esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of Bicoidal Effect  
To determine the biocidal activity of the obtained CuO-NPs, 5 microbial strains were used: 2 Gram-negative bacteria (_Escherichia coli_, CECT 101 and _Pseudomonas fluorescens_, CECT 378), 2 Gram-positive bacteria (_Bacillus cereus_, CECT 131 and _Staphylococcus epidermidis_, CECT 4183) and a yeast, _Rhodotorula mucaliaginousa_ 1S1, isolated from wastewater in a previous work [19]. To determine the minimum inhibitory concentration (MIC) of the nanoparticles against the microorganisms studied, two types of tests were performed. The first was carried out in Petri dishes with Mueller-Hitton agar (MHA) nutrient medium that was contaminated with increasing concentrations of nanoparticles ranging from 3.9 to 2000 \(\upmu\)g/mL, using the serial dilution method and starting from a colloidal suspension of CuO-NPs in ultrapure water (UPW) of known concentration (4000 \(\upmu\)g/mL). The microorganisms grown for 24 h in liquid medium were inoculated in triplicate and stored at 27 \({}^{\circ}\)C along with a non-toxic positive control to record the growth of the microorganism and a negative control to verify that the medium was free of external contamination. The second type of biocidal test was based on that described in the Clinical and Laboratory Standards Institute (CLSI) standard method M07-A9 with some modifications and was performed in 96-well microplates. In this case, polyvinyl alcohol (PVA) was previously added to the ultrapure water at 10% of the weight of the nanoparticles and heated to 60 \({}^{\circ}\)C to facilitate their complete dissolution [20]. The aim was to achieve better dispersion of CuO-NPs in the final colloidal suspension to improve its biocidal activity. Serial dilutions were performed to obtain a range of concentrations between 7.8 and 4000 \(\upmu\)g/mL. Mueller-Hitton (MH) medium was used, and a positive control (100 \(\upmu\)L MH/100 \(\upmu\)L NaCl 0.1 M/100 \(\upmu\)L UPW-PVA 10%) containing PVA was included to rule out its possible biocidal effect, and a negative control (100 \(\upmu\)L MH/100 \(\upmu\)L NaCl 0.1 M/100 \(\upmu\)L UPW-PVA 10%) to rule out contamination of the original medium. Colloidal suspensions were prepared from a 24 h culture at 27 \({}^{\circ}\)C in sterile 0.1 M NaCl electrolyte adjusted to 0.5 on the McFarland scale, and 100 \(\upmu\)L were inoculated into each well tested along with 100 \(\upmu\)L of MH medium and 100 \(\upmu\)L of CuO-NPs colloidal suspension. Assays were performed in triplicate, and plates were incubated at 27 \({}^{\circ}\)C for 24 h to obtain growth kinetics in a BioTek Synergy HT microplate reader (Santa Clara, CA 95051, USA), where readings were taken every 30 min at 630 nm.
# Hybrid Deep Learning-Based Epidemic Prediction Framework of COVID-19: South Korea Case
## 2 Background and Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research Studies** & **COVID-19 Related Application** & **Characteristics** & **Issues** \\ \hline [PERSON] et al. [7] & - Forecasting and prediction of & - Suitable for short series & - Limited assumption is used; past patterns \\  & ten-days-ahead COVID-19 patterns & - Capturing a variety of trends and seasonal & continue to apply \\  & - Used method: exponential smoothing & forecasting patterns & - Limited assumption is used; past patterns \\ \hline [PERSON] et al. [10] & - Predict the overall infections, peak, and & - Accurate prediction of the total number of & - Importance of human mobility \\  & one-week pattern before occurrence & - Infected people, daily infection curve, and & - Ignorance of human mobility \\  & - Used method: Verthisation & infection peak & - Related parameters depend on the used \\ \hline [PERSON] et al. [11] & - Estimation of COVID-19 epidemiology & - The incubation period of COVID-19 is & - Related parameters depend on the used \\  & - Used method: SEIR compartment model & considered & - Dataset only \\ \hline [PERSON] et al. [12] & - Analysis of active cases and deaths per day & - Utilization of the main factors affecting the COVID-19 spread and effects of lockdowns & - Ignorance of human mobility \\  & - Used method: QR-statistical functional form & - Effectiveness of the ARIMA model compared to & - ARIMA does not support volatility or \\ \hline [PERSON] et al. [13] & - COVID-19 pattern prediction & - Effectiveness of the ARIMA model compared to & - ARIMA does not support volatility or \\  & - Used method: ARIMA & that of basic regression and exponential methods & in-between changes in the prediction periods \\ \hline [PERSON] et al. [14] & - Estimation of the COVID-19 epidemic trend & - Usage of a Markov chain Monte Carlo (MCMC) algorithm to obtain posterior estimates and & - Ignorance of asymptomatic and \\  & - Used method: extended parameters & parameters & - Unconfirmed cases \\ \hline [PERSON] et al. [15] & - Prediction of epidemic trend & - Prediction with physiological data to alert the & - Limitation of the used detection method for \\  & - Used method: framework based on the & infection outbreak in advance & calculating the physiological anomaly rate \\  & - heart rate and sleep data collected from & - \\  & wearable devices & - \\ \hline [PERSON] et al. [16] & - Prediction model for diagnosing COVID-19 & - Summary of several diagnostic and prognostic models for COVID-19 & - Rare analysis and research studies \\  & - Review of existing relevant results studies & models for COVID-19 & considering human mobility \\ \hline [PERSON] et al. [17] & - Analysis of up-to-date transmission and & - Consideration of non-pharmacogical & - Uncertain effectiveness of the long-term \\  & - outbreak trajectories & interventions (NPI) & implementation of NPI \\  & - Used method: multivariate & - \\  & prediction model & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research studies on COVID-19 transmission and prediction.
# Recent Advances in Research from Nanoparticle to Nano-Assembly: A Review
## 3 Stimuli Dependent Techniques
### Chemical Stimuli-Based Self-Assembly of Nanomaterials
#### 3.1.5 Gas-Induced Self-Assembly  
Since gaseous substances could be easily removed from the reaction system through sonication, purging, or heating, vapor-induced assembly is likely to be a unique stimulus in NPs SA. Here, the assembly and disassembly of NPs can be easily tuned by a gassing and degassing process. Amongst all other gases, CO\({}_{2}\) was an exceptionally desirable candidate in gas stimuli-induced SA because of its biocompatibility and reactivity with the surface ligands. Amino group-functionalized NPs show more responsiveness towards  
Figure 5: **(A)** Represents the illustration of metal ion coordination with ligand carboxylate group. This image was cited and reproduced from the reference [79]. (**B**) (top) schematic illustration of Ag NCs self-assembly into nanotubes when coordinated with Ba\({}^{2+}\). (**i**-**iv) (down) are the TEM images at various concentrations (20 mmol\(\cdot\)L\({}^{-1}\), 40 mmol\(\cdot\)L\({}^{-1}\), 60 mmol\(\cdot\)L\({}^{-1}\), 100 mmol\(\cdot\)L\({}^{-1}\)) of Ba\({}^{2+}\) ion. These images were cited and reproduced from the reference [80]. (**C**) (**i**,**ii) depict the TEM images of Ag NCs before and after the addition of Zn\({}^{2+}\). These images were cited and reproduced from the reference [81]. (**D**) represents the schematic of site-selective metal co-ordinate self-assembly process of Au NPs. This image was cited and reproduced from the reference [82].  
this kind of SA because of its indirect deprotonation/protonation with the addition of CO\({}_{2}\) or the direct formation of carbamates. In a typical example, [PERSON] et al. designed a sophisticated, one-step method for the synthesis of CO\({}_{2}\)-responsive iron oxide NPs in the presence of 1,8-diaminoocatne. About 8 nm of monodispersed iron oxide NPs were synthesized, and they exhibited low solubility in water (low zeta potential value). The system was purged with CO\({}_{2}\), which resulted in the partial protonation of the terminal NH\({}_{2}\) groups on the surface of NPs, which resulted in a large increase in zeta potential and a high colloidal stability in water, as shown in the Figure 6(ii) digital image. This was due to the terminal NH\({}^{3+}\) groups with efficient hydration and a electrostatic double-layer repulsion-like charge on the NPs. Purging of N\({}_{2}\) gas triggered the NPs re-assembly, and these eliminated the CO\({}_{2}\) and increased the system pH to its initial value. DLS is used to monitor the reversible disassembly or assembly process; this results in an average NP size wave between 500 nm and 30 nm upon purging with N\({}_{2}\) and CO\({}_{2}\), respectively [84]. Variation in the hydrodynamic size of NPs in the presence of CO\({}_{2}\) and N\({}_{2}\) are shown in Figure 6(iii). The introduction of CO\({}_{2}\) in the NPs not only increases the surface polarity but also triggers the assembly [85].
