esa-sceva/satcom-chunk-collection huggingface dataset:
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 4 FOXS-GSC Performance Evaluation
### Scenario Coverage Evaluation
#### 4.2.2 Results  
In Figure (b)b, it can be noted that the number of packet collisions increases with the need to use multi-hop communication, as more messages are needed to reach the destination that is the most distant. Note that the greater the RSU coverage, the fewer messages are needed to reach the goal.  
Figure (a)a shows the number of packets re-transmitted. It can be seen that, as expected, the lower the number of RSUs and the greater the density of vehicles, the greater the number of re-transmitted messages. The method applied by FOXS-GSC for interactive intersection routing manages to keep the number of messages in the system low even with a large number of vehicles and reduced RSU coverage.  
FOXS-GSC has good coverage, as shown in Figure (c)c. When vehicle density is low, problems such as network disconnection reduce the coverage area. This problem can be reduced by applying a transmission methodology such as store-carry-forward. The FOXS-GSC protocol uses this methodology for its congestion control service; however, it is not implemented here, as regions with low density have a low probability of traffic congestion.  
As expected, the percentage of responses for messages sent (see Figure (d)d) is higher when the number of RSUs is higher. When using one RSU with a vehicle density of 50 vehicles/km\({}^{2}\), the number of collisions (76%) is influenced by the network partition problem. For a density of 500 vehicles/km\({}^{2}\), the results are influenced by packet collisions, as shown in Figure (b)b. Note the peak for a density of 150 vehicles/km\({}^{2}\) in the scenario with one RSU; this is due to the fact that this scenario has a sufficient number of vehicles to avoid network partition without having enough for packet collisions to reduce the delivery rate.  
Figure 8: Network comparison results with different levels of RSU coverage: (**a**) total number of packets re-transmitted; (**b**) number of collisions per vehicle; (**c**) receive coverage (%); (**d**) percentage of messages returned.
# Image Segmentation by Searching for Image Feature Density Peaks
## 2 Materials and Methods
### Superpixel Method for Image Preprocessing  
The conventional segmentation approach will process each pixel one by one, which will cause a massive amount of data have trouble in analyzing, especially when the picture size is relatively large. In the present work, we use superpixel segmentation as an intermediate step to reduce the complexity of images from millions of pixels [24]. Superpixel segmentation aims to partition a picture into multiple homogeneous cells, we called superpixels, which has been widely applied to the picture analysis and simplification. [PERSON] et al. [25] comprehensively evaluated a variety of advanced superpixel algorithms in their studies. Among these algorithms, the simple linear iterative clustering (SLIC) can effectively adhere to image boundaries as well as or better than other schemes [26]. We employ this algorithm to divide the original image into a number of irregular blocks according to their similarity, which changes a pixel-level image into a district-level image. Each block is a perceptually consistent unit, and all pixels in a little superpixel region are most likely uniform in color. The different quantity of superpixels can be set according to the different size of the picture, and the values of the pixels contained in each superpixel region are the same. Hence each superpixel can be abstracted as a sample point. It is obviously that this way makes system requires less computation compared with other algorithms.  
In this process, superpixel segmentation provides a more characteristic and significant representation easy to perceive of the digital image [27], most structures in the image are preserved. It is more helpful and meaningful for centralized processing of valid elements utilizing superpixel segmentation. There is very little loss in moving from the pixel-level map to the superpixel-level map. In the next task of image segmentation, superpixels are used as sample points for analysis. This method is illustrated by a simple example in Figure 2.
## 4 Results and Discussion
### Deflection of the Lower Cascades  
The impact of the thrust control configuration on the aircraft's thrust, lift, and pitch moment was measured under different angles of deflection of the lower cascades. As depicted in Figure 20, the additional lift showed a consistently negative trend, meaning that, as the lower plate was deflected, this created a downwash effect that reduced the overall lift produced by the wing. This negative lift intensified with an increase in the deflection angle, indicating a direct relationship between the angle of deflection and the aerodynamic penalty in terms of lift reduction.  
Figure 21 indicates that the aircraft exhibited a consistent nose-up pitch moment across all angles of deflection. This increase in the pitch moment indicated that deflecting the lower plate affected the airflow in a manner that increased the lift at the rear of the aircraft relative to the front, thereby pitching the nose upward. The pitch moment increased linearly with the deflection angle, suggesting a proportional effect of deflection on the pitching dynamics.  
