esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental
### Preparation of the AM-NaA/PVDF Membrane  
The polymer hydrogels AM, NaA, and AM-NaA in different AM/NaA monomer ratios were prepared by free radical polymerization in aqueous solutions. The AM and NaA monomers were mixed according to the mass ratios presented in Table 1. The solution was prepared by dissolving 0.134 g of the crosslinker BIS in 100 mL of distilled water. The water was first degassed with nitrogen gas for 15 min to remove any dissolved oxygen, as it inhibits radical polymerization by reacting with the active radicals and generates dead chain ends, which can interfere with the crosslinking process [58]. The resultant solutions were stirred by a heating magnetic stirrer at 40 \({}^{\circ}\)C for 30 min to ensure complete polymerization and the initiation of free radical reactions. Subsequently, 0.1 g of V-50 initiator was added dropwise to the mixture. The decomposition rate of the V-50 initiator is temperature-dependent and follows the Arrhenius equation (Equation (3)):  
\[\ln\left(\frac{k}{A}\right)=-\frac{Ea}{R}.\frac{1}{T}=f\left(\frac{1}{T}\right) \tag{3}\]where \(k\) is the rate constant (s\({}^{-1}\)), \(A\) is the pre-exponential factor (s\({}^{-1}\)), \(Ea\) is the activation energy (J/mol), \(R\) is the universal gas constant (8.314 J/(mol.K)), and \(T\) is the absolute temperature (K). While the free radicals can be generated at a temperature of 40 \({}^{\circ}\)C, the experimental attempts indicated that a temperature of 60 \({}^{\circ}\)C promoted solid gel formation. Therefore, decomposition into free radicals commences even at relatively moderate temperatures and is accelerated by temperature increase. The PVDF membrane was exposed to surface pretreatment using atmospheric plasma to activate its surface before hydrogel grafting, as chemical bonding with dissimilar materials is highly complicated in the presence of few available bonding sites. The PVDF membrane was sonicated for 10 min in water and dried at room temperature for 12 h before treatment with atmospheric glow discharge plasma (CTP-2000K, Coronalab, Nanjing, China). The electric field between the electrodes was 1000 V/mm, and the input power was 30 W. The rod electrode was wiped back and forth on the PVDF membrane for 4 min. Subsequently, the plasma-treated PVDF membrane (LEP-H1) was immersed in a hydrogel precursor solution for 30 min. After that, the membranes were withdrawn and subjected to further crosslinking via oven drying at 60 \({}^{\circ}\)C for 30 min, resulting in the modified membranes referred to as AM-NaA/PVDF. The preparation process is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Membrane Code** & **H-1** & **H-2** & **H-3** & **H-4** & **H-5** & **H-6** & **H-7** \\ \hline AM/NaA (monomer mass ratio) & 0:0 & 5:0 & 1:1 & 4:1 & 3:2 & 2:3 & 0:5 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of AM/NaA hydrogel coating on PVDF membranes.  
Figure 1: Schematic of the AM-NaA/PVDF membrane fabrication and filtration process.
## 5 Conclusions  
This study has made a significant contribution to the literature by demonstrating the potential of machine learning techniques in predicting pipeline damage caused by natural forces. This paper investigated the effectiveness of integrating climate data into pipeline incident data and achieved a high predictive accuracy of 92.3% for natural gas pipeline damage caused by natural forces. The findings of the study offer a framework for transportation agencies to efficiently manage their natural gas pipeline systems and preemptively avoid potential dangers. The developed models are reliable tools for identifying pipelines at high risk of damage and prioritizing inspection and maintenance activities. The implications of this study for the pipeline industry are vast, and it highlights the potential for cost savings and improved safety. Predicting pipeline damage accurately can help prevent failures and avoid costly emergency repairs. An accurate prediction can also enable operators to take proactive measures to prevent accidents and ensure that pipelines are safe for public use. Furthermore, the early prediction of potential damage can help mitigate the significant environmental consequences of pipeline failures.  
The potential for future research in exploring the performance of other machine learning techniques and investigating the impact of different variables on pipeline damage suggests that there is scope for further development in this area. Overall, the study underscores the importance of leveraging the power of machine learning in predicting pipeline damage and emphasizes the need for ongoing research to enhance our understanding of the complex interactions between climate change and pipeline infrastructure monitoring and maintenance.  
