esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Co-Authorship Analysis
### Author-Based Co-Authorship Analysis  
This subsection introduces a study on co-authorship among authors. A search using VosViewer found 2808 authors who have participated in this field. Of these, 198 authors fulfilled the specified criteria. Among them, 17 authors were recognized as part of the most interconnected co-author network. Table 3 presents the top thirteen authors from the group of disconnected sets with the highest co-authorship values according to TLS. The co-authorship-based TLS is always an integer, as the cumulative sum of fractional link strengths for an individual author in any published paper consistently equals one. Consequently, an author's co-authorship relationship strength, characterized by the count of publications, is directly linked to the volume of published works.  
The distribution of authors by countries shown in Table 3 corresponds to the leading research institutions presented earlier. It is easy to notice that only one of the authors ([PERSON], [PERSON]) has a distinguishably higher TLS rate, while the TLS of other authors is almost similar and equal to 6-7. This and the visualization presented in Figure 15 demonstrate that the current collaboration level remains low and collaboration possibilities are not yet fully utilized. Current research groups are fragmented and formed by a limited number of researchers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rank** & **Country** & **Author** & **Np** & **Links** & **TLS** \\ \hline
1 & Peoples R China & [PERSON] & 15 & 7 & 13 \\
2 & Peoples R China & [PERSON] & 7 & 7 & 7 \\
3 & United Arab Emirates & [PERSON] & 8 & 4 & 7 \\
4 & France & [PERSON] & 9 & 4 & 7 \\
5 & France & [PERSON] & 7 & 3 & 7 \\
6 & Singapore & [PERSON] & 9 & 7 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The top thirteen authors with the highest TLS values.  
Figure 15: A visual representation of the co-authorship network among researchers within the domain of OT security.  
Figure 15 presents a co-authorship network diagram with nodes in various colors, and the links connecting them represent their collaborative relationships. The size of the node depicts the role and importance of the researcher in a group. If the impact of different authors is similar, the node size is the same. Figure 15 analysis demonstrates the leading role of three research groups, led by [PERSON], [PERSON] (PRC), [PERSON], [PERSON] (India), and [PERSON], [PERSON] (France), while other groups remain of limited size and potential.
# Numerical Simulation of Thermo-Hydro-Mechanical Coupling of Model Test for Nuclear Waste Disposal
## 3 Coupling Model Parameters
### Gas Parameters and Constitutive Equations  
The parameters of gas are shown in Table 3.  
(1) The water vapor density is calculated as  
\[\rho_{v}=\frac{p_{v}M_{l}}{RT}=0.0021668\frac{p_{v}}{T} \tag{13}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline VG model parameters (\(a\)) & 0.009 m\({}^{-1}\) \\ VG model parameters (\(b\)) & 1.3386 \\ The density of water (\(\rho_{l}\)) & 1000 kg/m\({}^{3}\) \\ Fluid volume strain modulus (\(K_{l}\)) & 2.15 \(\times\) 10\({}^{6}\) kPa \\ Volume fraction of saturated liquid (\(\theta_{s}\)) & 1 \\ Residual liquid volume fraction (\(\theta_{r}\)) & 0 \\ Gravitational acceleration (\(g\)) & 9.8067 m/s\({}^{2}\) \\ Molar mass of water molecules (\(M_{l}\)) & 0.018 kg/mol \\ Latent heat of water evaporation (\(L\)) & 2.46 \(\times\) 10\({}^{6}\) J/kg \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of fluid.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Gas-phase bulk strain modulus & 142 kPa \\ Intake suction (\(s_{a}\)) & 1 kPa \\ Standard atmospheric pressure (\(atm\)) & 0.1013 MPa \\ \hline \end{tabular}
\end{table}
Table 3: Relevant parameters of gas [49; 50].  
