esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The development of DC distribution network technology has provided a more efficient way for renewable energy accommodation and flexible power supply. A two-stage stochastic scheduling model for the hybrid AC/DC distribution network is proposed to study the active-reactive power coordinated optimal dispatch. In this framework, the wind power scenario set is utilized to deal with its uncertainty in real time, which is integrated into the decision-making process at the first stage. The charging/discharging power of ESSs and the transferred active/reactive power by VSCs can be adjusted when wind power uncertainty is observed at the second stage. Moreover, the proposed model is transformed into a mixed integer second-order cone programming optimization problem by linearization and second-order cone relaxation techniques to solve. Finally, case studies are implemented on the modified IEEE 33-node AC/DC distribution system and the simulation results demonstrate the effectiveness of the proposed stochastic scheduling model and solving method.  
hybrid AC/DC distribution network; voltage source converter; active and reactive power coordination; two-stage stochastic programming +
Footnote †: journal: Applied Sciences
# A Comparison of Electrical Breakdown Models for Polyethylene Nanocomposites
## 2 Electrical Breakdown Models
### Electrical Breakdown Criteria  
In the EBEF breakdown model, the electric field concentration effect caused by the accumulation of space charges is mainly considered. It is assumed that when the highest electric field \(E_{max}\) inside the nanocomposite reaches a certain threshold value \(E_{C}\), the material is broken down. The breakdown criterion is \(E_{max}\geq E_{C}\)[23].  
In the EBEG breakdown model, the process of electron energy gain under the applied electric field after the mobile charge enters the free volume is mainly considered. When the energy gained by the mobile charges is greater than the trapping ability of the deep traps, the nanocomposite is broken down. Assuming that the free volume length is \(\lambda_{0}\), the energy gained by the electron in the free volume is \(e\lambda_{0}E(x)\). The maximum energy of electrons inside the nanocomposite is \(e\lambda_{0}E_{max}\). As shown in Figure 2a, the breakdown criterion of the EBEG model can be obtained as \(e\lambda_{0}E_{max}\geq u_{T}\)[27].  
In the EBMD breakdown model, the charge carrier injection and transport processes inside the nanocomposite under a strong electric field are considered. Furthermore, after the charges are captured by the deep traps on the molecular chain, the electric force acts on the molecular chain to cause its directional displacement. The time-dependent relationship of the displacement \(\lambda_{mol}\) of the molecular chain is calculated by the following equation [33]:  
\[\frac{\mathrm{d}\lambda_{mol}}{\mathrm{d}t}=\mu_{mol}E-\frac{\lambda_{mol}}{ \tau_{mol}} \tag{8}\]  
where \(\mu_{mol}\) is the molecular chain mobility and \(\tau_{mol}\) is the relaxation time constant of the molecular chain. In addition, \(\mu_{mol}\) decreases with the increase in the deep trap energy, \(\mu_{mol}=\mu_{0}\mathrm{exp}(\cdot u_{T}/k_{B}T)\), and \(\tau_{mol}\) increases with the increase in the deep trap level, \(\tau_{mol}=v_{0}{}^{-1}\mathrm{exp}(u_{T}/k_{B}T)\).  
The directional movement of the molecular chain will cause the expansion of the free volume. It is assumed that the free volume length is equal to the molecular chain displacement, that is, \(\lambda_{fo}(t)=\lambda_{mol}(t)\). The mobile charges gain energy in the expanded free volume. If the energy gained by the electrons exceeds the trapping ability of the deep trap, the nanocomposite will be broken down. As shown in Figure 2b, the breakdown criterion of the EBMD model is expressed as \([e\lambda_{fo}(t)E(t)]_{\mathrm{max}}\geq u_{T}\)[25].
## 3 Results
### Impact of ComBat Harmonization  
For the fourth approach, the slice thickness was used as the batch (Figure 3), the C-index values ranged (average \(\pm\) SD) between 0.30 and 0.79 (0.55 \(\pm\) 0.08) in Dataset 1, with intensity maximum being the most selected feature (94.8% of the times), followed by intensity peak position (84%) intensity minimum (79.2%), intensity skewness (59%), and intensity uniformity (51.4%).  
