esa-sceva/satcom-chunk-collection huggingface dataset:
# Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
## 4 Conclusions  
The main objective of this paper was to assess the ability of the L and C bands to penetrate the wheat, grassland, and maize canopies, which are the most frequent land covers in numerous agricultural zones in France. For wheat and grassland covers, the results showed that the L-band in HH polarization penetrates the canopy and the backscattered L-HH is sensitive to soil moisture. The  
Figure 6: Temporal behavior of the radar signal in the C-band and VV polarization over a nonirrigated maize plot. The vertical lines in red represent the rainfall events. To display NDVI and SSM values on the same axis, the NDVI-values were multiplied by 50.  
sensitivity of the L-band in HH polarization is approximately 0.27 dB/vol.% and 0.19 dB/vol.% for an NDVI less than and greater than 0.7 (vegetation height of 70 cm and LAI of approximately 1.5 m\({}^{2}\)/m\({}^{2}\)), respectively. On the other hand, the C-band in both VV and HV polarization penetrates the wheat and grassland cover only when the NDVI is less than 0.7. For an NDVI less than 0.7, the C-band sensitivity to SSM is approximately 0.12 dB/vol.% for both VV and VH polarizations. At an NDVI greater than 0.7, C-band SAR is not able to penetrate the wheat and grassland covers, and thus the C-band becomes insensitive to SSM of grassland and wheat.  
For maize, only the ability of the C-band to penetrate the canopy was assessed because the ALOS-2 images did not cover the maize plots. The results showed that the C-VV penetrates the maize canopy even when the maize is at peak biomass (NDVI > 0.7) and the backscattered C-VV is always sensitive to soil moisture. This is due to the significant scattering along the soil-vegetation pathway that includes a soil moisture contribution. The L-band, with a wavelength much higher than the C-band, should also penetrate the maize canopy cover. Finally, for the maize crop, the sensitivity of SAR data in L and C bands to SSM was not determined because in situ SSM measurements and ALOS-2 data were not available.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the results; [PERSON] wrote the article; [PERSON], [PERSON] and [PERSON] revised the paper.  
This research received funding from the French Space Study Center (CNES), the National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA), and the European Space Agency (ESA).  
The authors wish to thank the French Space Study Center (TOSCA 2018), the National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA), and the European Space Agency (project: ESA-ESTEC ITT AO/1-8845/16/CT) for supporting this work.  
The authors declare no conflict of interest.
# A Study on the Mechanism of the Precipitation-Induced Slope Instability of Colluvium
## 4 Analysis of Test Results
### Pore Water Pressure Response of the Slope  
It can be seen from Figure 12 that, when the slope angle is 40\({}^{\circ}\), the change process of pore water pressure inside the slope comprises three processes: smooth, accelerated growth, and decline. Before 50 min, it is in a slow growth stage, and due to the presence of gas in the soil, a stable seepage channel has not yet been formed in the slope. After 50 min, with the increase in rainfall, the pore water pressure begins to grow rapidly, reaching a peak at about 75 min. At this stage, the slope transitions from the unsaturated stage to the saturated stage, and a stable seepage channel is gradually formed inside the slope; with the increase in time, the rainwater inside the slope accumulates more and more, the pore water pressure of the soil increases, the effective stress is reduced, and, when the shear strength decreases to the shear stress of the sliding surface, the landslide starts. After that, the pore water of the slope is released, and the pore water pressure gradually decreases.  
Figure 11: Schematic diagram of towed landslide.  
Figure 12: Change process of pore water pressure.  
When the slope angle is 30\({}^{\circ}\), the pore water pressure first rises and then decreases; with the infiltration of rainfall, the soil at the foot of the slope slowly settles and compacts, and the consolidation of the soil at the foot of the slope is completed in about 40 min, while the pore water pressure decreases due to the formation of a small channel in the soil. Subsequently, the pore water pressure increases sharply, and the pore water pressure at the foot of the slope decreases at 110 min due to the release of pore water at the foot of the slope as the foot continues to slide.  
When the slope angle is 20\({}^{\circ}\), the pore water pressure gradually increases with the infiltration of rainwater in the early stage, reaching the maximum in about 75 min; the soil is in a saturated state, and the balance of drainage and infiltration maintains the balance of water volume, which is stable after 75 min, indicating that a stable drainage channel has been formed inside.  
