esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Track Irregularities on the Response of Two-Way Railway Tracks
## 2 Train-Track-Soil Interaction Model
### Train Model  
A quarter train model was used to simulate the vehicle body. The quarter train model consists of a quarter car body, half bogie, and a wheel. Each of these components is modeled as a rigid mass connected by two suspension systems (i.e., primary and secondary suspension systems). Figure 1 illustrates a schematic layout of the quarter train model, where the masses of quarter of a car body, half of a bogie and a wheel are denoted by \(m_{w1}\), \(m_{w2}\) and \(m_{w3}\) respectively. The primary suspension system which connects the wheel and bogie has stiffness \(k_{w2}\) and damping \(c_{w2}\) the secondary suspension system connects bogie and car body has stiffness \(k_{w1}\) and damping \(c_{w1}\). \(K_{H}\) presents the Hertz contact spring. The equations of motion for the given train model are derived as:  
\[\mathbf{M}\ddot{\mathbf{v}}_{w}(t)+\mathbf{C}\dot{\mathbf{v}}_{w}(t)+\mathbf{ K}\mathbf{v}_{w}(t)=\mathbf{F}(t), \tag{1}\]  
where \(\mathbf{M}\), \(\mathbf{C}\) and \(\mathbf{K}\) are the mass, damping and stiffness matrices of the train system, and \(\mathbf{F}\) is the load vector. Equation (1) can be written in expanded form as:  
\[\left[\begin{array}{cc}m_{\pi 1}&0&0\\ 0&m_{w2}&0\\ 0&0&m_{w3}\end{array}\right]\left[\begin{array}{c}\ddot{v}_{w1}\\ \ddot{v}_{w2}\\ 0\\ \end{array}\right]+\left[\begin{array}{cc}c_{\pi 1}&-c_{\pi 1}&0\\ -c_{\pi 1}&c_{\pi 1}+c_{\pi 2}&-c_{\pi 2}\\ 0&-c_{\pi 2}&c_{\pi 2}\\ \end{array}\right]\left\{\begin{array}{c}\dot{v}_{w1}\\ \ddot{v}_{w2}\\ \ddot{v}_{w3}\end{array}\right]+\left[\begin{array}{cc}k_{w1}&-k_{w1}&0\\ -k_{w1}&k_{w1}+k_{w2}&-k_{w2}\\ 0&-k_{w2}&k_{w2}\end{array}\right]\left\{\begin{array}{c}v_{w1}\\ v_{w2}\\ v_{w3}\end{array}\right\} \tag{2}\]  
In which:  
\[m_{w1}=\overline{m}_{w1}/8 \tag{3}\]  
\[m_{w2}=\overline{m}_{w2}/4, \tag{4}\]  
where \(\overline{m}_{w1}\), \(\overline{m}_{w2}\) and \(m_{w3}\) are the masses of car body, bogie, and a wheel, respectively and \(v_{w1}\), \(v_{w2}\) and \(v_{w3}\) are the vertical displacements of the car, bogie, and wheel, respectively.  
The coupling of wheel and rail can be obtained through the interaction forces between the wheel and rail and is given [15] as:  
\[F_{w/r}=\left\{\begin{array}{ll}\mathrm{K}_{H}(v_{w3}-v_{r}-irr)^{\frac{3}{2 }},&\mathrm{if}\ v_{w3}-(v_{r}-irr)<0,\\ 0,&\mathrm{if}\ v_{w3}-(v_{r}-irr)>0,\end{array}\right. \tag{5}\]  
where \(F_{w/r}\) is the interaction force between wheel and rail, \(\mathrm{K}_{H}\) is the contact spring constant, \(v_{w3}\) and \(v_{r}\) are the vertical displacements of the respective wheel and rail at the point of contact, and \(irr\) is the surface irregularity of the rail surface.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 3 Related Work and State-of-the-Art  
It should be noted that the proposed reputation model is not an instance of a reputation system. Reputation systems let parties rate each other and use aggregated ratings about a given party to derive a trust or reputation score [23]. Notable examples of reputation systems include Google's PageRank [24] or user rating systems on many e-commerce sites. The main challenge faced by reputation systems is that the transitive parties rate each other and potentially malicious parties do not rate each other. Compared to others, this challenge is not faced in the current case because the rating parties (client networks) and rated parties (remote servers) are disjointed.  
