esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**T**} & \multicolumn{3}{c}{**Smooth Image**} & \multicolumn{6}{c}{**Complex Image**} \\ \cline{2-9}  & **PSNRa** & **PSNRb** & **PSNRc** & **PSNRd** & **EC** & **PSNRe** & **PSNRf** & **PSNRg** & **PSNRh** & **EC** \\ \hline
10 & 33.5 & 32.6 & 35.6 & 34.8 & 309,600 & 29.6 & 30.7 & 28.1 & 27.4 & 39,528 \\
15 & 32.6 & 32.1 & 35.1 & 33.9 & 342,888 & 29.3 & 30.4 & 27.8 & 27.1 & 89,712 \\
20 & 31.8 & 31.6 & 34.6 & 33.2 & 356,832 & 28.8 & 29.7 & 27.5 & 26.4 & 135,912 \\
25 & 31.1 & 31.2 & 34.1 & 32.6 & 362,688 & 28.2 & 29.1 & 26.9 & 25.6 & 180,576 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of EC and PSNR under different smoothness. Figure 8: Two sets of images with different smoothness. (**a**) smooth image. (**b**) complex image. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**T**} & **Image** & **PSNROI-SI** & **PSNRCI-SI** & **EC** \\ \hline \multirow{4}{*}{T = 5} & Airplane & 31.852 & 57.0449 & \multirow{4}{*}{12,000} \\  & Baboon & 26.8096 & 53.4486 & & \\  & Bridge & 28.3714 & 54.2450 & & \\  & Man & 31.2343 & 53.7736 & & \\ \hline \multirow{4}{*}{T = 10} & Airplane & 31.7193 & 46.5513 & \multirow{4}{*}{63,768} \\  & Baboon & 26.6704 & 41.3761 & & \\  & Bridge & 28.1893 & 41.8135 & & \\  & Man & 30.9492 & 42.5611 & & \\ \hline \multirow{4}{*}{T = 15} & Airplane & 31.424 & 41.5739 & \multirow{4}{*}{111,216} \\  & Baboon & 26.3699 & 36.4424 & & \\  & Bridge & 27.7495 & 36.4353 & & \\  & Man & 30.4662 & 38.2309 & & \\ \hline \multirow{4}{*}{T = 20} & Airplane & 30.9865 & 38.3526 & \multirow{4}{*}{145,632} \\  & Baboon & 25.9763 & 33.5268 & & \\  & Bridge & 27.1726 & 33.3167 & & \\  & Man & 27.9083 & 35.6121 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of EC and PSNR under different thresholds. generated by [22; 23] are highly different. Although the shadow images are meaningful, they are not suitable for practical application scenarios. Many shadow images in [26; 27; 28] are meaningless ciphertext images. Thus, similar to traditional encryption methods, they also easily attract attackers' attention. The method proposed in [27] generates multiple meaningless shadow images of the same size by secret sharing of a cover image, and embeds the secret message in the shadow image, while the method in this paper uses multiple carrier images. Therefore, the embedding rate of [27] is higher than that of the method proposed in this paper, and the method proposed in this paper takes the smooth image as the carrier and its storage capacity is much higher than that of [27]. The embedding payload of [28] is bounded by the embedding method. When the PSNR \(\approx\) 35 dB, the maximum payload of [28] is around 0.65 bpp. In addition, the compression ratio of the method proposed in this paper is four, which means that the embedding capacity of our method has obvious advantages under the same storage space.
