esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurement System Modeling
### Description of Geomagnetic Vector in a North-East-Down Coordinate System  
As shown in Figure 1, the local North-East-Down (NED) coordinate system is the coordinate frame to describe the geomagnetic vector and fixed to the surface of earth. The origin denoted by \(\mathrm{O_{n}}\) is arbitrarily fixed to a point on the surface of earth, the X-axis denoted by \(\mathrm{N_{x}}\) points to the geodetic north, the Y-axis is denoted by \(\mathrm{E_{y}}\) points to the geodetic east, and the Z-axis is denoted by \(\mathrm{D_{z}}\) points downward along the ellipsoid normal [26].  
As shown in the Figure 1, elements to describe the field are as follows.  
\[\left\{\begin{array}{l}|\mathbf{h}|=|\mathbf{f}|\cos I\\ |\hat{f}_{\mathrm{down}}|=|\mathbf{f}|\sin I\\ |\hat{f}_{\mathrm{north}}|=|\mathbf{h}|\cos D=|\mathbf{f}|\cos I\cos D\\ |\hat{f}_{\mathrm{east}}|=|\mathbf{h}|\sin D=|\mathbf{f}|\cos I\sin D\\ \hat{f}^{2}=\mathbf{h}^{2}+\hat{f}_{\mathrm{down}}^{2}=\hat{f}_{\mathrm{north}}^ {2}+\hat{f}_{\mathrm{east}}+\hat{f}_{\mathrm{down}}^{2}\end{array}\right. \tag{1}\]  
Figure 1: Description of geomagnetic field.  
where, \(f\) is the geomagnetic vector and \(|f|\) is the total field intensity; \(h\) is horizontal component of total vector; \(D\) is declination, the angle between \(h\) and north, and positive when east; \(I\) is inclination, the angle between total vector \(f\) and horizontal plane, positive when downward;\(f_{\text{north}}\) is projection of \(f\) on north direction; \(f_{\text{east}}\) is projection of \(f\) on east direction; \(f_{\text{down}}\) is projection of \(f\) on downward direction.  
According to latitude, longitude, and elevation of the practical location, all components above relative to the geomagnetic information can be obtained from the World Magnetic Model (WMM) [27] and the International Geomagnetic Reference Field (IGRF) model.
# Parasitic-Based Active Gate Driver Improving the Turn-On Process of 1.7 kV SiC Power MOSFET
## 2 The Active Gate Driver  
Usually, the AGD input is connected to some feedback signal. A closed-loop signal can be obtained directly from the drain-source voltage, drain current or gate-source voltage [10; 11]. However, all of those signals except the gate-source voltage are hard to be measured in case of MV SiC MOSFETs. For example, to measure the \(v_{\text{DS}}\) voltage, the measuring resistance must be connected between the drain and the source, which makes the complete circuit more complicated and influences the main circuit performance [10]. An approach that is based on the feedback signal in the form of measurement of \(i_{\text{D}}\) and \(v_{\text{DS}}\) requires a complex subcircuit to be included in the AGD. In the proposed AGD, the closed loop is provided with the \(V_{\text{LS}}\) voltage (1). In order to explain this more clearly, a SiC MOSFET connected in the double-pulse test, as presented in Figure 1, is considered. The SiC MOSFET active gate driver is connected to the gate of the transistor through \(R_{\text{G}}\) and its reference ground is a Kelvin source (\(S_{\text{Kelvin}}\)). \(S_{\text{Kelvin}}\) in SiC MOSFETs is terminal, placed as close as possible to the actual chip inside the module and used for driving purposes. If the device was driven in reference to the power source (\(S\)), the source parasitic inductance \(L_{S}\) would have undesired effects on the gate current \(i_{\text{G}}\). The existence of the parasitic inductance \(L_{S}\) between \(S_{\text{Kelvin}}\) and \(S\) is naturally caused by internal connections of the SiC MOSFET. Dependence of the temperature for the value of \(L_{S}\) is very limited, and it decreases slightly with rising temperature [18]; hence, temperature fluctuations can be omitted when considering this method. From (1), it can be considered that the value of \(V_{\text{LS}}\) is proportional to the value of \(L_{S}\) and \(di_{\text{D}}/dt\). Thus, the value of \(i_{\text{D}}\) and its dynamics can be estimated from \(V_{\text{LS}}\). In an ideal case of the turn-on cycle, before \(i_{\text{D}}\) starts to rise, the \(V_{\rm Ls}\) value would be always 0 V; at the moment when \(i_{\rm D}\) begins to rise, it should have a constant value proportional to the slew rate of \(i_{\rm D}\). After the turn-on cycle, the value of \(V_{\rm Ls}\) should be 0 V. A feedback loop based on \(V_{\rm Ls}\) makes the AGD independent from the drain current values during the switching cycle because the drain current dynamic is defined by the changes in the \(V_{\rm Ls}\) value from 0 V to positive and negative values. Furthermore, \(V_{\rm Ls}\) peak values are not dependent on the maximal drain current values, but strictly on the \(di_{\rm D}/dt\) ratio (1) which is only reliant on the fixed values, thus making it an adequate signal to sense the individual stages of the turn-on cycle. Summarizing, the depicted closed-loop method simplifies the whole structure of the AGD and does not affect the power circuit of the SiC MOSFET.  
