esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Methodology  
### Governing Equations  
The following transport equations represent the dynamics of fluid flow, encompassing mass, momentum, energy conservation, and the transport of species [34].  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{k}}(\rho u_{k})=S \tag{1}\]  
\[\frac{\partial(\rho u_{i})}{\partial t}+\frac{\partial(\rho u_{i}u_{j})}{ \partial x_{j}}=\frac{\partial\sigma_{ij}}{\partial x_{i}}+S_{i} \tag{2}\]  
Figure 1: Diesel engine, piston bowl shape, and RCCI engine.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Cylinder volume (cc) & 510 \\ Compression ratio (CR) & 17.5:1 \\ Bore diameter/stroke (mm) & 85/90 \\ Number of valves & 2 \\ RPM & 3000 \\ IVO/IVC (\({}^{\circ}\)CA) & 339/592 \\ EVO/EVC (\({}^{\circ}\)CA) & 135/388 \\ Torque at rated speed (Nm) & 25 \\ Power (kW) & 7.88 \\ Injector hole diameter (mm) & 0.29 \\ Number of injector holes & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Conventional diesel engine Antor LD 510 specifications.  
where \(\sigma_{ij}\) represents the viscous stress tensor, and \(e_{ij}\) is the strain rate tensor.  
\[\sigma_{ij}=-\left(p+\frac{2}{3}\mu\
abla\cdot u\right)\delta_{ij}+2\mu e_{ij} \tag{3}\]  
\[e_{ij}=\frac{1}{2}\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{j}}{\partial x_{i}}\right) \tag{4}\]  
\[\frac{\partial}{\partial t}\left(\rho\left(e+\frac{1}{2}u_{k}^{2}\right)\right) +\frac{\partial}{\partial x_{j}}\left(\rho u_{j}\left(e+\frac{1}{2}u_{k}^{2} \right)\right)\]  
\[=-\frac{\partial}{\partial x_{j}}(u_{j}p)+\frac{\partial}{\partial x_{j}}(u_{ i}\sigma_{ij})+\frac{\partial}{\partial x_{j}}\left(K\frac{\partial T}{ \partial x_{j}}\right) \tag{5}\]  
\[+\frac{\partial}{\partial x_{j}}\left(\rho\sum_{m}D_{m}h_{m}\frac{\partial Y_ {m}}{\partial x_{j}}\right)+S\]  
\[\frac{\partial\rho_{m}}{\partial t}+\frac{\partial\rho_{m}u_{j}}{\partial x_ {j}}=\frac{\partial}{\partial x_{j}}\left(\rho D_{m}\frac{\partial Y_{m}}{ \partial x_{j}}\right)+S_{m} \tag{6}\]  
\[\rho_{m}=Y_{m}\rho \tag{7}\]  
where \(u\) represents the velocity, \(\rho\) is the density, \(\rho_{m}\) denotes the density of species \(m\), and \(Y_{m}\) is the mass fraction of species \(m\). \(D_{m}\) refers to the molecular mass diffusion coefficient for species \(m\), while \(S_{m}\) is the corresponding source term. Additionally, \(h_{m}\) indicates the species-specific enthalpy, \(e\) is the specific internal energy, \(K\) stands for thermal conductivity, and \(\frac{1}{2}u_{k}^{2}\) represents the kinetic energy term. Moreover, \(P\) denotes pressure, \(\mu\) is the viscosity, and \(\delta_{ij}\) is the Kronecker delta.
## 3 Processing Methods
### Spectrum Reconstruction
#### 3.1.1. Radiance Spectrum Reconstruction  
In order to extract the interference information of the target gas from the interferogram data cube, interferogram units with different interference dimensions in the same spatial dimension are extracted and concatenated to obtain the interference image sequence of the target gas. To correct errors in the interference image sequence caused by interfering factors such as environmental stray light and circuit instability, interference data processing is required, including baseline correction, apodization, and phase correction. Afterward, the corrected interference image sequence is subjected to Fourier transform to obtain the digital spectrum. To establish the mapping relationship between the digital number (DN) values of the DN spectrum and radiant flux density, spectral calibration, and radiometric calibration are performed to obtain the target radiance spectrum. The overall process of radiance spectrum reconstruction is shown in Figure 3.
