esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, the core area of Chengdu Plain was taken as a case example. The polarization coherence coefficient of rice on the basis of InSAR technology was analyzed, and the polarization backscatter coefficient and polarization coherence coefficient of rice on the basis of time-series SAR image were extracted. The two schemes based on time-series SAR image and time-series SAR combined with the coherence coefficient were analyzed to identify the rice-planting area. The following conclusions can be drawn:  
(1) The VV and VH polarization coherence coefficients of typical ground objects during the growing period of rice were obtained on the basis of InSAR technology. The analysis showed that the VV and VH polarization coherence coefficients can be used to well distinguish artificial buildings from other ground objects. The VV polarization coherence coefficient can be used to well distinguish rice from water area and vegetation in August and September; the VH polarization coherence coefficient can also be used to well distinguish rice from water area and vegetation in August and October.  
(2) The rice identification accuracy on the basis of time-series Sentinel-1 SAR image is lower than that of time-series Sentinel-1 SAR image combined with InSAR technology. The user accuracies of both methods are 78% and 81%, respectively. The rice identification result based on Sentinel-2 optical remote sensing images was compared with three rice identification results based on Sentinel-1 SAR images. Compared with single-time-phase SAR, the time-series SAR image can distinguish rice and vegetation information more significantly, thus improving the rice identification accuracy. The introduction of coherence coefficient not only improves the classification accuracy of artificial buildings but also significantly distinguishes vegetation and rice, thus improving the identification accuracy. Rice identification on the basis of time-series Sentinel-1 SAR image combined with InSAR technology can achieve a good classification result, which shows that the coherence coefficient improves rice identification.  
(3) In monitoring rice and other crops in cloudy regions, optical data are preferred if they are available. If optical data are lacking, SAR data can be used to monitor crops. The Sentinel-1 series images reach 250 km in width, which is significantly conducive to large-scale rice monitoring or mapping in plains or areas with small topographic relief.  
[PERSON] and [PERSON] designed and wrote the original draft; [PERSON], [PERSON] and [PERSON] assisted with the formal analysis; [PERSON] assisted with the methodology. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Asian Cooperation Fund Project: Demonstration Project of Water Resources Distribution and Land Use Dynamic Remote Sensing Monitoring Technology in the Lancang-Mekong River Basin; the National Key Research and Development Program of China (grant number 2016 YFB0501403); China Postdoctoral Science Foundation (2020M670609).  
Not applicable.  
The authors declare no conflict of interest.
# Citation Oriented AuthorRank for Scientific Publication Ranking
## 3 Methodology
### Author Ranking by PRP  
As described before, we used PRP for publication ranking by combining Labeled-LDA-based topic information and full-text publication information. We also applied this method of author ranking based on the assumption that if author\({}_{1}\)'s paper cites author\({}_{2}\)'s paper, then author\({}_{1}\) and author\({}_{2}\) are somehow related. The relation can be characterized on a directed graph with authors as vertices and citations as edges. In the author graph, \(G_{A}=(V^{\prime},\ E^{\prime})\), \(V^{\prime}\) is a set of vertices representing all the authors; and \(E^{\prime}\) is a set of edges representing author relationships as generated from the citation network.  
In most cases, one author has multi-publications, so \(v^{\prime}_{i}\), the vertex for a given author is a set of papers, i.e., \(v^{\prime}_{i}=\left\{p_{1},p_{2},p_{3},\cdots,\ p_{s}\right\}\). Similarly, the number of edges between two vertices \(\left\langle v^{\prime}_{i},v^{\prime}_{j}\right\rangle\) is always more than one, each edge \(e^{\prime}_{i}\) is expressed as \(\left\langle v^{\prime}_{i},v^{\prime}_{j}\right\rangle=\left\{\left\langle p _{1},p_{2}\right\rangle,\left\langle p_{1},p_{3}\right\rangle,\cdots,\left\langle p _{l},p_{k}\right\rangle\right\}\), only when \(p_{l}\in v^{\prime}_{i}\) and \(p_{k}\in v^{\prime}_{j}\).  