The thrust produced by the aircraft was inversely related to the deflection angle of the lower plate, as shown in Figure 22. As the angle increased, the effective exhaust area for the propulsion system diminished, leading to a reduction in thrust. The thrust reached a nadir at the maximum deflection, dropping to approximately 27.5% of its initial value, mirroring the obstruction caused by the plate to the outflowing jet stream.  
Figure 19: \(T\) variation with deflection angles in upper cascade deflection.  
Figure 20: \(C_{L}\) variation with deflection angles in lower cascade deflection.
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 2 The Metaverse and Mixed Reality
### Areas of Application
#### 2.2.6 Business  
The Metaverse technology has the potential to significantly enhance businesses's productivity by enabling users to peruse online stores, search for desired products, and engage with them via their digital avatars. It represents a paradigm shift in conducting e-commerce, as users can now experience products in an immersive manner rather than merely adding them to their shopping carts. Additionally, by integrating AR with the Metaverse, businesses can develop innovative advertising strategies that allow users to try the product instead of merely viewing it being displayed.  
The integration of the Metaverse into various sectors of our society is a critical undertaking. One of the key considerations in this process is ensuring the protection of user privacy, security, and confidentiality. Given the novelty and complexity of this emerging technology, there is a pressing need to establish precautions and standards that safeguard the interests of all stakeholders involved. Therefore, developing a regulatory framework that addresses potential risks and threats while promoting innovation and growth is essential. By doing so, one can harness the full potential of the Metaverse while upholding the ethical and legal standards that underpin our society.  
MR also holds tremendous potential to revolutionize business operations and enhance productivity by blending real-world environments with digital interactions. In e-commerce, MR allows users to interact with virtual representations of products in real time, examining them from different angles and manipulating them within the physical space, creating a more engaging and informed shopping experience. This immersive approach can increase consumer confidence and reduce the gap between online shopping and in-store experiences. Additionally, by integrating MR into advertising strategies, businesses can offer customers the ability to \"try before they buy\", allowing them to interact with products virtually within their environment--something far more interactive than static online displays.  
As MR technology becomes more integrated into various industries, safeguarding user privacy, security, and data confidentiality is crucial. Like the Metaverse, MR faces unique challenges regarding user data protection and the ethical use of immersive technologies. Establishing standards and regulatory frameworks is essential to mitigate risks such as unauthorized data access or the misuse of personal information. This framework should promote innovation while ensuring that users' rights are maintained, creating an environment where effectively adopting MR does not compromise ethical or legal standards.
# Enhancing Logistic Regression Using Neural Networks for Classification in Actuarial Learning
## 3 Prediction Models and Numerical Illustration
### Evaluation and Results
#### 3.7.3 Advanced Approaches
Let \(F_{LogReg}:\mathbb{R}^{d}_{2}\rightarrow\mathbb{K}^{m_{2}}_{2}\) be a logistic regression model implemented by a neural network. Let \(\{x_{1},\dots,x_{k}\}\) be a set of \(k\leq\min(d_{1},d_{2})\) categorical features that are used as input to an embedding layer in \(F_{\text{Poisson}}\), that are also used as input to the \(F_{LogReg}\) network. After training a model for Poisson regression on \(F_{\text{Poisson}}\), the weights of the embedding layers will then be used in \(F_{\text{LogReg}}\). We followed this approach and retrained the GAM2 model from [6]. We then extracted the embeddings for the variables VehBrand, Area and Region. The corresponding function implementation is shown in Listing 6. The input is a data frame and a pretrained model with embedding layers from which we extract the embedding for each feature using the get_layer function. Using these embeddings, instead of the embeddings trained by our architecture, as described in the previous section, we again trained a logistic regression and a neural network model. As we see, the results are essentially identical. This is not really surprising as, in both cases the embeddings capture the information about the relationships between different objects in each category. Figure 4: AUC values on the test set achieved using a neural network with feature normalization (**left**) and feature normalization + categorical embeddings (**right**). Figure 3: AUC values on the test set achieved using R’s logistic regression function (**left**) and logistic regression by a shallow neural network (**right**). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{Train} & \multicolumn{2}{c}{Test} \\ \hline  & **Method** & **Deviance** & **AUC** & **Deviance** & **AUC** \\ \hline  & & \(LogReg_{R}\) & 0.193 & 0.635 & 0.196 & 0.627 \\  & & \(LogReg_{Dev}\) & 0.193 & 0.634 & 0.196 & 0.627 \\  & & \(LogReg_{Dev+Emb}\) & 0.192 & 0.636 & 0.195 & 0.637 \\  & & \(LogReg_{Dev+TransferLearning}\) & 0.194 & 0.634 & 0.195 & 0.637 \\  & & \(LogReg_{Dev+Frozen}\) & 0.192 & 0.646 & 0.192 & 0.647 \\ \hline  & \(NN_{Dev}\) & 0.187 & 0.684 & 0.189 & 0.684 \\  & & \(NN_{Dev+Emb}\) & 0.186 & 0.689 & 0.189 & 0.687 \\  & & \(NN_{Dev+Skip}\) & 0.187 & 0.684 & 0.189 & 0.685 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The deviance loss and the corresponding AUC values on the training and test datasets. Figure 5: AUC values on the test set achieved using logistic regression with embeddings (**left**) and frozen weights learned by a neural network (**right**).
