esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, bearing stability was investigated experimentally by applying a GFPB with a high-damping polymer layer inserted into a biaxial rotor system with a diameter of 40 mm and a total length of 550 mm. The results were compared with those of the GFB.  
The polymer layer of the test GFPB used NBR, a type of natural rubber with excellent damping properties at temperatures ranging from \(-55\)\({}^{\circ}\)C to 125 \({}^{\circ}\)C. The polymer layer was 2 mm thick and 35 mm wide. The manufactured GFPB and GFB were measured for bearing clearance through a static load-deflection test, and both bearings had the same clearance of 200 \(\upmu\)m.  
As a result of the dual-rotor modal test, the first- and second-order natural frequencies were measured at 15 and 160 Hz, respectively, and the third-order natural frequency was 1835 Hz in the bending mode of the test rotor. Therefore, a separation margin of 26% was applied to the third-order natural frequency, limiting the maximum rotational speed of the dual-rotor rotordynamic test rig to 92 krpm.  
The GFPB and GFB were applied to the test rotor by adjusting the bearing clearance to 150 \(\upmu\)m using a shim. The GFPB instability began at 53 krpm and was stably suppressed to 80 krpm at an amplitude of approximately 10 \(\upmu\)m. However, the instability of the GFB tends to start at 44 krpm and has an amplitude of approximately 13 \(\upmu\)m at 57 krpm. This appears to be a feature of the GFPB with improved structural damping, i.e., the GFPB does not differ significantly in OSS compared with the GFB, but it is quite stable in the development of instability, especially on the NDE side.  
Consequently, summarizing the results of rotordynamic experiments for synchronous and subsynchronous motions and bearing frictions in this study, GFPBs have a great advantage in terms of stability compared to the widely used GFBs. Based on these results, the authors of this study carefully recommend the use of GFPB as a lubricating element for environments where rotordynamics is unstable or where the system is subjected to shocks or periodic disturbances. Of course, the temperature conditions of the operating environment must be suitable for the use of polymers.  
Conceptualization, K.S.; methodology, K.S.; software, J.P.; validation, J.P. and D.K.; formal analysis, J.P.; writing--original draft preparation, J.P. and D.K.; writing--review and editing, K.S.; supervision, K.S. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the research program funded by SeoulTech (Seoul National University of Science and Technology).  
Figure 12: Measured rotor speed versus time during the coast-down process for GFB and GFPB.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Works
### Other Related Works  
Aside from the proposals that have been reviewed, there are some others that should be taken into account due to the fact that include ideas that, while might not be explicitly related to the scenario of several autonomous maritime vehicles interoperating with each other while negating their own heterogeneity, are worth mentioning because they have been used in a similar context. They have been listed in a more detailed way in [24].  
Huxley [25] is aimed to offer a collection of software components that can be ported with ease from one vehicle to another while performing functionalities typical of middleware. This piece of work, though, offers little information about how information can be transferred from one vehicle or another without a centralized entity, or even if there is a procedure to do so via Publish/Subscribe components or any other communication paradigm.  
According to its developers, Sunrise has as its main objective creating an Internet of Underwater things [26] were reliable communications and data exchanges are made possible, regardless of the vendors that have manufactured the maritime vehicles. Unfortunately, there is no information about the location of the middleware components that are expected to be installed in each of the vehicles or what the performance of the solution is expected to be.  
T-REX is another proposal where the stress is put on underwater vehicles becoming capable of making their own decisions [27]. Its authors claim that they have made use of the sense-plan-act paradigm. Other aspects that are mandatory in this kind of application domain, such as the distribution of information among a plethora of underwater robots or features like context awareness or semantics have not been included in the proposal.  
Lastly, TRIDENT [28] targets cooperation among maritime vehicles by providing a solution where an Autonomous Surface Craft and an Intervention Autonomous Underwater Vehicle are deployed and interact with each other. Unlike other proposals, there is a semantic world model framework as a way to integrate several autonomous robots, where the different information sources are integrated seamlessly. Security, though, seems not to be considered.
## 6 Discussion  
In the following, we discuss several issues: the attack interval, a hybrid network and the capability of extensibility.  
