esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Study and Performance Benchmarking
### Mean Value and Standard Deviation of the Optimal Solution  
Next, to evaluate the stability of the algorithms, the mean value and the standard deviation of the optimal solution found in the 10 simulations was calculated. Since these were 10 independent simulations, the starting points were randomized in each simulation. These results are shown in Table 3.  
Even though HIEGA had a better performance than the proposed algorithm when converging to the best-known solution, the proposed algorithm was able to consistently converge to a better solution than HEIGA, as indicated by the mean value and standard deviation. For the Reeves problems, the proposed algorithm had a better performance than HEIGA. For 7 out of 9 problems, its average optimal value was much closer to the best-known solution than that of HEIGA, IEGA, sharing fitness, and CM. The standard deviation varied between 0.00 and 16.54 for the proposed algorithm, while it varied between 4.64 and 35.10 for HEIGA, 11.74 and 29.53 for IEGA, 15.61 and 39.63 for sharing fitness and 20.34 and
# Towards the Internet of Smart Trains:
## 6 IoT-Enabled Services: From More Efficient Operations to New Business Models
### Information
#### 6.3.1 Passenger Information System (PIS)
Note that in order to provide security to the ticketing system, dedicated communication lines should be deployed.
# Quality Factor Enhancement of 650 MHz Superconducting Radio-Frequency Cavity for CEPC
## 4 Conclusions  
Mid-T furnace baking was attempted at 650 MHz single-cell cavity, which achieved a state-of-the-art high \(Q_{0}\) at the accelerating gradient of 30 MV/m. Extremely low \(R_{\rm S}\) of 3.4 n\(\Omega\) at 25 MV/m was demonstrated, while the typical value of \(R_{\rm S}\) is 5-10 n\(\Omega\).  
Nitrogen doping is another kind of processing for SRF cavities, which can also improve \(Q_{0}\). It is also carried out in a vacuum furnace along with the injection of nitrogen gas by thermal diffusion, which is followed by light EP to remove the niobium nitrides inside cavity surface [16]. Compared with nitrogen doping, mid-T furnace baking is much easier and more convenient. These studies can help to enhance the performance and comprehension of large elliptical (<1 GHz) superconducting cavities, which are broadly used by synchrotron light sources, proton/neutron sources and high-energy colliders.  
Conceptualization, PS. and B.L.; Data curation, S.J. and Z.M.; Formal analysis, P.S.; Funding acquisition, P.S. and W.P.; Investigation, P.S. and J.Z.; Methodology, J.Z.; Project administration, W.P.; Resources, F.H.; Software, S.Z., L.Y. and X.H.; Supervision, P.S.; Visualization, C.D.; Writing--original draft, P.S.; Writing--review and editing, P.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 12075270, 11505197), Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB2500000) and the Platform of Advanced Photon Source Technology R&D.  
Not applicable.  
Figure 5: BCS resistance (\(R_{\rm BCS}\)) of 650 MHz cavity and 1.3 GHz cavity (at 2.0 K), which both received mid-T furnace baking.  
Figure 6: Residual resistance (\(R_{\rm res}\)) of 650 MHz cavity and 1.3 GHz cavity, which both received mid-T furnace baking.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing is not applicable.  
**Acknowledgments:** The authors would like to thank Beijing HE-Racing Technology Co., Ltd.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Numerical Results and Discussion  
The following material parameters are used in the numerical simulations. The volume of the computational domain \(\Omega\) is 100 um\({}^{3}\). The illumination of the volume of the biological media/cells is achieved at \(\lambda=830\) nm. Such a wavelength is in NIR, where the laser irradiation can both penetrate soft tissues (1-10 cm deep) with low absorption and overlap the absorption band of gold nanoparticles [5; 28]. The illumination beam has a power of \(P_{w}\approx 1.0\) W and a beam diameter of \(D_{beam}=0.15\) mm. The body temperature at the boundary of the computational domain \(\Omega\) is \(T_{0}=37.0\)\({}^{\circ}\)C. The electromagnetic and thermal parameters are given in Table 1[29; 30]. All constitutive materials of the nanoparticles are considered isotropic and homogeneous.  