###### Abstract  
Background: Stem cells harvested from human exfoliated deciduous teeth (SHED) are pluripotent and can be differentiated into insulin-secreting \(\beta\)-cells, i.e., SHED \(\beta\)-cells. Previously, we showed that zinc upregulates insulin secretion from SHED \(\beta\)-cells, potentially providing an extra source for insulin. Rationale: In this study, we determined the role of ionotropic \(\gamma\)-aminobutyric acid A (\(\mathrm{GABA_{A}}\)) receptor in zinc-enhanced insulin secretion from SHED \(\beta\)-cells. Autocrine/paracrine activation of \(\mathrm{GABA_{A}}\) receptors by GABA elevates calcium influx in pancreatic \(\beta\)-cells, in which intracellular chloride is maintained at high levels. Method and Findings: Differentiating SHED into SHED \(\beta\)-cells resulted in an increase in the expression of \(\mathrm{GABA_{A}}\) receptor subunits and Zrt-/irt-like protein3 (ZIP3), a zinc uptake transporter. Zinc pretreatment elevated the insulin gene transcription, whereas knockdown of ZIP3 reduced levels of intracellular zinc, and concomitantly reduced insulin secretion by SHED \(\beta\)-cells. Zinc-pretreated SHED \(\beta\)-cells exhibited a GABA-induced increase in \(\mathrm{Ca^{2+}}\) influx, detected with a ratiometric calcium-sensitive dye, suggesting zinc-mediated regulation of \(\mathrm{GABA_{A}}\) receptors. Conclusion: Our results indicate that elevated levels of zinc and \(\mathrm{GABA_{A}}\) receptors are indispensable for efficient insulin secretion by SHED \(\beta\)-cells. These findings suggest an opportunity for using SHED \(\beta\)-cells for treating diabetes.  
\(\beta\)-cells; insulin; GABA; SHED; zinc +
Footnote †: journal: Applied
## 3 Results
### Implementation Barriers of Sleeping Assets
#### 3.4.2 Stakeholder Barriers  
Stakeholders play a crucial role in logistics projects, and they can be categorized into three main groups: direct participants, indirect participants, and general stakeholders [51]. These stakeholders bring different perspectives and collaboration for the successful implementation of innovative logistics projects, as emphasized by all interviewees. However, it was noted that cooperation can be challenging due to competing interests among stakeholders.  
Based on the triangle of social acceptance by [PERSON] et al. [52], which defines three main groups (socio-political acceptance, community acceptance, and market acceptance), [PERSON] et al. [53] analyzed the factors influencing these forms of acceptance. Market acceptance is mainly impacted by potential economic benefits, incentives, trust in government and industry, as well as perceived costs and risks [53]. In highly competitive markets, cooperation becomes more difficult. Companies naturally prioritize maximizing their own economic benefits, which might sometimes conflict with the interests of others. Additionally, logistics operators competing with companies in other industries, such as those utilizing sleeping assets, such as wastelands or vacant land, further complicate the situation. To address these challenges, one interviewee suggested that actors should specialize in their respective areas of expertise. This would help to avoid entangled structures and inefficient regulations from mixed responsibilities among companies.  
When implementing cooperative logistics concepts, e.g., white-label solutions, it is also important to use synergies to increase the system's efficiency. [PERSON] et al. [54] implemented a simulation of logistics flows in cities using collaborative urban distribution centers. Their mathematical model coordinates maintenance and distribution strategies and minimizes costs by establishing collaborations [54]. In such concepts, the participation of all stakeholders can be crucial for success. [PERSON] et al. [16] also identified the early involvement of stakeholders in the planning processes of innovative urban logistics projects as crucial for their success.  
In summary, the opinions of the interview partners with regard to stakeholder barriers differed. On the one hand, cooperation is essential for the success of innovative logistics projects. On the other hand, it is a challenge to maintain communication and cooperation with all stakeholders and to overcome the barriers associated with the competitive market.