* Attack interval: In this paper, we assume that all attacks are independent and that the attack interval has an exponential distribution with a rate parameter \(\lambda\). In a realistic environment, there may be more than one adversary in the network, and the attack interval may not be in a regular pattern. The performance in this case shall be analyzed further, and the proposed scheme shall be enhanced to address this issue.  
Figure 7: The response time _vs_. the number of hops. (**a**) Low overhead; (**b**) high overhead.  
* Hybrid network: In this paper, we consider that the network is uniform and that all of the nodes in the network are the same. Nonetheless, the realistic AMI may be a hybrid network. As such, the network delay of remote nodes in different locations may not be alike, as well as the computation time of the checksum. Then, the verifier must have a proper mechanism to distinguish them and confirm compromised nodes in the network. Therefore, how to deal with the hybrid network is another issue for future study.
* Extensibility: In addition to the smart grid, there are numerous systems where the legacy system exists. Our proposed scheme is generic and can be extended to systems where the remote memory attestation is needed, but the hardware or computing capability is limited. As shown in our analytical and evaluation results, our proposed scheme can achieve a great performance with a lower overhead.
# Evaluation of Mixed Traffic Flow Efficiency and Safety on Hard-Shoulder-Running Freeways
## 3 Results  
### The Results of HSR Without CAVs  
As shown in Figure 3, if the hard shoulder remains closed to traffic, the overall road traffic efficiency will drop sharply after the temporary construction area starts to work, regardless of the free-flow, saturated flow, or congested flow variants. In contrast, if the hard shoulder is opened for traffic and the maximum speed limit of the hard shoulder is set at 60, 80, or 120 km/h, the overall road traffic efficiency will be significantly improved after the temporary construction area starts working. Additionally, the figure reveals the relationship between the speed limit and the traffic demands when the hard shoulder is open. In the case of free-flow and saturated flow traffic, a higher speed limit generally leads to higher overall traffic efficiency. However, the situation differs in congested traffic, as a speed limit of 60 km/h results in higher traffic efficiency than a speed limit of 120 or 80 km/h.  
Figure 3: The traffic efficiency of HSR at different limit speeds in different levels of traffic demands. The black line represents the values of traffic efficiency without HSR. The red, yellow, and green lines  
Figure 2: The simulation scenarios. The arrows represent the direction of traffic flow; solid lines denote the edges of traffic lanes, and dashed lines mark where drivers can cross the separators between lanes traveling in the same direction. The red-colored areas represent temporary construction zones where vehicular traffic is not permitted. (**a**) shows the traffic does not run on the hard shoulder when the temporary construction area exists; (**b**) shows the traffic running on the hard shoulder during the construction. The green area represents the HSR area.  
represent the traffic efficiency when the maximum speed limits on the hard shoulder are set at 120 km/h, 80 km/h, and 60 km/h, respectively, during the opening of the hard shoulder. (**a**) shows the scenario of HSR in free flow; (**b**) shows the scenario of HSR in saturated flow; (**c**) shows the scenario of HSR in congested flow.
## 3 Results and Discussion
### Scenario 1 (S1)
#### 3.1.1 Calculation Area of EC  
The scope of the sea area of Haizhou Bay for calculating the current value of EC in this study comprised from the Bergamot Tsui (35\({}^{\circ}\)05\({}^{\prime}\)55\({}^{\prime\prime}\) N, 119\({}^{\circ}\)21\({}^{\prime}\)53\({}^{\prime\prime}\) E) in Lanshan Town, Rizhao City, Shandong Province, in the north, to Gaogong Island (34\({}^{\circ}\)45\({}^{\prime}\)25\({}^{\prime\prime}\) N, 119\({}^{\circ}\)29\({}^{\prime}\)45\({}^{\prime\prime}\) E) in Lianyungang District, Lianyungang City, Jiangsu Province, in the south, to the connecting line of the two places in the east, and to the coastline of Haizhou Bay in the west (Figure 7 plan A). The EC of pollutants in Haizhou Bay were calculated according to the coastal shape formed by the superposition of reclamation working conditions (Figure 7 plans B-F).  
Working condition HZ1 was used for urban development, so the project area was not very large and the shape of the reclamation was relatively regular. Working conditions HZ2-HZ5 were used for port construction, so the shapes formed by reclamation were irregular polygons. The reclamation project will result in changes in the sea area of Haizhou Bay (Table 4).
# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 5 Conclusions  
In this work, we introduced an enhanced method for Arabic language dialect identification. We investigated several techniques such as logistic regression and recent advanced deep learning-based classifiers. We achieved the best performance using the BERT-based model that provided 90.45% accuracy on the Arabic Online Commentary (AOC) dataset. When we applied the BERT-based dialect recognition model for identifying the source region of COVID-19 related tweets, we managed to recognize the source region for 1.76 million Arabic tweets. After randomly sampling 2500 tweets from each region set and checking them manually, we got a weighted average accuracy of 97.36%. The tweets from January to April 2020 were used for experimentation, which was the starting critical period of the virus spread. The high accuracies of the results show that the proposed model can be used as a powerful tool for getting geo-based insights for disaster-related social data. We plan to expand dialect recognition to the country level in future work, which is challenging due to the overlap of the local dialects in neighboring countries.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & **Number of Tweets** & **Accuracy** \\ \hline MSA & 1,373,869 & 97.7\% \\ Gulf & 222,083 & 97.6\% \\ Egyptian & 139,143 & 92.8\% \\ Levantine & 34,037 & 99.6\% \\ Total & 1,769,132 & 97.36\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: COVID-19 labeled tweets and dialect recognition result.  
**Author Contributions:** Conceptualization, S.A.; methodology, N.E. and S.A.; software, N.E.; validation, N.E., A.M.M. and S.A.; formal analysis, A.M.M. and S.A.; investigation, N.E., A.M.M. and S.A.; resources, S.K., M.M.H., A.A. and M.A.A.; data curation, N.E. and K.M.E.; writing--original draft preparation, S.A.; writing--review and editing, A.M.M. and S.K.; visualization, S.A.; supervision, S.A., K.M.E. and M.R.; project administration, S.A., K.M.E. and M.R.; funding acquisition, N.E., A.M.M., K.M.E., S.A., M.R., S.K., M.M.H., A.A. and M.A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors are grateful to the Saudi Arabian Ministry of Education's Deputyship for Research and Innovation for supporting this research through project number 523.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [7; 12].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Results and Analysis
### Simulated Signal Experiments
#### 3.1.1 Fine Satellite Geometry
After this point, the value of \(C_{f}\) begins to decline, significantly reducing the algorithm's overall search space, with more emphasis on reproductive behavior and Gaussian mutation strategies. As seen in Figure 7a, in subsequent iterations, ADBO continues to effectively search for positions with smaller fitness values. Figure 6: Results of different algorithms under fine geometry. (**a**) Convergence curves. (**b**) Position errors curves. Figure 7: Convergence curves, FDC, and ruggedness curves from a single experiment. (**a**) Convergence curves by different algorithms. (**b**) FDC and ruggedness curves.
# ML-Based 5G Network Slicing Security:
###### Abstract  
Fifth-generation networks efficiently support and fulfill the demands of mobile broadband and communication services. There has been a continuing advancement from 4G to 5G networks, with 5G mainly providing the three services of enhanced mobile broadband (eMBB), massive machine type communication (eMTC), and ultra-reliable low-latency services (URLLC). Since it is difficult to provide all of these services on a physical network, the 5G network is partitioned into multiple virtual networks called \"slices\". These slices customize these unique services and enable the network to be reliable and fulfill the needs of its users. This phenomenon is called network slicing. Security is a critical concern in network slicing as adversaries have evolved to become more competent and often employ new attack strategies. This study focused on the security issues that arise during the network slice lifecycle. Machine learning and deep learning algorithm solutions were applied in the planning and design, construction and deployment, monitoring, fault detection, and security phases of the slices. This paper outlines the 5G network slicing concept, its layers and architectural framework, and the prevention of attacks, threats, and issues that represent how network slicing influences the 5G network. This paper also provides a comparison of existing surveys and maps out taxonomies to illustrate various machine learning solutions for different application parameters and network functions, along with significant contributions to the field.  
