esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### N\({}_{2}\) Adsorption-Desorption Measurements  
Nitrogen adsorption-desorption measurement was performed to determine the specific surface area (SSA) and to provide insights into the pore size and distribution. The N\({}_{2}\) adsorption-desorption isotherms are depicted in Figure 2a. A hysteresis loop is observed at P/P\({}_{0}\)\(\approx\) 0.5, due to capillary condensation of nitrogen in mesopores, especially for the sample SiCN-S-800 [30]. It indicates that all samples exhibit mesoporous character. Detailed SSA data and pore structure parameters, such as total pore volume (Vt) and isotherm type and average pore diameter (APD), are listed in Table 1. All the isotherms are described by type IV behavior, which is characteristic for materials containing mesopores [31]. From 800 \({}^{\circ}\)C to 900 \({}^{\circ}\)C, SSA and Vt of SiCN ceramics decrease with increasing pore size (corresponding to the value of APD). Due to equipment precision, larger mesopores could not be detected by a BET approach. With further increasing of the pyrolysis temperature to  
Figure 1: XRD patterns of (**a**) C-rich SiCN ceramics and (**b**) SiCN-S composites processed at different pyrolysis temperatures. The green dotted line in Figure 1b represents the position of the characteristic peaks of S\({}_{8}\).  
\(1100\)\({}^{\circ}\)C in argon, \(V_{\text{t}}\) and APD turn to increase, but there is no obvious change in SSA. The change in APD indicates a reduced the presence of bigger pores in the sample pyrolyzed at \(1100\)\({}^{\circ}\)C. The pore size distribution of all ceramic samples is presented in Figure 2b. With increasing pyrolysis temperature, the amount of mesopores in the range between 3 and 4 nm decreases.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **SiCN-800** & **SiCN-900** & **SiCN-1100** \\ \hline SSA (m\({}^{2}\)/g) & 162 & 116 & 115 \\ \(V_{\text{t}}\) (cm\({}^{3}\)/g) & 0.62 & 0.61 & 0.73 \\ Isotherm Type & IV & IV & IV \\ APD (nm) & 15.46 & 20.94 & 25.5 \\ I (A\({}_{\text{D}}\))/I (A\({}_{\text{G}}\)) ratio & 2.68 & 2.67 & 2.47 \\ L\({}_{\text{a}}\) (nm) & 6.28 & 6.29 & 6.81 \\ \hline \end{tabular}
\end{table}
Table 1: Pore structure parameters analyzed by BET and characterization of the developed free carbon measured by Raman spectroscopy. Specific surface area (SSA), total pore volume (V\({}_{\text{t}}\)), average pore diameter (APD), intensity ratio of I (A\({}_{\text{D}}\))/ I (A\({}_{\text{G}}\)), and crystallite size (L\({}_{\text{a}}\)) of samples SiCN-800, SiCN-900, and SiCN-1100 (Gaussian-type curve fitting applied [32]).  
Figure 2: N\({}_{2}\) adsorption-desorption isotherms (**a**) and pore diameter distribution (**b**) of C-rich SiCN ceramic matrix.
## 1 Introduction
### Contributions  
This review focuses on the advances in the IoV ecosystem and highlights the incorporation of blockchain as well as machine learning to provide a more secured and intelligent IoV application. The study seeks to identify the merits in the applications of this convergence and the solutions they proffer. Also, the technical challenges are discussed and the ways to overcome these challenges are highlighted. This study also incorporates some critical future research needs for successful deployment of these new converging technologies in the transportation system. The review seeks to address the research question \"Can ML-assisted approach integrated with blockchain techniques, improve the overall system performance in an IoV system?\". Table 1 provides all summaries of abbreviations used.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronyms** & **Definitions** \\ \hline AC & Average Correlation \\ \hline ADASs & Advanced Driver-Assistance Systems \\ \hline ANFIS & Adaptive Neuro-Fuzzy Inference System \\ \hline ANN & Artificial Neural Network \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of acronyms.  
