esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
The race simulation is implemented in Python. The computation time for a race with 20 participants (as in the 2019 season) is \(90\,\mathrm{ms}\) to \(110\,\mathrm{ms}\) on a common computer (Intel i7-6820 HQ) including the pre-simulation. For MCS, the races can be simulated independently from each other. Therefore, the calculation time benefits from multiple CPU cores almost linearly. This allows us to perform \(10,000\) simulation runs in about \(250\,\mathrm{s}\) to \(300\,\mathrm{s}\) using all four cores of the CPU.  
For reasons of clarity, only the six drivers of the three currently dominating teams (Mercedes, Ferrari, Red Bull) are simulated and shown in this section.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Race Track** & \(t_{\text{pit,in-lap/out-lap}}\) & \(t_{\text{pit,in-lap/out-lap,vsc}}\) & \(t_{\text{pit,in-lap/out-lap,sc}}\) \\ \hline Catalunya & \(19.04\,\mathrm{s}\) & \(10.03\,\mathrm{s}\) & \(7.88\,\mathrm{s}\) \\ Melbourne & \(17.85\,\mathrm{s}\) & \(12.95\,\mathrm{s}\) & \(12.61\,\mathrm{s}\) \\ Monza & \(20.60\,\mathrm{s}\) & \(15.42\,\mathrm{s}\) & \(10.16\,\mathrm{s}\) \\ Suzuka & \(19.48\,\mathrm{s}\) & \(15.05\,\mathrm{s}\) & \(13.60\,\mathrm{s}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time loss when driving through the pit lane under normal conditions, during a virtual safety car (VSC) phase, and during a safety car (SC) phase. The values were measured using videos from the cockpit perspective in the 2018 and 2019 seasons, which are available on F1 TV [13].  
Figure 9: Illustration of the safety car ghost (SCG) concept. For reasons of illustration, the lap-wise discretization is disregarded here. The circle symbolizes a lap on the race track starting and ending at the finish line (FL). During normal driving (left), a race driver drives with his calculated lap time \(t_{\text{lap}}\). As soon has his race time \(t_{\text{race}}\) exceeds the start of a safety car phase, he is slowed down to \(t_{\text{lap,vsc}}\) and his SCG starts driving on the finish line with a lap time of \(t_{\text{lap,sc}}\). This is the run-up stage (center). After some time, the driver will catch up with his SCG due to its slower lap time. As he is not allowed to overtake the SCG, he will follow it and keep the minimum temporal spacing \(p\cdot t_{\text{gap,sc}}\), even though his “free” lap time would still be \(t_{\text{lap,vsc}}\). This is the following stage (right). At the end of a safety car phase, the SCG always disappears at the finish line.
## 4 Results and Discussion
### First Query: Use of AI on CFD  
The Scopus database query retrieved a total of 9364 publications from 1984 to 2024. Despite this extensive time range, the number of annual publications remained below 50 until 2004 and did not exceed 100 until 2009. As illustrated in Figure 1, the past two decades had witnessed a more than 3500% increase in publications related to artificial intelligence in computational fluid dynamics, with the most significant surge occurring in the last decade, reaching 1898 publications in 2024.  
The fast rise in AI-related CFD publications was likely driven by multiple technological advancements that have facilitated the broader adoption of artificial intelligence across various disciplines. The expansion of training datasets and improvements in computational hardware were key enablers of large-scale AI model development. For instance, in 2010, most models required fewer than 100 petaflops of computational power, whereas by 2023, the majority demanded over 1 million petaflops on average [60]. This trend also  
Figure 1: Publications per year between 1984 and 2024 for the first query made.  
aligns with the continued evolution of computational modeling, which has historically progressed alongside these technological advancements, further contributing to the increasing integration of artificial intelligence [61; 62].  
Regarding the language distribution of the retrieved publications, Figure 2 shows that English was the predominant language (94.5%), as it was consistently used since the earliest recorded publication in the query. It was followed by Chinese (5.1%), which has gained prominence since the early 21 st century, experiencing a sharp increase in 2023 when the number of publications in this language doubled compared to 2022. The remaining languages, accounting for 0.48% of publications, were Korean, followed by French. While French presented in publications since 2002, Korean exhibited notable growth, with six articles published in this language in 2024.  