A better fitting abstraction is the problem of outlier ensemble models. In contrast to better-known classifier ensembles, outlier ensembles are designed to be applicable in an unsupervised setting and their design characteristics are much closer to our needs, as stated in Section 1.1. Individual client networks can be viewed as individual outlier detectors and a global reputation model can be viewed as their ensemble. The output of each outlier detector (client network) is in this case a local reputation model (LRM), associating an outlier score to every observed hostname in each network.  
One of the challenges is that individual outlier detectors observe different sets of hostnames. As argued in Section 4.2.1, this property can be treated systematically as an instance of missing data. In the following sections, these ideas are further investigated. By introducing outlier ensemble models, we explore relevant design decisions in outlier ensembles and discuss how they apply to our settings. Finally, the impact of missing values on outlier ensembles is assessed and algorithms dealing with this issue are discussed.
## 2 Construction of the Spatiotemporal Network for Subway Vehicle Maintenance and Scheduling
### Problem Description and Assumptions  
The integrated optimization of preventive maintenance and train scheduling for subway vehicles must address two core challenges. First, appropriate train assignments must be made for each operational route. Second, trains approaching their maintenance cycle  
Figure 5: Subway vehicle maintenance and scheduling spatiotemporal state network diagram.  
must be scheduled to return to the depot or maintenance base in time for necessary servicing. This ensures that all operational routes have adequately maintained trains and that these trains complete their required maintenance within the designated cycle [26]. From the perspective of train states, this process corresponds to paths within the spatiotemporal network. In the constructed spatiotemporal network, time represents the planning period, while space denotes the actual train locations, such as departure stations, terminal stations, and subway depots. The above analysis confirms that the preventive maintenance and scheduling plan for subway vehicles aligns with the fundamental properties of spatiotemporal networks, providing a solid theoretical foundation for the integrated optimization modeling of subway train scheduling and maintenance based on the spatiotemporal network diagram.  
Building upon the spatiotemporal network theory, a detailed analysis of train operations is conducted to clarify temporal and spatial operational processes. An integrated optimization model for preventive maintenance and scheduling is then established under the following assumptions to simplify the problem: (1) The daily train schedule is predetermined throughout the entire planning period, and existing operational routes remain fixed and unmodifiable. (2) The locations of subway depots, turnaround stations, turnaround times, and route structures are known. (3) The depot has a sufficient number of trains, and only a single type of train is considered in the model. Operations such as train coupling or decoupling are not included. (4) Trains in a depot that have completed maintenance are classified as being in a maintained state.
# A Crowdsensing Platform for Monitoring of Vehicular Emissions: A Smart City Perspective
## 6 Results and Discussions
### What Is the Amount of CO\({}_{2}\) Emitted by the Vehicles?
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Sensor** & **Samples** & **CO\({}_{2}\)** \\ \hline Volkswagen Voyage & MAP & 93 & 122.87 g \\ Ford Ecosport & MAP & 137 & 197.33 g \\ Fiat Palio & MAP & 379 & 483.37 g \\ Peugoet 208 Allure & MAP & 179 & 283.71 g \\ Honda Fit & MAP & 617 & 521.83 g \\ Toyota Etios & MAF & 303 & 325.05 g \\ Toyota Corolla & MAF & 691 & 2185.45 g \\ Nissan Versa & MAF & 169 & 269.80 g \\
**Total** & & 2777 & 4874.75 g \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of data collected from all considered vehicles. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Timetable** & **Duration** & **Samples** & **CO\({}_{2}\)** \\ \hline
17 May & 19 min & 294 & 251.37 g \\
19 May & 19 min & 288 & 245.68 g \\
23 May & 18 min & 271 & 236.28 g \\
**Total** & 56 min & 853 & 733.33 g \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of data collected from a Toyota Etios 2015. With this in mind, some interesting points can be listed about the results presented in Figure 4, especially in relation to the areas of higher incidence. * During times of departure, parking, or when stopped at traffic lights, the vehicle had increased emission readings. * Intermediate locations of the routes due to traffic jams or the presence of heavier traffic had high emissions. * Moments in which the vehicle had reduced speed to approach crossings and speed bumps also caused the increase of the emission of the gases.
