esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Selection of VNAR-Displaying Yeats against Antigens
To find the whole spectrum of VNARs that possibly recognize frcRn epitopes, we analyzed the protein sequences and their frequencies from the sorted clones before  
Figure 4: The amino acid composition of the different lengths of mutated CDR3 sequences. Shown in heatmaps is the percentage of each amino acid on each length of the randomly mutated CDR3 sequence. and after FACS biopanning. The NGS data of fFcRn-binding VNARs were summarized in Table 4. After three rounds of FACS screening, the unique fFcRn-specific VNAR protein sequences decreased from 7327 to 1962, and their frequencies in the library were calculated for further analysis. The amplification factor of individual sequences could be a helpful parameter to narrow down the possible candidates of antigen-specific binders [41]. The amplification factor can be defined as the frequency of a VNAR protein sequence in the library after biopanning divided by its frequency before biopanning. As shown in Table 4, the number of sequences with the increased frequency (amplification factor > 1) decreased from 5000 in the first round to 558 in the last round. When the stringency of selection was increased by 10-fold (amplification factor > 10), the number of sequences decreased from 2853 in the first round to 48 in the final round. The composition of the post-sorting fractions within the three rounds of sorting also changed due to antigen-binding selection. While the sequences are distributed according to the CDR3 length in the library, the distribution is shifted towards certain kinds of abundant sequences after rounds of sorting. As shown in Figure 3, the distribution shift of VNAR sequences with 18, 20, or 23 residues of CDR3 results in 1.6-, 2.2-, and 4.3-fold increases, respectively, over their frequency in the library after three rounds of sorting for fFcRn. On the contrary, the VNAR sequences with 12 or 13 CDR3 residues show a decreased frequency, with a 0.5- and 0.4-fold reduction, respectively. Meanwhile, there is a slight increase in the frequency of VNAR sequences with 16 residues of CDR3 after the sorting process. To determine whether specific CDR3 sequences are associated with target-binding after FACS sorting, we used pLogo (Probability Logo Generator) to identify and visualize statistically significant motifs within sorted sequences. The pLogo analysis was applied to every mutation site in CDR3 and the calculation was based on the individual residues in context to the library as the background frequency. Logos generated with pLogo are shown for the library and for the sorted fractions of fFcRn biopanning round 3 (Figure 5A). The pLogo result of the library shows that the two most highly abundant amino acids, which are arginine and proline, frequently occurred at each position in the randomized CDR3 sequence (Supplementary Figure S1). The most common CDR3 motifs are rep
## 3 Models and Methods
### Models
#### 3.1.2 Discrete Anisotropic Radiative Transfer (DART) Model  
The DART model is a 3-D physically-based RTM that has been under development since 1992 [45]. After years of development, it is now a comprehensive model with multiple functions that can simulate not only radiative budgets but also remote-sensing images from different sensors (e.g., passive and active sensors), different platforms (e.g., airborne and spaceborne), and different bands for any Earth scene (e.g., vegetation and urban landscapes). The lidar module in the DART model was implemented based on the so-called \"box method\" and \"Ray Carlo method\" [35], which can provide accurate lidar simulations for any landscape and lidar sensor configuration (e.g., view direction, footprint size, and pulse characteristics). In this study, the lidar module of the DART model was used to validate our method because the simulated data usually had corresponding ground truth values that have an absolute consistent temporal phase with lidar observation. These so-called ground truth values were an ideal dataset for verifying the method. To ensure that the lidar data were simulated more realistically, we used the same parameter configuration as the GLAS device as the input parameters of the DART model (Table 1). In addition, the forest structure parameters set in the model were directly based on the forest structure characteristics in our study areas.  
Figure 2: Workflow diagram for retrieving vertical foliage profile (VFP) and LAI from GLAS data.  