# Regional Analysis of Dust Day Duration in Central Iran
###### Abstract
Arid areas (deserts), semi-arid areas, seasonally dry coastal areas, lakes and wetlands, and areas with very low annual rainfall, very low soil moisture, abundant alluvial sediments, and poor vegetation are particularly prone to dust emission and hence to severe dust storms. In addition, dust storms can be worsened by human factors in the form of human intervention in land surface diversity, land use change through agriculture, deforestation, and overgrazing [4; 27]. Poor land management, climate change, and drought caused by human activities, followed by land drying and desertification, poor management of water resources such as surface water diversion, multiple dams, and overuse of water, are contributors to dust supply. Many studies have been performed in the field of how dust days are affected by meteorological and land surface conditions in arid parts of Iran, [28]. Severe sandstorms raise dust during atmospheric instabilities under dry and windy conditions. In general, the main sources of dust storms are located in the arid and semi-arid regions of the world. In the Middle East, deserts in Iraq, Iran, Egypt, Israel, Syria, and the Arabian Peninsula are known to be the main sources of dust [29]. In recent years, dust events considered serious environmental hazards [3; 11] have occurred with greater frequency and intensity in Asia [30]. Iran is located in a zone of southwest Asia where arid and semi-arid climate and sparse vegetation provide the conditions for dust storms, particularly in Central Iran, where the deserts of Loot and Kavir provide ideal conditions for this phenomenon. The Jazmourian, Gavkhori, Hamoon wetlands in the central plateau of Iran, when dry and under influence of the regional winds, acting major dust sources, particularly in drought conditions. The continuity of dust day duration (DDD) in the region, intensifies the human and environmental effects of this destructive phenomenon. The purpose of this study is to analyze periods of dust persistence and to identify the most affected areas. The return periods of different DDD values were calculated, and then these calculated values of this parameter were regionalized across the whole study area. One of the innovative aspects of this study is that no study has previously been performed on the persistence of dust days in the central plateau of Iran.
# Optimization and Experimental Verification of Lower
## 2 Optimization Model for Lower Articulation
### Structure and Fatigue Life Evaluation of Optimized Lower Articulation
#### 2.3.2 Sensitivity Analysis  
To examine the impact of the eight design factors on the objective functions of stress and weight for the lower articulation of the low-floor tram, sensitivity analyses were performed. Figure 8 shows the results of the sensitivity analyses for stress and weight. Figure 8a presents the sensitivity analysis results for stress. It was observed that factors \(x_{1}\) and \(x_{5}\) had little effect on the stress. Additionally, it was confirmed that changes in thickness for the remaining variables resulted in increased stress. Figure 8b shows the sensitivity analysis results for weight. As seen in the figure, factors \(x_{3}\), \(x_{5}\), and \(x_{6}\) significantly affected the weight due to changes in thickness. Conversely, the impact of the changes in thickness on weight for the other variables was minimal. The sensitivity analyses identified the factors that influence stress and weight.
###### Abstract  
The transmission rate (\(\beta(t)\)) plays a crucial role in disease spread, making its measurement essential for effective control. However, existing techniques for its estimation are impractical as they rely on typically unavailable data. To address this, a prior analysis of the most frequently reported data during the Coronavirus Disease 2019 (COVID-19) pandemic has been carried out, namely the number of new hospital admissions (\(y_{1}(t)\)) and deaths (\(y_{2}(t)\)). Based on this analysis, an SIHR epidemic model is presented, where \(S\), \(I\), \(H\), and \(R\) represent susceptible, infected, hospitalized, and immunized subpopulations, respectively, and various observers tailored to the available data are proposed. Assuming both \(y_{1}(t)\) and \(y_{2}(t)\) are available, an exponential observer has been designed for the state estimation, from which \(\beta(t)\) is obtained. If only \(y_{1}(t)\) is available, a combination of output injection and output diffeomorphism is employed to transform the nonlinear system into its observer canonical form, enabling the design of an adaptive observer for estimating both the states and \(\beta(t)\). By considering only \(y_{2}(t)\), an explicit equation has been obtained to estimate \(y_{1}(t)\), enabling the application of the previously developed methods. These approaches have been validated through a theoretical framework and numerical simulations; the first and third methods successfully estimate the unknown states and \(\beta(t)\) with high accuracy. The second method yields a bounded region where the true value is expected to lie.  
