esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Simulation Analysis of Earthquake Damage of Oil and Gas Pipeline Structure
#### 3.1.1 Analysis of Pipeline Structure Deformation  
The present study primarily investigated the fluid-structure coupling-induced deformation of the pipe bridge system under six distinct operational conditions. Figure 3 illustrates the maximum deformation observed in the pipe bridge system across various working scenarios.  
The maximum deformation of the oil and gas pipeline structure under different seismic waves was determined by extracting the highest deformation value from Figure 3, as presented in Table 3.  
The results presented in Figure 3 and Table 3 demonstrate that, across the six operational conditions, the pipeline experienced maximum deformation at 11 s under seismic wave action. Notably, the midspan positions of both the left- and right-side spans exhibited the most significant deformation, displaying an antisymmetric pattern. Comparative anal  
\begin{table}
\begin{tabular}{c c} \hline
**Working Condition** & **Combination Mode of Seismic Action Direction** \\ \hline
1 & X \\
2 & Y \\
3 & Z \\
4 & X + Y \\
5 & X + Z \\
6 & X + Y + Z \\ \hline \end{tabular} Note: X, Y, and Z represent the three translational components of seismic wave propagation along longitudinal, transverse, and vertical bridges.  
\end{table}
Table 2: Working condition setting.  
Figure 2: Simulation analysis process.  
ysis of deformation and damage caused by three unidirectional seismic waves revealed that longitudinal waves exerted the greatest influence on pipeline deformation, followed by vertical waves. Transverse waves primarily affected deformations in the pipe bridge system atop the bridge tower through transverse bending.  
Figure 3: _Cont._  
Figure 3: Cont.  
under the influence of three-way seismic waves, the pipe bridge system exhibited further reduced deformations. Consequently, it can be inferred that multi-directional ground motion leads to more reasonable deformations in the pipe bridge system.  
In conclusion, when considering the fluid-structure coupling effect of oil and gas pipelines, the deformation of the pipeline is similar to that of a pipeline considering the additional mass effect. The maximum displacement response occurs in the middle span between the left and right sides. Therefore, under strong earthquake action, excessive deformation of the side spans may lead to structural damage in the pipeline system. When analyzing seismic effects on bridges, particular attention should be paid to deformations occurring in these side spans. Among different seismic wave directions, longitudinal waves have the greatest influence on pipeline deformation, followed by vertical waves, while transverse waves primarily affect displacements at the top of bridge towers within pipe bridge systems. Furthermore, under combined multi-directional ground motion, pipeline deformations are smaller compared to those resulting from single earthquake events, thus indicating a weakening effect on pipeline deformations due to multi-directional seismic wave actions.
## 3 Verification Results
### Case Study 2  
The second verification case study was performed on a human face cast model (Figure 15(a)). Digitization was performed by contact scanning using a _Cyclton II--Renishaw_ system, and contains 905,931 points (Figure 15(b)). Bearing in mind moderate complexity of the point data from the filtering aspect, this case study was used firstly to verify the module for reduction of point data.  
Point-data pre-processing was started by the application of the tools for 3D filtering of point data. Considering that the model is symmetric about the \(x\)-axis (_i.e._, \(y\)-\(z\) plane) point-data pre-processing was started by applying 3D filtering with a segmented line filter and a total of four segments. Coordinates of the knot points defining the filter line's segments are given in Table 2. This resulted in a 351,292 filtered points (Figure 16).  
\begin{table}
\begin{tabular}{c c c} \hline
**Knot point** & \(y\)**-coordinate** & \(z\)**-coordinate** \\ \hline \(P_{1}\) & 130 & 260 \\ \(P_{2}\) & 145 & 235 \\ \(P_{3}\) & 165 & 215 \\ \(P_{4}\) & 290 & 215 \\ \(P_{5}\) & 333 & 238 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the applied segmented line filter.  
Figure 14: **(a)** Generated cross-sectional curves. **(b)** Created surface model.  
Figure 15: **(a)** Human face model. **(b)** Result of 3D digitization.  
The next phase of point data pre-processing was the reduction. The point cloud prepared for reduction contained 564,612 points in 979 cross-sections. The reduction was performed by the fuzzy-straightness method with \(MAD\) = 0.04 mm. Results are given in Table 3 and in Figure 17.  
\begin{table}
\begin{tabular}{l c} \hline
**Parameter** & **Value** \\ \hline MAD & 0.04 mm \\ Maximum error & 0.03993 mm \\ Average error & 0.00491 mm \\ Reduction level & 53.29 \% \\ Number of retained points & 275,811 \\ Reduction time & 95.45 min * \\ \hline \end{tabular}  
* Obtained on PC configuration: _Intel i7-870 (8M Cache, 2.93 GHz): DDR3 Kingston 8 GB 1,333 MHz._  
\end{table}
Table 3: The point data reduction results.  
