esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### FT-IR Spectroscopic Analysis  
The FT-IR spectra obtained for the coal samples before and after extraction are shown in Figure 1, showing that the spectra obtained from different methods of extraction are broadly similar in shape (i.e., the positions of absorption bands remain the same). However, the relative intensities of the absorption bands vary widely and all the major bands obviously stem from organic functional groups in coal samples. The absorption bands in the spectra were assigned according to the standard method in the literature [19; 20]. In Figure 1, the absorption band at 3400-3350 cm\({}^{-1}\) is assigned to the O-H stretching. The relatively weak band at 3080-3035 cm\({}^{-1}\) in Figure 1 is due to the vibrational absorption from C-H bonds in aromatic rings. Clearly, in the SE process, more small molecules main network than that in the other extraction processes, showing a poor extraction degree in the Soxhlet process. A broad band between 2975 and 2848 cm\({}^{-1}\), which is due to aliphatic -CH-, -CH2-, and -CH3 vibrations, is persisting for all the samples. The bands among 1745-1695 cm\({}^{-1}\) are caused by aliphatic and aromatic carbonyl groups. Apparently, the carbonyl groups are essentially absent in raw coal and in residues after SE. The broad band around 1615-1585 cm\({}^{-1}\) is assigned to the stretching vibration of C=C in aromatic compounds. The absorption peak of the extracted residual coal obtained by microwave and ultrasonic is enhanced at about 1600 cm\({}^{-1}\), indicating that there are more aromatic structures in the extracted residual coal. The results also show that more small molecular substances are dissolved in the extraction process, which emphasizes the macromolecular structure of the coal. On the other hand, the absorption bands between 900 and 700 cm\({}^{-1}\), which are assigned to C-H vibrations in aromatic compounds, can be observed for all the samples. The above observations suggest that all three extraction processes only destroy the weak interactions between small molecules and the network structure of macromolecules in coal, but do not break down the macromolecular structure of coal. The band intensities obtained for the residues after UAE and MAE are enhanced, while those from SE are remaining about the same.
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 6 Experimental Evaluation
### Analysis of Quantum Key Generation  
The distance between the main site and the branch site is about 33.6 km, and the outdoor temperature is 5 \({}^{\circ}\)C. When the QKD network is started, initialization work such as basis comparison is needed. In this process, [PERSON] and [PERSON] choose to keep the part of the data with the same basis and discard the data with different bases. After the basis comparison, [PERSON] and [PERSON] count the number of signal states, decoy states, and vacuum states and then obtain the corresponding detection rate. Let the optical error rate caused by the imperfect optical calibration of the system be \(e_{opt}\) and the detection rates of the signal state, decoy state, and vacuum state be \(S\), \(D\), and \(V\), respectively, then the error rates of signal state and decoy state are \(e_{s}=e_{opt}+0.5\times V/_{S}\), \(e_{D}=e_{opt}+0.5\times V/_{D}\). The calculation results are shown in Figure 9. QKD runs stably for 50 min; the signal state error rate is about 0.92%, and the decoy state error rate is about 2.07%.  
Figure 8: The video conference system network topology based on the quantum key.  
Figure 9: The quantum key error rate.  
In the QKD process, [PERSON] and [PERSON] generate different sifted keys due to noise such as channel interference, optical adjustment problems, and dark counting of detectors. As shown in Figure 10, the key rate of sifted keys is about 24.57 kbps. In order to ensure the correct encryption and decryption of data, the sifted keys are corrected. After processing, [PERSON] and [PERSON] generate the same final key. The final key is the symmetric key for information encryption and decryption, and the quantum key rate is about 3.64 kbps. The key rate after error correction is lower than that of the sifted key, and the quantum key is stored in the QKME.  
In order to observe the relationship between the quantum key rate and the storage space occupied by the image, this paper lists the memory space occupied by single-frame images with \(160\times 120\), \(320\times 240\), \(640\times 480\), \(800\times 600\), \(1024\times 768\), when the color depth is 3, 8, and 16 bits. As shown in Table 2, the image with a resolution of \(160\times 120\) and a color depth of 3 bits occupies the smallest space. The higher the resolution and the greater the color depth, the larger the space the image takes up. In order to ensure the stable and clear operation of the system, the resolution of this system is \(320\times 240\). In the quantum key generation, the quantum key rate is about 3.64 kbps, which is far lower than the storage space occupied by the image. Therefore, the QKD network needs to run for a long time and store enough quantum keys.
# Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
## 2 Materials and Methods
### Subjects and Datasets  
Inclusion and exclusion criteria were similar for the three studies, however, there were some differences (see Appendix B). Demographic and clinical features of subjects are shown in Table 2.  
The studies assessed a wide range of information, including demographics, clinical information (such as clinical stage, and the presence of metastatic disease), physical characteristics (such as weight), and concomitant medications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Study 1** & **Study 2** & **Study 3** \\ \cline{3-5}  & & **(_n_ = 1625_)** & **(_n_ = 1440_)** & **(_n_ = 588)** \\ \hline \multirow{5}{*}{**Demographics**} & \multirow{5}{*}{Mean age (years)} & 64 & 69 & 68 \\ \cline{2-5}  & & Cauc 84\% (1372) & Cauc 95\% (1365) & Cauc 99\% (584) \\ \cline{2-5}  & & Black 11\% (188) & Black 1\% (13) & Black 0\% (0) \\ \cline{2-5}  & & Race & Asian 0.7\% (12) & Asian 0.5\% (7) & Asian 0\% (0) \\ \cline{2-5}  & & Hispanic 3\% (46) & Hispanic 2\% (25) & \begin{tabular}{c} Hispanic 0.3\% \\ (2) \\ \end{tabular} \\ \cline{2-5}  & & Other 0.4\% (7) & Other 2\% (31) & Other 0.3\% (2) \\ \hline \multirow{5}{*}{**Clinical features**} & \multirow{5}{*}{\
\begin{tabular}{c} Clinical stage \\ \end{tabular} } \\ \cline{2-5}  & & Category 1 & 0.2\% (3) & 0.1\% (1) & 0\% (0) \\ \cline{2-5}  & & Category 2 & 1\% (23) & 11\% (153) & 7\% (44) \\ \cline{2-5}  & & Category 3 & 8\% (131) & 17\% (245) & 15\% (89) \\ \cline{2-5}  & & Category 4 & 63\% (1028) & 38\% (550) & 39\% (229) \\ \cline{2-5}  & & Category 5 & 27\% (440) & 31\% (446) & 36\% (213) \\ \cline{2-5}  & & Category 6 & 0\% (0) & 3\% (45) & 2\% (13) \\ \cline{2-5}  & &
\begin{tabular}{c} Metastatic node \\ disease \\ \end{tabular} & 28\% (462) & 3\% (41) & 4\% (24) \\ \cline{2-5}  & & Gleason score & & \\ \cline{2-5}  & & category & & \\ \cline{2-5}  & & Category 1 & 5\% (78) & 33\% (477) & 44\% (256) \\ \cline{2-5}  & & Category 2 & 48\% (788) & 40\% (574) & 45\% (266) \\ \cline{2-5}  & & Category 3 & 47\% (759) & 25\% (363) & 11\% (63) \\ \cline{2-5}  & & Category 4 & 0\% (0) & 2\% (26) & 0.5\% (3) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Demographics, and clinical features of subjects, based on the subjects available for final analysis.
## 4 Verification of Evaluation Methods
### Outline of Previous Experiments
In addition, the cracks increase when the tendon length becomes shorter, as can be seen in PCJ05, because the bending moment at the boundary of RC zone is larger. The maximum crack width at the maximum peak drift angle of 4% was just 0.2 mm, and the maximum residual crack width at the unloaded state was less than 0.06 mm. This suggests that even when the PC connection has experienced considerably large deformation under strong earthquakes, the postearthquake residual cracks could be very slight compared to the monolithic connections, reflecting the low-damage advantage of the PC connections. Figure 13 shows the beam shear force (\(P_{b}\))-beam deflection angle (\(R_{b}\)) relationship obtained from the tests. In the figure, the ultimate flexural points, which are the maximum strength points, and the yielding points of the tendons are plotted. From the test results, for PCJ05 with shorter tendon, the maximum strength point and the tendon yielding point occurred at the smaller deflection angle. When the initial prestressing force became lower (PCX01 and PCX02), the maximum strength and yielding of the tendons occurred at the larger deflection angles. Moreover, if the tendon amount was reduced (PCX02), the maximum strength decreased. It can be also seen in Figure 13 that the residual deformation gradually increased after the yielding of tendons, so setting the tendon length is very crucial to guarantee the self-centering behavior, especially for the case of debonded partial tendons. Figure 12: Crack maps: (**a**) PCJ05; (**b**) PCJ06. Figure 13: Load–deflection curves of PC beams and experiment–calculation comparisons: (**a**) PCX01; (**b**) PCX02; (**c**) PCJ05; (**d**) PCJ06. The dashed lines represents the evaluated values of \(P_{b,u}\) and \(R_{b,u}\).