In the two experiments with slope angles of 40\({}^{\circ}\) and 30\({}^{\circ}\), the pore water pressure at the foot of the slope experiences a sharp rise before sliding; this sharp increase in pore water pressure can be regarded as a feature of instability prediction, and the maximum pore water pressure value increases with the increasing slope angle. Due to the different times of the overall failure of the slopes at different slope angles, the end time of the test is also different.
# Parameter Optimization Method for Metal Surface pBRDF Model Based on Improved Strawberry Algorithm
## 3 Improved Strawberry Optimization Algorithm
### Improved Algorithm
#### 3.2.3 Optimization of Metal Surface pBRDF Model Parameters  
As mentioned above, the Tent chaotic mapping and Levy flight strategy are introduced into SBA, and an improved strawberry algorithm (ISBA) has been proposed, which is applied to the parameter retrieval of the metal surface pBRDF model on metal surfaces to obtain the optimal solution.  
The algorithm flowchart is shown in Figure 6.  
Figure 6: Flowchart of parameters retrieval.  
To avoid errors caused by randomness in the optimization, the SBA and ISBA were run 50 times continuously in the same environment. The minimum, average and maximum values of the objective function values were calculated. The extreme value in the optimization process was compared between ISBA and SBA and is shown in Figures 7 and 8. It can be seen that the optimization efficiency and accuracy of ISBA are enhanced compared to those of SBA.
## 3 Results and Discussion
### Results of Characterization of Materials
#### 3.1.1 Platinum-Based Powder and Sintered Material
The results of the characterization of the materials studied are summarized in Table 1, Appendix A.  
Figure 6: Scanning electron microscopy (SEM) image of the sintered line (dark grey central area) printed on alumina substrate (light grey lateral areas) (**A**) and the elemental mappings derived from the following EDX spectra: aluminum (**B**), platinum (**C**) and oxygen (**D**).
# 3D Reconstruction of a Single Bubble in Transparent Media Using Three Orthographic Digital Images
## 3 3D Bubble Reconstruction  
### Modified Stacking Ellipse Method  
The method in this paper proposed here included four steps as illustrated in Figure 4. The first step was to extract the projected contour of the bubble in three orthographic images, and the second step corrected the posture of the projected contour to make the \(c\)-axis (or \(z\)-axis) in the front view and the side view vertical or horizontal; the third step was also supposed to calculate the dimensionless major semi-axis (\(a_{0}\)) and minor semi-axis (\(b_{0}\)) of the horizontal elliptical cross-section by utilizing the projection relationship and the geometric information; the fourth step divided the bubble into \(n\) layers along the height direction (\(c\)-axis) and calculated the major semi-axis (\(a_{i}\)) and the minor semi-axis (\(b_{i}\)) of each horizontal ellipse slice. Finally, all the ellipse slices were stacked to form a 3D bubble shape.  
Figure 3: The relative mean square errors (K) vary with bubble diameters.
# Bounce: A High Performance Satellite-Based Blockchain System
## 3 Bounce Protocol
### Trust Model
#### 3.3.2 Satellite Assumptions  
**Non-traitorous satellite assumption**: Each satellite contains a single processor called a _Bounce Unit_ that implements the _Bounce_ protocol. As we will see, protocol is quite simple, so it can be verified and burned in as read-only memory. For this reason, we assume that the only failures a Bounce Unit can suffer are to stop processing, either permanently or intermittently. That is, we assume no traitorous (e.g., Byzantine) failures.  
This assumption is reinforced by the fact that it is impractical to capture a satellite to modify it and the Bounce Unit can be made tamper resistant (i.e., self-destruct in case of tampering). A would-be adversary can force omission failures by attacking the satellite or its communications, but cannot force traitorous failures.  
**Approximate synchrony assumption**: The satellites agree on the time to within a known bound, say 100 milliseconds of the real time. Satellite clocks fall easily within this bound given an occasional beacon from a time server.
# GAE and OBE Enhanced Interference Mitigation Techniques in LDACS
## 4 Nonlinear Estimators
### Genie-Aided Estimators  
When the received signal is modeled with K-GMM, the genie-aided estimator (GAE) assumes the knowledge of the state of the underlying noise generation process. For any impulse noise, when modeled as properly weighted mutually exclusive Gaussian events,  
Figure 5: General block diagram for GAE and OBE.  