These findings underscored the strong preference for English as the primary language of scientific publications. However, they also suggested a gradual shift towards Chinese, likely driven by the increasing number of articles published by Chinese researchers and the country's substantial investment in research through initiatives such as Made in China 2025 [63; 64].
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 4 Features and Preprocessing
### Operation Pattern Features  
To improve the ability to describe ICPSs' normal operation pattern, real industrial traffic collected from a coal mine factory was first analysed. This work then proposed using the operation sequence, time interval and execution time to describe the operation patterns. The first two are similar to the method proposed in [13; 24]; however, this work expands on this by improving the description of function types. Some proposals for modelling information systems [33] and performing functional safety assessments [34] can be useful when modelling operation sequences. They proposed using the Markov chain to describe the time-dependent processes; however, this work uses the determined finite state (DFA) because the ICPS operations usually follow a fixed sequence. The execution time is different from the runtime proposed in [35]. The runtime measures the time taken by master devices; however, the execution time focuses on measuring the time taken by slave devices to process the requests.  
These improved features are detailed as follows:  
* **Operation sequence.** This feature describes the sequence pattern of operation commands, such as the reading register \(reg_{i}\) of device \(A\), which is always followed by the  
Figure 2: An example of Modbus TCP request and response process.  
writing register \(reg_{j}\) of device \(B\). This feature enables detection models to identify malicious operations that violate the sequence patterns, such as tampering, reconnaissance, and unauthorised operation attacks.
* **Time interval.** This feature focuses on the time intervals between contiguous operations because they often follow fixed distributions [36]. In this way, the detection model is able to detect attacks that occur significantly earlier or later than expected, such as MITM and relay attacks.
* **Execution time.** The execution time describes the time taken by slave devices to process received requests. This feature is reasonable because slave devices are usually real-time systems, and the execution time only depends on the request commands' types and the computational resources [37; 38]. This feature enables models to detect anomalies whose responses are earlier or later than expected.
## 3 Test Results, Analyses, and Discussion
### Rheological Properties
#### 3.1.1 Temperature Sweep Analysis
Theoretically, low continuous grade of the asphalt-binders at high temperature means poor high-temperature rutting resistance. As the number of rejuvenating cycles increased, the high-temperature continuous grade of SBSMA with Type I and III rejuvenators decreased continuously, while the grade of the asphalt-binder with Type II rejuvenator maintained an increasing trend. After the third cycle of rejuvenation, with an increase in high temperature, the grade of the asphalt-binder with Type II demonstrated an insufficient effectiveness of the repeated rejuvenation with Type II. By contrast, Type I and III rejuvenators could be applied to the repeated rejuvenation of SBSMA to reduce its high temperature grade. However, the grade reduction associated with the Type I rejuvenator  
Figure 5: Fatigue factor of SBS asphalt-binders at different aging cycles: (**a**) Second cycle; (**b**) Third cycle. was relatively low. On the other hand, whilst the third rejuvenated asphalt-binder reached 84.1 \({}^{\circ}\)C, the third rejuvenated asphalt-binder with Type III was only 79.8 \({}^{\circ}\)C (see Figure 6a). For asphalt-binders rejuvenated with Type I and II rejuvenators, the intermediate temperature continuous grade decreased to 18.3 \({}^{\circ}\)C and 18.7 \({}^{\circ}\)C (Type I and Type II, respectively), and then increased by 19.0 \({}^{\circ}\)C and 21.6 \({}^{\circ}\)C (Type I and Type II, respectively) with an increase in the number of aging cycles. For the asphalt-binders rejuvenated with Type III, the continuous grade at the intermediate temperature decreased with the aging cycles (see Figure 6b). The continuous grading temperature of the first, second, and third aged asphalt-binders rejuvenated with Type I were extremely close, showing that the fatigue properties of SBSMA rejuvenated with Type I could satisfactorily reach the virgin asphalt-binder level. Based on the results of the rutting and fatigue behavior of the asphalt-binders in different aging and rejuvenation cycles, Type I was considered to achieve the expected rejuvenation effects among the three types of rejuvenators evaluated.
