esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Performance Analysis of the Proposed Method
According to the introduction of Section 4.1, for the reconstruction results, a higher CP means more good points in the reconstructed 3D point cloud. Here we define the reconstructed 3D points with an altitude error of less than 1m as good points (since the nadir resolution of the satellite image is 0.3 m, it is reasonable to set the error threshold at the 1m). The RMSE and ME metrics both represent the reconstruction accuracy of the 3D point cloud, and the RMSE finds the root-mean-square error of the altitude of all the reconstruction 3D points, including some 3D points with very large errors, but this kind of 3D points may be the reconstruction points that we are not concerned about, such as the altitude of trees. Besides RMSE, this paper also uses ME to represent the median error of the reconstructed 3D point cloud altitude, and ME can be understood as the altitude error of the first 50% of the 3D points with the best reconstruction altitude accuracy. Obviously, we hope the RMSE and ME of the reconstructed 3D point cloud are as low as possible. Figure 6: The ground truth DSMs and the reconstructed DSMs of the S2P method [7], the BA method [18], and the proposed method. Figure 7: The details of the reconstructed results of Site 1. As shown in Table 1, the proposed method has the highest CP in the reconstruction results of most sites, and the CP of the proposed method is also close to the best CP for Site 7. In addition, except for Site 4, Site 7, and Site 8, the CP of the proposed method is more than 65%, indicating that the 3D point cloud reconstructed by our method has good completeness. The slightly worse CP of the reconstructed results for Site 4, Site 7, and Site 8 indicates bad 3D reconstruction from satellite images, which is also verified in Figure 6. In terms of accuracy, the proposed method can achieve an RMSE of less than 5.0 m and an ME of less than 0.6 m for the reconstruction results of most sites (more than five sites), which means that the proposed method can generate 3D reconstructed point clouds with high accuracy.
## 2 Materials and Methods
### Anticancer Studies of Eugenol and Isoeugenol  
_Cell cultures_  
The experimental work was carried out using human colon cancer cells of the LoVo line (ATCC) and the human glioma-like line U-87 MG (Sigma-Aldrich, Poznan, Poland). The cells were cultured in F12K medium (Genos) for LoVo and EMEM (_Eagle's Minimum Essential Medium_) (Sigma-Aldrich, Poznan, Poland) for U87 MG. The media contained FBS (_Fetal Bovine Serum_) serum (10%, Sigma-Aldrich, Poznan, Poland), glutamine (2 mM, Sigma-Aldrich, Poznan, Poland), amino acids (1% NEA, Sigma-Aldrich), sodium pyruvate (1 mM, Sigma-Aldrich, Poznan, Poland), and antibiotics (penicillin [100 IU/mL] and streptomycin [10 \(\mu\)g/mL], Sigma-Aldrich, Poznan, Poland). The culture was carried out under optimal conditions (37 \({}^{\circ}\)C, in an atmosphere of 5%CO\({}_{2}\), with 95% humidity).  
_Cell viability test (MTT method)_  
Due to the negligible solubility of eugenol and isoeugenol in aqueous media, a starting solution using dimethylsulfoxide solvent (DMSO p.a., Sigma Aldrich, Poznan, Poland) was used to prepare intermediate solutions [7].  
The cytostatic/cytotoxic properties of eugenol and isoeugenol were tested by the determination of the percentage of the growth inhibition of cells treated with a specific concentration of each compound relative to control systems [40]. Incubation was conducted for 72 h, after which 20 \(\mu\)L of fresh MTT solution (2.5 mg/mL) was added to each sample [20]. Incubation with MTT was carried out for 3 h in an incubator under the optimal conditions [21]. Then, after removing the medium, the resulting formazan crystals were dissolved by adding 200 \(\mu\)L each of solvent (DMSO, Sigma Aldrich, Poznan, Poland) [8]. Spectrophotometric readings were taken using a UV-M340 absorption microplate reader (Asys Hitach, East Lyme, CT, USA) with the MicroWin 2000 software [29]. The results were exported to Microsoft Excel.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Concentration** & **0.5\%** & **1.5\%** & **2.5\%** \\ \hline  & mg/mL & mg/mL & mg/mL \\ \hline
**Eugenol (EU)** & 5 & 15 & 25 \\ \hline
**Izo-eugenol (IZO-EU)** & 5 & 15 & 25 \\ \hline \end{tabular}
\end{table}
Table 1: The concentrations of eugenol and isoeugenol expressed in mg/mL.  