2. The air density is calculated as \[\rho_{a}=\frac{0.2(p_{g}-p_{v})M_{\rm O}+0.8(p_{g}-p_{v})M_{N}}{RT}=0.00347\frac{p _{g}-p_{v}}{T}\] (14) (3) The dry air density is calculated as \[\rho_{g}=\frac{p_{v}M_{\rm I}+0.2(p_{g}-p_{v})M_{\rm O}+0.8(p_{g}-p_{v})M_{N}}{ RT}=0.0034\Big{(}\frac{p_{g}}{T}\Big{)}-0.0013\Big{(}\frac{p_{v}}{T}\Big{)}\] (15) (4) The gas thermal drive coefficient is calculated as \[k_{gT}=6\times 10^{-12}(1-S)\] (16) (5) The effective diffusion coefficient of water vapor molecules is calculated as \[D_{V}=D_{V0}(\frac{T}{273.15})^{1.8}\] (17) (6) The specific heat capacity of air is calculated as \[c_{g}=\frac{28.8\rho_{g}+5.4\rho_{V}}{0.0288\rho_{g}-0.0108\rho_{v}}\] (18) (7) The dry air solubility coefficient is calculated as \[H=\Big{\{}8.21\times 10^{-5}\exp\Big{[}1700(\frac{1}{T}-\frac{1}{298.15}) \Big{]}+1.35\times 10^{-4}\exp\Big{[}1300(\frac{1}{T}-\frac{1}{298.15}) \Big{]}\Big{\}}\] (19) (8) The dynamic viscosity coefficient of gas is calculated as \[\mu_{g}=0.00001827\Big{(}\frac{411.15}{T+120}\Big{)}\bigg{(}\frac{T}{120}\bigg{)} ^{1.5}\] (20) (9) The gas relative permeability is calculated as \[k_{gr}=\bigg{[}1-\bigg{(}\frac{S-S^{m}}{1-S^{m}}\bigg{)}\bigg{]}^{3}\] (21) where \(M_{\rm I}\) is the molar mass of water molecules and its value is 0.018 kg/mol, \(p_{v}\) is the vapor pressure, \(M_{\rm O}\) is the molar mass of O\({}_{2}\) and its value is 0.032 kg/mol, \(M_{N}\) is the molar mass of N\({}_{2}\) and its value is 0.028 kg/mol and \(D_{V0}\) is the molecular diffusivity and its value is \(3\times 10^{-10}\) m\({}^{2}\)/s.
## 3 Results  
### Mechanical Properties  
The tensile strength ratio refers to the ratio of the tensile strength of rock specimens with pre-existing cracks to that of intact specimens, while the failure displacement ratio refers to the ratio of the vertical displacement of rock specimens with pre-existing cracks to that of intact specimens when the peak load is reached. Figure 4 illustrates the variations in the tensile strength ratio and the failure displacement ratio as a function of the crack inclination angle for circular specimens with different inclination angles.  
From Figure 4, it can be observed that the notable reductions in both the tensile strength and failure displacement of rock specimens with pre-existing cracks compared to intact specimens. The tensile strength of specimens with pre-existing cracks amounts to approximately 60% to 80% of the tensile strength of intact specimens, signifying significant strength degradation caused by the presence of pre-existing cracks. Furthermore, the  
Figure 4: Tensile strength ratio and failure displacement ratio of circular specimens with pre-existing cracks at different inclination angles (black dots represent the tensile strength ratio, and red square dots represent the failure displacement ratio).  
Figure 3: Schematic diagram of the experimental system layout.  
tensile strength of specimens with pre-existing cracks exhibits considerable variability, with an approximate magnitude of variation of 18%. The tensile strength ratio of rock specimens with pre-existing cracks follows a \"V\" shape trend, initially decreasing and then increasing as the crack inclination angle increases. The minimum value is observed at a crack inclination angle of \(\alpha=45^{\circ}\), with a strength of only 60.3% of that of intact specimens, while the maximum value is obtained at a crack inclination angle of \(\alpha=90^{\circ}\), with a strength of 77.2% of that of intact specimens. Additionally, the difference in tensile strength is relatively minor within the crack inclination angle range of 30 to \(60^{\circ}\).  
Similarly, the failure displacement of specimens with pre-existing cracks also shows a decreasing-then-increasing trend with an increase in the crack inclination angle. However, in comparison to the tensile strength, the reduction in failure displacement is relatively small, and the variation range of the failure displacement ratio of specimens with pre-existing cracks is relatively small with an oscillation magnitude of approximately 12% as the crack inclination angle increases. The minimum value of the failure displacement ratio is observed at a crack inclination angle of \(\alpha=45^{\circ}\), corresponding to approximately 72.85% of that of intact circular specimens, indicating that the damage caused by the crack is the highest at this angle. Conversely, when the crack inclination angle is parallel to the loading direction (i.e., \(\alpha=90^{\circ}\)), the damage caused by the crack is minimized, and the failure displacement is approximately 85% compared to intact circular specimens.
