esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 4 Study on the Deformation and Instability Modes of the Slope Under Seismic Loading
### Acceleration Response Analysis  
Figure 12 shows the acceleration variation curves in the X and \(Z\) directions for three monitoring points on the slope surface, while Table 5 presents the absolute peak accelerations and their corresponding timestamps. Taking the X direction as an example, based on the seismic wave data for the applied X-direction acceleration time history, the peak acceleration at 4.44 s is 6.20 m/s\({}^{2}\). The peak accelerations at the five monitoring points occur between 5.08 and 8.85 s, indicating a lag in the acceleration response as the seismic wave propagates through the steep slope. The peak accelerations at monitoring points 1 to 4 are amplified to varying degrees compared to the initial values. Monitoring Point 2 exhibits the greatest amplification, with the peak acceleration being 4.5 times the initial value, while the other three monitoring points show an amplification effect between 1 and 2 times. Monitoring Point 5, located at the slope crest, shows a decreasing acceleration effect. The peak acceleration and corresponding timestamps in the \(Z\) direction follow a similar pattern to the X direction, but the amplification effect in the \(Z\) direction is stronger than in the X direction. The maximum amplification occurs at Monitoring Point 2, reaching 7.24 times the initial value. Overall, as slope elevation increases, the peak acceleration amplification factor decreases, and the peak acceleration at the slope crest is lower than the initial value [38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Monitoring Point**} & \multicolumn{2}{c}{**Direction X**} & \multicolumn{2}{c}{**Direction Z**} \\ \cline{2-5}  & **Timestamp (s)** & **Peak Acceleration (m/s\({}^{2}\))** & **Timestamp (s)** & **Peak Acceleration (m/s\({}^{2}\))** \\ \hline
1 & 8.04 & 11.0 & 4.75 & 10.50 \\
2 & 8.85 & 17.50 & 8.69 & 23.15 \\
3 & 8.24 & 12.30 & 5.62 & 13.38 \\
4 & 6.73 & 6.53 & 8.70 & 4.92 \\
5 & 5.08 & 2.14 & 5.02 & 2.44 \\ \hline \end{tabular}
\end{table}
Table 5: Acceleration at monitoring points under seismic conditions.
# An Image Authentication Scheme Using Merkle Tree Mechanisms
## 2 Background
### Image Authentication  
In order to securely transmit the digital image over the internet, image watermarking and the techniques of image encryption and authentication are the most important research issues in recent years. Therefore, when the receiver receives the image, whether the image is tampered with or not, it would be verified. So, the validity of the information source is extremely important. For protecting the integrity of images, several methods have been proposed [17]. These methods include traditional cryptography, fragile and semi-fragile watermarks, and digital signatures based on image content [1; 18]. [PERSON] et al. [3] collected several applications of watermarking, like broadcast monitoring, owner identification, proof of ownership, authentication, transactional watermarking, copy control, and covert communication.  
[PERSON] et al. [19] proposed a novel watermark-based multimedia blockchain framework. The watermark contains two parts of information: First, a cryptographic hash containing the history records (blockchain transactions); second, the image hash of the original media content. Later, [PERSON] et al. [20] proposed a blockchain framework for JPEG images that has its own block content. The image related information is hidden in the header of a JPEG image, and the image is stored in a multimedia server. The information for verifying copyright were stored in the blockchain framework.  
Storing image verification information on the blockchain is a good strategy, but the image is still stored in the centralized manner, or kept by the owner, which will affect the availability of image management. Thus, we proposed a method to solve this problem.
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 3 Cycloid Gear Profile Model  
### Dual Quaternion Algebraic Operations  
A quaternion can be viewed as an expression that is a combination of three complex numbers containing real parts, as in Equation (1):  
\[P=\text{A}+\text{B}_{1}i+\text{B}_{2}j+\text{B}_{3}k=\text{A}+\overset{\to}{ \text{B}} \tag{1}\]  
where A is the real part, and products of \(\text{B}_{\text{x}}\) (x = 1, 2, 3) and unit versor \((i,j,k)\) denote the imaginary part.  