The agglomerates are constituted by individual gold nanoparticles of varying diameters \(D_{np}\), with relative volume concentrations \(C_{rp}\) (or the relative mass concentration \(C_{rm}\))  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & \(\rho\) **(kg\(\cdot\) m\({}^{-3}\))** & \(C_{p}\) **(m\({}^{2}\cdot\) s\({}^{-2}\cdot\) K\({}^{-1}\))** & \(\kappa\) **(kg\(\cdot\)m\(\cdot\)s\({}^{-3}\cdot\) K\({}^{-1}\))** & \(e_{r}\) **(at 830 nm)** \\ \hline bio-media/cells & 1090 & 2185 & 1.20 & 1.77 \\ Au nanoparticle & 19, 300 & 129 & 310 & \(-26.61\) + j1.67 \\ \hline  & \(D_{np}\) (nm) & \(D_{bm}\) (μmm) & \(C_{rm}\) (in \%) & \(C_{rp}\) (in \%) \\ \hline range & (15; 180) & (1; 10) & (0.02; 35.0) & (0.001; 2.3) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of the parameters (the volume mass density \(\rho\), the specific heat capacity \(C_{p}\), the thermic conductivity \(\kappa\), and the relative permittivity \(e_{r}\)) for the photothermic model and for the media (biological media/cells and gold nanoparticles) with i\({}^{2}=-1\). The ranges of diameters (\(D_{np}\) and \(D_{bm}\)) and the relative concentrations (\(C_{rm}\) and \(C_{rp}\)) used in this study for both materials are also given.  
in the biological media/cells. The ranges of diameters and concentrations used in this paper are given in Table 1.  
In the following, we first analyze the relevant constitutive parameters for the agglomerates of identical nanoparticles before investigating the temperature elevation produced by mixtures of agglomerates.
# Self-Disclosure to an IoT Conversational Agent: Effects of Space and User Context on Users'
## 2 Theoretical Background and Research Hypotheses
### Effects of the Space Type on Self-Disclosure to IoT-CAs  
#### 2.2.1 Self-Disclosure in Living Spaces and Workspaces  
IoT-CAs are making interactions ubiquitous and are becoming a part of our everyday spaces such as living spaces and workspaces. People in living spaces and workspaces have different emotions and activities, which might affect their willingness to self-disclose to IoT-CAs in these spaces. [PERSON] et al. [28] pointed out that an individual tends to disclose more personal information when they feel safe in a space. Living spaces are safe and private for people, whereas workspaces are spaces to pursue promotions, opportunities, and high performance [29]. Furthermore, the interpersonal relationships in living spaces (close family or friends) are more intimate than those in workspaces (coworkers). People will disclose more personal information to intimate partners than to nonintimate partners [30; 31]. People might engage in more self-disclosure with an IoT-CA in a living space than they would in a workspace. Thus, the following hypothesis is proposed:  
**Hypothesis 2.**_Users in living spaces are willing to disclose more information to IoT-CAs than those in workspaces._
## 2 The Waveform Superposition Algorithm
### Equivalent Transmitted Power  
The electromagnetic wave transmitted by the nodes continues to transmit \(L_{2}\) forward to reach the target location; i.e., \(L=L_{1}+L_{2}\). Assume the equivalent transmitted power of the array is \(P_{t,array}\), and the jamming power in the target area is given by  
\[P_{j,array}(\theta,\phi)=P_{t,array}(\theta,\phi)\cdot G(\theta,\phi)\cdot \left(\frac{\lambda}{4\pi L_{2}}\right)^{2} \tag{23}\]  
Figure 6: The array factor of a sparse array.  
According to the equation: \(P_{j,array}(\theta,\phi)=P_{j,wave}(\theta,\phi)\), \(P_{t,array}\) is given by  
\[\begin{split} P_{t,array}(\theta,\phi)&=\eta_{e}\cdot\frac{2\pi L_ {2}^{2}\big{[}\text{sum}(\mathbf{AA^{T}})+\text{sum}(\mathbf{BB^{T}})\big{]}}{ P_{total}\cdot G(\theta,\phi)}\\ &=\frac{\eta_{e}}{2\eta_{a}}\cdot\frac{n^{2}L_{2}^{2}P_{total}}{ p_{total}}\cdot\frac{\text{sum}(\mathbf{AA^{T}})+\text{sum}(\mathbf{BB^{T}})}{|B( \theta,\phi)|^{2}}\end{split} \tag{24}\]  
Assume that the nodes consist of a \(4\times 4\) uniform rectangular array with \(0.5\lambda\) array element spacing; the sparse array consists of these nodes arranged randomly in a circle with a radius of \(5\) m. Assume \(A_{tk}=1,\theta=\pi/2,L_{2}=2500,\eta_{e}=\eta_{a}=1\), and the coordinate of the target in the Cartesian coordinate system is \((0,5000)\). The distribution curve of \(P_{t,array}(\phi)\) is shown in Figure 7, which is calculated according to (24), and the variance in the horizontal direction is \(1.55\times 10^{-5}\), showing that \(P_{t,array}(\phi)\) has little difference at all angles.  