## 3 Results and Discussion  
### Setting Time  
Figure 4 shows the measurements of initial and final setting times for different DSPB slurries. Figure 4a shows that both the initial and the final setting times of the slurries have a tendency to increase with a decreasing HCDA ratio. This may be attributed to high levels of f-CaO and SO\({}_{3}\) in HCDA, leading to changes in condensation time. The f-CaO reacted with free water to produce Ca(OH)\({}_{2}\), which made the pH value increase, which also favored the solubilization of reactive silica and aluminum in the GBFS and accelerated the generation of AFt and C-S-H gels, thereby shortening the coagulation time [52]. On the contrary, Ca\({}^{2+}\) dissolved to quickly form Ca(OH)\({}_{2}\) micelles and attract negatively charged HCDA particles, thus shortening the coagulation time. The high water demand of HCDA was another factor contributing to the shortening of the setting time [53]. The formation of hydration products and the consumption of free water were the main reasons for the shortening of the condensation time.  
From Figure 4b, it can be seen that the setting time was prolonged with the increase in PCE dosage. PCE molecules adsorbed on the surface of HCDA and GBFS particles prevented f-CaO from reacting with water, thus preventing Ca(OH)\({}_{2}\) from further reacting with other reactive substances, which had a certain inhibiting effect on the hydration of slag, which prolonged the setting time macroscopically. The results of other studies have also shown that the setting time of pastes always increases as the PCE dosage increases, due to the fact that PCE affects the dissolution of slag particles [54; 55].  
The results in Figure 4c show that the setting time gradually decreased as the SS content increased from 0. This was due to the fact that the increase in alkali activator accelerated the solution of Si and Al, releasing large quantities of AlO\({}_{4}\) and SiO\({}_{4}\) tetrahedras, which promoted the generation of gel and thus shortened the setting time. The findings of other  
Figure 4: Initial and final setting times of DSPB samples with different (**a**) HCDA-to-GBFS ratio; (**b**) PCE additions; (**c**) SS additions.  
scholars are similar to those of the present study and could support this interpretation well [56; 57].  
The initial setting time of DSPB samples was basically more than 2 h, and the final setting time was about 12 h, which indicates that the slurry would not be set too early during the transportation of the pipeline, leading to the phenomenon of a blocking pipe, and could also be set as soon as possible after the pouring to obtain early strength so that the construction can be continued.
# Study on Corrosion Monitoring of Reinforced Concrete Based on Longitudinal Guided Ultrasonic Waves
## 3 Finite Element Simulation of Corroded Reinforcement
### Finite Element Model
#### 3.1.4 Determination of Excitation Frequency  
In practical detection, the propagation pattern of GUWs in reinforcement is affected by the dispersion property, which will make the signal analysis difficult and the detection accuracy reduced, so it is especially important to select the appropriate excitation signal. [PERSON] et al. [33] found that it is feasible to select the L(0, 1) mode of GUWs to detect defects in steel rods at specific frequencies, and pointed out that the dispersion phenomenon of the L(0, 1) mode is weak at lower frequencies (such as less than 50 kHz). [PERSON] et al. [34] showed that the excitation of multiple modes should be avoided as much as possible in GUW tests of rods, and that the excitation frequency needs to be selected as flat as possible in the frequency dispersion curve. According to the frequency dispersion curve shown in Figure 2a, it is found that the slope of the curve with a frequency lower than 100 kHz is flatter, which indicates that the dispersion degree is smaller, and the deformation of the GUWs in the propagation process is smaller, which makes it easier to accurately identify  
Figure 4: Comparison of GUW signal propagation in smooth and threaded reinforcement.  
the reach time. Therefore, the GUW with a frequency within 50-100 kHz is selected as the excitation frequency. A modulated sinusoidal function with a center frequency of 50, 80, and 100 kHz is used for ultrasonic excitation at the non-completely fixed end face to avoid octave interference.
