esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Equations
### Soil Type A  
In this section, the proposed equation for the correlation factor when the colliding buildings are founded on soil type A is presented. Figure 2 presents the plot of the correlation factor when the colliding buildings are founded on soil type A versus \(T_{1}/T_{2}\). It can be seen that the plot is a piecewise function, and it is composed of two different functions. It can also be noticed that the correlation factor follows two different trends depending on whether \(T_{1}\leq\) 0.2 s or \(T_{1}\) > 0.2 s (see Figure 2). Therefore, the proposed equation for the correlation factor when the colliding buildings are founded on soil type A is composed of two equations depending on whether \(T_{1}\leq\) 0.2 s or \(T_{1}\) > 0.2 s (see Equation (11)). Figure 3 shows a comparison between the proposed equation (Equation (11)) and the actual values of the correlation factor obtained for different cases. Using Equation (9), the RMS error has been calculated as equal to 2.94% for \(T_{1}\leq\) 0.2 s and 12.92% for \(T_{1}\) > 0.2 s.  
\[\rho=\left\{\begin{array}{ccc}\left(\frac{T_{1}}{T_{2}}\right)^{-1.117}&,&T_{1} \leq 0.2\;\mathrm{s}\\ 57.343\left(\frac{T_{1}}{T_{2}}\right)^{4}-147.46\left(\frac{T_{1}}{T_{2}} \right)^{3}+141.74\left(\frac{T_{1}}{T_{2}}\right)^{2}-61.171\left(\frac{T_{1} }{T_{2}}\right)+10.548,&T_{1}>0.2\;\mathrm{s}\end{array}\right. \tag{11}\]  
Figure 3: Comparison between the actual trend and the proposed equation for the correlation factor when the colliding buildings are founded on soil type A.  
Figure 2: The correlation factor when the colliding buildings are founded on soil type A versus \(T_{1}/T_{2}\). Both lines correspond to the same thing (correlation factor). The graph of the correlation factor is not continuous.
## 3 System Model
### Energy Model  
We use a simple energy model for the radio hardware energy dissipation to calculate the energy consumption, which is called the first radio energy model [2; 3; 4]. Depending on the distance between source node and destination node, a free space (\(d^{2}\) power loss) or multipath fading (\(d^{t}\) power loss) channel model will be used. To transmit an l-bit data packet over a distance \(d\), \(E_{Tx}\) amount of energy will be consumed as follows:  
\[E_{Tx}(l,d)=\begin{cases}IE_{elec}+l\varepsilon_{fs}d^{2},d<d_{0}\\IE_{elec}+l\varepsilon_{mp}d^{4},d\geq d_{0}\end{cases}\]  
where \(E_{elec}\) represents the energy dissipation of radio device. Its value is depended on many factors such as digital coding, modulation, and spreading of signal. \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) represents the free space and multipath transmitter amplifier model respectively. To receive the same data packet, \(E_{Rx}\) amount of energy will be consumed with \(E_{Rx}(l)=IE_{elec}\).  
Depending on the distance from source to destination node, a threshold \(d_{0}\) can be defined as \(d_{0}=\sqrt{\varepsilon_{{}_{fh}}/\varepsilon_{mp}}\), when we let \(\varepsilon_{{}_{fh}}d_{0}^{2}=\varepsilon_{mp}d_{0}^{4}\). If the distance to receiver is less than the threshold \(d_{0}\), the free space model is used; otherwise, the multipath model is used. Thus, to forward \(k\) data packets, \(E_{Fw}\) amount of energy will be consumed, as is listed in Equation (1) below:  
\[E_{Fw}=\begin{cases}(k+1)IE_{elec}+l\varepsilon_{fs}d^{2},d<d_{0}\\ (k+1)IE_{elec}+l\varepsilon_{mp}d^{4},d\geq d_{0}\end{cases} \tag{1}\]
# Gait Recognition Based on Gait Optical Flow Network with Inherent Feature Pyramid
## 3 Proposed Approach
### Set-Level Feature from Unordered Set  
Assume a gait dataset contains N objects, and the corresponding gait sequence is \(X=\{X_{i}\}\), where \(i\in\) 1, 2, 3, \(\ldots\), N. The process to transform the origin gait sequences into the GOFI representation can be formulated as follows:  
\[G_{i}=P(X_{i}) \tag{6}\]  
where \(P\) represents the process of fusing the optical flow and the silhouette edge, and \(G=\{G_{i}\}\), where \(i\in\) 1, 2, 3, \(\ldots\), N represents the GOFI output. Then, the gait model, named GOFN, is used to extract both the image-level \(f_{IL}\) and set-level feature \(f_{SL}\) for higher recognition accuracy. The set-level feature can be formulated as follows:  
\[f_{SL}=ST(f_{IL}),\;\textit{where}\;f_{IL}=L(G) \tag{7}\]  
where \(L\) represents a group of CNN layers used to extract image-level features \(f_{IL}\in\mathbb{R}^{d_{x}\times d_{c}\times d_{l}}\), \(d_{n}\) is the batch size, \(d_{c}\) is the number of channels, and \(d_{l}\) is the length of the image-level feature. The Set Transition (ST) blocks are used to aggregate the image-level features to the set-level features.  
