esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
In particular, in the expression of PPAR-\(\gamma\) and SREBPP1, tilianin showed an increased expression level compared to the control at a concentration of 30 \(\upmu\)M (Figures 4D and 5E). In addition, as almost no effect was observed on the mRNA expression of CPT1 at a concentration of 30 \(\upmu\)M (Figure 5C). Thus, it can be assumed that the threshold dose of tilianin in the 3T3 cell line is 30 \(\upmu\)M or more. Several studies have shown that other single compounds which can found in _Agastache rugosa_ exert anti-obesity properties. For example, acacetin has anti-adipogenic effects on 3T3-L1 adipocytes and reduce lipid accumulation in high-fat-diet induced mice [21]. In addition, another component, rosmarinic acid, has been found to attenuate obesity in human adipocytes and HepG2 cells [31; 32]. However, there are insufficient research on tilianin to date. Consequently, we have filed a patent regarding the results obtained in this study. Still, several experiments are remaining to explore. For example, to deeply understand our results at an in vitro level, other obesity factors should be investigated, such as the mitogen-activated protein kinase and extracellular-signal-regulated kinase pathway which are known to be closely related to triglyceride and hormones [33; 34]. Moreover, research about mechanism of action should be investigated. Additionally, mitotic research could produce meaningful results, and lastly, in vivo experiments are required to clarify effects of tilianin and ARE on animals. Natural products are more easily accessible than pharmaceutical products. In addition, there are many different dietary supplements; however, ARE does not occupy a large portion of the dietary supplement market. Through this study, the anti-obesity function of ARE and that of its major bioactive compound tilianin are shown, revealing their potential use as components for dietary products. Conceptualization, J.M.H., M.-H.L., J.-[PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; formal analysis, [PERSON]-[PERSON], [PERSON] and [PERSON]; investigation, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON] and [PERSON]; supervision, [PERSON]-[PERSON] and [PERSON]; project administration, [PERSON]-H.L. ; funding acquisition, [PERSON]-H.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Acknowledgments: This work was supported by the Bio-Synergy Research Project (2013M3A9C407815934) of the Ministry of Science, ICT and Future Planning through the National Research Foundation. **  
**Conflicts of Interest: The authors declare no conflict of interest. **
## Nomenclature  
\begin{tabular}{l l l l} \(Re\) & Reynolds number & \(\rho\) & air density (kg/m\({}^{3}\)) \\ \(AR\) & aspect ratio & \(S_{t}\) & Strouhal number \\ \(AR_{\sigma}\) & critical aspect ratio & \(\sigma_{L}\)\({}^{2}\) & variance of local lift coefficient \\ \(H\) & height of cylinders (mm) & \(\theta_{+}\) & positive pressure range (\({}^{\circ}\)) \\ \(D\) & diameter of cylinders (mm) & \(C_{p,t}(t)\) & wind pressure coefficient \\ \(h\) & height above the wind tunnel ground (mm) & \(P_{t}(t)\) & measured wind pressure (Pa) \\ \(I_{u}\) & turbulence intensity (\%) & \(\overline{C}_{p}\) & mean wind pressure coefficient \\ \(P_{\infty}\) & static pressure (Pa) & \(S(\prime)\) & power spectral density \\ \(V_{H}\) & wind speed at the top of models (mm) & \(\overline{C}_{PB}\) & base pressure coefficient \\ \(H_{k}\) & tributary height (mm) & \(F_{D},F_{L}\) & drag and lift force (N) \\ \(U\) & mean wind speed (m/s) & \(C_{D,k}(t),C_{L,k}(t)\) & local force coefficient \\ \(U_{r}\) & mean wind speed at reference height (m/s) & \(C_{JD}(t),C_{JL}(t)\) & base force coefficient \\ \(\theta\) & angle between taps and the incoming flow (\({}^{\circ}\)) & \(\overline{C}_{D},\overline{C}_{L}\) & mean local force coefficient \\ \(f\) & frequency (Hz) & \(C_{JD},C_{L}\) & fluctuating local force coefficient \\ \(N\) & number of pressure taps at each layer & \(\overline{C}_{JD},\overline{C}_{JL}\) & mean base force coefficient \\ \(M\) & number of pressure tap layers & \(C_{JD},C_{JL}\) & fluctuating base force coefficient \\ \end{tabular}
## 4 Numerical Results
### Application for Real-World Engineering Problems
#### 4.2.2 Problem 2: Sonic Boom Uncertainty Quantification
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Statistics** & **Variable** & **Statistics** \\ \hline \(H_{1}\) (m) & \(\mathcal{N}(0,1)\) & \(RH_{4}\)(\%) & \(\mathcal{N}(66.96,15)\) \\ \(H_{2}\) (m) & \(\mathcal{N}\) (11,000, 1000) & \(RH_{5}\)(\%) & \(\mathcal{N}(24.38,10)\) \\ \(H_{3}\) (m) & \(\mathcal{N}\) (20,000, 1000) & \(RH_{6}\)(\%) & \(\mathcal{N}(8.49,5)\) \\ \(T_{1}\)(\({}^{\mathrm{c}}\)F) & \(\mathcal{N}(59,5)\) & \(WXH_{1}\) (m) & \(\mathcal{N}(0,1)\) \\ \(T_{2}\)(\({}^{\mathrm{c}}\)F) & \(\mathcal{N}\) (\(-69.7\),5) & \(WXH_{2}\) (m) & \(\mathcal{N}(5000,1000)\) \\ \(T_{3}\) (\({}^{\mathrm{c}}\)F) & \(\mathcal{N}\) (\(-69.7\),5) & \(WXH_{3}\) (m) & \(\mathcal{N}\) (20,000, 1000) \\ \(ZH_{1}\) (m) & \(\mathcal{N}(0,1)\) & \(WX_{1}\) (m/s) & \(\mathcal{N}(0,1)\) \\ \(ZH_{2}\) (m) & \(\mathcal{N}(1520,500)\) & \(WX_{2}\) (m/s) & \(\mathcal{N}(20,5)\) \\ \(ZH_{3}\) (m) & \(\mathcal{N}(6400,1000)\) & \(WX_{3}\) (m/s) & \(\mathcal{N}(30,10)\) \\ \(ZH_{4}\) (m) & \(\mathcal{N}(7620,1000)\) & \(WYH_{1}\) (m) & \(\mathcal{N}(0,1)\) \\ \(ZH_{5}\) (m) & \(\mathcal{N}\) (10,060, 1000) & \(WYH_{2}\) (m) & \(\mathcal{N}(5000,1000)\) \\ \(ZH_{6}\) (m) & \(\mathcal{N}\) (13,720, 1000) & \(WYH_{3}\) (m) & \(\mathcal{N}\) (20,000, 1000) \\ \(RH_{1}\)(\%) & \(\mathcal{N}(59.62,15)\) & \(WY_{1}\) (m/s) & \(\mathcal{N}(0,1)\) \\ \(RH_{2}\)(\%) & \(\mathcal{N}(67.06,15)\) & \(WY_{2}\) (m/s) & \(\mathcal{N}(10,5)\) \\ \(RH_{3}\)(\%) & \(\mathcal{N}(77.66,15)\) & \(WY_{3}\) (m/s) & \(\mathcal{N}(20,10)\) \\ \hline Temperature Profile & \(H_{i}\): Altitude & \(T_{i}\): Temperature & \\ Humidity Profile & \(ZH_{i}\): Altitude & \(RH_{i}\): Relative Humidity & \\ X-Wind Profile & \(WXH_{i}\): Altitude & \(WX_{i}\): X-wind & \\ Y-Wind Profile & \(WYH_{i}\): Altitude & \(WY_{i}\): Y-wind & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of atmospheric propagation aleatory uncertain variables. \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Statistics** \\ \hline Ground altitude (\(ft\)) & \(\mathcal{U}(0,5000)\) \\ Reflection factor & \(\mathcal{U}(1.8,2.0)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistics of atmospheric propagation epistemic uncertain variables. Uncertainty quantification results are shown in Table 8. Since a small number of simulations results are used while creating the surrogate model, the accuracy of the surrogate model adopted by the PCE method is low. Therefore, the obtained results have discrepancies with the MC method. Similarly, the MF-PCE method also yields poor accuracy metrics for this small number of samples. When the LRA and MF-LRA results are examined, it is observed that, although few analysis results are used, obtained results are in good agreement with the MC method.