The two following realistic canopy architecture scenarios were constructed in the DART model to validate our method: (1) homogeneous turbid scene (Figure 3a): the scene only included the gap within the canopy; this scene was composed of a horizontal ground and a cube plant canopy with randomly distributed foliage. The thickness of the canopy was 9 m, and its bottom was 4 m above the ground. In addition, to verify the performance of our method under different vegetation abundance, we set the LAI values to three levels in this scene: high (i.e., LAI = 8), medium (i.e., LAI = 6), and low (i.e., LAI = 4). (2) Discrete individual-tree scene (Figure 3b): the scene included large gaps between tree crowns; this scene consisted of trees with a canopy height of 9 m. The 3-D tree model is shown in Figure 3c. In this scene, the LAI of the GLAS footprint was 0.98 [i.e., the area covered by the red circle, as shown in Figure 3b].
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 4 Performance Analysis  
In this section, the proposed PL-AKA is theoretically analyzed in terms of the authentication error probability and signaling overhead under a MIoT system scenario. In addition, an experiment using USRP is performed to demonstrate the benefits of integrating PLA with an AKA protocol.
###### Abstract  
The safety and efficiency of underground coal mining are threatened by thick hard roofs characterized by large overhang areas, problematic spontaneous caving, and high dynamic load upon their breakage. In this study, a mechanical model of the bearing capacity of thick hard roofs in upward mining coalfaces associated with mining activities is built based on bending theories for beams with single generalized displacement and the elastic foundation beam theory. Using this method, we analyze the deformation and fracture mechanisms of a thick hard roof during upward mining. We further derive the mechanical equations of rotational angle, bending moment, shear force, and deflection of the free overhang and coal-bearing zone in the thick hard roof and an equation for calculating the limiting span. The mechanical behaviors of the thick hard roof bearing state are analyzed under different parameters. The results show that the foundation coefficient, roof thickness, and angle of upward mining have little influence on the roof bending moment but are positively correlated to the limiting span. Roof load and overhang length have a significant influence on the roof bending moment. They are negatively and positively correlated with the limiting span, respectively. Finally, a case study is performed on the III601 upward mining coalface in the Zhuzhuang Coal Mine. The distribution characteristics of the bending moment of the thick hard roof at different extraction stages are analyzed. At each stage, the limiting spans of the thick hard roof upon breaking were calculated as 13.18, 18.82, and 22.50 m, respectively, being close to the on-site measured periodic weighting lengths of 13.33, 19.33 m, and 22.67 m. This close fit proves the feasibility and accuracy of the developed mechanical model. The present study offers theoretical guidance for estimating the weighting length of thick hard roofs in coalfaces and for engineering technology control in similar scenarios.  
upward mining coalface; thick hard roof; mechanical model; bending moment; limiting span; field measurement +
Footnote †: journal: Applied Sciences  
1
## 4 Results  
### Study Area  
This study takes 327 cities at the prefecture level or above (including prefectures that are not termed as cities but play the same role in China's administrative system) in 2010 in China as research subjects (Figure 1). The lowest life expectancy in 2010 was 49.66 years located in Yushu of Qinghai, which might be related to the severe earthquake disaster occurred in 2010, which caused many deaths; therefore, it was excluded as a special case. At the same time, considering the availability of data and the adjustment of administrative divisions, nine other cities were also excluded, including Chaohu of Anhui; Qingyang, Linxia Hui Autonomous Prefecture, Dingxi, Gannan Tibetan Autonomous Prefecture, Longnan of Gansu; Wuzhong, Guyuan, and Zhongwei of Ningxia. Following these exclusions, 327 cities at the prefecture level or above were included in the study.  
In order to further investigate and analyze the association between economic development and change in life expectancy in China, the 327 cities are divided into three categories--109 low-income cities (per capita GDP in 2000 \(\leq\) 5000 yuan), 105 middle-income cities (per capita GDP in 2000 of 5000-8000 yuan), and 113 high-income cities (per capita GDP in 2000 \(\geq\) 8000 yuan)--using the quantile method in ArcGIS 10.6, based on per capita GDP of cities in 2000.