In Dataset 2, the c index values range (average \(\pm\) SD) was 0.05-0.95 (0.43 \(\pm\) 0.16), with none of the features being selected in more than half of the times. Intensity minimum was the most selected feature (43.7%) followed by intensity kurtosis (35.6%). In Dataset 3, the c index values ranged between 0.29 and 0.90 with an average (\(\pm\) SD) of 0.61 \(\pm\) 0.09. Intensity minimum was the most selected feature in this scenario (76%) followed by intensity peak position (68.2%). The remaining RFs were selected in less than 50% of the times.  
Using the pixel spacing the batch (the fifth approach) resulted in C-index values ranges (mean \(\pm\) standard deviation) of 0.25-0.75 (0.51 \(\pm\) 0.08), 0.05-0.97 (0.47 \(\pm\) 0.17), and 0.23-0.87(0.59 \(\pm\) 0.09) in Dataset 1, Dataset 2, and Dataset 3, respectively.  
On average, the models based on the original RFs values before ComBat harmonization significantly (\(p\)-value \(<\) 0.05) outperformed the models based on harmonized RFs across all datasets, except for the models based on RFs harmonized based on the slice thickness in Dataset 1.
# Statistical Analysis of Acoustic Emission in Uniaxial Compression of Tectonic and Non-Tectonic Coal
## 4 Analysis and Discussion  
### Probability Density Function Distribution of AE Absolute Energy  
Previous studies show that the probability density function of AE absolute energy in porous material collapse exhibits critical behavior, following the power law distribution [17, 26]. Figure 4a shows the probability density function of AE absolute energy for both kinds of coal sample. The AE energy follows the power law very well, especially for the distribution of non-tectonic, which spans near seven energy decades. The slope of the power law line is the exponent which controls this distribution. In order to investigate this critical exponent in more detail, we use the ML (maximum likelihood) method [27] to study the AE energy probability density function distribution. ML avoids the choice of bins and construction of histograms. If the AE energy following the power law probability density function distribution, \(P(E)\)-\(E^{-\varepsilon}\), the ML estimated probability density function distribution  
Figure 3: (**a,b**) are AE amplitude for non-tectonic and tectonic coal. (**c,d**) are AE absolute energy spectrum, cumulative energy curves, and compression stress curves for non-tectonic and tectonic coal.  
exponent \(\varepsilon\) is: \(\varepsilon(E_{\min})=1+n[\sum_{i=1}^{n}ln\frac{E_{i}}{E_{\min}}]^{-1}\), and the ML curve leads to a plateau for the exponent \(\varepsilon\). Moreover, theoretical study shows [28] that the shape of ML curves shows if the AE data correspond to a unique density function distribution, or mixing of two different sources. Figure 4b shows the maximum likelihood curves for tectonic and non-tectonic coal. The shape of maximum likelihood curve for non-tectonic coal shows that a typical one fixed the power law distribution behavior, and the estimated exponent values from the shoulder of maximum likelihood curve (about 1.38) also agrees with the mean filed theory for fracture mechanism [16]. For tectonic coal, the maximum likelihood curves show mixing fixed points behavior. The ML curve of mixing shows the horizontally arranged S shape, the maximum corresponds to a lower bound of the high exponent. The minimum of the curve approximately corresponds to an upper bound of the low exponent. For the tectonic coal's ML curves, the lower exponent is consistent with that of non-tectonic coal, so one source of the fixed point is also the fracture mechanism. Moreover, the higher exponent value is close to 1.7. The dashed curve comes from the theoretical prediction of mixing ML curve [28], which agrees with the result of tectonic coal well. This experimental exponent value is very similar to the theoretical prediction from friction source [29]. This result agrees with the material structure difference between these two kinds of coal samples. Non-tectonic coal is more intact, and the main collapse used by the crack propagation and fracture, while the tectonic coal has a weaker structure and even granular-like structure, which introduces the friction effect in the compression process.
## 3 Results and Discussion  
### TRMM Data Verification  
To assess the accuracy of the TRMM satellite precipitation data, they were compared with the meteorological data of the study area, using statistical indicators. The indicators' values computed in different stations are shown in Table 1.  
