esa-sceva/satcom-chunk-collection huggingface dataset:
# Developments on Perovskite Solar Cells (PSCs): A Critical Review
## 3 Modelling the Perovskite Electronic Structure  
Methylammonium lead iodide (MAPbI3) is the simplest and well-studied perovskite absorber compound. Its crystal structure consists of an organic MA+ molecule surrounded by three-dimensional inorganic Pb-I molecules. Figure 3a-f demonstrate the effect of MA+ \(-\)001, \(-\)110, and \(-\)111 ionic orientations in the bandgap structure of cubic and tetragonal MAPbI3, determined from Perdew-Burker-Ernzerhof function for solids. It is observed that the conduction (CB) and valence (VB) band of the MAPbI3 electronic structure vary with MA+ orientation; however, the overall electronic bandgap remains the same throughout 001-, 110-, and 111-MA+ ionic orientations. This, therefore, suggests that the MAPbI3 bandgap depends on the three-dimensional Pb-I system surrounding the MA+ ion, and not on the MA+ ion [33; 35; 36; 37]. In additional studies on the MAPbI3 electronic structure, [PERSON] (2014) studies on the density of states (DOSs) emphasizes more on the above suggestion. Figure 3g illustrates MAPbI3 total and partial DOSs. It is observed from the DOS positions of lead (Pb) and iodide (I) p-orbitals in Figure 3g occupy the CB and VB, respectively, indicating that MAPbI3 optical transitions rely on Pb and I p-p transitions (see Figure 3g) [35; 38].  
Figure 2: MAPbI3 phase transitions arranged in order of increasing temperature (**a**) orthorhombic, (**b**) tetragonal, and (**c**) cubic perovskite phases [34].
## 3 Results and Discussion
### GPP, NEE and eWUE Responses across ET Mean Gradients  
ET is a pivotal factor that can have a great impact on GPP, NEE and eWUE. To diminish the dispersion of our data, we depicted GPP, NEE and eWUE average values in relation to seven ranges of ET, from 0 to above 6 mm d\({}^{-1}\) (Figure 8). In Figure 8, it can be observed that the response of GPP to ET differed depending on the ET range. The higher the ET, the larger the GPP under a certain threshold (ET ca. 4-5 mm d\({}^{-1}\)). Around that point, GPP reached maximum values. Additionally, above that ET of ca. 4-5 mm d\({}^{-1}\), both NEE and eWUE tended to stabilize, failing to present statistical differences (see post hoc letters in NEE and eWUE in Figure 8). Particularly in agro-ecosystems, the increase in carbon assimilation in response to water availability and transpiration rates is maintained until  
Figure 7: Relationship between monthly averages of GPP and NDVI (**A**), eWUE and NDVI (**B**), and GPP and NDVI \(\times\) ET (**C**). The correlation coefficient (R\({}^{2}\)) and error bars, representing the standard deviation of the mean values, are also shown. All regressions are significant at \(p<0.05\).  
a certain point. From that point, the photosynthesis response to transpiration saturates due to non-stomatal limitations [108]. Hence, beyond that point, carbon assimilation and biomass production depend on other factors besides the sole water availability. In this sense, short-term environmental conditions [82; 109], physiological performance due to inherent phenological responses [110; 111] and biotic stresses [112] can lead to non-stomatal limitations, playing a key role in net CO\({}_{2}\) assimilation and, hence, in GPP, NEE and eWUE performance. This effect, where net carbon assimilation, biomass and yield gain do not increase from a certain threshold of water use, has been previously reported in other studies. For example, [113] observed that the net carbon assimilation tended to stabilize from a certain threshold stomatal conductance. This scaled-up effect was reported by [98], who reported that almond yield failed to improve above a certain threshold of irrigation volume.