Statistical analysis of the results obtained was carried out using the Statistica 13.3 software from TIBCO [12]. After examining the distribution of the data, which deviated from a normal distribution, nonparametric tests were used in further analyses [17]. The Wilcoxon test (*--\(p\leq 0.05\); **--\(p\leq 0.01\); ***--\(p\leq 0.001\)) was used to test for differences within a given group, between individual compounds at a given concentration [9].
## 6 Conclusions
**Funding:** This research was funded by the Key Research Project of Frontier Science of Chinese Academy of Sciences (QYZDY-SSW-DQC027), the National Key R&D Program of China (2018 YFC1506605), the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, 2019 QZKK0604), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA17010103). **Data Availability Statement:** The Terra/MODIS MOD06 cloud products were collected from the official NASA Goddard MODIS website ([[https://modis.gsfc.nasa.gov/data/dataprod/mod06.php](https://modis.gsfc.nasa.gov/data/dataprod/mod06.php)]([https://modis.gsfc.nasa.gov/data/dataprod/mod06.php](https://modis.gsfc.nasa.gov/data/dataprod/mod06.php)) (accessed on 29 January 2020)). The Fengyun-4A/AGRI cloud products were collected from the official Fengyun Satellite Data Center of NSMC/CMA ([[http://satellite.nsmc.org.cn/portalsite/default.aspx?currentculture=en-US](http://satellite.nsmc.org.cn/portalsite/default.aspx?currentculture=en-US)]([http://satellite.nsmc.org.cn/portalsite/default.aspx?currentculture=en-US](http://satellite.nsmc.org.cn/portalsite/default.aspx?currentculture=en-US)) (accessed on 29 January 2020)). **Acknowledgments:** The authors are grateful to [PERSON] from Environment and Climate Change of Canada for his very helpful suggestions. We would also like to thank [PERSON] from Sun Yat-Sen University for useful discussions. The authors would like to acknowledge NASA and NSMC/CMA for freely providing the MODIS and AGRI cloud products online. **Conflicts of Interest:** The authors declare no conflict of interest.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 5 Summary
Panoply is available for the most commonly used computing platforms (e.g., Windows, Mac). Several examples in the tropics are presented, using the data and tools introduced in this article. Most maps were obtained from Giovanni including time-averaged and time-accumulated maps. Users can obtain monthly or seasonal averages of variables for a quick look at their climatology.
# Practical Application of Mesh Opportunistic Networks
## 4 Results
### Communication Flow among Participants
#### 4.3.1 Communications, Locations and Pins  
The results regarding the communications, locations and pins maintained through the eight goTenna (GT) devices used are presented below. Communications involved text messages in the common chat, which involved the communication of information among the users. Locations were shared by each participant by indicating their GPS coordinates, coordinates that were automatically placed on an internal map in the mobile app. These locations enable the members of the team to indicate where they are to the rest of the team and/or the coordinator. When the map is accessed, the location of a user and the device's own location appears, making it possible to see how far you are from a specific participant. Finally, pins are fixed so that a user can establish stable locations of interest that can be placed on the map (other than their own coordinates), e.g., meeting points, points with Wi-Fi connections, points to go to cover information, etc.  
The weight of the different communication modalities (text, location, and pins) among all the terminals (GT01 to GT08) was represented (Figures 4 and 5). 60.9% of text communications were carried out by a single terminal (terminal GT07), one of the authors of this study. This participant was the coordinator of the pilot session and therefore, he was responsible for the communications in general. This user was in charge of giving instructions to the rest of the participants, requesting locations or pins, and coordinating their participation. From the communications of the rest of the users, it was notable that different users communicated distinctly. For example, while terminal GT04 made 12.5% of the communications, terminal GT05 barely made 1.6% of them. Moreover, terminal GT08 did not participate in the communication flow, as this was a professional from RTVE who was monitoring the session externally and who decided not to actively participate by sending text communications to the rest of the group. Regarding the locations, we see that a total of 25 were established during the experiment and again, the coordinator (GT07) was the most active user with 9 of these (36%). The rest of the participants also sent their location during the experiment, except for GT05 who, as seen previously, did not participate significantly and did not share the location at any time. Finally, with regard to the pins, these were established on two occasions, in both instances the pins were placed on the map by the coordinator (GT07) to indicate the start point and the arrival point during the experiment.