Figure 16: Results of filtering by segmented line. **(a)** 3D view. **(b)**_y-z_ plane view.  
The surface model was generated based on the reduced point data in IBL format. Generated cross-sectional curves and resulting surface models are shown in Figure 18. Certain problems noticeable at the base of the nose can be attributed to a very small number of points in that area produced by 3D digitization.
# Simplified Design of Magnetic Gear by Considering the Maximum Transmission Torque Line
## 2 Maximum Transmission Torque Line of Magnetic Gear
### Design Parameters of Magnetic Gear  
In this study, various magnetic gears were simulated with randomly different parameters. This was done to analyze the characteristic of transmission torque against the total volume of PM. However, a few parameters are fixed, as shown in Table 1, to ensure that the characteristic of the simulated magnetic gears are uniform. The gear ratio is fixed to 5:1, converting 1000 rpm speed of high-speed rotor to 200 rpm speed of low-speed rotor. This low gear ratio is suitable for the application with smaller torque improvement. However, a 25:1 ratio through double-stage gearing is considered high [23]. The reason 1000 rpm is used as the input speed is that most applications of gears are dependent on the speed of the prime mover as, for example, of the induction motor in References [24; 25] and for DC motor [26] applications. The number of pole-pairs at high-speed rotor is 2, which required four PMs, while the number of pole-pairs at low-speed rotor is 10, which required 20 PMs. Thus, the number of pole pieces is 12, which is the sum of the number of pole pairs at both high-speed and low-speed rotors, as in Equation (2). The magnetic gears are designed with 0.5 mm air gaps between the rotors and the pole pieces, as the narrower air gaps increase the permeance and reduce the flux resistance. The 0.5 mm air gaps have no issue in assembling, since it is proven that smaller than that also can be achieved in assembling an electric machine [27].  
Table 1 shows the fixed parameters, while the size of the magnetic gears' structure, as shown in Figure 2, is a variable parameter. The changed parameters are the outer radius of MG, \(r_{\rm o}\); inner radius  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Design Parameter** & **Value** \\ \hline Gear ratio, \(G_{\rm r}\) & 5:1 \\ Speed of high-speed rotor, \(n_{\rm hs}\) & 1000 rpm \\ Speed of low-speed rotor, \(n_{\rm ls}\) & 200 rpm \\ Number of pole-pairs at HS rotor, \(p_{\rm hs}\) & 2 \\ Number of pole-pairs at LS rotor, \(p_{\rm ls}\) & 10 \\ Number of pole pieces. \(N_{\rm pp}\) & 12 \\ Length of air gap, g & 0.5 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Value of fixed parameters of the magnetic gears.  
of MG, \(r_{i}\); thickness of outer yoke, \(t_{\rm o}\); the thickness of PMs of low-speed rotor, \(t_{\rm m2}\); the thickness of pole pieces, \(t_{\rm p}\); the thickness of PMs at high-speed rotor, \(t_{\rm m1}\); and the thickness of inner yoke, \(t_{\rm i}\). The performance of the MGs is influenced by the parameters, and the observation on its effect on magnetic flux distribution is discussed in Section 3.1.
## 3 Optical Phase Conjunction (OPC) Parametric Optimization  
Figures 2 and 3 illustrate the measured conversion efficiency and BERs at the receiver end versus the signal power and the pump signal power input into OPC, respectively. The conversion efficiency is defined as the power ratio of the resulting phase conjugate signal to the transmitted signal. It is well-known that the control of signal power and pump signal power at the input of OPC is an important approach to eliminate the nonlinear distortion such as SPM and pump stimulated Brillouin scattering (SBS) [23]. Figure 2 shows that the balance of BER and conversion efficiency at the receiver end requires the input signal power of OPC to be \(-2.5\), \(-3.3\), \(-2.4\), and \(1.7\) dBm for HNLF A-D, respectively. Likewise, Figure 3 shows that the balance of BER and conversion efficiency at the receiver end requires the pump signal power to be 23, 23, 23, and 21 dBm for HNLF A-D, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**HNLF** & **A** & **B** & **C** & **D** \\ \hline \(\lambda_{0}\)\([\mathrm{nm}]\) & 1541.3 & 1546.7 & 1542.9 & 1545.4 \\ \(S_{0}\)\([\mathrm{ps/nm^{2}km}]\) & 0.0074 & 0.017 & 0.072 & 0.07 \\ \(\alpha\)\([\mathrm{dB/km}]\) & 0.76 & 0.74 & 0.83 & 0.47 \\ \(n_{2}\)\([\mathrm{m^{2}/W}]\) & 30.467 & 31.989 & 27.392 & 18.951 \\ \(\gamma\)\([\mathrm{W^{-1}/km^{-1}}]\) & 10.8 & 11.3 & 9.7 & 6.7 \\ \hline \end{tabular}
\end{table}
Table 2: Four types of highly nonlinear fiber (HNLF) are considered for OPC.  