The quaternion can only be used to represent three rotational degrees of freedom of a rigid body. For example, in Equation (2), it means the \(\theta\)-angle rotation around the B-axis.  
\[P_{r}=\cos\frac{\theta}{2}+\text{B}\sin\frac{\theta}{2} \tag{2}\]  
It is defined as a dual quaternion after combining the duals in order to make the quaternion more complete in describing the variation of the rigid body degrees of freedom. The mathematical expression is shown in the form of Equation (3).  
\[\left\{\begin{array}{l}\hat{\text{P}}=P_{r}+\varepsilon P_{l}\\ P_{t}=\frac{1}{2}P_{t}t\end{array}\right. \tag{3}\]  
where \(\varepsilon\) is dual unit with the property \(\varepsilon^{2}\) = 0 (\(\varepsilon\) is nilpotent). \(P_{r}\) denotes the unit quaternion in Equation (4), which represents rotation, and t represents the translation vector.  
\[P_{r}=\left(\cos\frac{\theta}{2},\mathbf{n}\sin\frac{\theta}{2}\right) \tag{4}\]  
When exploring the kinematics, dual quaternions are typically used to rotate and translate coordinate points. The algorithm is shown in Equation (5). The unit quaternion is used to represent the rotation angle theta around the unit axis \(\mathbf{n}\) in three-dimensional space. Finally, coordinates are transformed by unit dual-quaternion multiplication.  
\[p^{\prime}=qpq^{*} \tag{5}\]  
where the dual quaternion \(q\) and its conjugate \(q^{*}\) represent coordinate transformation; \(p\) and \(p^{\prime}\) represent the quaternion information before and after the transformation.
## 3 Results and Analysis
### The Test of the Mediating Effect in Urbanization
From the estimation results of Column8 in Table 7, it can be seen that the partial regression coefficients of economic development level (\(lnG\)) for urbanization level (\(lnU\)) is 0.074, respectively, and it is statistically significant at the 1% significance level, indicating that the current level of economic development (\(lnG\)) can significantly promote the improvement of regional urbanization level (\(lnU\)) and carbon emissions (\(lnY\)) [42]. (3) The mediating effect test of urbanization. Because the coefficients of \(lnC_{2}\) and \(lnU\) in Column5 and Column6 are not significant, the Sobel test is performed. Since \(Z=\frac{\hat{a}\hat{b}}{S_{\hat{a}\hat{b}}}=-0.0118\), \(S_{\hat{a}\hat{b}}=\sqrt{\hat{a}^{2}{S_{a}}^{2}+\hat{b}^{2}{S_{\hat{b}}}^{2}}= 4.757\), and the statistics are not significant, we can get that there is no significant effect path of the tertiary industry ratio (\(lnC_{2}\)) through the role of urbanization (\(lnU\)) on affecting carbon emissions (\(lnY\)). In addition, since the correlation coefficients in Column7 to Column9 are statistically significant, this indicates that \(lnG\) significantly affects \(lnY\) by acting on \(lnU\), and the mediating effect of urbanization accounts for 54% of the total effect.
# MQC-MB: Multiphoton Quantum Communication Using Multiple-Beam Concept in Free Space Optical Channel
## 4 The Implementation of MQC-MB
### Multiple Beam Quantum Communication Phase
#### 4.2.2 Step 2: Multiple-Beam Processing  
As shown in Figure 4, the array transmitter is located at the center back of the transmitter's telescope. This channel follows the same rules based on an array of QKD transmitters and receivers paired at the transmitter and receiver telescope referred from [21] and [22]. The signal beams are propagated through the free space channel and collected by the receiver's telescopes. As suggested by [32] and [41], the signals that are transmitted using4 beams with 16 paths of laser beams achieved the best distance and low geometrical attenuation.
## 4 Experimental Results and Analysis
### Kernel Design  
As with the literature [14], we design 12 kernels in this work which include 7 Gaussian kernels, 4 polynomial kernels and a linear kernel. The Gaussian kernel function is defined as \(k(x_{i},x_{j})=\exp(-\left\lVert x_{i}-x_{j}\right\rVert_{2}^{2}/2l\sigma^{2})\), where parameter \(\sigma\) denotes the maximal distance between \(x_{i}\) and \(x_{j}\), and parameter \(l\) takes values in the set {0.01, 0.05, 0.1, 1, 10, 50, 100}. The polynomial kernel function is defined as \(k(x_{i},x_{j})=({x_{i}}^{T}x_{j}+a)^{b}\), where parameter \(a\) takes values in the set {0, 1} and parameter \(b\) takes values in the set {2, 4}. The linear kernel is defined as \(k(x_{i},x_{j})={x_{i}}^{T}x_{j}\).