\begin{table}
\begin{tabular}{c c} \hline
**Acronyms** & **Definitions** \\ \hline AI & Artificial Intelligence \\ \hline AIS & Artificial Immune System \\ \hline AV & Autonomous Vehicles \\ \hline BC & Blockchain \\ \hline Bi-LSTM & Bi-directional Long-Short Term Memory \\ \hline BPFL & Blockchain-based Privacy-preserving Federated Learning \\ \hline BPNN & Back Propagated Neutral Networks \\ \hline BSM & Basic Safety Messages \\ \hline CAVs & Connected and Autonomous Vehicles \\ \hline CIoV & Cognitive Internet of Vehicles \\ \hline C-LSTM & Centralized Long-Short Term Memory \\ \hline CNN & Convolutional Neural Networks \\ \hline CPABE & Cipher text Policy Attribute Based Encryption \\ \hline CRT & Classification and Regression Tree \\ \hline DAG & Directed Acyclic Graph \\ \hline DDL & Distributed Deep Learning \\ \hline DDoS & Distributed Denial of Service \\ \hline DL & Deep Learning \\ \hline DLT & Distributed Ledger Technology \\ \hline DRL & Deep Reinforcement Learning \\ \hline DSRC & Dedicated Short Range Communications \\ \hline ECUs & Electronic Control Units \\ \hline ELM & Extreme Learning Machine \\ \hline EKM & Efficient Key Management \\ \hline FL & Federated Learning \\ \hline GA & Genetic Algorithm \\ \hline IoT & Internet of Things \\ \hline IoV & Internet of Vehicles \\ \hline IPFS & Inter Planetary File System \\ \hline ITS & Intelligent Transportation Systems \\ \hline IVN & Intelligent Vehicular Network \\ \hline k-NN & k-Nearest Neighbors \\ \hline LSTM & Long Short-Term Memory \\ \hline LTE & Long Term Evolution \\ \hline MAPE & Mean Absolute Percentage Error \\ \hline MEC & Mobile Edge Computing \\ \hline ML & Machine Learning \\ \hline MLP & Multi-Layer Perceptron \\ \hline MVAEE & Multi-Vehicle Average Error Evaluation \\ \hline NFC & Near Field Communications \\ \hline NN & Neural Networks \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._
## 3 Results and Discussion
### Effect of Electrode Design on Machining Time (MT)
Thus, the gap between the inner walls of the eroded hole and the tool's sidewalls (of 1 mm length) was short as compared to the gap between the inner walls of the hole and tapered sidewalls of the tool. In this way the sparking only occurred at the bottom and 1 mm sidewalls of the tool. In this case the phenomenon was termed as the moderate side sparking. This design type (D\({}_{\text{RL}}\)) might overcome the above stated drawback of the previous design (D\({}_{\text{R}}\)). Moreover, upon the tool wear  
Figure 6: Sideway sparking during EDM: (**a**) conventional design; D\({}_{\text{C}}\), (**b**) relief angle design; D\({}_{\text{R}}\), and (**c**) relief angle with land design; D\({}_{\text{RL}}\). of \(\mathrm{D_{RL}}\) along its length, the cross section of the tool at the bottom side remained uniform due to the land of 1 mm thickness. As the input discharge energy was constant (as in case of present research) and the surface area also became constant after a depth of 1 mm therefore a steady rate of erosion was observed. Thus, it could be inferred that the provision of the relief angles in the tool designs allowed the side or peripheral sparking to be at a minimum or moderate level. It could also be noticed that the amount of side sparking continuously increased in the case of the conventional tool design whereas it remained constant when the relief angles were introduced in the tool design. Against a fixed level of input discharge energy, if the spark density was continuously reduced the erosion rate would be slowed down and if the spark density remained constant the steady rate of erosion could be maintained. That is why the machining time to complete the through hole by conventional tool design (\(\mathrm{D_{CR0}}\)) was significantly high as compared to the time taken by the relief angled tool designs (\(\mathrm{D_{R}}\) and \(\mathrm{D_{RL}}\)) to finish the same hole in the same material under the same machining conditions.
## 4 Conclusions  
To fully utilize the low-grade energy from ships and reduce carbon dioxide emissions, this paper established a wet steam model that considered the nonequilibrium phase change. The effect of the phase change on the internal flow of the steam eject was studied and the  
Figure 11: Mach number distribution along the steam eject axis under different \(P_{m}\). (**a**) Wet steam model; (**b**) ideal gas model.  
