esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis and Results
### Effect of O-Ring Pre-Compression (Compression Ratio) on Sealing Performance  
The compression ratio of the O-ring directly determines the contact stress of the II and III sealing surfaces. Working pressures of 10 MPa, 15 MPa, 20 MPa, and 25 MPa and compression rates of 15-25% (the size of each increment is 2.5%) were analyzed. The contact stress of the three sealing surfaces I, II, and III and the maximum stress of the combined sealing ring were obtained as shown in Tables 2-5.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Compression Ratio** & **15\%** & **17.5\%** & **20\%** & **22.5\%** & **25\%** \\ \hline Von-mises stress (MPa) & 28.991 & 23.562 & 25.373 & 19.367 & 20.633 \\ Contact stress at I (MPa) & 10.941 & 10.679 & 11.239 & 11.972 & 13.292 \\ Contact stress at II (MPa) & 16.53 & 17.426 & 18.575 & 19.067 & 19.731 \\ Contact stress at III (MPa) & 12.779 & 13.707 & 14.563 & 15.416 & 16.251 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contact stress and compression ratio (working pressure 10 MPa).  
Figure 5: Stress cloud diagram of combined sealing structure. (**a**) P1 = 10 MPa; (**b**) P2 = 15 MPa; (**c**) P3 = 20 MPa; (**d**) P4 = 25 MPa.  
From the table above, it can be seen that when the working pressure reaches 20 MPa, the maximum contact stress at I decreases with the increase in the compression rate. This is because the contact bandwidth between the wear ring and the O-ring increases with the increase in the compression rate, which leads to a decrease in the oil-bearing area and a decrease in the contact stress of the wear ring. The contact stress at II increases with the increase in the compression ratio, so it is feasible to increase the compression ratio if the contact stress is to be increased to match the sealing performance. However, when the working pressure is 25 MPa and the compression rate is 25%, the maximum contact stress at I decreases to less than the working pressure. This shows that the greater the compression rate is, the better the sealing effect is. It is particularly important to select a compression rate that is suitable for the working conditions.  
At the same compression rate, the maximum contact stress increases with the increase of pressure. When the compression rate is 15%, the maximum contact stress change range at I is 10.941-26.807 MPa, and the maximum contact stress change range at II is 16.53-29.916 MPa. The maximum contact stress variation range at III is 12.779-20.716 MPa. Under the same structural conditions, the main influence on the maximum contact stress at III is the pre-compression of the O-ring.  
When designing the groove for O-ring installation, the influence of working pressure should be fully considered, the most suitable compression rate parameter should be selected according to the range of the working pressure, and the piston groove size should be designed.  
Therefore, when designing a groove for O-ring installation, the influence of the working pressure should be fully considered. According to the range of working pressures, the most suitable compression rate parameter should be selected for the design of the piston groove.
## 4 Results
### Evaluating Generated Text
#### 4.4.1 Word-Overlap Metrics  
* **BLEU** The BLEU metric [4] performs n-grams comparison between the generated texts and original/reference texts. In our work, we have applied this metric at corpus level for the evaluation of generated texts. The BLEU score calculation is mathematically defined as below: \[BLEU-N=BP*exp(\sum_{n}^{N}w_{n}log(p_{n}))\] (4) where \(N\) is the maximum length for n-grams (in this paper, we have used BLEU-3) because implicitly it contains the BLEU-1 and BLUE-2, \(w\) is a uniform weighting and \(BP\) is the brevity penalty. In the next sections, BLEU score implicitly refers to BLEU-3.
* **METEOR** The METEOR metric [28] is used to correlate human evaluation more better for the generated texts. This score first create unigram alignment by assigning 0 or 1 to the unigram in the generated texts to the unigram in reference/original texts. This alignment not only considers the exact matches but also stemming, synonyms and  
Figure 8: Classification performance of conventional ML techniques on original and balanced dataset with new generated text.  
paraphrase matching. The precision and recall for unigrams are calculated based on this alignment. The METEOR score calculation is mathematically defined as below:  
\[METEOR=F_{mean}(1-p) \tag{5}\]  
where \(F_{mean}\) is the harmonic mean of recall and precision and \(p\) is a penalty.
* **ROUGE-L** The ROUGE-L metric [5] is a F-measure based calculation defined on the Longest Common Subsequence (LCS) between the generated and original texts.
