esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Effect of Activation Sequence
For instance, the activation of SMA2 in Figure 8a will distort the concrete beam not only in the \(y\)-direction but also in the \(z\)-direction. The bending distortion due to the SMA2 in the \(y\)-direction will shorten the SMA1 and relax the prestress of the SMA1 while the bending in the \(z\)-direction will tighten and increase the prestress of the SMA1. Moreover, if the concrete is locally cracked or crushed during these sequences, the degradation of the concrete properties will induce additional loss of the prestress in the Fe-SMA bars. Figure 9 shows the concrete cracking patterns due to the activation of the Fe-SMA bars predicted by the FE simulations, with sequential activation and activation at once. It evidently shows that the sequential activation of the Fe-SMA bars causes more damage in the concrete near the end of the RC beam than the activation at once. The activation of Fe-SMA bars induced damage of the concrete mainly near the bottom center of the RC beam and near the end of the RC beam (i.e., at regions A and B in Figure 9). The two FE models produced almost identical cracking patterns at region A, whereas pronouncedly severe shear-induced inclined cracks appeared in the sequential activation in comparison to the activation at once at region B. Tthe more severe cracking in this region in the sequential activation than the activation at once is probably due to the complex \(y\)- and \(z\)-direction distortions during the sequential activation of the Fe-SMA bars. The cracking patterns in this region influenced the loss of the prestress in the sequentially activated Fe-SMA bars due to the degradation of the concrete and consequently led to the lower \(x\)-axis stress in the Fe-SMA bars after the sequential activation in comparison to the activation at once. Figure 8: Normal \(x\)-axis stress distributions along Fe-SMA bars after activation, calculated by FE simulations. (**a**) With sequential activation of 5 Fe-SMA bars and (**b**) with 5 Fe-SMA bars activated at once. Figure 9: Concrete cracking patterns on \(x\)-\(y\) plane developed during activation of 5 Fe-SMA bars, with sequential activation and with activation at once. Regardless of the difference in the crack patterns and the \(x\)-axis stress distributions of the Fe-SMA bars, there was no clear difference in the bending load-deflection responses in the two different FE models with the sequential activation and the activation at once, as shown in Figure 10. The load-deflection curve obtained by the FE model with the sequential activation followed nearly the same trend as that of the activation at once, indicating that the effect of Fe-SMA bar activation in sequence is negligible on the bending behavior of the RC beam prestressed by the Fe-SMAs.
## 5 Simulation and Validation
### Disscussion
It can be seen that accuracy of vehicle responses were higher under road level ISO-\(A\) excitation than road level ISO-\(C\), i.e., higher road level lead to higher error. Roll angle and roll rate accuracy for vehicle responses of 6-DOF model was a maximum of 60% under road level ISO-\(A\)/\(C\) excitation. Roll angle and roll rate calculation accuracy for vehicle responses of 9-DOF model was 85% under road level ISO-\(A\)/\(C\) excitation. With increase of the steering wheel input, as in the case of \(F_{2}\), accuracy for 6-DOF model was a maximum of 58% under road level ISO-\(A\)/\(C\) excitation. However, for a 9-DOF model accuracy declined at a slower rate than 6-DOF model as shown in Figure 12b. From \(F_{3}\) perspective, an increase in road level resulted in accuracy for vehicle responses of 6-DOF and 9-DOF model to reach a maximum of 90% under road level ISO-\(A\ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Road Excitation** & \multicolumn{3}{c}{**Level ISO-\(A\) Excitation**} & \multicolumn{3}{c}{**Level ISO-\(C\) Excitation**} \\ \hline  & \multicolumn{3}{c}{Accuracy of state calculation/\%} \\  & Roll angle & Roll rate & Yaw rate & Roll angle & Roll rate & Yaw rate \\ \hline \(F_{1}\) (Kurtosis) & 99.8 & 86.9 & 98.2 & 98.3 & 77.4 & 80.3 \\ \(F_{2}\) (Crest factor) & 96.3 & 92.0 & 83.1 & 99.3 & 91.4 & 77.2 \\ \(F_{3}\) (Clearance factor) & 91.1 & 95.2 & 71.2 & 87.6 & 95.3 & 78.5 \\ \(F_{4}\) (Shape factor) & 99.9 & 94.2 & 99.8 & 99.7 & 97.4 & 96.8 \\ \(F_{5}\) (Impulse factor) & 96.4 & 86.7 & 82.9 & 99.6 & 88.9 & 85.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Simulation accuracy of comparing 9-DOF model and CARSIM® with steering wheel 15\({}^{\circ}\) input. Figure 12: Accuracy of statistical features for vehicle state calculations using 6-DOF and 9-DOF models under road Level ISO-\(A\)/\(C\) excitation and steering wheel 5\({}^{\circ}\), 10\({}^{\circ}\) and 15\({}^{\circ}\) conditions. **(a)** Kurtosis, **(b)** Crest factor, **(c)** Clearance factor, **(d)** Shape factor, **(e)** Impulse factor. Based on the above analysis, simulation results show that accuracy of statistical features calculation for 6-DOF model reached a maximum of 65% for all situations. Lower accuracy of statistical features follows increase in the angle of steering wheel input; accuracy of statistical features calculated for 9-DOF model was higher than 85%. Comparison of simulation results from MATLAB and CARSIM(r) allows verification of the proposed 9-DOF full-car model for roll behavior with or without road excitation condition. A 6-DOF half-car model was found to be suitable in conditions without road excitation. Influence of vehicle model on roll behavior should be adopted for the full-car model while analyzing vehicle roll behaviors with large steering wheel input and considering road excitation.