## 3 Mathematical Modeling
### Multi-Objective Model for FJSP-LS
#### 3.2.5. Maximum and Total Job Flowtime (\(Z_{4}\) and \(Z_{5}\))  
In the presence of lot streaming, the entrance time \(\hat{e}_{j}\) and the departure time \(\hat{d}_{j}\) of a job are the smallest and the largest entrance times of all its subplots, \(\min_{\forall s\mid\gamma_{sj}=0}\{e_{s,j}\}\) and \(\max_{\forall s\mid\gamma_{sj}=0}\{d_{s,j}\}\), respectively. The values of these variables are set by the constraints in Equations (32) and (33), and the objective function. The flowtime of a job, \(\hat{f}_{j}\), is the difference \(\hat{d}_{j}-\hat{e}_{j}\) as enforced by the constraint in Equation (34). The constraint in Equation (35) along the objective function enforces \(\hat{f}_{max}\) to assume the maximum flowtime of all the jobs, \(\max_{\forall j}\{\hat{f}_{j}\}\). The total job flowtime (\(f_{total}\)) is evaluated by the constraint in Equation (36). The values \(\hat{f}_{max}\) and \(\hat{f}_{total}\) correspond to the fourth and fifth terms, \(Z_{4}\) and \(Z_{5}\), of the objective function and their values are enforced by the constraints in Equations (37) and (38), respectively.  
\[\hat{e}_{j}\leq e_{s,j}+\Omega(1-\gamma_{s,j});\ \ \forall(s,j) \tag{32}\]  
\[\hat{d}_{j}\geq d_{s,j}-\Omega(1-\gamma_{s,j});\ \ \forall(s,j) \tag{33}\]  
\[\hat{f}_{j}=\hat{d}_{j}-\hat{e}_{j};\ \ \forall j \tag{34}\]  
\[\hat{f}_{max}\geq\hat{f}_{j};\ \ \forall j \tag{35}\]  
\[\hat{f}_{total}=\sum_{j=1}^{I}\hat{f}_{j} \tag{36}\]  
\[Z_{4}=\hat{f}_{max} \tag{37}\]  
\[Z_{5}=\hat{f}_{total} \tag{38}\]
## 2 Methodology
### Dynamic Inter-Satellite Network  
ISL measurement is a dual one-way observation generated by a pair of satellites. The two satellites are each other's transmitter and receiver. The phased array antenna equipped on BDS-3 satellites can realize the beam scanning in a wide space range of \(-60^{\circ}\) to \(60^{\circ}\)[15]. The pointing agility and fast-tracking characteristics of phased array antenna make it possible to complete dual unidirectional ranging in a short time of 3 s, which can establish a dynamic inter-satellite network [20]. Phased array antennas work following predesigned timeslot schedule by ground control personnel. The timeslot schedule records the chain building information for a week, including when and how one satellite connects with the other satellites.  
The timeslot schedule of the BDS-3 ISL takes 1 h as a pre-designed period. Each pair of satellites is assigned a short-time segment of 3 s to finish a dual one-way ranging. A forward link is established in the first 1.5 s, and a backward link is established in the second 1.5 s [20]. Therefore, a satellite can establish contact with up to 20 satellites or anchor stations in one minute without regard to visibility. In the following 59 min, the order and rules for establishing the links remain the same as in the first minute.
# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 1 Introduction
In this paper, based on deep learning, a four-dimensional electron density model is established using a physical lattice neural network to study the distribution characteristics of electron density.
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### Non-Tech Analysis
Moreover, the Taiwan Information Security Center and National Chung Hsing University introduced a clone detection method from multidimensional scaling [86]. Universities and laboratories from other countries, such as Israel, Singapore, the United Kingdom, Australia, Germany, and Canada, are also engaged in related research. All the research cooperation among countries is close. For example, Singapore University of Technology and Design developed an abnormal detection framework to detect malicious behavior with the Imperial College of London [76].
# Color Point Cloud Registration Algorithm Based on [PERSON]
## 3 Algorithm Implementation
### Optimization Method  
The optimization aims to find the optimal solution through minimizing the error function. It is a convex optimization process and we can use Gauss-Newton Method to solve it. The transformation \((w,t)\) consists of 6 independent variables \(\xi=(w;t)\), where \(w=(\varphi,\theta,\psi)^{T}\) is the rotation angle of the three axes and \(t=(a,b,c)^{T}\) is the translation vector. The rotation can be divided into three independent rotation around these axes:  
\[R(w)=R_{x}(\varphi)R_{y}(\theta)R_{z}(\psi) \tag{15}\]  
where \(R_{x}(\varphi)\) represents the rotation of \(\varphi\) around the x-axis, and \(R_{y}(\theta)\) and \(R_{z}(\psi)\) are the same operation on the y-axis and z-axis, respectively.  
Because the error function consists of two parts, which are hue error and geometric error, the Jacobian matrix can be divided into two parts, \(J_{e_{H}}\) and \(J_{e_{G}}\), which are about the hue and geometry respectively:  
\[J_{e}=\begin{bmatrix}J_{e_{H}}\\ \sqrt{\sigma}J_{e_{G}}\end{bmatrix} \tag{16}\]  
Figure 4: The space relationship of the points.  
Let \(\hat{\xi}^{k}=(\varphi^{k};\theta^{k};\psi^{k};\mu^{k};b^{k};c^{k})\) be the current transformation after the \(k\)th iteration, and the Jacobian matrix can be seen as the gradient of error function about the transformation \(\xi\), so the Jacobian matrix at \(\xi^{k}\) can be calculated as follows:  
\[J_{e_{H}}=\
abla e_{H}^{(p,d)}(\xi)\big{|}_{\xi=\hat{\xi}^{k}} \tag{17}\]  
\[J_{e_{G}}=\
abla e_{G}^{(p,d)}(\xi)\big{|}_{\xi=\hat{\xi}^{k}} \tag{18}\]  
According to the chain rule, \(\
abla e_{H}^{(p,d)}\) and \(\
abla e_{G}^{(p,d)}\) can be calculated as follows:  
\[\
abla e_{H}^{(p,d)}(\xi) =[\frac{\partial}{\partial\xi_{i}}(H_{p}(f(s(q,w,t))-p)-H(q))] \tag{19}\] \[=\
abla H_{p}(f)J_{f}(s)I_{\xi}(\xi)\]  
\[\
abla e_{G}^{(p,d)}(\xi) =[\frac{\partial}{\partial\xi_{i}}(s(q,w,t)-p)^{T}n_{p}] \tag{20}\] \[=n_{p}^{T}I_{\xi}(\xi)\]  
where \(\
abla H_{p}(f)=d_{p}\) according to Equation (3) and \(J_{f}(s)\) is the Jacobian matrix of \(f\) about \(s\) while \(J_{s}(\xi)\) is that of \(s\) about \(\xi\). They can be found by the Equations (3) and (11). The optimization method we used is Gauss-Newton method, and the transformation \(\xi\) is updated as follows:  
\[\xi^{k+1}=\xi^{k}-(J_{e}^{T}I_{e})^{-1}J_{e}^{T}e \tag{21}\]  
where \(e=\left[\frac{e_{H}}{\sqrt{\sigma}e_{G}}\right]\). \(e_{H}\) and \(e_{G}\) are the matrix formed through the hue and geometric error according to Equations (10) and (11).  
The whole optimization is a process of iteration, and if the number of iterations reaches the maximum or the value of error function is less than the threshold preset, then iteration should be stopped and output the current transformation as the result; otherwise, the iteration will continue until reaching the stopping criteria.
