esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Linear Interpolation Method (LIN)  
In this model, the interpolation of tropospheric delay is performed as follows, based on the coordinates between the GNSS station to be interpolated and the reference station.  
\[\Delta ZWD_{g,n}=\left[\begin{array}{cc}\Delta X_{g,n}&\Delta Y_{g,n}\\ \end{array}\right]\left[\begin{array}{c}a\\ b\\ \end{array}\right] \tag{10}\]  
\(\Delta X_{g,n}\) and \(\Delta Y_{g,n}\) are the coordinate differences between the GNSS station and the reference station.  
The interpolation model is written as follows:  
\[\left[\begin{array}{c}ZWD_{1,n}\\ ZWD_{2,n}\\ \vdots\\ ZWD_{n-1,n}\end{array}\right]=\left[\begin{array}{ccc}\Delta X_{1,n}&\Delta Y_{1,n }\\ \Delta X_{2,n}&\Delta Y_{2,n}\\ \vdots&\vdots\\ \Delta X_{n-1,n}&\Delta Y_{n-1,n}\end{array}\right]\left[\begin{array}{c}a\\ b\end{array}\right]. \tag{11}\]  
In the linear interpolation method, the coefficients \(a\) and \(b\) are determined according to the least squares method by Equation (12).  
\[\left[\begin{array}{c}a\\ b\end{array}\right]=\left[A^{T}A\right]^{-1}A^{T}V \tag{12}\]  
A coefficients matrix and \(V\) correction values are calculated by the following equations:  
\[A=\left[\begin{array}{ccc}\Delta X_{1,n}&\Delta Y_{1,n}\\ \Delta X_{2,n}&\Delta Y_{2,n}\\ \vdots&\vdots\\ \Delta X_{n-1,n}&\Delta Y_{n-1,n}\end{array}\right]V=\left[\begin{array}{c}V _{1,n}\\ V_{2,n}\\ \vdots\\ V_{n-1,n}\end{array}\right]. \tag{13}\]  
In the study, observations collected from 40 stations of the EPOSA GNSS Network were used for an assessment of the interpolation methods. The distribution of the GNSS stations is given in Figure 2.  
The GNSS observations were evaluated in summer, winter, and spring seasons to observe the behavior of the interpolation methods in humid and dry periods. The meteorological parameters were obtained from the TAWES Meteorological Network for the relevant days. In order to analyze the performance of the ANN, IDW, and LIN methods, the RMSE values of the errors between the  
Figure 2: EPOSA Global Navigation Satellite System (GNSS) Network.  
interpolated values and known values were calculated. RMSE values were obtained by the following Equation (14):  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(ZWD_{i}^{GNSS}-ZWD_{i}^{INTERP.} \right)^{2}}{n-1}}, \tag{14}\]  
where the variable \(n\) denotes the number of observations. \(ZWD_{i}^{GNSS}\) and \(ZWD_{i}^{INTERP.}\) are the \(i\)th ZWD value estimated using GNSS data and the ANN model, respectively.
## 2 Materials and Methods
### Microbial Community Analysis  
In order to study the microbial community attached to the electrodes, three samples were obtained from the electrodes (Acetone, DMF, and Control). Genomic DNA from these electrodes was extracted with the PowerSoil(r) DNA Isolation Kit (MoBio Laboratories Inc., Carlsbad, CA, USA), following the manufacturer's instructions. The entire DNA extract was used for the pyrosequencing of the 16S-rRNA gene-based massive library, targeting the eubacterial region V1-V3 16S-rRNA and performed at MR DNA (www.mrdnalab.com, accessed on 20 January 2021, Shallowater, TX, USA) utilizing MiSeq equipment (Illumina, San Diego, CT, USA). The primer set used was 27 Fmod (5\({}^{\prime}\)-AGGRTTTGATCMTGGTCAG-3\({}^{\prime}\))/519 R modBio (5\({}^{\prime}\)-GNTTTACNGGCGKGCTG-3\({}^{\prime}\)). Diluted DNA extracts were used as a template for PCR reactions. The obtained DNA reads were compiled in FASTq files for further bioinformatics processing. Trimming of the 16S-rRNA bar-coded sequences into libraries was carried out using QIIME software version 1.8.018. Quality filtering of the reads was performed at Q25 quality prior to grouping into operational taxonomic units (OTUs) at a 97% sequence homology cut-off. The following steps were performed using QIIME: a denoising procedure by using a denoiser algorithm19. Final OTUs were taxonomically classified using BLASTn against a database derived from the Ribosomal Database Project II (RDPII, [[http://rdp.cme.msu.edu](http://rdp.cme.msu.edu)]([http://rdp.cme.msu.edu](http://rdp.cme.msu.edu)), accessed on 20 January 2021) and the National Centre for Biotechnology Information (NCBI, www.ncbi.nlm.nih.gov accessed 20 January 2021).
