esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Terrain Model
### Initial Geometry  
The initial geometry of the model can be based on a DEM (Digital Elevation Model) [46] or generated by a procedural method. For the proposed solution, the layer was constructed using a modified Poisson Faulting (or fault formation) algorithm [47]. In a common form, model geometry is divided by random generated lines (fault edges). In the sequential process, one side is raised by  
Figure 3: General schema of the proposed terrain model.  
Figure 2: Natural mesa and buttes. Public domain photos found on pxhere.com under the Creative Commons Zero (CC0) license. (**a**) View of the mesa, Dead Horse Point, Utah, USA. (**b**) The Mittens (West, East) and Merick Butte, Arizona, USA.  
a random offset using the step function. In this modification, fault edges are determined by the circular function. The resulting geometry has fBs (fractional Brownian surface) properties [9; 20; 40]. A rendered comparison of the geometry generated by both variants is presented in Figures 4 and 5.  
In the article, the linear method is referred to as LFF (Linear Fault Formation) and the circular one as CFF (Circular Fault Formation). The circular fault was selected for three main reasons:  
* The LFF combined with a step-based height modifier introduces sharp transitions between points lying on the fault sides, resulting in a rough surface, more adequate in irregular arrangement of rocks and stones than in structured terrain [42];
* The CFF in combination with a new height modifier emulates the distribution of the sediments at the terrain base in the further described erosive procedure;
* The CFF can be limited to a certain area of the height-field, thus the generation of buttes is simplified.
# Development and Preliminary Evaluation of a Lower Body Exosuit to Support Ankle Dorsiflexion
## 3 Preliminary Evaluation
### Participants  
Nine neurologically intact subjects (_n_ = 6 men; age 31 \(\pm\) 6 years; mass 88 \(\pm\) 15 kg; \(n\) = 3 women; age 24 \(\pm\) 3 years; mass 59 \(\pm\) 1 kg) participated in this study. Only participants without any gait abnormalities were considered eligible for the study.
# Air Quality Effects on Human Health and Approaches for Its Assessment through Microfluidic Chips
## 5 OoC for Assessing Health Effects
### Cancer Types Caused by PM  
PM2.5 particles are considered as potential carriers of diverse harmful substances causing a variety of diseases [165; 166; 167]. The effect of PM2.5 particles on human health depends on the physical and chemical properties of the PM, especially on the chemical composition. The chemical composition of PM2.5 particles can be complex. PM2.5 may contain heavy metals, such as lead (Pb), cadmium (Cd), arsenic (As), mercury (Hg), and chrome (Cr); but also, organic compounds, such as polycyclic aromatic hydrocarbons (PAH). PM2.5 particles can cause damage to the respiratory and the cardiovascular systems, leading to coughing, dyspnea, asthma, chronic bronchitis, arrhythmia, etc. [164; 167; 168]. Also, some reports showed that PM2.5 might cause severe cancer, such as lung cancer, uterine leiomyomata, and leukemia [169; 105; 169; 170; 171].  
Figure 3: One can visualize how small the air particles are by comparing them to the human hair. The hair cross-section diameter being \(\sim\)60 \(\upmu\)m, about six PM10 particles and 24 PM2.5 particles could extend over the hairs cross-section perimeter. PM2.5 (or fine PM), tend to penetrate into the gas-exchange regions of the lung (alveolus).  
The development of cancer, such as lung cancer, is known to be a long process. The currently high prevalence of lung cancer may be the result of the long-term exposure to the polluted environment (such as exposure to traffic exhaust fumes and combustion products) and risk factors arising from an unhealthy lifestyle (such as obesity and smoking). Among other industrial countries, China is known for a high incidence of lung cancer. The reason for this has always been considered to be found in long-term excessive smoking in the past. However, in the recent years, with significantly increased PM2.5 values in many Chinese cities, the carcinogenic nature of PM2.5 attracted a wider attention. Although the increase in the incidence of lung cancer cannot be attributed entirely to the increased PM2.5 values, some studies have shown that polycyclic aromatic hydrocarbons (PAH) and heavy metal ions in PM2.5 play an important role in the occurrence of lung cancer [172]. More experimental and case studies are required to confirm this hypothesis (Figure 4).
