esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Modifying Mohr-Coulomb Compaction Concept from the Study  
The Mohr-Coulomb theory has been used to characterize the asphalt mixture compaction in terms of initial cohesiveness, friction angle, and viscosity effects. However, this theory cannot explain the compaction of the asphalt mixture well, in that the effect of aggregate interactions is missing due to the change in temperature, which causes the change in volumetrics. Based on the results of the energy index and aggregate orientation studies corresponding to the change in viscosity, a modified compaction concept can be introduced. The failure line in the original concept depends on the viscosity of the mass. However, this study shows that the viscosity effect does not linearly increase in the Mohr-Coulomb failure line. It was found that the aggregate friction varies with the volume change in the asphalt binder for lubrication due to the change in viscosity. It was obvious that cohesion governs the compaction of mixture below a certain optimum mixing temperature. After passing the optimum mixing temperature, compaction appears to be more dependent on the aggregate contact rates, which  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Mixture** & \({}^{1}\)**G\({}_{ab}\)** & **Asphal Contents (\%)** & **Compaction Temperature (\({}^{\circ}\)C)** & **\%Gm at \({}_{\text{Malign}}\)** & **Air Vioid** & **Cmm** & **VMA** & **VFA** & \({}^{1}\)**Vacc** & \({}^{2}\)**Pba** \\  & & & & & & & & & & & \\ \hline \multirow{4}{*}{PG 58-28} & \multirow{4}{*}{2.722} & \multirow{4}{*}{5.0} & \multirow{4}{*}{110} & 96.0 & 4.0 & 2.541 & 15.1 & 70.8 & 11.1 & 0.54 \\  & & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline \multirow{4}{*}{PG 64-28} & \multirow{4}{*}{2.722} & \multirow{4}{*}{5.1} & \multirow{4}{*}{115} & 95.9 & 4.1 & 2.532 & 15.4 & 73.1 & 11.3 & 0.35 \\  & & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline \multirow{4}{*}{PG 64-28} & \multirow{4}{*}{2.722} & \multirow{4}{*}{5.1} & \multirow{4}{*}{115} & 95.9 & 4.1 & 2.532 & 15.4 & 73.1 & 11.3 & 0.35 \\  & & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline \end{tabular} \({}^{1}\) G\({}_{ab}\) = the bulk specific gravity of aggregates, Vacc = the volume of effective asphalt. \({}^{2}\)Pba = the percent of absorbed asphalt binder by weight of aggregates.  
\end{table}
Table 2: Volumetric properties of asphalt mixtures.  
Figure 11: Relationships of aggregate orientation and energy index corresponding to volume change in the PG64-28 mixtures.  
can create the internal friction. Thus, this change in internal friction can be expressed with \(\phi_{1}\), which denotes the influence of aggregate friction on the compaction resistance of the asphalt mixture, as shown in Figure 12.
## 5 Conclusions
\begin{tabular}{l l} \(c_{\text{op},c}\) & Central unit annual operation cost (e/y) \\ \(c_{\text{op},u}\) & Unit annual operation cost (e/y) \\ \(c_{p}\) & Specific heat(Kj/kg K) \\ \(c_{\text{p}vp}\) & PV panels investment cost (e/m) \\ \(c_{\text{stp}}\) & Solar thermal panels (ST panels) investment cost (e/m\({}^{2}\)) \\ \(c_{\text{stp},c}\) & Central ST panels investment cost (e/m\({}^{2}\)) \\ \(c_{\text{tot}}\) & Total annual cost (e/y) \\ \(\mathbf{C_{ts}}\) & Cooling energy storage input (kWh) \\ \(c_{ts}\) & Thermal Storage (TS) investment cost (e/kWh) \\ \(c_{ts,c}\) & Central TS investment cost (e/kWh) \\ \(d\) & Generic day \\ \(\mathbf{E_{bgt}}\) & Electricity bought from the network (kWh) \\ \(E_{cc}\) & Electricity required by the CC (kWh) \\ \(\mathbf{E_{hp,c}}\) & Electricity required by the HP when producing cold (kWh) \\ \(\mathbf{E_{dem}}\) & User electricity demand (kWh) \\ \(\mathbf{E_{hp,h}}\) & Electricity required by the HP when producing heat (kWh) \\ \(E_{tp}\) & Electricity required by the HP (kWh) \\ \(\mathbf{E_{ice}}\) & Electricity produced by the ICE (kWh) \\ \(\mathbf{E_{ice,c}}\) & Electricity produced by the centralized ICE (kWh) \\ \(E_{ice,lim}\) & ICE operation limits (kW) \\ \(\mathbf{em_{d}}\) & Electricity carbon intensity (kgCO\({}_{2}\)/kWh) \\ \(\mathbf{em_{fboi}}\) & BOI fuel carbon intensity (kgCO\({}_{2}\)/kWh) \\ \(\mathbf{em_{f,cen}}\) & Central CHP fuel carbon intensity (kgCO\({}_{2}\)/kWh) \\ \(\mathbf{em_{f,chp}}\) & CHP fuel carbon intensity (kgCO\({}_{2}\)/kWh) \\ \(\mathbf{E_{mgt}}\) & Electricity produced by the MGT (kWh) \\ \(\mathbf{em_{lim}}\) & Emission limit in the s-constrained optimization (kgCO\({}_{2}\)/kWh) \\ \(\mathbf{em_{tot}}\) & Total annual CO\({}_{2}\) emissions (kg) \\ \(\mathbf{E_{ppp}}\) & Electricity produced by the PV panels (kWh) \\ \(\mathbf{E_{sol}}\) & Electricity sold to the network (kWh) \\ \(f_{abs}\) & ABS amortization factor (y\({}^{-1}\)) \\ \(F_{bi}\) & Fuel required by the BOI (kWh) \\ \(f_{boi}\) & BOI amortization factor (y\({}^{-1}\)) \\ \(F_{boi,c}\) & Fuel required by the central BOI (kWh) \\ \(f_{cc}\) & CC amortization factor (y\({}^{-1}\)) \\ \(f_{hp}\) & HP amortization factor (y\({}^{-1}\)) \\ \(F_{ice}\) & Fuel required by the ICE (kWh) \\ \(F_{ice,c}\) & Ice amortization factor (y\({}^{-1}\)) \\ \(F_{mgt}\) & Fuel required by the MGT (kWh) \\ \(f_{mgt}\) & MGT amortization factor (y\({}^{-1}\)) \\ \(f_{net}\) & DHCN amortization factor (y\({}^{-1}\)) \\ \(f_{pvp}\) & PV panels amortization factor (y\({}^{-1}\)) \\ \(f_{stp}\) & ST panels amortization factor (y\({}^{-1}\)) \\ \(f_{ts}\) & TS amortization factor (y\({}^{-1}\)) \\ \(h\) & Generic hour \\ \(H_{abs}\) & Heat required by the ABS (kWh) \\ \(\mathbf{H_{boi}}\) & Heat produced by the BOI (kWh) \\ \(\mathbf{H_{boi,c}}\) & Heat produced by the central BOI (kWh) \\ \(H_{boi,lim,c}\) & Centralized BOI operation limits (kW) \\ \(H_{dem}\) & User thermal demand (kWh) \\ \(H_{hp}\) & Heat produced by the HP (kWh) \\ \(H_{ice}\) & Heat produced by the ICE (kWh) \\ \end{tabular}
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 2 Preliminaries  
This section describes the MIESM-based FLA mechanism, and introduces the existing calibration method.
## 4 Results and Discussion  
The flow field is simulated using the given method when the train passes through the enclosed housing for sound emission alleviation at a speed of 350 km/h. The time history of the pressure on sections S1, S3, S6, S9 and S11 is monitored and shown in Figure 10. When the aerodynamic pressure is a pushing action, the aerodynamic pressure is defined as positive; conversely, when the aerodynamic pressure is a suction action, the pressure is defined as negative.  
Figure 10: Time history of aerodynamic pressure on measuring points.  
Figure 9: Comparison of u/Ut_MAX between the numerical and the experimental results. (**a**) VT1. (**b**) VT2.  
