esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis of iPyroxy Block
### S11 Reflection Coefficient Without iPyroxy Block  
The material's reflective properties are noteworthy, as evidenced by a consistently low reflection coefficient that remains well below \(-10\) dB throughout the operational frequency range. The S11 reflection coefficient, measured without incorporating an iPyroxy block in the composite, demonstrates the material's inherent ability to minimize signal reflection and energy loss (Figure 6). This suggests that the composite, likely comprised of elements such as epoxy resin, lacks significant impedance mismatches that would otherwise lead to signal reflection. The achievement of a reflection coefficient below \(-10\) dB without the iPyroxy block underscores the material's intrinsic characteristics, making it suitable for applications where signal integrity and minimal energy loss are critical considerations. This performance trait enhances the material's viability in various electromagnetic applications, including antennas, wireless communication systems, and other devices where efficient signal transmission is essential across a diverse frequency range.  
Figure 5: (**a**) S11 VNA with iPyroxy block; (**b**) reflection coefficient with iPyroxy block.
## 3 VVCM Evaluation
### Influence of VMA on the Mechanical Properties of LSAM-50 Asphalt Mixture and Design Criteria
#### 4.3.3 VMA Design Criteria  
From the previous discussion, it is clear that VMA has no evident correlation with the mechanical properties of asphalt mixtures. Currently, in the internationally accepted Superpave design method, the guiding role of the VMAmin standard in mixture design and practical engineering application is not significant. The commonly accepted view is that the VMA is directly related to the nominal maximum size of the aggregate in the mixture. However, a unified standard correlating VMA with the performance of the mixture has not been established. Up to now, in most scenarios, China considers VMA as a reference indicator. A relatively relaxed VMA design standard has been established. After the design is completed, the actual VMA of the mixture is evaluated. For mixtures that do not meet the VMAmin standard, appropriate adjustments should be made to their design process.  
In summary, the VMA design standard for LSM-50 mixture is not recommended as a mandatory design indicator. However, it should still be considered as a reference index to constrain its minimum value. The Chinese specification JTG F40-2004 specifies the technical requirements for VMA corresponding to different nominal maximum aggregate sizes in dense graded asphalt mixtures. Based on the VV design standard discussed in this article, the calculated values for VMAmin are presented in Figure 12.  
Figure 11: Mechanical properties of LSM\(-50\) asphalt mixture versus VMA.  
As seen in Figure 12, the VMA\({}_{\text{min}}\) decreases with increasing nominal maximum particle size. When the nominal maximum aggregate size exceeds 13.2 mm, VMA\({}_{\text{min}}\) decreases linearly with the increase in nominal maximum aggregate size. Based on this trend, it can be calculated that when VV is 3.5%, 4.0%, 4.5%, and 5.0%, the VMA\({}_{\text{min}}\) for the LSAM-50 mixture is 7.5%, 8.0%, 8.5%, and 9.0% respectively. Therefore, taking into account both the mechanical properties and practical engineering applications, the design standard for the VMA of the LSAM-50 mixture is set as VMA\({}_{\text{min}}\geq 7.5\%\).
## 3 Material and Methods
### DL-Based Text Classification
#### 3.2.3 LSTM and CRF Model  
This stage enhances sequence labeling and contextual dependency modeling by integrating LSTM networks with CRF layers. The goal is to effectively capture both local and global structures in the text, enabling precise identification of propaganda techniques at the token and sequence levels.  
In our proposed framework, we integrate LSTM networks with CRF to enhance sequence labeling and contextual analysis in propaganda detection. This hybrid approach capitalizes on the strengths of both models, with CRF excelling in structured prediction tasks and LSTM networks providing robust context-aware representations. The CRF component operates as a global model for sequence labeling, efficiently handling flexible features and assigning optimal label sequences to input text. By leveraging probabilistic modeling, the CRF assigns weighted values to input sequences, optimizing the label predictions. This enables the CRF to capture relationships between labels and input features, which is particularly useful for identifying sentence-level propaganda techniques [57]. The ability of the CRF to model dependencies among labels ensures a coherent and accurate labeling process, which is critical in complex classification tasks.  