# Vertical GaN MOSFET Power Devices
## 4 Techniques to Boost the Breakdown Voltage
### Drift Layer Thickness and Doping Concentration
The breakdown voltage under punch-through conditions is given by [85]  
\[V_{BD\_PT}=V_{BD\_PP}\bigg{(}\frac{d_{Drift}}{W_{pp}}\bigg{)}\bigg{(}2-\frac {d_{Drift}}{W_{pp}}\bigg{)}, \tag{5}\]  
Figure 6: The schematic representation of the electric field distribution profiles for two distinct breakdown scenarios: (**a**) Non-punch-through (parallel-plane) breakdown, and (**b**) punch-through breakdown [88]. where \(V_{BD\_PT}\) is the breakdown due to punch-through, \(V_{BD\_PP}\) is the parallel-plane breakdown voltage, \(W_{pp}\) is the parallel-plane depletion region, and drift is the drift layer thickness. \(V_{BD\_PP}\) is given by \(V_{BD\_PP}=(E_{crit}.W_{pp}/2)\) and \(W_{pp}=(\varepsilon_{s}EC_{\_{F}it}/\varepsilon N_{\_}d)\). The validity of the previously mentioned equation for the breakdown voltage of the punch-through region holds when the critical electric field for punch-through (\(E_{crit,PT}\)) closely approximates the \(E_{crit,NPT}\), meaning that \(E_{\_{C}it,NPT}\approx E_{\_{C}it,PT}\approx E_{\_{C}it}\). According to findings reported in [89], the disparity between the critical electric fields for parallel-plane and punch-through breakdown is exceedingly minimal. In the punch-through state, the \(V_{BD}\) is influenced not only by the doping concentration but also by the thickness of the drift region. It is worth noting that the typical breakdown voltages for punch-through and parallel-plane conditions can significantly differ under certain doping conditions [91], as shown in Figure 7. To obtain the optimum drift doping concentration, the relation between the PFOM and the doping concentration shown in Figure 7 should be considered. The region where PFOM has the highest value is considered to be the optimized drift doping concentration.
## 3 Results and Discussion
### Proteolytic Degradation Analyzed by SDS-PAGE
#### 3.7.2 Principal Component Analysis (PCA)  
It should be noted that there may be some correlation between the changes of 35 volatile compounds detected. A certain overlap in the reflection of the odor characteristics of these volatile compounds should be considered. Therefore, PCA analysis was carried out to discover the correlation among aldehyde, ketone, acid, and other volatile compounds. The results showed three principal components were extracted, which could explain 47.98%, 35.41%, and 16.62% of the total information respectively (Figure 5). The PC1 was mainly negatively correlated with most aldehydes (hexanal, octanal, nonanal, decanal); it was negatively correlated with hexadecaldehyde, 1-[3,3-dimethylbicyclo [2.2.1] heptyl-2-yl] ethanone, octanol, 2-phenylisopropanol, osiridine, tetradecane, acenaphthene, tetramethylpyrazine, and all polycyclic aromatic hydrocarbons, octyl alcohol, 2-phenylisopropanol, osilane, and tetradecane. The second principal component mainly reacted with benzaldehyde, 1-[3,3-dimethylbicyclo [2.2.1] hept-2-yl] ethanone, nonanoic acid, 1-octen-3-ol, hexadecane, 2,6,10,14-tetramethylpentadecane, heptadecane, and di-tert-butyl-p-cresol. It can be seen from Figure 5B that the PC3 (16.62%) mainly explains the changes of 1,3-Dichlorobenzene; 6,10-dimethyl-5,9-undecane-2-one, and hexanol. These substances may be the main contributors to the odor changes of sturgeon during storage.  
Figure 5: Variable loadings on planet-2 (**A**) and 1-3 (**B**) of principle component analysis (PCA) on volatile compounds.  
On this basis, the principal component scores of each experimental group were calculated (Table 2). The results showed that in the _Pseudomonas fluorescens_ group, the greatest contribution comes from PC1, which indicated that some related volatile compounds might be the main source of odor of spoilage fish treated by _Pseudomonas fluorescens_. The PC2 had the greatest effect on the putrefaciens of _Shewanella putrefaciens_ group, and only changed significantly in the fish treated by _Shewanella putrefaciens_, which may be related to the putrefaction characteristics of _Shewanella putrefaciens_.
## 2 Review of PV Model and MPPT Control Methods  
### Mathematical Model of Solar Panels  
There have been many advancements in PV cell modeling through the years, which have helped to closely reflect the behavior of actual PV cell panels. According to [23], a balance between computational speed and accuracy can be obtained using the current source plus diode model shown in Figure 1 in combination with Equations (1)-(4), and five parameters.  