# An SQP Algorithm for Structural Topology Optimization Based on Majorization-Minimization Method
## 4 Numerical Examples
### MBB Beam
#### 4.1.2 Convergence Rate  
Figure 4a shows the convergence curves of the compliance under different methods and parameters. For the QPOC method, when the curvature parameter decreased, the corresponding purple (\(q=3\)), red (\(q=2\)), and black (\(q=1\)) convergence curves were below the previous curve, and the same phenomenon was also observed for the IPC method. This indicates that the curvature parameter was negatively related to the convergence rate. However, this does not mean that the curvature parameter can be arbitrarily small; otherwise, it may lead to the surrogate function being smaller than the original function resulting in non-convergence of the algorithm.  
Figure 4b illustrates that the curvature parameter and the average iteration step of the design variables were negatively correlated, and a larger average iteration step could enhance its capability to find the extreme points and improve the convergence rate. It is worth noting that Figure 4b also indirectly illustrates that the selection of the filter radius in the IPC method was positively correlated with the average iteration step of the design variables.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{\(q\)} & **Figure 3** & **Radius \(r\)** & \multicolumn{1}{c}{**Iterations**} & **Compliance \(M_{\text{nd}}\) (\%)** & \multicolumn{1}{c}{**Time Cost**} & \multicolumn{1}{c}{**Per\_Time**} \\  & & & & & & & & **(s)** & **Cost (s)** \\ \hline OC & None & a & 4.8 & 0.5 & 93 & 219.2 & 26.5 & 4.0 & 0.04 \\ \hline \multirow{4}{*}{QPOC} & 1 & b & 4.8 & 0.5 & 101 & 219.2 & 26.5 & 4.2 & 0.04 \\  & 1.5 & None & 4.8 & 0.5 & 72 & 219.5 & 26.8 & 3.1 & 0.04 \\  & 2 & c & 4.8 & 0.5 & 65 & 220.5 & 27.3 & 2.8 & 0.04 \\  & 3 & d & 4.8 & 0.5 & 64 & 222.4 & 28.4 & 2.7 & 0.04 \\ \hline \multirow{4}{*}{IPC} & 1 & e & 4.8 & 0.5 & 200 & 209.0 & 15.2 & 49.2 & 0.25 \\  & 1.5 & f & 3.6 & 0.5 & 200 & 202.2 & 11.7 & 41.2 & 0.21 \\ \cline{1-1}  & 2 & g & 4.8 & 0.5 & 200 & 211.4 & 18.0 & 38.7 & 0.19 \\ \cline{1-1}  & 2 & h & 2.4 & 0.5 & 137 & 199.1 & 8.7 & 27.0 & 0.20 \\ \cline{1-1}  & 2 & i & 2 & 0.5 & 200 & 196.1 & 8.5 & 42.9 & 0.21 \\ \cline{1-1}  & 3 & None & 2.2 & 0.5 & 142 & 201.4 & 9.6 & 29.0 & 0.20 \\ \hline \hline \end{tabular}  
* \(f^{*}\) is the volume fraction of the final optimized structure.  
\end{table}
Table 1: Optimization data for different methods and parameters.  
Overall, the IPC (\(q=1\)) method had the fastest convergence rate, and the OC and QPOC (\(q=1\)) converged at about the same rate. The compliance of all the methods rapidly decreased in the first few steps, and then the rate of decrease slowed down, with the compliance of all methods reaching convergence in fewer than 60 steps.
# Model Modification of the Soil-Water Characteristic Curve of Unsaturated Weak Expansive Soil
## 3 Test Process
### Filter Paper Method Test  
The paper filtration method followed the principle of thermodynamic equilibrium. The contact test method realized the equilibrium measurement of material suction through the water transfer between the filter paper and the soil. The non-contact test method measured the total suction through the balance between the filter paper and the water in the air. The test schematic is shown in Figure 3.  