# Optimal Sliding Mode Control for an Active Suspension System Based on a Genetic Algorithm
## 4 The OSMC Controller Design  
As a variable structure control strategy, the SMC strategy is different from other control strategies because of its discontinuity, which ensures the structure of the system can change over time. The variable structure control with sliding mode aims to lead the state trajectory of the system to a surface in the state space and ensure that it holds to this surface by means of a high-speed switching control law [19]. The whole design process of OSMC consists of three steps: the design of sliding surface, the build of control law, and the optimization of switching function based on GA.
## 5 Guidance and Control Algorithm
### Differential Air Drag Control
\[\delta\mathbf{A}(t)=K_{1}\tilde{y}_{1}+K_{2}\tilde{y}_{2}+K_{3}\tilde{x}, \tag{18}\]  
where \(K_{1,2,3}\) denotes the control gains. For radial and along-track separation, the navigation error is generally greater than the nominal value, and the control state easily becomes noisy, which makes \(K_{2}\) and \(K_{3}\) small. In this control algorithm, it is only possible to switch the attitude orientation once every two to three days, owing to the limited power. For formation flying, the altitudes of the two spacecraft are slightly different, so the ballistic coefficient (\(C_{B}\)) has the most significant impact. The ballistic coefficient is determined by the drag coefficient and the area-to-mass ratio. \[C_{B}=\frac{C_{D}A}{m} \tag{19}\]  
The attitude of the deputy is controlled to the maximum area if the summation of the equality area and the control area is larger than the maximum, otherwise it is maintained as the minimum. \[\mathbf{A}_{ctrl}=\mathbf{A}_{Eq}+\delta\mathbf{A}, \tag{20}\]  
where \(\mathbf{A}_{Eq}=\frac{\mathbf{A}_{ chief}}{m_{ chief}}\cdot m_{ deputy}\) is the area of the deputy with the same area-to-mass ratio as that of the chief. When DADC was performed at a distance of approximately 1.4 km with an initial drift rate of 0.5 cm/s, the relative trajectory for the five orbital periods was expressed as shown in Figure 8. Because it is initially positioned in a positive along-track direction, it has a negative drift rate by controlling its attitude to have a minimum effective cross-sectional area (top panel). Owing to the differential air drag, the direction of the relative velocity of the satellite changes from negative along-track to positive when the control drag area reaches its maximum effective cross-sectional area. In the negative along-track direction, the direction of velocity changes at approximately 2.8 km, and differential gravity occurs due to relative distance, thereby distorting the initial shape of the orbit (second panel). In the direction of the positive along-track, the distance of the orbit is the greatest (third panel), as it is separated by up to 14 km, and the differential gravity is the greatest. Because DADC is a passive control, the maximum distance at zero relative velocity gradually increases over time as the control error caused by perturbations accumulates (second and bottom panels).