## Conclusions  
**In conclusion, ethanoanthracene cycloadducts (5-7)** _anti, (5-7)** _syn,_ **and (5-7)** _dec_ **were synthesized, and the substituent effects on the regioselectivity of these cycloadducts were described. The antidepressant and anti-phobic activities of these cycloadducts (5-7)** _anti, (5-7)** _syn_**, and marotiline **9** as a reference drug, were theoretically predicted through PASS and molecular docking. The PASS activity of the (5-7)** _anti_ **and (5-7)** _syn_ **against phobic disorders were high and comparable to marotiline **9**, but variable and much less than marotiline **9** as antidepressant. The MolDock scores of (5-7)** _anti_ **and (5-7)** _syn_ **are in the line with PASS values. The pharmacokinetic and druglikeness properties of (5-7)** _anti_ **and (5-7)** _syn_ **are valuable and provide an informative, promising analysis, as they are possible antidepressant-like compounds. Further in vitro and in vivo studies would be needed for confirmation of the chemoinformatics investigation.**  
**Supplementary Materials: The following are available online at [[http://www.mdpi.com/2076-3417/10/21/7727/s1](http://www.mdpi.com/2076-3417/10/21/7727/s1)]([http://www.mdpi.com/2076-3417/10/21/7727/s1](http://www.mdpi.com/2076-3417/10/21/7727/s1)), Figure S1. Full \({}^{1}\)H-NMR spectrum of **5** _anti, **5** _syn_ **and 5** _dec_**; Figure S2. Partial \({}^{1}\)H-NMR spectrum of **5** _anti, **5** _syn_ **and 5** _dec_**; Figure S3. Thematic representation of the migration of **6** _anti, **6** _syn_ **and 6** _dec_ **on TLC; Figure S4. Full \({}^{1}\)H-NMR spectrum of 7** _anti, **7** _syn_ **and 7** _dec_**; Figure S5. Partial \({}^{1}\)H-NMR spectrum of 7** _anti, **7** _syn_ **and 7** _dec_**; Figure S6. Full \({}^{1}\)H-NMR spectrum of compound 8; Figure S7. Partial \({}^{1}\)H-NMR spectrum of compound 8; Figure S8. Structure of the 4**xx** **model complex with the poses of compounds; Figure S9. Structure of the 3**GWU** **model complex with the poses of compounds**  
**Author Contributions: Conceptualization, M.A.S. and A.B.; methodology, M.A.S., M.M.O., and A.B.; software, M.A.S., M.M.O., and A.B.; validation, A.B., A.M.A., M.S.A.G., and M.A.-Q.; formal analysis, A.B., and M.A.S.; investigation, A.B., M.A.S., A.M.A., M.S.A.G., and M.A.-Q.; resources, A.B., AM.A., M.S.A.G., and M.A-Q.; data curation, A.B., M.A.S., and M.A.-Q.; writing--original draft preparation, A.B. and M.A.S.; writing--review and editing, M.S.A.G. and M.A.-Q.; visualization, A.B., M.A.S., and M.M.O.; supervision, A.B., M.S.A.G., and M.A.-Q.; project administration, A.B., M.M.O., and M.A.S.; funding acquisition, A.B. All authors have read and agreed to the published version of the manuscript.**  
**Funding: King Saud University-Researchers Supporting Project Number (RSP-2020/64).**  
**Acknowledgments: The authors would like to extend their sincere appreciation to Researchers Supporting Project Number (RSP-2020/64), King Saud University, Riyadh, Saudi Arabia.**  
**Conflicts of Interest: The authors declare no conflict of interest.**
## 2 Methods
### Model Design
#### 2.2.3 Decoder  
The last part of the separation system is the decoder, which takes the output of encoder and the output of the separation network together as the input of the decoder. It adopts 1-D transpose convolution to turn the features of different channels back to the features of a single channel to reconstruct the separated speech signal. The stride and kernel size of the decoder are consistent with encoder. The speech is restored to the length before input to the encoder. The input of the decoder is the dot product of the output \(e_{N}\) of the separation network and the output \(e\) of the encoder. The speech signal of the separated \(N\)th speaker is defined by the following Formula (22):  
\[\hat{s}_{N}=\text{conv1d-transpose }(e_{N}\cdot e). \tag{22}\]  
Figure 5: The internal structure of the speaker enhancement module, as the post-processing module of the model, uses the temporal correlation characteristics of the same speaker’s speech to improve the separation performance of the model.  
where conv1d-transpose (\(\cdot\)) denotes 1-D transpose convolution.