The filter paper method test adopted the domestic \"double circle\" brand NO. 203-type slow filter paper to measure the matric suction. The test steps were as follows: The filter paper was dried in the oven for 12 h and set aside. The soil sample was finely milled, passed through a 2 mm geo sieve, and then dried for 8 h in an oven at a constant temperature of 105 \({}^{\circ}\)C. Using a ring knife sampler and a hydraulic jack, five sets of standard ring knife samples with a diameter of 61.8 mm and a height of 20 mm were prepared. The samples had dry densities of 1.4 g\(\cdot\)cm\({}^{-3}\), 1.5 g\(\cdot\)cm\({}^{-3}\), 1.6 g\(\cdot\)cm\({}^{-3}\), 1.7 g\(\cdot\)cm\({}^{-3}\), and 1.8 g\(\cdot\)cm\({}^{-3}\), with an initial moisture content of 4% and a gradient of 2%. A total of 10 groups of soil samples, with different moisture content, were prepared. An additional 2 groups of parallel specimens were prepared separately.  
A circular sealing box was used as a test container. Notably, 3 pieces of filter paper were placed at the bottom of the box, with those on both sides acting as protective material. The protective filter paper had a slightly larger diameter than that of the ring knife. The middle filter paper was the test filter paper, and its diameter was the same as the diameter of the ring knife.  
The matching sealing gasket was used for sealing treatment. It was placed in a test chamber maintained at a constant temperature of 25-27 \({}^{\circ}\)C for 10 days.  
Figure 3: Profile of the principle of the filter paper test: 1 and 7 are filter paper, of which 1 is non-contact filter paper and 7 is contact filter paper; 2 is the sealing device; 3 is the gaseous migration of water molecules in the air; 4 is the air in the sealing device; 5 is the water molecule in the soil sample; 6 is the sample soil sample.  
An electronic balance with an accuracy of 0.0001 g was used to measure the moisture content of filter paper quickly. Based on the filter paper method test, the soil-water characteristic curves of expansive soils with different densities in the high suction range (0.5-40 MPa) were obtained.
## 2 Analysis of the Operating Mechanism of a Hybrid Plant Station Primary Frequency Regulation
A hybrid plant station should release energy equal to that of the synchronous generator in order to guarantee that the inertia response effect is the same as that of the synchronous generator, as indicated by Equation (2):  
\[\Delta E_{\rm W}=\Delta E_{\rm k\_max}=P_{\rm W}\times\Delta t=\frac{2500-f_{1} ^{2}}{5000}P_{\rm N}T_{\rm J} \tag{2}\]  
In the equation, \(P_{\rm N}\) is the rated power of the wind turbine; and \(T_{\rm J}\) is the inertia time constant of the synchronous generator. Assuming that the time to complete the inertia response of a hybrid plant station is equivalent to that of the synchronous generator, that is, \(T_{\rm J}=\Delta t\), then the compensation power required by a hybrid plant station in the process (1) as shown in Equation (3):  
\[P_{1}=\frac{2500-f_{\rm a}^{2}}{5000}P_{\rm N} \tag{3}\]  
In the equation, \(f_{\rm a}\) is the system frequency at the beginning of a hybrid plant station's simulated drop characteristic. (2) The system frequency is regulated by the inertia response and drops characteristics of the simulated synchronous generators of a hybrid plant station when the system frequency lowers from \(f_{\rm a}\) to the lowest point \(f_{\rm b}\). The synchronous generator's power frequency characteristic curve indicates that when the system frequency falls to \(f_{1}\), the drop characteristic provides a power supply, as indicated by Equation (4):  
\[P_{\rm V}=K\times(50-f_{1}) \tag{4}\]  
In the equation, \(K\) is the drop coefficient of the generator. Therefore, the power required by a hybrid plant station in the process (2), as shown in Equation (5),  
\[P_{2}=\frac{f_{\rm a}^{2}-f_{\rm b}^{2}}{5000}P_{\rm N}+K\times(f_{\rm a}-f_{ \rm b}) \tag{5}\]  
(3) Damping the system's frequency change represents the synchronous generator's inertia response process. Thus, as demonstrated by Equation (6), a hybrid plant station only simulates the drop characteristic of the synchronous generator and the needed power as the system frequency rises from the lowest point \(f_{\rm b}\). \[P_{3}=K\times(f_{\rm c}-f_{\rm b}) \tag{6}\]  
In the equation, \(f_{\rm c}\) is the system frequency when a hybrid plant station completes the primary frequency regulation. In summary, when the hybrid plant station's simulated synchronous generator engages in primary frequency regulation, the total power that must be adjusted in the process is displayed in Equation (7):  
\[P_{\rm WSP}=\frac{2500-f_{\rm b}^{2}}{5000}P_{\rm N}+K\times(f_{\rm a}+f_{\rm c }-2f_{\rm b}) \tag{7}\]  
As shown in Equation (7), the compensation power required by a hybrid plant station when the system frequency drops is \(P_{\rm WSP}\), and the electric hydrogen production load reduction and the power release of the energy storage device respond to the frequency regulation power. Therefore, the dynamic characteristics of each unit of the hybrid power plant are analyzed below. Model Establishment and Dynamic Characteristics Analysis of the Wind-Energy Storage-Hydrogen System
## 6 Simulation Results
### Simulation Setup  
The experimental evaluations are performed on MATLAB environment using the Monte-Carlo method. The overall cost, in which safety performance, average route length and energy efficiency performance are involved, is set as the criteria.  