Figure 5: Embedding (token/word/position) model and transformer 12-layer encoder.  
The labeled outputs from the CRF are then fed into LSTM networks for deeper contextual analysis. LSTMs, with their ability to capture long-term dependencies, are particularly well suited for processing sequential data such as text. In our framework, the LSTM refines the predictions by incorporating context across the sequence, enhancing the overall accuracy and interpretability of the classification results.  
This integration of CRF and LSTM addresses key challenges in propaganda detection by combining the structured prediction capabilities of CRF with the context-aware processing power of LSTM networks. By focusing on the synergy between these models, our approach represents a significant advancement over traditional sequence labeling methods, ensuring both scalability and robustness in handling complex multi-label classification tasks. This hybrid model is a cornerstone of our methodology, emphasizing its role in advancing propaganda detection within COVID-19-related public discourse.
# Closed Form Solutions for Thermal Buckling of Functionally Graded Rectangular Thin Plates
## 3 Closed Form Solutions of Thermal Buckling
### Case 1 Temperature-Independent Homogeneous Thin Plates  
For homogeneous isotropic plates, the material properties are independent of geometry and temperature. According to Equation (14), \(\hat{G}_{0}\) can be written as  
\[\hat{G}_{0}=\frac{-G_{0}}{D_{11}+G_{2}-\frac{\left(B_{11}+G_{1}\right)^{2}}{A _{11}+G_{0}}}=\frac{(1+\
u)\alpha\Big{(}\int_{-h/2}^{h/2}T\text{d}z-T_{0}h \Big{)}}{\frac{h^{3}}{12}-(1+\
u)\alpha\Big{(}\int_{-h/2}^{h/2}Tz^{2} \text{d}z-\frac{T_{0}h^{3}}{12}\Big{)}} \tag{33}\]  
then, the analytical critical temperature can be solved from Equation (31). For a uniform temperature field, it is  
\[\Delta T_{\text{cr,uni}}=\frac{\hat{G}_{0\text{cr}}h^{2}}{\alpha(1+\
u)\big{(} \hat{G}_{0\text{cr}}h^{2}+12\big{)}} \tag{34}\]  
where \(\Delta T_{\text{cr}}=T_{\text{cr}}-T_{0}\); for a linear temperature field, it is  
\[\Delta T_{\text{cr,line}}=\frac{2\hat{G}_{0\text{cr}}h^{2}}{\alpha(1+\
u) \big{(}\hat{G}_{0\text{cr}}h^{2}+12\big{)}} \tag{35}\]  
It can be seen that, for the temperature-independent homogeneous isotropic thin plates, we have  
\[\Delta T_{\text{cr,line}}=2\Delta T_{\text{cr,uni}} \tag{36}\]  
The nonlinear temperature field does not exist for homogeneous isotropic plates since the thermal conductivities at the top and bottom surfaces are the same. Substituting \(k_{\text{t}}=k_{\text{b}}\) into Equation (23), we can find that the temperature distribution of the nonlinear temperature field reduces to the linear temperature field.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**B.C.** & **Eigenvalue Equations for \(y=0\) and \(b\)** & **Eigenvalue Equations for \(x=0\) and \(a\)** \\ \hline S-S & \(\sin\beta_{1}b=0\) & \(\sin\alpha_{1}a=0\) \\ G-G & \(\sin\beta_{1}b=0\) & \(\sin\alpha_{1}a=0\) \\ C-C & \(\frac{1-\cos\beta_{1}b\cosh\beta_{2}b}{\sin\beta_{1}b\sinh\beta_{2}b}=\frac{ \beta_{1}^{2}-\beta_{2}^{2}}{2\beta_{1}\beta_{2}}\) & \(\frac{1-\cos\alpha_{1}a\cosh\alpha_{2}a}{\sin\alpha_{1}a\sinh a_{2}a}=\frac{ \alpha_{1}^{2}-\alpha_{2}^{2}}{2\alpha_{1}a_{2}}\) \\ S-G & \(\cos\beta_{1}b=0\) & \(\cos\alpha_{1}a=0\) \\ S-C & \(\beta_{2}\tan\beta_{1}b=\beta_{1}\text{tanh}\beta_{2}b\) & \(\alpha_{2}\tan\alpha_{1}a=\alpha_{1}\text{tanh}a_{2}a\) \\ G-C & \(\beta_{1}\tan\beta_{1}b=-\beta_{2}\text{tanh}\beta_{2}b\) & \(\alpha_{1}\tan\alpha_{1}a=-\alpha_{2}\text{tanh}a_{2}a\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Eigenvalue equations for various boundary conditions.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 4 Dimensioning Tools: Interference and Fading Characterization
### Single-User: Case of Single Antenna Receiver  
In this subsection, we review first from [9] the characterization of the Laplace transform of interference and the distribution of the fading in the SISO case averaged on the random noise plus interference.