## 5 Conclusions  
In this study, we performed feature selection in order to prioritize inputs in the predication of energy consumption in an artificial illumination system for a CPPS using linear and nonlinear regression models. A dataset was generated with electrical measurements for proprieties such as intensity, light wavelength (RGB and W), frequency, and duty cycle.  
The algorithms used for the linear models to identify the elimination order of the features included the variance threshold, Pearson correlation, univariate liner F-regression, and sequential backward feature selection with linear regression.  
On the other hand, for nonlinear models, the algorithms used were the variance threshold, mutual information gain, and sequential backward feature selection with tree decision regression, using a tree depth from 2-5. The Kruskal-Wallis test served to validate the elimination order distributions.  
The best order for eliminating features with the linear model was duty cycle, light color, frequency, and intensity, with \(p_{value}=0.012364\). The best order with nonlinear models was white, green, blue, duty cycle, frequency, red, and intensity, with significance at \(p_{value}=0.007161\). The elimination order for the duty cycle and R in the linear and nonlinear models differed enormously because the linear algorithms considered them the most suitable elimination features, while nonlinear algorithms marked them as essential features. This discrepancy was because the duty cycle and R were nonlinear features. Thus, only nonlinear models could map them correctly. Moreover, this supports the hypothesis that the energy consumption in LED lamps for CPPSs has nonlinear behavior and that nonlinear models should be used to predict it.  
This technique allows various deductions to be drawn from the analysis of the data obtained, including the estimation of the average energy consumption and its comparison with the quality of the crop, as well as the determination of the circumstances under which energy use is efficient. The selection of characteristics can be used as a reference for the agro-industrial community.  
Figure 10: Order of elimination for features in nonlinear models.  
**Author Contributions:** All authors conceived the experiments; [PERSON], [PERSON] and [PERSON] collected the data for the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] conducted the experiments, performed the statistical analysis, and generated the figures. All authors wrote and reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** We acknowledge the support of the Consejo Nacional de Ciencia y Tecnologia (CONACYT) in Mexico for supporting this work through funds for the projects INFRA-2016-01, Project No. 270665, and CB-2016-01, Project No. 287818.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Geotechnical Evaluation of Diesel Contaminated Clayey Soil
## 3 Results and Discussion
### Influence of Diesel Contamination on the Consistency Limits  
Table 5 provides the plastic characteristics of the soils. According to their Atterberg limits, both the natural and the contaminated soils are considered as very high and expansive ones. Despite the variations of the values of the Atterberg limits, both the natural and the contaminated soils were classified as silts of high compressibility. The natural soil was considered as a normal active clay, while contaminated soil was considered as a very active one. This increase in the activity of contaminated soil, in comparison with its activity at uncontaminated condition, was attributed to the physical and chemical effect of diesel on clay particles.  
After soil contamination, its liquid and plastic limits increased by 9% and 4%, respectively. It is known that the liquid behavior of soils is influenced by free water, which is not absorbed by the particles of soil and can freely move through soil pores. When diesel was added to the natural soil, it covered the surfaces of the clay particles, making the contact between the free water and the clay particles more difficult.  
Since diesel is a non-polar liquid, it could not allow that the water added during the test, which created a link with clay surface, to provide the required condition for clay to flow. When water was added to the contaminated soil to evaluate its Atterberg limits, it gradually displaced the diesel adhered to clay surface, allowing the interaction between clay and water. Thus, more water was needed in the contaminated soil to achieve its plastic state and, thus, higher plasticity was obtained.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Natural Soil** & **Contaminated Soil** \\ \hline Liquid limit (\%) & 77.3 \(\pm\) 0.4 & 83.9 \(\pm\) 1.9 \\ Plastic limit (\%) & 35.8 \(\pm\) 2.2 & 37.3 \(\pm\) 1.0 \\ Plasticity index (\%) & 41.5 \(\pm\) 2.5 & 46.7 \(\pm\) 1.5 \\ USCS classification & MH & MH \\ Average clay activity (\%) * & 1.03 & 2.14 \\ \hline \hline \end{tabular} Values in the table: mean value \(\pm\) standard deviation (\(n=5\)). * Calculated as: average plasticity index/average clay fraction.  
\end{table}
Table 5: Influence of diesel contamination in the consistency limits of soil.