Figure 6: General block diagram for pulsepeak processors.  
the GAE claims to know which is the \(k^{th}\) Gaussian component of the (pdf) combination affecting at each time of epoch and causes the actual noise. The received signal \(y\) can be expressed as the sum of transmitted signal \(x\) and noise \(i|_{k}\) where \(x\) and \(i|_{k}\) are two zero-mean independent Gaussian random variables with variances \(\sigma_{x}^{2}\) and \(\sigma_{i}^{2}\). For each time epoch, the powers of the transmitted OFDM signal and receiver noise power are \(\sigma^{2}x_{t}=E[|x_{t}[n]|^{2}]\) and \(\sigma^{2}i_{t}=E[|i_{t}[n]|^{2}]\). Under these conditions, GAE removes the noise as per the expression in (7) [32, 45].  
\[\hat{x}_{|kG}(y)=\rho_{k}.y \tag{7}\]  
where \(\rho_{k}=\frac{\sigma_{x}^{2}}{\sigma_{x}^{2}+\sigma_{k}^{2}}\) and,  
\[\sigma_{k}^{2}=(1+\frac{k}{A\Gamma})\sigma_{0}^{2}=\frac{k/A+\Gamma}{1+\Gamma }\sigma_{i}^{2}=\frac{k}{A\Gamma}\sigma_{1}^{2}+\sigma_{0}^{2} \tag{8}\]  
'\(A\)' is the average of Gaussian mixture components. From (7), it can be understood that the linear GAE works with a variable slope at the time epoch with the knowledge (perfect and non-realistic) of the present noise state. It is to be noted that (7) is applicable for the real and imaginary parts of the complex-valued signals we are dealing with in the LDACS system. Hence, it is possible to use (9) to express the operation of GAE to complex valued data and a K-component complex Gaussian noise mixture in LDACS.  
\[\hat{x}_{|kG}^{*}(y)=\rho_{k}.|y|e^{j\arg(y)} \tag{9}\]  
The GAE is not expedient in practical systems, as the receiver is unable to predict the noise component which causes actual noise. It is possible to improve the performance of GAE by utilizing other side information, such as impulsive noise arrival time or relationship of the impulsive noise [46, 47, 48]. The following two estimators described in Sections 4.2 and 4.3 exploit threshold-based noise detection along with GAE to attain a lower bound performance, especially at low SNR values, which is very important in the DME mitigation scenario.
Entropy_**2012**, _14_, 654-664; doi:10.3390/e14040654  
_Artcle_  
**A Rotated Quasi-Orthogonal Space-Time Block Code for Asynchronous Cooperative Diversity**  
**[PERSON] *, [PERSON] and [PERSON]**  
School of Electrical Engineering and Information, Anhui University of Technology, Ma'anshan,  
243002, China; E-Mails: [EMAIL_ADDRESS] (F.-K.Y.); [EMAIL_ADDRESS] (L.Z.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-0555-236-8431; Fax: +86-0555-231-6596.  
_Received: 16 February 2012; in revised form: 4 March 2012 / Accepted: 22 March 2012 / Published: 29 March 2012_  
**Abstract:** The rotated quasi-orthogonal space-time block code (RQSTBC) for asynchronous cooperative diversity is proposed in this paper. The source selects half of the symbols from a signal constellation set and the other half of them from that constellation rotated with the optimum angle. Meanwhile, it constructs orthogonal frequency division multiplexing (OFDM) frames to counterbalance time delays of the signals. Then, relays create the frequency domain quasi-orthogonal space-time block transmitted signals matrix in such a way that its items are staggered to take on the Jafarkhani code structure or time-reversion of it. These three stages let the received signals at the destination take on RQSTBC structure with diversity order 4, which results in the fast symbol-pair-wise maximum likelihood (ML) decoder. Simulation results have shown that the proposed scheme outperforms the other asynchronous cooperative diversity schemes considered in this paper.  