5G network; network slicing; security; threats; machine learning +
Footnote †: journal:  
_Future Internet_  
Footnote †: journal:
## 5 Power Optimization
### The Optimization Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source of** & **Sum of** & **Degree of** & **Mean Square** & \(f\)**Value** & \(p\)**Value** & **Significance** \\
**Variation** & **Squares** & **Freedom** & **Mean Square** & \(f\)**Value** & \(p\)**Value** & **Significance** \\ \hline Model & 7.97 & 9 & 0.8851 & 4.81 & 0.0052 & **\({}^{**}\)** \\ X\({}_{1}\) & 2.02 & 1 & 2.02 & 10.98 & 0.0290 & \({}^{*}\) \\ X\({}_{2}\) & 0.4399 & 1 & 0.4399 & 2.39 & 0.1660 & \({}^{**}\) \\ X\({}_{3}\) & 1.05 & 1 & 1.05 & 5.68 & 0.0486 & \({}^{*}\) \\ X\({}_{3}\)X\({}_{3}\) & 0.0261 & 1 & 0.0261 & 0.1417 & 0.7178 & \({}^{**}\) \\ X\({}_{3}\)X\({}_{3}\) & 0.1197 & 1 & 0.1197 & 0.6604 & 0.4465 & \({}^{**}\) \\ X\({}_{3}\)X\({}_{3}\) & 0.6650 & 1 & 0.6650 & 3.61 & 0.0491 & \({}^{*}\) \\ X\({}_{4}^{2}\) & 0.0796 & 1 & 0.0796 & 0.4325 & 0.5318 & \({}^{*}\) \\ X\({}_{5}^{2}\) & 2.18 & 1 & 2.18 & 11.83 & 0.0108 & \({}^{*}\) \\ X\({}_{5}^{2}\) & 1.58 & 1 & 1.58 & 8.60 & 0.0220 & \({}^{*}\) \\ Residuals & 1.29 & 7 & 0.1841 & & \\ Lack of Fit & 0.8433 & 3 & 0.2811 & 2.53 & 0.1961 & \({}^{**}\) \\ Pure Error & 0.4452 & 4 & 0.1113 & & \\ Cor Total & 9.25 & 16 & & & \\ \hline \hline \end{tabular} Note: \(p\leq 0.01\) means extremely significant, marked with \({}^{***}\); \(p\leq 0.05\) means significant, marked with \({}^{***}\); \(p<0.05\) means insignificant, marked with \({}^{***}\); \(p<0.05\) means insignificant, marked with \({}^{***}\); \(-\)**.  
\end{table} 
Table 5: Analysis of variance of operating power. According to the above analysis of variance and the regression coefficient of each factor in the regression model, each factor that has an influence on the cutting power consumption can be obtained. The obvious indigentity of each factor on the target optimization value is as follows: the first term \(X_{1}\) and \(X_{3}\), the interaction term \(X_{2}X_{3}\), and the second term \(X_{2}^{2}\) and \(X_{3}^{2}\). The influence order is forward speed \(X_{1}>\) bending angle \(X_{3}>\) rotating speed \(X_{2}\). The response surface based on the power consumption regression equation is shown in Figure 11. When the forward speed is 1100 m/h, the power consumption increases with the increase of tool roller speed and bending angle.
# Assessment of the Structural State of Dissimilar Welded Joints by the Acoustic Emission Method
## 3 Results
In the scatterplot with \(\sigma_{\text{max}}\) = 300 MPa (Figure 10b), a similar region is also presented. It contains a larger number of indications and combines more AE hits. At \(\sigma_{\text{max}}\) = 400 MPa, the scattering diagram (Figure 10c) displays more points, just as in the defect-free case. Many horizontally oriented groups of indications are formed. Figure 11 shows the frequency histogram of the AE hits' rise times obtained at \(\sigma_{\text{max}}\) = 200 MPa. This histogram can be considered as an empirical distribution of the rise time parameter. In Figure 11a, corresponding to a defect-free welded joint, the rise time distribution is close to uniform, and in Figure 11b, corresponding to a welded joint with diffusion interlayers, we can see a single mode with a high frequency of observations. For AE signals obtained during loading of a dissimilar welded joint with diffusion interlayers, cluster analysis was performed using the same method as for a defect-free specimen. It was found that AE hits with equal rise time are grouped in a scatterplot into horizontally oriented regions (marked with a red ellipse in the Figure 9a,b). All of them have a similar waveform and correlate with \(r\) = 0.7-0.9. Figure 12 shows the signals corresponding to weld deformation at stress \(\sigma_{\text{max}}\) = 200 MPa (Figure 12a) and \(\sigma_{\text{max}}\) = 300 MPa (Figure 12b). As can be seen, the AE signals have the same waveform and characterize practically identical physical processes. Figure 10: Rise time vs. amplitude scatterplots for AE hits registered under cyclic loading of a specimen with diffusion layers at \(\sigma_{\text{max}}\) = 200 MPa (**a**) \(\sigma_{\text{max}}\) = 300 MPa (**b**) and \(\sigma_{\text{max}}\) = 400 MPa (**c**).