## 2 Materials and Methods
#### 2.1.6. MABAC (Multi-Attributive Border Approximation Area Comparison) Technique  
Developed by [PERSON] and [PERSON] in 2015, MABAC evaluates decision options by taking into account the distance of the criteria to the border proximity areas [70]. MABAC is an MCDM technique that aims to select the best alternative in a problem with many criteria in institutional and individual decision-making processes. MABAC is implemented with six steps by using Equations (30)-(37) [70; 71].  
**Step 1. Creating the decision matrix** The matrix X in Equation (1), which consists of m rows and n columns, is created.  
**Step 2. Normalizing the decision matrix** The normalized matrix is derived from decision matrix (1) using Equation (31) for maximization-oriented criteria and Equation (30) for minimization-oriented criteria.  
\[r_{ij}=\frac{X_{ij}-X_{j}^{min}}{X_{j}^{max}-X_{j}^{min}} \tag{30}\]  
\[r_{ij}=\frac{X_{ij}-X_{j}^{max}}{X_{j}^{min}-X_{j}^{max}} \tag{31}\]  
**Step 3. Weighting the normalized matrix**: The normalized matrix is weighted with Equation (32). In this context, the normalized matrix is multiplied by the weights (\(w_{j}\)) obtained by the PSI method.  
\[V_{ij}=W_{j}*\left(1+r_{ij}\right) \tag{32}\]  
**Step 4. Obtaining the boundary proximity matrix**: Using Equation (33), the boundary proximity area matrix (G) shown in Equation (34) is obtained.  
\[g_{i}=\left(\prod_{i=1}^{m}V_{ij}\right)^{\frac{1}{m}} \tag{33}\]  
\[G=\left[g_{i}\right]_{1*g} \tag{34}\]  
**Step 5. Determining the distances of the alternatives from the boundary proximity values:** These distances are calculated using Equations (35) and (36).  
\[Q=(v_{i}-G)=\begin{bmatrix}v_{11}-g_{1}\ v_{12}-g_{2}&\ldots&v_{1n}-g_{n}\\ v_{21}-g_{1}\ v_{22}-g_{2}&\cdots&v_{1n}-g_{n}\\ \ldots&\ldots&\ldots&\ldots\\ v_{m1}-g_{2}\ v_{m2}-g_{2}&\cdots&v_{mm}-g_{n}\end{bmatrix}=\begin{bmatrix}q_ {11}\ q_{12}&\ldots&q_{1m}\\ q_{21}\ q_{22}&\cdots&q_{2m}\\ \vdots&\ddots&\vdots\\ q_{n1}\ q_{n2}&\cdots&q_{nm}\end{bmatrix} \tag{35}\]  
\[A_{i}\ \in\ \begin{cases}G^{+}\ if\ q_{ij}>0\\ G\ if\ q_{ij}=0\\ G\ if\ q_{ij}<0\end{cases} \tag{36}\]  
**Step 6. Ranking the alternatives:** The alternatives specified in the decision matrix X are ranked using Equation (37).  
\[S=\sum_{j=1}^{n}q_{ij} \tag{37}\]  
The process is completed by sorting the \(S_{i}\) values of the options from biggest to smallest.