# Unfair and Risky? Profit Allocation in Closed-Loop Supply Chains by Cooperative Game Approaches
## 3 Problem Description and Assumptions
Following the principle of utility maximization, if \(t_{n}^{L}\geq\max\{t_{r}^{L},\,0\}\), consumers will purchase a new product, leading to a new product demand function \(q_{n}^{L}\big{(}p_{n}^{L},p_{r}^{L}\big{)}=1-\frac{p_{n}^{L}-p_{r}^{L}}{1- \delta}\). If \(t_{r}^{L}\geq\max\{t_{n}^{L},\,0\}\), consumers will purchase a remanufactured product, resulting in a remanufactured product demand function \(q_{r}^{L}\big{(}p_{n}^{L},p_{r}^{L}\big{)}=\frac{p_{n}^{L}-p_{r}^{L}}{1-\delta }-\frac{p_{r}^{L}}{\delta}\)[25; 33; 34]. Likewise, the utility of a consumer receiving from Retailer F for a new product is \(t_{n}^{F}(\theta)=\theta-p_{n}^{F}\), and for a remanufactured product is \(t_{n}^{F}(\theta)=\delta\theta-p_{r}^{F}\). If \(t_{n}^{F}=\max\{t_{r}^{F},\,0\}\), a new product demand function is \(q_{n}^{F}\big{(}p_{n}^{F},p_{r}^{F}\big{)}=1-\frac{p_{n}^{F}-p_{r}^{F}}{1- \delta}\). If \(t_{r}^{F}\geq\max\{t_{n}^{F},\,0\}\), a remanufactured product demand function is \(q_{r}^{F}\big{(}p_{n}^{F},p_{r}^{F}\big{)}=\frac{p_{n}^{F}-p_{r}^{F}}{1-\delta }-\frac{p_{r}^{F}}{\delta}\). Retailer F's fairness concern is a reaction towards adverse inequality related to the upstream agent [31]. Given this assumption, Retailer F's fairness concern becomes unrelated in the models of CC and MF due to the absence of financial transaction between M and F. However, in Models DC, LF, and ML, Retailer F shows fairness concerns with M.  
With similar arguments in studies [35; 36; 37; 38; 39; 40], it is assumed that a risk-averse retailer evaluates his or her profit on the basis of a mean-variance function. The utility function \(U(L)\) of Retailer L considering its profit is presented as below:  
\[ \begin{array}{rl}U(L)=\Pi(L)-&\eta Var(L)/2\\ &=\big{(}p_{n}^{L}-w_{n}^{L}\big{)}*q_{n}^{L}+\big{(}p_{r}^{L}-w_{r}^{L}\big{)}* q_{r}^{L}-\eta\sigma^{2}\big{[}\big{(}p_{n}^{L}-w_{n}^{L}\big{)}^{2}\\ &+\big{(}p_{r}^{L}-w_{r}^{L}\big{)}^{2}+2\rho\big{(}p_{n}^{L}-w_{n}^{L}\big{)} \big{(}p_{r}^{L}-w_{r}^{L}\big{)}\big{]}/2\end{array}  \tag{1}\]  
where \(\eta\) (\(>0\)) is the risk aversion parameter for Retailer L and \(\sigma^{2}\) is the variance of demand uncertainty. A risk pooling effect exists [41] when the correlation coefficient between new products and remanufactured products is negative (\(-1<\rho<0\)). If the risk aversion parameter is too high, the risk-averse [PERSON] does not participate in the market. We assume that \(0<\eta<\frac{\theta}{\beta}\) to ensure [PERSON] participates in the game.
# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 4 Experimental Results
### Comparative Evaluation Results
The experimental results are shown in Table 7, where the Mean describes the average values of the _DICE_ and _JSI_ for all test image data. The foregoing experimental analysis demonstrates that our proposed method is designed based on an improved SPF function and the LIF method. The model combines the merits of global image information and local image information and can segment noisy images and multiobjective images well. By contrast, the C-V model and the SBGFRLS model are constructed with global image information alone, based on the assumption that the region to be segmented is homogeneous. Unfortunately, this assumption is not suitable for intensity-inhomogeneous images [2; 31; 35]. The LBF model and LIF model use local information to segment intensity-inhomogeneous images and obtain desirable segmentation results; thus, the models are sensitive to the initial position and image noise [2; 36]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image ID** & **C-V** & **LBF** & **LIF** & **SPFLIF-IS** \\ \hline  
\hline Mean & 0.9568 & 0.9339 & 0.8483 & 0.9699 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: DICE values of the image segmentation results using the four models for Berkeley color images. The LSACM model is proposed based on the local statistical information of an image; therefore, this model is robust to noise while suppressing intensity overlap to some extent. Nevertheless, this model is assumed that the image gray is separable in a relatively small area, and the offset is smooth in the entire image area. The model is easily trapped in a local minimum and involves high computational complexity [58; 59]. It follows from Table 7 that the values of the _DICE_ and _JSI_ of the SPFLIF-IS method are the highest for the eight real image data, and the Mean is also the largest. Thus, the experimental results obtained for synthetic and real images further demonstrate the superior performance of our method. Therefore, our model is able to obtain better _DICE_ and _JSI_ values than those yielded by the methods compared.