# Buckle Pose Estimation Using a Generative Adversarial Network
## 4 Experiments and Results
### Evaluation Methodology  
To evaluate the performance of our algorithm, the evaluation metric for edge detection was used to measure the quality of the results, and the absolute distance of the error  
Figure 7: Different labels during training: (**a**) image; (**b**) edge label; (**c**) mask label; (**d**) line label.  
was used as the evaluation metric for the final result. For the edge extraction network, the optimal dataset scale (ODS), optimal image scale (OIS), average precision (\(AP\)), and R50 measures were used, and the \(F\)-measure was the reconciled average of precision (\(P\)) and recall (\(R\)). ODS represents the global optimal threshold, which is fixed in all images to maximize the overall \(F\)-measure. OIS represents the optimal threshold of each image and was used to maximize the \(F\)-measure of the image. AP is the integral of the \(P/R\) curve, and R50 is the recall rate at 50% precision. For edge detection, a distance tolerance parameter was used to determine whether the predicted boundary pixels were correctly predicted, allowing for small positioning errors between the predicted and ground-truth boundaries. The distance tolerance was obtained by multiplying the width and height of the image by the maxDist (0.0075). The formulae for \(P\) and \(R\) are shown in Equations (10) and (11); the formulae for the \(F\)-measure and AP are shown in Equations (12) and (13).  
\[P=nnz(matchE)/nnz(E1), \tag{10}\]  
\[R=sum(matchG)/sum(allG), \tag{11}\]  
\[F=\left(1+\beta^{2}\right)\times\frac{P\times R}{\beta^{2}\times P+R}, \tag{12}\]  
\[AP=\int_{0}^{1}P(R)dR, \tag{13}\]  
where \(E1\) represents the binarization result of all edge images, \(matchE\) is the predicted edge point that matches the ground-truth point, \(allG\) is the ground-truth edge point, and \(matchG\) is the number of predicted edge points that were ground true. The number of non-zeros (\(nnz\)) reflects the number of non-zero elements and \(sum\) represents the sum of the points. The F-measure was tuned by adjusting the value of \(\beta\) to obtain the optimized proportion of \(P\) and \(R\), where \(\beta=1\).  
The evaluation indices of the center point and angle prediction were evaluated using the Euclidean distance and absolute value error, respectively. The calculation formulae are as follows:  
\[\rho=\sqrt{(x_{2}-x_{1})^{2}+(y_{2}-y_{1})^{2}}, \tag{14}\]  
\[\theta=|\alpha-\alpha_{0}|, \tag{15}\]  
where (\(x_{1},y_{1}\)) and (\(x_{2},y_{2}\)) represent the center point coordinates of the true value and those of the predicted value, respectively. \(\alpha\) and \(\alpha_{0}\) represent the deflection angles of the true and predicted values, respectively.
# Atomic Receiver by Utilizing Multiple Radio-Frequency Coupling at Rydberg States of Rubidium
## 3 Conclusions  
In this paper, the feasibility of an atom-based FDM receiver was discussed in detail through the method of dual RF-carrier. Various factors affecting the communication quality, such as BER and SNR, were studied. Our new scheme is of great significance to the field of atomic communication. By using Rydberg atom as a receiving antenna, the AM and FM signal was directly captured by the fast photodetector and can be obtained without any demodulation means. A demo experiment of image  
Figure 5: Test results of the proposed atomic FDM receiver for image data reception at different data transfer rates.  
Figure 6: Bit error ratio (BER) for image data reception at different channels.  
data reception at two channels has shown that the maximum acceptable data transfer rate was 200 kHz with a BER of less than 5%, for both AM and FM communication. Comparing with the traditional electronic-based receivers, the new atomic approach can realize the communication with high sensitivity and weak signal detection ability. Moreover, the signals transmitted by the two channels worked in parallel, and do not interfere with each other under a certain bandwidth, which greatly saved the spectrum resources. Based on the above excellent performance, these atomic receivers have great development potential in numerous applications. In the future, the performance of the atomic receiver will also be optimized for various communication technologies in our subsequent experiments.  
Conceptualization, Z.S.; Formal analysis, H.Z., H.M., Z.F., J.Q., Q.W., and Z.S.; Methodology, Z.S., H.Z., and Z.F.; Writing--original draft, H.Z.; Writing--review & editing, Z.F., Q.W., and Z.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key Research and Development Program of China (Grant No. 2016 YFF0200104).  
The authors declare no conflicts of interest.
## 4 Simulation of the Proposed Control
### Case B: Turbine Deloading with Fast Communication  
For Case B, the same fault scenario as the deloading control case was studied. When the fault occurred, the on-shore side converter detected the AC voltage dip and communicated with the DFIG turbine to decrease the power generation. Once the on-shore low voltage was detected, the RSC converter current reference was reduced by the voltage. Finally, the DFIG electrical power was reduced by the rotor current reference of RSC. By deloading the turbine, power transferred through the off-shore converter was reduced, resulting in stabilized DC voltage. Compared to the DC chopper case, a stable DC voltage was observed in this case. The consequence of deloading was that the DFIG rotor accelerated.