## 4 Analysis of the Relationship Between the Number of Input Ground Motions and Seismic Fragility
### Example Facility: 13-Story Commercial Building
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Performance Level**} & \multicolumn{4}{c}{**Median (g)**} & \multicolumn{2}{c}{**Difference**} \\ \cline{2-6}  & **Ref** & **Min** & **Max** & **Avg** & **(\%)** \\ \hline
1—IO & 0.4137 & 0.3838 & 0.4451 & 0.4138 & 0.0168 \\
2—LS & 1.1322 & 1.0597 & 1.2098 & 1.1324 & 0.0156 \\
3—CP & 1.2440 & 1.1615 & 1.3226 & 1.2442 & 0.0156 \\ \hline \multirow{2}{*}{Performance Level} & \multicolumn{4}{c}{Log-standard deviation} & Difference \\ \cline{2-6}  & Ref & Min & Max & Avg & (\%) \\ \hline
1—IO & 0.9013 & 0.8460 & 0.9562 & 0.9011 & 0.0272 \\
2—LS & 0.8779 & 0.8208 & 0.9311 & 0.8776 & 0.0238 \\
3—CP & 0.8568 & 0.8058 & 0.9060 & 0.8567 & 0.0197 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Range of seismic fragility curve parameters of the 13-story building (N = 350). As the number of ground motions considered in deriving the seismic fragility curves increases, the overall difference from the reference values decreases despite slight differences for each performance level. This indicates that increasing the number of ground motions reduces the uncertainty in selecting the ground motions for fragility curve generation. Similar to the six-story example, Figure 13 illustrates the differences between the mean values of each fragility input variable and the reference values for the three performance levels. Figure 12: Seismic fragility curves and histograms of the 13-story building (N = 30). (**a**) Performance Level 1; (**b**) Performance Level 2; (**c**) Performance Level 3. Figure 13: No. of ground motions vs. difference of the 13-story building. (**a**) Performance Level 1; (**b**) Performance Level 2; (**c**) Performance Level 3. When deriving the seismic fragility curve for the 13-story example facility, the median and log-standard deviation values decreased as the number of ground motions considered increased when compared to the reference values. In cases with fewer than 30 ground motions, the rate of change was substantial in both example facilities. However, when 30 or more ground motions were used, the differences decreased at a relatively smaller rate as the number of ground motions increased.
## 3 Results
### Biomarkers Analysis
#### 3.3.1 Insulin Levels
\begin{table}
\begin{tabular}{c c} \hline \hline
**General Characteristics** & **N** \\ \hline
**Participants (total)** & 24 \\ _Men_ & 11 \\ _Women_ & 13 \\ _Age (years)_ & 31.23 \(\pm\) 9.8 \\
**Anthropometric characteristics** & **Mean \(\pm\) SD** \\ _Weight (kg)_ & 73.48 \(\pm\) 19.24 \\ _Height (cm)_ & 171.24 \(\pm\) 9.45 \\ _BMI (kg/m\({}^{2}\))_ & 24.71 \(\pm\) 4.76 \\ _FAT (\%)_ & 25.59 \(\pm\) 6.08 \\ _Muscle Mass (kg)_ & 51.43 \(\pm\) 12.28 \\ _TBW (\%)_ & 51.86 \(\pm\) 3.69 \\ _BM (kcal/day)_ & 1633.29 \(\pm\) 368.37 \\ _WHR_ & 0.80 \(\pm\) 0.09 \\
**General habits** & N \\ Smoking & \\ _Smokers_ & 9 \\ _Non-smokers_ & 15 \\ Physical activity _Low_ & 14 \\ _Medium_ & 6 \\ _High_ & 4 \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline  & **INS** & **INS** \\
**(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** \\
**at Baseline (t1)** & **at 1 h (t2)** & **at 2 h (t3)** & **at 4 h (t4)** \\ \hline \hline \multirow{3}{*}{CTRL} & \multirow{3}{*}{\(11.570\pm 1.084\)} & \multirow{3}{*}{\(31.250\pm 3.311\)} & \multirow{3}{*}{\(25.353\pm 3.936\)} & t1 & 9.328 & 13.812 & \multirow{3}{*}{\(13.812\)} \\  & & & t2 & 24.401 & 38.099 & \\ \cline{3-8}  & & & t3 & 17.211 & 33.495 & \\ \cline{3-8}  & & & t4 & 7.778 & 11.530 & \\ \hline \multirow{3}{*}{OLT} & \multirow{3}{*}{\(10.387\pm 0.642\)} & \multirow{3}{*}{\(24.614\pm 2.418\)} & \multirow{3}{*}{\(31.513\pm 3.383\)} & \multirow{3}{*}{\(9.840\pm 0.879\)} & t1 & 9.058 & 11.715 \\ \cline{3-8}  & & & t2 & 19.612 & 29.616 & \\ \cline{3-8}  & & & t3 & 24.515 & 38.510 & \\ \cline{3-8}  & & & t4 & 8.020 & 11.659 \\ \hline \multirow{3}{*}{OLB} & \multirow{3}{*}{\(10.158\pm 0.757\)} & \multirow{3}{*}{\(26.607\pm 3.356\)} & \multirow{3}{*}{\(29.773\pm 4.471\)} & \multirow{3}{*}{\(11.330\pm 1.529\)} & t1 & 8.593 & 11.724 \\ \cline{3-8}  & & & t2 & 19.665 & 33.550 & \\ \cline{3-8}  & & & t3 & 20.524 & 39.021 & \\ \cline{3-8}  & & & t4 & 8.167 & 14.493 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: General characteristics of participants at baseline. Although graphical trends suggested differences in the timing and magnitude of the insulin peak between groups, the group-by-time interaction was not statistically significant (\(p=0.054\)). Overlapping 95% confidence intervals across groups (e.g., t3: CTRL, OLT [24.515, 38.510], OLB [20.524, 39.021]) further indicate the absence of significant differences. Overall, these results demonstrate a transient postprandial increase in insulin followed by a return to baseline, with similar temporal patterns across groups. A possible delay in the insulin peak observed in the OLT and OLB groups compared to CTRL warrants further investigation into future studies.
## 3 Experimental Results and Discussion
### Security Analysis
#### 3.2.3 Correlation Analysis of Two Adjacent Pixels  
Correlation is a measurement tool for the relationship between two variables or pixels in an image; in fact, adjacent pixels of an original image are highly correlated in the horizontal, vertical, and diagonal directions [11]. Therefore, correlation in a cipher image should be sufficiently low to demonstrate that the proposed encryption algorithm can resist statistical attacks.  
In total, 3000 pairs of adjacent pixels in each direction were randomly chosen from a plain image and a cipher image, so the correlation coefficient of each pair could be calculated using the following formulas [9]:  
\[\begin{array}{c}E(x)=\frac{1}{N}\sum\limits_{i=1}^{N}x_{i},\\ D(x)=\frac{1}{N}\sum\limits_{i=1}^{N}(x_{i}-E(x_{i}))^{2},\\ \mathrm{cov}(x,y)=\frac{1}{N}\sum\limits_{i=1}^{N}\ (x_{i}-E(x_{i}))(y_{i}-E(y_{i})), \\ r_{xy}=\frac{\mathrm{cov}(x,y)}{\sqrt{D(x)}\sqrt{D(y)}},\end{array} \tag{13}\]  
where \(x\) and \(y\) are gray values of two adjacent pixels in the image. Figures 11 and 12 show the correlation of two adjacent pixels. The high correlation of adjacent pixels from the original image in three different directions can be observed on the left-hand side in Figures 11 and 12; every dot is located linearly along the diagonal, whereas in the cipher image, every point is scattered on the entire plane. Thus, the correlation between adjacent pixels was reduced after applying the encryption process.  
