esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
(2005) verified the positive correlation between the permeability and the relaxation time for water-saturated sandstones [17]. Some scholars have shown that permeability calculations can be carried out by using the correlation between relaxation time and pore size and that the calculated results are typically in good agreement with directly measured permeability [23]. The relaxation time spectrum inversion method is used for obtaining the induced polarization relaxation time spectrum of a sample via the time-domain decay curve; in turn, the induced polarization relaxation time spectrum can then be used for characterizing porous media distribution and to estimate its permeability [24; 25; 26]. By introducing the formation factor, [PERSON] et al. (2016) proposed that the relaxation time can be used to better predict the permeability of the samples in their study [27]. In addition, some scholars have studied from the perspective of Debye decomposition that the relaxation time and permeability conform to a power-law relationship in the sandstone samples; this suggests that the relaxation time parameter is a good indicator for permeability evaluation [28]. The above research shows that relaxation time and permeability could have a good correlation. In this paper, we mainly expand the factual database of water-bearing sand samples. Then relaxation time and permeability curves using grain size as the link are established, providing an expression of the relationship between these two parameters suitable for water-bearing sand samples. Eight kinds of quartz sand samples were used to simulate a water-bearing sand layer under different conditions, and in situ experiments and laboratory testing were carried out through multiple predesigned experimental devices. The first was an induced polarization experiment, where tanks of different sizes were filled with quartz sand samples of different grain sizes, and the relaxation times were measured. The second experiment involved the measurement of permeability. Using the same sample, a Darcy percolation experiment was performed to determine the sample's permeability parametersin order to obtain its actual permeability. By treating the grain size of the water-bearing sand samples as an intermediate quantity, the relationship between relaxation time and permeability was established. This relationship is further clarified to verify the effectiveness of the use of this method for estimating induced polarization permeability. The results of this study could provide a theoretical basis for the advanced detection of permeability via tunnel-induced polarization.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 4 Video Compressive Sensing
### Spatio-Temporal VCS  
Video compressive sensing approaches are mostly based on either temporal or spatial domain. These approaches consider one single domain to compress data which is not optimal. However, spatio-temporal data can convey more features that can be used to optimize the sensing and the recovery processes. In fact, the spatio-temporal approach consists in sampling both the temporal and spatial information simultaneously. In this case, the sensing matrix becomes a sensing cube that encode the video i ints 3 rd dimension. In [57], [PERSON] et al. implemented a hardware-friendly algorithm for video compressive sensing where the sensing cube, that is composed of either 1 or 0, is used to encode the video signal into a single coded image. Then, the recovery phase is processed using dictionary and simple sparse recovery. However, the computational cost of the recovery process used in [57] remains one the major limitations of this spatiotemporal VCS algorithm. In [58], the same research team improved their previous work, by adding a CNN layer to extract key features from the frames to enhance the recovery process and improve the sensing quality. [PERSON] et al. [59] propose a video sampling process divided into 2 steps. Firstly, the 3D image volume is decomposed by a 3D Wavelet transform. Then, a second measurement is obtained by a Noiselet transform. Using this sampling paradigm, the CS reconstruction, with Total Variation, performs successfully.  
Motivated by the success of convolutional neural network(CNN) in image processing, 3D CNN are commonly used for decades to extract useful features from video signals. In [60], the authors apply a 3D CNN network to extract spatial and temporal features for action recognition. This architecture is used later in [61] to design a 3D video compressive sensing algorithm. One other similar approach is proposed in [62] which proposes a 3D Convolutional network that is more suitable to extract spatiotemporal features compared to 2D ConvNets by exploring the effect of different depths and filter sizes.  
In the later work of [PERSON] et al. [63], an improved version of ISTA-Net+ is proposed which learns an adaptive sampling matrix by simultaneously optimizing the sampling and reconstruction procedures. A two-phase joint deep reconstruction is adopted to selectively exploit spatial-temporal information, consisting of a temporal alignment with a learnable occlusion mask and a multiple frames fusion with spatial temporal feature weighting (see Figure 8). The separated frames (key and non-key) reconstructions are based on the attention mechanism that applies an adaptive shrinkage-thresholding for discriminative transform coefficients suppression. A specific measure loss is also proposed to ease the network optimization by reducing the inverse mapping space. Accordingly, the reconstruction network is able to adaptively exploit spatial-temporal correlations to recover the full video from few 3D samples of the original video tensor.  
Figure 8: Overall architecture of STEM-Net.