Therefore, the publication topic prior can be formulated as:  
\[p_{z_{key_{m}}}(v^{\prime})=\sum p_{z_{key_{m}}}(v)=\sum_{d}\frac{\text{P}( key_{m}|p_{d})}{\sum_{x=1}^{|V|}\text{P}(key_{m}|p_{x})} \tag{6}\]  
where \(p_{d}\in v^{\prime}\), and the transitioning probability score for an edge can be calculated as:  
\[p_{z_{key_{m}}}(v^{\prime}_{i}|v^{\prime}_{j})=\sum\ p_{z_{key_{m}}}(v_{i}|v_{ j})=\sum\frac{\text{P}(key_{m}|citation_{j,i})}{\sum_{x=1}^{d_{out}(v_{j})}\text{P}( key_{m}|citation_{j,x})} \tag{7}\]  
where \(citation_{j,i}\) is the citation context from \(v^{\prime}_{i}\) to \(v^{\prime}_{j}\).  
So, the (topic-relative) importance of an author (vertex) can be calculated by the same formula as: \(I_{key_{m}}(v^{\prime}|R)=\pi_{key_{m}}(v^{\prime})\). For output, we can obtain ranking lists for each specific topic as a result.  
Author topical ranking by PRP may be more accurate than paper ranking, as each vertex is represented by a list of papers from an author, and more textual information results in more accurate topic inference.
# Synthesis of Biodiesel by Interestricification of Triglycerides with Methyl Formate
## 1 Introduction
Studies of the quality indicators of biofuels made from insects (black soldier fly larvae (BSFL)) oil and methyl acetate have shown that they are not inferior in quality to biodiesel made from rapeseed oil. The basic physico-chemical indicators of biofuels produced from insect fat have been found to meet the requirements of standard EN14214, and many properties meet the requirements of ASTM D6751, with the exception of the flash point and acid count [29]. The use of alkylates of longer alcohol chains (propyl, butyl, propionate, and butyrate) decreases the intereesterification efficiency [37; 38]. It has been found that the use of longer alcohol chains for intereesterification improves the low-temperature properties of products (pour point, flash point, cold filter plugging point, and cloud point). When a longer chain of alcohol is used for oil intereesterification, the resulting fuel can be used at in areas of colder climate. However, these fuels have higher viscosity, density, and molecular weight compounds. Such biofuels do not meet the density and viscosity requirements for biodiesel. Studies on the intereesterification of oil with acetates under standard conditions using both chemical and enzymatic catalysts or supercritical conditions have been performed, with their results summarized [39; 40]. Figure 1: Triglyceride intereesterification reaction with carboxylate esters. This study is intended to review the process of triglyceride intereesterification with methyl formate and analyze the quality of the resulting product. The intereesterification of triglycerides with methyl formate yields fatty acid methyl esters and formyl glycerides. In an efficient process, triglycerides react completely with methyl formate to form 84 wt.% fatty acid methyl esters (FAME) and 16 wt.% triformyl glycerol (TFG) [37]. It should be mentioned that the intereesterification of oil with methyl formate has not been studied much, but is pespective, since the methyl formate is a rather cheap and easily obtained compound in the chemical industry produced from methanol and formic acid.