## 2 Materials and Methods  
### Hydrodynamic Model  
MIKE 21 is a simulation tool for physical, chemical, or biological processes in two-dimensional waters developed by the Danish Hydrodynamic Institute [34; 35]. It is widely used in ocean, bay, lake, and river environments. The system is based on the numerical solution of the two-dimensional incompressible Reynolds-averaged Navier-Stokes equations, invoking the Boussinesq assumption and the hydrostatic pressure assumption [34]. Integrating the horizontal momentum and continuity equations in the depth direction yields the following shallow water equations, and the integral form of the shallow water system of equations can be written as:  
\[\frac{\partial\mathbf{U}}{\partial t}+\
abla\cdot\mathbf{F}(\mathbf{U})=\mathbf{S}(\mathbf{U}) \tag{1}\]  
where \(\mathbf{U}\) is the vector of conserved variables; \(\mathbf{F}\) is the flux vector function, and \(\mathbf{S}\) is the vector of source terms. In Cartesian coordinates, the system of 2D shallow water equations can be written as:  
\[\frac{\partial\mathbf{U}}{\partial t}+\frac{\partial\big{(}\mathbf{F}_{x}^{I}-\mathbf{F}_{ x}^{V}\big{)}}{\partial x}+\frac{\partial\Big{(}\mathbf{F}_{y}^{I}-\mathbf{F}_{y}^{V} \Big{)}}{\partial y}=\mathbf{S} \tag{2}\]  
where the superscripts \(I\) and \(V\) denote the inviscid (convective) and viscous fluxes. \[\mathbf{U}= \begin{bmatrix}h\\ h\overline{u}\\ h\overline{v}\end{bmatrix} ,\mathbf{F}_{x}^{I}= \begin{bmatrix}h\overline{u}\\ h\overline{u}^{2}+\frac{1}{2}g\big{(}h^{2}-d^{2}\big{)}\end{bmatrix} ,\mathbf{F}_{x} ^{V}= \begin{bmatrix}0\\ hA\Big{(}\frac{2\overline{u}}{g\overline{u}}\Big{)}\\ hA\Big{(}\frac{\partial\overline{u}}{\partial y}+\frac{\partial\overline{v}}{ \partial x}\Big{)}\end{bmatrix} ,\] \[\mathbf{F}_{y}^{I}= \begin{bmatrix}h\overline{v}\\ h\overline{v}^{2}+\frac{1}{2}g\big{(}h^{2}-d^{2}\big{)}\end{bmatrix} ,\mathbf{F}_{y} ^{V}= \begin{bmatrix}0\\ hA\Big{(}\frac{2\overline{u}}{\partial y}+\frac{\partial\overline{v}}{ \partial x}\Big{)}\\ hA\Big{(}\frac{2\overline{v}}{\partial x}\Big{)}\end{bmatrix} , \tag{3}\]  
\[\mathbf{S}= \begin{bmatrix}g\eta\frac{\partial d}{\partial x}+f\overline{v}h-\frac {h}{\rho_{0}}\frac{\partial p_{a}}{\partial x}-\frac{gh^{2}}{2\rho_{0}}\frac{ \partial p}{\partial x}-\frac{1}{\rho_{0}}\Big{(}\frac{\partial s_{xx}}{ \partial x}+\frac{\partial s_{xy}}{\partial y}\Big{)}+\frac{\tau_{xx}}{\rho_{ 0}}-\frac{\tau_{yx}}{\rho_{0}}+hu_{s}\\ g\eta\frac{\partial d}{\partial y}+f\overline{u}h-\frac{h}{\rho_{0}}\frac{ \partial p_{a}}{\partial y}-\frac{gh^{2}}{2\rho_{0}}\frac{\partial p}{\partial y }-\frac{1}{\rho_{0}}\Big{(}\frac{\partial s_{xx}}{\partial x}+\frac{\partial s _{xy}}{\partial y}\Big{)}+\frac{\tau_{xy}}{\rho_{0}}-\frac{\tau_{yy}}{\rho_{0 }}+h\
u_{s}\end{bmatrix}
## 4 Methodology
### Object Pose Detection Method  
The first step of the detection part is detecting the 2D position of the targeted object. With YOLOv5, we detected the 2D object position \((u,v)\) in the received frame, as shown in Figure 4.  
However, as we need the 3D position of the object \((U,V,W)\), we proceeded with the backward projection method. From the pixel coordinates, we were able to determine the world coordinates after calibrating the camera. To do so, we used the inverse of the following method illustrated in Figure 5:  
\(M_{int}\) represents the intrinsic matrix, which provides the relationship between the camera coordinate system and its image space, and \(M_{ext}\) represents the extrinsic matrix, which, in turn, represents the relationship between the camera coordinate system and the object coordinate system.  
\(M_{ext}=\)  
\(\begin{bmatrix}r_{11}&r_{12}&r_{13}&t_{x}\\ r_{21}&r_{22}&r_{23}&t_{y}\\ r_{31}&r_{32}&r_{33}&t_{z}\\ 0&0&0&1\end{bmatrix}\)  
With \(r_{ij}\) representing the rotations and \(t_{k}\) representing the translations.  