## 2 Feature Analysis of Existing RRSA Concepts
### Regular-Panel Antenna  
With respect to the regular-panel antenna concept, i.e., the PTDA mentioned in Section 1, its reflective surface is segmented into a series of identical panels in the radial direction, with a circle-shaped hub left in the center, as shown in Figure 4a. Its unfolding process is like a blooming flower; hence, it is referred to as the petal-type deployable antenna [32; 36; 39], and the panel together with its supporting structure (framework) is usually called a petal. All the panels are fixed on their respective framework and can be easily linked to the hub with tilted revolute joints (purple rectangles) through these frameworks, so the deployed or folded PTDA can be implemented by rotating the panels around their respective joint axes. This scheme has been widely adopted by many designs [31; 32; 33; 35; 36], owing to its structure simplicity. Some designs introduced a base to connect the panel [38]. No matter what schemes are adopted, the structure of the PTDA exhibits high axisymmetry at any phase of deploying or folding, possessing excellent commonality of elements in deployment balance [39] compared with the aforementioned antenna concepts. In the folded state, all the panels form a pattern similar to the vertical view of a fan impeller, as shown in Figure 4b.  
The gap between adjacent panels is large enough, and the back of one panel is exactly opposite to the face of another, thereby ensuring a well-suited gap for accommodating satisfactory supporting structures to realize high surface accuracy. For instance, the surface accuracy of the FIRST antenna [31] and Millimetron antenna are 8 \(\upmu\)m and 6 \(\upmu\)m, respectively.  
Figure 4: Petal-type deployable antenna. (**a**) Deployed state. (**b**) Folded state. (1) This antenna has 18 units, each of which has only one panel; (2) The position of the revolute joint must be finely arranged for precise antenna deployment.  
In terms of manufacturing, only two sets of molds are required for fabricating all the panels, where, by adopting them, the extra economic merit of this antenna type outperforms those of the other two.  
The above advantages make this antenna concept become the most prevailing one among those three antenna concepts, for which the characteristics of the above concepts are listed in the first three rows of Table 1 for a comprehensive comparison.  
Although three commonly adopted RRSA concepts exist, their RSEs are still low. According to Table 1, the regular panel is the preferred concept, based on which we propose a new antenna concept while utilizing the advantages of the regular panel in subsequent sections.
# Agrochemical Contamination and Ageing Effects on Greenhouse Plastic Film for Recycling
## 5 Discussion
Each film was tested as a cover for two low tunnels: one of them was sprayed on its inside face with commercial agrochemicals containing iron, chlorine and sulphur, while the other one was not sprayed, so as to be used as a reference. Radiometric tests were carried out on new film samples and obtained at the end of the test. Laboratory analysis of the uptake of selected contaminants was performed on the samples obtained at the end of the field exposure, in order to compare the relative effectiveness of the stabilizers. The final results showed that, within six months of the experimental field trials, the combined effect of natural weathering and agrochemicals did not significantly alter the radiometric properties of the films in the wavelength ranges of solar radiation and photosynthetically active radiation (PAR). In contrast, variations in up to 70% of the initial value were observed for stabilized films in the long-wavelength infrared (LWIR) wavelength range.
# Optimal Gliding Trajectories for Descent on Mars
## 3 Results
### Uncertainties on Aerodynamic Coefficients: Sensitivity Analysis
The banking angle (Figure 12) has a more dispersed evolution, with trajectories that differ considerably with a slight variation in \(l/d\), adjusting the direction of the flight closer to the final point. In summary, the OCP results were presented in this Section, obtaining feasible trajectories that satisfied the objectives and constraints (Equations (9)-(11)). At the same time, a sensitivity analysis from Monte Carlo simulations was performed, calculating optimal solutions from changes in the values of the lift and drag coefficients. The optimal control problem was formulated and solved via nonlinear programming to calculate the control history of optimal gliding descent above Mars. The results show feasible solutions, minimizing the four cost functions at a terminal time. The results showed that all the trajectories landed on Mars, so neither escapes from the planet nor collisions on its surface occurred for the \(l/d\) values used. We also saw that most of the trajectories ended with flight path angles near \(-18.6\) degrees, but the final azimuth angle had a large dispersion, from zero to more than 100 degrees. So, the final azimuth angle is very sensitive to the \(l/d\) ratio, and it is impossible to make an accurate prediction of this value without knowing the spacecraft's \(l/d\) ratio.
# Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
## 5 Spoofing Detection Performance
### Results of Data Set 2 (Dense Urban Environment)
A missed detection probability is calculated if in each observation interval (\(t_{u}\)), in the spoofing case, the detection metric is below the detection threshold. The measured missed detection rate for GPS-based trajectory for \(t_{u}\) = 5 s, \(t_{u}\) = 10 s and \(t_{u}\) = 20 s is 21%, 7% and 2% respectively. As demonstrated increasing the observation period reduces the miss detection performance. The mean spoofing detection time for the given data set is 3.2 s, 3.4 s and 3.5 s for \(t_{u}\) = 5 s, \(t_{u}\) = 10 s and \(t_{u}\) = 20 s cases respectively. Figure 16: ROC curves (**a**) GPS-based trajectory (u-blox receiver) and different update intervals. Increasing \(t_{u}\) improves the detection performance. (**b**) ROC curves for true-reference and GPS trajectories for \(t_{u}\) = 10 s. The detection performance is degraded due to GPS measurement errors. Figure 15: RIO horizontal error for various update intervals (\(t_{u}\)) in the spoofing case for MEMS IMU.
# Dynamic Induction Heating Temperature Field Analysis of Spiral Bevel Gears
## 2 Modeling of Induction Heating Coils for Spiral Bevel Gears
### Induction Heating Modeling  
A three-dimensional model of the spiral level gear induction heating with two coil structures was established using COMSOL finite element software (Figure 4). The coil structure consisted of a spiral level gear, an air domain, and an induction coil. The yellow arrow in the figure indicates the induction coil's current flow direction, while the white arrow indicates the gear's rotation direction. The gear area between the red dashed lines is the coil opening area, used as the projectile coverage area during the gear shot-peening process.  
Transient analysis was solved using the magnetic field module and the solid heat transfer module of COMSOL finite element software. Tetrahedral mesh cells were used for the induction coil and air domain parts. To reduce the calculation time, the mesh near the induction heating coil was finely divided, and the mesh far from the coil was coarsely divided. The spiral bevel gear was meshed with a hexahedral mesh cell, the tooth surface was meshed with a rectangular mapping mesh, and the mesh inside the gear was in-creased gradually along the shaft diameter direction of the gear center. To improve the accuracy of the temperature solution, the boundary layer mesh of the tooth surface was di-vided into three layers [18; 19].  
The spiral bevel gear was made of 20 CrMnTi gear steel. 20 CrMnTi is a material commonly used to manufacture transmission gears in the automotive field, and has high hardenability, high strength and impact toughness. Its chemical composition is shown in Table 3. The electrical and thermal parameters related to the temperature are shown in Figure 5. The spiral bevel gear was set to rotate at a constant speed of 25 r/min around the center of rotation; the induction heating parameters were a current of 1000 A, current frequency of 15 kHz, heating time of 120 s and an air gap of 3 mm.  
Figure 4: Spiral bevel gear induction heating model: (**a**) transverse coil, (**b**) longitudinal coil.  
Figure 5: Parameter curves of the 20 CrMnTi with temperature: (**a**) thermal parameters, (**b**) electrical parameters.  
In the electromagnetic field solution process, magnetic insulating boundary conditions are set on the outer surface of the air domain to ensure that the magnetic lines of force are parallel to the outer surface of the air domain. The boundary conditions of heat convection and heat radiation were considered in the simulation analysis of the temperature field of the spiral bevel gear. The convective heat transfer coefficient was set to 10 W/(m\(\cdot\)K), radiation rate was 0.7, and external ambient temperature was set to 293 K.
###### Abstract
These computational techniques enable the extraction of complex patterns and relationships from datasets, revealing correlation and causation relationships between SM and various predictive factors that may not be immediately discernible through traditional statistical methods [19]. These techniques have been used for spatiotemporal SM monitoring, SM downscaling, and upscaling, modeling non-linear relationships between SM and environmental covariates, generation of global and regional spatially continuous high-resolution SM datasets, and testing and validation datasets, among others [20]. Recently, efforts have been made to collect and harmonize in situ SM data generated from different organizations and groups. The International Soil Moisture Network (ISMN) is a collaborative effort that integrates and standardizes data from 80 networks and 3147 stations worldwide [21]. These in situ measurements are essential for validating satellite observations, improving climate models, and supporting hydrological and meteorological research [22]. This extensive database, particularly dense in semi-arid lands, plays a pivotal role in validating and enhancing global satellite observations and is crucial for SM monitoring. The objectives of this review article are threefold: (a) to compile and summarize the datasets, statistical methods, and available online resources for measuring and monitoring soil moisture (SM) in semi-arid zones; (b) to identify the advantages and limitations of available satellite datasets for measuring and monitoring SM; and (c) to evaluate and systematize available SM platforms, geoportals, measurement networks, and products. Our review highlights the critical role of multidisciplinary approaches in advancing SM research. The primary contributions of this review lie in the identification and systematization of key methods, tools, and models for SM measurement and monitoring at various scales, from large landscapes to smaller plots; furthermore, we highlight the gaps in effective SM monitoring.