## 3 The Serge Middle Wave Multiplier
Then, we apply KCL at the output node of the circuit, which gives us  
\[I=I_{\rm F}-I_{\rm R}. \tag{33}\]  
Combining Equation (32) with Equation (33) and applying [PERSON]'s diode equation gives us  
\[\frac{V_{\rm in}-V_{\rm x}}{R_{1}}=\underbrace{I_{\rm s}\left(\exp\left(\frac{ V_{\rm x}}{\eta V_{\rm T}}\right)-1\right)}_{I_{\rm F}}-\underbrace{I_{\rm s} \left(\exp\left(\frac{-V_{\rm x}}{\eta V_{\rm T}}\right)-1\right)}_{I_{\rm R}}. \tag{34}\]  
As before, we assume the diodes will not conduct simultaneously and arrive at the piecewise relationship  
\[V_{\rm in}-V_{\rm x}=\lambda R_{1}I_{\rm s}\left(\exp\left(\frac{\lambda V_{ \rm x}}{\eta V_{\rm T}}\right)-1\right), \tag{35}\]  
where once again \(\lambda=\text{sgn}(V_{\rm in})\). To further simplify this expression we neglect the constant factor \(\lambda R_{1}I_{\rm s}\) that results from its expansion. This gives us:  
\[V_{\rm in}-V_{\rm x}=\lambda R_{1}I_{\rm s}\exp\left(\frac{\lambda V_{\rm x} }{\eta V_{\rm T}}\right). \tag{36}\]  
Next, we rearrange this equation in the Lambert-W form described by Equation (22) by dividing both sides by \(\exp\left(\lambda V_{\rm x}/\eta V_{\rm T}\right)\). This yields  
\[\left(V_{\rm in}-V_{\rm x}\right)\exp\left(-\frac{\lambda V_{\rm x}}{\eta V_{ \rm T}}\right)=\lambda R_{1}I_{\rm s}, \tag{37}\]  
which can be solved for \(V_{\rm x}\) as:  
\[V_{\rm x}=V_{\rm in}-\lambda\eta V_{\rm T}W\left(\frac{R_{1}I_{\rm s}}{\eta V _{\rm T}}\exp\left(\frac{\lambda V_{\rm in}}{\eta V_{\rm T}}\right)\right). \tag{38}\]  
As a final step, we insert Equation (38) into Equation (31) to derive a complete expression for the transfer function of a single wavefolding stage in the Serge middle VCM:  
\[V_{\rm out}=V_{\rm in}-2\lambda\eta V_{\rm T}W\left(\frac{R_{1}I_{\rm s}}{ \eta V_{\rm T}}\exp\left(\frac{\lambda V_{\rm in}}{\eta V_{\rm T}}\right) \right). \tag{39}\]  
Figure 7 shows the transfer function of the circuit, evaluated in MATLAB for values of \(V_{\rm in}\) between \(-1.5\) and \(1.5\) V. As before, the model was discretized trivially and is presented against its corresponding SPICE simulation. Parameter values used in this simulation are compiled in Table 4. The value of parameters \(I_{\rm s}\) and \(\eta\) for the 1N4148 diode were matched to those of its corresponding SPICE model [61]. Figure 7b shows the absolute difference between both simulations. These results indicate a good match between the models, as the maximum difference was once again found to be below \(1\) mV. Figure 5.0 **(a)** Transfer function of a single wavefolding stage in the Serge middle VCM measured using SPICE and the proposed model; and **(b)** the absolute difference between these two curves. Finally, Figure 8 shows the output of the Serge wavefolder for a 500-Hz sinusoidal input. As expected, the circuit behaves as a wavefolder, folding portions of the input waveform whose absolute value exceeds approximately 0.3 V. This behavior is similar to that of the Lockhart wavefolder (cf. Figure 4a).
_Applied_  
_sciences_  
Modeling of Structure-Property Relationships of Polymerizable Surfactants with Antimicrobial Activity  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 1(c), [PERSON] 2(c), [PERSON] 3 and [PERSON] 2(c)**  
1 Research Institute on Membrane Technology (ITM-CNR), University of Calabria, Via P. Bucci, I-87036 Arcavacata di Rende (CS), Italy; [EMAIL_ADDRESS]
2 Laboratory of Industrial and Synthetic Organic Chemistry (LISOC), Department of Chemistry and Chemical Technologies, University of Calabria, Via Pietro Bucci 12/C, 87036 Arcavacata di Rende (CS), Italy; [EMAIL_ADDRESS] (R.A.); [EMAIL_ADDRESS] (R.M.); [PERSON](B.G.)