## 5 Simulation Results
### Training Performance  
In this section, we will provide comprehensive details on the training performance of the LSVM, CGSVM, and ANN algorithms. The training performance of the LSVM and CGSVM classifiers were determined using a receiver operating characteristic (ROC) curve, while the ANN algorithm performance was assessed using a mean square error (MSE) graph. Figure 8a,b show the training performance of the linear SVM (LSVM) and coarse Gaussian SVM (CGSVM) in the form of an ROC. The LSVM and CGSVM show higher training performance as both contain a large portion of the dataset under the curve area, showing remarkable training performance. The training ROC curve shows the capability of the LSVM and CGSVM models to classify the normal and attack traffic, with accuracy of 99.9% and 98.8%, respectively. In Figure 8a, the LSVM classifier demonstrates a false positive rate (FPR) of 0.14 (14%), as indicated on the x-axis, and a true positive rate (TPR) of 1.00 (100%), as shown on the y-axis. This high TPR highlights the exceptional ability of the LSVM classifier to accurately identify true positives while maintaining a relatively low FPR. Similarly, in Figure 8b, the CGSVM classifier achieves an FPR of 0.19 (19%) and a TPR of 1.00 (100%). While both classifiers exhibit perfect true positive rates, the CGSVM shows a slightly higher FPR compared to the LSVM.  
Figure 9 shows the ANN-based model's training performance for the AV DoS attack dataset [42]. The mean square error (MSE) graph shows robust performance by continuously minimizing the error between the predicted and actual outputs. We also take care of model overfitting and underfitting during training of the ANN algorithm by splitting the subjected dataset into training (70%), validation (15%), and testing (15%), as shown in Figure 9. In Figure 9, the solid blue line shows the training, the dashed green line shows the validation and the dotted red line shows the testing curve respectively. The training algorithm used in the training of the ANN scheme is the scaled conjugate gradient (SCG) and achieves a training accuracy of 91.79%, validation accuracy of 91.02%, and testing accuracy of 90.54%. It is also important to note that, this testing and validation is solely used for model underfitting and overfitting during the training of the ANN algorithm; however, for the actual testing of the dataset, we utilize the hold  
Figure 8: (a) linear SVM training ROC curve. (b) coarse Gaussian SVM training ROC curve.  
Figure 10 illustrates an error histogram graph of the ANN training model, which shows the occurrence of instances around the zero error line, which is shown as a yellow colored line. Figure 10 depicts the high concentration of the training, validation, and testing instances at the zero error line. The error histogram graph also reveals the skewed behavior of instances around the zero line, which was determined through another analysis of the performance of the training model.
# Shock Initiation of a Satellite Tank under Debris Hypervelocity Impact
## 5 Conclusions  
* The minimum power density of liquid hydrazine \(P_{dmin}=3.98\times 10^{5}\) kbar\({}^{2}\) us is determined based on the WSTF#HYP-2 test, SAIC test and the designed TITANK test, the expressions of shock wave pressure and pressure duration are constructed based on the one-dimensional wave theory, and the initiation criterion for the liquid hydrazine tank is established.
* The initiation criterion for the liquid hydrazine tank is used to calculate the power density in WSTF#HYP-2 test. The relative error between the initiation criterion calculation and the TDL simulation is 9.71%, which verifies the effectiveness of the initiation criterion for the liquid hydrazine tank.
* Numerical simulation and the initiation criterion are adopted to calculate the power density in the satellite tank under the debris impact at the velocity of 10 km/s, and the power densities obtained are \(4.8206\times 10^{6}\) kbar\({}^{2}\) us and \(5.2705\times 10^{6}\) kbar\({}^{2}\) us, respectively. The two results agree well with each other and they are both larger than the minimum power density, indicating that the satellite tank explodes under the debris hypervelocity impact.  
Conceptualization, J.Z.; methodology, C.C.; software, B.Z.; validation, B.Z.; formal analysis, B.Z.; investigation, B.Z.; resources, J.Z.; data curation, Y.W.; writing--original draft preparation, B.Z.; writing--review and editing, J.Z.; visualization, C.C. This research received no external funding.  
This research was done with the financial support of the State Key Laboratory of Laser Propulsion and Application. We wish to express sincere gratitude to [PERSON] for his valuable help.  
The authors declare no conflict of interest.
# Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification
## 3 Method and Algorithm  
### Overall Model  
The cost-sensitive variational autoencoding classifier (cost-sensitive VAE) proposed in this paper is divided into three parts: encoder, decoder, and cost-sensitive modules. The function of the encoder is to obtain the distribution of the hidden variable \(Z\) of the original data \(X\). The decoder's function is to sample the hidden variable \(Z\) and then reconstruct the data \(X\) and obtain the label \(Y\) of the data. The function of the cost-sensitive module is to adapt to the situation of imbalanced data. A flow chart of the proposed cost-sensitive VAE classifier is shown in Figure 1.  
The cost-sensitive method adopts a cost matrix; the form of the matrix is shown in Equation (1). The order \(c\) of the matrix is the same as the number of data categories. The element \(cost[i,j]\) (\(1\leq i,j\leq c\)) in the matrix represents the cost of a sample originally classified as class \(i\) being misclassified as class \(j\).  
\[Cost=\left[\begin{array}{cccc}cost[1,1]&\cdots&cost[1,j]&\cdots&cost[1,c]\\ \vdots&&\vdots&&\vdots\\ cost[i,1]&\cdots&cost[i,j]&\cdots&cost[i,c]\\ \vdots&&\vdots&&\vdots\\ cost[c,1]&\cdots&cost[c,j]&\cdots&cost[c,c].\end{array}\right] \tag{1}\]  
The cost function of this model is shown in Equation (2).  
\[L=-E_{z\sim q(z|x)}[logP(x|z)]+KL(q(z|x)||p(z))+\psi*L(\hat{y},y)*cost[y,\hat{ y}]. \tag{2}\]  
where \(-E_{z\sim q(z|x)}[logP(x|z)]\) represents the minimized reconstruction error of the original data. \(KL\) is the Kullback-Leibler divergence, which is used to measure the difference between two probability distributions in the same event space; thus, \(KL(q(z|x)||p(z))\) represents whether the distribution of the hidden variable \(Z\) is close to the Gaussian distribution. \(L(\hat{y},y)\) represents the model classification loss function (in this paper, the cross-entropy loss function was adopted), \(cost[y,\hat{y}]\) represents the classification cost, and \(\psi\) represents the importance of the classification task.
## 2 An Overview of the Weighted Modulus Algorithm  
In this section, we review the weighted modulus algorithm proposed by [PERSON] et al. [32]. Our proposed scheme makes use of the variants of their algorithm to conceal secret messages. Their scheme, denoted by WM(\(n\), \(m\)), embeds an \(m\)-ary secret message in a pixel cluster with \(n\) cover pixels using the modulus operator. Let \(\mathbf{P}=(P_{1}\), \(P_{2}\),..., \(P_{n})\) be a pixel cluster, \(s_{m}\) be an \(m\)-ary secret digit, and \(\mathbf{W}=(w_{1}\), \(w_{2}\),..., \(w_{n})\) represent an \(n\)-tuple weight vector. The stego pixel cluster, \(\mathbf{P^{\prime}}=(p^{\prime}_{1}\), \(p^{\prime}_{2}\),..., \(p^{\prime}_{n})\), which has conveyed the hidden digit \(s_{m}\), is produced through three steps, as detailed below:  
**Step 1:** Referring to Equation (1) to determine the remainder value, \(r\), where \"\(\bullet\)\" represents the dot product of vectors and \"mod\" represents the modulus operator.  
**Step 2:** Calculating the difference, \(d\), between \(s_{m}\) and \(r\) using Equation (2).  
**Step 3:** Inquiring about the pixel alteration vector, \(\mathbf{A_{d}}\), from the pixel alteration (PA) table by \(d\) before producing \(\mathbf{P^{\prime}}=(p^{\prime}_{1}\), \(p^{\prime}_{2}\),..., \(p^{\prime}_{n})\) using Equation (3).  