## 4 Discussion  
### EOA Computation with MRI  
EOA computation of BMHV is largely performed clinically [11] using echocardiography, and investigation of the MRI as a complementary tool has been limited. However, MRI is considered the gold standard for heart function assessment [11], and if able to be accurately used for BMHV assessment, it may reduce the need for multiple exams using different imaging modalities. It is known that assessment of the BMHV by EOA continuity using echocardiography results in an overestimation of the EOA as this approach is not considering that the orifice of the valve is divided into three zones. Echocardiography is not inherently a 3D technique, and MRI provides a true 3D alternative for the computation of the EOA for BMHV. This work, through simplified Computational Fluid Dynamics modeling of BMHV, provides an important insight into the capacity of EOA\({}_{\text{AST}}\) as a means to provide a meaningful assessment of the valve, the influence of the plane, as well as design and patient specificity.  
No specific shape of this blurring effect could be expected according to the positioning of the valve (see Table 2), which limits the possibility of providing reference values dedicated to MRI. Moreover, resolution of the available 4D flow sequences will be an issue for such generalization of EOA\({}_{\text{AST}}\) computation.
## 3 Results and Discussion
### Outdoor Experimental Results
#### 3.2.1 Environmental Conditions  
Environmental parameters were monitored throughout the whole season to calculate net crop water requirements. The atmospheric temperature ranged from 15 \({}^{\circ}\)C to 32 \({}^{\circ}\)C. The relative humidity varied from 62% to 96% during the experimental season (Figure 12). Due to frequent rainfall, irrigation was carried out only four times, while at other times, sufficient rainfall was occurring to fulfill tomato plant water requirements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Irrigation System**} & \multicolumn{2}{c}{**Irrigation Distribution Uniformity**} \\ \cline{2-3}  & **Indoor** & **Outdoor** \\ \hline Subsurface drip irrigation & 0.88 & 0.72 \\ Surface drip irrigation & 0.92 & 0.82 \\ \hline
9000 & & \\
8000 & & \\
7000 & & \\
6000 & & \\
5000 & & \\
5  
Figure 12: Environmental parameters: (**a**) temperature, (**b**) humidity, and (**c**) rainfall during the tomato growing season.
## 5 Experimental Results and Analysis
### Simple Harmonic Excitation Analysis
Figure 8a-h plots the structural acceleration response peaks and RMS values at different locations. Figure 8: Structural responses of the original structure and vibration-damping structure under simple harmonic excitation various positions: (**a**) STMD peak acceleration; (**b**) STMD RMS acceleration; (**c**) DTMD peak acceleration; (**d**) DTMD RMS acceleration; (**e**) MTMD peak acceleration; (**f**) MTMD RMS acceleration; (**g**) MDTMD peak acceleration; and (**h**) MDTMD RMS acceleration. The results presented in Figure 8 show that the different arrangement positions of the same control strategy were different. Although the arrangement at the top of the structure can still have a certain control effect, the arrangement at the bottom of the vibration reduction effect was more obvious. The RMSA attenuation rate of the top layer increased from 31.59% to 47.56%, even with the addition of a single TMD. Therefore, for high-frequency vibration control of the structure, there is an optimal arrangement, and the vibration absorption performance should be based on reasonably arranged TMDs, optimized according to the structural response under the control mode. The vibration of the PSPP varies greatly between the various parts of the structure, and the TMD set on a single floor cannot control the complex vibration of the PSPP structure. Therefore, the operation mechanism of coordinated multi-point TMD array damping was studied by placing the TMD array system in multi-point space to optimize the vibration reduction performance. In the test, three strategies were considered: the traditional 4 MTMD system was formed by arranging the STMD on four corner posts at the top and bottom of the structure, and the space multi-point TMD array system was formed by arranging the STMD on the top and bottom corner posts on the same side. The cooperative vibration reduction performance of the structure and space multi-point TMD array was tested. As can be seen from Figure 9, the vibration control effect of the 4 MTMD-B group was the best, and the RMSA attenuation rate of the top layer of the structure was 80.73%. This behavior agreed with the observation in Figure 8 that the vibration response of the laid-out bottom was less than that of the laid-out top. It is due to the controller's position at the maximum controlled modal response, which absorbs the vibration response of the structure to the maximum extent and dissipates vibration energy to reduce the response of the structure. Combined with the controller groups arranged in the same layer with different vibration reduction strategies in Table 3, the vibration reduction effects differed little. Therefore, in the actual project they can be flexibly arranged according to the site, in order to achieve the best control effect.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Microsatellites
