esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Waveform from Different GPS Satellites  
With the purpose of understanding how the reflected signal varies with the change of soil moisture and GNSS satellite elevation, some raw data from the GPS in-view satellites PRN 02, PRN05, PRN06 and PRN 10 (Figure 6) were selected for the days 8 and 9, September 2014. Soil moisture was constant at 0.1 m\({}^{3}\)/m\({}^{3}\) until 21:00 UTC on 9 th September, when 11 mm precipitation occurred and soil moisture raised to 0.22 m\({}^{3}\)/m\({}^{3}\). Figure 6 then shows the GPS satellites skylplot of the MELBEX ELBARA-II site at 22:00 UTC on 9 th September 2014.  
Figure 6: Skyplot at 22:00 UTC on 9 th September, 2014 showing the in-view GPS satellites available and the respective elevation angles.  
Figure 7 shows the correlation power of direct and LHCP/RHCP-reflected signals of four GPS satellites on two different days with different soil moisture. It also includes the satellite elevations and incidence angles to antenna (the angle between the incident signal and the antenna boresight). According to Figure 7, the correlation power of direct signals (D) were considered to be very stable because they only depend on the antenna gain pattern. In contrast, the correlation power of LHCP-reflected signals (RI) were significantly affected by soil moisture. In Figure 7b,d,f,h, the correlation power of RHCP-reflected signals (Rr) also increase with rising soil moisture, but less obviously. It is worth noting that the signals with incidence angle larger than 45\({}^{\circ}\)-50\({}^{\circ}\), for both direct and reflected signals have very low correlation power, lower than 0.015. This might be caused by antenna multipath reduction and, therefore, all direct signal data with correlation power lower than 0.015 were discarded during data processing.  
Figure 7: _Cont._
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 2 Materials and Methods  
### Cygnss Data  
#### 2.1.1 CYGNSS Mission  
CYGNSS is a NASA Earth Venture mission consisting of eight small satellites that receive direct and reflected signals from GPS satellites. CYGNSS is designed to observe the wind speed on the ocean surface during a hurricane and, therefore, it scans the tropical orbit at an inclination of 35 degrees, covering only 38\({}^{\circ}\) South latitude to 38\({}^{\circ}\) North latitude, and its receiver and software are optimized for ocean surface remote sensing [27]. Each CYGNSS satellite carries two downward antennas and a GNSS-R receiver, which can simultaneously provide GNSS-R observations from four different GPS satellites. CYGNSS has announced marine and terrestrial products, and has provided free data access and download services since March 2017 at [[https://podaac-opendap.jpl.nasa.gov](https://podaac-opendap.jpl.nasa.gov)]([https://podaac-opendap.jpl.nasa.gov](https://podaac-opendap.jpl.nasa.gov)), (accessed on 30 July 2021). CYGNSS data products are divided into three levels. The Level 1 (L1) dataset contains surface bistatic radar cross section (BRCS) measurements; the Level 2 (L2) dataset includes derived spatial average wind speed and mean square slope (MSS); and the Level 3 (L3) dataset is gridded wind speed. The product has a time resolution of 1 h and a spatial resolution of 0.2\({}^{\circ}\)\(\times\) 0.2\({}^{\circ}\). Since the data interval of CYGNSS is 1 s, 32 sets of measurements can be obtained simultaneously per second. And the CYGNSS constellation composed of eight small satellites can provide a relatively short revisit time so that it can provide observations with high temporal and spatial resolution.  
This paper uses CYGNSS Level 1 ScienceData products (Version 3.0.), and the data period is from 19 July to 27 July 2021. Compared with the previous data (Version 2.1.), this version re-estimates GPS EIRP (Effective Isotropic Radiated Power) by using direct signal power and antenna gain assessed by the CYGNSS time delay doppler measuring instrument [28].