## 5 Current Treatments to Avoid or Reduce PPO Activity in Fresh-Cut Produce
### Antioxidants and Antimicrobials
#### 5.4.9 Other Washing Treatments  
Many washing treatments not mentioned before have been used in minimal processing, whose relevancy should be noted. Thus, the use of ClO\({}_{2}\) has been proved for fresh-cut processing, showing different results regarding the following aspects: reduction of the risk of foodborne infections and intoxications, decrease in microbial spoilage, preservation of fresh attitudes and nutritional quality or absence of unacceptable levels of toxic residues. In that sense, its microbiological control has been widely proved [115]. On one hand, some authors have pointed out its negative effect (in gaseous form) on visual appearance in lettuce and cabbage [107]. On the other hand, different ClO\({}_{2}\) washing treatments (10, 50 and 100 mg/L) seemed to control PPO activity for fresh-cut lotus root [33]. For fresh-cut lettuce, the use of ClO\({}_{2}\) (100 mg/L) inhibited PPO from day 8 of storage [30]. Sodium chlorite was used in fresh-cut apples, inhibiting browning reaction and PPO activity [44]. Finally, a recent study considered chlorogenic acid as a treatment to reduce PPO activity in fresh-cut potatoes, significantly inactivating this enzyme [116].
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 6 Electricity Load-Forecasting Model
### Clustered Federated Learning Load-Forecasting Process Based on Louvain Algorithm  
The entire process of personalized adaptive differential privacy clustering federated learning for electricity load forecasting using the Louvain algorithm consists of the following five steps:  
1. All clients participate in training with the classical differential privacy federated averaging algorithm until convergence.
2. The central server calculates the cosine similarity based on the local update vectors at the convergence of the model from local clients.
3. Each client serves as a network node, constructing a network edge set and edge weights between nodes based on the similarity of learned client parameters, forming an undirected weighted complex network.
4. Community clustering is performed using the Louvain algorithm.
5. The pADP-FedAvg algorithm is utilized for personalized adaptive differential privacy clustering federated learning for electricity load forecasting.
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 3 Model Construction
### Feature Extraction Module
During training, the VAE aims to minimize two key objectives: the reconstruction error, which ensures the fidelity of the generated data, and the KL divergence, which measures the difference between the latent variable distribution \(q(z|x)\) and a predefined prior distribution \(p(z)\). The overall objective function of the VAE can be formally expressed as follows:  
\[L_{VAE}=\text{E}_{q(z|x)}[\log p(x|z)]-KL(q(z|x)\|p(z)) \tag{6}\]  
where \(\text{E}_{q(z|x)}[\log p(x|z)]\) represents the reconstruction error, which is the difference between the training data \(x\) and the data generated by the decoder \(x^{\prime}\), calculated with the cross-entropy method, and \(KL(q(z|x)\|p(z))\) denotes the KL divergence between the latent variable distribution and the prior distribution, which measures the distance between \(q(z|x)\) and \(p(z)\). Since \(q(z|x)\) is typically modeled as a Gaussian distribution, the encoder network maps the input data \(x\) to a mean vector \(\mu\) and a variance vector \(\sigma\). The latent variable \(z\) is then sampled from this distribution using the reparameterization trick, which can be expressed as follows:  
\[\mu=f_{\mu}(x) \tag{7}\]  
\[\sigma=f_{\sigma}(x) \tag{8}\]  
\[z=\mu+\varepsilon\odot\exp(\frac{\sigma}{2}) \tag{9}\]  
where \(\varepsilon\sim N(0,1)\) is the noise vector, and \(\odot\) represents element-wise multiplication.