## 2 FE-SEA Hybrid Model for Bending Vibration of a Simply Supported Beam
### Formulation of the Dynamic Equation
Denoting the corresponding shape functions \(\varphi_{n}^{g}(x)=\varphi_{n}(x)(n=1,2,3,\cdots,s)\) and \(\varphi_{n}^{l}(x)=\varphi_{n}(x)(n=s+1,s+2,\cdots,N)\), Equation (5) can be rewritten  
\[U(x,t)=\sum\limits_{n=1}^{s}q_{n}^{g}(t)\varphi_{n}^{g}(t)+\sum\limits_{n=s+1 }^{N}q_{n}^{l}(t)\varphi_{n}^{l}(t) \tag{8}\]  
Here, superscripts \(g\) and \(l\) represent the global and local modal components, respectively. Similarly, Equation (7) can be reformulated in matrix form  
\[ \begin{bmatrix}D^{gg}&D^{gl}\\ D^{gl}&D^{ll}\end{bmatrix}\begin{bmatrix}Q^{g}\\ Q^{l}\end{bmatrix} = \begin{bmatrix}F^{g}\\ F^{l}\end{bmatrix}  \tag{9}\]  
By expanding and simplifying, the coupled equations can be expressed  
\[ \begin{bmatrix}D^{gg}-D^{gl}(D^{ll})(D^{gl})^{T}\end{bmatrix} Q^{g}=F^{g}-D^{gl} (D^{ll})^{-1}F^{l} \tag{10}\]  
\[D^{ll}Q^{l}=F^{l}-(D^{gl})^{T}Q^{g} \tag{11}\]  
Equations (10) and (11) correspond to the global and local motion equations, which are solved using the FEM and Statistical Energy Analysis (SEA), respectively. First, the element-level mass, stiffness, and generalized force matrices are computed. According to the finite element formulation, these are expressed  
\[\left\{ \begin{array}{l}M_{mn}^{gg}=\rho A\int_{0}^{L}\varphi_{m}^{g}(x)\varphi_{ n}^{g}(x)dx\\ M_{mn}^{gl}=\rho A\int_{0}^{L}\varphi_{m}^{g}(x)\varphi_{n}^{l}(x)dx\\ M_{mn}^{ll}=\rho A\int_{0}^{L}\varphi_{m}^{l}(x)\varphi_{n}^{l}(x)dx\\ \end{array} \right. \tag{12}\]  
\[\left\{ \begin{array}{l}K_{mn}^{gg}=EI\int_{0}^{L}\varphi_{m}^{g^{\prime\prime }}(x)\varphi_{n}^{g^{\prime\prime}}(x)dx\\ K_{mn}^{gl}=EI\int_{0}^{L}\varphi_{m}^{g^{\prime\prime}}(x)\varphi_{n}^{l \prime}(x)dx\\ K_{mn}^{ll}=EI\int_{0}^{L}\varphi_{m}^{l\prime}(x)\varphi_{n}^{l\prime}(x)dx \\ \end{array} \right. \tag{13}\]  
\[F_{m}^{g}=F_{0}\varphi_{m}^{g}(x_{0}),F_{m}^{l}=F_{0}\varphi_{m}^{l}(x_{0}) \tag{14}\]  
Assuming the structure exhibits Rayleigh damping, the damping matrix is defined as a linear combination of the mass and stiffness matrices:  
\[C=\alpha M+\beta K \tag{15}\]  
where \(\alpha\) and \(\beta\) are the Rayleigh damping coefficients.