# Trajectory Planning and Optimization for a Par4 Parallel Robot Based on Energy Consumption
## 3 Trajectory Planning of the Par4 Parallel Robot
### Trajectory Planning Based on Lame Curve  
The general expression of the Lame curve is  
\[\left(\frac{u}{e}\right)^{n}+\left(\frac{w}{f}\right)^{n}=1\qquad e>0,f>0,n=1,2,\cdots \tag{4}\]  
It is known that when \(n\geq 3\), the points B and C, where the Lame curve and the line cross, will be continuous [36]. For simplicity, let us take \(n=3\). The half-way trajectory is shown in Figure 3, where the dotted line is the trajectory of the Lame curve.  
In Figure 3, AB is the vertical movement, BC is the Lame curve movement (shown in dotted lines in Figure 3), and CE is the horizontal movement. \(H_{a}\) is the pick-up height, and the horizontal length  
Figure 3: The half-way motion trajectory.  
of IE is half the pick-up span \(B_{a}\). Setting up the local plane coordinate system **UO'W**, the geometric meaning of parameters \(e\) and \(f\) in Equation (4) are shown in Figure 3. \(\varphi\) is the turned angle from the starting point B to the point \(k_{i}(u_{i},w_{i})\) on the Lame curve, which is noted clearly in Figure 3. The turned angle from the starting point B to the point C is \(\pi/2\).  
The arc length of the curve from B to C is  
\[l_{BC}=\int_{0}^{\frac{\pi}{4}}\frac{(1+\tan^{2}\varphi)\sqrt{e^{2}\tan^{4} \varphi+f^{2}}}{(1+\tan^{3}\varphi)^{4/3}}d\varphi+\int_{0}^{\frac{\pi}{4}} \frac{(1+\tan^{2}\varphi)\sqrt{f^{2}\tan^{4}\varphi+e^{2}}}{(1+\tan^{3} \varphi)^{4/3}}d\varphi. \tag{5}\]  
In the local coordinate system **UO'W**, the coordinates of any point on the Lame curve can be expressed as Equation (6), based on the turned angle \(\varphi\):  
\[\left\{\begin{array}{l}u(\varphi)=\frac{2e+(2\epsilon-B_{a})(1+\tan^{3} \varphi)^{1/3}}{2(1+\tan^{3}\varphi)^{1/3}}\\ w(\varphi)=\frac{f\tan\varphi+(H_{a}-f)(1+\tan^{3}\varphi)^{1/3}}{(1+\tan^{3} \varphi)^{1/3}}\end{array}\right. \tag{6}\]
# Damping and Natural Period Evaluation of Tall RC Buildings Using Full-Scale Data in Korea
## 3 Empirical Formulae for Damping Ratio of RC Buildings for Wind Design
### Single-Value Damping Ratio  
#### 3.1.1 Eurocode 1  
Eurocode 1 presents a logarithmic decrement of damping for the wind design of buildings depending on the type of structure, regardless of structure height or response amplitude. The logarithmic decrement of damping \(\delta\) for the fundamental bending mode can be expressed as follows:  
\[\delta=\delta_{s}+\delta_{a}+\delta_{d} \tag{8}\]  
where \(\delta_{s}\) is the logarithmic decrement of structural damping, \(\delta_{a}\) is the logarithmic decrement of aerodynamic damping for the fundamental mode, and \(\delta_{d}\) is the logarithmic decrement of damping due to damping devices such as tuned mass dampers and sloshing tanks. Unlike masts with small mass and slim towers, the logarithmic decrements of aerodynamic damping for buildings are relatively small compared to the logarithmic decrements of structural damping and the logarithmic decrements due to damping devices.  