## 3 Methods
### Facial Expression Recognition  
Feature extraction for facial expression recognition incorporates geometric, temporal, and deep learning-based features to enhance representation. Geometric features capture facial structure through landmark-based distances and angles, while temporal features track variations in expressions over time. Additionally, deep learning features are extracted using pre-trained convolutional neural networks (VGG16 model) to encode high-level spatial patterns. For classification, Support Vector Machines (SVMs) and Logistic Regression are applied independently to generate initial predictions. These outputs are then fused using a Dynamic Bayesian Mixture Model (DBMM), which dynamically adjusts classifier contributions based on confidence scores and temporal dependencies, ultimately improving classification accuracy and robustness. The facial expression model was trained on the KDEF dataset [20], which consists of adult facial images. While children may exhibit expressions that differ in intensity or geometry, we addressed this limitation by extracting geometric features (e.g., distances between eyes, eyebrows, and mouth corners) that are less dependent on texture. An ensemble classifier combining these geometric features with EEG-based concentration detection helped reduce bias and improve emotional state recognition. Algorithm 3 presents all the steps for facial expression recognition. ```
1:Input: Face image of size \(224\times 224\)
2:Output: Predicted facial expression class
3:Step 1: Preprocessing
4: Detect facial landmarks using DLib or MediaPipe. 5: Normalize and resize the image to \(224\times 224\). 6:Step 2: Feature Extraction
7:(a) Geometric Features:
8: Compute pairwise distances among facial landmarks:
9:\(d_{ij}=\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}}\)
10: Compute landmark triangulation (Delaunay). 11: Compute angles between facial landmark triangles:
12:\(\theta_{ijk}=\cos^{-1}\left(\frac{d_{ij}^{2}+d_{jk}^{2}-d_{jk}^{2}}{2d_{ij}d_{jk}}\right)\)
13:(b) Covariance-Based Features:
14: Compute covariance matrix (given the landmarks):
15:\(s_{k\ell}=\frac{1}{N-1}\sum_{i=1}^{N}(y_{ki}-g_{k})(y_{\ell i}-g_{\ell})\)
16: Compute logm of covariance matrix:
17:\(e^{\mathbf{B}}=\mathbf{I}_{K}+\sum_{n=1}^{\infty}\frac{\mathbf{S}^{n}}{n! }, \quad\mathbf{B}=\text{logm}(\mathbf{S})\)
18:(c) Deep Learning Features (given the face images):
19: Extract deep features from VGG16 fully connected layers. 20:(d) Other image-based features (given the face images):
21: Extract Histogram of Gradients and Linear Binary Patterns. 22:(6) Temporal Features:
23: Compute temporal difference:
24:\(F_{T}=F_{t}-F_{t-1}\) (difference between current and previous frame feature vector)
25:Step 3: Feature Fusion
26: Concatenate all extracted features:
27:\(F=\{F_{\text{geo}},F_{\text{HtoG}},F_{\text{VGG16}},F_{T}\}\)
28:Step 4: Feature Selection
29: Select features using Information Gain:
30:\(IG(F_{i})=H(\mathbb{C})-H(\mathbb{C}|F_{i})\)
31:Step 5: Train SVM and Logistic Regression
32: Train Support Vector Machine (SVM):
33:\(\min_{\mathbf{w},\mathbf{b}}\frac{1}{2}\|\mathbf{w}\|^{2}\)
34: s.t.