###### Abstract
This encompasses a wide range of activities, including regulatory compliance with international, national, and regional regulations and standards set by organizations such as the International Maritime Organization (IMO). These regulations cover various aspects of maritime operations, including ship construction, equipment, crew qualifications, and operational procedures [6; 7; 8; 9]. Preventive measures include the implementation of systems and protocols designed to prevent accidents, such as regular maintenance, safety drills, and the use of advanced navigation and communication technologies. These measures aim to address potential risks before they lead to incidents [11; 12]. Emergency preparedness and response involve the development of contingency plans and training programs to ensure that crew members are prepared to respond effectively to emergencies, such as oil spills, fires, and collisions. This includes the provision of necessary equipment and the establishment of coordination mechanisms with relevant authorities [7; 10]. Risk management in maritime transportation involves the systematic identification, assessment, and mitigation of risks associated with maritime operations. Risk assessment is the process of identifying potential hazards, evaluating the likelihood of their occurrence, and assessing the potential impact on human life, property, and the environment. This often involves the use of risk assessment models and tools to analyze various risk scenarios [13; 14]. Risk mitigation includes the implementation of strategies and measures to reduce the likelihood and impact of identified risks. This can involve engineering controls, administrative controls, and the use of advanced technologies such as AI for real-time monitoring and predictive analytics [13; 14]. Continuous monitoring and review are essential for ongoing monitoring of maritime operations to detect new risks and evaluate the effectiveness of existing risk management measures. Regular reviews and updates to risk management plans ensure that they remain relevant and effective in addressing emerging challenges [6; 7; 8; 9] (Table 1). Despite numerous advances, gaps remain in the integration of AI into maritime safety and risk management. Previous studies have focused primarily on individual applications of AI without a comprehensive analysis of their collective impact and integration challenges. This study addresses this gap by providing a detailed examination of how various AI technologies can be synergistically applied to enhance maritime safety and operational efficiency. By clarifying these concepts, this review provides a foundation for understanding how AI technologies can be applied to enhance safety and risk management in maritime transportation. The following sections will delve deeper into specific AI applications and their impact on the maritime industry.
## 4 Influencing Factors and Error Analysis  
### Effect of Aperture on Correction Error  
To verify the applicability of the correction method and analyze the error source further, experiments were conducted using \(\varphi\)12 mm and \(\varphi\)28 mm holes as examples. Internal surface defects were simulated by pentagonal stars with known sizes. Endoscopic images were obtained and corrected under the same conditions and methods. The correction process and results are shown in Figure 11. Where (a)-(e) represent 12 mm holes, and (f)-(j) represent 28 mm holes; (a) and (f) show the original images, (b) and (g) display image center detection results, (c) and (h) display the circumferential correction results, (d) and (i) represent the axial correction results, and (e) and (j) represent the final correction results. The image centers of 12 mm is (305, 386) and the 28 mm is (318, 378).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Column Number**} & \multirow{2}{*}{**Mean Value**} & \multirow{2}{*}{**Standard Deviation**} & \multirow{2}{*}{**Total Deviation**} \\ \cline{3-3} \cline{5-10}  & & **1** & & & & & & & & & \\ \hline \multirow{8}{*}{Number} & 1 & 11 & 12 & 12 & 12 & 11 & 11 & 11.50 & 0.548 & \(-11.66\%\) \\  & 2 & 11 & 12 & 13 & 12 & 10 & 12 & 11.67 & 1.033 & \(-10.38\%\) \\  & 3 & 14 & 14 & 12 & 13 & 13 & 13 & 13.17 & 0.753 & 1.14\% \\  & 4 & 13 & 15 & 14 & 13 & 12 & 14 & 13.50 & 1.049 & 3.70\% \\  & 5 & 14 & 16 & 14 & 13 & 13 & 13 & 13.83 & 1.169 & 6.26\% \\  & 6 & 14 & 16 & 15 & 14 & 13 & 13 & 14.17 & 1.169 & 8.82\% \\  & 7 & 14 & 15 & 14 & 13 & 13 & 12 & 13.50 & 1.049 & 3.70\% \\  & 8 & 12 & 15 & 13 & 13 & 13 & 13 & 13.17 & 0.983 & 1.14\% \\  & 9 & 12 & 13 & 12 & 13 & 14 & 12 & 12.67 & 0.816 & \(-2.70\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the calibration block height/pixel.  
Figure 10: Axial correction results.  
Figure 9: Fitting results of axial distance growth function.  
As shown in Figure 11, the pentagonal star in the corrected image has been restored to its original positive five-pointed star. To verify the accuracy of the correction algorithm, we extracted the vertex coordinates of the pentagonal star, calculated its edge length in pixel distance and the measurement error based on unit pixel size. The results are presented in Table 3.  
Table 3 shows that the deviation range of the experimental measurement results has been reduced from nearly 20% to less than 5%, indicating that the correction method is suitable for correcting distortion in inner surface images with different apertures. The decrease in the deviation range demonstrates that the correction and measurement accuracy of the experiment have shown a significant improvement in both correction and measurement accuracy. One reason for this improvement is that the verification experiment used a larger area of calibration paper as background, and the grid on the calibration paper improved the accuracy of the image center detection algorithm.