Figure 3: Measured conversion efficiency in square symbol line and BERs in triangle symbol line versus the pump signal power input into OPC. **(a)** HNLF A; **(b)** HNLF B; **(c)** HNLF C; **(d)** HNLF D.  
Figure 2: Measured conversion efficiency in square symbol line and bit error rates (BERs) in triangle symbol line versus the signal power input into OPC. **(a)** HNLF A; **(b)** HNLF B; **(c)** HNLF C; **(d)** HNLF D.
## 4 Clustering Routing Algorithm Design  
The clustering routing algorithm has two main phases: CHN selection phase and data transmission phase. In the algorithm, the network is divided into cubes, and each cube is seen as a cluster. In every cluster, nodes run for CHN. One of them will be selected as the CHN and the others become CMNs. CMNs collect data and send them to the CHN by a single hop. After receiving the data from the CMNs, the CHN processes the data and then transmits them to the SN in one data packet by multiple hops.  
Figure 2: The searching scope.  
The relay nodes on multi-hop paths are other CHNs and the optimal path to the SN is found by using the improved ACO algorithm.
## 3 Results and Discussion  
### Definition of Dissociated Methanol Gas Substitution Ratio  
In the simulations, the total energy is assumed to be constant, so:  
\[E_{total}=M_{m}\cdot H_{m}+M_{d}\cdot H_{d}=M_{d0}\cdot H_{d} \tag{1}\]  
In Equation (1), \(E_{total}\) is the total energy consumption rate of the original engine fueled with diesel; \(M_{m}\) and \(M_{d}\) are the mass consumption rates of methanol and diesel when the engine fueled with diesel-DMG mixtures, respectively; \(H_{m}\) and \(H_{d}\) are the low calorific values of methanol and diesel, respectively; \(M_{d0}\) is the consumed mass of diesel when the engine is fueled with neat diesel. Therefore, the DMG substitution ratio \(l\) is calculated as follows:  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Engine speed (rpm) & 1500 \\ Load & 100\% \\ Injected mass (mg/cyc) & 237 \\ Injected pressure (MPa) & 180 \\ SOI (CA ATDC) & \(-5^{\circ}\) \\ Injected duration (CA) & 38\({}^{\circ}\) \\ Intake pressure (bar) & 2.9 \\ Intake pressure (K) & 350 \\ EGR ratio (\%) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Operating conditions for the validation.  
Figure 2: Model validation of in-cylinder pressure and HRR.  
\[l=\frac{M_{m}\cdot H_{m}}{M_{m}\cdot H_{m}+M_{d}\cdot H_{d}} \tag{2}\]  
According to experimental data, the cycle diesel injection quantity of the original engine is 237 mg/cyc at 1500 rpm and full load. For each substitution ratio, the diesel and methanol mass fraction can be calculated according to Equations (1) and (2). Thus, the DMG composition can be obtained when the methanol consumption is determined. In this study, the dissociation by-products were negligible since they were small in proportion. The fuel consumptions of different DMG substitution ratios are shown in Table 4.  
Since the simulations began at the IVO, the DMG-air mixture had already formed. The component of air should be considered. Therefore, the initial in-cylinder composition included N\({}_{2}\), O\({}_{2}\), CH\({}_{3}\)OH, CO, and H\({}_{2}\). Their mass fractions are set in Table 5.