**Keywords:** asynchronous cooperation diversity; rotated quasi-orthogonal space-time block code; time-reversion; orthogonal frequency division multiplexing
## 1 Introduction  
Nowadays, hyperspectral remote sensing is a hot topic in the remote sensing field. It was originally proposed for minerals. In the 1980s, based on the study of mineral reflectance spectroscopy models by [PERSON] and [PERSON] [1; 2; 3], the unique advantages of hyperspectral remote sensing in geological applications were gradually recognized by many scholars. Traditional hyperspectral imaging is mostly used in aircraft or satellite platforms and suitable for large-scale geology mapping. On the other hand, emerging terrestrial imaging spectroscopy methods, such as laboratory-based and field-based methods, have been used for various geoscience applications, including mineral exploration [4; 5; 6; 7], sedimentology analysis [8; 9], mineral identification, and alteration zonation mapping, etc. [10; 11; 12]. Compared to those of airborne and satellite-borne, the laboratory-based hyperspectral imaging can obtain mineralogical maps at centimeter to millimeter scale, thus providing more detailed information about the internal spatial distribution of rock specimens.  
Anshan-Benzi area, Liaoning province, is one of the most important BIFs ore resource bases in China, among which the Qidashan is interpreted as the second largest one. The iron ores in this area are mainly composed of BIFs and BIFs-related high-grade iron ores. The high-grade iron ore has the characteristics of high iron grade and more industrial economics. Chloritization shows close relations to the high-grade iron ore, which can be regarded as the crucial prospecting criteria [13]. Unfortunately, the chlorite does not merely distribute around the high-grade ores, actually, it is widespread everywhere in the mining district, which is thought to be related to the low-medium grade regional metamorphism. Furthermore, high-grade iron ores are also distributed in other deposits of the Anshan-Benzi area. Examples include the Gongchangling, Yanqianshan, Waitoushan, Dawanggou, Banpanling, and Nanfen iron deposits. Their wall rock alteration consists of chloritization, sericitization, etc., which is similar to the Qidashan [14]. Although many researches such as geophysical exploration, geochemistry, and geochronology have been carried out on these mines, the hyperspectral imaging method, which has the advantages of quick-evaluation of the high-grade iron ore resources, has rarely been conducted.  
In this paper, the laboratory-based HySpex-320m spectrometer was adopted to study the wall rock of Qidashan Iron Deposit. The alteration minerals, assemblage, texture, and the spectral features of chlorite were identified. A model of hyperspectral image processing and spectral feature extraction was promoted, and the spectral differences of two kinds of chlorites extracted from wall rock of BIFs and high-grade iron ore were analyzed. Finally, the relationship between cations of the chlorite octahedral layer and the spectral absorption features was discussed.
## 3 Results and Discussion
### Effect of blockage Ratio on Friction Factor  
A friction factor analysis was conducted with an evaluation of the difference in pressure between the inlet and outlet of the test section. This pressure difference is associated with heat transfer which occurs at the surface. An assessment of the friction factor is not only based on the heat transfer but also based on the flow length of the heat exchange, the free-flow area, and the hydraulic diameter of the passage [29]. Figure 7 shows the influence of the blockage ratio (\(R_{b}\)) on the friction factor (\(f\)) as a function of the Reynolds number (\(Re\)). A downtrend in \(f\) corresponds to an increase in \(Re\) number for all test conditions. These results are consistent with those reported in the authors' earlier publications [16; 23; 24]. As shown in Figure 7, the friction factor of the tube with DWT or LS insertions is higher than with the plain tube. When turbulent disturbance and core main flow increased, the  
Figure 6: Effect of blockage ratio (\(R_{b}\)) on convective heat-transfer behavior (\(Nu\)) with Reynolds number (\(Re\)).  
flow friction factor also increased. In addition, the blockage effect due to the thickness of the inserts significantly improved the flow friction factor [30]. The average friction factor in the inner tube with the addition of the DWT with \(R_{b}\) = 0.28, 0.35, and 0.42 increased by 11.8, 13.4, and 15.5, respectively, compared with the much lower friction factor values of the LS and plain tube, although the values of LS were slightly better those of the plain tube. \(R_{b}\) = 0.42 showed the highest \(f\) value, which may be attributed to the increased surface area (solid-winglet/tape) that induced a larger recirculation zone behind the winglet, leading to higher vortex strength, higher turbulence intensity, and an enhanced friction factor [28].