# Orientation of Liquid Crystalline Molecules on PDMS Surfaces and within PDMS Microfluidic Systems
## 1 Introduction
For this reason, their quality must meet different standards than those required in typical microfluidic systems. Therefore, it seems reasonable to perform analyzes concerning the orientation of liquid crystal molecules in microstructures fabricated using cheaper technologies. Even if liquid crystalline waveguides in a PDMS arrangement have been previously investigated, their fabrication process has been exclusively limited to the photolithographic technique, with no particular studies dedicated to the determination of the conditions favoring the orientation of molecules on the PDMS surface [35]. With such a motivation of our work, the mutual presentation and comparison of different fabrication technologies is the main point of this paper's novelty. It allows for qualitative and quantitative data to be collected, leading to the benchmarking of available fabrication methods and emphasizing that the attention of this work has been devoted to the investigation of the possibilities of using various technologies for LC:PDMS waveguide creation. Several geometries of LC:PDMS structures, featuring a novel approach for electric field applications, have been analyzed. Specifically, using microchannels filled with an electrically conductive fluid to control LC molecular orientation is an innovative solution proposed in this paper. This feature is far more advanced than the previously studied LC:PDMS waveguiding structures with electrodes made of sputtered ITO [43] or gold deposited on PDMS by electroplating technique [37]. Besides their lack of stiffness and brittleness, electrodes in the form of microchannels (of almost any shape) filled with an eutectic gallium-indium alloy may be placed in the close vicinity of the LC channel. Because the possibility of the electrical control of the LC molecular orientation is not the main topic of the present work, detailed results on this subject (including electrodes of different, complex geometries) are reported in a future publication.
# A Grey Wolf-Based Method for Mammographic Mass Classification
## 4 Experimental Results
### Evaluation Criteria  
To assess the results of the proposed method, a set of popular benchmark metrics have been applied. Considering the confusion matrix obtained from the classification step, as shown in Figure 5, the following metric are used and they are briefly highlight as follows:  
Accuracy: This is to show percentage of correctly predicted classes to the total number of classes. It can be calculated as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]  
Precision: This is show the percentage of correctly positive predicted classes to the total positive predicted classes. A high precision confirms a low FP rate.  
\[Precision=\frac{TP}{TP+FP}\]  
Recall (Sensitivity): This is show the percentage of correctly positive predicted samples to all samples in an actual class. A high recall confirms a low FN rate.  
\[Recall=\frac{TP}{TP+FN}\]  
The area under the ROC (Receiver Operating Characteristics) curve is a graph of two-dimensional plot: sensitivity versus specificity. This curve is considered one of the important factors for evaluating the performance of classification systems where value 1.0 reflects that the system has a perfect performance.
## 5 Conclusions  
Based on the data of a high-frequency underwater acoustic communication experiment, this paper studied and analyzed the channel characteristics of a typical deep-sea incomplete channel under the shallow-depth high-frequency transmitted condition.  
The deep-sea field zone was divided into the direct-arrival zone and the shadow zone, and the channel characteristics were obviously different in the two zones. In the direct-arrival area, from the perspective of communication, the focus was mainly on the path clusters, such as D, S, B, and SB. The channel structure was relatively simple and stable, the RMS delay spread was usually less than 0.1 s, and the temporal coherence was 0.6-0.8.  
On the contrary, in the shadow zone, there was no D. In addition to S, B, and SB, the contribution of BS and SBS was non-negligible. The channel structures were more complex, and the channel temporal variations were more obvious than those in the direct-arrival zone. The RMS delay spread ranged from 0.1 s to 0.6 s, and the temporal coherence was between 0.2 and 0.6.  
Figure 20: Phase variations of individual clusters over time at 3405 m.  
Different path clusters were separated and their randomness was analyzed. D and B were the most stable, and their temporal coherence stayed above 0.8 within 160 s. Their coefficients of variation were less than 0.2. On the contrary, the temporal coherence of the surface-related paths clusters decreased significantly. Their coefficients of variation were generally larger. In most of the cases, the coefficients of variation were greater than 0.2.  
Table 4 is listed below to summarize the channel characteristics of the shadow zone and the direct-arrival zone in the communication scenario of this paper.  
Understanding these characteristics of high-frequency deep-sea channels can provide guidance for the development of communication algorithms and system design in typical communication scenarios.