# Systematic Literature Review of Challenges in Blockchain Scalability
## 8 RQ3: How Researchers Address the Scalability Issue in Blockchain?
### On-Chain Solution
#### 8.1.2 Segwit  
In Segwit method [120], the block size is kept constant while adding more transactions to the block. This approach aims to extract the signature data from the transaction and store it outside the base transaction block to effectively allow more spaces for new transactions. In this approach, the validating part is kept separated from actual data of the transaction. As the digital signature contributes almost 70% to the transaction, the signature must be stored in the data structure called the witness and is isolated from the transaction to resolve the malleability of the transaction [8]. In addition, Segwit has launched a new transaction size unit. A single transaction is divided into two sections. Non-witness (it should be stored in the block as usual) and witness data (it will transfer to extended block). Moreover, non-witness data bytes are counted as 4 WU each, whereas the witness data byte is counted as 1 WU each. The highest storage space for a block is 4 WU, which is equivalent to the old maximum block size of 1 MB, if no node uses Segwit.
## 4 Model and Methodology of the Adaptive Protocol
### Double-Q-Value Learning Model  
The Q-learning algorithm is a value function-based algorithm in RL, and for any finite Markov decision process, Q-learning can find an optimal policy. Q-learning involves an agent, a set of states \(S\), and a set of actions \(A\). By performing actions in the environment that cause the agent to move from one state to another, the action in a particular state is rewarded. That is, \(Q(s,a)\) is the expected reward for performing action \(a(ainA)\) in state \(s(sinS)\) at a given time [33]. The algorithm used in this study is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Representation** \\ \hline \(E_{elec}\) & Energy consumed to transmit a unit of _bit_ data \\ \(\varepsilon_{fs}\) & Power amplifier normal loss \\ \(\epsilon_{amp}\) & Power amplifier for multipath attenuation \\ \(d_{0}\) & Distance constant \\ \(E_{ec}\) & The energy spent for computation \\ \(DA(s,a)\) & The degree of data aggregation of node s to the node pointed to by its action a \\ \(E_{s}^{a}\) & The remaining energy of the node pointed to by action a \\ \(L_{s}^{a}\) & Link strength between node s and the node pointed to by action a \\ \(d(a,\sin k)\) & The distance from the node pointed to by action a to the sink \\ \(T_{s}^{a}\) & Forwarding time from node s to the node pointed to by its action a \\ \(DA_{nor}\) & Data Aggregation normalized value \\ \(E_{nor}\) & Residual energy normalized value \\ \(A_{nor}\) & The normalized value of the number of hops to the sink node \\ \(T_{nor}\) & Forwarding delay normalized value \\ \(L_{nor}\) & Link strength normalized value \\ Rec\({}_{sp}\) & Received signal power \\ \(\alpha_{sp}\) & Adaptive weight factor \\ \hline \hline \end{tabular}
\end{table}
Table 4: Protocol parameters and definitions.
## 5 Security Analysis
### Malicious User Mobility  
By setting the time threshold period of the revocation PKBL blacklist update and \(th_{cs}\) threshold discussed in Section 4.4 for security events, the user can no longer attack other domains after the key is revoked, even if it moves and tries to access within other SNR domains.
## 5 Simulation Results and Discussions
### Performance Analysis of Users
#### 5.3.3 Quick Decision under the Dynamic Environment  
Furthermore, we notice that in Figure 9, User 2 adopted single Q-learning in the first time stage and adopted multi-agent Q-learning to cooperate with User 1 in the second time stage; while in the third stage, User 2 employed single Q-learning method again, and it could achieve high cumulative normalized utility via using the training experience of the first stage. The proposed ICADCSA algorithm only needed to converge once in one certain case, as each user maintained different Q-table for different cases. When users encountered the same case that had been trained before, they could take advantage of the previous experience and make quick decisions, which means the proposed algorithm worked well in UAV communication networks with high mobility.
## 1 Introduction
### Characterization of the Research Area
Figure 1: The study area: (**a**) the location of King George Island in West Antarctica, (**b**) the location of the ASPA-128 and ASPA-151 on King George Island; glaciated areas indicated by light grey color, ice-free areas are in black; 1-Bellingshausen Station, 2-Arctowski Station, 3-Ferraz Station, 4-Jubany Station, 5-Fildes Peninsula, 6-Arctowski Icefield.