# A Calibration Method for Time Dimension and Space Dimension of Streak Tube Imaging [PERSON]
## 3 Calibration Method
### Angle Calibration Experiment
#### 3.2.2 Data Processing for Angle Calibration  
A calibration array is also needed, as with the time calibration. At first, we should calculate the centroid coordinates of the laser-point streak detected in Experiment II. The centroid coordinate is expressed by \((x,y)\)  
\[\begin{array}{l}x=\frac{\sum_{p=1}^{P}\left(x_{p}\times I_{p}\right)}{\sum_ {p=1}^{P}I_{p}}\\ y=\frac{\sum_{p=1}^{P}\left(y_{p}\times I_{p}\right)}{\sum_{p=1}^{P}I_{p}}\end{array} \tag{4}\]  
where \(P\) is the number of all pixels of the CCD (655,360, i.e., 1280 \(\times\) 512, in this work), \(I_{p}\) is the gray value of the \(p\)th pixel on the streak image, \(x_{p}\) is the horizontal coordinate of the \(p\)th pixel, and \(y_{p}\) is the vertical coordinate of the \(p\)th pixel. In this experiment, the horizontal axis is the time axis, and the vertical axis is the spatial axis. The horizontal and vertical coordinates are the sequence number of the pixel on the time-resolved axis and the sequence number on the space-resolved axis. The calculation results are shown in Figure 9. Different from Equation (1), which is the centroid of a row, Equation (4) is the centroid of the whole image. Moreover, threshold denoising was required for the image before extracting the centroid.  
Similarly to Equation (3) and Figure 7, we took the average of the horizontal and vertical coordinates of all centroids under the same delay and angle to reduce the calibration error, and one of the averages is shown in Figure 10. The black points are the centroids, and the green star is the average of the centroids.  
After taking all of the average values under every delay and angle, we obtained a series of points that filled the entire area of interest, as shown in Figure 11. We can find that the arrangement of these points was not horizontal or vertical, and the spacing between points was also different because of optical and electronic imaging diffusion and distortion. This phenomenon explains why STIL requires calibration.  
Figure 11 shows the result after thinning because an overly dense point array is not conducive to observing the arrangement phenomenon described in the previous paragraph. However, during the actual calibration process, we aimed to obtain a denser matrix by two-dimensional interpolation, such that a point that falls at any position on CCD can be indexed to its angle value. We found that these points had both angle and time information, but we did not use these points for time calibration because the light source was an auxiliary laser rather than the laser of the lidar. There is a time jitter that cannot be ignored between the auxiliary laser and the lidar.  
Figure 9: The centroid of the streak of the laser point in Experiment II.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 3 Model
### Antenna Model [22]  
Table 3 illustrates the relationship between coverage radius and antenna gain. As shown in the table, beams with a smaller coverage radius exhibit a higher concentration  
of energy and consequently greater antenna gain, while the opposite is true for larger coverage radii.  
The normalized antenna gain calculation formula is:  
\[G(\theta)=10\times\lg(|\frac{\sin(N\times\pi\times\sin(\frac{\theta}{2}))}{N \times\sin(\pi\times\sin(\frac{\theta}{2}))}|) \tag{10}\]  
Based on Equation (10), the antenna radiation patterns of the three beams are visualized, and the corresponding results are presented in Figure 2.
# A Simple Model for Estimating the Hydraulic Conductivity of Unsaturated Soil
## 5 Conclusions  
A simple model for estimating the hydraulic conductivity of unsaturated soil has been proposed in the present study, and the main conclusions are summarized below.  
1. Depending on the different soil-water interaction mechanisms, the soil-water characteristic curve and the hydraulic conductivity curve can be divided into three segments: the boundary effect zone, transition zone and adsorptive dominant region, where the air-entry value and residual suction are two inflection points.
2. A simple hydraulic conductivity model for unsaturated soil composed of three straight lines has been proposed. Model parameters can be conveniently calibrated by the measured SWCC and the saturated hydraulic conductivity.