## 3. Freestanding Position-Based Routing Protocol
### Data Dissemination  
After the source node receives the destination geographic position, the data is sent towards the destination using the MFWAR mechanism. It is important to note that as the destination geographic position is included in the data packet header, every relay node can dynamically decide the next hop for the data packet. Furthermore, as every node has a digital map, the anchor-based approach [47,48] can be used where an intersection is found. In FPBR it is assumed that the IEEE 802.11a or the IEEE 802.11p RTS/CTS mechanism is disabled for data dissemination, this assumed to decrease the delay of the data packet and the overhead. Furthermore, implementing the RTS/CTS mechanism requires the transmission of several control packets before sending the actual data. A drawback of this strategy is that one or more of the RTS/CTS control packets could be lost because of the constraints imposed by the highway channel. This may lead to significant performance drops because at least 4 packets (RTS + CTS + DATA + ACK) must be sent to perform a single data packet transmission.
# Optimization Design of Centrifugal Fan Blades Based on Bezier Curve Method
## 2 The Experiment and Numerical Methodology of the Fan  
### The Model of the Voluteless Centrifugal Fan  
A three-dimensional model of our voluteless centrifugal fan is shown in Figure 1a. The main components were the inlet duct and the impeller. The impeller consisted of a shroud, backplate, and blades. The operating conditions and design parameters are shown in Table 1. In order to fully develop the fluid in the calculation domain and better fit the actual testing situation, a spherical inlet and outlet domain was used, and the diameter of the inlet and outlet domain was five times the impeller diameter, as shown in Figure 1b.
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 2 Optical CDMA with MUI Cancellation
### Conventional Liu's Scheme  
In the conventional Liu's scheme, group information is added to every codeword in MPSCs [21]. This scheme is sometimes called _padded MPC_[13]. The part indicating the group information requires \(q\) chips. If the assigned codeword belongs to the \(i\)th group, only the \(i\)th chip in that part is 1 and the other chips are 0. Therefore, the length of the spread sequence is \(q^{2}+q\) and the maximum number of users \(N_{\max}\) is \(q^{2}\). Table 2 also presents an example of the codeword assignment in [PERSON]'s scheme. Because this cancellation scheme is based on OOK signaling, the \(k\)th user's encoder outputs the codeword \(\mathbf{w}_{k,1}\) when \(I=1\), and it outputs the all-zero sequence when \(I=0\ (k=0,\ 1,\ \cdots,\ q^{2}-1)\).  
The block diagrams illustrated in Figure 2a,b are also applied to the encoder and decoder of [PERSON]'s scheme, respectively. For [PERSON]'s scheme, \(\mathbf{w}_{k,0}\) in the encoder is an all-zero sequence with a length of \(q^{2}+q\). At the \(k\)th user's decoder, Correlator 1 computes the correlation \(\Gamma_{1}\) between the received sequence \(\mathbf{r}\) and the sequence in which the first \(q^{2}\) chips are the same as those of \(\mathbf{w}_{k,1}\), and the last \(q\) chips are all zeros. In contrast, Correlator 0 computes the correlation \(\Gamma_{0}\) between \(\mathbf{r}\) and the sequence in which the last \(q\) chips are the inverted chips of those of \(\mathbf{w}_{k,1}\) and the first \(q^{2}\) chips are all zeros. For example, \(\Gamma_{1}\) and \(\Gamma_{0}\) at the fourth user's decoder are represented as follows:  
\[\Gamma_{1}=\Gamma(\mathbf{r},\ (1000\ 0100\ 0010\ 0001\ 0000)), \tag{8}\]  
\[\Gamma_{0}=\Gamma(\mathbf{r},\ (0000\ 0000\ 0000\ 0000\ 1011)), \tag{9}\]  
where \(\mathbf{w}_{4,1}=(1000\ 0100\ 0010\ 0001\ 0100)\). Then, the decoder calculates the difference \(\Gamma_{1}-\Gamma_{0}\). If the difference is greater than or equal to the threshold, the decision circuit outputs \(\hat{I}=1\). Otherwise, it outputs \(\hat{I}=0\). The ideal threshold is \(q/2\) when we suppose that the light intensity of each user's positive chip is 1. [PERSON]'s scheme cancels MUI completely at the decoder, if the receiver can estimate the received light intensity of a single positive chip correctly. Because \(\Delta(1)=q\) and \(\Delta(0)=0\) for an ideal link, the decision distance in [PERSON]'s scheme is \(q\).  