To aggregate the temporal information and obtain set-level features from unordered gait sequences, a permutation invariant function is need to process the image-level features extracted by the CNN. Regardless of the order of objects in the set, the value of a permutation invariant function remains the same. As shown in Figure 2, the ST block can be represented as follows, where \(\omega\) denotes the parameters in ST block:  
\[ST(f)=LN(f+rFL(f)), \tag{8}\]  
where \(f=LN(f_{IL}+Multihead(f_{IL},f_{IL},f_{IL};\omega))\).  
LN is the layer normalization, FL is the row-wise feedforward layer, and \(r\) is the weight factor. \(Multihead()\) is the multi-head attention, which collects the global information to refine image-level features. First, \(f_{IL}\) is copied and input into multiple linear layers to extract the multi-head subspace features. The length of subspace features is \(d_{sub}\). For example, in head 0, the multi-head subspace features are denoted as \((f_{q}^{0},\;f_{k}^{0},\;f_{0}^{0})\). The attention \(z^{0}\) is calculated by \(z^{0}=softmax(f_{q}^{0}f_{k}^{0T}/\sqrt{d_{sub}})\times f_{0}^{0}\). The attentions of each head are then concatenated. The output is resized to the original shape by multiplying it with a linear layer. The output is then fed into the FL module. Each feature is multiplied with a share-weight linear layer, and the outputs are aggregated. ST takes the unordered gait sequences and applies self-attention between the image-level features, resulting in set-level features of the same size. The image-level feature is transformed to the set-level feature through ST. The structure will stabilize the network and is beneficial for recognition accuracy.  
Figure 1: The examples of GOFI. (**a**) Bag-carrying condition under 144\({}^{\circ}\) view. (**b**) Coat-wearing condition under 36\({}^{\circ}\) view.
## 3 Results and Discussion
### Validation Using HFSS  
In order to verify the reliability and consistency of the performance of the proposed MM, the CST result for the transmission coefficient (S\({}_{21}\)) is authenticated by Ansys HFSS. The simulated result obtained with this software also shows quad-band resonance peaks, with amplitudes remaining nearly unchanged and yielding four resonance peaks at 2.37 GHz, 7.66 GHz, 9.20 GHz and 11.0 GHz with amplitudes of \(-\)40.71 dB, \(-\)33.08 dB, \(-\)44.70 dB and \(-\)30.36 dB, respectively. For comparison, the results are shown in Figure 19, in agreement with the CST result.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resonance Peak (GHz)** & **Unit Cell** & **1 \(\times\) 2 Array** & **2 \(\times\) 2 Array** &
\begin{tabular}{c} **Max. Shift of Frequency** \\ \end{tabular} \\ \hline First & 2.35 & 2.57 & 2.57 & 0.22 \\ Second & 7.72 & 7.60 & 7.60 & 0.12 \\ Third & 9.23 & 9.47 & 9.48 & 0.25 \\ Fourth & 10.68 & 10.36 & 10.36 & 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Deviation of frequency for various arrays of unit cells.  
Figure 17: Array combinations: (**a**) 1 \(\times\) 2 array; (**b**) 2 \(\times\) 2 array.  
Figure 18: Comparison of S parameters for a array and unit cell (**a**) transmission coefficient (\(S_{21}\)) and (**b**) reflection coefficient (\(S_{11}\)).