## 2 Materials and Methods
### Preparation and Characterization of the Activated Carbon  
PS was prepared with air-drying and sieving to a fraction between 0.5 and 1 mm and then stored in a plastic container before being employed as a precursor for activated carbon production. In an initial step to establish an inert atmosphere in the oven (Nabertherm GmbH, Lilienthal, Germany), nitrogen flow (34 L h\({}^{-1}\)) was kept for 30 min. Sawdust carbonization (666.3 g, 60 g per lot) was carried out at 600 \({}^{\circ}\)C for 1 h under a nitrogen atmosphere (34 L h\({}^{-1}\)) in an oven, using a temperature ramp of 5 \({}^{\circ}\)C min\({}^{-1}\) until reaching the target temperature. After that, the biochar (25.6 g) was mixed with the activating agent (KOH pellets, Probus, Badalona, Spain) at a ratio of 1:4 (w\({}_{\rm carbon}\)/w\({}_{\rm KOH}\)) with milling. The mixture was activated in the oven at 850 \({}^{\circ}\)C for 2 h with a temperature ramp of 5 \({}^{\circ}\)C min\({}^{-1}\). The same nitrogen flow (34 L h\({}^{-1}\)) was applied. After cooling down, the activated carbon was put in contact with agitation with HCl (0.1 M) at the ratio of 1 g carbon:100 mL HCl for 15 min followed by washing with double-distilled water until a stable pH was reached. The activated carbon (PSAC, 9.2 g) was then dried overnight at 105 \({}^{\circ}\)C, and the activated carbon yield was calculated from the weight loss. N\({}_{2}\) and CO\({}_{2}\) adsorption isotherms were obtained as described by [PERSON] et al. [13] to determine the carbon surface area and pore volume distribution. The isotherms were obtained using an ASAP 2020 sorption analyzer (Micromeritics, Norcross, GA, USA) and were recorded at \(-\)196 \({}^{\circ}\)C for N\({}_{2}\) and 0 \({}^{\circ}\)C for CO\({}_{2}\) using a Dewar with liquid nitrogen or with an ice bath, respectively. Prior to the experiments, the samples were degassed under vacuum at 300 \({}^{\circ}\)C for 1 h. The surface area was determined using the Brunauer-Emmet-Teller (BET) equation, and the Barret-Joyner-Halenda (BJH) isotherm was used to determine the mesopore volume. The total pore volume was determined at a relative pressure of 0.99. The value obtained was used to calculate the micropore volume based on the mesopore volume, neglecting the macropore volume (from the BJH equation). The pore size distribution was determined using the two-dimensional, non-local density functional theory (2D-NLDFT) included in the MicroActive v.6.00 software package (Micromeritics Instruments Corporation) that allows for the calculation of this distribution using CO\({}_{2}\) and N\({}_{2}\) adsorption data simultaneously. The last calculation takes into account a wider analysis of pore size distribution that includes the ultramicroore range.