Conceptualization, B.A. and Y.H.; methodology, B.A. and Y.H.; formal analysis, B.A., Y.H. and N.Y.; investigation, B.A., Y.H. and N.Y.; resources, Y.H.; data curation, B.A. and Y.H.; writing--original draft preparation, B.A., Y.H. and N.Y.; writing--review and editing, B.A., Y.H. and N.Y.; visualization, B.A., Y.H. and N.Y.; supervision, N.Y. and Y.H.; project administration, Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was made possible through a grant from the National Science Foundation (NSF), EPSCoR RII Track-2 Program, through NSF award # OIA-2119691, and the APC was funded by MDPI.  
Not applicable.  
The data will be made available upon request.  
This research was made possible through a grant from the National Science Foundation (NSF) and data from the United States Pipeline and Hazardous Materials Safety Administration (PHMSA). The findings and opinions presented in this manuscript are those of the authors only and do not necessarily reflect the perspective of the agencies.  
The authors declare no conflict of interest.
## 2 Risers' Geometries and Materials and FE Modeling
### Materials for the Risers
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density** & **\(E_{1}\)** & **\(E_{2}=E_{3}\)** & **\(G_{12}=G_{13}\)** & **\(G_{23}\)** & **\(\sigma_{23}\)** & **\(\sigma_{1}^{\intercal}\)** & \(\sigma_{1}^{C}\)** & \(\sigma_{2}^{\intercal}\)** & \(\sigma_{2}^{C}\)** & \(\tau_{12}\)** \\  & **(kg/m3)** & **(GPa)** & **(GPa)** & **(GPa)** & **(GPa)** & **(GPa)** & **(GPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline Steel & 7850 & 207 & & & 0.3 & & & & 625 & & & \\ PEEK & 1300 & 3.64 & & & 0.4 & & & & 120 & & & \\ AS4-PEEK & 1561 & 131 & 8.7 & 5 & 0.28 & 2.78 & 0.48 & 1648 & 864 & 62.4 & 156.8 & 125.6 \\ \hline \multicolumn{10}{c}{Subscripts: 1, fiber direction; 2, in-plane transverse; and 3, through-thickness direction. Superscripts: \(T\), Tension; C, Compression. Note: For steel and polyetheretherketone (PEEK) in the table, \(\sigma_{1}^{\intercal}\) is used for their ultimate stress.} \\ \end{tabular}
\end{table} 
Table 2: Properties of the materials for the risers. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Riser**} & \(\rho_{effective}\) & \(E_{x}\) & \(E_{y}\) & \(E_{z}\) & \(G_{xy}\) & \(G_{xz}\) & \(G_{yz}\) & \(\sigma_{yz}\) & \(\
u_{xy}\) & \(\
u_{xz}\) & \(\
u_{yz}\) \\  & **(kg/m3)** & **(GPa)** & **(GPa)** & **(GPa)** & **(GPa)** & **(GPa)** & **(GPa)** & **(GPa)** & **(GPa)** \\ \hline Riser 2 & 1524.4 & 45.7 & 73.8 & 9.83 & 4.42 & 2.87 & 2.95 & 0.0316 & 0.479 & 0.453 \\ Riser 3 & 1513.3 & 30.4 & 5
## 4 Discussion  
Today's computer and server systems need to support high-speed transmission rate buses. A comparison of the performance of different CM noise suppression filters is shown in Table 3. It can be observed that the highest CM rejection frequency is only 10.8 GHz in the literature when using DGS structure filters. In addition, the highest CM rejection frequency is only 13.41 GHz in the literature on mushroom structure filters. Among the studied filter structures, the ring structure filter can support the highest fractional bandwidth in the literature, but the highest CM suppression frequency for a ring structure filter is only 7.87 GHz. Therefore, these different structures' filters struggle to support high-speed buses in today's computer systems.  