## 6 Numerical Experiments
### Discussion  
This paper compares the optimization results with the solution results determined by the basic slot allocation based on delayed delivery. The collaborative optimization results of this paper can increase the total revenue of a liner company by 4.76%; at the same time, the utilization rate of slot resources can be increased by 10.151%.  
Compared to the findings in [1, 17], this research highlights the pronounced cyclical characteristics of container transportation, particularly for empty container repositioning. Furthermore, the results identify key factors influencing the trade off between laden and empty container transportation, including the unit revenue of laden containers, transportation costs, and storage costs for empty containers. When the unit revenue of laden containers is low or port storage costs are high, it is a wise choice for liner companies to allocate more slots for empty container transportation.
# A Novel Digitized Method for the Design and Additive Manufacturing of Orthodontic Space Maintainers
## 3 Results  
### Finite Element Analysis Results  
Finite element analysis was used to evaluate the planar stiffness (x-direction stiffness) of the space maintainer before and after undergoing severe plastic deformation in the bending test (Figure 7) caused by an external element pushing during the occlusion movement. Planar stiffness is a crucial parameter defining the component's behavior when in use, since it is the main parameter that defines the device's functionality during operation. The quantification of planar stiffness involved calculating the reaction force at a specific displacement of boundary condition D (Figure 7). The results in Figure 7 depict the magnitude of planar stiffness during successive iteration steps, where displacement in the simulation increases. The decreasing trend of planar stiffness primarily arises from the elastic-plastic response and the consideration of large displacements. However, as displacements become larger, the gradient of planar stiffness decreases, reaching a plateau typically observed around the fourth and fifth iteration steps. Overall, there is a slight increase in stiffness after plastic deformation (referred to as post-deformation) compared to the pre-deformation state. Nevertheless, this increase is not significant and can be attributed to the residual  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Set Value** \\ \hline Layer thickness (mm) & 0.05 \\ Exposure time (s) & 10 \\ Z lift distance (mm) & 8 \\ Z lift speed (mm/s) & 1 \\ Z retract speed (mm/s) & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main printing parameters adopted.  
Figure 6: Three-dimensional printing process: (**a**) printing file after the supports’ creation and (**b**) manufactured space maintainer.  
stress state resulting from the plastic deformation process. Figure 8 provides visual representations, including top and bottom views, of the extensively plasticized region within the simulated space maintainer. The highlighted red areas indicate regions that underwent significant plastic deformation, surpassing a plastic equivalent strain value of 0.1 mm/mm.  
The findings indicate that the planar stiffness remained relatively constant before and after the occurrence of plastic deformation, suggesting a consistent response attributable to the elastic-perfectly plastic material behavior exhibited by the \"OD-Clear MF Bio monomer free\" resin. This consistency implies that the space maintainer can maintain its structural integrity and functional properties, even when subjected to severe plastic deformation.  
Figure 7: Planar stiffness: (**a**) space maintainer stiffness pre- and post-plastic deformation and (**b**) force vs. displacement curve pre- and post-plastic deformation. Dashed lines represent the planar stiffness plateau reached during the simulation.
### Related Works
A recurrent neural network (RNN) is an artificial neural network in which nodes are directed to form a ring. The internal state of such a network can exhibit dynamic timing behavior. In theory, it can use historical information of any length, so it can model time series more completely [26]. However, RNN has the problem of gradient disappearance and gradient explosion during training. Therefore, researchers have improved it and proposed the Long Short-Term Memory (LSTM) network [27]. One study [28] proposes a deep Long Short-Term Memory (LSTM)-based ultra-short-term prediction method for regional-level loads based on big data resources. It visually demonstrates the extraction of abstract features from the load data by using a deep learning algorithm and confirms its good feature learning capability. Reference [29] predicted daily peak load based on Bi-directional Long Short-Term Memory (Bi-LSTM) and feature correlation analysis. Another study [30] proposes an LSTM-based ultra-short-term prediction method for regional-level loads based on big data resources. It visually demonstrates the extraction of abstract features from the load data by using a deep learning algorithm and confirms its good feature learning capability. However, the authors did not take into account the issue of the weighting of adjacent individual load values on the prediction results. This results in the hidden layer missing such critical information, which ultimately reduces the prediction accuracy. This paper addresses the method of daily load forecasting for groups of residents by first clustering similar load curves. The prediction models in each cluster are then fitted. Finally, the predictions from each cluster are summed to obtain the final predicted value.