The value of R2 could imply the satisfactory precision of estimated precipitation in different locations of the study area. The results of EF attest that TRMM satellite is able to both predict the real precipitation of the province and estimate the extreme precipitations at an admissible precision. As stated by [20], the EF greater than 0.5 indicates that the performance of the model in predicting the data is land satisfying. The values of IA connote an agreement between monthly precipitation rates in the selected stations and TRMM precipitation data. The best agreements between time series of estimated and observed precipitations were discovered for Kangavar, Sonqor, Javanrud, Qasr-e-Shirin, and Islamabad-e-Gharb stations. It is to be also noted that the values of IA in the other stations exceeded the value of 0.9. The results regarding the spatial distribution of NRMSE have inferred an acceptable estimation of precipitation with not so much error of TRMM. The NRMSE values showed that there are few precipitation deviations in different stations. This issue was observed commonly in the southern and western halves of the study area. The slope values, which lie in the range of 0.7-1.1, confirmed that in most parts of the province, the estimated data were not so far away from the fit line. For instance, Figure 3 illustrates the comparison between the data of TRMM precipitation and those of the selected stations in the study area based on of BIAS indicator.  
According to Figure 3, the model, apart from Sanqor and Gilan-e-Gharb stations, overestimates the precipitation at all stations. The maximum overestimation for Kermanshah station was 8.38 mm, which is about 2% relative to the total BIAS average monthly rainfall at 439 mm. [PERSON] et al. [20] stated that the relative BIAS values were less than 10% of the model's very good performance, between 10% and 15% of the model's good performance, and between 15% and 25% of the model's relatively good performance. Generally, the precision assessment of TRMM precipitation data based on various statistical models in Kermanshah province verified that this data set enjoys a suitable accuracy. The high precision of TRMM precipitation data is pointed out in the research of [PERSON] et al. [13] and [PERSON] et al. [15].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
\begin{tabular}{c} **Statistical** \\ **Indicator** \\ \end{tabular} & \(\mathbf{R^{2}}\) & **EF** & **IA** & **NRMSE** & **Slope** & **BIAS** \\ \hline
**Values** & 0.7–0.8 & 0.6–0.8 & 0.91–0.95 & 0.6–0.85 & 0.7–1.1 & \textless{}8.38 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical indicators of TRMM precipitation and observed data in different stations.
## 3 Results
### Validation of Hybrid Models  
In total, eleven cloud-free Sentinel-2 images were used to derive indices for 2019 and 2020. All six hybrid models, as shown in Table 3, were used as a proxy for f\({}_{\text{PAR}}\) to model GPP using light use efficiency model (Equation (1)) for the \(\varepsilon\) values presented in Table 2. The modelled GPP was validated against EC-derived GPP for years 2019 and 2020, which were climatologically wet years (Table 4). GPP estimates using Models 2 and 4 showed a strong correlation with the EC-derived GPP, using all the literature-based \(\varepsilon\) values, with Spearman correlation coefficients of 0.96 and 0.89, respectively, as shown in Figure 6. Models 5 and 6 exhibited moderate correlation from approximately 0.61 to 0.69, with the EC-derived GPP, while Models 1 and 3 were poorly correlated with the EC GPP, with Spearman coefficients of 0.2 to 0.25, respectively.  
Figure 5: The seasonal temporal dynamics of meteorological parameters: (**a**) Daily gross primary productivity (g C m\({}^{-2}\) d\({}^{-1}\)) and (**b**) Light use efficiency parameter (\(\varepsilon_{\text{site}}\)) (g C MJ\({}^{-1}\) d\({}^{-1}\)).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Modelled GPP for \(\epsilon\) = 0.61**} & \multicolumn{10}{c}{**Modelled GPP for \(\epsilon\) = 0.83**} & \multicolumn{10}{c}{**Modelled GPP for \(\epsilon\) = 1.2**} \\ \cline{3-14}
**Date** & \begin{tabular}{c} **GPP** \\ **(EC)** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **1** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **2** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **3** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **4** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **5** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **6** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **1** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **2** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **3** \\ \end{tabular} & \begin{tabular}{c} **Model** \\ **4** \\ \end{tabular} & \
\begin{tabular}{c} **Model** \\ **5** \\ \end{tabular} &
\begin{tabular}{c} **Model** \\ **6** \\ \end{tabular} \\ \hline
10/2/2019 & 0.880 & \(-\)0.106 & 1.017 & \(-\)0.176 & 0.148 & 0.111 & 0.127 & \(-\)0.095 & 1.002 & \(-\)0.185 & 0.136 & 0.107 & 0.135 & \(-\)0.112 &
## 5 Estimation Equation for Maximum Load Bearing Capacity Considering Buckling Position
### Proposed Estimation Equation  
Nonlinear regression analysis was performed using the statistical analysis software R release 3.6.2 for each group of buckling positions in the PCFST discussed above. The estimation equation for the group where the steel tube buckled at the base, or in two locations, is shown in Equation (7). For the group where the steel tube buckled at the section just above the concrete, the estimation equation is shown in Equation (8).  