state estimation; time-varying parameter estimation; transmission rate estimation; nonlinear dynamical system; epidemic model; COVID-19; adaptive observer; observer canonical form; diffeomorphism Article
# The Dependence of Ultrasonic Velocity in Ultra-Low Expansion Glass on Temperature
## 5 Results and Discussion
### Accuracy Validation of \(c_{\mathrm{L}}\)-\(T\) Model  
The accuracy of the \(c_{\mathrm{L}}\)-\(T\) model was decisive for its application in engineering. Samples 1#, 3#, and 5# were randomly selected, and their ultrasonic velocities were measured at ten random temperature points in the range of 10-30 \({}^{\circ}\)C. The measured values were compared with the model predictions in this paper, and the results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Temperature** & \multirow{2}{*}{\(c_{\mathrm{m}}\)**(m/s)**} & \multirow{2}{*}{\(c_{\mathrm{p}}\)**(m/s)**} & \multirow{2}{*}{\(c_{\mathrm{m}}\)**\(-c_{\mathrm{p}}\)**(m/s)**} & \multirow{2}{*}{\(\delta\)1} \\  & **(\({}^{\circ}\)C)** & & & & **\%** \\ \hline \multirow{8}{*}{1\#} & 10.2 & 5745.5 & 5746.3 & \(-\)0.8 & 0.014 \\  & 12.7 & 5748.1 & 5748.2 & \(-\)0.  
Considering that the measurement locations of the three samples in the model validation procedure may vary, it was reasonable to use a recalculation of \(c^{{}^{\prime}}\) based on the standard ultrasonic velocity at 20 \({}^{\circ}\)C to determine the \(c_{\rm L}\)-\(T\) exponential model, which may differ slightly from the \(c^{{}^{\prime}}\) obtained by modeling the above \(c_{\rm L}\)-\(T\) data. The \(c_{\rm L}\) values in the tested glasses at an average room temperature of 20 \({}^{\circ}\)C were substituted into the derived exponential model based on the average temperature coefficient from the above fitting analysis to determine a \(c_{\rm L}\)-\(T\) exponential model for each tested glass. As seen in Table 3, the standard deviation of the predicted values for three samples were all within 0.90 m/s, and the relative errors between measured and model-predicted values were mostly within 0.020%, which suggests that the models fitted in this work exhibit high precision.  
Of course, it should be noted that increasing the sample thickness may further reduce the errors in \(c_{\rm L}\) measurement, but it also imposes more stringent requirements on the frequency of the transducer and time required for the sample to reach thermal equilibrium.
# An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP
## 5 Impact of BDS Satellite DCB on Positioning
### Performance of PPP
Figure 21 shows the mean values and RMS values of the horizontal and 3D positioning errors for BDS dual-frequency PPP with the two strategies. The mean horizontal and 3D positioning errors are less than 2 cm and 3 cm, respectively. Without considering the DCB correction effect of B1C+B2C, the horizontal and 3D positioning accuracies are improved by 13.53% and 13.84% with DCB correction. The DCB correction for B1I+B3I shows the greatest improvement in positioning accuracy, with an improvement of 21.00% in horizontal direction and 17.68% in 3D. The mean values of the horizontal and 3D positioning errors are reduced from 2.46 cm and 5.03 cm with no correction to 2.16 cm and 4.68 cm with DCB correction, as shown in Figure 21 The mean RMS values of the horizontal and 3D positioning errors are changed from 1.22 cm and 1.78 cm with no correction to 1.03 cm and 2.01 cm with DCB correction. With DCB correction, the mean horizontal positioning errors of B1I+B2a, B1I+B3I, B2a+B3I, B2a+B1C, and B3I+B1C PPP are reduced by 4.11%, 17.65%, 7.64%, 24.21%, and 11.23%, respectively. Meanwhile, the mean 3D positioning errors are also reduced by 2.52%, 20.01%, 2.03%, 12.03%, and 9.42%, respectively. Figure 20: PPP positioning errors of the ULAB, URUM, and WUH2 stations.
## 2 Materials and Methods  
### Feedstocks and Catalyst  
The feedstocks used in this study were commercial FTS waxes, commercial fossil diesel, and commercial vacuum gas oil (VGO). The FTS waxes were pre-treated to remove water and light fractions (gases and gasoline, <2 wt.%) in a standard laboratory rotary evaporator. Later, the FTS waxes were distilled, obtaining a diesel fraction (180-360 \({}^{\circ}\)C) and a heavy fraction (360-700 \({}^{\circ}\)C). Distillation was performed in a PETRODIST 500 Q (semi-automatic operation) PILODIST laboratory & process technology, Prague, CZ. Both petroleum feedstocks were supplied by a commercial refinery.  