3. The estimated results, in comparison with the corresponding experimental data, have shown that the proposed model performs well in the estimation of the hydraulic conductivity of different types of soils over a wide suction range.
4. Compared with existing approaches, the proposed hydraulic conductivity model has a simple form, and it is unnecessary to calibrate the scaling parameter by the measured unsaturated hydraulic conductivity curve.  
Conceptualization, Z.Z. and M.Z.; methodology, Z.Z.; validation, Z.Z.; formal analysis, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, M.Z.; supervision writing, M.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Basic Research Program of Shaanxi, grant number 2022 JM-167.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
We are grateful for the educational and scientific resources provided by Chang'an University.  
The authors declare no conflicts of interest.
# Verifiable (2, \(n\)) Image Secret Sharing Scheme Using Sudoku Matrix
###### Abstract  
As Internet technology continues to profoundly impact our lives, techniques for information protection have become increasingly advanced and become a common discussion topic. With the aim to protect private images, this paper splits a secret image into \(n\) individual shares using a Sudoku matrix with authentication features. Later, the shares can be compiled to completely reconstruct the secret image. The shares are meaningful ones in order to avoid detection and suspicion among malicious users. Our proposed matrix is unique because the embedding rate of the secret data is very high, while the visual quality of the shares can be well guaranteed. In addition, the embedded authentication codes can be retrieved to authenticate the integrity of the secret image. Experimental results prove the advantages of our approach in terms of visual quality and authentication ability.  
Sudoku; secret image sharing; data hiding; image authentication +
Footnote †: journal: Computer Science and Information Engineering, Tamkang University, New Taipei City 251301, Taiwan 2
## 2 Literature Review
A comparison of existing state-of-the-art works is shown in Table 1. The proposed methods in [30; 31; 11; 32] provide effective navigation support in supermarket environments; however, they rely heavily on electronic hardware such as radio-frequency identification (RFID) technology, in-store Internet-of-Things (IoT) devices, and ultra-wideband (UWB) technologies. While Refs. [30; 31] integrate these hardware modules with machine learning methods to enhance store navigation, product selection, and localization, these approaches face challenges in scalability and adaptability within dynamic large-scale retail supermarket environments. In contrast, the ML-DProSA algorithm innovatively combines machine learning principles with dynamic proximity-driven sorting algorithm (DProSA). This approach leverages real-time proximity data between products and advanced machine learning techniques to dynamically adapt and optimize shopping list management. By enhancing navigation efficiency and refining product selection through situational adaptive sorting, it marks a significant advancement in retail technology. \begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**Authors** & **Key Contributions** & **Optimization** & **ML-Based Sorting** & \begin{tabular}{l} **Simulation** \\ **Environment** \\ \end{tabular} & \begin{tabular}{l} **Dynamic** \\ **Proximity** \\ \end{tabular} \\ \hline [PERSON] et al. [11] & \begin{tabular}{l} Real-time location tracking \\ based on UWB in an indoor \\ environment \\ \end{tabular} & No & No & Yes & No \\ \hline [PERSON] et al. [30] & \
\begin{tabular}{l} ReQL-Net algorithm-based \\ store navigation \\ \end{tabular} & Yes & Yes & Yes & No \\ \hline [PERSON] et al. [31] &
\begin{tabular}{l} Machine learning- \\ based Real-Time and \\ Item-Level (RETaIL) \\ indoor localization system \\ \end{tabular} & No & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of existing state-of-the-art work.
# A Performance Analysis of VANETs Propagation Models and Routing Protocols
## 3 Proposed Work
### Propagation Models
#### 3.1.2 Two-Ray Ground Propagation Model  
This model is one of the most common models used in VANETs for multipath radio propagation and is also an ideal model for many wireless applications. As stated in [42], this model has a more practical implication than FRIS as it considers the direct path and the ground reflected direction. It predicts the path loss between the transmitter and receiver antenna within the Line-of-Sight (LOS) with nodes present in a planar structure.  
\[P_{r}=\frac{P_{t}G_{t}G_{r}\big{(}H_{t}^{2}*H_{r}^{2}\big{)}}{d^{4}*L} \tag{2}\]  
The reception power, the transmitter and receiver antenna's height, and the distance through which the antenna's receive and transmit are signified by \(P_{r}\), \(H_{t}\) and \(H_{r}\), and \(d\), respectively. \(G_{t}\) and \(G_{r}\) denotes the antenna's gain of receiving and the transmitting, respectively, with \(L\) as system Loss. An oscillation parameter is induced in this model by the ray's constructive and destructive combination, making it unsuitable for shorter distances.  