## 2 Datasets and Methods
### Methods
#### 2.2.3 Regression Analysis  
(1) Single driver regression analysis  
We conducted linear regressions to investigate the relationships between NDVI and single climate driver over the past 30 years as following:  
\[\mathrm{NDVI}=\upbeta_{0}+\upbeta_{1}\,\mathrm{CLM} \tag{5}\]  
where: CLM denotes climatic drivers, including air temperature, precipitation and incoming shortwave solar radiation; \(\upbeta_{1}\) is the regression coefficient of climate driver to NDVI. Since there is no significant autocorrelation in the growing season NDVI series under 95% confidence bounds, autocorrelation will not be considered in our regression analysis. The influences of climatic factors on the variation of NDVI can be inferred via the linear regression coefficients. Despite the discrepancy between causation and correlation, as most of the previous studies [19, 30, 36, 38], we assumed that it was highly possible that the NDVI variation was influenced by the correlated climatic factor, yet one should notice that uncertainties exist in this kind of \"causation\". Here the single regression coefficients indicate the direct response of NDVI to unit change in single climate driver, meanwhile can be compared with previous studies to highlight our findings.  
Figure 2: Spatial distribution of the growing season for non-evergreen vegetation in China. (**a**) start of season (SoS); (**b**) end of season (EoS). \(a\) and \(b\) in the legend denote the first half and the second half of a month respectively.  
(2) Multiple drivers regression analysis  
To consider the effects of multiple climate drivers simultaneously and compare their relative effects, a multiple regression model was applied:  
\[\frac{\mathrm{NDVI}-\overline{\mathrm{NDVI}}}{\sigma_{\mathrm{NDVI}}}=\beta_{0}+ \beta_{1}\frac{\mathrm{CLM}_{1}-\overline{\mathrm{CLM}_{1}}}{\sigma_{1}}+\ldots+ \beta_{n}\frac{\mathrm{CLM}_{n}-\overline{\mathrm{CLM}_{n}}}{\sigma_{n}} \tag{6}\]  
where: \(\mathrm{CLM}_{i}(i=1,2,\ldots,n)\) are climatic drivers; \(\overline{\mathrm{NDVI}}\) and \(\overline{\mathrm{CLM}_{i}}\) are interannual mean values of NDVI and climatic drivers respectively; \(\sigma_{\mathrm{NDVI}}\) and \(\sigma_{i}\) are standard deviations of NDVI and climatic drivers respectively. Each time series, was normalized by subtracting its mean value and divided by its standard deviation thus the unit change in each variable has the same statistical meaning. Therefore the regression coefficients \(\beta_{i}\) reflect the relative effect or contribution of each climate driver on NDVI respectively and can be compared directly. T-test was used to test the significance of these regression coefficients. With the multi regression above, climatic factor with larger coefficient should have contributed more to the variation of NDVI, hence was identified as the dominant climatic factor for the NDVI variation.  
Figure 3: Interannual variation of mean temperature and precipitation in (**a**) growing season; (**b**) spring; (**c**) summer and (**d**)autumn in China from 1982 to 2010.
## 5 Conclusions  
In this paper, we analyzed the changes of SPAD, LAI, and canopy spectral characteristics of rice under increasing diffuse radiation fraction. The results show that the increase in diffuse radiation fraction led to the increase in SPAD and LAI of rice. Additionally, the reflectivity of canopy spectra in the visible light region decreased, while that in the near-infrared region increased. The absorption features, calculated with the baseline normalization method, changed remarkably as the diffuse radiation fraction increased. The correlation between spectral parameters and SPAD and LAI of rice was analyzed, and it seems more accurate monitoring models of SPAD and LAI values in rice under changes in the diffuse radiation fraction can be established based on the results of this paper. Further analysis of the different morphology of the canopy spectra between T1 and T2 shows that the increase in the diffuse radiation fraction will also affect the structure of rice leaf and canopy and the process of water vapor absorption. Building on this paper, in the future, the spectral parameters, which were highly correlated with SPAD and LAI in this paper, will be used to establish rice monitoring models with high accuracy and efficiency. Further, these models can be used in hyperspectral images to analyze the impact of increasing the diffuse radiation fraction on rice in large geographic regions. In addition, more rice field experiments should be designed to analyze the influence of increasing the diffuse radiation fraction on the water vapor absorption process of rice.  