## 2 Design and Development of 3D Curvature Sensor of FBG
### Development of 3D Curvature Sensor of FBG  
Based on the curvature sensing principle of FBG in Section 2.1, this study designed and developed a 3D curvature sensor of FBG suitable for the complex underground environment  
Figure 1: Section Diagram of FBG Series Arrangement on Flexible Substrate Surface. The A is the position of the FBG string 1 on the Y-axis, and A\({}^{\prime}\) is the projection of A onto the neutral plane MM\({}^{\prime}\). The B is the position of the FBG string 2 on the X-axis, and B\({}^{\prime}\) is the projection of B onto the neutral plane MM\({}^{\prime}\). The O is the origin of the coordinates. The meanings of M and N have been given in the manuscript. The \(\beta\) is the angle between the Y-axis and the neutral plane MM\({}^{\prime}\).  
of a coal mine, which can be used to perceive curvature information of discrete points in the orthogonal direction. The sensor comprised a mining rubber tube with grooves in the orthogonal direction of the surface of the flexible substrate and pasted with a fiber grating string, including a flexible substrate and two fiber Bragg grating strings. In order to ensure that the 3D curvature sensor of FBG can be coordinated with the bending deformation of the scraper conveyor, the selection of the flexible substrate adopts a mining rubber tube with a large deformation ability and strong recovery ability. The cross-section of grooves on the surface of the flexible substrate in the orthogonal direction is shown in Figure 2. In order to improve the sensing accuracy of the 3D curvature sensor of FBG as much as possible, the grating interval of the FBG string is set to 0.5 m. Considering the wavelength demodulation range of the FBG demodulation instrument, 10 grating points are evenly arranged on each FBG string, and one end of the FBG string is equipped with an FC/APC connector. The FBG is laid in the grooves of two orthogonal directions of the rubber pipe for mining using a butt joint. The specific butt joint method is shown in Figure 3. The design and encapsulation of the sensor are shown in Figure 4.
## 4 Results and Discussion
### Population Size Analysis  
In order for the model to be effective, it is necessary to perform the optimal setting of the model parameters. The topology of the population affects the speed of propagation, i.e., the effect of optimization [63]. An adequate selection of the population size is an important factor in the performance of this metaheuristic optimization method. According to [64], the optimal number of particles varies between 70 and 500 for the most complex optimization problems, although it is necessary to analyze the influence of the number of particles on the optimization efficiency for each individual optimization problem.  
In this research, special emphasis is placed on determining the size of the population needed for the exploration of searched space. The optimal number of particles that search the area is the one where the results of the function objective are the best. Therefore, the influence of the number of particles on the optimization results was analyzed. However, due to the use of a greedy algorithm, it is not possible to examine the influence of the number of particles on the first test performed. Namely, the greedy algorithm prioritizes the most energy-demanding consumer. Therefore, the test was carried out when several ships with similar energy needs were connected to the shore-based power infrastructure. The distribution of sampling, i.e., determining the size of the population, is performed according to the exponential function \(f(x)=e^{x}\) with the addition of one intermediate point with the aim of increasing the resolution, as shown in Figure 11. It can be seen from the figure that the greatest reduction in peak power is achieved when the number of particles is set to 256. Compared to the initial reduction of 0.76% when the number of particles was 1024, a reduction in peak power in the amount of 13.47% was achieved. The relationship of the required number of particles to achieve a certain objective function cannot be generalized and must be analyzed and optimized for each individual problem [64].  
Figure 12a shows the convergence history when the population size is 1024 particles and Figure 12b when the population size is 256 particles. Although the convergence history looks similar, there is a significant difference in the achieved reduction in the required charging power of the ESS group of ferries.  
Figure 10: Optimized charging when several similar ships are connected to the shore network.  
Figure 13 displays an optimized charging schedule when the particle count is 256.  
Figure 11: The influence of the selection of the number of particles on the reduction of the peak charging power.  
Figure 12: Convergence history (**a**) No. of particles = 1024 (**b**) No. of particles = 256.  
Figure 13: Optimized charging schedule when the number of iterations is set to 256.
## 5 Conclusions  
This paper proposes a small target detection method based on improved YOLOv7 polarization imaging of photovoltaic cell defects. First, the polarization camera is used to collect small defects of photovoltaic modules and the analyzed DOP image information is used as network input to make the edge of the small target prominent, increase the available information of images and increase the detection accuracy of the model. Secondly, a COT attention mechanism module is added to increase the accessible information of small targets by using the context of small targets and improve the detection accuracy of small targets. Finally, by improving the target loss function and replacing the IOU measure with the NWD measure, the sensitivity of IOU to the position deviation of small target objects can be overcome, the prior box regression effect can be improved, and the detection accuracy of small target can be further improved. Therefore, the mAP has obvious improvements.  