\[V_{\rm Ls}=L_{\rm s}\frac{di_{D}(t)}{dt} \tag{1}\]
# DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism
## 1 Introduction
Security platforms such as VirusTotal no longer provide the WHOIS information of the domain name. Therefore, DGA detection and classification by context information such as WHOIS of domain names is no longer feasible. Therefore, we can only determine whether it is a DGA domain name and which type of DGA it belongs to by analyzing the domain name string. There has been a lot of work based on the textual characteristics of domain names. [PERSON] et al. [8] perform DGA detection using the distribution characteristics of the characters and the 2-gram character set in the domain name. [PERSON] et al. [9] use domain name length, character frequency, randomness, and other characteristics to unsupervised clustering of domain names to achieve DGA domain name detection. The traditional machine learning method has problems, such as feature extraction, relying on expert experience, which makes it easy for an attacker to bypass. Aiming at the problems existing in the current DGA domain name classification, this paper proposes a DGA domain name classification method based on LSTM with attention mechanism through the research of DGA and DGA domain name. This method neither needs to reverse the malware sample nor uses context information such as WHOIS of the domain name, but only uses the character sequence of the domain name. For the character sequence of the domain name, the LSTM algorithm combined with the attention mechanism is used to construct the DGA domain name classifier, to achieve fast and accurate classification of the domain name. The contributions of this paper mainly include two aspects:  
1. We only use the character sequence of the domain name for DGA domain name classification to further prove that the character sequence contains DGA features. 2. We combine LSTM with attention mechanisms and apply them to DGA domain name classifications to prove that the weights of characters in DGA domain names are different. The remainder of the present paper is organized as follows. In Section 2, we briefly present related work to detect and classify DGA domains. In Section 3, we briefly describe the techniques used in our approach. In Section 4, we describe the architecture of our approach. The experimental results are presented in Section 5, and the summaries of the whole paper are presented in Section 6.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 3 Lightning Analysis of PR Data
### Lightning Activity Associated with Convective and Stratiform Rainfall  
Unsurprisingly, the studies found that lightning occurred more frequently for convective rainfall than stratiform rainfall [82; 83], and it was found that the absence of lightning could be explained by the stratiform nature of the precipitation patterns [76]. Meanwhile, flashes in stratiform rainfall accounted for a larger fraction of lightning over oceans than over land [77]. Convective rainfall usually had larger vertical velocities and developed at higher altitudes and thus had a greater possibility for the occurrence of lightning in comparison with stratiform rainfall.
## 3 Distance-Based Opportunistic Push/Subscribe Content Dissemination Model
### Decision-Making Layer  
#### 3.4.1 Principle of Publishing Content  
Among all users, some users may have the requested contents of other users. In this paper, we call the node that has others' requested content a provider. However, even though a provider has the requested content, it is not obligatory that the provider publish its content. If the provider is very far away from the subscriber, the provider will not publish the requested content because the probability of the subscriber receiving the content is very low.  
Let \(d_{ps}\) be the distance from the provider to the subscriber, \(d_{t}\left(x\right)\) be the distance of the provider travels in past x days. For instance, \(d_{t}\left(2\right)\) means the distance of the provider travelled in past 2 days. If a provider accesses CSS and has the subscribed content in one of the subscriptions, the provider publishes the content if Equation (1) is true:  
\[d_{ps}\leqslant Max\left(\left\{d_{t}\left(1\right),d_{t}\left(2\right), \ldots,d_{t}\left(Rd\right)\right\}\right) \tag{1}\]  
In Equation (1), \(Rd\) is the remaining days from the current date to the deadline. \(Max\left(\left\{d_{t}\left(1\right),d_{t}\left(2\right),\ldots,d_{t}\left(Rd \right)\right\}\right)\) is the longest distance that the provider has travelled in the past \(Rd\) days. For example, if the deadline is the day after tomorrow, \(Rd\) is 2. If the subscriber's location is at location 1 in Figure 2, the provider will publish the content. If the subscriber's location is at location 2, the provider will not publish the content.