The message extraction is straightforward as we can adopt the dot vector again to obtain \(s_{m}\), as shown in Equation (4).  
\[r=[P\bullet W]\bmod m. \tag{1}\]  
\[d=(s_{m}-r)\bmod m. \tag{2}\]  
\[\mathbf{P^{\prime}}=\mathbf{P}+\mathbf{A_{d}}. \tag{3}\]\[s_{m}=\left[\mathbf{P}^{\prime}\bullet\mathbf{W}\right]\bmod m. \tag{4}\]  
As an example, let \(\mathbf{P}=(18,\,20,\,42)\) be a cover pixel cluster (\(n=3\)). Let \(s_{8}=2_{8}\) be an \(\delta\)-ary secret digit to be concealed (\(m=8\)) and \(\mathbf{W}=(7,5,2)\) represent a 3-tuple weight vector. In step 1, we derive \(r=[\mathbf{P}\bullet\mathbf{W}]\bmod 8=6\). In step 2, we calculate the difference and derive \(d=4\). Finally, we inquire the pixel alteration by \(d=4\) shown in Table 1 and obtain \(\mathbf{A}_{4}=(1,\,1,\,0)\), thus producing the stego pixel cluster \(\mathbf{P}^{\prime}=\mathbf{P}+\mathbf{A}_{d}=(19,\,21,\,42)\). The recipient can extract the secret message using \(s_{8}=[\mathbf{P}^{\prime}\bullet\mathbf{W}]\bmod 8=2_{8}\).  
The WM algorithm can effectively conceal secret messages using the optimal weight, providing a smaller pixel distortion caused by the message concealment. However, the algorithm does not belong to the approach of reversible data embedding, which means that the original cover image after the data extraction cannot be restored after extracting the secret message. In this paper, we take advantage of the weight modulus and introduce an image reversible data-hiding algorithm, which can provide a high embedding capacity through a multi-hider approach, producing dual stego images, each of which has a high image quality, and retrieving the cover image after the message extraction. We detail our algorithm in the next section.
# Effect of Dynamic Loading Conditions on Maximizing Energy Dissipation of Metallic Dampers
## 2 Design Concept and Optimization Approaches
### Static and Dynamic Optimization  
Since metallic dampers can be easily fabricated [24], they can be inexpensively produced with various geometries. Thus, this study attempted to make each optimized shape specific to different optimization conditions. These conditions represent the diverse installation environments of the subject metallic dampers. In other words, the dampers can be optimized with respect to the specific characteristics of individual structures.  
Generally, a slope of the elastic region in the hysteresis loop becomes stiffer during the shape optimization process. It eventually contributes to an increased area of the hysteresis loop, which is equivalent to the energy dissipation. However, in the case of low loading conditions, such as mild earthquakes, an elastically stiffer damper might be unfavorable, as there is little deformation. According to the structural analysis conducted in this study, a novel topology of the optimized damper was found to be more effective with moderate ground accelerations with smaller peak ground acceleration and higher frequencies. To customize the damper shapes diversely for various expected installation conditions, several loadings with different maximum strokes or numbers of cycles were applied to the optimization process. Moreover, to overcome the limitation of general static optimization, the dynamic characteristics of the damper were considered. This advanced optimization process was conducted based on the dynamic FE analysis.
