esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result Analysis and Verification
### Comparison under the Hypothesis of Non-Uniform Moisture Content
Meanwhile, it can be seen from Figure 12, for the same rainfall intensity, due to the moisture content of the wetting front, it gradually increases and is unsaturated during the rainfall process, and the wetting front depth of the improved model in this paper is higher than the calculation results of the traditional model. Moreover, due to the condition that the initial moisture content increases uniformly with depth, the internal matrix suction is smaller than the hypothesis of the uniform moisture content. Under the same surface moisture content, the matrix suction gradient of the former is significantly greater than the latter. According to Equation (1), the larger the suction gradient, the faster the seepage velocity in soil. Therefore, the expansion speed of the wetting front under the non-uniform moisture content condition is greater than the latter. At the same time, it can be found that when the rainfall intensity is low, there is little difference in the wetting front depth, and with the increase in rainfall intensity, the difference gradually increases. Figure 11: Variation law of surface moisture content under different hypotheses: (**a**) Variation trend of surface moisture content with time; (**b**) Relationship between time difference and moisture content.
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 2 Working Principle of CW Doppler Radar  
Single-tone Doppler CW radar transmits an unmodulated RF signal to the target, which reflects part of the signal back to the radar receiver. The moving target modulates the carrier signal nonlinearly in the phase. Further DSP techniques can be used to recover the target's movement from the nonlinear phase modulation.
# Online Monitoring of Power Converter Degradation Using Deep Neural Network
## 2 Classification of Health States of Power Converter
### BLDC Motor Drive  
The BLDC motor drive system is operated by the feedback from the rotor position sensor, which is obtained every 60 electrical degrees where commutation of the phase currents occurs. The circuit diagram of the three-phase BLDC motor drive system is shown in Figure 1. The commutation timing is determined by hall sensor signals \(H_{A}\), \(H_{B}\), and \(H_{C}\), as shown in Table 1. The \(+\) and \(-\) sign indicate the direction of the current flow, while OFF means no current flow in the phase. Based on the position of the rotor, there are six switching intervals. At each interval, the six switches are controlled in such a way that the current only flows in two phases in opposite direction. A shunt resistor is placed in a series connection with the lower switch of each phase. As shown in Figure 1, a voltage sensor is used to measure the voltage of each shunt resistor for current control scheme.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Switching** \\ **Interval** \\ \end{tabular} & \(\mathbf{0^{\circ}}\)**–\(\mathbf{60^{\circ}}\)** & \(\mathbf{60^{\circ}}\)**–\(\mathbf{120^{\circ}}\)** & \(\mathbf{120^{\circ}}\)**–\(\mathbf{180^{\circ}}\)** & \(\mathbf{180^{\circ}}\)**–\(\mathbf{240^{\circ}}\)** & \(\mathbf{240^{\circ}}\)**–\(\mathbf{300^{\circ}}\)** & \(\mathbf{300^{\circ}}\)**–\(\mathbf{360^{\circ}}\)** \\ \hline \begin{tabular}{c} Sequence \\ Number \\ \end{tabular} & 0 & 1 & 2 & 3 & 4 & 5 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Hall \\ Sensors \\ \end{tabular} } & H\({}_{A}\) & 1 & 1 & 1 & 0 & 0 & 0 \\ \cline{2-6}  & H\({}_{B}\) & 0 & 0 & 1 & 1 & 1 & 0 \\ \cline{2-6}  & H\({}_{C}\) & 1 & 0 & 0 & 0 & 1 & 1 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Phase \\ Current \\ \end{tabular} } & A & OFF & \(+\) & \(+\) & OFF & \(-\) & \(-\) \\ \cline{2-6}  & B & \(-\) & \(-\) & OFF & \(+\) & \(+\) & OFF \\ \cline{1-1} \cline{2-6}  & C & \(+\) & OFF & \(-\) & \(-\) & OFF & \(+\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Switching patterns of the BLDC motor drive.  
Figure 1: BLDC motor drive.
## Materials and Methods  
### Simulation Data  
The SCOPE model (version 1.61) was employed to simulate the SIF emission and APAR of the leaves and canopy with different biochemical and biophysical parameters under different environmental conditions. The inputs to the simulation experiment are listed in Table 1. The SCOPE model is a 1-D vertically integrated radiative transfer and energy balance model linking radiance observations at the top of the canopy with land surface characteristics, thus providing estimation of the amount of photosynthesis, leaf and canopy chlorophyll fluorescence and reflectance [38]. A range of values of the vegetation parameters, Cab, LAI, LIDFa (leaf angle distribution function) and LIDFb, as well as the geometrical parameters, solar zenith angle (SZA) and view zenith angle (VZA), were used in the simulation. Values of Cab ranging from 5 ug/cm\({}^{2}\) to 80 ug/cm\({}^{2}\) were used to represent the different chlorophyll levels. The LAI inputs were set from 0.5 to 7, indicating different levels of vegetation coverage. Six different leaf inclinations represented by combinations of LIDFa and LIDFb were used to represent different plant types. Different values of the SZA were set to correspond to different incident light conditions. All other SCOPE parameters were set to their default values.