Figure 3: (**a**) FRIS and (**b**) Two-Ray Ground propagation models [44].
# Stochastic Models to Qualify Stem Tapers
## 2 Materials and Methods  
### SDE Stem Tapers  
This study focuses on univariate continuous diffusion processes \(Y^{i}(x)\), \(i=1,\ldots,M\), evolving in \(M\) different individuals (tree stems). We suppose that the evolution of the relative diameter \(Y^{i}=\frac{D}{d^{i}}\) against the relative height \(x^{i}=\frac{h}{h^{i}}\) (\(x^{i}\in[0;1]\), in the sequel \(x\)) is expressed by the Ito-type stochastic differential equation [29], where \(D\) is the diameter at any specified height \(h\), \(d^{i}\) is the diameter at breast height of the i-th tree, and \(h^{i}\) is the total stem height of the i-th tree. In the following, we will consider three different univariate diffusion models given by the SDEs with explicitly known transition distributions. The Gompertz-type stochastic differential equation of the relative diameter against the relative height is formulated as follows:  
\[dY^{i}(x)=[\alpha_{1}Y^{i}(x)-\beta_{1}Y^{i}(x)\ln(Y^{i}(x))]dx+\sigma_{1}Y^{ i}(x)dW^{i}_{1}(x),\ i=1,\ldots,M, \tag{1}\]  
where \(\alpha_{1}\), \(\beta_{1}\), and \(\sigma_{1}\) are fixed effects parameters (identical for all stems from the specified tree species) and \(y^{i}_{0}\) is the initial relative diameter. In this study, we focus on two initial distributions, namely, the deterministic initial value \(Y^{i}(0)=y^{i}_{0}\), which is a degenerate initial distribution \(P\big{(}Y^{i}(0)=y^{i}_{0}\big{)}=1\), and a log-normal distribution, \(Y^{i}(0)\sim LN_{1}\big{(}\mu_{0};\sigma_{0}^{2}\big{)}\). Here, \(W^{i}_{1}(x)\), \(i=1,\ldots,M\) represents independent standard Brownian motions. The diffusion process, \(Y^{i}(x)\), conditioned on the initial value \(y^{i}_{0}\), has a log-normal distribution where \(LN_{1}\big{(}\mu_{1}(x|\alpha_{1},\beta_{1},\sigma_{1},y^{i}_{0});v_{1}(x| \beta_{1},\sigma_{1})\big{)}\) with the conditional mean, variance, and density, respectively. \[\mu_{1}(x|\alpha_{1},\beta_{1},\sigma_{1},y^{i}_{0})=e^{-\beta_{1}x}\ln(y^{i} _{0})+\left(\frac{1-e^{-\beta_{1}x}}{\beta_{1}}\right)\left(\alpha_{1}-\frac {\sigma_{1}^{2}}{2}\right) \tag{2}\]  
\[v_{1}(x|\beta_{1},\sigma_{1})=\frac{\sigma_{1}^{2}}{2\beta_{1}}\big{(}1-e^{-2 \beta_{1}x}\big{)}, \tag{3}\]  
\[p_{G}\big{(}y,x|\alpha_{1},\beta_{1},\sigma_{1},y^{i}_{0}\big{)}=\frac{1}{y \sqrt{2\pi v_{1}(x|\beta_{1},\sigma_{1})}}\exp\!\left(-\frac{\left(\ln y-\mu_{ 1}(x|\alpha_{1},\beta_{1},\sigma_{1},y^{i}_{0})\right)^{2}}{2v_{1}(x|\beta_{1 },\sigma_{1})}\right). \tag{4}\]  
A geometric Brownian motion stochastic differential equation of the relative diameter against the relative height is formulated as follows:  
\[d\ Y^{i}(x)=\alpha_{2}Y^{i}(x)dx+\sigma_{2}Y^{i}(x)dW^{i}_{2}(x),i=1,\ldots,M, \tag{5}\]where \(\alpha_{2}\), and \(\sigma_{2}\) are fixed effect parameters (identical for all stems from the specified tree species) and \(W_{2}^{i}(x)\) denotes independent standard Brownian motions.