The experimental and research results show that the proposed method is 1.01 percentage points higher than the mAP of the original YOLOv7 algorithm. Subsequent research work will involve more comparative experiments in the field of PV cell defect detection and verify the applicability of the method proposed in this paper under different types of defect conditions. Attention will also be paid to the lightweighting of the model so that it can be run in mobile devices while maintaining accuracy.  
Conceptualization, H.W. and F.W.; methodology, H.W. and D.Z.; software, H.W.; validation, X.G. and P.W.; investigation, H.W. and R.W.; data curation, X.G.; writing--original draft preparation, H.W.; writing--review and editing, H.W., F.W. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Anhui Natural Science Foundation (2008085 UD09); Anhui University Collaborative Innovation Project (GXXT-2021-010); Anhui Construction Plan Project (2022-YF016, 2022-YF065, 2023-YF050); Anhui Province Higher Education Science Research Project (2022 AH04004); Anhui Province University Outstanding Youth Research Project (2022 AH020025).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We would like to acknowledge the support from Anhui Natural Science Foundation, Anhui Provincial Department of Education, Anhui Provincial Department of Housing and Urban Rural Development.  
The authors declare no conflicts of interest.
# Fracture Prediction in Weldox 700E Steel Subjected to High Velocity Impact Using LS-DYNA
## 2 Materials and Methods
### Model Description  
In this study, a numerical investigation is conducted to analyze the fracture behaviour of Weldox 700E steel plate using varying critical fracture parameter and fracture strain criteria for varying target plate thickness, varied from 2 to 6 mm. If the ratio of the thickness to the length of a plate is less than or equal to 0.1, it is termed a thin plate, whereas if the ratio is greater than 0.1, it is termed a thick plate [31]. In view of the above, a 2 mm plate is categorized as a thin plate, whereas 4 and 6 mm are categorized as thick plates. The plate is subjected to impact from a blunt-nosed cylindrical projectile at high velocity. Due to the symmetry of the model and loading conditions, the FE study has been performed on one-quarter of the model. The schematic of the quarter model is presented in Figure 3a. The relevant dimensions are as follows: radius of the target plate (R) = 21 mm, radius of the projectile (r) = 2.97 mm, and length of the projectile (L) = 29.80 mm. The mass of the projectile is taken as 6.4 g [26]. The coefficient of friction between the contact surfaces was set to 0.3. The boundary conditions for the quarter model, including the target and projectile, are defined with x-axis and y-axis symmetry. Further, the target plate is assumed to be clamped, and therefore, all degrees of freedom are fixed along the perimeter of the target plate. The projectile is free to move downward along the z-axis to strike the target plate. The FE model is prepared using LS-DYNA/explicit incorporating 8-noded hexahedral elements with one-point quadrature. The contact between the projectile and target plate was modelled using the *contact-automatic-surface-to-surface algorithm in LS-DYNA. The initial gap between the contact surfaces of the target and the projectile is controlled to optimize analysis time. The hourglass control is used using suitably adjusted parameters [28]. The element size in the impact region is also controlled to effectively capture the stress gradients and stress wave propagation [32; 33; 34]. The element length along the thickness direction is adjusted to account for varying plate thicknesses. The FE mesh used in the analysis is shown in Figure 3b. To analyze the fracture behaviour the representative elements at the \"top\", \"mid\", and \"bottom\" locations are identified in the target plate as shown in Figure 3.
## 4 Results and Discussion
### Discussion  
The propagation characteristics of channel waves in extra-thick coal seams were discovered through wavefield forward modeling and actual data application, which conform to the propagation laws of channel waves. According to [PERSON] and [PERSON]'s equations, it is determined that the extra-thick coal seam is mainly a Love-type channel wave. We demonstrated suitable detection to extra-thick coal seams, with areas of strong dispersion and weak dispersion along the detection direction of the extra-thick coal seam. When the source is excited, the Love-type channel wavefront propagates as follows, where the incident angles of SH-waves are equal to the critical angle; SH-waves are both totally reflected and critically refracted, which forms an interference system referred to as the \"normal mode\" range of Love-type channel waves within the extra-thick coal seam; and no energy escapes into either adjacent half-space. Where the angles of the incident SH-waves are less than the critical angle, there is a rapid decrease in energy.  