The pressure curves show that all sections suffer both positive and negative aerodynamic pressures. The extreme pressures and variation tendencies are different in different sections depending on the distance away from the entrance. In the same section, the pressure curves share a similar tendency; in particular, the pressure curves are very close when the aerodynamic pressure is close to 0 Pa. Meanwhile, the pressure amplitudes at the near-train side are always higher than those at the far-train side when extreme pressure appears. These results indicate that most of the time, the wind flow inside the housing for sound emission alleviation has one-dimensional characteristics; however, when extreme pressure appears, the wind flow should have a three-dimensional structure. These aerodynamic pressure characteristics on the enclosed housing for sound emission alleviation are similar to those for tunnels reported by [PERSON] et al. [10]. However, as most enclosed housings for sound emission alleviation are made of steel structures and their stiffness is much lower than that of tunnels, they are more sensitive to train-induced wind loads, and consequently, the characteristics of the train-induced wind load are more important for the structural design of enclosed housing for sound emission alleviation.  
Figure 10 shows that the aerodynamic pressure on the housing for sound emission alleviation is much more complicated than normal dynamic loads. The aerodynamic pressure is not a periodic wave and always rapidly increases or decreases. Therefore, the amplitude and frequency spectra are not enough to describe the dynamic characteristics of the aerodynamic pressure, and the impact effects should be considered. In the following sections, the dynamic characteristics of the aerodynamic pressure will be discussed in terms of wave propagation, impact effects, extreme pressure, duration of extreme pressure, dominant frequency and decay rate.
## 3 Proposed Method
### Scenic Score Calculation  
The scenic score is used to measure the quality of the landscape of the POI or the edge. The larger the scenic score is, the higher the quality of the landscape of the POI or the edge, and the more popular the users. In this paper, the scenic value of POI and the scenic value of edge are calculated, respectively. The specific process is as follows.  
1. The scenic score of POI. This is determined mainly by the corresponding score, level, pictures, and comments of POI. The score, pictures, and comments are contributed by the checking in of users, and the level is determined by the characteristics of the POI itself. The greater these values are, the greater the scenic score of the POI. The specific calculation is shown in Formulas (17) and (18). \[s(i)=\log(\sqrt[3]{score(i)\times pictures(i)\times comments(i)}+level(i))\] (17) \[scenicScore(i)=s(i)\times[1+\max(s(j))\times w_{i})]\] (18) where \(s(i)\) is the inherent scenic score of \(POI_{i}\) and \(scenicScore(i)\) is the scenic score felt by different users. Here, combined with user preferences, \(score(i)\), \(level(i)\), \(picture(i)\) and \(comments(i)\) represent the data after \(POI_{i}\)'s score, grade, number of photos, and positive comments, respectively, are normalized to [0; 5]. The higher the score of the POI, the more photos, the more positive comments, and the higher the star rating, the higher the scenic score corresponding to the POI. \(max(s(j))\) is the highest scenic score of the POI. \(w_{i}\) is the cosine similarity between the feature vector \(T_{i}\) of \(POI_{i}\) and the user preference vector \(P(u)\), which is called the reward factor here. The scenic score of a POI is inherent. However, from the user's perspective, those who like this type of POI actually have a much better viewing experience than those who do not. That is, POI will reward users. The more users like this type of POI, the greater the reward factor and, hence, the greater the reward value. When \(w_{i}\) is 0, the user does not like this POI or does not have a historical data linked to it. Then, the POI may be recommended to the user according to its inherent scenic score.
2. The scenic score of the edge. The scenic score of an edge is determined by the scenic score of the POI of its neighbours. The more neighbouring POIs there are, the greater the POI correlation, and the higher the POI scenic score, the higher the scenic score of the corresponding edge. The specific calculation is shown in Formula (19). \[scenicScore(e_{i,i+1})=scenicScore(1)+\sum_{j=2}^{m}scenicScore(j)\times r(j-1,j)\] (19) where \(m\) is the number of POI adjacent to edge \(e_{i,i+1}\), and \(r(j-1,j)\) is the correlation between \(POI_{j-1}\) and \(POI_{j}\).
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 2 Materials and Methods
### Performance Evaluation  
After the development of major network interfaces, the performance of the integrated system was evaluated throughout the interworking experiments. To verify the fidelity of the data-transmission and saving processes, a verified signal with certain amplitude and frequency was applied to the ECG device, and the measured data were compared with the input signal. The waveforms recorded in each text file created in the internal network were investigated, and the loss rates were calculated.
## 3 Result and Discussion
### Comparison with the Previous Study  
From the current experimental results, it can be concluded that any addition of cells significantly improves the load-carrying capacity of the proposed floor slab panels. This is attributed to the presence of overlapped sheets between individual cell panels. Triple-cell panels have the highest ultimate load while having the same width and length as double-cell panels. Fibres improve the ductility of the panels and offer a comparatively better load capacity than panels without any fibres. The primary failure noticed in all panels is flexural failure. This failure is initiated by the buckling and tearing of the steel sheets, and then the cracking of the geopolymer concrete.  