## 3 System Model
### Ecosystem Monitoring  
A wireless path signifies the flow of data communication between nodes connected in the network domain in a specified range. Then nodes act as a router to broadcast packets to the multi-hop network. The nodes are placed in the region of \([I*J]\) in meters. Each node has its own character and they are grouped in clusters to make the transmission more efficient by minimizing delay and packet loss. The sink node is fixed in a location that enables it to send and receive data packets from sensor nodes. The position of nodes is measured with coordinate values and data are transferred by the routing protocol. The network is separated into \(c\) count of clusters and \(S_{c}\) cluster heads. Accordingly, nodes are grouped into clusters such that a cluster head (CH) is formed and thereafter nodes are allowed to exchange the data packets. Each node \(G_{r}\) sends data bytes to the intended CHs, which broadcast data packets to a sink node [38]. The system model of WSN is illustrated in Figure 1.  
Energy Model  
Let us define nodal initial energy and note that the nodes' energy is not rechargeable. The energy during the transmission of data bytes from the normal node to CH is based on multipath blurring and a free space strategy that mainly depends on the distance from sender to receiver [11]. The source considers radio electronics as well as powerful speakers to scatter energy, though the recipient utilizes radio hardware to disseminate the energy [39]. Equation (1) describes the loss of energy by normal node while broadcasting bytes of data:  
\[H_{dis}(G_{r})=\begin{cases}H_{\text{elec}}*I+H_{amp}*I*\|G_{r}-S_{a}\|^{4}& \text{; if }\|G_{r}-S_{a}\|\geq g_{0}\\ H_{\text{elec}}*I+H_{fs}*I*\|G_{r}-S_{a}\|^{2}&\text{; if }\|G_{r}-S_{a}\|<g_{0}. \end{cases} \tag{1}\]  
Specify in Equation (2) that:  
\[g_{0}=\sqrt{\frac{H_{fb}}{H_{amp}}}, \tag{2}\]  
where \(H_{elec}\) signifies electronic energy, and it is shown Equation (3) that:  
\[H_{elec}=H_{tr}+H_{agg}. \tag{3}\]Here, \(H_{tr}\) shows the energy of the transmitter, and \(H_{agg}\) implies the energy of the data aggregation. Accordingly, the energy parameter correlated with the power amplifier is indicated as \(H_{amp}\); \(\left\|G_{y}-S_{a}\right\|\) symbolizes the distance from the \(r\)th node to the \(a\)th CH. The loss of energy during the reception of data bytes is specified in Equation (4).  
\[H_{dis}(S_{a})=H_{elec}*l. \tag{4}\]  
The nodes update their energy during the transmission and reception of packets such that the node tends to die when the energy is moved to zero. In such a case, energy is updated by Equations (5) and (6):  
\[H_{d+1}(G_{r}) =H_{d}(G_{r})-H_{dis}(G_{r}) \tag{5}\] \[H_{d+1}(S_{a}) =H_{d}(S_{a})-H_{dis}(S_{a}). \tag{6}\]
# Super-Resolution Reconstruction of Depth Image Based on Kriging Interpolation
## 5 Discussion  
This study has shown that most of the classical image interpolation algorithms are not suitable for super-resolution of complex depth images with voids and rapid edge changes. Through comparisons of the point clouds of interpolated depth images, it is found that only nearest-neighbour interpolation of the classical image interpolation algorithms is applicable to depth images. The Kriging interpolation model, especially the variant based on spatial distance, can obtain better results that reflect the spatial characteristics to a greater extent. The introduction of fractional-order differential edge recognition successfully addresses the edge blurring problem of Kriging interpolation. The experimental results demonstrate the superiority of the improved Kriging interpolation based on fractional-order differentiation in terms of the super-resolution of depth images, which preserve texture information and edge properties. Objective evaluations show that the Kriging interpolation with fractional-order differential edge detection significantly improves the interpolation effect compared with the classical interpolation algorithm, with a PSNR improvement of 3-18 dB. Furthermore, the images produced by Kriging interpolation have more fractal characteristics, as would be expected for super-resolution based on interpolation. Overall, the Kriging interpolation super-resolution method based on fractional-order differential edge detection has better image reconstruction ability and stronger robustness than the classical interpolation algorithms.  