# A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal
## 3 Local Search Maximum Likelihood Parameter Estimator  
After denoising, parameter estimation is performed by local search estimator using denoised signal \(y^{\prime}(n)\) and observation \(y(n)\). As shown in Figure 4, the proposed approach consists of a TF coarse estimator, which uses TF distribution of \(y^{\prime}(n)\) to coarsely estimate the initial frequency \(f_{0}^{\prime}\) and chirp rate \(k^{\prime}\), then an ML fine estimator applied on the \(\big{(}\hat{f_{0}^{\prime}},\ k^{\prime}\big{)}\) and \(y(n)\) to get the fine estimation results of the initial frequency \(f_{0}\) and chirp rate \(\hat{k}\).
## 1 Introduction and Background
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case Study** & **Coastline Extraction** & **Study Area (km2)** & **Input Data** & **Limitations** \\ \hline
[24] & \
\begin{tabular}{c} Semiautomatic \\ (histogram threshold/ frequency band ratio) \\ \end{tabular} & 5100 &
\begin{tabular}{c} Low-resolution imagery \\ (Thematic Mapper (TM); Enhanced Thematic \\ Mapper (ETM)) \\ \end{tabular} & Not applicable for large scale mapping [44] \\ \hline
[14] & \
\begin{tabular}{c} Semiautomatic \\ (maximum likelihood method) \\ \end{tabular} & Few km2 &
\begin{tabular}{c} High-resolution imagery \\ (WorldView multispectral \\ Imagery) \\ \end{tabular} & Not applicable for large scale mapping [44]; \\ \hline
[46] & \
\begin{tabular}{c} Semiautomatic \\ (edge detection operators) \\ \end{tabular} & Few km2 &
\begin{tabular}{c} High-resolution imagery \\ (Aerial Images/WorldView \\ multispectral imagery) \\ \end{tabular} & Not applicable for large scale mapping [44]; \\ \hline
[12] & \begin{tabular}{c} Semiautomatic \\ (adaptive thresholding) \\ \end{tabular} & Several hundred km2 & \
\begin{tabular}{c} Low-resolution imagery \\ (Landsat 8 OLI) \\ \end{tabular} &
\begin{tabular}{c} Based only on \\ spectral information \\ \end{tabular} \\ \hline
[15] & \
\begin{tabular}{c} Semiautomatic \\ (adaptive thresholding) \\ \end{tabular} & Several thousand km2 &
\begin{tabular}{c} High-resolution imagery \\ (Geoeye- \\ 1/Quickbird/WorldView) \\ \end{tabular} & Based only on spectral information \\ \hline
[16] & \begin{tabular}{c} Semiautomatic \\ (edge detection operators) \\ \end{tabular} & Few km2 & \
\begin{tabular}{c} High-resolution imagery \\ (GF1/GF2/Quickbird/ZY3) \\ \end{tabular} &
\begin{tabular}{c} Based only on \\ spectral information \\ \end{tabular} \\ \hline
[17] & \begin{tabular}{c} Semiautomatic \\ (adaptive thresholding) \\ \end{tabular} & Few km2 & \
\begin{tabular}{c} Low-resolution imagery \\ (Landsat TM, ETM+, OLI) \\ \end{tabular} &
\begin{tabular}{c} Based only on \\ spectral information \\ \end{tabular} \\ \hline
[45] & \begin{tabular}{c} Semiautomatic \\ (adaptive thresholding (edge detection)) \\ \end{tabular} & Several thousand km2 & \
\begin{tabular}{c} Low-resolution imagery \\ (Landsat) \\ \end{tabular} &
\begin{tabular}{c} Based only on \\ spectral information \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of various existing remote sensing coastline extraction methods.