Our novel MDCRP structure filter can support high-speed buses in today's computer and server systems. This also achieves application benefits in the industry and makes great contributions and innovations to academics. The filter size is only 0.75 \(\lambda\)g \(\times\) 0.75 \(\lambda\)g, and the corresponding center frequency is 12.85 GHz. It can suppress CM noise at high frequencies of up to 20.62 GHz with a fractional bandwidth of up to 120.8%. The filter size and fractional bandwidth are calculated concerning the center frequency value. The MDCRP structure filter has the highest rejection bandwidth and the highest center frequency, so the filter size is the largest, and the fractional bandwidth is the worst after calculating the results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Max. Eye Open** & **Max. Eye Width** & **Jitter** \\ \hline Reference Board (20 Gb/s) & 458 mV & 48.50 ps & 0.62 ps \\ \hline MDCRP Board (20 Gb/s) & 452 mV & 47.25 ps & 0.94 ps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the differential eye diagram parameters of the measured results.  
Figure 10: Eye diagram of the differential signals of the measured results. (**a**) The reference boards. (**b**) The MDCRP structure filter board.  
The most difficult part of our research was the geometric parameter simulation process. All geometric parameters and dependencies need to be considered, and finding the optimal configuration requires constant tuning and simulations over a long period. Another difficulty is the equivalent circuit process. The equivalent component values need to be adjusted over a long period to reach the most effective value. These efforts are all about finding the widest and best CM noise suppression effects.  
Our future work will involve studying higher transmission rates exceeding 32 Gb/s to meet the design requirements of PCI Express 5.0, which will be used in future computer products. In addition, this design philosophy can be extended to the development of absorbing CM filter structures so that the whole CM filter can be designed more optimally.
## 2 Materials and Methods
The analysis was performed with MATLAB(r), by compressing the data at 10-ping intervals after removing sea-level signals made by the sea conditions and noise generated by sensors and near-field communication during transmission, and then extracting SV and nautical area scattering coefficient (NASC, m\({}^{2}\) nm\({}^{-2}\)), which was converted from SV in linear form for comparison of the relative fish density [20]. The NASC extracted during the analysis was calculated as the average value for 1 h and 1 day. The data from the ship-based echosounder were analyzed for both 38 kHz and 120 kHz. Noise was removed from the SV data obtained while the ship was in motion, and the difference in mean volume backscattering strength (\(\Delta\)MVBS) was used to separate adult fish (2 dB \(<\)\(\Delta\)MVBS\({}_{38-120}\)\(<\) 20 dB) from fish larvae and zooplankton (2 dB \(<\)\(\Delta\)MVBS\({}_{120-38}\)\(<\) 20 dB) [12; 21]. In addition, the data were extracted by converting into average NASC, in the same way as the results from the moored echosounder for each frequency. An elementary distance sampling unit (EDSU) of 0.1 nautical mile was set for the extraction, by considering the stopping distance. Figure 2: Design of the acoustic monitoring system in Jinhae Bay, Korea. Before the acoustic survey, a scuba survey was conducted to photograph and identify fish larvae on the Nanpo-ri Coast (35\({}^{\circ}\)4.979\({}^{\prime}\) N, 128\({}^{\circ}\)37.704\({}^{\prime}\) E, depth 5-8 m) in Jinhae Bay in February 2016. Data from an ocean observation buoy (35\({}^{\circ}\)2.633\({}^{\prime}\) N, 128\({}^{\circ}\)45.700\({}^{\prime}\) E) in Jinhae Bay, which were provided by the Korea Ocean Observing and Forecasting System of the Korea Hydrographic and Oceanographic Agency, were used to examine the fluctuation of water temperature, which is one of the major environmental factors for the spawning of the Pacific herring in the survey area.
## 2 Detecting Principle  
### Principle of EOFM  
When the laser beam is focused on the switching transistor, many laser-semiconductor interactions occur. As a result, the phase of the reflected light is modulated at the switching frequency. These phase modulations of the reflected light can be converted into amplitude modulations by interferometric effects. EOFM uses a high-sensitivity photodetector to detect the amplitude modulation of the reflected light in the parts-per-million range, thereby locating the position of the transistor operating at the frequency of interest [12; 16; 17]. The 1319 nm laser wavelength is widely used in the EOFM, and the main effect of this wavelength is the free carrier effect [11]. The free carrier effect in silicon describes the change in absorption coefficient and refractive index as a function of free carrier density [18]:  
\[n_{fc}=-\frac{q^{2}\lambda^{2}}{8\pi^{2}c^{2}\epsilon_{0}n_{0}}\left(\frac{ \Delta N_{e}}{m_{ce}^{*}}+\frac{\Delta N_{h}}{m_{ch}^{*}}\right) \tag{1}\]  
\[\alpha_{fc}=\frac{q^{3}\lambda^{2}}{4\pi^{2}c^{3}\epsilon_{0}n_{0}}\left( \frac{\Delta N_{e}}{m_{ce}^{*}{}^{2}\mu_{e}}+\frac{\Delta N_{h}}{m_{ch}^{*}{ }^{2}\mu_{h}}\right) \tag{2}\]  
where \(n_{fc}\) and \(\alpha_{fc}\) are the changes in refraction index and the absorption coefficient, respectively. \(q\) is the elementary charge, \(\lambda\) is the wavelength, \(\epsilon_{0}\) is the permittivity of free space, \(n_{0}\) is the refractive index of the unperturbed crystalline silicon, and \(c\) is the speed of light in vacuum. \(\Delta N_{e}\) and \(\Delta N_{h}\) are, respectively, the change in the electron and hole density; \(m_{ce}^{*}\) and \(m_{ch}^{*}\) are, respectively, the effective masses of the electron and hole.  