## 3 Results and Discussion
### Reliability Assessment of Web Service Composition
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Stage in Growth** \\ **Testing** \\ \end{tabular} } & **Expert 1** & **Expert 2** & **Expert 3** & **Expert 4** & \multicolumn{2}{c}{**Beta Distribution**} & \multicolumn{2}{c}{**Hyperparameter**} \\ \cline{3-8}  & & & & & **Expected** & **Variance** & \(a\) & \(b\) \\ \hline \multirow{2}{*}{1} & (0.003, & (0.0035, & (0.0040, & (0.0038, & \\  & 0.0072) & (0.0075) & (0.0074) & (0.0072) & \multirow{2}{*}{0.0054} & \multirow{2}{*}{1.17 \(\times\) 10\({}^{-6}\)} & \multirow{2}{*}{17.58} & \multirow{2}{*}{140.41} \\ \hline \multirow{2}{*}{2} & (0.0080, & (0.0060, & (0.0065, & (0.0063, & \\  & 0.0088) & (0.0086) & (0.0090) & (0.0085) & \multirow{2}{*}{0.0077} & \multirow{2}{*}{3.42 \(\times\) 10\({}^{-6}\)} & \multirow{2}{*}{21.22} & \multirow{2}{*}{270.78} \\ \hline \multirow{2}{*}{3} & (0.0075, & (0.0077, & (0.0076, & (0.0078, & \\  & 0.0095) & (0.0093) & (0.0092) & (0.95) & \multirow{2}{*}{0.0085} & \multirow{2}{*}{2.48 \(\times\) 10\({}^{-6}\)} & \multirow{2}{*}{33.27} & \multirow{2}{*}{311.92} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Information of experts. Thus, the experiments demonstrated that in the case of identical prior information, MMR was the most accurate method to calculate the prior distribution, and it significantly reduced the number of test cases required during reliability demonstration assessment. In the case of ESM, the opinions of experts made the value of hyperparameters more subjective, which yielded a greater influence on the postdistribution. In the case of BMCM, the lack of prior information induced a uniform initial distribution of the hyperparameter over the domain. Subsequently, the distributions were repeatedly sampled to obtain the expected values of the hyperparameters. This procedure usually involves complex numerical calculations and is difficult to implement.
## 3 Results and Discussion  
### Construction of P&A Bacterial Consortium  
#### 3.1.1 Optimization of PAHs Degradation by Individual Bacteria  
Optimization of Individual Bacteria by Single PAHs  
Each of 10 bacteria was used to consume 500 mg/L NAP, 50 mg/L PHE, and 100 mg/L PYR, respectively [37; 38], and the absorbances of the solutions were determined by UV-vis spectrophotometer on Days 2, 4, 6, 8, and 10. Within 10 days of consumption, the average degradation rates of NAP, PHE, and PYR were continuing to increase up to 90.24%, 34.17%, and 34.36%, respectively (Figure 2), but the increase rates on Days 8-10 were significantly slower than others, so the degradation time of subsequent experiments was determined to be 8 days.  
It could be also seen that the degradation rates of NAP by 10 strains were all about 80% (Figure 2a) within 8 days. The top six bacterium with the best degradation effect on NAP, PHE, and PYR and their degradation rates within 8 days are shown in Table 2.  
Figure 1: Flow chart of heavy metal ions and PAHs degradation experiment.  
Figure 2: Degradation rates of single PAHs by purebred strain.  
Figure 2 and Table 2 show that, NAP, PHE, and PYR degradation rates reach 87.40%, 38.58%, and 44.88% when 1-1 was used, and 84.28%, 37.15%, and 30.29% when 2-1 was used. Bacteria 1-1 and 2-1 belong to _Pseudomonas_, with the most extensive degradation object range and species so far [39]. One strain of _P. anguiliseptica_ isolated from an urban area was reported with both degradation of PAHs and improved degradation efficiency of bacterial consortium [40].  
Similarly, based on the results in Figure 2 and Table 2, NAP, PHE, and PYR degradation rates reach 84.36%, 31.99%, and 45.83% when 1-5 was used, and 85.25%, 37.10%, and 31.02% when 1-6 was used. Bacteria 1-5 and 1-6 belong to _Bacillus_ and _Alcaligenes_, respectively. Four potential candidates with higher degradation effect single substance of PAHs were selected from 10 bacteria.