3 Department of Biology, Ecology and Earth Sciences, University of Calabria, 87036 Arcavacata di Rende (CS), Italy; [EMAIL_ADDRESS]
* Correspondance: [EMAIL_ADDRESS]; Tel.: +39-0984-492080  
Received: 11 August 2018; Accepted: 2 October 2018; Published: 18 October 2018  
Featured Application: Some of the investigated ammonium salts were used as surfactants in the antimicrobial coating of commercial membranes. The correlations between the structures of this class of compounds and the properties controlling their antimicrobial activity are of fundamental importance for optimizing the synthesis of new hanging functional groups, allowing for the increase of the antibacterial properties of surfaces. Therefore, the findings of this _ab initio_ modeling could have different applications, such as the preparation of membranes for wastewater treatments or air purification, without forgetting biomedical devices, where biofouling is a major problem for the development of more efficient systems.  
**Abstract:** Polymerizable quaternary ammonium salts (PQASs) were synthesized in a previous work and some of them were used as surfactants in the antimicrobial coating of commercial membranes. Herein, the electrostatic charges, maximum length, and aspect ratio of these antibacterial surfactants were calculated with the aim of investigating the relationship between the properties, recognized to control the biocidal activity of these molecules, and the molecular structures. The effect of the water molecules was considered through a quantum and molecular mechanics approach. The correlation between the number of carbons in the main aliphatic chain of PQAS and the above properties was investigated, by finding that the net charge on the ammonium group does not increase as the number of carbons in the aliphatic chain increase. Thus, although this number influences the antibacterial activity of the surfactants, this influence is not correlated with an increase of the ammonium positive charge. Unlike the partial charges, a different trend was obtained for the surfactants' maximum length and aspect ratio in agreement with the experimental behavior. As this modeling does not use empirical or adjustable parameters, it can assist the synthetic plan of new structures for surface functionalization, in order to improve the biofouling resistance of the membranes.  
**Keywords:** membrane surface functionalization; biofouling resistance; polymerizable surfactants; antibacterial activity; structure-property relationship; molecular based modeling
## 3 Frequentist Estimation
### EM Algorithm
When performing the M-step during the iteration process, for the purpose of maximizing the pseudo log-likelihood function \(\ln{L_{C}(\mathbf{a},b)}\) based on the complete data, through (24), we can derive that  
\[a^{*(k)}=n(\sum_{i=1}^{n_{1}}F_{2,i}^{(k)}+\sum_{i=n_{1}+1}^{n_{1}+n_{2}}E_{2,i}^{(k)})^{-1}, \tag{25}\]  
and  
\[b^{(k)}=n[(a^{*(k)}+1)(\sum_{i=1}^{n_{1}}F_{3,i}^{(k)}+\sum_{i=n_{1}+1}^{n_{1} +n_{2}}E_{3,i}^{(k)})-(\sum_{i=1}^{n_{1}}F_{1,i}^{(k)}+\sum_{i=n_{1}+1}^{n_{1}+ n_{2}}E_{1,i}^{(k)})]^{-1}. \tag{26}\]  
In addition, we say the process converges when  
\[|a_{j}^{(k+1)}-a_{j}^{(k)})|<\epsilon,j=1,2,\ldots,s,\]  
and  
\[|b^{(k+1)}-b^{(k)})|<\epsilon,\]where \(\epsilon\) represents the tolerance threshold fixed in advance, and proper values of \(\epsilon\) need to be obtained by numerical tests. Based on the above, the sequential steps of the EM algorithm are expressed in Algorithm 1 systematically. ```
