esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Model Parameter Analysis  
The above results indicate that the constitutive model based on Gamma distribution is the optimal model in both weak softening and strong softening curves. Therefore, the influence and the physical meaning of two parameters \(a\) and \(b\) of Gamma distribution on shear curve are further analyzed as below.  
Figure 12 shows the constitutive model parameters \(a\) and \(b\) from Gamma distribution. In type A curves (Figure 12a), parameter \(a\) decreases, and parameter \(b\) increases with the increase of normal stress. In type B curves (Figure 12b), parameter \(a\) increases with the increase of normal stress. The softening characteristics of the two types of shear curves are opposite; thus, the changing trend of the constitutive model parameters \(a\) is also opposite.  
The evolution curves of the damage variable based on Gamma-constitutive model (in Figure 13) demonstrate that the damage evolution changes nonlinearly with shear displacement. When the displacement reaches 5 mm, the damage variable of type A is more than 95% (Figure 13a); when the displacement reaches 5 mm, the damage variable of type B is more than 90%, and the damage degree is close to 95% when the displacement reaches to 10 mm (Figure 13b). It shows that the damage variable has reached a higher degree of damage when the shear displacement is small. The above analysis also reveals that the soil is easy to be damaged under small deformation.  
Figure 12: Parameters of the constitutive model based on Gamma distribution. (**a**) Gamma distribution parameter of type A curves; (**b**) Gamma distribution parameter of type B curves.  
Figure 13: Evolution curves of damage variable based on Gamma distribution fitting results. (**a**) Damage evolution curves of type A; (**b**) damage evolution curves of type B.  
The two parameters \(a\) and \(b\) of Gamma-constitutive model determine the shape of the shear softening curve and softening degree. Figure 14 shows how these two parameters affect the shape of the shear constitutive model curve by changing the values of parameters \(a\) or \(b\) and keeping other parameters constant. In Figure 14a, with the increase of parameter \(a\), the peak stress and softening degree increase, but the peak displacement and the displacement reaching the residual strength also remain unchanged. It indicates that parameter \(a\) mainly determines the peak strength in the shear process. In Figure 14b, with the increase of parameter \(b\), the peak strength and the corresponding peak displacement increase, and the displacement reaching the residual strength also increases. The increase of parameter \(b\) can also increase the softening degree and peak strength, but the influence on the strength is much less than parameter \(a\). In sum, parameter \(a\) mainly determines the peak strength in shear deformation process, and the small increase of parameter \(a\) makes the peak value of shear constitutive model increase sharply; parameter \(b\) mainly determines the displacement reaching the peak strength and residual strength.
# Comparison of Dual-Combustion Ramjet and Scramjet Performances Considering Combustion Efficiency
## 2 Materials and Methods
### Mathematical Model
#### 2.1.1 Flow Control Equations  
The flow control equations include the mass, momentum, energy, and component transport equations, and the unified matrix form is as follows:  
\[\frac{\partial Q}{\partial t}+\frac{\partial(E-E_{v})}{\partial x}+\frac{ \partial(F-F_{v})}{\partial y}=H \tag{1}\]  
where  
\[Q=\begin{bmatrix}\rho\\ \rho u\\ \rho v\\ \rho e\\ \rho Y_{i}\end{bmatrix}E=\begin{bmatrix}\rho u\\ \rho uu+p\\ \rho uv\\ u(\rho e+p)\\ \rho uY_{i}\end{bmatrix}E_{v}=\begin{bmatrix}0\\ \tau_{xx}\\ \tau_{xy}\\ u\tau_{xx}+v\tau_{xy}-q_{x}\\ \rho_{i}D_{im}\frac{\partial Y_{i}}{\partial x}\end{bmatrix} \tag{2}\]  
\[F=\begin{bmatrix}\rho v\\ \rho vu\\ \rho vv+p\\ v(\rho e+p)\\ \rho vY_{i}\end{bmatrix}F_{v}=\begin{bmatrix}0\\ \tau_{yx}\\ \tau_{yy}\\ u\tau_{xy}+v\tau_{yy}-q_{y}\\ \rho_{i}D_{im}\frac{\partial Y_{i}}{\partial y}\end{bmatrix}H=\begin{bmatrix} 0\\ 0\\ 0\\ 0\\ \omega_{i}\end{bmatrix} \tag{3}\]  
Here, \(Q\) is a conserved variable; \(E\), \(F\), \(E_{V}\), and \(F_{V}\) are the inviscid flux and viscous flux in the \(x\) and \(y\) directions, respectively; \(u\) and \(v\) are the velocities along the coordinate \(x\) and \(y\) directions, respectively; \(p\) is the pressure; \(\rho_{i}\) is the density of component \(i\); \(\rho\) is the density of the mixture; \(Y_{i}\) is the mass fraction of component \(i\); \(\omega_{i}\) is the mass generation rate of component \(i\); and i = 1, 2..., \(\text{Ns}-1\), where \(\text{Ns}\) is the number of components.  