# Extending the Coverage of IEEE 802.15.4z HRP UWB Ranging
## 3 Link Analysis of HRP UWB Communication
### Communication and Ranging Principles of HRP UWB  
Section 3.1 explains the structure and modulation method of the physical layer packet of the IEEE 802.15.4z HRP UWB standard. This section examines the principle of how HRP UWB technology measures distance based on wireless communication. Since HRP UWB is basically a wireless communication method, packets are exchanged to send and receive data. Among the packets, the preamble performs the role of synchronization for data demodulation, and the payload among DPs is used for data transmission. The principle of ranging between UWB devices using this packet exchange is as shown in Figure 5a. Figure 5a is a method of measuring distance by exchanging only two packets, which is called single-sided two-way ranging (SS-TWR). Among the two UWB devices, the device that sends the packet first is called the initiator, and the other is called the responder. The initiator transmits a Poll packet and records the time \(t_{1}\) when the transmission of the Poll packet begins, while \(t_{1}\) is the moment when the preamble ends, and this time record is called a timestamp. The UWB responder device that received the Poll message sends a Response packet after the time \(t_{reply}\). Now, the UWB initiator records the time at which the Response packet was received as a timestamp \(t_{2}\). Now, the distance between the two UWB devices is \(1/2\) of the time of propagation (ToF), which is given by Equation (1):  
\[R=c\times\frac{t_{2}-t_{1}-t_{reply}}{2}, \tag{1}\]  
where \(c\) means the speed of light. In this way, ranging using two packets is possible through wireless communication, and this is called two-way ranging (TWR).  
Meanwhile, the types of TWR that measure distance in the UWB can be divided into SS-TWR, which exchanges packets twice (Poll-Response), and double-sided-TWR (DS-TWR), which exchanges packets three times (Poll-Response-Final), as shown in Figure 5b. DS-TWR is further classified into symmetrical DS-TWR (SDS-TWR) and asymmetrical  
Figure 5: Two-way ranging: (**a**) principle of UWB two-way ranging and (**b**) classification of HRP UWB two-way ranging and related packets.  
DS-TWR (ADS-TWR). Among these, ADS-TWR, which can freely use the length of the packet, is mainly used for ranging. ADS-TWR is used when high distance accuracy is required because it can minimize the influence of clock errors between the transmitter and receiver. In ADS-TWR, the length of the packet is the shortest for the Poll packet and the longest for the Final packet, as shown in Figure 5b. Regardless of the TWR method used, in order to measure the distance between UWB devices with TWR, two packets must be successfully exchanged for SDS-TWR, and three packets must be successfully exchanged for DS-TWR. To this end, both the preamble and the payload of the packets must be successfully received.
## 2 Materials and Methods
### Research Data
#### 2.2.2 Characteristic Variables for Permafrost Mapping
The DEV was determined using the NASA Land Processes Distributed Active Archive Center's free 30 m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) (SRTMGL1_003 [[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)), (accessed on 1 October 2022)) [48]. In this study, the radius used to calculate DEV was taken as 1 km. * Solar radiation (SR). Solar radiation can reflect the amount of solar radiation energy that can be absorbed by the Earth's surface. For a long time, the permafrost in Northeast China has been in a degraded state, and its degradation trend has a certain degree of correlation with the solar radiation absorbed [24]. The method in the hemispheric horizon algorithm created by [PERSON] et al. [49] and further developed by [PERSON] [50] was adopted to calculate the solar radiation received by the research area in the non-snow period (from 15 April to 15 October). The solar radiation results are considered as one of the characteristic variables in the mapping of permafrost distribution. The DEM data used to calculate solar radiation were also SRTMGL1_003. * NDVI and NDWI. There are a large number of virgin forests in the permafrost region of Northeast China. The growth pattern of plants on the ground alters during the permafrost degradation process due to changes in soil moisture and heat, and even tree species succession [51]. Such vegetation changes lead to corresponding changes in NDVI values. Therefore, NDVI was applied as one of the characteristic variables of permafrost mapping. On the other hand, to eliminate the interference of surface water in mapping, NDWI was also applied as a characteristic variable. To reduce the uncertainty caused by seasonal changes in vegetation and water, we used Landsat-8 OLI image data and adopt the simple cloud-mask algorithm [52] to synthesize the annual minimum cloud amount image of the study area in 2021. NDVI and NDWI data are obtained by band operation with this image. The spatial resolutions of LST1, LST2, LST3, DEV, SR, NDVI, and NDWI mentioned above are all 30 m. The relationship between these characteristic variables and permafrost prior data is further discussed in Section 4.1.