esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Theoretical Derailments Caused by Cross-Winds with Frequency
## 4 Evaluation of Derailment Safety under Cross-Wind
### Derailment Safety According to the Cross-Wind Regulations
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Track Condition**} & \multirow{2}{*}{**Wind Speeds**} & \multicolumn{2}{c}{**Ratios of Wheel Unloading**} & \multirow{2}{*}{**Differences**} \\  & **[m/s]** & & **Theory** & **Simulation** & \\ \hline \multirow{3}{*}{Straight track} & 0 & 0 & 0 & 0 \\  & 30 & 0.120 & 0.119 & 0.001 \\ \cline{1-1}  & 40 & 0.233 & 0.220 & 0.013 \\ \cline{1-1}  & 45 & 0.292 & 0.276 & 0.016 \\ \hline \multirow{3}{*}{Curved track (Lateral Acc. = 0.5 m/s\({}^{2}\))} & 0 & 0.128 & 0.120 & 0.008 \\  & 30 & 0.248 & 0.248 & 0 \\ \cline{1-1}  & 40 & 0.343 & 0.353 & 0.010 \\ \cline{1-1}  & 45 & 0.398 & 0.395 & 0.003 \\ \hline \multirow{3}{*}{Curved track (Lateral Acc. = 1.0 m/s\({}^{2}\))} & 0 & 0.256 & 0.242 & 0.014 \\ \cline{1-1}  & 30 & 0.376 & 0.364 & 0.012 \\ \cline{1-1}  & 40 & 0.471 & 0.463 & 0.008 \\ \cline{1-1}  & 45 & 0.526 & 0.526 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The ratio of wheel unloading (\(\Delta\)Q/Q) between the theoretical formula and the simulation for cross-winds. Figure 10: The ratios of wheel unloading to cross-wind speeds under the overturning derailment condition. \[F_{H}=\frac{\Big{[}\frac{P}{Q}(1+\gamma)-\mu\gamma\Big{]}F_{LV1}+\Big{[}\mu(1+ \gamma)-\frac{P}{Q}\gamma\Big{]}F_{RV1}}{\Big{[}(1+\mu\beta)-\frac{P}{Q}\beta \Big{]}} \tag{32}\]  
Here, \(\beta=\frac{h_{0}}{c_{1}}\), \(\gamma=\frac{2c_{0}}{c_{1}}\) (see Figure 4). \(h_{0}\): Radius of wheel  
\(e_{0}\): Track width (distance between two contact points of both treads)  
\(e_{1}\): Distance between the contact point of a wheel tread and the primary suspension  
\(\mu\): Friction coefficient
## 5 Results and Discussion  
### Calculation of CPTs and Construction of the FBN  
Once CPr and FPr have been quantified, the next step is to calculate the CPTs. For this study, CPr calculated from RTO data are used for sixteen BEs, and for the remaining ten BEs, FPr are used (refer to Figure 3). For the second case (C-2), CPT calculations are based on FPr for all twenty-six BEs, and the results are compared to determine the effects of using expert elicitation. Table 5 shows the two cases developed to compare the FBN using CPr and FPr. It is important to highlight that this comparison excludes cases where only historical data are utilized. This exclusion is due to limitations in the availability of historical data for all basic events; only 16 out of 26 BEs have historical records. It is also crucial to recognize that using incomplete data for this analysis may lead to unreliable risk assessment results.
## 6 Example and Validation
### Simulations  
During simulations (PSIM 2022 software), the transient responses of the DC link voltage and grid-side current to a zero-to-rated power step-like load variation, and the corresponding steady-state performance under the proposed control methodology were evaluated. The results are depicted in Figures 11 and 12 for 50 Hz and 60 Hz mains, respectively.  
Figure 10: Bode diagram of the DC link voltage loop.  
Figure 9: Grid-interfacing converter under study.  
Figure 11: Simulation results, 50 Hz mains. DC link voltage and grid-side current response to 100% load increase at 50 ms for (**a**) \(\omega_{\rm{G}}=\) 2\(\pi\)-49.5 rad/s; (**b**) \(\omega_{\rm{G}}=\) 2\(\pi\)\(\cdot\)50 rad/s; and (**c**) \(\omega_{\rm{G}}=\) 2\(\pi\)\(\cdot\)50.5 rad/s.  