Based on Figure 1, Equation (1) can be written as follows:  
\[I_{PV}=I_{ph}-I_{d}-I_{sh}, \tag{1}\]  
where \(I_{ph}\) represents the current generated by the PV panel under a given solar irradiance \(G\)[23].  
\[I_{ph}=\left[I_{sc}+K_{l}\ (T_{c}-T_{r}\ )\right]\frac{G}{G_{STC}}, \tag{2}\]  
where  
\(I_{sc}\)--the short circuit current under Standard Test Conditions (STC) (model first parameter) \(K_{l}\)--the short circuit temperature coefficient  
\(T_{c}\)--the cell temperature  
\(T_{r}\)--the reference temperature (298.15 K)  
\(G\)--solar irradiance  
\(G_{STC}\)--solar irradiance STC  
The diode current in (1) is represented by \(I_{d}\), and in (3), it exhibits exponential behavior [23].  
\[I_{d}=I_{0}\left[exp\bigg{(}\frac{q\ V_{d}}{A\ k\ T_{c}}\bigg{)}-1\right], \tag{3}\]  
where  
\(I_{0}\)--the diode saturation current (second parameter of the model)  
\(q\)--the electron charge constant (\(1.6\times 10^{-19}\))  
\(V_{d}\)--the diode voltage  
\(A\)--the ideal factor of the diode (third parameter of the model)  
\(k\)--Boltzmann's constant (\(1.38\times 10^{-23}\))  
The current \(I_{sh}\) in (1) is the current in the parallel resistor \(R_{sh}\), and can be calculated by:  
\[I_{sh}=\frac{(V_{PV}+I_{PV}\ \ R_{s})}{R_{sh}}, \tag{4}\]  
where  
\(V_{PV}\)--the photovoltaic cell voltage  
\(I_{PV}\)--the photovoltaic cell current  
\(R_{S}\)--the series resistance of the solar panel (fourth model parameter)  
\(R_{sh}\)--the parallel or shunt resistance of the solar panel (fifth model parameter).  
A PV characteristic curve can be obtained using Equations (1)-(4), although choosing the proper model coefficients (\(I_{sc}\), \(I_{0}\), \(A\), \(R_{S}\), and \(R_{sh}\)) can be challenging [34]. Figure 2 depicts the characteristic curves of the current and power versus the voltage for the PV used in this study.  
Figure 1: Equivalent electrical model of PV cell.  
The PV coefficients of A10-M60-240 from A10 Green are already implemented in MATLAB, derived from a PV list based on data from the National Renewable Energy Laboratory (NREL) [35]; however, the PV coefficients are not disclosed by the manufacturer. Instead, manufacturers sometimes provide other characteristics, such as \(V_{\alpha c}\) (open circuit voltage), \(I_{sc}\) (short circuit current), \(V_{MPP}\), \(I_{MPP}\), and voltage, as well as the temperature coefficients (\(K_{\
u}\), \(K_{i}\)) [36]. According to [34; 37], some methods can be used to model a PV cell when the coefficients are not disclosed.
## 2 Governing Equations
### Energy Conservation  
[PERSON] et al. [32] proposed the governing equations for a fully coupled hydro-thermal model deduced from energy conservation, which considered the effects of latent heat,  
Figure 1: Schematic illustrations of adverse effects of seepage flow and spatial variability of soil thermal conductivity on AGF for tunnel excavation.  
thermal conductivity and seepage velocity on temperature distribution. The temperature governing equation in freezing porous media is expressed as:  
\[C_{\rm eq}\frac{\partial T}{\partial t}+\rho_{\rm w}C_{\rm w}\mathbf{v}_{\rm w^{-}}( \
abla T)+\mathbf{q}\ =\ 0 \tag{1}\]  
where \(C_{\rm eq}\) is equivalent to the volumetric thermal capacity; \(T\) is temperature; \(t\) is freezing time; \(\mathbf{v}_{\rm w}\) is the velocity field of fluid water. \(\rho\) and \(C\) are density and specific heat capacity, respectively. The solid matrix, liquid water and crystal ice are subscripted by s, w and i, respectively. \(\mathbf{q}\) is conductive heat flux that can be written based on the Fourier law:  
\[\mathbf{q}\ =\ -\lambda_{\rm e}\
abla T \tag{2}\]  
where \(\lambda_{\rm e}\) is the effective thermal conductivity of porous ground structure, which mainly depends on the porosity of the ground, thermal conductivities and spatial distribution of each phase (soil, ice and unfrozen water). The volumetric average approximation method is usually used to calculate the value of effective thermal conductivity in the porous media [11; 12; 33; 34; 35]. It computes the effective thermal conductivity as the weighted arithmetic mean of fluid and porous matrix conductivities:  
\[\lambda_{\rm e}\ =\ (1-n)\lambda_{\rm s}+n\gamma\lambda_{\rm w}+n(1-\gamma) \lambda_{\rm i} \tag{3}\]  
where \(n\) is porosity; \(\lambda\) represents the thermal conductivity; \(\gamma\) is the unfrozen water content within the pore fluid, its value decreases from 1 to 0 as the freezing development, regarded as a function of temperature as [6]:  