\(\tau_{i,j}\) is the viscous stress classification, which is expressed as  
\[\begin{split}\tau_{xx}&=-\frac{2}{3}\mu(\
abla \cdot V)+2\mu\frac{\partial u}{\partial x}\\ \tau_{yy}&=-\frac{2}{3}\mu(\
abla \cdot V)+2\mu\frac{\partial v}{\partial y}\\ \tau_{xy}&=\tau_{yx}=\mu\left(\frac{\partial u}{ \partial y}+\frac{\partial v}{\partial x}\right)\end{split} \tag{4}\]  
\(q_{x}\) and \(q_{y}\) are the energy flux due to thermal conduction and component diffusion, respectively, which are expressed as follows:  
\[\begin{split} q_{x}&=-k\frac{\partial T}{\partial x }-\rho\sum\limits_{i=1}^{\text{Ns}}D_{im}h_{i}\frac{\partial Y_{i}}{\partial x }\\ q_{y}&=-k\frac{\partial T}{\partial y}-\rho\sum \limits_{i=1}^{\text{Ns}}D_{im}h_{i}\frac{\partial Y_{i}}{\partial y}\end{split} \tag{5}\]\(e\) is the total energy, and \(h_{i}\) is the enthalpy of component \(i\):  
\[e=\sum_{i=1}^{N_{h}}Y_{i}h_{i}+\frac{1}{2}\left(u^{2}+v^{2}\right)-\frac{P}{\rho} \tag{6}\]  
\[h_{i}=\int_{T_{0}}^{T}c_{pi}dT+h_{i}^{0} \tag{7}\]
# Roto-Translational Control of Spacecraft in Low Earth Orbit Using Environmental Forces and Torques
## 2 Spacecraft Dynamics
### Translational Dynamics  
Considering the gravitational influence of the Earth including \(J_{2}\), which captures the largest perturbation on the orbit of the spacecraft due to the Earth's mass distribution, and the non-gravitational influence of atmospheric drag and lift; the acceleration of a spacecraft in Low Earth Orbit (LEO) can be written in the ECI coordinate system as  
\[\bar{x} \triangleq-\frac{GM_{\oplus}}{\|\mathbf{r}\|^{3}}x+\frac{3}{2}\Bigg{(} \frac{J_{2}GM_{\oplus}R_{\oplus}^{2}}{\|\mathbf{r}\|^{4}}\Bigg{)}\bigg{(}\frac{x} {\|\mathbf{r}\|}\bigg{(}\frac{5z^{2}}{\|\mathbf{r}\|^{2}}-1\bigg{)}\bigg{)}+\bar{r}_{D,x}+\bar{r}_{L,x}, \tag{5}\] \[\bar{y} \triangleq-\frac{GM_{\oplus}}{\|\mathbf{r}\|^{3}}y+\frac{3}{2}\Bigg{(} \frac{J_{2}GM_{\oplus}R_{\oplus}^{2}}{\|\mathbf{r}\|^{4}}\Bigg{)}\bigg{(}\frac{y} {\|\mathbf{r}\|}\bigg{(}\frac{5z^{2}}{\|\mathbf{r}\|^{2}}-1\bigg{)}\bigg{)}+\bar{r}_{D,y}+\bar{r}_{L,y},\] (6) \[\bar{z} \triangleq-\frac{GM_{\oplus}}{\|\mathbf{r}\|^{3}}z+\frac{3}{2}\Bigg{(} \frac{J_{2}GM_{\oplus}R_{\oplus}^{2}}{\|\mathbf{r}\|^{4}}\Bigg{)}\bigg{(}\frac{z} {\|\mathbf{r}\|}\bigg{(}\frac{5z^{2}}{\|\mathbf{r}\|^{2}}-3\bigg{)}\bigg{)}+\bar{r}_{D,z}+\bar{r}_{L,z}. \tag{7}\]  
In (5)-(7), \(G\in\mathbb{R}\) is the universal gravitational constant, and \(M_{\oplus}\), \(R_{\oplus}\in\mathbb{R}\) are the mass and radius of the Earth, respectively. Vectors \(\mathbf{\bar{r}_{D}}\triangleq[\bar{r}_{D,x}~{}\bar{r}_{D,y}~{}\bar{r}_{D,z}]^{T} \in\mathbb{R}^{3}\) and \(\bar{r}_{L}\triangleq[\bar{r}_{L,x}~{}\bar{r}_{L,y}~{}\bar{r}_{L,z}]^{T}\in \mathbb{R}^{3}\) are the accelerations due to aerodynamic drag and lift expressed in the ECI coordinate system, respectively.  