## 3 Results and Discussion
### Adsorption Kinetics and Isotherms
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Kinetictic Models** & **C\({}_{0}\)** & **50** & **75** & **100** & **125** & **150** \\ \hline \multirow{6}{*}{PFO} & \(\mathrm{q_{e}}\) & 51.2 & 77.6 & 101.5 & 126.1 & 153.2 \\  & \(\mathrm{k_{1}}\) & 0.026 & 0.024 & 0.018 & 0.022 & 0.022 \\  & \(\mathrm{R^{2}}\) & 0.990 & 0.990 & 0.993 & 0.997 & 0.995 \\  & Adj-\(\mathrm{R^{2}}\) & 0.989 & 0.989 & 0.992 & 0.997 & 0.994 \\  & \(\mathrm{\chi}\) & 3.27 & 8.03 & 8.72 & 5.42 & 15.43 \\ \hline \multirow{6}{*}{PSO} & \(\mathrm{q_{e}}\) & 64.8 & 100.0 & 137.9 & 164.4 & 199.2 \\  & \(\mathrm{k_{2}}\) & 3.85\(\cdot\)10\({}^{-4}\) & 2.19\(\cdot\)10\({}^{-4}\) & 1.09\(\cdot\)10\({}^{-4}\) & 1.20\(\cdot\)10\({}^{-4}\) & 1.02\(\cdot\)10\({}^{-4}\) \\  & \(\mathrm{R^{2}}\) & 0.975 & 0.976 & 0.986 & 0.989 & 0.985 \\  & Adj-\(\mathrm{R^{2}}\) & 0.972 & 0.974 & 0.985 & 0.988 & 0.984 \\  & \(\mathrm{\chi}\) & 8.90 & 19.35 & 17.65 & 21.85 & 45.94 \\ \hline \multirow{6}{*}{Elovich} & \(\mathrm{\alpha}\) & 2.10 & 2.72 & 2.41 & 4.01 & 4.99 \\  & \(\mathrm{\beta}\) & 0.05 & 0.03 & 0.02 & 0.02 & 0.01 \\ \cline{1-1}  & \(\mathrm{R^{2}}\) & 0.953 & 0.958 & 0.977 & 0.977 & 0.970 \\ \cline{1-1}  & \(\mathrm{\chi}\) & 16.65 & 35.02 & 30.31 & 47.73 & 91.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Kinetic modeling parameters for adsorbing MB onto BCA-Mag/AB. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Adsorbate** & **Langmuir** & \multicolumn{2}{c}{**Temkin**} & \multicolumn{2}{c}{**Freundlich**} \\ \hline \multirow{6}{*}{MB} & \(\mathrm{q_{m}}\) & 69.76 & \(\mathrm{B_{T}}\) & 6.34 & \(\mathrm{K_{F}}\) & 0.15 \\  & \(\mathrm{K_{L}}\) & 0.001 & \(\mathrm{A_{T}}\) & 0.04 & 1/n & 0.879 \\ \cline{1-1}  & \(\mathrm{R^{2}}\) & 0.995 & \(\mathrm{R^{2}}\) & 0.966 & \(\mathrm{R^{2}}\) & 0.996 \\ \cline{1-1}  & Adj-\(\mathrm{R^{2}}\) & 0.993 & Adj-\(\mathrm{R}\) & 0.955 & Adj-\(\mathrm{R}\) & 0.995 \\ \cline{1-1}  & \(\mathrm{\chi}\) & 0.05 & \(\mathrm{\chi}\) & 0.35 & \(\mathrm{\chi}\) & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Isotherm parameters for the adsorption of MB onto BCA-Mag/AB.