\[\gamma\ =\ \left[1+\left(\frac{T_{0}-T}{w}\right)^{\frac{1}{1-n}}\right]^{-m} \tag{4}\]  
where \(w\) and \(m\) are material constants related to pore size distribution in soil; \(T_{0}\) is the freezing point. Compared with the unfrozen zone; water/ice phase transition and release of latent heat take place in the frozen zone, so the equivalent volumetric thermal capacity; \(C_{\rm eq}\), is recorded by [PERSON] et al. [32] as below:  
\[C_{\rm eq}\ =\ \begin{cases}C_{\rm f}+n\rho_{\rm w}L_{\rm f}\frac{\partial \gamma}{\partial T}&T\leq T_{0}\\ C_{\rm u}&T>T_{0}\end{cases} \tag{5}\]  
where \(L_{\rm f}\) is the heat latent. \(C_{\rm f}\) and \(C_{\rm u}\) are the volumetric thermal capacities in the frozen zone and unfrozen zone, respectively. They can be expressed as the sum of volumetric heat capacities of three phases divided by their volume fractions in the frozen or unfrozen zone. To simplify Equation (5), \(C_{\rm eq}\) can be expressed as [16]:  
\[C_{\rm eq}\ =\ (1-n)\rho_{\rm s}C_{\rm s}+n\gamma\rho_{\rm w}C_{\rm w}+n(1- \gamma)\rho_{\rm i}C_{\rm i}+n\rho_{\rm w}L_{\rm f}\frac{\partial\gamma}{ \partial T} \tag{6}\]
## 5 Experiments and Performance Evaluation
### Perception Experiment
#### 5.1.3 Experimental Result  
In the B1 the tester holds the perception system and passes through the test area according to the route. The perception results are shown in Figure 31. We use line graphs to indicate the signal perception result. The dashed line indicates that the signal is not recognized at all, and the solid line indicates that the signal is recognized. At the same time, the red solid line represents the perception result is error, yellow solid line represents the signal perception result is correct but unstable, and green represents the result is correct, stable, and continuous.  
Figure 30: Test site plan and signal source layout location: (**a**) B1 of parking lot; (**b**) B2 of parking lot.  
Figure 29: The deployed signal nodes: (**a**) Wi-Fi node; (**b**) Bluetooth node; and (**c**) ZigBee node.  
Experimental results show that the system can simultaneously perceive WiFi, Bluetooth and ZigBee signals in a mixed wireless signal environment. However, accidental misrecognition and unstable recognition also occurred during the experiment. The reason of unstable recognition or even unrecognizable during the experiment are as follows: (1) the weak signal power causes weak signal characteristics. When the signal power is less than the noise, signal perception failed; (2) the signal broadcast cycle is too long to guarantee that the signal will be captured in each time-frequency image. These two situations are more common in the Bluetooth signal perception. Due to its low power consumption design, the signal transmission power is lower and the period is longer. The weak signal power also easy to appear due to 'non-line of sight'.  
In the B2, we tested the perception ability at different distances, respectively. The distance between signal source and test points are a multiple of 8 m. At each test point, we continuously record the perception results for 2 min and calculate the recognition rate. The result is shown in Figure 32.  
It can be seen from the experimental results that the effective distance of perception is: WiFi > ZigBee > Bluetooth, and the main factors that affect the perception are signal power and signal bandwidth. The stronger the power, the wider the signal bandwidth, and the more obvious the characteristics of the target signal on the time-frequency image, the easier it is to be accurately identified, and the longer the perception distance. It should be noted that when the signal received power is less than the noise, no signal characteristics can be reflected in the time-frequency image, and the perception method is invalid.
# Synthesizing 3D Gait Data with Personalized Walking Style and Appearance
## 5 Experimental Results
### Applications  
The direct application is gait recognition, which is also the main motivation for this research. Our generator can be used to provide training data for recognition model training, to analyze and compare the performance of different recognition methods. Compared with existing datasets, a main advantage of our generator is the ability to provide many different forms of gait data. We can provide silhouette-based features like GEI images for appearance-based methods. As we can see from Figure 9, our method provides much better silhouettes than extraction from videos using cutting-edge methods like mask-RCNN [49].  