## 2 Named Data Networking: Nuts and Bolts
### Security Principles in NDN  
In a named data network, the primary objective is to locate and retrieve data objects within a specified context by their names. The security features of the NDN design are intrinsic and require signatures to be used in _Data_ packets [12]. Producers are responsible for digitally signing all _Data_ they create, which enables consumers to validate the authenticity of the received information. Additionally, routers and repositories can store _Data_, and consumers can receive _Data_ packets from any source. The NDN architecture relies on several security components to ensure data security [13].
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 5 Performance Evaluation
### Evaluation Metrics  
The proposal will be validated with a simple application that calculates the Body Mass Index (BMI), using POST requests. It is important to note that the request could be made to any application running in the cluster and duly registered with the SR. Therefore, this application's use is restricted to confirm the viability of the proposed API.  
To do this, three scenarios will be run with different numbers of users (1, 10, 50, and 100), simulating the ability to respond to concurrent requests. Each scenario will be run at different execution times 5, 10, 30, and 60 s.  
This variation aims to evaluate performance variations over continuous periods. The Locust tool will make HTTP requests and obtain the average response time and the average number of requests [42].  
In addition, the production server will be tested with four and eight threads to evaluate performance. The test sets will be run ten times to obtain reliable results, and a 95% confidence interval will be calculated to assess the variability of the results. The confidence interval (\(ci\)) is calculated using the equation:  
\[ci=\overline{x}\pm\mathcal{Z}\cdot\frac{\sigma}{\sqrt{n}},\]  
where \(\overline{x}\) represents the sample mean response times obtained in 10 executions. \(\mathcal{Z}\) is the normal distribution of 1.96 for a confidence level of 95%. \(\sigma\) represents the standard deviation of the response times, while \(n\) indicates the sample size. The \(\pm\) indicates the upper and lower result intervals.  
End-to-End Communication Test  
This test aims to demonstrate the viability of end-to-end communication. To perform this, requests will be made to the application developed using the integration service. The following aspects will be analyzed: (i) the response to the request and whether it corresponds to what was expected; (ii) the time taken to check and send the first response;  
Figure 4: VM structure used in validation.  
and (iii) the response time of the requests. In addition, the UPF interface with the application will be monitored to capture the packet for later analysis. For items 1 and 4, only one request will be made, considering that the information observed will not be altered. However, 30 runs will be made for the other items to obtain the average time. Confidence intervals will also be calculated for the times discussed. For this test, no specific testing tools will be used; the request and time measurement will be carried out utilizing native Ubuntu applications.
# Dual-Polarization Conversion and Coding Metasurface for Wideband Radar Cross-Section Reduction
###### Abstract  
Modern stealth application systems require integrated meta-devices to operate effectively and have gained significant attention recently. This research paper proposes a 1-bit coding metasurface (CM) design. The fundamental component of the proposed CM is integrated to convert linearly polarized incoming electromagnetic waves into their orthogonal counterpart within frequency bands of 12.37-13.03 GHz and 18.96-32.37 GHz, achieving a polarization conversion ratio exceeding 99%. Furthermore, it enables linear-to-circular polarization conversion from 11.80 to 12.29, 13.17 to 18.44, and 33.33 to 40.35 GHz. A second element is produced by rotating a fundamental component by 90\({}^{\circ}\), introducing a phase difference of \(\pi\) (pi) between them. Both elements are arranged in an array using a random aperiodic coding sequence to create a 1-bit CM for reducing the radar cross-section (RCS). The planar structure achieved over 10 dB RCS reduction for polarized waves in the frequency bands of 13.1-13.8 GHz and 20.4-30.9 GHz. A prototype was fabricated and tested, with the experimental results showing a good agreement with the simulated outcomes. The proposed design holds potential applications in radar systems, reflector antennas, stealth technologies, and satellite communication.  
metasurface; circular polarization; RCS reduction; 3 dB axial ratio; PCR +
Footnote †: journal: _Photonics_  
00202411454[[https://doi.org/10.3390/photonics11050454](https://doi.org/10.3390/photonics11050454)]([https://doi.org/10.3390/photonics11050454](https://doi.org/10.3390/photonics11050454))
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
### Main Contributions  
In this paper, we design a solution for the uplink multi-user broadband mmWave mMIMO systems, where the hardware constraints inherent to these systems are considered. Therefore, our design options are the following:  
* The use of CE-OFDM, which has the benefits of OFDM schemes to deal with multi-path effects, but has a PAPR equal to 0 dB, which solve the problem of nonlinear distortions caused by the power amplifiers;
* The use of low-complexity UTs, using an analog-only precoder, to reduce the hardware costs and power consumption. The precoder is based on partial channel state information (CSI) knowledge, shifting the complexity to the BS;
* The use of hybrid analog-digital architecture at BS side, to reduce the power consumption and the hardware costs. The full-digital systems use a dedicated RF chain per antenna, which is impractical for mMIMO;
* The use of iterative equalizer based on IB-DFE principles, to efficiently remove the inter-user and inter-carrier interferences.  