The values of logarithmic decrements of structural damping of buildings in Eurocode 1 are categorized according to the following building structure types: RC structure, steel structure, and SRC structure (Table 2). Because the use of the damping ratio is more popular in Korea than the use of the logarithmic decrement of damping, the equivalent damping ratios, which can be obtained by dividing the logarithmic decrement of structural damping with \(2\pi\), are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Code** & **Structural Type** & **Natural Period** \\ \hline KBC 2009 & RC moment frame & \(T=0.073H^{0.75}\) \\ ASCE 7-10 & RC moment frame & \(T=0.0670H^{0.9}\) \\  & Other RC building & \(T=0.043H\) \\ Lagomarsino & RC & \(T=0.018H=H/55\) \\ Eurocode 1 & RC & \(T=0.022H=H/46\) \\ [PERSON] et al. & RC & \(T=0.015H=H/67\) \\ [PERSON] & RC & \(T=0.0193H=H/52\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Empirical formulae for estimating natural period.
## 5 Application and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Car as Driver** & **Car as Passenger** & **Non-Motorized Mode** & **Public Transport** \\ \hline \multirow{8}{*}{Null scenario} & Run1 & 1,384,997 & 333,402 & 818,210 & 154,913 \\  & Run2 & 1,386,325 & 332,784 & 818,387 & 155,000 \\  & Run3 & 1,383,779 & 332,003 & 820,958 & 154,692 \\  & Run4 & 1,383,371 & 333,463 & 815,492 & 156,014 \\  & Run5 & 1,384,407 & 334,249 & 817,158 & 154,650 \\  & Run6 & 1,384,264 & 333,080 & 818,885 & 154,290 \\  & Run7 & 1,380,316 & 333,205 & 816,984 & 155,437 \\  & Average & 1,383,923 & 333,169 & 818,011 & 154,999 \\ \hline \multirow{8}{*}{Light rail scenario} & Run1 & 1,384,855 & 344,431 & 779,447 & 167,609 \\  & Run2 & 1,386,632 & 335,106 & 781,763 & 166,223 \\ \cline{1-1}  & Run3 & 1,386,480 & 331,012 & 780,364 & 167,564 \\ \cline{1-1}  & Run4 & 1,386,265 & 333,690 & 782,130 & 167,427 \\ \cline{1-1}  & Run5 & 1,388,175 & 332,199 & 781,431 & 166,685 \\ \cline{1-1}  & Run6 & 1,385,187 & 333,697 & 781,025 & 167,742 \\ \cline{1-1}  & Run7 & 1,388,022 & 332,826 & 779,965 & 166,980 \\ \cline{1-1}  & Average & 1,386,478 & 333,280 & 780,875 & 167,176 \\ \hline \% change among & & \([-0.13\%,0.57\%]\) & \([-0.97\%,0.93\%]\) & \([-5.06\%,-4.09\%]\) & \([6.54\%,8.72\%]\) \\ \cline{1-1}  & the runs & & \(0.18\%\) & \(0.03\%\) & \(-4.54\%\) & \(7.86\%\) \\ \hline \% change between & & & & & \\ the average values & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison of daily travel demand on different traffic modes based on two scenarios. mode of travel. However, concerning the car-related transport modes, including both the car as driver and car as passenger, the change in the number of related trips is negligible, since the value of zero is located within the difference range among the runs, which is \([-0.13\%,0.57]\) and \([-0.97\%,0.93\%]\), respectively. Such a result implies that the implementation of a light rail system has only limited effects on car-related transport, especially in the short term.