FTS waxes and their distillation fractions were analysed in terms of density (ASTM D 4052) [31], elemental analysis (ASTM D5291) [32], nitrogen and sulphur content (ASTM D5453 [33]; ASTM D4629 [34]), and Simdis (ASTM D 2887) [35]. Petroleum-based feedstocks were analysed in terms of the EN590 norm, including but not limited to density, viscosity, and elemental analysis, among other analyses.  
A commercial metal sulphide-supported catalyst, NiWSx/SiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\), was used for the hydrocracking of heavy FTS wax fractions with VGO. The catalyst was supplied in extrudate form (0.5-2.0 mm). It is widely used in the industry for hydrocracking petroleum-heavy fractions to produce suitable light products. The same catalyst has been used by the authors and reported in previous references [36; 37]. As the catalyst was commercially sourced, detailed information about the fresh or used catalyst samples is not included in the manuscript. Standard refinery gas was used as the H\({}_{2}\) supply for hydrocracking, consisting of 97.5-99.5 vol.% H\({}_{2}\) and 0.5-2.5 vol% of C\({}_{1}\)-C\({}_{2}\) hydrocarbons.
## 4 Conclusions  
The flow characteristics of VRFBs were analyzed, and flow channel shapes were proposed to enhance the system efficiency. The shapes were examined to quantify the flow non-uniformity of the electrolyte discharged to the electrode. Numerical analysis showed that uniformity was improved by the proposed flow channel shapes. Thus, the pressure drop was decreased. This reduced pump energy consumption and increased the overall system efficiency.  
The visualization experiment and CFD analysis of a conventional VRFB indicates that the flow path causes the unbalanced problem, which reduces the active surface area of the electrode. According to the analysis of the conventional design problems in the existing S-shaped flow channel, dead zones were formed and the electrolyte flow to the electrode was concentrated at the outlets on one side. In addition, there was an increase in the pressure drop because the electrolyte was discharged to the electrode via 90 outlets. To resolve these problems, the initial inlet of the flow channel was divided into five sections and an elliptical guide was installed the first section.  
The maximum velocity deviations for the existing and proposed shapes were 11.89% and 54.16%, respectively. This indicated that the proposed shape increased flow uniformity. In addition, the pressure drop in the entire flow frame was reduced by 44% for the proposed shape as compared with the existing shape. The aforementioned results show that the overall efficiency of the RFB can be increased by increasing the maximum active surface area and the pump efficiency can be increased by understanding the flow characteristics of the flow channel and modifying its shape. However, as the factors that affect the actual battery system performance vary, it is considered that additional charging/discharging experiments must be performed according to flow uniformity. In addition, it is necessary to verify the system performance of the VRFB according to the flow uniformity through charging and discharging experiments on the unit cell and stack configuration.  