To further understand and highlight the role of infill material, a detailed comparison of the test results is presented against a similar study by the authors on composite floor panels without infill [28]. Thus, the performance of single- (SC), double- (DC), and triple-cell (TC) panels with and without infill were evaluated in terms of ultimate load capacity and failure mode for all test specimens.
## 2 Numerical Simulation Methodology
### Definition of Materials and Boundary Conditions  
#### 2.2.1 Materials  
In Chinese commercial kitchens, the flowing fluid is a complex mixture of gas (VOCs and exhaust gases) and solid (particles). In this paper, to investigate the kitchen indoor air quality, benzene (C\({}_{6}\)H\({}_{6}\)) and CO\({}_{2}\) were considered to represent the VOCs in cooking frame and combustion exhaust gas, respectively. The oil particles were dispersed in the gas phase. Firstly, cooking emissions can result in human exposure to both coarse particles (2.5-10 \(\upmu\)m) and ultrafine particles (UFPs) (<0.1 \(\upmu\)m) [26]. In order to understand the volume-based  
Figure 3: Grid independence test.  
Figure 2: Mesh division of the CCKEF system.  
size distribution during the Chinese cooking process, [PERSON] et al. [27] indicated that the typical Chinese-style cooking process, such as frying, can be simplified as a 2 min oil heating stage, and they found that the volume frequency of fume particles in the range of 1.0-4.0 \(\upmu\)m accounts for nearly 100% of PM 0.1-10 \(\upmu\)m with the mode diameter around 2.5 \(\upmu\)m during the heating stage. Secondly, the purification performance of electrostatic purifiers is also often evaluated by the removal efficiency of PM2.5 in the literature [25; 28]. As a result, PM\({}_{2.5}\) was selected as the representative cooking particulate in this study. As the surrounding temperature near the gas stove was quite high, the Boussinesq approximation was not suitable for buoyancy-driven flows with large density differences in the flow field inside the kitchen. The incompressible ideal gas equation was applied to obtain the variation in fluid density with different temperatures in this study according to Equation (1).  
\[\rho_{f}=\frac{PM}{nRT} \tag{1}\]  
where \(\rho_{f}\) is the density of fluid [kg/m\({}^{3}\)]; \(P\) is the pressure [Pa]; \(M\) is the mass of fluid [kg]; \(n\) is the amount of substance [mol]; \(T\) is the temperature [K]; \(R\) is a constant, 8.314 []/(mol\(\cdot\)K)].
## 5 Simulation Verification
### Impact of Current-Limiting Reactor and Fault Location  
Under FRT control, the proposed methodology enables quantitative assessment of current-limiting reactor (CLR) influences on fault currents. For DC fault \(F_{1}\), Figure 19 illustrates the response profile of \(I_{1}\) as CLR inductance varies from 50 mH to 300 mH. Correspondingly, Figure 20 depicts the dynamic behavior of \(I_{2}\) under identical parametric variations during DC fault \(F_{2}\).  
Figure 19: Calculation and simulation values of \(I_{1}\) with different CLR during DC fault \(F_{1}\).  
Figure 17: Comparative analysis of calculated versus simulated \(I_{2}\) under voltage FRT control, with \(U_{dcryl}\) clamped at zero post-FRT activation.  
Figures 19 and 20 demonstrate that reduced CLR inductance correlates with increased fault current rise rates and peak magnitudes. Correspondingly, the current attenuation rate exhibits an inverse relationship with CLR values. Post-FRT activation, diminished CLR inductance accelerates fault clearance duration and advances current zero-crossing points.  
Assuming a DC fault \(F_{3}\) on the overhead line. Figure 21 presents the transient profile of \(I_{1}\) with the fault distance variation (50-200 km) from MMC\({}_{1}\), while Figure 22 documents corresponding \(I_{2}\) characteristics relative to MMC\({}_{2}\). Analysis of Figures 21 and 22 reveals an inverse correlation between fault proximity and current transient metrics; reduced fault distance elevates both the rise rate and peak magnitude of fault current, while accelerating its attenuation rate. Consequently, post-FRT activation, distant faults exhibit prolonged clearance durations and delayed zero-crossing attainment.  