# Recent Developments of Solar Cells from PbS Colloidal Quantum Dots
## 5 PbS Quantum Dot Heterojunction Solar Cells
introduced a CdIr treated CdSe quantum dot buffer layer between ZnO nanoparticles and PbS quantum dots in colloidal PbS heterojunction solar cells [102], while [PERSON] et al. used an ultrathin Mg(OH)\({}_{2}\) layer for this purpose [103]. [PERSON] et al. examined the influence of CdS thin films used for electron transport in TiO\({}_{2}\)/PbS heterojunction colloidal quantum dot solar cells. They used a single-source precursor to prepare solution-processed CdS thin films by spin-coating and annealing, and found increased power conversion  
Figure 10: Scheme of energy band alignment of the solar cell with inserted mixed nanocrystal (MNC) layer between ZnO, PbS treated with 1-ethyl-3-methylimidazolium iodide (EMII) as an active layer and PbS-EDT electron blocking layer, in short-circuit condition. Reprinted with permission from [101]. Copyright (2017) American Chemical Society. efficiencies compared to cells with CdS layers prepared with chemical bath deposition or RF magnetron sputtering [104]. Another semiconductor, SnO\({}_{2}\), was suggested by [PERSON] et al. to produce colloidal quantum dot heterojunction solar cells. Based on its larger band-gap and higher electron mobility, as compared to the often applied ZnO, as well as adequate band alignment with the PbS quantum dots, light absorption and photocarrier extraction were supported, resulting in high power conversion efficiency [105]. Similarly to [74], [PERSON] et al. found a significant influence of the solvent used to prepare PbS/TiO\({}_{2}\) heterojunction solar cells from different PbS solutions. They reached the highest power conversion efficiency of 7.64% in an n-octane/isooctane hybrid solvent [106]. To summarize, for heterojunction solar cells similar points as forthe aforementioned types of solar cells are important, tuning the quantum dot band gap to desired values and increasing the time-stability of the common short ligands on PbS quantum dots. Special difficulties also occur according to the band alignment of semiconductor, the PbS quantum dot layer and possible additional charge transport layers. Nevertheless, PbS quantum dots offer a wide range of possible utilizations in heterojunction solar cells, underlined by the large number of studies published in this research area.
# MUSIC: Cardiac Imaging, Modelling and Visualisation Software for Diagnosis and Therapy
## 4 Functionalities
### Electromatomical Map Analysis  
ElectroAnatomical Mapping (EAM) systems are used to create a 3D model of the heart and to record the cardiac electrical activity at the mapped points [7]. More than two decades of device improvements and technological refinements have made the clinical EAM systems routinely used during invasive radio-frequency (RF) ablation procedures for treating dangerous cardiac arrhythmia [8].  
An electroanatomical map (EAM) is a combination of a 3D heart geometry (i.e., a mesh) and electrophysiological data in the form of 1D electrical signals (e.g., uni/bipolar maps, local activation times, intracardiac electrograms). After the EP procedure, the study containing the maps can be exported from navigation systems. Our application is capable of importing EAMs from navigation systems used in the clinics: CARTO (Biosense Webster,  
Figure 6: Polygon ROI features. **(a)** Repulsor: the orange circle handled by the user allows to push the boundary of a previously segmented region of interest **(b)** Multiple views: a segmentation is done simultaneously in two views. The arrows show an ingoing segmentation of lesions in blue and green in two reformatted brain images, where the user can easily follow the differences.  
Johnson & Johnson) and RHYTHMIA (Boston Scientific), respectively. Once imported, these maps can be visualised and further processed using various toolboxes.
# Probabilistic Analysis of Fatigue Behavior of Single Lap Riveted Joints
###### Abstract
[13] performed experimental and numerical investigations on riveted panels. Analytical expressions for local stress as a function of the squeezing force and coefficient of friction were proposed, based on a response surface technique. A Monte Carlo simulation, which integrated both squeezing force and friction coefficient variables into the models, was developed to determine the fatigue life distribution to visible cracks. A good agreement between predicted fatigue life and results from the experimental test was achieved. [PERSON] et al. [9] investigated the fatigue behavior of riveted joints in a coach peel configuration under various load ranges and ratios. Results showed the compliance of multi-axial fatigue equations with the experimental data, in terms of fatigue life. [PERSON] et al. [12] performed a series of fatigue tests on aluminum alloy sheet coupons with an open hole and filled hole subjected to remote tension, pin loading and out-of-plane bending. It was shown that the fatigue response of the open hole specimens could be predicted with a satisfactory accuracy employing the Neuber fatigue notch factor [18]. For the filled hole specimens, considerably longer fatigue lives were observed than for the open hole specimens under the same type of loading. Concerning the industrial context, the Detail Fatigue Rating (DFR) method [19; 20], widely used by Boeing and based on the definition of a benchmark that is the result of numerous experiments, allows assessing the fatigue reliability of the whole structure. Starting from the knowledge of the DFR of the analyzed (riveted) structure and the relative stress concentration factor, the method provides important information for a preliminary dimensioning. In this paper, fatigue tests at three different levels of stress were carried out on 200 small single lap multiple-riveted joints, in order to find the statistical distribution of such joints in terms of fatigue life and critical crack size. In particular, the research activity was addressed in finding the statistical distribution function which better fits the experimental results. Different distribution types, such as normal, log-normal, Weibull, Extreme Value, Exponential, etc., were considered.
# TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSNs) play a key role in the ecosystem of the Industrial Internet of Things (IIoT) and the definition of today's Industry 4.0. These WSNs have the ability to sensor large amounts of data, thanks to their easy scalability. WSNs allow the deployment of a large number of self-configuring nodes and the ability to automatically reorganize in case of any change in the topology. This huge sensorization capacity, together with its interoperability with IP-based networks, allows the systems of Industry 4.0 to be equipped with a powerful tool with which to digitalize a huge amount of variables in the different industrial processes. The IEEE 802.15.4e standard, together with the access mechanism to the Time Slotted Channel Hopping medium (TSCH) and the dynamic Routing Protocol for Low-Power and Lossy Networks (RPL), allow deployment of networks with the high levels of robustness and reliability necessary in industrial scenarios. However, these configurations have some disadvantages in the deployment and synchronization phases of the networks, since the time it takes to synchronize the nodes is penalized compared to other solutions in which access to the medium is done randomly and without channel hopping. This article proposes an analytical model to characterize the behavior of this type of network, based on TSCH and RPL during the phases of deployment along with synchronization and connection to the RPL network. Through this model, validated by simulation and real tests, it is possible to parameterize different configurations of a WSN network based on TSCH and RPL.  
WSN; industrial internet of things; synchronization; TSCH; RPL +
Footnote †: journal: Sensors  
1
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
## 3 Results

Table 2: Correlations between the scores on the scales of the questionnaire tool and the level of involvement in physical activity. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Effect Type** & **Effect** & **Estimate** & **SE** & \multicolumn{2}{c}{**95\% C.I.**} & \multicolumn{2}{c}{**\(\beta\) 95\% C.I.**} \\  & **Power** & **Upper** & **Power** & **Power** & **Upper** \\ \hline \multirow{4}{*}{Indirect} & PA level \(\Rightarrow\) Self-control \(\Rightarrow\) SwL & 0.813 & 0.234 & 0.431 & 1.365 & 0.127 & 0.059 & 0.195 \\  & PA level \(\Rightarrow\) Reappraisal \(\Rightarrow\) SwL & 0.309 & 0.151 & 0.073 & 0.687 & 0.048 & 0.004 & 0.093 \\  & PA level \(\Rightarrow\) Suppression \(\Rightarrow\) SwL & \(-\)0.003 & 0.105 & \(-\)0.230 & 0.195 & \(-\)0.001 & \(-\)0.031 & 0.030 \\ \hline \multirow{4}{*}{Component} & PA level \(\Rightarrow\) Self-control & 3.632 & 0.640 & 2.361 & 4.872 & 0.402 & 0.274 & 0.530 \\  & Self-control \(\Rightarrow\) SwL & 0.224 & 0.051 & 0.128 & 0.329 & 0.317 & 0.189 & 0.444 \\  & PA level \(\Rightarrow\) Reappraisal & 1.229 & 0.473 & 0.307 & 2.193 & 0.168 & 0.042 & 0.294 \\  & Reappraisal \(\Rightarrow\) SwL & 0.252 & 0.059 & 0.139 & 0.367 & 0.288 & 0.157 & 0.418 \\  & PA level \(\Rightarrow\) Suppression & 0.014 & 0.419 & \(-\)0.780 & 0.840 & 0.002 & \(-\)0.137 & 0.142 \\  & Suppression \(\Rightarrow\) SwL & \(-\)0.240 & 0.072 & \(-\)0.379 & \(-\)0.101 & \(-\)0.221 & \(-\)0.351 & \(-\)0.091 \\ \hline Direct & PA level \(\Rightarrow\) SwL & 0.528 & 0.401 & \(-\)0.253 & 1.322 & 0.083 & \(-\)0.041 & 0.206 \\ Total & PA level \(\Rightarrow\) SwL & 1.646 & 0.418 & 0.810 & 2.440 & 0.258 & 0.133 & 0.383 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Raw and standardized estimates with bootstrapped 95% confidence intervals for indirect effects and their components and direct and total effects in mediation analysis.