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 4 Results and Discussion
### Average Shear-Wave Velocities \(V_{s30}\)
#### 4.4.2 \(V_{s30}\) Predictive Equation for the Oran Region  
Predictive equations for \(V_{s30}\) using the corresponding wavelength can allow for calculating \(V_{s30}\) values directly without performing the inversion process, which cannot be a straightforward process. It has been widely used in sedimentary basins (e.g., [22; 43; 54]).  
For each site, the average \(V_{s30}\) has been identified from the obtained Vs model. One must note that the Rayleigh wave-phase velocity depends on the Vs and Vp to a depth of one wavelength [43]. The corresponding wavelength (\(\lambda_{30}\)) is then retrieved from each dispersion curve using Equation (5):  
\[\lambda_{30}=\frac{V_{s30}}{f(V_{s30})} \tag{5}\]  
where \(V_{s30}\) is the average Vs velocity of the first 30 m, calculated from Equation (2). \(f(V_{s30})\) is the frequency at the \(V_{s30}\) value, read directly from the dispersion curve plotted in Figure 5.  
After the wavelength (\(\lambda_{30}\)) is figured out for each dispersion curve, the average wavelength is obtained as \(\overline{\lambda}_{30}=47\pm 4\) m.  
The \(V_{R47}\), which represents the Rayleigh wave velocity corresponding to the wavelength \(\overline{\lambda}_{30}\), is presented in Figure 8 as the intersection between the dispersion curve and the \(V_{S}=\overline{\lambda}_{30}\cdot f\) line (represented by the red color in Figure 8). The \(V_{s30}\) values were then aligned with the \(V_{R47}\) ones to obtain the most optimal linear fit between them. As a result, Equation (6) was deduced experimentally for the area under study, with a correlation coefficient of R\({}^{2}\) = 0.8703.  
\[V_{S30}=0.9853\times V_{R47} \tag{6}\]  
Figure 9 shows the obtained regression line, as well as the corresponding residual values. The \(V_{s30}\) values have been obtained with a maximum error not greater than 6.6%, and the associated site classifications have also been established (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & \(V_{s30}\) **(m/s)** & **Predicted \(V_{s30}\) (m/s)** & **Error (\%)** & **Actual Site** & **Predicted Site** \\  & & & **Classification** & **Classification** \\ \hline P1 & 620 & 590 & 5.00\% & C & C \\ \hline P2 & 590 & 560 & 5.30\% & C & C \\ \hline P3 & 630 & 650 & 3.00\% & C & C \\ \hline P4 & 680 & 640 & 6.25\% & C & C \\ \hline P5 & 510 & 480 & 6.20\% & C & C \\ \hline P6 & 540 & 570 & 5.30\% & C & C \\ \hlineFigure 8: Dispersion curves (black lines) obtained from the f-k analysis and already shown in Figure 5 above. The V\({}_{\rm R47}\) results from the intersection between the dispersion curve and the red line corresponding to the wavelength of 47 m.
## 6 Model Validation  
This section discusses the validation of the proposed model. We constructed a prototype to test our proposed model, which allows us to compare the MRT calculated by the model to the MRT achieved in real-world experiments. Figure 8 depicts the experiment's setup. There have been several omissions. In the validation scenario, we simulate the existence of a request generator, a gateway, a router, a fog, and a cloud. Four fog/cloud cores, each with its container, are available. The request is divided across the containers using a round-robin technique. We used the sixth machine as a world clock. A synthetic system (Request generator) was constructed to send requests with an exponential distribution. The configuration of the machines used to do the validation, as well as their respective functions, are presented in Table 3.  
Figure 7: Results changing the arrival rate with routing strategies variation.  
All graphics were generated according to the results extracted from the JMT tool. Only the validation has used an application prototype developed in Java. The validation code is available on Bitbucket ([[https://bitbucket.org/laecioandrade/simulatorcode/src/master/](https://bitbucket.org/laecioandrade/simulatorcode/src/master/)]([https://bitbucket.org/laecioandrade/simulatorcode/src/master/](https://bitbucket.org/laecioandrade/simulatorcode/src/master/)), accessed on 20 December 2021).  