Figure 10: Mach number contour under different \(P_{m}\) for wet steam model and ideal gas model.  
results for the wet steam model and the ideal gas model under different \(P_{m}\) were compared. The main conclusions were as follows:  
Under different \(P_{m}\), the structures of the shock wave train for the wet steam model and the ideal gas model were different. For the wet steam model, the first shock wave changed from a compression shock wave to an expansion shock wave when the \(P_{m}\) was 55,000 Pa. For the ideal gas model, the compression shock wave changed to an expansion shock wave when the \(P_{m}\) was 75,000 Pa.
2. When the \(P_{m}\) was 50,000 Pa, the primary temperature at the nozzle exit for the wet steam model increased by 72.1%, and the choke temperature at the constant section decreased by 3.4% compared with the ideal gas model. With the increase in the \(P_{m}\), the variation in the primary temperature at the nozzle exit increased by 60%, the variation in the choke temperature decreased by 50% and the variation in the temperature before the shock wave in the diffuser decreased by 92.3%.
3. The phase change reduced the fluctuation of the \(P_{m}\) and the \(T_{m}\), which reduced the energy loss of the shock wave. When the \(P_{m}\) increased from 50,000 Pa to 80,000 Pa, the variation in the length of the shock wave train for the wet steam model decreased by 61% compared with the ideal gas model.  
In conclusion, there were significant differences between the wet steam model and the ideal gas model on the flow characteristics inside the steam ejector under different working conditions. The research in this paper provides a reference for the design theory of a ship's waste heat steam ejector. In future research, the influence of various models on the flow characteristics inside steam ejectors should be further studied, especially the real gas model.  
Validation, investigation, writing and editing, N.H. and X.C.; conceptualization, investigation, writing and editing, project administration and funding acquisition, J.D.; methodology and investigation, C.F., M.L. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (grant no. 51979022).  
Not applicable.  
All data have been provided in the paper.  
The authors declare no conflict of interest.
## 3 Results
### Physicochemical Properties of Surface Water  
Table 1 summarizes the physico-chemical properties of surface water collected in the study.  
All surface water collected in the study was tested using the HANNA multi-parameter to gather physicochemical properties of water as this affects heavy metal concentration in it [56]. These parameters could aid in the analysis to identify how Mn concentration in surface water and soils accumulates with time.  
These physicochemical properties were compared with the available guidelines set by Philippine National Standards for drinking water (PNSDW) and the World Health Organization (WHO). The pH of the surface water ranged from 5.7 to 8.05 with an average of 7.2, which is within the PNSDW and WHO guidelines, but the results show some  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Max** & **Min** & **Mean** & **SD** & **PNSDW 2017 Guideline** & **WHO** \\  & & & & & **Guidelines** & **Guidelines** \\ \hline Temperature & 34.5 & 27.4 & 31.2 & 0.972 & n/a & n/a \\ pH & 8.05 & 5.70 & 7.20 & 0.364 & 6.5–8.5 & 6.5–9.2 \\ EC (\(\mu\)S/cm) & 2430 & 424 & 763 & 187.780 & n/a & 1500 \\ TDS (mg/L) & 1360 & 216 & 390 & 112.526 & 600 & 1200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physicochemical properties of surface water.  
Figure 6: Spatial Grid map of (**a**) pH SW, (**b**) EC SW, (**c**) TDS SW, (**d**) Temperature SW, (**e**) Curve Number, (**f**) Flood hazard map, (**g**) Mn concentration in SW and (**h**) Mn content of soils with a 500 m grid size.  
areas in the study with acidic pH values. Figure 6a shows the spatial distribution of pH in surface water in the study area. Most of the regions in Mogpog have acidic surface waters, especially near the abandoned mine pit and TSFs. Meanwhile, the highest EC measured in the area was 2430 \(\upmu\)S/cm while the minimum was 763 \(\upmu\)S/cm with an average of 424 \(\upmu\)S/cm. Electric Conductivity was correlated to the salt content of water as the water salinity hazard is measured using this parameter. Although the average EC was less than the WHO guidelines, some locations in the river have high EC even though they are far from the shore (Figure 6b). These high EC samples were concentrated close to the abandoned pit and TSFs, suggesting that the higher EC values could be attributed to mine drainage. The highest value of TDS, a measure of inorganic salts present in solution, was 1356 mg/L which exceeds the WHO guidelines. The lowest TDS was 216 mg/L with an average value over the whole area of 390 mg/L, which is less than the environmental limit (Figure 6c). The TDS was also highest in the areas where EC is highest, showing the direct relationship between these two parameters. Lastly, the temperature doesn't have any limits identified by NSDW and WHO. The highest surface water temperature in the area was 34.5 \({}^{\circ}\)C, while the lowest was 27.4 \({}^{\circ}\)C, with a mean of 31.2 \({}^{\circ}\)C (Figure 6d).