# Data-Driven Redundant Transform Based on Parseval Frames
## 4 Image Denoising
### Robustness Analysis  
In this subsection, we illustrate the robustness of DRTPF by training DRTPF using the image 'Barbara' and testing DRTPF for denoising the same image with Gaussian white noise added. The noise level (standard deviation) \(\delta\) ranged from 20 to 60 with a step size of 2. In the experiment, the frames \(\mathbf{\Phi}\) and \(\mathbf{\Psi}\) of size \(100\times 200\) were initialized as 1D overcomplete DCT (ODCT) and \(10\times 10\) overlapping mean-subtracted patches were used. The patch size was set as \(8\times 8\) with stripe 1. We set the parameters \(\eta_{1}=1.1\) and \(\eta_{3}=1e+7\), and \(\eta_{2}\) was replaced by the \(\ell_{0}\) thresholding \(0.6\sigma\) (i.e., \(\|\mathbf{Y}\|_{0}\leq 0.6\sigma\)). For comparison, our proposed algorithm was compared with K-SVD [14]. The size of dictionary learnt from K-SVD is \(8\times 256\) at its optimal state, according to the previous work.  
We show the denoising result in Figure 1, from which it is apparent that with higher noise, our DRTPF method outperformed K-SVD more and more. In other words, our proposed model has good robustness. In fact, in our model, the sparse coefficients are calculated accurately by the inner product of the signals and the frame \(\mathbf{\Psi}\), and are limited to a certain range. Theoretically, it should be more robust. The learnt transforms \(\mathbf{\Phi}\) and \(\mathbf{\Psi}\) are illustrated in Figure 2. These figures show that our frame learning method can capture the features in both analysis and synthesis ways. Figure 3 shows two exemplified visual results on the images 'Babara' at noise level \(\sigma=30\) and \(\sigma=50\). From Figure 3 we know that our proposed DRTPF can obtain more clearer features than K-SVD [14].  
Figure 1: Robustness Analysis. DRTPF is trained and tested using the image ’Barbara’. The \(X\)-label is the noise level \(\delta\) and the \(Y\)-label is the PSNR. It can be seen that DRTPF performs more robustly than K-SVD.  
Figure 2: The learnt operators \(\mathbf{\Phi}\) (**left**) and \(\mathbf{\Psi}\) (**right**) for _barbara_.
## 5 Conclusions  
Multi-angle tower-based optical spectra and flux datasets acquired during the period from April to September 2013 were used to study the ability of PRI to act as a proxy of LUE in a sub-tropic planted coniferous forest stand in Southern China. The following conclusions could be drawn from this study.  
1. Both half-hourly PRI and LUE decreased with increases of VPD, Ta, and PAR. LUE is more sensitive to changes of these bioclimatic factors than PRI. Significantly positive diurnal correlations between PRI and LUE were mostly found on clear or partially cloudy days.
2. Significant logarithmic relationships were found between LUE and PRI at both half-hourly and daily scales across the study period. Correlations of PRI with LUE varied dramatically throughout the growing season. The correlation was the strongest (\(R^{2}\) = 0.6427, \(p\) < 0.001) in July and the poorest in May.
3. The ability of PRI to track LUE varied with bioclimatic factors. Generally, the effectiveness of PRI in indicating diurnal change of LUE increased with the increases of VPD, Ta, and PAR. As to the entire study period, PRI is more effective in detecting the changes of LUE under clear or partially cloudy skies (CI > 0.3) with moderate to high VPD (>20 hPa) and high temperatures (>31 \({}^{\circ}\)C).
4. Overall, we found that PRI is most sensitive to variations in LUE under stressed conditions, and the sensitivity decreases as the growing conditions become favorable when atmosphere water vapor, temperature and soil moisture are near the optimum conditions.  