## 2 Non-Linear VCSEL Behavior
The rate equations can be used to describe the behavior of the LASER (edge emitting and VCSEL). As it is a set of differential equations, they describe the derivation of the carrier density \(n(t)\) and the photon density \(N(t)\). Solving the rate equations enables the estimation of the photon density, which is proportional to the optical output power. Since the current through the active area is a  
Figure 3: All 12 possible PAM-4 pulse responses at a symbol rate of 56 Gbaud. coefficient of the differential equation, the VCSEL exhibits non-linear behavior when the current is modulated. The parameters are extracted by fitting model behavior to measurement data. One of the formulas used is given in Equation (1), with \(f_{r}\) being the resonant frequency, \(I\) being the VCSEL operating current and \(I_{th}\) being the threshold current of the VCSEL. \[f_{r}=\frac{\sqrt{B(I-I_{th})}}{2\pi} \tag{1}\]  
When trying to increase the data rate of a VCSEL-based transmission system, two approaches are possible: One either increases the band rate, which is only possible if the bandwidth of the components is also increased. Knowing that the bandwidth of the VCSEL \(f_{3 db}\) is proportional to the resonance frequency \(f_{r}\), it is only possible by designing the VCSEL in a way so the rate equation parameter \(B\) (with the unit s\({}^{-2}\)A\({}^{-1}\)[14]) is increased, which in turn would mean that the difference in resonance frequencies \(\Delta f_{r}\) also has to increase. The other possibility is to switch from lower order modulation (like NRZ) to higher-order (like PAM-4 or PAM-8), which comes at the drawback of needing larger signal amplitudes for similar error rates. This is only possible by increasing the modulation current amplitude \(\Delta I\), which in turn also increases \(\Delta f_{r}\). Hence, specialized filtering for non-linear VCSEL behavior becomes increasingly important and is discussed in the following chapter.
## 3 Materials and Methods  
### Core Data  
The measured gas content, porosity, coal proximate and petrographic analyses, and Langmuir Isotherm adsorption data were collected from the CBM wells in the Dacun Block. The porosity was measured using the helium expansion method according to Chinese Standard SY/T5336-1996 [34]. The coal proximate, including moisture, ash yield, volatile matter, and fixed carbon, were conducted using a 5E-MAC III infrared fast core analyzer and performed following ASTM Standards D3173-11 [35] and D3174-11 [36]. The R\({}_{o,max}\) was performed using a Leitz MPV-3 photometer microscope based on the Standard of ISO7404.5-1994 [37].  
The Langmuir Isotherm adsorption experiments were conducted based on Chinese standard GB/T 19560-2004 [38]. All coal samples were crushed and sieved to a size range of  
Figure 2: Comprehensive stratigraphic column of Permian coal measures in the Dacun Block.  
60-80 mesh, and then 100-125 g samples were subjected to moisture equilibrium treatment. The moisture equilibrium treatment was processed for at least four days for each sample. After these pretreatments, the coals were put into the sample cell of the IS-100 for the adsorption isotherm experiment. The experimental temperature and equilibrium pressure were 30 \({}^{\circ}\)C and up to 10 MPa.  
In this work, the total gas content (Q\({}_{\rm t}\)) data of coal samples were determined based on the direct method of United States Bureau of Mines (USBM) [39], and they consisted of three parts: (1) the measured desorbed gas (Q\({}_{1}\)) in desorption canisters; (2) the measured residual gas (Q\({}_{2}\)) released during the crushing of coal sample; and (3) the lost gas (Q\({}_{3}\)) estimated based on Q\({}_{1}\); that is, Q\({}_{\rm t}\) = Q\({}_{1}\) + Q\({}_{2}\) + Q\({}_{3}\). The Qt in this work is reported on a d.a.f. basis.  
The gas saturation was calculated through the following equation:  
\[S=\frac{V_{a}(p+p_{L})}{V_{L}p}, \tag{1}\]  
where \(S\) is gas saturation, in %; \(V_{a}\) is the measured gas content of coal seam, in m\({}^{3}\)/t; \(p\) is the measured reservoir pressure, in MPa; \(p_{L}\) is the Langmuir pressure, in MPa; and \(V_{L}\) is the Langmuir volume, in m\({}^{3}\)/t.
## 2 Materials and Methods
### Instrumentation  
Each operator was asked to perform the experiment with three devices, a standard goniometer, a smartphone device, and the proposed IMU glove. A universal goniometer with a plastic 360\({}^{\circ}\) goniometer face and 7-inch movable arms was used as a standard goniometer. An iPhone 6 with a measurement application was used as the smartphone device. The proposed IMU glove is equipped with a six-axis MPU-6050 MEMS MotionTrackingTM, which contains a three-axis gyroscope and a three-axis accelerometer. The measurement signal is sent to the application for recording and visualization.