Table 3 compares the properties of the three MUI cancellation schemes using MPSCs from the viewpoints of the spread sequence length \(n\), the maximum number of users \(N_{\text{max}}\), and the decision distance \(d\).
# Calculation Method of Loose Pressure in Surrounding Rock Mass
## 5 Upper Bound Solution for Loose Pressure in the Surrounding Rock Mass
### Upper Boundary Solution for Loose Pressure for Extra-Large Section Tunnels
#### 5.1.2 Description of Failure Mechanism  
The basic shape of the loose zone obtained from the orthogonal test shows that the failure forms of the deeply buried tunnel include the tunnel vault subsidence and side wall shrinkage. Based on the determination of the failure zone, the mass of rock and soil in the failure mechanism is divided into rigid blocks that meet the conditions. The velocity and direction of each rigid block are obtained according to the geometric relationship and the sine theorem corresponding to the failure mechanism.  
In summary, the failure mechanism of a deeply buried tunnel is represented in Figure 12. The tunnel section is approximately rectangular, \(ABCD\), with height \(h\) and span \(B\). \(M\) and \(N\) are located at the midpoint of the tunnel vault and the midpoint of the loose zone boundary curve, respectively. \(O\) is located on the straight line \(MN\) and is the auxiliary point. \(H\) represents the burial depth of the cavity \(H_{1}\) represents the distance from \(O\) to the ground plane, and the support reaction forces of the tunnel arch and the side walls are the uniform loads \(q\) and \(e\), respectively.  
Owing to the symmetry of the failure mechanism, this study takes the left half of the failure mechanism for the corresponding description. \(AB\) is extended to intersect the boundary curve at point \(A_{m}\). \(\angle A_{m}ON\) is divided into \(m+1\) parts with angles for \(\beta_{0},\beta_{1},\ldots,\beta_{m}\). \(\angle AA_{m}C\) is divided into \(n\) parts with angles \(\beta_{m+1},\beta_{m+2},\ldots,\beta_{m+n}\). The intersections of each angle and the boundary of the loose zone are recorded as \(A_{0}\), \(A_{1},\ldots,A_{m},\ldots,A_{m+n+1}\). Connecting \(NA_{0},A_{0}A_{1},A_{1}A_{2},\ldots,A_{m}A_{m+1},\ldots,A_{m+n-1}C\), when \(m\) and \(n\) are large enough, the error between each connecting line and the boundary curve is therefore small enough.  
According to the associated flow rule, the velocity vector sum of the rigid slider is zero, and the angle between the relative velocity vector and the discontinuity line should be \(\varphi t\). Figure 13 shows the kinematically admissible velocity field corresponding to the failure mechanism. The velocity of block \(\Delta A_{0}ON\) is \(V_{0}\) in the vertically downward direction. The velocity of block \(\Delta AOM\) is \(V_{m+n+1}\) in the vertically downward direction. The velocities of the other blocks, from top to bottom, are \(V_{1},\ldots V_{m+n}\), and the relative velocities of each rigid slider on the discontinuity line are \(V_{0,1},\ldots,V_{m+n-1,m+n}\), \(V_{m+n,m+n+1}\). The shape of the blocks is determined by \(\alpha_{1},\ldots,\alpha_{m}\) and \(\beta_{1},\ldots,\beta_{m}\). With reference to [PERSON]'s theory and the [PERSON] theory, the shapes can be assumed to be \(\beta_{0}=\pi/4-\varphi^{\prime}/4\).  
Figure 12: Failure mechanisms.
## 2 Materials and Methods
### LC-MS/MS Analysis
#### 2.4.2 Instrumental Analysis  
An Agilent 1260 Infinity HPLC system combined with an Agilent 6460 TripleQuad mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) equipped with an electro-spray ionization interface (ESI) was used to quantify estrogens. Separation was achieved using Poroshell 120 EC-C18 (2.1 \(\times\) 100 mm; 2.7 \(\upmu\)m) fitted with a security guard column of the same packing material (Agilent Technologies, Santa Clara, CA, USA). Mobile phases A and B consisted of formic acid (7 mM) and acetonitrile, respectively. The solvent gradient program concerning mobile phase B was as follows: 0 min 50%; 0-10 min 100%; 10-11 min 100%; and 11-11.1 min 50%, followed by equilibration for 5 min. The flow rate was 350 mL/min, and the injection volume was 10 \(\upmu\)L. Mass data were acquired using MassHunter Workstation software (Agilent Technologies, Santa Clara, CA, USA) and multiple reaction monitoring (MRM) in the positive mode. The detector settings were capillary voltage 3500 V, nozzle voltage 2000 V, gas temperature (N\({}_{2}\)) 200 \({}^{\circ}\)C, gas flow 10 mL/min, nebulizer 50 psi, sheath gas temperature (N\({}_{2}\)) 350 \({}^{\circ}\)C and sheath gas flow 10 mL/min. The precursor and product ions, collision energy and fragmentor voltage used for MRM are summarized in Table 2.