This work was supported by the National Natural Science Foundation of China (41271352 and 41371070), the Special Climate Change Fund under Grant (CCSF201412), Jiangsu Graduate Innovation Program (CXZZ12_0041), China Postdoctoral Science Foundation under Grant (2015T80148), National Science Foundation of China under Grant (41401418/D0106). We gratefully acknowledge the support from the Qianyanzhou station of Chinese Academy of Sciences for providing flux and meteorology datasets. We further acknowledge [PERSON] (CSIRO, Australia) and [PERSON] (University of Technology Sydney, Australia) for their valuable discussion on photosynthetic mechanism. Many thanks to three anonymous reviewers who provided helpful comments to the improvement of the manuscript.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] designed the research. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] processed spectral and flux observations, and data preparation. [PERSON], [PERSON], and [PERSON] mainly analyzed data and prepared the manuscript and figures. [PERSON], [PERSON], and [PERSON] reviewed and polished the manuscript. All the authors contributed to the data analysis and paper-writing and shared equally in the editing of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Appendix**  
_Appendix A. Description of iAMSPEC II_
## 3 Experiment Results and Analysis
### Clustering Algorithm Complexity Analysis  
The proposed noevel FPSO-GAK algorithm consists of three parts. To facilitate analysis, the following notation is used: the number of GPS data points is \(N\), the number of clustering is \(K\), the particles population of PSO is \(M\). In the first part, the k-Means++ initialization method is employed to obtain particles \(M\) population. The time complexity of this part is \(\mathcal{O}(P1)=MKlog(K)\). In the second part, the hybrid heuristic initialization is utilized to determine optimal initial clustering centers. The iterations number of PSO algorithm is \(T_{1}\), The iterations number of GA algorithm is \(T_{2}\), number of calculations to fitness function is \(T_{3}\). The time complexity of the fuzzy system is related to the number of iterations and the number of fuzzy rules; it takes very little time and is negligible in terms of computational time complexity. The time complexity of the second part is \(\mathcal{O}(P2)=MNKT_{1}+MNKT_{2}+MT_{3}\). In the third part, the K-Means clustering algorithm is  
Figure 20: The urban hotspots in multi-source trajectory data set. (**a**) K-Means++; (**b**) GAK; (**c**) PSO-GAK; (**d**) DSCK; (**e**) PSOK; (**f**) PSOK; (**g**) RS; (**h**) FPSO-GAK.  
used to locate the urban hotspots. The iterations number of K-Means clustering algorithm is \(T_{4}\).The time complexity of the third part is \(\mathcal{O}(P3)=NKT_{4}\).  
The time complexity of the proposed clustering algorithm is \(\mathcal{O}(FPSO-GAK)=\mathcal{O}(P1)+\mathcal{O}(P2)+\mathcal{O}(P3)\approx NK( (T_{1}+T_{2})M+T_{4})\). In practical applications, \(K\) and \(M\) are very small relative to the number of GPS data points \(N\). The number of iterations \(T\) of the K-means++ algorithm in which the random initialization procedure fails to acquire the better initialization tends to be extremely high for large-scale datasets. Due to the good non-homogeneous search capability of the proposed hybrid heuristic initialization algorithm, the value of \((T_{1}+T_{2})M\) is relatively small. Furthermore, the better-initialized centers makes \(T_{4}\) would be much smaller than \(T\). The result of their interaction makes: the complexity of the proposed algorithm is of the same order of magnitude as that of k-Means, but it will be slightly larger numerically.  
In the actual experimental operation, the PSO-GAK algorithm in literature [55] consumes the most time since its complexity tends to \(\mathcal{O}(N^{2})\). The proposed algorithm can better adjust the search parameters automatically, and it will converge significantly faster than the PSO-GAK algorithm. The complexity of an intelligent algorithm is generally higher than the general iterative, recursive algorithm [56]. Collectively, it can be seen that the complexity of the proposed algorithm is much less than \(\mathcal{O}(N^{2})\) but greater than \(\mathcal{O}(NKT)\). The complexity of the proposed algorithm is roughly linearly related to the number of samples, and it is very efficient and scalable for handling large data sets.