## 1 Introduction
Owing to the higher sensitivity of the sensor made of NR-latex, not only the pressing force but the shear force can be easily measured by the same sensor [15]. In addition, NR-latex is more effective than silicon oil rubber for the sensor requirements of softness and large extension the because of its large elasticity. If a sensor is made of NR-latex, large extension and softness can be achieved. On the other hand, in a sensor made of PSERC, the shear force cannot be measured easily. The shear force is also known as the friction force; it depends on the normal force, although the normal force is larger. The currently used PSERC sensor is good at sensing the normal force. There is a notion that it might be possible to add to PSERC to measure shear force. However, this idea has the same problems of vulnerability as strain gauges or piezoelectric elements, and would be ineffective, as explained above. Therefore, the MCF rubber made of NR-latex can simultaneously sense normal, shear forces and temperature, as well as possess large extension and softness. However, the problems of secular changes in the electric resistance [24], higher sensitivity with F\({}_{\rm s}\) less than 0.01 N, etc. still remain unresolved. The previous method of making the MCF rubber is based on the process by drying the MCF rubber under a constant temperature because of the vulcanization of the rubber as well as by the application of a magnetic field because of the creation of the magnetic clusters in the rubber [15]. By this method its vulcanization becomes unstable, whether the MCF rubber is vulcanized or not. In addition, the characteristics of the MCF rubber undergo secular changes. Therefore, the method used to make the MCF rubber must certainly be investigated. In the present paper, to fulfill all sensor requirements including haptic sensibility, sensing both shear stress and normal force, sensing temperature, a strong and simple structure, softness, and large extension, we propose a new method of producing sensitive rubber by introducing an electrolytic polymerization method and utilizing NR-latex, MCF, and a magnetic field. We evaluate the effectiveness of the new method by investigating the electric and dynamic characteristics of the rubber. In addition, vulcanization of the MCF rubber by the new method is clarified by analyzing not only its changes of voltage, electric current, and temperature, but also its viscosity using a viscometer. Where we must note that the same force sensor can measure temperature. This is because sensing can be obtained by changes of electric current or voltage, and change in temperature corresponds to that of electric current or voltage. Therefore, we have only to focus on the force apart from the temperature in the case of measuring the property of the rubber in order to investigate the sensitivity of the rubber sensor. In this study, we deal with the electric and dynamic characteristics of the rubber.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Qualitative Analysis
#### 5.4.3 Qualitative Ablation Results
When the Sailing Location Preference is not considered, the VEPO-S2S model is not correct. Figure 17 shows the results with and without the Anchoring Time Preference, which is related to working and resting habits. The trajectory of the tugboat changes more significantly when it is in working condition. Therefore, it is difficult for the model to accurately determine the current movement of the tugboat without the Anchoring Time Preference, which leads to deviations from the true trajectory points. Figure 18 demonstrates the visual comparison results of the Portrait Selection Component. Without the Portrait Selection Component, the result deviates from the real track. This is because the Portrait Selection Component can select the most relevant characteristics for prediction and without it, irrelevant characteristics may be introduced into the prediction process, which results in significant deviations in the predicted outcomes. Figure 19 shows the visual comparison results of the Feature Fusion Component, which effectively integrates the trajectory information with the vessel features and enhances the correlation between them. Without the Feature Fusion Component, the vessel characteristics struggle to be fully expressed, which leads to incorrect predictions. Figure 20 denotes the comparison results of the Multi-head Decoder Component, which can adjust the level of engagement in the trajectory information and vessel features. The model's adaptability decreases when removing the Multi-head Decoder Component. Figure 16: The predicted trajectories of a tugboat, where the green and red lines represent the prediction results of the VEPO-S2S model with and without considering the Voyage Time Preference, respectively. The Voyage Time Preference is related to the habits of the crews. When the Voyage Time Preference is not considered, the VEPO-S2S model may produce inaccurate predictions. Figure 17: The predicted trajectories of a tugboat, where the green and red lines represent the prediction results of the VEPO-S2S model with and without considering the Anchoring Time Preference, respectively. The Anchoring Time Preference helps the model identify the working and resting habits of the vessel. In the absence of the Anchoring Time Preference, the model produces an incorrect estimation for each timestamp.
## 4 Experiment
The charge coupled device (CCD) is placed on the focal plane of the focusing lens, i.e., the Fraunhofer plane of the DOEs. The light intensity is adjusted by a continuously neutral density filter, to ensure that none of the pixels on the CCD are overexposed. The diffracted patterns are captured by the CCD and are normalized to 0-1. The pseudo color images of the normalized patterns are shown in Figure 7. Because we use the calculation result of the GS algorithm as the initial value of the SA-PSO algorithm, the speckle distributions of the GS algorithm and the SA-PSO algorithm are quite similar. Each intensity distribution in Figure 7 is normalized. Because there is no truth value in the experiment, the standard deviation of each result is calculated to evaluate the quality. The standard deviation is expressed as:  
\[S=\sqrt{\frac{\sum_{m,n=1}^{m=M,n=N}\left(I_{m,n}-\overline{I}\right)^{2}}{M \times N}} \tag{6}\]  
where \(I_{m,n}\) is the intensity diffracted by the calculated DOE, \(\overline{I}\) is the mean value of the target light intensity, and \(M\) and \(N\) represent the number of rows and columns of the matrix, respectively. The standard deviation of Figure 7a,b is 0.0440 and 0.0383, respectively. The relative reduction by the SA-PSO hybrid algorithm is 13.0%, which is consistent with the simulation result. Figure 6: Schematic of the experimental setup. Figure 7: The diffracted intensity patterns obtained by the CCD. (**a**) The GS algorithm and (**b**) the SA–PSO algorithm. To show the feasibility of the SA-PSO algorithm on display, we use this algorithm to design a DOE for a 2D Rubik's cube, and compare the experimental reconstruction result with those by the GS algorithm and the single inverse diffraction method, as shown in Figure 8.  