\(M_{int}=M_{affine}.M_{projection}=\)  
\(\begin{bmatrix}a_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ 0&0&1\end{bmatrix}\)\(\cdot\begin{bmatrix}f&0&0&0\\ 0&f&0&0\\ 0&0&1&0\end{bmatrix}\)  
Figure 4: Object recognition process.  
Figure 5: Forward projection.  
\(M_{affine}\) is the matrix that represents the affine transformation, which is a geometric transformation that conserves lines and parallelism. It occurs between film coordinates and pixel coordinates. On the other hand, \(M_{projection}\) is the matrix that describes the mapping from the 3D points in the world to 2D points in an image of the camera.  
Since our purpose is to determine the 3D coordinates of the object in the world and not the other way around, we used the inverse of our matrices, and the computation was realized with the following equation.  
\[\begin{bmatrix}U\\ V\\ W\\ 1\end{bmatrix}\quad=M_{int}^{-1}.M_{ext}^{-1}.\begin{bmatrix}u\\ v\\ 1\end{bmatrix}\]
# Thin CdTe Layers Deposited by a Chamberless Inline Process using MOCVD, Simulation and Experiment
## 2 Experimental Section
\[Uv_{mat}=\frac{v_{\text{exp}}}{v_{theor}}\times 100\% \tag{1}\]  
where \(v_{exp}\) was calculated using:  
\[v_{\text{exp}}=d\times A_{dep} \tag{2}\]  
where \(d\) is the average film thickness, \(A_{dep}\) is the deposited area, \(v_{theor}\) is the theoretical volume of CdTe deposit, limited by the molar supply of the metal-organic precursor [10], which was calculated using the relationship  
\[v_{theor}=\frac{p_{mo}F_{mo}t_{d}N_{A}}{22.4}\times\frac{a_{0}^{3}}{4} \tag{3}\]  
where \(p_{mo}\) and \(F_{mo}\) are the partial pressures and the mass flow rate, respectively, of the limiting precursor (DiPTe in this series of experiments), \(t_{d}\) is the deposition time, \(N_{A}\) is the Avagadro constant, \(a_{0}\) is the lattice constant of CdTe (6.48 A). The RFO diameters were varied from 0.5 to 1.0 mm, and the differential pressure of the active exhaust was controlled from \(-5\) to \(-15\) Torr (relative to ambient pressure). For the dynamic mode experiments, CdTe layers were deposited on 3.2 mm thick 15 \(\times\) 15 cm\({}^{2}\) TEC\({}^{\text{TM}}\) C15 glass substrates provided by NSG Pilkington (Nippon Sheet Glass Co Ltd., Mita Minato-ku, Tokyo), moving at a translation speed (\(v_{sub}\)) of 1.8 cm/min. The mass deposit (\(m_{\text{exp}}\)) and d were measured from a 5 \(\times\) 5 cm\({}^{2}\) specimen cut out from the centre of the substrate after deposition, due to the size restriction on the micro-balance used. The material utilisation based on gravimetric measurements (\(Uw_{mat}\)) was calculated from:  
\[Uw_{mat}=\frac{m_{\text{exp}}}{m_{theor}}\times 100\% \tag{4}\]  
The resolution of the measured mass \(m_{\text{exp}}\) was 0.1 mg (micro-balance resolution). The theoretical mass (\(m_{theor}\)) of the deposit was calculated using:  
\[m_{theor}=v_{theor}\times\rho_{CdTe} \tag{5}\]  
Figure 3: Controller temperature set-point vs averaged measured substrate surface temperature. with \(\rho_{\text{CdTe}}\) being the density of CdTe (5.86 g/cm\({}^{3}\) at 25 \({}^{\circ}\)C) [18]. In these experiments, the deposition surface temperature (\(T_{s}\)) was varied between 305 \({}^{\circ}\)C and 470 \({}^{\circ}\)C, the precursor concentrations (i.e., partial pressures) were varied from 1.38 to 3.48 Torr for DMCd and 0.44 to 1.08 Torr for DiPTe, and \(F_{Total}\) from 0.5 to 1.0 L/min. The layer thicknesses in static and dynamic modes were measured using a Veeco Dektak 150 (Veeco Instruments Inc. Planview, New York, United Staes) stylus profilometer over the available data points (Figure 2). The dynamic growth rates (GR) given were determined using the averaged final thickness in relation to the time of deposition, \(t_{d}\) (i.e., duration of substrate passing through the active deposition zone).