###### Abstract  
The objective of this study was to assess the impact of digital waxing expertise on patient satisfaction with anterior interim computer-aided design and computer-aided manufacturing (CAD-CAM) prosthetic rehabilitations designed either by a CAD and dental technician or by a dental clinician with basic CAD-CAM training. This in vivo preliminary study was conducted on a total of 18 patients receiving anterior CAD-CAM rehabilitations (at least from canine to canine) with fixed bridges and/or single crowns. Only patients that had conventional chairside temporary restorations were enrolled. Three within-patient groups were defined at different time points: group CONTROL refers to all 18 patients at their first appointment, when they were using their initial temporary conventional prostheses; group DENT refers to all patients immediately after trying in a set of CAD-CAM prostheses designed by a dental clinician (with basic one-week CAD-CAM training); and group CAD refers to all patients after trying in a set of CAD-CAM prostheses for the same tooth elements but designed by an experienced CAD technician (who was also an experienced dental prosthetic technician). All CAD-CAM restorations were milled in polymethyl methacrylate (PMMA) with high translucency and strength properties (Prime, Zirkonzahn). Satisfaction with comfort and esthetics was assessed for all patients for the three different time points (groups CONTROL, DENT and CAD). Statistically significant differences among groups were assessed with the Friedmann's test. Group CAD significantly outperformed the other groups in both assessed variables (\(p\) = 0.001). The group DENT, in turn, outperformed group CONTROL for satisfaction with esthetics (\(p\) = 0.006) but not for comfort (\(p\) > 0.05). In conclusion, CAD operator background and expertise level significantly affect patient self-reported outcomes for anterior CAD-CAM rehabilitations. Single crowns and fixed bridges digitally designed by an experienced professional trained in both CAD-CAM and dental technology will likely offer high satisfaction and comfort to patients receiving anterior prosthetic rehabilitation.  
Computer-aided design; 3D printing; interim single crown; patient satisfaction; self-assessment; questionnaire study; prosthetic rehabilitation; anterior esthetic restoration
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 2 Applications of Personalized Video Summarization
### Personalized Trailer of Automated Serial, Movies, Documentaries and TV Broadcasting  
For the same film, there is a large discrepancy in users' preferences for summarization, so nowadays, it is necessary to create a personalized movie summarization system [19]. Personalized video summarization is useful for creating series and movie trailers to make each viewer watch the series or movies more interesting, as they focus on their interests. Examples of such works are presented below.  
[PERSON] and [PERSON] [35] proposed a framework to produce personalized video summaries based on ratings assigned by video experts in video frames, and their second method could demonstrate good results for the movie category. [PERSON] et al. [19] proposed a system to create movie summaries with semantic meaning for the same video, which are tailored to the user's preferences and interests and which are collected from a query interface. [PERSON] et al. [36] proposed an event detection-based framework and expanded it to create a personalized summary of video film and soccer. [PERSON] et al. [28] presented a personalized movie summarization scheme using deep CNN-Assisted Recognition of facial expressions. [PERSON] et al. [31] proposed a lightweight client-driven personalized video summarization framework that aimed to create subsets for long videos, such as documentaries and movies.
# _Thuja occidentalis_ L. (Cupressaceae): Ethnobotany, Phytochemistry and Biological Activity
## 2 Ethnobotany  
_Thuja occidentalis_ originated in the Eastern North America and is cultivated in Europe and Brazil as an ornamental tree [57; 58; 59], known as the \"tree of life\" or \"white cedar\" (Figure 1) [21].  
In traditional medicine, _T. occidentalis_ has been used in the treatment of liver diseases, bullous bronchitis, psoriasis, enuresis, amenorrhea, cyositis, uterine carcinomas, diarrhea, and rheumatism [58; 60; 61; 62]. Essential leaf oil was used in the treatment of fungal infections, cancer, and intestinal worms [41]. As a tincture, _T. occidentalis_ has been used in the treatment of warts, papillomas, and condydomas caused by human papilloma virus (HPV) [57; 63].  