While our proposed method shows promising results, there are still some limitations that should be addressed in future work. For instance, our current approach assumes that the depth images are noise-free, which may not be realistic in practise. Future work could explore the integration of denoising techniques with our approach to improve its robustness. Additionally, while we have demonstrated the effectiveness of our approach on synthetic depth images, its performance on real-world data should be further evaluated.  
Overall, our work provides a valuable contribution to the field of depth image super-resolution and has the potential to be applied in a range of practical applications, such as 3D reconstruction, augmented reality, and medical imaging.  
Conceptualization, T.H., X.L. and C.W.; methodology, T.H. and X.W.; software, T.H. and Y.Y.; formal analysis, T.H. and X.W.; data curation, X.W. and Y.Y.; writing--original draft preparation, T.H. and X.W.; writing--review and editing, T.H., X.L. and C.W.; funding acquisition, X.L. and C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number 2022 YFC3803702.  
Not applicable.  
The authors declare no conflict of interest.
# Optimal Attitude Determination for the CR200 Underwater Walking Robot
## 3 Results
### Attitude Determination  
Before conducting the CFD analysis, it is important to define the posture of the CR200. In this study, the posture is determined by the two parameters. First, the pitch angle \(\alpha\) between the uniform inflow and the geometric center (CG) defines the tilt angle of the body. Second, the seafloor clearance \(\hat{\kappa}\) is defined as \(\hat{\kappa}=z_{CG}-0.5H\), where \(z_{CG}\) is the z-directional coordinate of the CG, and \(H\) is the height of the robot body. Defining the clearance this way allows for the robot's body's attitude to be uniquely established by first raising the body center to \(z_{CG}=\hat{\kappa}+0.5H\) and then rotating it by the angle \(\alpha\) in a counterclockwise direction with respect to the body center (CG). Figure 6 illustrates the parameters that define the posture.  
The angle of attack varies from \(10^{\circ}\) to \(14^{\circ}\) at intervals of \(2^{\circ}\), resulting in three levels. Similarly, the distance from the seabed has three levels: 100 mm; 120 mm; and 400 mm. The corresponding values of parameters are presented in Table 2.  
Based on the two parameters, \(\alpha\) and \(\hat{\kappa}\), which define the range, a computational mesh necessary for the CFD simulation was generated. Figure 7 shows the mesh and the corresponding number of meshes for all nine cases, considering the variations in both angle and distance. The central cross-section and surface mesh used the same configuration values regardless of the posture.  
Figure 5: Detailed view of the computational mesh.
## 3 Numerical Simulation and Laboratory Testing
### Laboratory Testing  
According to the experimental design specifications, defective concrete beams were manufactured in the laboratory. During the fabrication of the specimens, plastic tape was chosen to control the distance between the defects and the upper surface of the concrete rather than using fine wire. This decision was made because electromagnetic waves are more sensitive to metallic materials, which helps avoid interference from metals and other substances that could affect the test results. Polystyrene foam was employed to represent the small impermeable solid structures found within concrete due to its porous nature. A square wooden box containing slurry-free crushed stone was used to simulate larger, less dense entities. Wood was selected as the exterior material for the impermeable solid because its dielectric constant is similar to that of air.  
In another set of experiments, bending tests were conducted on concrete beam H_B0. In the final damaged state, the peak load of the vertically concentrated load force reached 41.27 kN, and the diagonal crack on the surface of the concrete beam reached a 3 mm width. The measurement equipment used in this experiment is the SIR-4000 ground penetrating radar system produced by GSSI company with a 1.6 GHz antenna. The system utilizes high-frequency antennas to provide high detection resolution and can detect structures and defects with smaller scales. Figure 11 shows the precast defects in the concrete beam used in the experiment and the damaged state of the concrete beam after the bending test.  
The handheld GPR device used in this study is shown in Figure 12. The two antennas were horizontally offset by dx = 58 mm.