Figure 12: Simulation results, 60 Hz mains. DC link voltage response to load increase for (**a**) \(\omega_{\rm G}=2\pi\cdot 59.4\) rad/s; (**b**) \(\omega_{\rm G}=2\pi\cdot 60\) rad/s; and (**c**) \(\omega_{\rm G}=2\pi\cdot 60.6\) rad/s.  
It may be concluded that the system operates as expected under the proposed methodology in terms of dynamic response (a \(\sim\)10 V DC link voltage deviation is evident irrespectively of mains frequency). As to the grid-side current quality, Table 1 summarizes the corresponding steady-state THD values. It is evident that the system designed according to the proposed approach complies well with \(THD^{*}\) at \(\omega_{C}=\alpha_{\min}\)-100\(\pi\). Moreover, the THD attains near-zero values at the nominal values of the mains frequency, as predicted above.
## 2 Background  
### Tailored Forming  
Tailored forming is a novel technology for the production of hybrid high-performance components. In contrast to other manufacturing technologies for solid multi-material components, a forming process takes place with previously joined semi-finished products so that two different materials are formed simultaneously. This has the advantage that, on the one hand, load-adapted material topologies can be achieved locally and, on the other hand, the joining zone can be influenced both geometrically and thermally during the forming process, resulting in a longer service life of the components [10]. With the use of tailored forming, the number of necessary manufacturing steps usually increases, which also increases the complexity of the corresponding process chain [11]. Complex process chains, such as that of tailored forming technology, are characterized by the fact that process uncertainties also increase in conjunction with the many interdependencies of the processes involved [12; 13]. Therefore, the greatest possible conformity of product designs with later manufacturing restrictions of the process chain is particularly desirable [14]. From a design perspective, multi-material design opens up great potential for new concepts in lightweight design [15]. However, the handling of manufacturing-induced design constraints becomes more complicated due to the increased interdependence within the tailored forming process chain. The process-oriented design of the joining zone in particular takes a central role in the design of tailored forming components [16]. The current challenge lies in the tailored forming process chain itself, which is being fundamentally researched for the first time in the Collaboration Research Center (CRC) 1153 for the manufacture of solid multi-material components. Therefore, cross-process manufacturing knowledge is in part only implicit. The development of tools for forming is especially complex and cost-intensive [17]. Therefore, in addition to empirical investigations, research is increasingly using validated simulation models to develop knowledge about the tailored forming process chain. This knowledge is fed back into product development in the form of knowledge-based methods and tools to support the manufacturing-oriented design of tailored forming components and to make the complex technology applicable for design engineers [18].  
The tailored forming process chains investigated in CRC 1153 consist of three process areas [16; 19]:  
1. First, two monomaterial semi-finished products made of different materials are joined together;
2. This is followed by forming the hybrid semi-finished product. This produces structural changes that have a positive effect on the strength and service life of the components;
3. Finally, the shape and the edge zone and surface properties for the component are set by machining.  
Within this process chain framework, various manufacturing processes are available for the production of a multimaterial shaft (see Figure 1).
# Covert Surveillance Video Transmission with QAM Constellations Modulation
## 1 Introduction
Diverging from the Gaussian codebooks that are prevalent in many theoretical covert communication studies [22; 23; 33], we adopt QAM codebooks that are commonly employed in video transmission to explore the theoretical bounds of covert transmission. In [36], the authors analyzed the covert transmission performance of phase-shift keying modulation in intelligent reflection surface systems; however, the covertness performance of QAM has not yet been investigated. In this work, we aim to investigate the performance of covert surveillance video transmission with QAM modulation. Leveraging the Taylor series expansion theory, we systematically conduct a comprehensive analysis, encompassing both transmission rates and covert aspects. Our investigation yields theoretical insights into facilitating the derivation of the necessary conditions governing the optimal modulation probability of constellation points under QAM modulation. Following this theoretical groundwork, we conduct a series of experimental simulations to rigorously validate our derived conclusions. Remarkably, our approach demonstrates a commendable performance, exhibiting superior transmission rates and enhanced covertness compared to existing methodologies. This success underscores the efficacy of our optimized strategy in the covert transmission of surveillance videos. The rest of this paper is organized as follows: Section 2 introduces the surveillance video transmission scheme at the transmitter and receiver, as well as the hypothesis test of covert surveillance video transmission at an adversary, and an optimization problem of constellation transmission probability and amplitude gain is also formulated, aiming to maximize the transmission rate; Section 3.1 analyzes the transmission rates of surveillance videos; Sections 3.2 and 3.3 present the covertness performance of the surveillance video transmission and the theorem of the optimal conditions of QAM modulation; Section 4 provides the experiments and analysis to verify the performance of our research; Section 5 concludes the paper.