Figure 21: Calculation and simulation values of \(I_{1}\) with different locations of \(F_{3}\)  
Figure 20: Calculation and simulation values of \(I_{2}\) with different CLR during DC fault \(F_{2}\).  
Figures 19-22 validate that the proposed methodology's estimates exhibit precise alignment with simulation results across diverse CLRs and fault locations. The proposed method can be used to analyze the DC fault current of the hybrid MMC and provides a theoretical basis for the design of system and control parameters under FRT conditions. It should be noted that this paper concentrates on the low-resistance fault, since it generates the most severe fault current. Given that real fault resistances can exhibit complex time-varying behavior (e.g., thermal effects, arc formation), the fault current calculation method considering the high-resistance and time-varying resistance fault conditions will be further improved in the future research.
## 3 Experimental Results and Analysis
### Experimental Results of Grille Turbulence Generator  
The measurements were first conducted for the two grilles. Figure 7 illustrates the time series of the instantaneous velocity at the central measuring point of the 6 th section. As shown in Figure 7, the incoming wind became highly disturbed, for this reason, a statistical analysis was done for the flow field. The decay of turbulence intensity from the grilles towards the airfoil test section was reported in Figure 8a. Within the scope of the test, the averaged turbulence intensity was recorded along 8 downstream sections (each measured with 15 points). Besides, the margin of error in Figure 8a was expressed as the standard deviation of each section data. According to Figure 8a, the average turbulence intensity of downstream in scheme one dropped from 13.5% at the position of 1.0 m behind the grille to 9.4% at the position of 2.0 m; the average turbulence intensity of the downstream in scheme two was higher than scheme 1, which decreased from 27.7% at the position of 1.0 m behind the grille to 15.9% at the position of 2.0 m. The standard deviations of turbulence intensity of the two schemes drops quickly towards the test section. The change in turbulence level indicates that there were very high turbulence fluctuations just behind the grille which yielded very unstable flow field. The flow became gradually stabilized owing to the mutual dissipation over a propagation distance. To ensure a homogeneous and isotropic turbulent field, so the distance 1.6 m (from the leading edge of the airfoil to the grille) was selected for placing the leading edge of the airfoil. The turbulence intensity measured at the 6 th section of the two schemes was 10.5% and 19.0%, respectively. Figure 8b illustrates the power spectral density (PSD) of the wind speed at the two grille schemes measured at the central measuring point of the 6 th section (1.6 m measured from grille to the hot-wire probes). Obviously, the larger the turbulence intensity was generated, the larger the amplitude of the power spectral density was observed. Moreover, the power spectra amplitude in the downstream direction was consistent with the corresponding Karman spectra, and it dropped down rapidly after the frequency over 30 Hz, which suggests that the grille distance was more proportional to the turbulent kinetic energy.  
Figure 7: The time series of the instantaneous streamwise velocity (the 6 th section, central measuring point).
# Aero-Engine Remaining Useful Life Estimation Based on CAE-TCN Neural Networks
###### Abstract  
With the rapid growth of the aviation fields, the remaining useful life (RUL) estimation of aero-engine has become the focus of the industry. Due to the shortage of existing prediction methods, life prediction is stuck in a bottleneck. Aiming at the low efficiency of traditional estimation algorithms, a more efficient neural network is proposed by using Convolutional Neural Networks (CNN) to replace Long-Short Term Memory (LSTM). Firstly, multi-sensor degenerate information fusion coding is realized with the convolutional autoencoder (CAE). Then, the temporal convolutional network (TCN) is applied to achieve efficient prediction with the obtained degradation code. It does not depend on the iteration along time, but learning the causality through a mask. Moreover, the data processing is improved to further improve the application efficiency of the algorithm. ExtraTreesClassifier is applied to recognize when the failure first develops. This step can not only assist labelling, but also realize feature filtering combined with tree model interpretation. For multiple operation conditions, new features are clustered by K-means++ to encode historical condition information. Finally, an experiment is carried out to evaluate the effectiveness on the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets provided by the National Aeronautics and Space Administration (NASA). The results show that the proposed algorithm can ensure high-precision prediction and effectively improve the efficiency.  
remaining useful life estimation; aero-engine; convolutional autoencoder; temporal convolutional network +
Footnote †: journal: _Sciences_