Take the equivalent transmitted power at the target orientation \(\phi_{0}\) as the equivalent transmitted power of the array; i.e., \(P_{t,array}(\phi)=P_{t,array}(\phi_{0})\). Furthermore, since the nodes' beams are aligned to the target, so \(f(\phi_{0}^{\prime})=1\), the beampattern at the target orientation \(B(\phi_{0})\) is equal to \(N\), and \(P_{t,array}\) is written as  
\[P_{t,array}=\frac{L_{2}^{2}}{2}\cdot\left(\frac{n}{N}\cdot\frac{P_{total}}{P_{ total}}\right)^{2}\cdot\left[\text{sum}(\mathbf{AA^{T}})+\text{sum}(\mathbf{BB^{T}})\right] \tag{25}\]  
As shown in (25), \(P_{t,array}\) of the array is only related to the nodes themselves and the distribution of the array. Moreover, there is no need to calculate the relative positions between the nodes and the target, reducing the computational complexity.
## Economic Viability
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**CRCA Incorporation** & **Thickness** & **Theoretical Volume** & **Estimated Volume with** & **Shotcrete Cost Per** & **Lining’s Cost Per** & **Variation [f/m]** \\
**Ratio [\%]** & **[mm]** & **[m\({}^{3}\)/m]** & **Rebound Losses [m/m]** & **Unit Volume [f/m\({}^{3}\)]** & **Unit Length [f/m]** & **Variation [f/m]** \\ \hline \multicolumn{7}{c}{Case 1—Similar stiffness criterion} \\ \hline
0 & 200 & 3.64 & 4.86 & 140.00 & 680.26 & - \\ \hline
20 & 206 & 3.75 & 5.00 & 139.20 & 695.94 & +15.68 (2.3\%) \\ \hline
50 & 214 & 3.89 & 5.19 & 138.05 & 716.00 & +35.74 (5.3\%) \\ \hline
100 & 283 & 5.08 & 6.78 & 136.19 & 922.98 & +242.7 (35.7\%) \\ \hline \multicolumn{7}{c}{Case 2—Similar yield stress criterion} \\ \hline
0 & 200 & 3.64 & 4.86 & 140.00 & 680.26 & - \\ \hline
20 & 224 & 4.06 & 5.42 & 139.20 & 754.40 & +74.14 (10.9\%) \\ \hline
50 & 255 & 4.60 & 6.14 & 138.05 & 847.13 & +166.87 (24.5\%) \\ \hline
100 & 290 & 5.20 & 6.94 & 136.19 & 944.66 & +264.40 (38.9\%) \\ \hline \end{tabular}
\end{table} 
Table 9: Lining cost per unit length of the tunnel for Scenario 1 (50% Coarse Natural Aggregates). Figure 3 shows the variation of the lining's cost per unit length of tunnel for the three hypothetical costs of CRCA. Figure 3 shows that the lining's cost per unit length of the tunnel is fairly insensitive to the cost of CRCA. In particular for Case 1, similar stiffness criterions-mixes IR20 and IR50 lead to marginal increases in the lining's cost (between 1.2% and 5.3%), while the other cases studied lead to variations higher than 10%. So, IR20 and IR50 are interesting alternatives for CRCA incorporation in dry-mix shotcrete solutions. The remaining cases require a larger increase in lining's thickness, which is economically unattractive.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## 1 Introduction
However, the data assimilation usually occupies a large amount of computation. Some other studies tried to up-scale the inputs of the GPP model firstly, then estimated the regional GPP by using the up-scaled inputs [30; 31], but the errors of the input data in the up-scaling process would have an influence on the accuracy of the results, and the uncertainty was difficult to qualify. To sum up, although some up-scaling methods of GPP, using statistical regression, data assimilation and machine learning models, have been proposed in recent years, few studies focused on the difference in the accuracy and feasibility these up-scaled models. In this condition, comparing the precision of these methods and analyzing the applicability of these models is urgent. It is an effective way to validate the estimated GPP by comparison with the observed data. The flux net observed network, such as FLUXNET, AmeriFLUX, AsiaFLUX, ChinaFLUX, which contained more than 500 total stations, had been constructed to monitor carbon flux regionally or globally, and to validate the simulative GPP from different kinds of models [32; 33; 34]. Some other projects, such as Validation of Land European Remote Sensing Instruments (VALERI) [35] and BigFoot [36; 37], had also been designed to validate the satellite products. Considering the difference in the footprint between field observed data and the satellite data would bring some uncertainty in direct validation; some indirect validation methods were also proposed to assess the precision of the remote sensing