#### 3.3.2 MicroSAR (65 kg)  
The MicroSAR system is a proposal by Kongsberg Satellite Services and Space Norway, tailored for maritime monitoring. It is equipped with a C-band 800 W radar transmitter and an onboard automatic identification system (AIS) for vessel tracking [114]. The launch mass of MicroSAR is 65 kg including a 3.8 m \(\times\) 1.8 deployable reflector illustrated in Figure 19 (left). Figure 19 (right) provides a pictorial description of expected revisit times achieved by eight satellites in two orbit planes in the arctic region. Low-latency tasking and data delivery are possible via a network of twenty ground stations adapted for small satellites across continents and oceans including the Arctic Circle (Svalbard) and Antarctica (Troll Station). Although any MicroSAR satellite has not been launched yet, it represents an example of prioritizing the ground station infrastructures, which have been jointly utilized by Cosmo-SkyMe  
Figure 18: ICEYE series: (**left**) ICEYE-X1. (**middle**) ICEYE-X2, X4, X5. (**right**) ICEYE-X3 (Harbinger). Source: ICEYE, York Space Systems.  
and NovaSAR. As the demand for SAR data increase and small satellites play a bigger role therein, its market will be segmented into launch services (e.g., Electron rocket), ground stations (e.g., KSAT), data analytics (5. Discussion), and so forth dedicated to small satellites and/or spaceborne SAR.
## 4 Results and Discussion
### Capillary Porosity  
#### 4.2.1 Effect of Slag Content on Capillary Porosity  
The permeability of the specimen is one of the important factors affecting its efflorescence, and capillary porosity is an important indicator for evaluating the permeability of the specimen. Figure 8 shows the capillary porosity of RPSG with different slag contents. It can be seen from Figure 8 that the capillary porosity of RPSG decreases with the increase of slag content. The capillary porosity of the specimens RP-S0-M1.3N6, RP-S15-M1.3N6, RP-S30-M1.3N6, RP-S45-M1.3N6 are 0.33%, 0.24%, 0.18% and 0.13%, respectively. Compared with the specimens without slag, the capillary porosity of the specimens with 15 wt.%, 30 wt.% and 45 wt.% slag content decreased by 27.3%, 45.5% and 60.6%, respectively. This is because the incorporation of slag can introduce more active calcium components into the matrix, which significantly increases the content of active components, thereby promoting the formation of hydration products such as CSH, CASH and NASH. The generated hydration products can fill the pores of the RPSG and improve the density of the slurry, which in turn leads to a decrease in the capillary porosity of the specimen. In addition, unreacted slag can also fill the pores of the RPSG, improve the density of the slurry and reduce the capillary porosity of the slurry.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 4 Results and Evaluations
### The Power Consumption of the System  
The power consumption of the single-board computer was measured by running it for one hour. The power consumption of the satellite terminal was determined by a current clamp meter that probed the cable connecting the battery to the PWM charge controller. The single-board computer was  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Item** & **Price (USD)** \\ \hline Satcom Inmarsat BGAN 50-unit Prepaid SIM card & \$115 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The operation cost per 3 months of using a prepaid SIM card.  
Figure 4: The three-dimensional (3D) rendered enclosure for housing the solar controller, single-board computer, and DC boost converter.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Item** & **Price (USD)** \\ \hline Hughes 9201 INMARSAT BGAN Terminal & \$361 \\
100 W solar panels & \$37 \\ FGWX 12 V 60 AH li-ion battery & \$62 \\ Raspberry Pi 3 model B & \$35 \\ PWM solar controller with USB & \$4 \\ Enclosure & \$55 \\ \hline Total & \$504 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The capital cost of the access point.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Item** & **Price (USD)** \\ \hline Outfitter Satellite Inmarsat BGAN Standard PLUS & \$65 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The operation cost of using a monthly plan for under 20 MB of data usage.  
connected to the USB of the PWM charge controller through a USB tester, which was connected to the 720 Wh lithium-ion battery of the system to fully simulate the actual situation. The measurement results are shown in Table 6.