The simulated area has a dimension of 500 m \(\times\) 500 m, while the radio range of each node inside is set as 250 m. By taking the nominated WSN applications in this paper into consideration, IEEE 802.15.4 is adopted for the physical and data link layer, as it is initially designed for applications with low data rate but very long battery life [56]. In addition, CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) is adopted as a Media Access Control (MAC) protocol, as defined by 802.15.4 standards. Based on specifications addressed in [56], the traffic type is set as CBR (constant bit rate) at a data rate of 20 Kbps, and the length of each packet is set as 127 Bytes. Since the prediction of transmission cost is partly dependent on previous research [36], the same values of those parameters required for the prediction process are retained as previously adopted in [36]. For more details, see Table 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Descriptions** \\ \hline Simulation Area & 500 m \(\times\) 500 m \\ \hline Node Radio Range & 250 m \\ \hline Traffic Type & CBR \\ \hline Packet Size & 127 bytes \\ \hline Data Rate & 20 kbps \\ \hline SNR Threshold \(\beta\) & 10 dB \\ \hline Processing Power Level \(P_{c}\) & \(10^{-4}\) W \\ \hline Receiving Power Level \(P_{r}\) & \(5\times 10^{-5}\) W \\ \hline Outage Requirement \(\mathcal{P}^{*}_{out}(i,m)\) & 0.01\% \\ \hline Variance of AWGN & \(10^{-9}\) W/Hz \\ \hline Path-loss Exponent & 2.33 \\ \hline Maximum Output Power of Solar Cell on & \(3.75\times 10^{-3}\) W \\ Sensor Nodes & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation parameters.  
In every simulation, regular nodes are assumed to be mixed up with a certain fraction of malicious nodes. These compromised nodes are randomly placed in the simulation area, and they have certain preset behaviors that may further impact the route discovery process.  
The destination node is set as stationary, which suits the scenario in surveillance applications (such as enemy detection or environment monitoring). The engineer stays at a fixed site where the WSN is deployed and gathers data from the nodes. Nodes number varies from 60 to 200.
###### Abstract  
This paper proposes an _M_-ary amplitude shift keying (MASK) power and information synchronous transmission system based on phase-shifted full-bridge (PSFB) for applications in wireless power transmission (WPT). The Pulse Width Modulation (PWM) waveform uses different phases to control the MOSFET in the full-bridge inverter for MASK modulation. The inverter voltage generates \(M\) amplitude transformation, forming a comprehensive power information flow. The demodulation circuit processes the information transmitted to the secondary side, following the power supply with a differential amplifier, to realize synchronous transmission of power supply and information. Compared with conventional amplitude modulation, the system's volume is significantly reduced, and the DC-DC modulation circuit has no filtering effect. It transmits comprehensive high-level data and improves the information transmission rate from the perspective of bit width. In the experiment, 16-bit width data are transmitted, and the bit rate is increased by four times compared to conventional amplitude modulation. Combined with DSP, the designed demodulation circuit reduces the voltage amplitude fluctuation at the receiving end to 5% and minimizes the impact of amplitude modulation voltage fluctuation on the system.  
wireless power transmission (WPT); information transmission; synchronous; phase-shifted full-bridge (PSFB); _M_-ary amplitude shift keying (MASK)