Because the GS algorithm is an iteration method with many forward and backward diffractions, the reconstruction quality is better than that of the single inverse diffraction method, as shown in Figure 8c,d. The SA-PSO algorithm uses the result of the GS algorithm as the initial search value, so the imaging quality is slightly better than that of the GS algorithm, as shown in Figure 8b,c.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### ABE Scheme Based on SM9 IBBE
#### 4.2.2 KeyGen (\(\mathrm{M_{pk}}\), \(\mathrm{M_{sk}}\), \(\mathrm{U}\))  
Given the master public key \(M_{pk}\), the master secret key \(M_{sk}\), and a set of attributes \(U\), this algorithm converts the set of attributes \(U\) into an identity \(ID_{U}\in\{0,1\}^{|u|}\) by running the algorithm \(\varphi\), and then calls the Private-Key-Extract algorithm of SM9. It outputs the ABE's private key \(SK_{U}\).  
\[SK_{U}\gets SM9.\textbf{Private-Key-Extract}\Big{(}M_{pk},M_{sk}, \varphi(U,u)\Big{)} \tag{6}\]
###### Abstract  
In this study, the influence of small additives on the spectral and optical properties of Na\({}^{+}\)-Ag\({}^{+}\) ion-exchanged silicate glass is presented. Polyvalent ions, for example, cerium and antimony, are shown to reduce silver ions to atomic state and promote the growth of photoluminescent silver molecular clusters and plasmonic silver nanoparticles. Na\({}^{+}\)-Ag\({}^{+}\) ion-exchanged and heat-treated glasses doped with halogen ions, such as chlorine or bromine, exhibit formation of photo- and thermochromic AgCl or AgBr nanocrystals. Growth of a silver nanoisland film on the glass surface was observed in the case of undoped sample. The presented results highlight the vital role of small additives to control properties of the silver nanostructures in Na\({}^{+}\)-Ag\({}^{+}\) ion-exchanged glasses. Possible applications of Na\({}^{+}\)-Ag\({}^{+}\) ion-exchanged glass ceramics include but are not limited to biochemical sensors based on surface-enhanced Raman scattering phenomena, temperature and overheating sensors, white light-emitting diodes, and spectral converters.  
ion exchange; silver; photo-thermo-refractive glass; silver nanoparticles; silver molecular clusters; silver bromide nanocrystals +
Footnote †: : 2021: 11, 3891. [[https://doi.org/10.3390/app11093891](https://doi.org/10.3390/app11093891)]([https://doi.org/10.3390/app11093891](https://doi.org/10.3390/app11093891))  
Footnote †: : 2021: 11, 3891. https://doi.  
be modified with the method, whereby the typical thickness of the silver-enriched region is below 2 um[9; 10]. The IE technique, on the other hand, is characterized by simplicity, flexibility, and applicability for mass production. For example, Na+-K+ IE is used for glass strengthening on an industrial scale for producing cover glass for smartphones [11]. The method is based on the substitution of alkali (Na+, K+, Rb+, Cs+) or transition (Ag+, Cu+, Tl+) metal ions from glass by another one from the salt melt, metal film, or another source [12; 13; 14]. A comparison of ion implantation and ion exchange technologies for modification of glass is shown in Table 1.  
It must be noted that most research papers devoted to the study of silver nanostructures formed in ion-exchanged glasses focused on the influence of the ion exchange (concentration of silver in salt melt, temperature, and duration) and subsequent heat treatment (temperature and duration) parameters, while glass composition and influence of the glass additives have not received much attention. As it is shown further, these uncharacterized factors play a key role in determining the properties of silver nanostructures and, consequently, the functionality of the glass doped with them. This research deals with the influence of typical additives of PTR glass on the spectral and optical properties of Na+-Ag+ ion-exchanged layers. However, as PTR glass is based on the well-known and widely used sodium-aluminosilicate matrix, our results can be extended to other silicate glasses.