## 6 COTS Hardware Components
### Transceiver--Fmcomms5  
To generate RF signals, we interface the ZCU102 with an Analog Devices FMCOMMS5 RF evaluation board [87]. This board features dual AD9361 RF Agile Transceivers and supports up to 4 x 4 MIMO configurations [88]. This board interfaces with the ZCU102 using two standard FMC connectors. This device is depicted in Figure 8.  
Figure 6: Block diagram of the hardware architecture of the CHP2 prototype implementation. The system is driven by a ZynqMP ZCU102 evaluation kit, which interfaces with and ADI FMCOMMS5 RF evaluation board to produce RF signals, a transmit-receive switching and amplifier board to manage transmit and receive operations, and supporting hardware such as power supplies, data interfaces, and antennas.  
Figure 7: Front (**left**) and back (**right**) view of all hardware mounted in an aluminum frame. The front is the Xilinx ZCU102 evaluation board. The back contains the ADI transceiver card, the TR switching board, and the power supply module.
## 5 Experiment  
The data from an experiment in the Yellow Sea of China in 2019 were processed for testing the proposed and reference methods. A 2.8 m movable horizontal array was used. The horizontal array had 16 elements. The sampling frequency was 20 kHz. The source was fixed as shown in Figure 3a. The source transmitted wideband noise within a frequency band of 2-4 kHz. Both the source and the horizontal array were located at a depth of 5 m. The trajectory of the horizontal array is shown in Figure 3a.  
The extracted Green's function at Position #1 (988.9 m) in Figure 3a is shown in Figure 3b. The beam-time migrations obtained using the reference and proposed methods are shown in Figure 3c,d, respectively, which have the same scale. The peak position of beam-time migrations is marked by the red square. Based on the beam-time migrations in Figure 3c,d, the range estimated with the reference and proposed methods were 1244.3 m and 1130.2 m, respectively, and the relative estimation errors of the reference and proposed methods were 25.9% and 14.4%, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**True Range**} & **Proposed** & **Reference** \\  & **(Estimated Range, Relative Error)** & **(Estimated Range, Relative Error)** \\ \hline
1000 m & 1038.7 m, 3.9\% & 850.2 m, 15.0\% \\ \hline
1500 m & 1615.1 m, 7.7\% & 1216.3 m, 18.9\% \\ \hline
2000 m & 2146.8 m, 7.3\% & 2454.3 m, 22.7\% \\ \hline
3000 m & 3232.3 m, 7.7\% & 2251.0 m, 24.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The range estimates and relative errors of the proposed and reference methods at different ranges in the simulation.  
Figure 3: (Color online). (**a**) Positions of the array and source in the experiment. (**b**) Green’s function obtained with blind deconvolution at Position #1. (**c**) Beam-time migration obtained using the reference method. (**d**) Beam-time migration obtained using the proposed method.  
The received signals at Positions #2 (718.6) and #3 (580.1) in Figure 2(a) were also processed with the proposed and reference methods. Table 2 shows the range estimates and relative errors of the proposed and reference methods at different positions. It can be seen that the relative error of the proposed method is about half of that of the reference method.
###### Abstract  
A devastating earthquake took place on 8 April 1893, close to the town of Svilajnac, central Serbia. Over the past decade, significant historical data on the effects of this earthquake has been collected from a variety of sources, including books, scientific publications, reports, newspapers, and coeval chronicles. Additionally, this earthquake was recorded 750 km from the epicenter at the seismological station Rocca di Papa in Rome, Italy. Based on critical review and analysis of the historical data, we demonstrate that the epicentral area of this earthquake was 531 km\({}^{2}\), and the macroseismic effects were recorded at epicentral distances up to 600 km towards the west (Vienna, Austria) towards the north, up to 500 km (Kosice-Michalovec, Slovakia), towards the east up to 460 km (Brasov-Borsec, Romania); and towards the south up to about 300 km (Radovis, North Macedonia). Finally, we show that the key parameters of the 1893 Svilajnac earthquake are as follows: (1) epicentral intensity, \(I_{0}\) = IX EMS-98, (2) the estimations of the moment magnitude and focal depth based on the observed intensities, \(M_{W}\) = 6.8 and h = 13 km, respectively, and (3) the epicenter coordinates, \(44.160^{\circ}\) N and \(21.354^{\circ}\) E.  