## 3 Results and Discussion
### Validation of Natural Frequency Parameter and Acoustic Power Calculation
Furthermore, from Figure 6 it is observed that for concentric stiffener patches the (0, 1) mode of plate with all combinations of patches shows the highest value of percentage variation of \(\lambda^{2}\) due to greater stiffness associated with this mode. However, for all the remaining modes (0, 0), (0, 2) and (0, 3) of plates with concentric stiffener patches the stiffness decreases and as a result the percentage variation of \(\lambda^{2}\) decreases associated with these modes. Figure 7 shows the numerical comparison of natural frequency parameters \(\lambda^{2}\) with modes for an unloaded plate and for a plate with 4 rectangular and 4 concentric stiffener patches for taper ratio, \(\rm T_{x}=0.00\). It is clear from Figure 7 that the effect of natural frequency parameter due to 4 rectangular stiffener patches is almost same as that of the unloaded plate. However, a plate with 4 concentric stiffener patches shows little decrease in the frequency parameter due to greater stiffness associated with this plate with concentric patch. Tables 4-6 numerically compare the first four natural frequency parameter \(\lambda^{2}\) of a plate with different combinations of rectangular and concentric stiffener patches for different cases of thickness variation with different taper ratios. It is observed from Tables 4-6 that the  
Figure 4: Comparison of sound power level analytically, numerically and experimentally for unloaded plate with uniform thickness for taper ratio \(\rm T_{x}=0.00\). natural frequency parameter for a plate with concentric patches for all thickness variations reduces more in comparison to rectangular patches with increasing taper ratios. This may be due to the lower stiffness of the plate associated with concentric patches. Furthermore, it is observed that the frequency parameter for a Case II plate (parabolically decreasing-increasing thickness variation) for all cases of thickness variation with different combinations of rectangular and concentric stiffener patches reduces more in comparison to a Case I plate (parabolic decreasing thickness variation). This is due to the lower stiffness associated with the Case II plate than that of the Case I plate. It is further investigated that the effect of the frequency parameter for a Case III plate with different attachment of rectangular and concentric stiffener patches (parabolic increasing-decreasing thickness variation) is almost same as that of uniform unloaded plate due to more stiffness associated with the Case III plate. However, for all cases of different rectangular and concentric stiffener patches, plate with different parabolically thickness variations alters its modes at higher taper ratios. Figure 5: Comparison of % variation of natural frequency parameter with different modes for plate with taper ratio, \(\mathrm{T_{x}}=0.00\) and with different attachments of rectangular stiffener patches.
## 4 Model Development  
As discussed earlier, this study aims to develop an accurate predictive model of RSI in the form of a simple, straightforward equation. The GEP technique can generate a straightforward mathematical function, making it one of the most suitable tools for developing such empirical models [16]. However, developing a GEP model with a limited-size dataset would result in a non-generalized and susceptible-to-overfitting model. Hence, hybrid machine learning is adopted in this study for developing the RSI predictive model. The ANN approach, which is known as an efficient and robust tool for generating predictive models in terms of numerical codes, will be employed first to generate new reliable datasets of different combinations. These data will be fed along with the raw dataset into the GEP software for developing the final mathematical equation for predicting the RSI value.