**Assumption 1**.: _Each CubeSat is assumed to be in circular LEO, and the inter-spacecraft distance is small as compared with their orbit radius._The Schweighart-Sedwick equations [23], which include influence of the \(J_{2}\) perturbation, can be used to express the motion of a target spacecraft with respect to the LVLH frame centered at the CoM of the chaser as  
\[\Delta\dot{x} \triangleq 2(\Omega c)\Delta\dot{y}+(5c^{2}-2)\Omega^{2}\Delta x+u_{x}, \tag{8}\] \[\Delta\dot{y} \triangleq -2(\Omega c)\Delta\dot{x}+u_{y},\] (9) \[\Delta\dot{z} \triangleq -q^{2}\Delta z+2 lq\cos(qt+\varnothing)+u_{z}, \tag{10}\]  
where \(\mathbf{\Delta r}\triangleq[\Delta x\ \Delta y\ \Delta z]^{T}\in\mathbb{R}^{3}\) is the LVLH position of the target, the vector \(\mathbf{u}\triangleq[u_{x}\ u_{y}\ u_{z}]^{T}\in\mathbb{R}^{3}\) is the control input (i.e., experienced differential aerodynamic acceleration). \(\Omega\in\mathbb{R}\) is the constant angular velocity of the orbit of the chaser; \(c\triangleq\Big{(}1+\frac{3J_{2}R_{\mathbf{\omega}}^{2}}{8r_{ref}^{2}}\Big{(}1+3 \cos(2i_{ref})\Big{)}\Big{)}^{1/2}\in\mathbb{R}\); \(r_{ref},\ i_{ref}\in\mathbb{R}\) are the radius and inclination of the orbit of the chaser. Parameters \(l,\ q,\ \varnothing\in\mathbb{R}\) in the decoupled out-of-plane component are defined in [23].
## 4 Topology Control
### Re-Deployment
#### 4.4.1 Integer Linear Programming  
The node placement problems can be formalized as a linear programming (LP) problem or an integer linear programming (ILP) problem. ILP could be an optimal approach to find the optimal numbers or positions of new nodes to improve or maintain connectivity if the network is small, but for a large network, the LP or ILP are not applicable.  
For instance, the energy optimization for \(k-connected\) networks can be regard as an LP problem. The constraints and objective functions are given in [35]. The work in [98] formulates the optimal number of relay nodes to guarantee connectivity and coverage to be an ILP problem. The work in [99] investigates the node placement strategies to achieve five objectives: mean overall residual energy, mean minimum residual energy, mean number of nodes over the threshold, mean overall traveled distance and mean maximum distance. The problem is formalized to be a ILP, but unfortunately, it has a huge number of constrains, including the positions of all deployed nodes and their status information.  