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Argon Ion Irradiation
Thus, the plasma treatments induced the lateral sliding of the top S layer and the 2\(H\) phase transited to the 1\(T\) phase. [PERSON] et al. [96] grew MoS\({}_{2}\) mono-layers on SiO\({}_{2}\) substrates and treated them in a vacuum under 500 eV Ar\({}^{+}\) ions at room temperature. The beam current density was 3.1-11.2 \(\upmu\)A/cm\({}^{2}\). The molybdenum content of the MoS\({}_{2}\) layers remained essentially constant while the amount of sulfur decreased significantly during irradiation. The sulfur content of the layers could be reduced to 50%. The average sputter yield was 0.03 sulfur atom per Ar\({}^{+}\) ion. During the irradiation procedure, MoS\({}_{2}\) mono-layers were selectively de-sulfurized while the basic physical structure of the MoS\({}_{2}\) remained largely intact. The photoluminescence (PL) yield decreased as sulfur atoms were removed by the Ar\({}^{+}\) irradiation. Ar\({}^{+}\) plasma was also employed to thin MoS\({}_{2}\) sheets at room temperature [118]. It was reported that the top MoS\({}_{2}\) layers were entirely removed by the Ar\({}^{+}\) plasma while the bottom MoS\({}_{2}\) layers remained largely unaffected. Thus, MoS\({}_{2}\) multi-layers with controllable thickness, even MoS\({}_{2}\) single-layers, could be prepared reliably (with almost 100% success rate). [PERSON] et al. [117] prepared MoS\({}_{2}\) mono-layers using a liquid exfoliation method, and annealed them at 300-450 \({}^{\circ}\)C in a vacuum. The treated layers were then deposited on Au substrates and exposed to 0.5 keV Ar\({}^{+}\) ions for 1 min. Defects were introduced into the MoS\({}_{2}\) layers, influencing the electronic structures of MoS\({}_{2}\). Figure 11 shows XPS spectra of irradiated MoS\({}_{2}\) mono-layers. Peaks shifted because of induced defects. Figure 10: Raman spectra of MoS\({}_{2}\) bi-layers before (bottom) and after (top) Ar\({}^{+}\) irradiation at the fluence of \(2.26\times 10^{15}\) ions/cm\({}^{2}\)[84]. Reprinted with permission from Reference [84]. Copyright © 2017 by the American Physical Society.
## 5 Discussion
### Performance of the Calibrated Biome-BGC Model  
Model parameter calibration has long been considered as a difficult task, especially for complex process-based ecosystem models such as the Biome-BGC. In this study, we proposed a practical method for model calibration by combining a global sensitivity analysis with the simulated annealing optimization algorithm. The calibration results show that the carbon fluxes simulated by the calibrated Biome-BGC model are in good agreement with the EC measurements. However, it should be noted that the NEE simulated by the calibrated model is still overestimated during the non-growing season, and its reason can be traced back to the overestimation of TER. Furthermore, the overestimation of TER can be largely attributed to the soil respiration simulation, which is the main source of respired carbon during the non-growing season. The problem of soil respiration module in the Biome-BGC has also been reported in previous studies [17; 19; 64], and its reason was attributed to either the flaw in the respiration algorithm or the oversimplified simulation of soil temperature [19]. Nevertheless, for the TP, the problem of soil respiration module may also be attributed to the widespread distribution of frozen soil in the non-growing season. Specifically, the presence of frozen soil usually has a large impact on soil temperature, which will further affect the activity of soil microbes and ultimately act on soil respiration. Another study conducted on the TP has also demonstrated the important effects of frozen soil on soil respiration during the non-growing season [65].  