Figure 13: Buckling position relationships with \(n\) and \(L_{\text{c}}/L\): (**a**) Model C-009-020; (**b**) Model C-009-040.  
\[\frac{P}{P_{\rm u}}=\left(2.91\times\frac{L_{\rm c}\,^{0.06}}{L}\times R^{0.27} \times\lambda^{0.01}\right)-\left(0.15\times\frac{L_{\rm c}\,^{-0.31}}{L}\times R ^{-0.72}\times\lambda^{0.84}\times n^{0.95}\right) \tag{7}\]  
\[\frac{P}{P_{\rm u}}=\left(2.02\times\frac{L_{\rm c}\,^{0.41}}{L}\times R^{-0.06} \times\lambda^{-0.03}\right)-\left(3.61\times\frac{L_{\rm c}\,^{0.37}}{L}\times R ^{0.05}\times\lambda^{0.15}\times n^{1.34}\right) \tag{8}\]  
\(P_{\rm u}\) indicates the maximum bearing capacity at \(L_{\rm c}/L=0\) and \(n=0\) for each model.
## 5 Conclusions  
This study aimed to determine a simple way to age the binder-filler system within asphalt mixtures called mastics. The rheological properties and the rate of oxidation of the mastics with different combinations of binders and filler contents were then evaluated. Key findings were determined as follows:  
* Based on the statistical analysis, the oven study shows that the oxidation level of the asphalt mastics in the oven is influenced significantly by the distance from the plate to the resistive heater in X-axis since the limitation of the circulation.
* The mastics could become stiffer, the phase angle and relaxation spectrum decreases with the increasing aging times, and it was observed that 8 h of aging could change the rheological properties of the mastics significantly by visualization.
* When compared to the aging process of mastics with and without fillers, it was found that the addition of fillers could accelerate the aging of mastics dramatically. The hypothesis is the addition of filler would shorter the binder film thickness and therefore speed up the aging process.
* From the analysis of aging index, it could be found that both the modification of binder and filler contents inside the mastics could influence the aging of mastics. The mastic produced by SBS modified asphalt present a lower aging rate when compared to the mastic from the neat binder. The aging index of the mastics increases with the filler content in a quadratic function, which may be attributed to the changing binder distribution within the mastics with a different volume of fillers.  
This study could help to promote an understanding of the oxidative aging of the mastics, and a more precise evaluation and prediction of the performance of the mastics with different filler contents or modification after aging. But the influence of the interaction between the filler and binder on the aging properties of mastics needs to be further studied in the future by considering different mineral fillers and modification of binders.  
Conceptualization, H.L.; Data curation, S.L.; Formal analysis, J.J.; Funding acquisition, H.L.; Investigation, J.J.; Methodology, H.L., J.J. and X.M.; Resources, X.M.; Supervision, H.L.; Writing--original draft, J.J.; Writing--review & editing, S.L. and X.M.  
The authors are grateful for the financial support from the Scientific Research Foundation of Nanjing Institute of Technology (YKJ201728).  
The authors gratefully acknowledge [PERSON] for the help on experiments of the study.  
The authors declare no conflict of interest.  
Figure 8: The aging index of different mastics: (**a**) effect of filler contents on the PG76-22 mastics; (**b**) effect of binder types.