# Effects of Random Values for Particle Swarm Optimization Algorithm
## 4 Experiments and Analysis
### Application and Analysis  
#### 4.3.1 Application in Engineering Problem  
The pressure vessel design, which was initially introduced by [PERSON] [64], is a real world engineering problem. There are four involved variables, including the thickness (\(x_{1}\)), thickness of the head (\(x_{2}\)), the inner radius (\(x_{3}\)), and the length of the cylindrical section of the vessel (\(x_{4}\)). The highly constrained problem of pressure vessel design can be expressed as,  
\[\text{Min}:\ f(x)=0.6224x_{1}x_{3}x_{4}+1.7781x_{2}x_{3}^{2}+3.1611x_{1}^{2}x_{ 4}+19.84x_{2}^{2}x_{4} \tag{10}\]  
\[\text{Subject to}:\] \[\text{S}_{1}=0.0163x_{3}-x_{1}\leq 0,\] \[\text{S}_{2}=0.00954x_{3}-x_{2}\leq 0,\] \[\text{S}_{3}=1296000-\pi x_{3}^{2}x_{4}-\tfrac{4}{3}\pi x_{3}^{3} \leq 0, \tag{11}\] \[\text{S}_{4}=x_{4}-240\leq 0,\] \[\text{S}_{5}=1.1-x_{1}\leq 0,\] \[\text{S}_{6}=0.6-x_{2}\leq 0.\]  
where \(x_{1}\) and \(x_{2}\) are integer multipliers of \(0.0625\). \(x_{3}\) and \(x_{4}\) are continuous variables in the ranges of \(40\leq x_{3}\leq 80\) and \(20\leq x_{4}\leq 60\). In this study, the standard PSO and LDIW-PSO algorithms with different types of random values are utilized to solve this engineering problem.  
The parameters of standard PSO and LDIW-PSO algorithms for the engineering problem are the same as those for benchmark functions. In order to eliminate random discrepancy, the results are averaged over 30 independent runs. The optimization results are shown in Table 4. Obviously, the results of all algorithms are similar. However, the performances of LDIW-PSO algorithm random values generated by \(U[-1,1]\) and \(G(0,1)\) are slightly poorer than those of other algorithms. This is because the pressure vessel design is a low dimensional optimization problem. Although the random values generated by \(U[-1,1]\) or \(G(0,1)\) are beneficial to improve the diversity of particles, the local searching ability may be decreased due to the finite particles.  
Figure 6: The mean best fitness of LDIW-PSO algorithm with different types of random values for benchmark functions: (**a**) Sphere1; (**b**) Sphere2; (**c**) Rastrigin; (**d**) Rosenbrock; (**e**) Griewank; (**f**) Ackley; (**g**) Levy and Montalvo 2; (**h**) Sinusoidal; (**i**) Rotated Expanded Scaffer; (**j**) Alpine; (**k**) Moved axis parallel hyper-ellipsoid; (**l**) Schwefel. (The solid, dash, short dash and short dash dot lines represent the random values generated by uniform distribution in the ranges of [0, 1] and [\(-\)1, 1], Gauss distribution, and 0.5, respectively; the black, red and blue lines represent the space dimensions 10, 30, and 100, respectively).