products. For example, [PERSON] et al. validated the estimated biomass and Net Primary Production (NPP) with field data firstly, then up-scaled NPP to 1 km and validated the estimated NPP at a regional scale [38]. [PERSON] et al. found that the difference in the estimated annual average global GPP could be as high as 50% when validating from field data to regional data [39]. The validation of up-scaled GPP had typically been on training (flux tower) sites,which are limited in number and mostly not independent. Few studies concentrated on the validation of scale transform results at different scales systematically. In this case, studying the validating methods of up-scaled GPP at several scales is of great significance. The aims of this paper are to up-scale GPP at flux tower sites to a regional scale, by using five machine learning models (Cubist regression tree, RF, SVM, ANN and deep belief network (DBN)), based on remote sensing data (MODIS land surface reflectance products, and derived fractional vegetation cover, land-cover products), and to compare the accuracy of the different up-scaling approaches. The results of this paper demonstrated the applicability and feasibility of machine learning models in up-scaling GPP at flux tower sites.
## 3 Experimental Program
### Description of the Test Specim
#### 3.1.2 Details of the Concrete Elements  
Figure 3 shows the details of precast concrete elements in the test specimen designed based on Chinese Code GB50011 [33] and GB50010 [34]. The precast concrete wall panel was cast in a local precast concrete factory with a thickness of 200 mm, a length of 820 mm, and a height of 3475 mm. An enhanced loading beam was also made as a portion on the panel with a width of 240 mm, a length of 660 mm, and a height of 320 mm. A 30 mm deep groove was reserved on the top surface of the foundation beam for the embedment of the panels and the 15 mm thick grouting layer. PVC pipes with a diameter of 75 mm were embedded at the centers of the wall panels and the corresponding locations of the foundation beam. Thru-holes were reserved at appropriate locations of the wall panels to facilitate the installation and replacement of the energy dissipaters, and threaded sleeves were preembedded into the foundation beam for the attachment of MBSFs. Considering that crushing of the concrete is undesirable for the self-centering system with additional energy dissipaters, 10 mm thick steel plates were employed at the appropriate positions of the wall panel to protect the crucial local areas, especially the wall toes. Rebar with diameters of 16 mm and 8 mm were adopted as the longitudinal reinforcing rebar of the wall panels, and lateral reinforcing rebar with a diameter of 10 mm were spaced at 200 mm intervals.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test No.** & **Post-Tension Force (kN)** & **Number of SEDPs** & **Number of MBSFs** & **Spacing of Belleville Spring (mm)** \\ \hline RW1-1 & 480.4 & 4 & 0 & \\ RW1-2 & 480.4 & 4 & 4 & 7 \\ RW1-3 & 480.4 & 4 & 4 & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Test description.  
Figure 3: Details of the precast concrete elements (unit: mm).
## 3 Influence of Stator Configuration on Thrust Force
### Influence of End Effects  
Actuators with E-shaped cores consist of two unit C-shaped cores and the optimal width is \(2\tau_{p}=64\) mm [8]. End effects were not considered here during the design of the unit configuration. Therefore, a reduction would be brought to the thrust force to some extent if end effects are considered.  
The configuration shown in Figure 3 was adopted to compensate for the reduction in thrust force caused by the end effects and to reduce the ripple of thrust force: two radially magnetized magnets are mounted on both sides of the mover and the E-shaped core has tooth tips on either side. The arrows represent the orientations of magnetization.  
Figure 4 shows the thrust forces produced by the PM LOA over the stroke range with an ideal C-core and with the E-core. As shown, the average thrust forces were 350 N and 361 N with and without end effects, respectively. The thrust force of ideal C-core LOA without end effects was greater than the E-core LOA with end effects, but the thrust force ripple was also greater.