## Materials and Methods
### Total RNA Extraction, cDNA Synthesis, and Quantitative Real-Time PCR (qRT-PCR) Analysis  
Total RNA was extracted from 0.20g frozen flowers using TRIzol reagent (CoWin Biotech Co., Ltd., Beijing, China) as per the manufacturer's instructions. DNA contamination was removed by treating the total RNA with the DNase I enzyme. cDNA was synthesized using a one-step gDNA removal and cDNA synthesis supermix (Transgene, Wuhan, China) according to the manufacturer's instructions. The synthetic first-strand cDNAs were diluted 10-fold for gene expression analysis. The gene expression of _CpMYC2_ and _bHLH13_ was studied by qRT-PCR in 5 developing stages of the flower of H29, i.e., FB (flower bud), DP (display petal), POF (partially-open flower), OF (open flower stage), and SF (senescing flower) (Figure 1) on an Applied Biosystems 7500 Fast Real-Time PCR platform with the SYBR Premix Ex TaqMan(tm) II mix (Takara Biotechnology Co., Ltd., Dalian, China), according to the manufacturer's instructions, and the results were analyzed using the Applied Biosystems 7500 software (Applied Biosystems Life Technologies). Three biological replicates were tested, and reactions carried out in triplicate. Relative transcript levels were calculated by the 2\({}^{-\Delta\Delta\text{CT}}\) method [71] using the previously reported most stable genes _RPL8_ and _Tubulin_ (_CpTublin_) for wintersweet [72; 73], _NADH_ dehydrogenase _(NtNADH_) and chaperonin _CPN60-2_ (_NtCPN60-2_) for tobacco [74], and clathrin adaptor complex subunit (CACS) (_AtCACS_) and \(\beta\)-tubulin2 (_AtTubulin_) [20; 75] for arabidopsis as the endogenous control genes for data normalization according to the recommendations [76] and described method [77]. The primers 35S F and 35S R were used for the expression of empty gene transformation. The transcript level of monoterene-producing _At1g61680_ and _At3g25810_ along  
\begin{table}
\begin{tabular}{c c c} \hline
**Primer Name** & **Primer Sequence (5’-3’)** & **TM C\({}^{0}\)** \\ \hline Salk\_083483 LP & TGGTTTTTTCTGTTCGATG & \\ Salk\_083483 RP & CTCTAATCATTGCGCTCCAACC & 60 \\ \hline LBb1.3 & ATTTTTGCCGATTCCGGAAC & 60 \\ \hline \end{tabular}
\end{table}
Table 3: Primers used for PCR for homozygous T-DNA insertion lines.  
with the sesquiterpene-producing _At5g23960_ and _At5g44630_ genes in arabidopsis was also measured. Primer Premier 5 software was used to design the primers used in the experiments. The primers used for qRT-PCR analysis are listed in Table 4.
## 2 Materials and Methods  
### Process Description  
The schematic block diagram of an integrated co-gasification system of BC blended with MSW is presented in the Figure 1, which consists mainly of three processes: (1) air separation unit (ASU), (2) co-gasification of MSW and BC, and (3) CO\({}_{2}\) absorption unit and syngas treatment. The mixtures of MSW and Shenmu BC act as the feedstock, and their proximate and the ultimate analyses are displayed in Table 1. The raw MSW and BC are first dried and then sent to be co-gasified in a fluidized bed gasifier using O\({}_{2}\) and CO\({}_{2}\) as the gasifying agents. Moreover, the H\({}_{2}\)O component from the raw MSW drying process can also act as an agent to improve the BC gasification. The main chemical reactions for the MSW/BC co-gasification process are illustrated in the Table 2. Among them, the R1 reaction represents the drying process of MSW/BC, and R2 reaction stands for the pyrolysis process of dried MSW/coal, which starts at above 230 \({}^{\circ}\)C [21], where the char and volatile matters are released and participate in gasification reaction. The R3-R11 reactions represent a series of gasification reactions, including heterogeneous (R3-R6) and homogeneous reactions (R7-R11). The required oxygen can be obtained by the ASU, which uses cryogenic technology due to its high separation efficiency and relatively low cost for large-scale oxygen production [22]. After being cooled and purified, the raw syngas enters the CO\({}_{2}\) absorption unit to remove CO\({}_{2}\). Some of the CO\({}_{2}\) absorbed is recycled in the gasifier to act as the agent and the rest is captured. The syngas leaving the absorption is treated as syngas product.  
Figure 1: The schematic of the MSW/BC co-gasification system in CO\({}_{2}\)/O\({}_{2}\) atmosphere.