## 4 Simulation Results
### Network Lifetime  
We also study the network lifetime of each approach. A sensor node is considered to be dead if it doesn't have enough energy to receive or transmit data. We ran the simulations using the numbers of cells 6 \(\times\)6, 8 \(\times\)8, and 10 \(\times\)10, respectively, for CBDAS. As shown in Figures 8a-c, CBDAS has longer network lifetime than the other approaches. The simulation result shows that CBDAS lasts on average 1.3 times more the number of rounds than PEGASIS. It also shows that a grid of 10 \(\times\)10 cells is the most appropriate among the three numbers of cells for CBDAS. Figure 8c shows that the number of dead  
Figure 7: Impact of node density. (**a**) Each node with initial energy 0.25 J; (**b**) Each node with initial energy 0.5 J; (**c**) Each node with initial energy 1.0 J.  
nodes is increasing after 2000 rounds for CBDAS but after 1200 rounds for PEGASIS though. In CBDAS, each node in a cell takes turn to be cell head and each cell head takes turn to be cycle leader, the energy depletion is evenly distributed so that the nodes' lifetime extend. As a result, the lifetime of the whole sensor network is prolonged.
## 2 Related Works
### ROI-Based Video Coding  
Various researchers adopt ROI-based video coding schemes to increase video quality without increasing the file size. ROI-based video coding schemes divide video images into two regions, ROI and non-ROI, and allow a system to encode with two different bit rates. The means of setting and managing ROI is a popular research topic in ROI-based video coding schemes. The research by [PERSON] et al. uses the Means-Absolute-Difference (MAD) prediction method to set ROI and adjust the Quantization Parameter (QP) value of H.264 encoder to differentiate the compression rates of ROI and non-ROI [20]. In the research by [PERSON] et al., the authors utilize the Flexible Macroblock Ordering (FMO) tool in H.264 video coding standard [21]. The work encodes the background and foreground regions separately by dividing them into two slices. The writers claim that the proposed technique improves the objective quality of the foreground by more than 1 dB. [PERSON] et al. use the visual perceptual mechanism of the Human Visual System (HVS) on the H.264 standards [22]. The paper claims that using HVS improves overall visual quality and states that the PSNR of the important region improves by 1.2 dB. The aforementioned researchers set ROI in a video to separate the important and unimportant regions. By following the standards, the researchers present a method to improve the quality of the important region. In this paper, the system predicts the ROI differently from H.264 or VP9 [23] so that regions of detected UAVs in the video are determined more accurately, and eventually improving QoE by applying encoding at the correct region.
# Corrosion Behavior of Pre-Rusted Rebars in Cement Mortar Exposed to Harsh Environments
## 1 Introduction  
The corrosion of rebar embedded inside reinforced concrete structures is affected by various factors, such as the water-cement (W/C) ratio during the concrete mixing process, use of admixtures, design deficiencies, environmental conditions, and chloride penetration through external concrete cracks [1; 2]. The corrosion of rebar embedded in concrete involves an initiation phase and a propagation phase, and the aforementioned conditions can affect the rate of the corrosion [3]. Rebar embedded in reinforced concrete composites, in general, does not undergo corrosion due to the formation of a passive layer in a high-alkali environment. However, there are still issues such as the increased cost of repairing structures damaged by chloride or carbonation [4]. Researchers have predicted the amount of corrosion of stainless steel (SS) [5; 6] and carbon steel [7; 8], and have measured the level of corrosion in various environments [9; 10].  
In the field of civil engineering and construction, many studies have been conducted to analyze the effect of corroded rebar on the mechanical performance of reinforced concrete. Studies have investigated the effects of corrosion in seawater or a chloride solution environment [11; 12] on the bond strength [13; 14; 15; 16] and structural behavior [17; 18; 19] of reinforced concrete, as well as the effects of concrete mixed with rust inhibitors on corrosion [20; 21; 22; 23]. However, studies have rarely focused on the corrosion of pre-rusted rebar embedded in cement composites [24], and the findings reported thus far are inconsistent [20; 21]. Therefore, in this study, the effect of pre-rusting by two different chloride-ion-containing solutions on the corrosion of rebar in cement mortar was examined by electrochemical measurement and microscope image analysis. In this study, the effect of a pre-rustedrebar on corrosion in a cement composite was investigated using two pre-rusting agents: CaCl\({}_{2}\), a common deicing agent [25], and an HCl solution based on a previous study [16].