Therefore, the aim of this paper is to design an iterative/nonlinear multi-user equalizer for the uplink broadband mmWave mMIMO CE-OFDM systems. At the UTs we consider a low-complex, yet efficient, only analog precoder, with the goal of maximizing the beamforming gain between each UT and the BS and implicitly remove some multi-user interference. At the BS, the analog part of the equalizer is derived by assuming a free multi-user interference system. Then, a per carrier basis nonlinear/iterative multi-user equalizer, based on the IB-DFE principle, is designed, to explicitly remove both the multi-user and residual inter carrier interferences, not tackled in the analog part. The design metric is the sum of the mean square error (MSE) of all subcarriers, whose minimization is shown to be equivalent to the minimization of a weighted error between the hybrid and the full digital equalizer matrices. We also propose a simple, yet accurate, semi-analytical approach for obtaining the performance of the proposed hybrid scheme for CE-OFDM systems.  
The remainder of this paper is structured as follows: In Section 2, the system model adopted in the paper is presented. In Section 3, we describe the proposed analog precoder and the iterative hybrid analog-digital multi-user equalizer. The main performance results are shown in Section 4, and finally, in Section 5, the main conclusions of the paper are discussed.
## 2 6G Convergence with Red AI Perspective in Terrestrial and Non-Terrestrial Applications
### Application 2: Red-AI-Enabled 6G Virtual Emotion Flow and Map  
_Definition: Red-AI-Enabled 6G Virtual Emotion Flow and Map_, referred to as _RedAI-6G-VEmoFLOWMAP_, is a map with flow by _Red AI_ view that can be derived from the accumulated and the detected virtual emotion information by already formed _RedAI-6G-VEmoBAR_ with 6G communications. _RedAI-6G-VEmoFLOWMAP_ is generated for specific emotion type during specific period and at specific region. Furthermore, the requested detection accuracy and the given request response speed are considered as system performance.  
_System Goal_: Ultimately, _RedAI-6G-VEmoFLOWMAP_ aims to increase the performance of the system such as delay, creation accuracy, proper virtual emotion service matching. So, the first objective is to minimize derivation and creation delay of _RedAI-6G-VEmoFLOWMAP_. The second purpose is to maximize the creation accuracy of _RedAI-6G-VEmoFLOWMAP_ for specific emotion type. The third goal is to maximize virtual emotion service matching correctness to people appropriately with anonymity and security for people.  
_Operation_: From _RedAI-6G-VEmoBAR_, AI agents at edge computing level receive accumulated virtual emotion data sets. It follows that we collect a large amount of virtual emotion information through autonomous vehicles, UAVs and smart devices in public roads, building, institutions, then the flow and map of specific emotion type is created so that the independent _RedAI-6G-VEmoFLOWMAP_ for each emotion type can be generated as well as the complex _RedAI-6G-VEmoFLOWMAP_ representing several emotion types can be obtained. With _Red AI_ view, the _RedAI-6G-VEmoFLOWMAP_ aims to increase the accuracy of virtual emotion derivation by deliberating on extra computations and expanded amount of information. For operations, there are available service cases based on virtual emotion such as current emotion flow check, criminal prevention, virtual-emotion-based advertisement, violent driver tracking, etc. For example, some pedestrians in public streets make requests for _RedAI-6G-VEmoFLOWMAP_ to the system to monitor current status of neighbor places whether there are potential criminals due to unstable emotion types such as fury and rage, the closest AI agents then send the _RedAI-6G-VEmoFLOWMAP_ to those pedestrians in response. If a sole AI agent bears a large burden from numerous requests through 6G convergent system, the request can be forwarded to next closest AI agent gradually. Because the reliability of system depends on the derived accuracy, _RedAI-6G-VEmoFLOWMAP_ focuses on how to increase the accuracy. Since the virtual emotion information of people is changed frequently, the large amount of virtual emotion data should be collected and treated repeatedly.