In the estimation of velocity and critical angle, potential sources of error and uncertainty, aside from data quality, data processing, inversion, and factors related to instrument reception, primarily stem from discrepancies between the model assumptions and the actual coal thickness and coal seam velocity. These discrepancies significantly affect phase velocity, leading to deviations from the true values. According to Snell's Law, the critical angle can be directly calculated using the information from the shear waves in the coal seam and the roof and floor. Numerical simulations show that at the critical angle, the corresponding phase velocity exhibits weak dispersion, while medium-to-thick coal seams demonstrate more pronounced dispersion characteristics. In scenarios where the coal seam  
Figure 11: Image of the apparent channel wave velocity.  
thickness varies significantly at the working face, wave analysis may not be the most suitable method, as large variations in coal thickness can lead to fractures in the roof and floor. This irregularity may distort wave propagation, resulting in inaccurate velocity estimations. However, using critical angle estimation can be more effective in helping to plan mining routes with greater precision, enhance safety protocols, and optimize resource extraction.  
The main energy of the apparent channel wave propagates in the coal seam, and the characteristics conform to the above definition of a channel wave. Due to the extra-thick coal seam, the seismic event is distinct in the raw recording, the first arrival is identifiable, and the frequency is stable. The apparent channel wave velocity represents the first part of the channel wave in the seam and is consistent with the wavefront velocity, which can be picked up accurately with minor errors. All the conditions provide theoretical support for the accurate exploration of extra-thick coal seams.
# Electrically Tunable Propagation Properties of the Liquid Crystal-Filled Terahertz Fiber
## 4 Conclusions  
An electrically tunable bandgap-guiding fiber for THz frequencies was designed by infiltrating the cladding air holes of an index-guiding MOF with nematic LC. The Topas polymer was chosen as the fiber base material because of its relatively low absorption loss and extremely low material dispersion in the 0.2-2 THz range. In addition, 5 CB was chosen as the filling material because of its wide nematic range around room temperature, relatively small extinction coefficient, large birefringence, and, thus, higher electrical tunability in the frequency range of 0.2-1.0 THz. Structural dependence of the bandgap for the designed THz LC-MOF without an external static electric field across the fiber was studied in detail. The structural parameters of the LC-MOF that had PBG centered at 1 THz are given. The PBG could also be transposed to certain frequency by scaling the fiber structure. Due to the strong anisotropy of 5 CB, external electric field applied across the fiber can cause polarization-dependent bandgap splitting, and thus broaden the effective transmission bandwidth and achieve single mode single polarization guidance. Propagation properties, such as mode property, confinement loss, and the dispersion of the designed THz LC-MOF show electrical tunability. A flattened near-zero GVD of 0 \(\pm\) 1 ps/THz/cm was obtained for the y-polarized fundamental mode within a broad normalized frequency range. These results can provide theoretical references for applications of LC-MOF for dynamic polarization control and tunable fiber-optic devices in THz frequencies.  
[PERSON] designed the structure and wrote the article; [PERSON] performed the numerical simulations; [PERSON] put forward the idea and analyzed the data; [PERSON] checked the spelling and grammar of this article, and put forward some comments (data curation, [PERSON]; funding acquisition, [PERSON]; investigation, [PERSON]; methodology, [PERSON]; software, [PERSON]; supervision, [PERSON]; writing--original draft, [PERSON]; writing--review and editing, [PERSON]). This research was funded by the National Natural Science Foundation of China (Grant Nos. 11604260 and 11605133). This work was supported by the National Natural Science Foundation of China (Grant Nos. 11604260 and 11605133). The authors declare no conflict of interest.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 3 System Performance Simulation  
After designing the watermarking scheme, it is necessary to estimate the performance of the watermarking scheme. In this paper, the original system parameters are designed as follows: the sampling rate is 30.72 M, the subcarrier spacing is 15 kHz, the system occupies 24 subcarriers, the modulation format of user's signal is 16 QAM, and the modulation format of DMRS is QPSK. In the design of the watermark system, the key is unified as \"keyforphone1,\" the hash algorithm is SHA-1, and the watermark embedding strength is 0.2. Under each channel condition, 10,000 data frames are sent and received. In this section, we consider two channel environments: Gaussian channel and fading channel. In each channel, we discuss two cases: ideal carrier synchronization and synchronization with carrier frequency offset to evaluate the performance comprehensively.