## 3 Results  
Table 2 shows the results from the MANOVA analysis. First, the main effects of TN and SPL on both acoustic comfort and noise disturbance were found to be significant, with no interaction effect. Meanwhile, the main effects of IN were also found to be significant in both evaluations. Furthermore, the interaction effect between the interior noise source (IN) and traffic sound level (SPL) was also significant in both subjective evaluations.  
According to the MANOVA analysis, the influential factors of acoustic comfort and noise disturbance are the same. There was a strong negative correlation between acoustic comfort and noise disturbance (r = \(-0.7\), \(p\) < 0.001). To reduce the repetitive results, only results related to acoustic comfort are presented in the following sections.  
According to the size factor (\(\eta_{\text{p}}^{2}\)), traffic noise level (SPL) was found to be the most influential factor, followed by the interior noise type (IN), and the traffic noise type (TN). This result indicates that controlling the noise level is the most efficient measure to reduce the negative impact of combined noises. As for the noise sources, participants were more affected by the interior noise source than the exterior traffic noise source.
# Correction of Young's Modulus Calculation in the Flexural Mode of Resonant Column Test
## 4 Conclusions  
On the basis of the calculation formula of [PERSON]'s modulus for the flexural mode resonant column test by [PERSON] et al. [6], the present study proposed a modified formula that considers the rotational inertia effect of the resonant column device. For verification, three aluminum calibration rods with different diameters but similar height were used to perform torsional and flexural mode resonant tests. An additional test was performed on a soil specimen to evaluate the proposed calibration formula. The following conclusions were drawn:  
1. After rotational inertia correction, the calculated \(E\) of the calibration rod and \(\
u\) were close to the actual values.
2. Several tests are required to obtain an accurate equivalent centroid height. Hence, the results must be averaged to eliminate the error for the calculated equivalent centroid height.
3. After the rotational inertia correction, the difference between the calculated \(E\) and \(\
u\) was reduced with the same variation level in the measured resonant frequency, i.e., the sensitivity of \(E\) and \(\
u\) estimation is reduced.
4. The rotational inertia correction is required for the \(E\) estimation of soil specimen to avoid underestimation although the difference is not significant. However, the change may be great due to the different equipment and needs further investigation.  
This work was supported by the Ministry of Science and Technology, Taiwan under Award No. MOST 108-2628-E-005-001-MY3. The authors gratefully acknowledge such support.  
The authors declare no conflict of interest.
## 2 Results  
### Isolation and Identification of Secondary Metabolites  
The total methanol extract (TME) of freeze-dried _B. oleraca_ (26 g) was subjected to various and repeated techniques of chromatography to yield three pure compounds: caffeic acid (**1**) [19; 20], gallic acid (**2**) [21], and methyl gallate (**3**) [22] (Figure 1). The structures were elucidated via the inspection of 1D-and 2D-NMR spectroscopic data, including \({}^{1}\)H,\({}^{13}\)C, DEPT, HMQC, and HMBC (Supplementary Materials). The results were compared with those available in the literature.  
Caffeic acid **(1)**: \({}^{1}\)H NMR (DMSO-\(d\)6, 400 MHz) \(\delta\): 7.43 (d, J= 15.9 Hz, H-7), 7.04 (1H, s, H-2), 6.96 (1H, d, J = 7.9 Hz, H-6), 6.76 (1H, d, J = 7.9 Hz, H-5), 6.18 (1H, d, J = 15.8 Hz, H-8); \({}^{13}\)C NMR (DMSO-\(d\)6, 100 MHz) \(\delta\): 125.66 (C-1), 115.71 (C-2), 145.5 (C-3), 148.08 (C-4), 115.08 (C-5), 121.13 (C-6), 144.55 (C-7), 114.58 (C-8), 167.88 (C-9).  
Gallic acid **(2)**: \({}^{1}\)H NMR (400 MHz, DMSO-\(d\)6): 6.94 (2H, s, H-2, H-6); \({}^{13}\)C NMR (100 MHz, DMSO-\(d\)6): 8 120.21 (C-1), 108.7 (C-2, C-6), 137.94 (C-4), 145.35 (C-3, C-5), 167.46 (C-7).  