## Appendix A
The above mentioned eigenfunctions are formed by 80 Laguerre-Gauss functions playing the role of orthonormalised bases which, for the forward beam, are  
\[ \begin{array}{rl}G_{fp}(\rho,z)=&\sqrt{\frac{2}{\pi}}\frac{1}{W(z)}L_{p} \left(\frac{2\rho^{2}}{W(z)^{2}}\right)\exp\left(-\frac{\rho^{2}}{W(z)^{2}} \right)\\ &\times\exp\left\{+1\left[\frac{k\rho^{2}}{2\kappa_{\mathrm{c}}(z)}-(2p+1) \Phi(z)\right]\right\},\end{array}  \tag{10}\]  
and for the backward beam are  
\[ \begin{array}{rl}G_{bp}(\rho,z)=&\sqrt{\frac{2}{\pi}}\frac{1}{W(z)}L_{p} \left(\frac{2\rho^{2}}{W(z)^{2}}\right)\exp\left(-\frac{\rho^{2}}{W(z)^{2}} \right)\\ &\times\exp\left\{-i\left[\frac{k\rho^{2}}{2\kappa_{\mathrm{c}}(z)}-(2p+1) \Phi(z)\right]\right\},\end{array}  \tag{11}\]  
where \(k=2\pi/\lambda\). The forward and backward quantities are distinguished by the subscripts \(f\) and \(b\), respectively. The quantities \(W(z)\), \(R_{\mathrm{c}}(z)\), and \(\Phi(z)\) are defined by Equations (2)-(4). \(L_{p}(X)\) is the Laguerre polynomial of order \(p\). Note that the field distribution in the bare cavity made up of mirrors \(\mathrm{M_{p}}\) and \(\mathrm{M_{c}}\) and is identical for the forward and backward beams. However, it has been demonstrated theoretically [66] and experimentally [35] that if there are any diffraction effects inside the resonator, then the longitudinal and transversal intensity distributions associated with the forward and backward waves are different. To facilitate the use of the following model for the determination of the resonant field of a cavity with diffracting objects, the reader may wish to refer for details to a previous work [67] on cavities with a super-Gaussian aperture or a binary circular phase plate. (a) Cavity with a diaphragm and a binary phase mask (Figure 10):  
The forward and backward fields are assumed to be linearly polarised and are expressed as linear combinations of the basic functions on both sides of the DOE:  
\[E_{fj}(\rho,z)=\exp[i(kz-\omega t)]\underset{p}{\sum}f_{jp}G_{fp}(\rho,z), \tag{12}\]  
\[E_{bj}(\rho,z)=\exp\{i[k(2L-z)-\omega t]\}\underset{p}{\sum}b_{jp}G_{bp}(\rho, z). \tag{13}\]  
The index \(j\) is equal to 1 in the region 1 (by \(0<z<z_{DOE}\)) and equal to 2 in the region 2 (\(z_{DOE}<z<L\)), where \(z_{DOE}=(L-D)\) is the position of the DOE. We study the stationary field for \(t=0\) and then \(\exp(-i\omega t)=1\). The functions of the basis satisfy the orthonormalisation condition given by:  
\[2\pi\underset{0}{\int}\ G_{fp}(\rho,z)G_{fm}^{*}(\rho,z)\rho\ d\rho=\delta_{ pm} \tag{14}\]  
\[2\pi\underset{0}{\int}\ G_{bp}(\rho,z)G_{bm}^{*}(\rho,z)\rho\ d\rho=\delta_{ pm} \tag{15}\]  
where the symbol \({}^{*}\) is used to describe the complex conjugate of the quantity. The knowledge of the forward and backward fields involves the determination of the four (\(\rho\) and \(z\), independently) coefficients, \(f_{1p},f_{2p},b_{1p}\) and \(b_{2p}\), of the field expansion for \(0<z_{DOE}<L\).
###### Abstract  
In mining and civil engineering applications, a reliable and proper analysis of ground vibration due to quarry blasting is an extremely important task. While advances in machine learning led to numerous powerful regression models, the usefulness of these models for modeling the peak particle velocity (PPV) remains largely unexplored. Using an extensive database comprising quarry site datasets enriched with vibration variables, this article compares the predictive performance of five selected machine learning classifiers, including classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for PPV analysis. Before conducting these model developments, feature selection was applied in order to select the most important input parameters for PPV. The results of this study show that RF performed substantially better than any of the other investigated regression models, including the frequently used SVM and ANN models. The results and process analysis of this study can be utilized by other researchers/designers in similar fields.  
Keywords:ground vibration; prediction; machine learning; feature selection; environmental issue of blasting +
Footnote †: journal: Applied Sciences