Figure 7: Schematic diagram of the morphological changes of the second and third absorption bands on T1 and T2.  
**Author Contributions:** Conceptualization, [PERSON] and X.J.; methodology, [PERSON] and X.J.; investigation, [PERSON] and X.L.; formal analysis, [PERSON] and X.L.; writing--original draft preparation, [PERSON] and X.J.; writing--review and editing, [PERSON], [PERSON], S.Y., and Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, Grant Number 41875140, the National Key Research and Development Program of China, Grant Number 2019 YED1002202, and the Special Fund for Meteorological Scientific Research in the Public Welfare of China, Grant Number GYHY201506018.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Renewable-Based Microgrids: Design, Control and Optimization
## 1 Introduction  
To achieve carbon neutrality by 2050, additional measures must be taken, including the extensive incorporation of renewable energy sources (RESs). Additionally, we must pave the way for new market and grid structures that facilitate the integration of these clean technologies, along with initiatives for demand response (DR), electric vehicles (EVs), and energy storage.  
In this context, microgrids (MGs) have emerged as a highly valuable framework. Specifically, a microgrid can be described as a small-scale network equipped with local power generation capabilities, typically connected to a larger power grid but being capable of operating independently [1]. As a result, microgrids offer numerous opportunities for various entities to optimize the utilization of local resources while ensuring a reliable electricity supply in remote and isolated areas [2].
## 3 Results and Discussions
### RAP Mortars Mixed with RTFO Binder  
#### 3.2.1 Stiffness Values and m-Values of RAP Mortars  
This section presents the test results of the RAP mortars mixed with various RAP sources and a short-term aged (RTFO) binder of PG 58-28. The fabricated BBR samples were tested at three temperatures (\(-6~{}^{\circ}\)C, \(-12~{}^{\circ}\)C, and \(-18~{}^{\circ}\)C). The main test results are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Min. Temp**} & \multicolumn{3}{c}{**Stiffness**} & \multicolumn{3}{c}{**m-Value**} & \multicolumn{3}{c}{**Low Temperature Determination**} \\ \cline{2-10}  & \multicolumn{2}{c}{**Aged Binder Percentage**} & \multicolumn{2}{c}{**Aged Binder Percentage**} & \multicolumn{2}{c}{**Aged Binder Percentage**} & \multicolumn{2}{c}{**Aged Binder Percentage**} \\ \cline{2-10}  & **5\%** & **10\%** & **15\%** & **5\%** & **10\%** & **15\%** & **5\%** & **10\%** & **15\%** \\ \hline A & \(-21\) & \(-12.2\) & \(-  
As shown in Figure 4, it can be found that, at \(-6\)\({}^{\circ}\)C, all RAP mortars mixed with sieved RAP and RTFO aged PG 58-28 binder showed increased m-values and the decreased stiffness values during a loading process. It is also noted that, as expected, RAP mortars with a higher aged binder content containing the aged soft binder have a higher stiffness and a lower m-value, following logarithmic trends regardless of RAP source and test time. In addition, different from the RAP mortars mixed with virgin binder PG 58-28, the RAP mortar with a 5% aged binder mixed with RTFO binder can show stiffness values and m-values during a loading procedure. Moreover, these stiffness values are significantly higher compared to those values of the modified binders mixed with virgin binder due to the RTFO aged binders.
###### Abstract  
Flow measurements in open channels have often utilized velocity-area methods. Thus, estimations of the average velocity in a cross-section of rural canals play an important role in the flow measurement of an irrigation district. This paper derives a model for calculating depth average velocity. This model considers the classical logarithmic formula describing the velocity distribution and flow partitioning theory, which is aimed at finding out a location that represents the depth average velocity (LDAV) along the vertical line from boundary to water surface. Subsequently, the average flow velocity of the whole channel can be further determined by using the velocity-area method in different regions. Moreover, the LDAV has different expressions in different sub-regions according to flow partitioning theory under various aspect ratios. The results are verified by experiments under different experimental conditions, and the formula is highly applicable and has a high theoretical significance and practical value.  
Keywords:flow partitioning; velocity; flow measurement; log-law +
Footnote †: journal: Applied Physics Letters