# An Efficient Library for Reliability Block Diagram Evaluation
## 5 Results and Discussion: Evaluation of librbd Performance
### Evaluation of librbd Execution Time
The difference between the Intel i5 CPU and the other Intel CPUs may be due to both differences in the CPU architecture and in the OS used. Finally, we observe a sensible difference between all Intel-based PCs and the ARM-based one. This may be due to several architectural differences of the two CPU architectures. The principal one could be that, while the tested ARM CPU incorporates 4 physical cores, i.e., with the number of concurrently executing threads equal to 4, the tested Intel CPUs incorporate 4 physical cores with Hyper-Threading technology, thus \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**\# Times**} & \multicolumn{5}{c}{**Execution Time (ms)**} \\  & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** \\ \hline
50,000 & 0.253 & 0.263 & 0.240 & 1.361 & 2.962 \\
100,000 & 0.406 & 0.449 & 0.452 & 1.736 & 5.967 \\
200,000 & 0.853 & 0.823 & 0.774 & 3.375 & 10.985 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance evaluation of bridge generic block. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Topology**} & \multirow{2}{*}{**\# Times**} & \multicolumn{5}{c}{**Execution Time (ms)**} \\  & & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** \\ \hline
1 oo15 & 100,000 & 0.663 & 0.430 & 0.635 & 2.083 & 4.653 \\
2 oo15 & 100,000 & 0.828 & 0.883 & 0.884 & 2.908 & 8.284 \\
3 oo15 & 100,000 & 1.154 & 1.185 & 1.064 & 3.054 & 11.596 \\
4 oo15 & 100,000 & 1.454 & 1.401 & 1.402 & 3.385 & 14.935 \\
5 oo15 & 100,000 & 1.848 & 1.660 & 1.535 & 3.183 & 18.000 \\
6 oo15 & 100,000 & 2.147 & 1.898 & 1.723 & 3.513 & 21.169 \\
7 oo15 & 100,000 & 2.478 & 2.136 & 1.968 & 3.734 & 24.497 \\
8 oo15 & 100,000 & 3.063 & 2.475 & 2.831 & 5.842 & 27.787 \\
9 oo15 & 100,000 & 2.731 & 2.118 & 2.596 & 6.283 & 24.446 \\
10 oo15 & 100,000 & 2.432 & 1.900 & 2.418 & 4.917 & 21.156 \\
11 oo15 & 100,000 & 2.072 & 1.646 & 1.931 & 3.696 & 18.043 \\
12 oo15 & 100,000 & 1.562 & 1.414 & 1.666 & 3.144 & 14.822 \\
13 oo15 & 100,000 & 1.240 & 1.181 & 1.340 & 2.564 & 11.546 \\
14 oo15 & 100,000 & 0.933 & 0.935 & 1.128 & 2.342 & 8.221 \\
15 oo15 & 100,000 & 0.635 & 0.464 & 0.641 & 1.980 & 4.719 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance evaluation of Koo15 identical block. providing a number of concurrently executing threads ranging from 4 to 8. Nonetheless, the performed experiments show promising results also on low-power and low-cost CPUs.
## 2 The Development of the Fluoropolymer Industry from 1930s \(\sim\) 1990s
### 1960s
Nafion can be cast into thin films by heating in aqueous alcohol at 250 \({}^{\circ}\)C in an autoclave. By this process, Nafion can be used to generate composite films, coat electrodes, or repair damaged membranes. The combination of the stable Teflon backbone with the acidic sulfonic groups gives Nafion many special characteristics. It is highly conductive to cations, making it suitable for many membrane applications. The Teflon backbone interlaced with the ionic sulfonate groups gives Nafion a high operating temperature, e.g. up to 190 \({}^{\circ}\)C. The combination of fluorinated backbone, sulfonic acid groups, and the stabilizing effect of the polymer matrix make Nafion a very strong acid, with pKa of -6. Nafion's superior properties allowed for broad application. Nafion has found use in fuel cells, electrochemical devices, chlor-alkali production, metal-ion recovery, water electrolysis, plating, surface treatment of metals, batteries, sensors, Donnan dialysis cells, drug release, gas drying or humidification, and superacid catalysis for the production of fine chemicals ([PERSON], 2005). Nafion is also often cited for theoretical potential (_i.e._, thus far untested) in a number of fields. Although fuel cells have been used since the 1960s as power supplies for satellites, recently they have received renewed attention for their potential to efficiently produce clean energy from hydrogen. Nafion was found effective as a membrane for proton exchange membrane (PEM) fuel cells by permitting hydrogen ion transport while preventing electron conduction. Solid Polymer Electrolytes, which are made by connecting or depositing electrodes (usually noble metal) to both sides of the membrane, conduct the electrons through an energy requiring process and rejoin the hydrogen ions to react with oxygen and produce water. Fuel cells are expected to find strong use in the transportation industry. Nafion is the first and still one of the most important functional fluoropolymer materials. The further development in the fuel cell industry and the automotive industry offered the prominent future for the interesting polymer systems. Figure 6: The preparation of Nafion.