frames.  
of the enhanced signal was assessed by means of the log-likelihood ratio (also known as Itakura distance) (LLR) [39]. LLR represents the degree of discrepancy between the smoothed spectra of the target and reference signals, computed over the active speech segments of the linear prediction coefficients. For LLR, the closer the target feature to the reference, the lowest the spectral distortion; therefore, smaller values indicate better speech quality.
## 4 Discussion
Multiple interactive data visualization servers and terrain servers use WebGL to store, distribute, and display 3D geospatial data more conveniently in a web browser environment without any plugin. A practical application test in the coal mine is carried out in the case study. It demonstrates that each process within the framework can be manipulated and performed seamlessly and smoothly. The spatial accuracy results of the web-based system by two types of validation data further indicated that the application accuracies have a good agreement with the true values.
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 4 Time-Varying Characteristics of DCB and OSB on BDS Satellites
### Comparison of DCB and OSB Obtained from the CAS  
To compare DCB and OSB products released by the CAS, we calculated the differences between the DCBs and the combined OSB values of different satellites. Figure 13 shows the time series of the differences between DCBs and combined OSB values for nine signal pairs for BDS. As shown in Figure 13, the average differences of each signal pair are stable, varying between \(-\)3 ps and 3 ps. The smallest mean difference is \(-\)0.1 ps, appearing in the C2I-C7I channel of BDS-2, while the largest mean difference reaches 2.7 ps, appearing in the C1P-C6I channel of BDS-3. The mean values of the differences between DCBs and the combined OSB values of each signal pair of satellite C35 are higher than those of other satellites, except for C2I-C6I. The differences between DCBs and combined OSB values for C1X-C8X fluctuate between \(-\)20 ps and \(-\)10 ps. It can also be seen that the differences between DCBs and the combined OSB values of different signal pairs of different satellites have remained constant every ten days since March 2021. Figure 14 shows the mean values of the differences between DCBs and the combined OSB values of different satellites with different markers. The mean values and STDs of the differences between DCBs and the combined OSB values of C2I-C7I are both the smallest. The overall magnitude of the differences between DCBs and combined OSBs is very small, which indicates that the DCBs  
Figure 10: Monthly STDs of C2I, C6I, and C7I OSBs for BDS-2.  
and combined OSBs have good consistency. Combined OSB values should have the same impact on positioning as DCBs.  
Figure 11: Monthly STDs of C1P, C1X, CSP, and C5X OSBs for BDS-3.  
Figure 12: Monthly STDs of C2I, C8X, C6I, and C7Z OSBs for BDS-3.
# State Merging and Splitting Strategies for Finite State Machines Implemented in FPGA
## 2 Materials and Methods
### Estimation of Optimization Criteria  
For the estimation of the optimization criteria, all states (for splitting) or couples of states (for merging) should be considered in sequence. For each state or couple of states, a trial splitting or merging is executed. Next, the internal states are encoded applying one of the common encoding techniques, and the set of logic functions relating to the combinational part of the FSM is constructed. After that, for the state to split or pair of states to merge, power consumption \(P_{i}\), critical delay path \(S_{i}\) or transformation quality \(Q_{i}\) ratios are estimated.
## 2 Materials and Methods
### Feature Selection, Modeling, and Performance Evaluation
with an initial exploration of 10 random points, followed by 30 iterations of optimization within a defined search space. The final number of components was chosen based on the best-performing model. Once the optimal number of components was determined, a PLRR model was trained using the training dataset and internally validated through cross-validation. Additionally, variable importance in projection (VIP) scores were calculated to assess the relative contribution of each feature to the model's predictions. The modeling was implemented using the \"pls,\" \"rBayesianOptimization,\" and \"vip\" packages [40, 41, 42]. All analyses were performed in R version 4.3.0, and the necessary packages were implemented accordingly [43]. Four statistical indices were used to evaluate model performance and complexity: the adjusted coefficient of determination (R\({}^{2}\)), relative prediction deviation (RPD), normalized root mean squared error (NRMSE), and corrected Akaike information criterion (AICc). The RPD is categorized into three levels: excellent (RPD > 2.0), satisfactory (1.4 \(\leq\) RPD \(\leq\) 2.0), and inadequate (RPD < 1.4). These four indices collectively provide a comprehensive assessment of model accuracy, predictive capability, and generalizability. The optimal model was determined based on the principle of minimizing the AICc (favoring lower model complexity) while maximizing the RPD (indicating better predictive performance).
# Influence of Stress Anisotropy on Petrophysical Parameters of Deep and Ultradeep Tight Sandstone
## 2 Geological Setting
In the depression, the productivity of gas wells with similar petrophysical properties in the same structure differs greatly, and even the productivity of two adjacent wells in the same structure differs significantly. Therefore, in situ stress-related factors should be considered in reservoir evaluation [51]. The gas reservoir is buried over more than 6500 m, which is a strike-slip stress field. Analysis of the numerical simulation results of the stress field shows that the present in situ stress is high, the horizontal stress difference is large, and the heterogeneity is strong. The maximum horizontal principal stress will be deflated in the plane and longitudinal directions, and the maximum deflection can be 90\({}^{\circ}\). Complex geological boundary conditions and differences in rock mechanical properties are important factors for the strong heterogeneity of the in situ stress field distribution, and gas reservoir development affects the in situ stress state around wells [52]. The exploration breakthrough of Bozi 9 reflects the great difference in the structural transformation between different zones and the strong heterogeneity [39]. In the Kelasu tectonic belt, the reservoirs buried more than 4500 m are strike-slip fields in an in situ stress state. The maximum horizontal principal stress of the Dabei 12 gas reservoir is 125-161 MPa, the minimum horizontal principal stress is 118-130 MPa, and the maximum horizontal principal stress is always greater than the minimum horizontal principal stress. The heterogeneity of in situ stress is strong, and the difference between wells is large. The conditions of the complex geological boundary and the interbedded lithology of reservoirs result in an extremely heterogeneous distribution of in situ stress. Favorable and unfavorable areas of in situ stress are distributed alternately, and unfavorable areas of in situ stress may be encountered in high structural positions [51]. [PERSON] et al. (2016) recovered the Cenozoic tectonic stress field in the Kuqa Depression, and a study using elastic finite element numerical simulation showed that the direction of the maximum principal compressive stress in the Kuqa Depression in the late Himalayan period was generally nearly north-south, which was highly fitted with the paleostress field [53]. In the Yangxiahe area, the direction of the maximum principal stress is slightly deflected due to the influence of the turning point of the fold hub, which reflects the local stress direction.