The service times obtained in this experiment were entered into the model. Each request is a file containing a 10,000 \(\times\) 10,000 matrix filled with random data in the experiment. The request generator simulates many consumers sending requests at once, and the router gets them all at once and distributes them among the fog/cloud, which then distributes them among the inner containers. To guarantee that the results were reliable, we repeated the experiment 30 times.  
We utilized the one-sample \(t\)-test (sample \(t\)-test [[https://tinyurl.com/yanthw4e](https://tinyurl.com/yanthw4e)]([https://tinyurl.com/yanthw4e](https://tinyurl.com/yanthw4e)), accessed on 20 December 2021) to compare the MRT generated by the model with the MRT obtained in the tests. Normal distribution was found in all of the samples. To check if the \(t\)-test was significant, we looked at the \(p\)-value. The results are presented in Table 4. The \(p\)-value in each case was more than 0.05. As a result, we cannot reject the null hypothesis of equality with 95 percent confidence in all circumstances. As a result, we may deduce that the model's output is statistically identical to the outcome of the experiment. Because it is based on reality, the model is useful for planning IoMT designs.  
Given the consistency of the data obtained during the experiment, one may infer that the model is dependable and accurate. The estimated value of the model is within the experimental error range. Because it is based on reality, the model is useful for planning IoMT designs.
## 4 Results and Analysis
### Long-Term Deformation and Time Series Deformation in the Northern QTP
(**a**) Time series deformation of study points during 2007–2009. (**b**) Time series deformation of study points during 2017 to 2021. (**c**) The deformation of selected profile. thermal melting collapses of permafrost from 2015 to 2020. The result shows that L-band ALOS-2 can effectively detect the location of thermal melting collapses. The result shows spatial consistency with the GF-1/2 datasets. Compared with the ALOS-2 results, C-band Sentinel-1 rarely detected the location of thermal melting collapses. The results show that the ALOS-2 data can detect displacement that cannot be detected by C-band sensors. To reveal the relationship between the time series deformation and the thermal melting collapses. We selected four points and plotted the time series deformation. The details of the four points are provided in Table 5.  
From Table 5, from 2015 to 2020, the L-band ALOS-2 result is different from the C-band Sentinel-1 result. For the ALOS-2 result, the permafrost under points A, B and C is undergoing subsidence. The Sentinel-1 result shows that the permafrost is still stable. Due \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point** & **Position** & \
\begin{tabular}{c} **Long-Term Deformation** \\ **Velocity during 2015–2020** \\ \end{tabular} &
\begin{tabular}{c} **Long-Term Deformation** \\ **Velocity during 2017–2021** \\ \end{tabular} \\ \hline Point A & 92.771\({}^{\circ}\) E, 35.059\({}^{\circ}\) N & \(-\)24.9 mm/year & 3.4 mm/year \\ Point B & 92.790\({}^{\circ}\) E, 35.051\({}^{\circ}\) N & \(-\)8.1 mm/year & 9.1 mm/year \\ Point C & 92.803\({}^{\circ}\) E, 35.044\({}^{\circ}\) N & 7.1 mm/year & 13.0 mm/year \\ Point D & 92.823\({}^{\circ}\) E, 35.032\({}^{\circ}\) N & \(-\)11.1 mm/year & 14.1 mm/year \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The details of the four selection points. Figure 6: The thermal melting collapse, average deformation and time series deformation of the study points. (**a**) Thermal melting collapse from GF-1/2 during 2019–2020. (**b**) Average deformation rate during 2015–2020. (**c**) Average deformation rate during 2017–2021. (**d**) Time series deformation of points during 2015–2020. (**e**) Time series deformation of points during 2017–2021. to the different observation periods of the two sensors, ALOS-2 has a longer observation period. In addition, results are also affected by the internal characteristics of the sensor, such as wavelength and incident angle.