## 5 Conclusions  
The proposed model in this paper considered all stages of the whole life cycle of the products and self-recovery properties, and improved the accuracy level of product reliability evaluation. As shown in Figure 9, compared with only one degradation model to evaluate product reliability, the accuracy of the proposed model was higher. Moreover, compared with the model that did not consider self-recovery properties, the accuracy of the proposed model was also higher. Therefore, when predicting the life or evaluating the reliability of products with self-recovery properties, the influence of self-recovery properties on reliability evaluation, and of performance degradation and threshold variation on product reliability should be considered in modeling. Through the analysis and proof of this paper, the following conclusions were drawn:  
(1) If the influence of the degradation threshold variation on product reliability is not considered, the reliability of the product is overestimated, and the accident rate is increased.  
(2) Ignoring the self-recovery properties of the product results in the reliability of the product being underestimated, which leads to increased costs of early operation and maintenance.  
(3) With considering the impact of product degradation at the storage stage on product reliability, the proposed model had better accuracy.  
In further research, in order to establish an improved reliability evaluation model with better accuracy that is more in line with actual working conditions, the influence of various stochastic processes and their combinations on the proposed model should be considered.  
Conceptualization, X.W. and B.W.; methodology, X.W.; software, X.W.; validation, X.W., B.W. and Z.H.; formal analysis, B.W.; investigation, X.W.; resources, X.W.; data curation, X.W.; writing--original draft preparation, X.W., B.W. and Y.N.; writing--review and editing, X.W. and Z.H.; visualization, Y.N.; supervision, X.W.; project administration, X.W. and B.W. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Reliability curves with varying models.  
Funding: This research was funded by the Projects of Talents Recruitment of GDUPT (grand number 2020 rc034), the State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment (grand number JMDZ2021012), the Special Project in Key fields of the General Universities of Guangdong Province (grand number 2022 ZDZX3013), the Natural Science Foundation of Hebei Province (grand number E2020501013), the Natural Science Foundation of Fujian Province (grand number 2022)01527), and CAST-BISEE2019-019. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
# Development of a Self-Powered Magnetorheological Damper System for Cable Vibration Control
## 2 Description of the Self-Powered MR Damper System for Cable Vibration Control  
### Configuration and Principle  
The proposed self-powered MR damper system consists of a current-adjusted MR damper and an energy harvesting device. Figure 1 depicts a schematic diagram of the system used in a real environmental setting for cable vibration control. The energy harvesting device includes a rotary DC generator and a linear-to-rotational conversion mechanism. The MR damper and the generator are attached to the cable at different locations rather than being integrated into one device. To improve the energy harvesting capability, the generator is attached at a higher location relative to the MR damper. Accordingly, the roller chain-sprocket is selected as the linear-to-rotational conversion mechanism since it can be conveniently installed at a higher location through a flexible connection without detracting from the aesthetics of the bridge.  
Under environmental excitation, the reciprocating linear motion of the stay cable can be converted into the rotational motion of the rotary DC generator through the linear-to-rotational conversion mechanism and a pre-tensioned spring. Accordingly, the rotary DC generator will produce electromotive force (EMF), and the induced EMF/current is then used as an input to the MR damper. Consequently, cable vibration can be mitigated by the self-powered MR damper system. It is worth noting that there are two sources of damping in the self-powered MR damper system for suppressing cable vibration. The major one is the self-powered MR damper, and the other one is the electromagnetic damping due to the generator itself.  
In addition, the EMF induced by the generator is proportional to the velocity of the stay cable [32], which implies that both the supply EMF/current and the damping force will increase with the increase in cable vibration. Consequently, the proposed self-powered MR damper system is expected to be capable of adaptively attenuating cable vibration without an external power supply or any controller. Nonetheless, the system is actually passive.