The work in [100] proposes an ILP method to solve minimal number of relay node placement issues related to the energy consideration to guarantee network connectivity. There are 20 constraints and many variables depending on the number of relay node positions. It shows that for \(k\) small (one or two) and a network that is not that big (tens of nodes), the problem is solvable within a reasonable time. However, if the network is large, and for a bigger \(k\) value, it seems that it is still intractable with ILP. Therefore, few problems can be solved in polynomial time. In turn, most of them are very difficult to solve with the optimal solution. If the original problem is NP-complete, the corresponding LP or ILP is also NP-complete.
## 5 Antenna Model
\begin{array}{c}G_{a}(\theta,\phi)=\sum\limits_{m=1}^{M}\sum\limits_{n=1}^{N}A_ {m,n}\cdot G_{a,sub}(\theta,\phi)\cdot E_{m,n}\\ \cdot\exp\left[jk\sin\theta\cos\phi\left(\frac{N+1}{2}-n\right)\Delta x\right] \\ \cdot\exp\left[jk\cos\theta\sin\phi\left(\frac{M+1}{2}-m\right)\Delta y\right] \end{array}  \tag{3}\]  
where \(A_{m,n}\) denotes the beam excitation coefficient for the \(m\)-th row and \(n\)-th column TRM, \(G_{a,sub}(\theta,\phi)\) represents the subarray pattern, \(E_{m,n}\) denotes the error matrix for the \(m\)-th row and \(n\)-th column TRM, \(\Delta x\) and \(\Delta y\) represent the distances between the subarrays in the \(x\)- and \(y\)-directions, respectively, and \(k=\frac{2\pi}{\lambda}\) denotes the wavenumber. Figure 18a,b show the element antenna patterns in azimuth and elevation, respectively. The subarray pattern \(G_{a,sub}(\theta,\phi)\) can be calculated as follows:  
\[G_{a,sub}(\theta,\phi)=G_{a,az}(\theta)\cdot G_{a,el}(\phi), \tag{4}\]  
where \(G_{a,az}\) and \(G_{a,el}\) denote the antenna beams in azimuth and elevation, as depicted in Figure 19a,b, respectively. Figure 20a,b show the total antenna patterns in azimuth and elevation, respectively. Figure 19: Subarray antenna patterns of the S-STEP SAR payload in (**a**) azimuth and (**b**) elevation. Figure 18: Element antenna patterns of S-STEP SAR payload in (**a**) azimuth and (**b**) elevation.
## 2 General State-of-the-Art
### Automation in 3D Modeling  
The next stage in the 3D pipeline at the end of point cloud classification is 3D modeling. This step once again presents a bottleneck point in the pipeline. This project will not address 3D modeling automation in detail as it remains a future work; however, some preliminary results in the case of wooden beams will be presented in the paper.  
In light of the largely manual operation in 3D modeling [25], its automation has therefore been a very interesting subject of research in recent years. The modeling of planar surfaces or facades has been studied in many works [47; 48]. These approaches often employ surface-normal similarity and coplanarity in patches of vectorial surfaces. Another uses robust algorithms such as the Hough transform or RANSAC to detect the surface [30]. Once a planar region is detected, the parameters of the plane can be estimated using total least squares fitting or robust methods that are less affected by points not belonging to the plane [49]. In regard to indoor modeling, the methods are mainly based on geometric primitive segmentation. Some approaches are based on space segmentation before applying a more detailed segmentation [40]. The segmentation of planes is then performed using robust estimators such as MLESAC, which uses the same sampling principle as RANSAC, but the retained solution maximizes the likelihood rather than just the number of inliers [50].  
In particular regard towards HBIM, problems related to the complexity of certain heritage buildings exist. In this case, the Level of Detail (LoD) requirements of heritage objects are often higher [14; 51]. One of the main challenges is the standardization offered by current BIM technology used to manage simple buildings and constructions [52], limited by the irrelevance of object libraries and the inability of 3D scans to determine structures in buildings of dissimilar age and construction [53]. Indeed, several research [53; 54] focused on enhancing the existing libraries of historical parametric objects, but few address the automation in HBIM generation.