## 2 Literature Review
### STS Theory and Cybersecurity
For instance, a recent study by [PERSON] et al. [11] highlights prior literature considering multiple sectors and lacks empirical investigation of cybersecurity issues exclusively for HERS. Secondly, previous research studies have outlined challenges and solutions based on various reports published through security bodies and in prior literature [11; 18; 45]. Thirdly, despite being significant stakeholders, cybersecurity experts and top managers are not considered primary participants in the research. The majority of the studies focus on students as the unit of analysis [13; 14]. Fourth, prior literature has not exclusively focused on HERS to empirically identify and classify cybersecurity issues. Most of the research has reported cybersecurity issues and countermeasures considering multiple sectors [11; 19]. Few studies have considered the health sector to address this issue [9; 20]. Some studies have chosen HEIs [13; 14]. However, [PERSON] et al. [13] emphasize exploring cybersecurity awareness in connection to digital platforms rather than investigating core cybersecurity issues in a Malaysian institute, whereas the study by [PERSON] et al. [14] focuses on exposure to cyberscrimes and digital technology rather than identifying primary cybersecurity threats in HEIs. Fifth, previous literature has not used any theory to support their findings [9; 18]. Finally, as outlined in Table 2, the studies addressing cybersecurity challenges through the lens of STS theory primarily consist of systematic reviews and theoretical works, with limited focus on specific industries and specific contexts, including crises [35; 36; 39; 40]. This study aims to fill these identified gaps and conduct an extensive empirical investigation on the identification and classification of emerging cybersecurity challenges in the HERS using the STS theoretical lens to consider recent major crises. The use of STS theory to identify and classify cybersecurity challenges is further elaborated in the Sections 4 and 5.
## 2 Materials and Methods
### Methods
#### 2.2.3 Affine Transformation of SPD Covariance Matrix  
We propose to affine transform the SPD covariance matrix to make the data from different subjects comparable. In the Riemannian Geometry framework, the cross-subjects signal variability can be understood as the geometric transformation of the SPD covariance matrix on the Riemannian manifold. In [27], [PERSON] et al. tried to solve the cross-subject variability by performing the affine transformation of the covariance matrix; however, their work did not consider the geometric structure of the covariance matrix. In [28], [PERSON] et al. tried to use the Riemannian Geometry framework to perform cross-session MI transfer learning; however, their method is related to the sequence of experiment task. In this article, we propose a method that combines the ideas of Riemannian Geometry classifier and affine transformation, and specially uses the Riemannian Geometry mean to complete  
Figure 4: The Riemannian minimum distance to the mean for classification problems. Two types of Riemannian Geometry mean (G1 and G2) are calculated from the training data. When the data to be classified (indicated by a question mark) come in, they are classified as the ones whose Riemannian distances is smallest.  
Figure 3: The distance between C1 and C2 is called Riemannian distance \(\delta\left(\mathbf{C}_{1},\mathbf{C}_{2}\right)\), where G represents the Riemannian Geometry Mean of C1 and C2.  
the selection of the reference matrix for the affine transformation. We know that the distribution of the covariance matrix of different subjects on the manifold is inconsistent, but there is a certain reference state; thus, as long as we find this reference state and express it in the form of matrix, through affine transformation, data from different subjects become comparable. We estimate a reference matrix for each subject's data, and then we use these reference matrix to perform an affine transformation on their data. After this transformation, the Riemann distance and geometric structure of the SPD covariance matrix on the manifold will not be changed. Although the reference matrix is different for every subject, it will cause the covariance matrix on the manifold to move in different directions, but, as long as a common and stable reference matrix is found, the data from different subjects will move in the same direction on the manifold and become comparable. The Riemannian Geometry Mean is a good choice to represent this reference matrix. We use Riemannian Geometry mean to calculate the reference matrix \(R_{i}\) (i = 1,2,...,k.) for each subject, respectively. The affine transformation can be represented as follows:  
\[C_{i}=R_{i}^{-\frac{1}{2}}C_{i}R_{i}^{-\frac{1}{2}} \tag{5}\]  
\(C_{i}\) represents SPD covariance matrix of the \(i\)th subject.
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
## 4 Discussion
### Future Work  
Our proposed method uses only one vanishing point to estimate the absolute orientation with one known rotation angle. Actually, some IMUs can give two directions with a high accuracy, or some positioning devices can be mounted on the camera. In some cases, more than one vanishing point exist in the FOV of camera. Hence, for the future work, we will follow the outline and idea of this paper to use more prior knowledge (i.e., two directions, different camera positions, or more vanishing points) to simplify the orientation estimation or to estimate more extrinsic parameters (orientation and position) and some intrinsic parameters (principal point and focal length). This will extend the applicable range of our proposed method. The ultimate goal is to use our proposed method in practice, e.g., SfM and SLAM.