1:Input:\(data\), initial parameter \(a^{*(0)}\), \(b^{(0)}\) and \(a_{j}^{(0)}=\frac{m_{j}a^{*(0)}}{n}\)
2:Initialization:\(k=0\)
3:while not converged do
4:E-step (Expectation step):
5: Under the current parameter estimate \((\mathbf{a}^{(k-1)},b^{(k-1)})\), calculate the expectations: \[E_{1,i}^{(k)},E_{2,i}^{(k)},E_{3,i}^{(k)}\ for\ i=n_{1}+1,n_{1}+2,\ldots,n_{1}+n_{2}\] \[F_{1,i}^{(k)},F_{2,i}^{(k)},F_{3,i}^{(k)}\ for\ i=1,2,\ldots,n_{1}\]
6:M-step (Maximization step):
7: Update the parameter estimates by using Equations (25) and (26)
8:\(k=k+1\)
9:endwhile
10:Output: Estimated parameter \(a_{j}^{*(k)}\) and \(b^{(k)}\)
```  
**Algorithm 1** The EM algorithm for obtaining MLEs under middle-censored data with competitive risks
# Electro-Optic Properties of High-Efficiency Organic Electronics with the Addition of An Interlayer
## 2 Materials and Methods
### SAMs and Device Deposition Process  
In the deposition of each type of SAM to the surface of the ITO substrate, the following procedures were performed. Taking the washed ITO in a 500 mL beaker, a 5 mM hybrid solution was mixed with ethanol for 1 h to produce the solution, and the ITO substrate was placed in the solution for 1 h for filming. In the comparative analysis of the filming by time, the washed ITO substrate was placed in a 5 mL PFA container with the hybrid solution and a 500 mL PFA container simultaneously to mediate vapor deposition, after which the ITO substrate was washed with IPA. Following hot plate drying, the substrate properties were analyzed based on the contact angle, while the performance characteristics were comparatively analyzed by fabricating N,N'-Bis(naphthalene-1-yl)-N,N'-bis(phenyl)-2,2'-dimethylbenzidine(\(\alpha\)-NPD) (50 nm), Alq3 (50 nm), LiF (0.6 nm), and Al (100 nm) devices using the vacuum thermal evaporator and the ITO substrate with the SAM film coating. The molecular structure of SAMs consists of a head group that chemically adheres to the substrate, a central alkyl chain that allows a regular pattern of molecular film formation, and a functional group that affects the function of the molecular film [14]. A hybrid solution was used as the SAM material; \(\alpha\)-NPD was used for the HTL. Next, using a vacuum thermal evaporator, three devices were fabricated: one with an HIL and an HTL, one with only an HTL, and one with neither an HIL nor HTL. Figure 1 shows schematics of the materials used in this study as energy bands. The hole injection barrier is high due to the basic ITO work function of 4.8 eV and HOMO level of 5.5 eV to induce a difference in the hole injection barrier between HIL and HTL so that the hole carrier transport is not efficient [29; 30; 31; 32]. In SAM filming, the perfluorododecyl-1H,1H,2H,2H-triethoxysilane-perfluorotetradecyl 1H,1H,2H,2H-triethoxysilane mixture (hybrid solution) exhibits a level of at least 5.6 eV at F10 SAM [33; 34] and F12 SAM [34]. Table 1 describes the fabricated OLED device thickness in Device (A) with the HIL (hybrid solution) and HTL (\(\alpha\)-NPD) composing an interlayer; Device (B) without HIL; and Device (C) without either. The goal was to ensure that the hole carrier transport is efficient via the sequential lining up of HOMO levels through the role of the interlayer.  
Figure 1: Energy band schematic.