Since historical times, the essential oil of _T. occidentalis_ has been used in folk medicine. _T. occidentalis_ containing thujone was used for hepatoprotection, bronchial catarrh, rheumatism, psoriasis, and even uterine carcinomas.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Thuja Species** & \multicolumn{2}{c}{**Benefits According to Biological Activities**} \\ \cline{2-3}  & \multicolumn{1}{c}{**Medicial Use**} & **Industrial Use** \\ \hline _T. koraiensis_ & antimicrobial [22; 23]; antioxidant [24] & - \\ \hline \multirow{6}{*}{_T. occidentalis_} & antimicrobial [26; 33; 34; 35]; antioxidant and & \\  & anti-inflammatory [33; 36; 37; 38; 39; 40]; & antifungal agent for \\  & antitumor [41; 42; 43; 44]; hepatoprotective and & biocontamination control in \\  & gastoprotective [45; 46]; antidiabetic [47]; & libraries and archives storage \\  & antiatherosclerotic [48]; & areas [51]; insecticidal activity [52] \\  & radioprotective [49]; antipyretic [50] & \\ \hline \multirow{2}{*}{_T. plicata_} & antimicrobial [25; 26; 53]; & antimicrobial agent for \\  & anti-inflammatory, immunomodulatory, & decontamination of buildings [25] \\  & and tissue remodeling [54] & \\ \hline _T. standishii_ & antimicrobial [30; 31; 32]; antitumor [30; 31; 32] & - \\ \hline _T. sutchuenensis_ & antimicrobial [55; 56] & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the bioactive potential of _T. occidentalis_ with other _Thuja_ genus species.  
Figure 1: Specim of _T. occidentalis_ from the “[PERSON]” University Botanical Garden of Macea, Romania; detail: twig of leaves showing the flattened branches (**a**) and small pins containing the seeds (**b**).  
The mother tincture of _T. occidentalis_ is used in homeopathy against a number of diseases. Homeopathy uses thuja as one of the main remedies for psychotic constitutions, in case of snake bite, small-pox, and vaccination-induced toxicity, respectively the proliferation or pathological vegetation [64].  
Thuja preparations with thujone were regularly used by the American Indian traditional healers. The decoct and the tea prepared from the inner bark of the unwoody twigs could relieve constipation and headache [65]. It has also been used for the treatment of polyps, birthmarks, and wounds and as a painkilling and anthelmitic remedy [66; 67].
## 4 Equilibrium Analysis and Inference  
### The Impacts on the Service Level  
When the products provided by merchants are homogeneous, the difference in service differentiation between CD and DD scenarios can be articulated as follows.  
\[\Delta s^{c}-\Delta s^{d}=\frac{2(\alpha\eta K_{2}\Delta\delta-\beta^{2}\delta_{2}) \beta\delta_{2}\Delta\delta}{K_{7}K_{8}} \tag{11}\]  
In Equation (11), \(K_{7}\sim K_{8}\) are represented as follows.  
\[\begin{array}{l}K_{7}=2\alpha\eta\Delta\delta-\beta^{2}\\ K_{8}=8\alpha\eta K_{1}^{2}\Delta\delta-\beta^{2}K_{6}\end{array} \tag{12}\]  
Given that \(\delta_{1}>\delta_{2}>0\), \(\Delta s^{c}>0\), \(\Delta s^{d}>0\), it follows that \(K_{7}>0\) and \(K_{8}>0\). When \(\alpha\eta K_{2}\Delta\delta-\beta^{2}\delta_{2}>0\), \(\Delta s^{c}>\Delta s^{d}\); if the opposite is true, then \(\Delta s^{c}<\Delta s^{d}\). Therefore, the service differentiation is influenced by consumer behavior (\(\delta_{i},\alpha,\beta\)) and operation cost (\(\eta\)). This further demonstrates that CBEC merchants should consider these factors comprehensively when formulating their service strategies. Furthermore, by analyzing the impact of various variables on the degree of service level differentiation, the following corollaries can be drawn.  
The service differentiation \(\Delta s\) is monotonically related to customer channel preference \(\delta_{i}\) in the CD, but is non-monotonically related in DD.  