Figure 8: Performance gap and closing: the first row plots curves of accuracy against training set size for the CASIA-B test set (**a**) and OU-MVLP test set (**b**); the second row shows accuracy curves with real data added in the training of the synthetic model for the CASIA-B test set (**c**) and OU-MVLP test set (**d**).  
We can also generate a skeleton directly for those model-based approaches and provide accelerometer data (Figure 10a) or depth images (Figure 10b) for gait analysis algorithms relying on them [50,51]. Although the accelerometer may be too smooth compared with the real one, the issue can be allieviated with appropriate noise introduced.  
With the synthetic environment, we can easily investigate the performance of related algorithms under various extreme circumstances. Figure 9c shows how mask extraction performs under different lighting conditions, while Figure 9b shows mask extraction with occlusion. Additionally, our method can also be helpful in multi-subject gait recognition (Figure 4c bottom), which has not been investigated so far.  
Finally, our method can also be used in scenarios where a new surveillance system is installed in a novel location without any instances of real gait data, similar to [52]. We can then reconstruct the 3D scene and use our method to generate training data for the recognizer.  
Figure 10: Simulated gait data other than silhouette provided by our system: (**a**) accelerometer data; the left shows the case with a mobile device represented by the red square in the trouser pocket, while the right shows the case in a jacket pocket; (**b**) depth map for two different scenes.  
Figure 9: Performance of mask extraction: columns 1–4 (**a**) show the comparison between mask-RCNN [49] and our system; columns 5–6 (**b**) show the extraction quality of mask-RCNN under various lighting conditions; column 7 (**c**) shows mask extraction of mask-RCNN with occlusion.
## 2 Normal Cloud Model Theory
### Cloud Model
#### 2.2.2 Cloud Generator  
Cloud models are executed by cloud generators. There are four commonly used cloud generators: the forward cloud generator, the backward cloud generator, the two-dimensional cloud generator, and the \(x\)-condition cloud generator (Figure 1). The forward cloud generator (CG) is used to generate the cloud drops with a membership degree that conforms to the three numerical characteristics. On the contrary, in the backward generator (CG\({}^{-1}\)), the transferring process is used to derive the qualitative concept represented by the three descriptors from cloud drops. The two-dimensional cloud generators can establish a three-dimensional cloud droplet (\(x_{1}\), \(x_{2}\), \(y\)) that satisfies the two-dimensional cloud distribution on the basis of the one-dimensional cloud generator. Lastly, the \(x\)-condition cloud generator (CG\({}_{x}\)) uses the three numerical characteristics of cloud and the specified \(x=x_{0}\) to generate the cloud drops.  
By analyzing the determination of membership function in both natural and social sciences, [PERSON] et al. (2018) determined that the normal distribution is the most suitable distribution for the membership function. According to the central-limit theorem, when a random variable is affected by many small and independent random factors, and each factor does not play an overwhelming role in the controlling distribution, this random variable has a normal distribution. In the case of tunnel collapse, there are many factors that can lead to tunnel damage, and each factor does not play a leading role. Therefore, the normal distribution cloud generator is used for the quantitative risk assessment of deep tunnel failure.  
Figure 1: Graphic expression of different cloud generators. (**a**) The forward cloud generator; (**b**) the backward cloud generator; (**c**) the two-dimensional cloud generator; (**d**) the x-condition cloud generator.
# Marine UAV-USV Marsupial Platform: System and Recovery Technic Verification
## 2 Fully Autonomous Recovery System
### UAV Motion Simulation and Wave Compensation for Stabilization  
The attitude of the UAV has a significant influence on the positioning and cooperative control of the UAV-USV platform, the active adjustment of the arresting cable, and the stable grasping of the manipulator. The uncertainty of the attitude of the USV due to wind and wave interferences will pose a serious challenge to the safe release and precise recovery of the UAV on the USV. Aiming at this problem, a wave motion model and a USV motion model are established to effectively predict the six-dimensional motion of the USV. Based on this predicted information, the attitude of the arresting cable is actively adjusted to maintain its position with the attitude of the UAV.  
In order to overcome the impact of USV attitude changes on the system, a wave compensation system based on an adaptive Fast Fourier Transform (FFT) prediction algorithm and \(H_{\infty}\) filter is proposed [29]. The algorithm is used to predict the heave motion model of the wave, to compensate the wave as synchronously as possible, and to eliminate high-frequency interference and make the compensation platform movement more stable. Utilizing the simulation software and the above algorithm, a USV platform and a three-degrees-of-freedom ocean wave simulation platform were constructed to perform six-degrees-of-freedom wave compensation. In the physical verification experiment, we use a six-axis simulation platform to simulate the six-dimensional movement of the USV and verify the relevant methods.