## 4 Results and Discussion  
### Experimental Consistency of Static and Dynamic Tests  
The static elastic modulus (\(Ec\)), initial chord elastic modulus (\(Ei\)), and compressive strength (\(fc\)) values were collected through static testing of the concrete according to ASTM C469/C469M-14 and ASTM C39/C39M-14a [3,26], and the _Eds_ (\(ASTM.LT\), \(f1\)\(f2\)\(LT\), and \(ASTM.TR\)) values calculated as the first and second resonance frequencies were calculated using Equations (1)-(4), for frequencies obtained by dynamic testing according to ASTM C215-14. The ranges of physical and mechanical properties of the specimens in the Mix1 and Mix2 were summarized in Table 2 and the coefficient of variation (COV) which means the standard deviation (\(\sigma\)) divided by the mean value (\(\mu\)) of the same group was used to evaluate the consistency of the test results in Table 3. Outliers were detected using the Z-score method, and some data were removed from the statistical analysis [34]. The COV of the \(Ec\) by static testing ranged from 4.60% to 14.31%, and the COV of the fc ranged from 3.03% to 5.47%. The 28-day fc values of Mix1 and Mix2 were 19.26 MPa and 43.99 MPa, respectively, i.e., similar to the target fc. However, the COV of the \(Ec\), which was slightly higher in Mix 1 (low curing age), was slightly distorted on the opposite side of the test piece; thus, the deformation during compression was not uniform. This appears to make the \(Ec\) more sensitive than the fc in static testing. The COV of the \(Ei\) ranged from 4.31% to 7.89%, and by using the method shown in Figure 4, it was possible to overcome the instability in the initial part of the stress-strain curve to collect consistent values. The COV ranges of \(ASTM.LT\) and \(ASTM.TR\) as measured by the longitudinal and transverse resonance frequency tests  
Figure 6: Procedure of artificial neural network from an input sample.  
were 2.14%-4.36% and 2.71%-5.16%, respectively. The COV of _f1/2.LT_, in which the first and second frequencies in the longitudinal mode of the resonance frequency test were used, ranged from 2.35% to 4.71%. For ASMT.LT, f1,f2.LT, and ASTM.TR, the COV remained reasonably consistent.
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 3 Experimental Work  
Design of Information Downlink Communication System While Drilling Based on Anisotropic Magnetoresistance Detection  
The whole system mainly includes the following parts: the transmitter includes the instruction sending circuit, processor, and signal excitation circuit; The receiver mainly includes the reluctance sensor and its position/reset circuit, amplifier circuit, ASK demodulation circuit, A/D acquisition circuit, processor, external clock reference circuit, a memory circuit, communication circuit. The overall framework is shown in Figure 7.  
Due to the complexity of downhole conditions, the channel greatly impacts the transmission of code elements. Golay (23,12,7), the only nontrivial multi error correction complete code in the binary domain, is used to reduce the BER of the system. The system adopts pure software decoding mode, and the upper computer performs sampling decision and Golay (23,12,7) decoding operation according to the envelope data collected, which has strong portability.
## 4 Results and Discussion
### Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Learning Model** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{ANN} & Low & 0.705 & 0.804 & 0.751 \\  & Moderate & 0.638 & 0.623 & 0.631 \\  & Severe & 0.868 & 0.787 & 0.826 \\  & Overall (average) & 0.737 & 0.738 & 0.738 \\  & Low & 0.707 & 0.763 & 0.734 \\  & Moderate & 0.639 & 0.596 & 0.616 \\  & Severe & 0.844 & 0.848 & 0.846 \\  & Overall (average) & 0.730 & 0.736 & 0.733 \\  & Low & 0.656 & 0.628 & 0.641 \\  & Moderate & 0.547 & 0.510 & 0.528 \\  & Severe & 0.773 & 0.838 & 0.804 \\  & Overall (average) & 0.659 & 0.698 & 0.659 \\  & Low & 0.729 & 0.761 & 0.745 \\  & Moderate & 0.644 & 0.605 & 0.624 \\  & Severe & 0.841 & 0.860 & 0.851 \\  & Overall (average) & 0.738 & 0.742 & 0.740 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of overall ML confusion matrix results (building register information and fire scenario).