## 4 IoMT Authentication Taxonomy
### Authentication Schemes
#### 4.6.4 Cryptography-Based Authentication  
Currently, cryptography is an essential part of authentication, and various cryptography techniques offer a good opportunity to empower IoMT security. Figure 17 illustrates different cryptographic-based authentication schemes. Cryptographic-based authentication commonly uses a hash function, which is efficient for resource-constrained IoMT devices [116]. [PERSON] et al. [28] adopted a keyed-hash authentication message code (HMAC) calculated by an iterative hash function such as SHA-1 or MD5.  
In the IoMT environment, many researchers adopted symmetric cryptography, such as Data Encryption Standard (DES) and advanced encryption standard (AES) [106; 117]. [PERSON] and [PERSON] [106] proposed authentication in IoMT based on AES and a bio-hash function to improve the cryptographic scheme. In [118], [PERSON] et al. improved real-time access to IoMT devices through a Diffie-Hellman shared key DES for credential encrypting. [PERSON] et al. [119] used another symmetric technique, called Addition/ Rotation XOR (ARX), to support lightweight cryptography for IoMT devices.  
Asymmetric cryptography, also known as the public-key technique, recently gained attention. Rivest-Shamir-Adleman (RSA) is a public-key cryptography that adopts two types of keys: one is called a public-key, and the other used as a signature is called a private-key [120]. The investigation clarifies that RSA has a higher level of security than the AES and DES techniques [29]. However, Elliptic Curve Cryptography (ECC) offers similar level of security as RSA and less storage requirement [2; 6]. Therefore, most of reviewed authentication schemes adopted ECC, which supports small keys and certificates [22; 34; 48; 99; 100; 114; 111; 102; 113; 114]. In the same direction, [PERSON] et al. [55] used an elliptic curve Diffie-Hellman (ECDHE) scheme for generating keys, as well as ECDSA for authenticating entities.  
Recently, it has become popular to combine multiple techniques for cryptography to increase the security level. This is referred to as hybrid cryptography. That combination of more than one technique overcomes the shortcomings of depending on one technique. B.  
Figure 17: Cryptography-based authentication.  
[PERSON] et al. [77], [PERSON] et al. [121], and [PERSON] et al. [122] adopted a hybrid authentication method through Chaotic-Map. Furthermore, [PERSON], [PERSON] [76] and [PERSON] et al. [87] accomplished authentication through fuzzy extractor and hash functions.
# Damage Evaluation in Tempered Vacuum Glazing via Multivariate Statistical Methods
## 3 Results
### Evaluation Model Verification  
The damage evaluation model of TVG can be summarized according to the evaluation process described above. First, six characteristic parameters were tested for the new samples. Each parameter was then standardized and two principal components were found. After that, Euclidean distances of the average values were calculated for main components, allowing one to extract various grades of the samples. Finally, the sample grade could be classified into the kinds with the minimum distance. The whole process of evaluation and verification is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grade** & **Grade Meaning** & **Corresponding Sample Number** \\ \hline Grade I & Minor damage & 1, 4, 7, 12, 13 \\ Grade II & Mediate damage & 2, 3, 9, 11 \\ Grade III & Serious damage & 5, 6, 8, 10 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Grade classification of TVG.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grade** & **Sample Number** & **Average Value of Element 1** & **Average Value of Element 2** \\ \hline Grade I & 5 & 1.09760 & \(-\)0.12889 \\ Grade II & 4 & \(-\)0.62672 & 1.23063 \\ Grade III & 4 & \(-\)0.74528 & \(-\)1.06952 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Average values for two principal components of each grade.  
Figure 3: Tree diagram as a result of HCA.  