Figure 10: Lena image decryption. (**a**) Decrypted cipher image with different initial conditions; (**b**) histogram of (**a**).  
Table 4 indicates the correlation coefficient values. Results showed that the correlation coefficient was very close to zero in the ciphered image, highlighting the fact that the proposed encryption algorithm is secure and that the final result had nonpredictable tendency.  
Figure 11: Correlation of two adjacent pixels in the Lena image.  
Figure 12: Correlation of two adjacent pixels in the [PERSON] image.  
Furthermore, we compared our proposed method with other algorithms proposed in the past using the Lena image (\(256\times 256\)) as a reference. Table 5 displays the comparison and concludes that the proposed system completely destroyed correlation between pixels. In addition, other larger images were added to the comparison to expand the scope of possibilities regarding the size of an image. Values close to zero showed the current system achieved the main goal in both large and small images.
# On the Needs and Requirements Arising from Connected and Automated Driving
## 5 Relationship to 3 GPP KPIs and the Role of 5 GAA
Both of the values are higher in the 5 GCAR use case, due to the need for better video quality in order to allow for the engagement of the driver with the specific service. * In 5 GCAR, the **HDLM** description assumes the existence of an off-board system (e.g., application server) that is used to gather and process all of the information collected from different sources available on and along the road. In 3 GPP, there is a similar use case entitled, sensor and state map sharing (SSMS) [20], but the focus is on the exchange of raw or processed sensor data among vehicles to build collective situational awareness. Hence, these two use cases follow different approaches for building and updating the in-vehicles maps. * **Network-assisted VRU protection** use case that has been described in 5 GCAR is not included in the list of 3 GPP use cases [20]. Finally, in the 5 GCAR project, the Remote Driving use case has been specifically described for Automated Parking, in order to limit the scope of the investigation so that the results of this use case evaluation could be achieved within the project duration. Although it takes place in a specific road environment (i.e., parking area), the performance requirements proposed by the 5 GCAR project are similar to the ones discussed in 3 GPP, which theoretically considers all types of road environments and longer period of time for remote driving.
# Robust Analysis and Laser Stripe Center Extraction for Rail Images
## 4 Conclusions  
A robust, fast, and accurate light stripe extraction method for rail images was proposed in the present work. Via the self-adaptive multi-threshold segmentation method, the light  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Noise Parameter \(\sigma\)** & **UM** & **Steger** & **The Proposed Method** \\ \hline
0.1 & 0.0045 & 0.4230 & **0.0009** \\
0.15 & 0.0051 & 0.4231 & **0.0015** \\
0.2 & 0.0057 & 0.4230 & **0.0019** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative results of detection accuracy.  
Figure 11: The synthetic image for the accuracy test.  
stripe can be completely extracted from the background without the interference of variable illumination and reflection. Additionally, the image quality is improved by the proposed image enhancement method. Moreover, the light stripe is segmented into several parts via the geometry of the rail profile, which simplifies the procedure of light stripe segmentation. Piecewise fitting based on polynomials makes the centerline smoother, and is a fast process. The results of the conducted experiments validate the performance of the proposed method, and center-points of different rail images were successfully extracted. For rail profile stripes with different widths and brightness, the proposed method can obtain comparable robustness with the UM method, and its average run time is only about one-fourth and one-fifth of the Steger and UM methods, respectively. Besides, the proposed method can achieve higher center extraction accuracy level for synthetic images. Consequently, the proposed method is suitable for the light stripe extraction of rail profile images.  
Methodology, investigation, validation, writing original draft preparation, H.G.; review, supervision, funding acquisition, G.X. 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 (2018 YFB2003803), National Key R&D Program of China (2017 YFB107300), National Key R&D program of China (2016 YFB1102600), National Natural Science Foundation of China (62073161), and Starting Fund for New Teachers of Nanjing University of Aeronautics and Astronautics (YAH18066).  
The image data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.