## 4 Results and Discussions
### Interface Transition Zone Analysis  
The thickness of the interface transition zone and interface structure for the C-S-H/asphal interface are shown in Figure 17. It can be seen that SCA modification has an important impact on the transition zone thickness and interface structure. For the unmodified interface, the thickness of the transition zone is 5.04 A. This may be due to the diffusion of water molecules in C-S-H into the area near the surface of the asphalt, where the water molecules overlap with the asphalt molecules. After SCA modification, the thickness of the transition zone increased from 5.04 A to 11.2 A, indicating that SCA modification enhanced the attraction of the C-S-H surface to asphalt. Moreover, two distribution patterns of SCA molecular chains on the C-S-H surface can be observed at the interface structure between C-S-H and asphalt: one is parallel to the C-S-H surface, and the other is inserted into the asphalt layer. The insertion of SCA molecular chains into the asphalt layer is strongly entangled with asphalt molecules, thereby enhancing the weak interface transition zone between C-S-H and asphalt.  
The influence of interface water on the asphalt nanostructure is shown in Figure 18. The results indicate that SCA modification reduces the effect of water on the relative concentration of SARA components at the interface. For the unmodified interface, interface water causes a varying degree of decrease in the relative concentration of asphalt SARA components within 0-7 A. This is why the interaction energy between  
Figure 16: Interaction energy for C-S-H/asphal interface.  
asphalt and C-S-H decreases when interface water exists. Within the distance of 7-15 A, the relative concentration of asphaltene, aromatics, and resins increases, indicating that the addition of water molecules causes the asphalt components at the interface to aggregate towards the center, also demonstrating the hydrophobicity of asphalt. After SCA modification, the relative concentration of resins and aromatics at the interface within 0-5 A remains basically unchanged, while the concentration of saturates slightly increases. It is worth noting that the relative concentration of asphaltene increases at 4-8 A and forms a peak. This indicates that after SCA modification, the interface structure of SARA components can remain relatively stable even under the action of water, thereby increasing the resistance to water damage between C-S-H and asphalt.  
Figure 17: Interface transition zone thickness and interface structure for C-S-H/asphalt interfaces: (**a**) C-S-H/asphalt interface zone thickness; (**b**) C-S-H/SCA/asphalt interface transition zone thickness; (**c**) C-S-H/asphalt interface structure; (**d**) C-S-H/SCA/asphalt interface structure.
## 5 Damage Detection in Wavefield Images Based on NCA-RPCA Algorithm
### Experimental Verification of Wavefield  
#### 5.3.1 Experimental Configuration  
For further validation of the algorithm, a publicly available dataset from the University of Gottingen, Germany, is used in this paper [46]. These data are for a CFRP plate with dimensions of 500 \(\times\) 500 mm and a thickness of 2 mm with the material properties shown in Table 5, the structure of the plate is shown in Figure 30. A piezoelectric transducer was located in the center of the structural plate for acoustic field measurements. An aluminum disc was mounted on the surface of the CFRP plate using adhesive tape to simulate damage. The excitation signal was a 5-cycle Hanning window-modulated sine wave. The experiment was carried out using a PSV-400-3D from Polytec GmbH (Waldbronn, Germany) for full wavefield measurements. Due to symmetry, only the lower left quarter of the plate was swept. Therefore, the sensor was located in the lower-left corner of the measured wavefield. The experiment is shown in Figure 31.
## 3 Results and Discussion
### Two Classical Methods Introduced to Establish PLS Models
#### 3.4.2 iPLS Prediction Model Built for Comparison with the Proposed Model  
The modeling setting of the iPLS method was as follows: By moving windows, the interval size was set to 20. The result included 280 bands in total that were selected from 14 intervals, which were located near 850, 1200, 1350, 1600, 1800, 2200, 2400, 2500, and 2600 nm. As shown in Figure 8, about 280 variables were selected as the modeling objects to establish the iPLS model, whose \(R_{c}^{2}\) was 0.9294, RMSEC was 0.2713, \(R_{cv}^{2}\) was 0.9021, RMSECV was 0.3088, \(R_{p}^{2}\) was 0.9232, and RMSEP was 0.2838. By selecting the interval, the bands with a stronger correlation with moisture characteristics were obtained, which increased the accuracy and stability of the model. However, the performance of the iPLS model was slightly worse than that of the DWT-BOSS-PLS model because the selected variables were still redundant bands.  
As shown in Table 2, among the three different wavelength selection methods, the DWT-BOSS algorithm performed the best. The PLS model established using the DWT-BOSS algorithm not only had the best stability and prediction ability but also used the least number of wavelength points. In summary, the ranking of the number of selected variables was as follows: DWT-BOSS \(<\) iPLS \(<\) GA, whereas the ranking of the prediction accuracy was: DWT-BOSS \(>\) GA \(>\) iPLS.