In CD, the first-order derivatives of the \(\Delta s^{c}\) with respect to \(\delta_{\rm i}\) are denoted as follows.  
\[\left\{\begin{array}{l}\frac{\partial\Delta s^{c}}{\partial\lambda_{1}}= \frac{-\beta^{3}}{K_{7}^{2}}\\ \frac{\partial\Delta s^{c}}{\partial\lambda_{2}}=\frac{\beta^{3}}{K_{7}  
Given that \(\delta_{1}>\delta_{2}>0\) and \(\alpha,\beta,\gamma,K_{1}\sim K_{8}\) are all greater than 0, it follows that \(\partial\Delta s^{c}/\partial\alpha<0\), \(\partial\Delta s^{d}/\partial\alpha<0\), \(\Delta s^{c}/\partial\beta>0\), and \(\partial\Delta s^{d}/\partial\beta>0\). This indicates that the service differentiation \(\Delta\)s is negatively correlated with consumers' price sensitivity \(\alpha\) and positively correlated with consumers' service sensitivity \(\beta\). QED. \(\square\)  
The service differentiation \(\Delta\)s is negatively correlated with the service cost coefficient \(\eta\).  
The first-order derivatives of \(\Delta s^{c}\) and \(\Delta s^{d}\) with respect to \(\eta\) are denoted as follows.  
\[\left\{\begin{array}{c}\frac{\partial\Delta s^{c}}{\partial\eta}=\frac{-2 \Delta\theta(\Delta s)^{2}}{K_{2}^{2}}\\ \frac{\partial\Delta s^{d}}{\partial\eta}=\frac{-8\alpha\beta K_{8}^{2}K_{8}( \Delta s)^{2}}{K_{8}^{2}}\end{array}\right. \tag{17}\]  
Given that \(\delta_{1}>\delta_{2}>0\) and \(\alpha,\beta,K_{1}\sim K_{8}\) are all greater than 0, it follows that \(\partial\Delta s^{c}/\partial\eta<0\) and \(\partial\Delta s^{d}/\partial\eta<0\). This indicates that the service differentiation \(\Delta\)s decreases as the service cost coefficient \(\eta\) increases. QED. \(\square\)
## 3 Computational Methods
### Large Eddy Simulation  
According to LES, the large-scale eddies in a fully turbulent flow are computed directly and the influence of the small eddies on the large-scale eddies is modeled. The continuity equation and the Favre averaged compressible Navier-Stokes equations, as expressed mathematically in Equations (1) and (2), respectively, are solved implicitly.  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho\overline{ u_{i}})=0 \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho\overline{u}_{i})+\frac{\partial}{\partial x_ {i}}(\rho\overline{u}_{i}\overline{u}_{j})=\frac{\partial}{\partial x_{j}}( \alpha_{i,j})-\frac{\partial\overline{p}}{\partial x_{i}}-\frac{\partial \tau_{i,j}}{\partial xj} \tag{2}\]  
where \(\sigma_{i,j}\) and \(\tau_{i,j}\) are defined as Equations (3) and (4), respectively.  
\[\sigma_{i,j}=[\mu(\frac{\partial\overline{u}_{i}}{\partial x_{j}}+\frac{ \partial\overline{u}_{j}}{\partial x_{i}})]-\frac{2}{3}\mu\frac{\partial \overline{u}_{i}}{\partial x_{j}}\delta_{i,j} \tag{3}\]  
\[\tau_{i,j}=\rho\overline{u_{i}u_{j}}-\rho\overline{\overline{u}_{i}} \overline{u}_{j} \tag{4}\]  
where \(\overline{u}_{i}\) is the resolved filtered velocity vector, \(\sigma_{i,j}\) is the stress tensor caused by molecular viscosity, and \(\tau_{i,j}\) is the sub-grid scale (SGS) stress tensor, representing the diffusive effect of the sub-grid scale eddies on the resolved ones. The Smagorinsky model [20] is used to model the SGS stresses because of its simplicity.