1893 Svilajnac earthquake; central Serbia; historical data; macroseismic intensity maps; magnitude 1  
M.I.M.Bulajic, B.D.B.
# Numerical Analysis via Mixed Inverse Hydrodynamic
## 4 Conclusions  
In this study, a numerical analysis method of thermal mixed IHL considering the effect of temperature rise due to friction in the sealing zone was presented. The sealing performance was analyzed by applying this method to a U-cup within the range of 25-115, sealed pressure of 20-35 MPa, and rod velocity of 0.1-1.0 m/s. From the present results, the following conclusions can be drawn:  
1. The oil film distribution according to operating conditions, such as rod velocity and sealed pressure, was analyzed. Regardless of stroke direction, as the rod velocity and sealed pressure increased, the lubrication performance improved, and a thick film was formed in the sealing zone, thus increasing the reverse pumping rate.
2. In addition to the rod velocity and sealing pressure, the sealing performance according to the ambient temperature was considered. The ambient temperature plays an important role in the sealing performance. A higher ambient temperature corresponded to the rough surface asperities having a greater effect on the seal owing to the thinner oil film, increasing friction, and reducing the reverse pumping rate.
3. Considering the effect of temperature rise, it was predicted that the total friction was larger and the reverse pumping rate was smaller; it was further confirmed that the difference increased as the ambient temperature increased. These results show that when predicting the performance of rubber seals, it is very important to consider the thermal effect under the operating conditions of high temperature or partial lubrication with a high asperity contact ratio.  
In the above studies, effects--such as thermal expansion of the housing and rod, 3D motion, and transients--were not considered or were deemed negligible. However, and future work will improve the analysis model via a more sophisticated analysis that considers these effects.  
Conceptualization, B.K. and Y.Y.; Methodology, B.K., B.L. and Y.Y.; Software, B.K. and B.L.; Validation, B.L.; Investigation, B.K., B.L., Y.C., G.H. and J.P.; Resources, Y.C.; Data curation, J.P.; Writing--original draft, B.K.; Writing--review & editing, B.L., G.H. and Y.Y.; Visualization, G.H., J.P. and Y.Y.; Supervision, J.S. and Y.Y.; Funding acquisition, J.S., Y.C. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1C1C2003523) and by the Nano-Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (No. 2020M3H4A3106186).  
Figure 10: Variation in flow rate according to different ambient temperatures and whether thermal effects are considered.
# Chaos-Based Underwater Communication With Arbitrary Transducers and Bandwidth
## Appendix A The Underwater Acoustic Channel  
The characteristics of the underwater acoustic channel are given in References [14;37], and the parameters used in this paper are given here in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter Name** & **Parameter Value** \\ \hline sea depth \(h\) & 100 m \\ temperature of seawater \(T\) & 20 \({}^{\circ}\)C \\ transmitter depth \(d_{1}\) & 30 m \\ receiver depth \(d_{2}\) & 80 m \\ propagation distance \(R\) & 1000 m \\ seawater sound speed \(c\) & 1539 m/s \\ seabed sound speed \(c_{1}\) & 1650 m/s \\ seawater density \(\rho\) & 1023 kg/m\({}^{3}\) \\ seabed density \(\rho_{1}\) & 1500 kg/m\({}^{3}\) \\ seawater salinity \(S\) & 35 ppt \\ transducer RMS movement \(\sigma_{d}\) & 0.25 m \\ number of multiple paths & 6 \\ path 1 with surface/bottom reflections \(s\)\(b\) & 2 1 \\ path 2 with surface/bottom reflections \(s\)\(b\) & 1 2 \\ path 3 with surface/bottom reflections \(s\)\(b\) & 3 2 \\ path 4 with surface/bottom reflections \(s\)\(b\) & 3 4 \\ path 5 with surface/bottom reflections \(s\)\(b\) & 2 3 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for the underwater acoustic channel.