Figure 1 shows the cross-section of a metal-oxide-semiconductor field-effect transistor (MOSFET). The drain potential oscillation caused by the gate voltage pattern can result in carrier density perturbations. According to Equations (1) and (2), the refractive index and light absorption of silicon will change due to the presence of free carriers. Therefore, the reflected light is modulated in both amplitude and phase. Previous studies indicate that the EOFM signals of the DUT come from the channel underneath the gate and the space  
charge region (SCR) underneath the drain [13,14]. If we want to use the signal from the channel to obtain the voltage information of the DUT, the model will be very complicated due to the existence of the inversion charge in the channel. Additional electrical simulations are required to obtain the inversion charge carrier density in the channel. By contrast, the drain signal comes from the SCR thickness variation of the reverse biased junction underneath the drain. The reverse biased junction is a part of every transistor in modern ICs; on the other hand, the electrical model of the reverse biased junction is simple. Because of this, we built a physical model of the reverse biased junction to obtain the accurate voltage level of the DUT through the reverse biased junction signal.
## 5 Experimental Results
### Noise Attacks
#### 5.2.4 JPEG Compression  
This is an image processing standard designed by the Joint Photography Experts Group. It uses DCT for compression, which is lossy image compression. Different compression ratios can be applied to the images based on the requirement. The proposed method is tested with compression 90.  
Figure 8: Noise attacks on sample images and extracted watermarks.
# Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints
###### Abstract  
The accurate prediction of joint shear strength is crucial for rock mass engineering design and geological hazard assessment. However, traditional machine learning (ML) models often suffer from local optima and limited generalization ability when dealing with complex nonlinear problems, thereby compromising prediction accuracy and stability. To address these challenges, this study proposes a hybrid ML model that integrates a multilayer perceptron (MLP) with the slime mold algorithm (SMA), termed the SMA-MLP model. While MLP exhibits strong nonlinear mapping capability, SMA enhances its training process through global optimization and parameter tuning, thereby improving predictive accuracy and robustness. A dataset with five input variables was constructed to evaluate the performance of the SMA-MLP model comprehensively. The proposed model was compared with other ML models. The results indicate that SMA-MLP outperforms these models in key metrics such as the root mean squared error (RMSE) and the correlation coefficient (R\({}^{2}\)), achieving an R\({}^{2}\) of 0.97 and an RMSE as low as 0.10 MPa on the test set. Furthermore, feature importance analysis reveals that normal stress has the most significant influence on joint shear strength. This study demonstrates the superiority of SMA-MLP in predicting joint shear strength, highlighting its potential as an efficient and accurate tool for rock mass engineering analysis and providing reliable technical support for geological hazard assessment.  
rock joints; shear strength; machine learning; slime mold algorithm +
Footnote †: journal: Applied sciences
# 5G V2X System-Level Architecture of 5 GCAR Project
## 3 Overall 5 GCAR Architecture Design
### Multi Connectivity Cooperation  
V2X services are expected to exploit infrastructure-based links (i.e., Uu) as well as direct vehicle-to-vehicle (V2V) links (i.e., sidelink--SL). These two links have different characteristics and consequently are associated with different features. For instance, SL is expected to provide low latency reduction and out-of-coverage support while Uu is expected to offer higher reliability as well as possibilities of connectivity towards remote backends. 5 GCAR considered that the joint exploitation of the two communication modes (i.e., Uu and SL) might bring benefits in order to meet the requirements of complex V2X environments. 5 GCAR also identified the issue that the selection of a suitable communication mode or technology should not only be driven by the QoS requirements of the related traffic. Indeed, as many V2X use cases (e.g., lane merge) focus more on the completion of a certain action (e.g., a vehicle safely merging into the main road), such type of information should be taken into consideration on the chosen communication mode/technology to allow the completion of the use case, i.e., the management of multi-connectivity options should be carried through the utilization of use case-aware mechanisms.  