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Asphalt Mixtures**} & \multirow{2}{*}{**F\%**} & \multirow{2}{*}{**Ab\%**} & \multirow{2}{*}{**AC\%**} & \multicolumn{4}{c}{**Average Values**} \\ \cline{5-8}  & & & & **Va\%** & & **S1 (kN)** & **S2 (kN)** & **RSI \%** \\ \hline \multirow{8}{*}{Mix A} & 2.62 & 1.334 & 3.5 & 8.8 & 10.934 & 7.900 & 72.25 \\ \cline{2-8}  & 2.62 & 1.334 & 4.0 & 7.5 & 11.425 & 8.752 & 76.61 \\ \cline{2-8}  & 2.62 & 1.334 & 4.5 & 6.1 & 14.024 & 11.560 & 82.43 \\ \cline{2-8}  & 2.62 & 1.334 & 5.0 & 4.5 & 14.151 & 12.286 & 86.82 \\ \cline{2-8}  & 2.62 & 1.334 & 5.5 & 3.7 & 13.710 & 11.943 & 87.11 \\ \hline \multirow{8}{*}{Mix B} & 4.97 & 2.08 & 3.5 & 7.9 & 11.905 & 8.516 & 71.53 \\ \cline{2-8}  & 4.97 & 2.08 & 4.0 & 6.5 & 13.036 & 10.114 & 77.58 \\ \cline{2-8}  & 4.97 & 2.08 & 4.5 & 5.1 & 14.386 & 11.850 & 82.37 \\ \cline{2-8}  & 4.97 & 2.08 & 5.0 & 4.0 & 15.014 & 12.615 & 84.02 \\ \cline{2-8}  & 4.97 & 2.08 & 5.5 & 3.0 & 15.377 & 12.957 & 84.26 \\ \hline \multirow{8}{*}{Mix C} & 7.34 & 3.77 & 3.5 & 7.0 & 12.062 & 8.494 & 70.42 \\ \cline{2-8}  & 7.34 & 3.77 & 4.0 & 5.4 & 14.926 & 10.912 & 73.11 \\ \cline{2-8}  & 7.34 & 3.77 & 4.5 & 4.2 & 16.220 & 12.739 & 78.54 \\ \cline{2-8}  & 7.34 & 3.77 & 5.0 & 3.3 & 15.759 & 12.661 & 80.34 \\ \cline{2-8}  & 7.34 & 3.77 & 5.5 & 2.1 & 15.485 & 12.296 & 79.41 \\ \hline \multirow{8}{*}{Mix D} & 6.06 & 2.521 & 3.5 & 7.3 & 11.768 & 8.313 & 70.64 \\ \cline{2-8}  & 6.06 & 2.521 & 4.0 & 5.9 & 14.739 & 11.324 & 76.83 \\ \cline{2-8}  & 6.06 & 2.521 & 4.5 & 4.4 & 15.563 & 12.724 & 81.76 \\ \cline{2-8}  & 6.06 & 2.521 & 5.0 & 3.5 & 15.367 & 12.689 & 82.57 \\ \cline{2-8}  & 6.06 & 2.521 & 5.5 & 2.4 & 14.582 & 11.940 & 81.88 \\ \hline \multirow{8}{*}{Mix E} & 3.72 & 1.86 & 3.5 & 8.4 & 10.768 & 7.734 & 71.83 \\ \cline{2-8}  & 3.72 & 1.86 & 4.0 & 6.9 & 11.797 & 9.150 & 77.56 \\ \cline{1-1} \cline{2-8}  & 3.72 & 1.86 & 4.5 & 5.4 & 13.386 & 11.160 & 83.37 \\ \cline{1-1} \cline{2-8}  & 3.72 & 1.86 & 5.0 & 4.2 & 14.092 & 11.959 & 84.86 \\ \cline{1-1} \cline{2-8}  & 3.72 & 1.86 & 5.5 & 3.3 & 13.749 & 11.757 & 85.51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average results of RSI values.
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
## 3 Formation of Weighted Path Aware Greedy Perimeter Stateless Routing Protocol  
### Influence of Node Movement Direction on Communication Reliability  
As shown in Figure 1a at T moment, the source node S sends a data packet to the destination node D. The direction of movement of node B is opposite to those of nodes S, A, and D. In the traditional greedy forwarding strategy, node B is closer; thus, node S selects node B for data transmission. However, as shown in Figure 1b at T + 1 moment, the node location changes, which will cause additional end-to-end delays, because node B will again select node A as the next hop node after receiving the packet. Therefore, when choosing the next hop node, not only does the distance need to be considered, but the direction of movement of the node also needs to be considered. Node S selects node A for data transmission, which will reduce the packet loss rate and average end-to-end delay.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Protocol** & **Scenario** & **Selection** & **Forwarding** & **Recovery** & **Performances** & **Sender/Receiver** \\  & **Parameters** & **Communication** & **Mechanism** & **Mechanism** & **Metrics** & **Oriented** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} SFTD \\ [25] \\ \end{tabular} } & \multirow{2}{*}{Urban} & \multirow{2}{*}{Link quality} & \multirow{2}{*}{V2V} & Smart data & \multirow{2}{*}{-} & Packet delivery & \multirow{2}{*}{-} & Receiver \\  & & & & & & & & -oriented \\