Conceptualization, Y.-H.C. and J.-Y.P.; methodology, J.-Y.P. and D.-Y.S.; software, J.-Y.P.; formal analysis, [PERSON]-Y.P. and [PERSON]-H.C.; supervision, Y.-H.C.; project administration, [PERSON]-Y.P., [PERSON]-Y.S., and [PERSON]-H.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# Hydraulic Vibration and Possible Exciting Sources Analysis in a Hydropower System
## 4 Results and Discussions
### Pressure Fluctuation in the Flow Passage
#### 4.1.2 Pressure Fluctuation in the Draft Tube  
When turbines work under off-design conditions, the rotational velocity component of fluid in the runner outlet will probably cause vortex rope accompanied by low-frequency and large-amplitude pressure fluctuations in the draft tube. According to the empirical formula, the estimated frequency range of vortex rope is \(f\) = (0.167\(\sim\)0.5) \(f_{n}\), and the frequency of 0.2-0.4\(f_{n}\) captured in the experiment is in the range of the estimated frequency. Figure 9 shows the pressure fluctuations in the draft tube (CH6) under S01, S02, and S03. The time domain characteristics illustrate that the pressure fluctuation in the draft tube cone is the highest compared to other regions. From the frequency domain characteristics, we can infer that 0.2\(f_{n}\) is the leading frequency that occurred in all conditions. Additionally, frequencies of 1.6\(f_{n}\) and 1.5\(f_{n}\), measured at the inlet of the draft tube only, occurred in S01, and these may propagate from the upstream region affected by RSI.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Mode** & **CH0** & **CH1** & **CH2** & **CH3** & **CH4** & **CH5** & **CH6** & **CH7** & **CH8** & **CH9** & **CH10** & **CH11** \\ \hline S01 & 0.1 & 0.1 & 15 & 15 & 0.2 & 0.2 & 1.6 & 1.5 & 0.2 & 0.2 & 0.2 & 0.2 \\ S02 & 0.0 & 0.1 & 0.2 & 0.2 & 1.0 & 0.2 & 0.2 & 0.2 & 0.4 & 0.4 & 0.2 & 0.2 \\ S03 & 0.0 & 0.0 & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(f/f_{n}\) in frequency domain at different monitoring points.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mode** & **a (°)** & \(n_{\textbf{11}}\) & \(Q_{\textbf{11}}\) & \(H_{\textbf{m}}\) & \(P\) & \(P_{\textbf{r}}\) \\  & **(r/min)** & **(L/s)** & **(m)** & **(MW)** & **(\%Pb)** \\ \hline S01 & 7.88 & 60.21 & 145.99 & 29.91 & 301.11 & 29.67 \\ S02 & 12.63 & 60.15 & 249.77 & 30.32 & 607.72 & 59.87 \\ S03 & 14.12 & 73.16 & 260.64 & 29.88 & 346.43 & 48.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of operating conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Mode** & **CH0** & **CH1** & **CH2** & **CH3** & **CH4** & **CH5** & **CH6** & **CH7** & **CH8** & **CH9** & **CH10** & **CH11** \\ \hline S01 & 1.88 & 1.86 & 1.84 & 1.78 & 1.51 & 1.74 & 3.95 & 3.53 & 3.12 & 3.13 & 1.81 & 2.86 \\ S02 & 1.91 &
## 3 Results
### Time Distribution of Construction Accidents
#### 3.2.3 Construction Accident Number per Hour of Day  
The final step in this section is to identify the correlation between the hour of day and the number of accidents. Figure 7 displays number of construction accidents by hour. Time of accident for each year was divided into 24 groups, in which each group contained the same hours (from 0 to 23). The outcome of ANOVA yields a rejection of hypothesis that diversity between construction accident number among different months is not significant. Therefore, a strong correlation between construction accident number and the hour of day is identified. A double-hump distribution with 12 o'clock axis of symmetry is obtained in Figure 7, and most accidents took place from 7 AM to 10 AM and 2 PM to 5 PM. In China, construction workers start working at 7 AM and continue with their work until noon. Accidents happened frequently during this time period, reaching a peak at around 10 AM. Then they had a one-hour lunch break. At 1 PM, when the temperature is the highest in the day, workers returned to work. In the afternoon, most accidents occurred from 2 PM to 5 PM, reaching their peak at around 3 PM. Most workers finish working at 6 PM, leaving only a few workers on duty at night. The number of accidents is low during nighttime and nearly zero around midnight.  
Note that an abnormally high number of accidents were reported between 0:00 to 0:59 AM, as evidenced in Figure 7. Only the year 2012 saw a high number of accidents occurring at midnight (marked with rectangular box), accounting for more than 50% of the total accidents at that time from 2012 to 2018. The number of accidents in the evening (5 PM to 11 PM) and midnight (1 AM to 5 AM) hours are low. Therefore, this high number can be regarded as a virtual peak which is attributed to the fact of false reporting or concealed reporting of accidents [36]. Frequently, when a construction worker dies because of an accident, some construction managers would first contact their family and pay them to compensate for their loss. If the family was satisfied with the amount of compensation for the loss, this accident would be concealed. Although the Chinese government requires that the accident should be reported to local government within one hour [37], some construction managers did not report accidents in order to avoid penalty. However, if they failed to reach a settlement with the worker's family, they were required to report the accident to local government, which delayed the report for several days after the accident.  
Figure 7: Number of construction accidents per hour of day.  
By then, no witnesses remembered the accurate time of the accident and simply picked 0:00 as the time of death in the report, leading to the virtual peak at midnight.