The benefits deriving from multi-connectivity cooperation are in terms of improved performance (e.g., reliability and data rate), together with higher resilience to link failure (e.g., redundancy).  
More details on the analyses conducted by 5 GCAR on different options for achieving redundancy of Uu and PC5 links are given in Section 4.6, while an analysis on how to achieve use case-aware multi-link connectivity is given in Section 4.3.
## 4 Discussion
### Case Simulation and Results Analysis
The details of the three schemes are shown in Table 5.  
Table 6 presents the comparison of day-ahead economic benefits and risk indicators of the wind/storage system under the three scheduling schemes. As shown in Table 6, Scheme 2 achieves the highest expected revenue of CNY 16,903.17, which is CNY 669.39 higher than Scheme 1 (CNY 16,233.78). Its maximum scenario revenue also reaches CNY 17,721.76, exceeding that of Scheme 1 (CNY 16,263.00) by CNY 1437.27. However, Scheme 2 does not incorporate CVaR and thus fails to effectively manage risk under extreme scenarios. As a result, its minimum scenario revenue drops to CNY 14,445.90, which is CNY 1086.58 lower than that of Scheme 1 (CNY 15,532.48). In Scheme 3, which excludes peak-valley arbitrage based on Scheme 1, the expected revenue decreases to CNY 14,894.26--lower than both Scheme 1 and Scheme 2. Its minimum scenario revenue is also the lowest, at CNY 14,192.96. Therefore, incorporating both CVaR and peak-valley arbitrage is crucial for mitigating risk and enhancing the economic performance of the wind/storage system.
## Materials and Methods
### Analytical Methods
#### 2.3.2 Mapping Analysis  
Mapping analysis can be used to visualize the spatial characteristics of surface water quality. Based on the values of surface water quality indicators, the inverse distance weighting (IDW) method was used to interpolate the average concentrations of each indicator within the whole watershed area [20; 21; 22].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Description** & **Meaning** \\ \hline \multirow{2}{*}{_CA_} & \multirow{2}{*}{Total (class) area} & Total area of patches of a certain landscape composition \\  & & type. A fundamental measure of landscape composition. \\ \hline \multirow{2}{*}{_PLAND_} & \multirow{2}{*}{Percentage of landscape} & Percentage of CA in total landscape area. Another \\  & & fundamental measure of landscape composition. \\ \hline \multirow{2}{*}{_LPI_} & \multirow{2}{*}{Largest patch index} & Percentage of the area of the largest patch of a certain \\  & & landscape composition type in the landscape total area. \\ \multirow{2}{*}{_NP_} & \multirow{2}{*}{Number of patches} & It helps confirm the dominant landscape \\  & & composition type. \\ \hline \multirow{2}{*}{_CONTIG_} & \multirow{2}{*}{Contiguity index} & Total number of patches of a certain landscape \\  & & composition type, which can reflect landscape spatial \\ \hline \multirow{2}{*}{_COHESION_} & \multirow{2}{*}{Contiguity index} & Average contiguity index among patches of certain \\  & & landscape composition types. \\ \hline \multirow{2}{*}{_COHESION_} & \multirow{2}{*}{Patch cohesion index} & Physical connectivity of patches in the same landscape \\  & & composition type that can describe the connectivity \\ \hline \multirow{2}{*}{_AI_} & \multirow{2}{*}{Aggregation index} & Adjacency relation among pixels that can aggregate into \\  & & patches in certain landscape composition types. \\ \hline \multirow{2}{*}{_DIVISION_} & \multirow{2}{*}{Landscape division index} & Possibility of two random pixels not in the same patch of \\  & & being a certain landscape composition type, which \\ \cline{1-1}  & & reflects the spatial pattern of the landscape. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abbreviations, descriptions, and meanings of the selected landscape indices.