On the other hand, some problems regarding model parameter calibration by numerical inversion methods must be acknowledged. Since the mathematical methods used to estimate model parameters usually treat the model as a \"black box\", that is, the calibration procedure is only based on mathematical optimization criteria without considering the physiological processes within the model, so the calibrated results may not be true [20]. Therefore, some studies suggest avoiding the use of mathematical methods for model calibration and instead using field measurements to determine parameter values [66]. However, for complex process-based ecosystem models like the Biome-BGC, it usually contains a large number of tunable parameters (e.g., 28 ecophysiological parameters for the Biome-BGC), which means that it is almost impossible to obtain all (or even some specific) model parameters through field measurements, especially for applications over large areas. Admittedly, we can choose a subset of parameters for observation, but considering the correlation between model parameters and the large difference in the sensitivity of the parameters to the model output [61], it is still a difficult task, especially when some parameters cannot be observed directly. Therefore, in the absence of alternatives, combining sensitivity analysis with numerical inversion methods for model calibration is still a relatively feasible and reliable approach, which is also the method used in this study.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 11 Discussion on Private Campus Networks
### Business Impact of 5G  
5G's new use-cases are generating tension currently for service providers, device manufacturers, and the industry in general. It is clear that 2G-3G and 4G networks are designed primarily for people in terms of use-cases. Of course, a significant number of machines are connected to these networks as end-devices, as well. The almost untapped resources to be provided by 5G are likely to be utilized by machines. These use-cases can be the simplest household appliances, vehicles, and of course complex industrial robots [131]. Hard real-time connectivity=-when a sensor-to-machine delay is less than 1 ms--will be utilized by several use-cases, however mostly not human applications [5; 26]. With 5G, we will be able to achieve a real-time production monitoring, knowing the current state of equipment we are ordering, or the time when it is expected to be manufactured, with proper access rights from anywhere around the globe. It also facilitates the development of real-time business and the advancement of services where precision has immense business importance. All in all, this means that the spread of 5G will benefit the economy as a whole [131].  
To understand the exact economic effects, we must examine which parts of the industry are affected and to what extent. There are many studies for this phenomenon, certainly updated or revised year by year. According to a study published by Ericsson, it is estimated that 5G-enabled industry digitization revenue for ICT players will reach $ 1.3 Trillion in 2026 [132]. When examining the most prominent domains, energy and utilities account for 19%, industrial production are there for 18%, and it is worth highlighting the automotive industry, which accounts for 8%. In terms of industrial networks, naturally, the factories are the main stakeholders. The short term impact of 5G and IoT is expected between $1.2 and $3.7 trillion. Of course this includes other high-impact domains utilizing 5G, such as connected cities and vehicles [133].
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 2 Signal Collection and Preprocessing
### Data Quality Assessment Algorithm Based on Blind Source Separation and Sample Entropy  
The fundamental concept of ICA technology is that under the assumption of independence, it can decompose multi-channel mixed signals into several independent components. Since the heart of the mother and the fetus are two independent bioelectrical signal sources, it is assumed that maternal and fetal ECG signals are statistically independent. The data quality assessment algorithm proposed in this paper aims to select pregnant women's ECG signals where the fetal heart is more apparent. Therefore, it is not sufficient to perform a data quality assessment on maternal ECG signals; instead, the aim is to assess the quality of the amplified fetal signals. A novel unsupervised algorithm for detecting fetal QRS complexes, which integrates ICA and maternal ECG signal cancellation, was introduced in the literature [5]. The process begins with signal preprocessing to remove baseline drift and power line interference, followed by the steps below: ICA to extract maternal ECG signals; maternal QRS complex detection; maternal ECG signal elimination through weighted SVD; and the enhancement of fetal ECG signals through secondary ICA. This paper adopts the algorithm by [PERSON] et al. to amplify fetal ECG signals from four channels of raw abdominal signals.  
Sample entropy is a metric that quantifies the complexity of a time series by measuring the likelihood of new pattern generation within a signal [30]. This study calculates sample entropy values based on ten randomly selected segments, each comprising 500 points from the data, to better represent the overall condition of the data through averaging [31]. The formula for calculating sample entropy is as follows:  
\[\begin{split} B^{m}(r)=\frac{1}{N-m+1}\sum_{i=1}^