## Appendix A Proof of Lemma 1  
For a random variable \(Z\overset{\Delta}{=}\frac{a\mathcal{X}}{bY+1}\) with independent \(X\sim\mathcal{X}_{2d_{1}}^{2}\) and \(Y\sim\mathcal{X}_{2d_{2}}^{2}\), the PDF of \(Z\) is derived as  
\[f_{Z}\left(z\right)=\int_{0}^{\infty}f_{Z|Y}\left(z|y\right)f_{Y} \left(y\right)dy\] \[=\int_{0}^{\infty}\left(\frac{by+1}{a}\right)\frac{\left(\left( \frac{by+1}{a}\right)z\right)^{d_{1}-1}e^{-\left(\frac{by+1}{a}\right)z}}{ \Gamma\left(d_{1}\right)}\cdot\frac{y^{d_{2}-1}e^{-y}}{\Gamma\left(d_{2} \right)}dy\] \[=\frac{\left(\frac{z}{a}\right)^{d_{1}-1}e^{-\frac{z}{a}}}{a \Gamma\left(d_{1}\right)\Gamma\left(d_{2}\right)}\int_{0}^{\infty}\left(by+1 \right)^{d_{1}}y^{d_{2}-1}e^{-\left(\frac{b}{a}z+1\right)y}dy\] \[\overset{\left(a\right)}{=}\frac{d_{1}\left(\frac{z}{a}\right)^ {d_{1}-1}e^{-\frac{z}{a}}}{a\Gamma\left(d_{1}\right)\Gamma\left(d_{2}\right)} \int_{0}^{\infty}\sum_{i=0}^{d_{1}}\left(\begin{array}{c}d_{1}\\ i\end{array}\right)\left(by\right)^{i}y^{d_{2}-1}e^{-\left(\frac{b}{a}z+1 \right)y}dy\] \[\overset{\left(b\right)}{=}\frac{d_{1}\left(\frac{z}{a}\right)^ {d_{1}-1}e^{-\frac{z}{a}}}{a\Gamma\left(d_{2}\right)}\sum_{i=0}^{d_{1}}\frac {\left(i+d_{2}-1\right)!b^{i}}{\left(d_{1}-1\right)!i!}\left(\frac{b}{a}z+1 \right)^{-\left(i+d_{2}\right)}, \tag{10}\]  
where (a) follows the binomial expansion of \(\left(by+1\right)^{i}\), and (b) comes from the equality \(\int_{0}^{\infty}y^{M}e^{-ay}dy=M!a^{-\left(M+1\right)}\). The MGF of \(Z\) in (7) is straightforwardly derived from the definition of MGF \(\mathcal{M}_{Z}\left(s\right)=\int_{0}^{\infty}e^{sx}f_{Z}\left(z\right)dz\) and \(\Psi\left(a,b,s\right)=\frac{1}{\Gamma\left(a\right)}\int_{0}^{\infty}e^{-st} t^{a-1}(1+t)^{b-a-1}dt\)[27].
# Global Mechanical Response Sensing of Corrugated Compensators Based on Digital Twins
## 2 Method
### Performance Criterion  
Once the digital twin model has been built, the performance criterion is to evaluate its similarity to the physical entity. Only digital twins with qualified accuracy can be applied to real engineering problems as a valid alternative to simulation calculations. As the test set of leave-one-out cross-validation (LOO CV) is sampled from the training set and does not need additional expensive simulations, LOO CV is widely used to validate the performance of the surrogate model [23]. For the training set with \(n\) samples, LOO CV uses the model built by \(n-1\) samples to predict the response of the remaining sample and repeats the above calculation process until the responses of \(n\) samples are calculated. In this paper, the standardized average LOO CV (\(CV_{\text{avg}}\)) [24] criterion described in Equation 12 is employed to evaluate the performance of digital twin:  
\[CV_{\text{avg}}\,=\,\frac{\sqrt{\frac{\frac{1}{n}\sum\limits_{i=1}^{n}\left( \hat{y}_{-i}^{(i)}-y^{(i)}\right)^{2}}}}{\text{range of }y^{(1)},\dots,y^{(n)}} \tag{12}\]  
where \(\hat{y}_{-i}^{(i)}\) and \(y^{(i)}\) are the predicted response value and true response value of the \(i\)-th omitted sample, respectively. The tolerable level of inaccuracy is application-specific but typically takes \(CV_{\text{avg}}<0.1\) as the target for a useful surrogate model [24].  
For further quantitative evaluation of DT performance, the correlation of determination (\(R^{2}\)) shown in Equation 13 is chosen as the global performance metric in this paper:  
\[R^{2}=1-\frac{\sum_{i=1}^{n_{t}}\left(y^{(i)}-\hat{y}^{(i)}\right)^{2}}{\sum_ {i=1}^{n_{t}}\left(y^{(i)}-\overline{y}\right)^{2}} \tag{13}\]  
where \(n_{t}\) is the number of test samples, \(y^{(i)}\) is the true response at the \(i\)-th test sample, \(\hat{y}^{(i)}\) is the corresponding predicted value, and \(\overline{y}\) is the mean of the response values. The closer \(R^{2}\) is to 1, the higher accuracy of the DT model will be. The surrogate model is generally considered to have good predictive ability for a value of \(R^{2}\) greater than 0.8 [25].