## 6 Investigating Several RL-Based Routing Methods
### ECTS  
[PERSON] et al. in [62] have suggested the efficient charging information transmission strategy (ECTS) in hybrid vehicular networks. Large-scale hybrid vehicular networks include a large number of electric vehicles, which must exchange a large volume of charging information. ECTS is a scalable and efficient routing framework that can greatly reduce communication overhead. In ECTS, an accurate mathematical model is designed to estimate the probability of local connectivity in the two-line road segments. This model improves the performance of the routing process in ECTS. Then, an efficient routing algorithm is presented to transfer the charging information between the server and the electric vehicle in the VANET environment. In ECTS, each electric vehicle can achieve its location and the server position using a digital map and GPS. In addition, an immobile node such as RSU is located at each intersection. It is responsible for storing the routing information and receiving the density and location of vehicles at each road segment. This routing approach tries to choose the most suitable intersection-based path with maximum connectivity. This issue is solved using a Q-learning algorithm. When transferring data between the source vehicle and the server, the first step is  
Figure 16: Learning structure in Q-LBR.  
to determine source and destination intersections. These intersections are determined with regard to movement direction and curvilinear distance between the desired node and the intersection. Now, the source vehicle transfers its charging data to the source intersection. Then, the source intersection runs a Q-learning-based route discovery operation. In this operation, the state space consists of all intersections in the network, and the set of actions includes the selection of the latter intersection. Figure 17 shows the learning structure in ECTS. When selecting the road segment with high connectivity, the discount parameter is dynamically adjusted to deliver charging data efficiently and maintain a tradeoff between the future and immediate rewards. In ECTS, the size of the state and action sets is limited. Therefore, this algorithm has an acceptable convergence speed.
## 5 Conclusions  
In this article, we used the 2021-2022 time-series NDVI image that was generated from PlanetScope satellite images to obtain LUCC maps of the study area. PlanetScope satellite products have the advantages of high temporal and spatial resolution, which makes generated NDVI data have a good interpretation for classification. By comparing the classification accuracy of the MLC method, the evaluation metrics demonstrate that time-series NDVI is far better than single NDVI and mean NDVI as basic data for land-cover classification under complex environments. Meanwhile, editing samples based on MLC results can also be an efficient way to train a deep-learning model. The other method of LUCC detection is the U-Net image-segmentation model. Through confusion matrix analysis, it was found that U-Net is better than MLC and can improve the situation of excessive classification to changed land, which is significant in LUCC detection. However, the classification of small samples, such as tidal flats, needs to be improved.  
The results show that the process framework is an efficient and reliable method for information extraction in LUCC. In the future, the application and research of the PlanetScope satellite data will continue to increase; the ability of the deep-learning model to be transferred to other regions also makes it preferred by remote-sensing application researchers.  
**Author Contributions:** Conceptualization and methodology, Z.C., J.L.; data collection, J.W., M.Y., J.L., L.Z.; data process, J.W., M.Y.; data analysis, J.W., M.Y., L.Z.; writing--original draft preparation, J.W., M.Y.; writing--review and editing, Z.C., J.L.; visualization, J.W., M.Y.; supervision, J.L.; funding acquisition: Z.C., J.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key Research and Development Program of China (2018 YFC1506506); the Frontier Project of Applied Foundation of Wuhan, China (2019020701011502); the Key Research and Development Program of Jiangxi Province, China (20201 BBG71002); and the LIESMARS Special Research Funding.  
Not applicable.  
**Data Availability Statement:** PlanetScope satellite data are available at [[https://www.planet.com](https://www.planet.com)]([https://www.planet.com](https://www.planet.com)) (accessed on 1 July 2022).  
We acknowledge data support from [[https://www.planet.com](https://www.planet.com)]([https://www.planet.com](https://www.planet.com)) (accessed on 1 July 2022). All authors are sincerely grateful to anonymous reviewers and editors for their insightful comments.  
The authors declare no conflict of interest.