Methyl gallate **(3)**: \({}^{1}\)H NMR (400 MHz, DMSO-\(d\)6): \(\delta\) 6.96 (2H, s, H-2, H-6), 3.75 (3H, s, COOCH3); \({}^{13}\)C NMR (100 MHz, DMSO-\(d\)6): \(\delta\) 119.28 (C-1), 108.48 (C-2, C-6), 138.37 (C-4), 145.53 (C-3, C-5), 166.30 (C-7), 51.54 (CH3).
## 4 Quality-Aware Routing and Channel Assignment in Multi-Channel Multi-Gateway WOBAN
### Our Proposed Algorithm for Coloring the Planar Subgraph
In our scheme, we only apply a genetic algorithm on the vertices in GCL, which is much lesser than the total number of vertices in the conflict graph and thus the genetic algorithm converges much faster. Notice that a graph is non-planar if it contains a subgraph that is a subdivision of the _complete bipartite graph_\(K_{3,3}\) or the _complete graph_\(K_{5}\), which are shown in Figure 6. In general, a complete bipartite graph \(K_{m,n}\) has \(m+n\) vertices and \(mn\) edges, whereas a complete graph \(K_{n}\) has \(n\) vertices and \(\frac{n(n-1)}{2}\) edges. We use the following properties of vertex deletion to show its effectiveness in our proposed scheme. **Definition 5** (Vertex deletion). : _The non-planar vertex deletion or vertex deletion \(vd(G)\) of a graph \(G\) is the smallest non-negative integer \(k\) such that the removal of \(k\) vertices from \(G\) produces the planar subgraph \(\hat{G}\)[46]. The vertex deletions of the complete bipartite graph \(K_{m,n}\) and complete graph \(K_{n}\) are known and can be expressed as follows:_  
\[vd(K_{m,n}) = min\{m,n\}-2\quad\text{if }min\{m,n\}>2, \tag{4}\] \[= 0\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \text{otherwise},\] \[vd(K_{n}) = n-4\quad\quad\quad\quad\quad\quad\quad\text{if }n>4,\] \[= 0\quad\quad\quad\quad\quad\quad\quad\quad\text{otherwise}. \tag{5}\]  
Thus, in case of a \(K_{m,n}\) graph, \(\min\{m,n\}-2\) vertex deletions are required to make the remaining graph planar [46]. For \(m=n>>2\), this corresponds to \(\sim\)50% of vertex deletions. Consequently, the length of GCL also reduces by \(\sim\)50% which results in reduced convergence time of the genetic algorithm. However, in case of a complete graph \(K_{n}\), \(vd(K_{n})=n-4\). Thus, almost all the vertices will be added to the GCL, and the vertex deletion is less effective. However, in reality, the conflict graphs are hardly complete graphs. In that case, our proposed scheme will greatly reduce the convergence time of the genetic algorithm.
## 5 Discussion
### Comparison with Other NPP Reports  
The data sources used in NPP studies can be broadly divided into two categories: existing NPP products [43; 44; 45; 46; 47] (Table 6), which have a fixed spatial resolution, and estimations of NPP using NDVI and FPAR. To analyze the relationship between NPP changes and influencing factors, studies have mainly explored the overall response mechanism of NPP to climate change and human activities; in addition, they have considered mainly the effects of temperature and precipitation [36; 43; 46; 48; 49]. Other studies have considered additional influencing factors, such as relative humidity, sunshine hours, chlorophyll-a, photosynthetic availability, wind stress curl, salinity, nitrates, phosphates, and CO\({}_{2}\) concentration [44; 47; 50]. Compared with other studies, this paper conducted a relatively comprehensive study of the climatic and anthropogenic influences on vegetation NPP;  
Figure 20: PLS-SEM model plots: **(a)** 2001–2005, **(b)** 2005–2010, **(c)** 2010–2015, and **(d)** 2015–2020 for the relationship between changes in each variable and changes in NPP. Ovals indicate latent variables, rounded rectangles indicate explicit variables, arrows indicate their association, red dashed lines indicate negative correlations, and green solid lines indicate positive correlations.  
however, other influencing factors, such as CO\({}_{2}\) concentrations, nitrates, and phosphates, could be considered in subsequent studies.