Taking a new sample of TVG and testing six characteristic parameters result in: \(Z_{1}=8\), \(Z_{2}=9\), \(Z_{3}=7\), \(Z_{4}=7\), \(Z_{5}=8\), and \(Z_{6}=8\). Standardizing them using data in Table 9 provides the values: \(z_{1}=0.41278\), \(z_{2}=1.09529\), \(z_{3}=-0.61574\), \(z_{4}=-0.09875\), \(z_{5}=0.91877\), and \(z_{6}=0.41279\). Putting them into Formula (8) gives two principal components as \(p_{1}=0.24766\), \(p_{2}=-0.61149\). Applying Formula (7) ensures Euclidean distances between the new samples' principal components and the corresponding average values from Table 17, and the results can be found in Table 18. According to these data, the new sample is close to Grade I and can thereby be classified with respect to this grade.
## 2 Materials and Methods
### Data Sources
#### 2.2.4 NPP Observation Data  
In this work, the estimated NPP by CASA model was verified through the observed NPP based on FLUXNET2015 datasets ([[https://fluxnet.org/data/fluxnet2015-dataset](https://fluxnet.org/data/fluxnet2015-dataset)]([https://fluxnet.org/data/fluxnet2015-dataset](https://fluxnet.org/data/fluxnet2015-dataset)), accessed on 24 August 2021) and ChinaFLUX datasets ([[http://www.chinaflux.org/](http://www.chinaflux.org/)]([http://www.chinaflux.org/](http://www.chinaflux.org/)), accessed on 30 October 2021), hosted at the Lawrence Berkeley National Laboratory (Berkeley, CA, USA) and Institute of Geographic Sciences and Natural Resources Research (Beijing, China), respectively. FLUXNET2015 and ChinaFLUX datasets contains the gross primary production (GPP) [65; 66], which could be converted into the NPP value by multiplying the ratio of NPP/GPP at 0.55 [67]. The observed GPP data from four flux sites located in Inner Mongolia during different periods were applied in this study. The details of the flux sites are given in Table 2.
## 4 Results and Analysis
### Validation Experiment
#### 4.3.1 Environmental Factors  
We experimented with varying the image contrast and brightness of the original data. The aim was to investigate the effect of the environment on the system's performance. Specifically, we employed data from the same person and conducted a single-factor comparison analysis by defining the parameters of brightness and contrast variation. The following steps were taken:  
1. We normalized the input image \(f(x,y)\) from the range [0, 255] to [0, 1];
2. The parameters of brightness and contrast were defined with the following equations: \[g(x,y)=a\times f(x,y),0<a<2\] (5) where \(g(x,y)\) represents the pixel value of the output image, \(a\) represents the changed brightness value or contrast value, and \(f(x,y)\) represents the pixel value of the original image.
3. Finally, we normalized the resulting image \(g(x,y)\) from the range [0, 1] to [0, 255].  
Figure 19: Examples of the YOLOv7 misjudgment. (**a**) The presence of a complex background can result in inaccurate identification of needles. (**b**) The presence of a finger in the background causes recognition errors.  
As depicted in Figure 20, we compared the accuracy of YOLOv7 in recognizing each frame and predicting the puncturing time by altering the brightness of the original video.  
The evaluation method employed for this purpose is as follows:  
\[Recognition\ of\ frame\ (\%)=\frac{\text{\emph{Successfully recognized frames}}}{\text{\emph{Total frames}}}\times 100\% \tag{6}\]  
\[Accuracy\ of\ time\ (\%)=\left(1-\frac{|Ground\ truth-Predicated|}{Ground\ truth} \right)\times 100\% \tag{7}\]  
The results in Figure 21 illustrate that the recognition rate of the needle frames increased significantly from a light intensity of 0.4 and maintained a high level until 1.4. Moreover, the accuracy of the recognition time reached 100% within the range of 0.7-1.0 in the recognition time accuracy results under the influence of light intensity.  
Next, the same Equation (5) is used to compare the contrast, as shown in Figure 22, to change the contrast of the original data.  
Using the same evaluation metric, the final outcomes were compared, as depicted in Figure 23. The proposed system exhibits a high level of performance in response to contrast variation, with the ability to maintain recognition of the needle at light intensity levels as low as 0.4, and reaching 100% recognition time accuracy at 0.9-1.1. Notably, the system's accuracy results remained consistently high at various levels of contrast.  
Figure 21: The result of brightness changes.  
Figure 20: The change in brightness.