## 2 Materials and Methods
### HR Determination Using Sliding Window and Temporal Filtering  
In the previous steps, we extracted the HR signal from an input image. However, even after considerable signal processing, detecting a subtle HR signal in a driving environment, which is characterized by severe interference, is challenging. In order to produce stable estimation results, our method used sliding windows of different lengths to derive several candidate estimation results at a single time point. The number of candidate estimates was set at seven, with a window length ranging from 8 to 14 s, and the Mahalanobis distance measure was employed for the final estimated HR to  
Figure 3: Heart rate (HR) estimation over time. _RoverG\({}_{\text{JBSS+EEMD}}\)_ adds JBSS to [PERSON] et al.’s method [15]. _RoverG\({}_{\text{excluded}}\)_ employs the consistent correlation assumption by subtracting the noisy components in the background from the face signal. _RoverG\({}_{\text{NLMS}}\)_ is our proposed method. EEMD—ensemble empirical mode decomposition; NLMS—normalized least mean squares.  
exclude those results that differed the most from the others, as in the same framework as Reference [14]. The final estimation result was, thus, the average of the remaining candidate HRs. To transform the HR into a time-series signal, the Welch method was employed [13]. Finally, temporal filtering was applied to smooth the intermittently fluctuating final HR as follows:  
\[HR^{t}=\frac{1}{s}\sum_{r=t-s}^{t-1}HR^{r}\;when\;HR^{t}-HR^{t-1}\geq\alpha, \tag{5}\]  
where \(HR^{t}\) denotes the HR at time \(t\). \(HR^{t}\) is determined by whether the threshold \(\alpha\) of the difference between the current final HR at time \(t\) and the previous HR at time \(t-1\) is exceeded.
# Efficient Estimation of Generative Models Using Tukey Depth
## 4 ABC Based on Tukey Depth Statistics  
As pointed out in the introduction, TDCs are highly flexible and able to capture almost any property of a multivariate sample. Therefore, they are appealing to use as statistics in ABC to efficiently compare the level of difference in properties between the observations and the generated samples from the model.  
An open question is how to compare the difference between the Tukey depth contours of the observations and the generated samples. One natural and powerful metric is Intersection over Union (IoU) [42], which is highly popular for comparing segments in image analysis. However, in this paper we use the computationally simpler alternative of computing the difference between the contours in the directions defined by \(u_{i},i\in 1,\ldots,n_{u}\). This is illustrated in Figure 3. The red and blue lines show an illustration of a TDC computed from a set of observations and generated samples for \(n_{u}=6\). The regions within the lines are the \(\alpha\)-depth regions. The green arrows show the difference between the TDCs along the directions \(u_{i},i\in 1,\ldots,6\).  
As a comparison, IoU is computed by dividing the area of the intersection of the two Tukey depth regions by the area of the union of the two regions.  
Further, it is possible to compare the TDCs of the observations and generated samples for multiple values of \(\alpha\), i.e., multiple TDCs. The resulting ABC rejection sampling algorithm using TDCs as statistics is shown in Algorithm 2.  
Figure 3: Illustration of the method used to measure the difference between the TDCs of the observations and the generated samples. The green arrows represent the measured distances between the TDCs.  
Figure 2: Scatter plot of a sample of size \(n=250\) generated from \(h(x)\) with \(\theta=0.3\). Tukey depth contours with \(\alpha=0.05\) and \(\alpha=0.1\) are visualised as blue lines in the last two plots, respectively.  
```
0:\(N\) // Number of iteration \(\mathbf{x}=x_{1},x_{2},\ldots,x_{n}\) // Dataset \(\alpha_{1},\ldots,\alpha_{k}\) // \(\alpha\)'s used to define TDCs \(\epsilon\) // Threshold \(\widehat{\theta}=\varnothing\) // The set of accepted approximate posterior samples Method:
1:for\(n\in 1,2,\ldots,N\)do
2:\(\widehat{\theta}\gets P(\theta)\) // Sample from prior distribution
3:\(\widetilde{\mathbf{x}}=\widetilde{x}_{1},\ldots,\widetilde{x}_{n}\gets P (x|\widehat{\theta})\) // Sample from generative model
4:\(\rho\leftarrow\sum_{j=1}^{k}\sum_{i=1}^{n_{n}}\left|\widehat{Q}_{i}(\alpha_{j}, \mathbf{X})-\widehat{Q}_{i}(\alpha_{j},\widetilde{\mathbf{X}})\right|\)
5:if\(\rho<\epsilon\)then
6:\(\widehat{\theta}\leftarrow\widehat{\theta}\cup\widetilde{\theta}\) // Add the accepted proposal \(\widetilde{\theta}\) to the set of accepted samples \(\widehat{\theta}\)
7:endif
8:endfor
9:return\(\widehat{\theta}\)
```  
**Algorithm 2** ABC rejection sampling using TDC statistics.  
Specifically, line 4 in the algorithm measures the difference between a TDC computed from the observations and a TDC computed from generated samples.
## 3 Results and Discussion
It is found that the transmission at 3\(\omega\) keeps decreasing with the increasing of PI concentration and drops below 90% when PI concentration reaches 3%, proving the predicted absorption of 3\(\omega\) laser energy by UV PI. It is also found that the transmission at 1\(\omega\) hardly changes, but the transmission at 2\(\omega\) slightly decreases after the addition of PI, which might be ascribed to the refractive index variation caused by the PI occupancy of micropores inside the coating (the transmission at 2\(\omega\) is more sensitive to the refractive index than that at 1\(\omega\) because 2\(\omega\) is located around a peak in the spectrum). This drawback can be overcome by the precise control of coating refractive index, and related study is currently in progress. Unfortunately, the sol becomes invalid for neither coating nor curing if PI concentration keeps increasing over 4% because the presence of too much PI molecule affects the original status of sol (like viscosity, volatilization rate, matrix concentration) and prevents the MPS prepolymer from cross-linking (leading to incomplete curing of the coating).
# Unconstrained Bilingual Scene Text Reading Using Octave as a Feature Extractor
## 2 Related Work
### Scene Text Recognition  
In the text-reading phases of natural images, text recognition is the second phase after scene text detection. This method can be implemented independently or after scene text detection phases. In the scene text recognition phase, the cropped text regions are fed either from the scene text detection phase or from the prepared input dataset, from which the sequences of labels are decoded. Previous attempts were made by detecting individual characters and refining misclassified characters. Such methods require training a strong character detector for accurately detecting and cropping each character out from the original word. These types of methods are more difficult for Ethiopic scripts due to their complexities. Apart from the character level methods, word recognition [12], sequence to label [30], and sequence to sequence [31] methods have been presented. [PERSON] et al. [32] and [PERSON] et al. [15] presented a spatial attention mechanism to transform a distorted text region from irregular input images into canonical pose suitable recognition. However, both the detection and recognition task performance are determined based on the extracted features. Previously proposed scene text detection and recognition of deep learning-based and conventional machine learning feature extraction methods do not consider the frequency of the input image. Following [21], in this paper, we propose an OctConv with ResNet-50 feature extractor, which extracts features by factorizing based on their frequencies.
## 6 Conclusions  
In this paper, the spatiotemporal evolution process before the Hanyuan landslide is studied based on UAV images, a digital elevation model, and the Sentinel-1 data set. Firstly, the average rate and time series of the landslide along the radar line of sight were obtained by using SBAS-InSAR technology. The results show that the maximum average deformation rate of the landslide along the radar line of sight is 25 mm/a, the maximum cumulative displacement is close to 40 mm, and the deformation is unevenly distributed in space. Then, we used GCD software to extract the elevation changes of the terrain before and after the landslide. Finally, by analyzing factors related to rainfall, human activities, and earthquakes, it was inferred that the Hanyuan landslide was directly caused by short-term heavy rainfall under the influence of multiple factors. This study utilizes SBAS-InSAR technology to significantly enhance the ability of local governments to prevent and control landslides through high-precision and large-scale surface deformation monitoring. The application of this technology can not only reduce the losses caused by landslide disasters but also provide a scientific basis and technical support for local governments' disaster prevention and reduction work.  
Conceptualization, S.X. and X.Z.; data curation, S.X. and X.Z.; formal analysis, S.X. and X.Z.; investigation, S.X. and X.Z.; methodology, S.X. and X.Z.; writing--original draft, S.X.; writing--review and editing, S.X. and X.Z. All authors have read and agreed to the published version of the manuscript.  
Financial support for this study was provided by the National Natural Science Foundation of China (grant No. 41421002 and 41930217).  
Not applicable.  
All data generated or analyzed during this study are included in this published article.  
The authors declare no conflicts of interest.