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 2 Methods and Materials
### Experimental Design  
To investigate the effects of water content and cementation degree on the shear characteristics of fault gouge, various parameter combinations are designed in the experiment. The water content is set at five levels: 10%, 15%, 20%, 25%, and 30%. Here, water content refers to the gravimetric water content, defined as the mass ratio of added water to the dry mineral mass. The cementation ratio, defined as the ratio of Portland cement mass to the total mineral mass, is established at 0%, 1%, and 3%. Normal pressures are applied at four levels: 100, 200, 300, and 400 kPa. Based on these parameters, a total of 60 fault gouge samples are prepared and subjected to direct shear tests. The experimental design is shown in Table 1. Additionally, to further understand the microstructural changes in fault gouge under varying conditions, scanning electron microscopy (SEM) analyses are performed on samples at different stages of the experiment to observe their microstructural characteristics under different water contents, cementation degrees, and normal pressures.
## 4 Conclusions  
In summary, triphenylamine- and acridine-based hole-transporting material TPA-1A was designed and synthesized. The synthesized hole-transporting material showed excellent thermal stability due to its rigid acridine moiety on both sides of the triphenylamine central core. The current efficiency of TPA-1A was 49.13 cd/A, which is higher than the NPB-based reference device (27.21 cd/A). Our material manifested an excellent power efficiency of 27.56 lm/W at 1200 cd/m\({}^{2}\). Moreover, the external quantum efficiency of TPA-1A was 13.74%, while the reference NPB exhibited a lower efficiency of 7.64%. Furthermore, the hole injection properties of TPA-1A (HIL) were demonstrated by an outstanding external quantum efficiency of 14.59%. Triphenylamine-acridine derivatives enhanced the device properties to ensure the proper charge balance than the NPB-based device. TPA-1A is a good candidate for highly efficient and thermally stable OLED applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **TPA-1A (HTL)** & **NPB (Reference)** & **TPA-1A (HIL)** \\ \hline Current (mA) & 0.006  & 0.006  & 0.01  \\  & 0.10  & 0.18  & 0.18  \\ Current efficiency (cd/A) & 44.29  & 33.38  & 37.09  \\  & 49.13  & 27.21  & 30.57  \\ Power efficiency (lm/W) & 31.62  & 27.56  & 19.88  & 19.21  \\  & 62.81  & 48.4  & 146.2  \\  & 1282  & 1197  & 1341  \\ External quantum efficiency (EQE \%) & 15.50  & 13.39  & 14.59  \\  & 13.74  & 7.64  & 8.53  \\ \multicolumn{6}{l}{17.1 pt}{International Commission on Illumination} \\ CIE (x,y)  & 0.33, 0.62 & 0.33, 0.63 & 0.34, 0.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Device properties of TPA-1A (HTL) with reference \(N\),\(N^{\prime}\)-Di(1-naphthyl)-\(N\),\(N^{\prime}\)-diphenyl-(1,1\({}^{\prime}\)-biphenyl)-4,4\({}^{\prime}\)-diamine NPB and TPA-1A (HIL).  
Figure 6: Electroluminescent (EL) spectra of fabricated green PhOLEDs.  
**Author Contributions:** [PERSON] and [PERSON] designed the project; [PERSON] and [PERSON] synthesized and characterized the material; [PERSON]-[PERSON] and [PERSON] fabricated the OLED device; [PERSON] supervised the device fabrication process; and [PERSON] analyzed and wrote the manuscript. All authors read and approved the final manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This research was supported by Wonkwang University (2016).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Simulation Cases for Greenhouse Model
#### 3.2.1 CO\({}_{2}\) Distribution toward Open and Closed Side Ventilation Inside the Greenhouse  
Figure 11 shows the simulation of CO\({}_{2}\) distribution inside the greenhouse when 1160 ppm CO\({}_{2}\) concentration was supplied through perforated tubes inside the plants. The appearance of CO\({}_{2}\) emissions from the perforated tube was confirmed with the degradation colors inside of the plants. CO\({}_{2}\) distribution in case open and closed side vents were showed a slight difference in CO\({}_{2}\) concentration (Figure 12).  
When the sidewall was opened (ventilated), the inflow wind from outside the greenhouse was continually updated to match the current wind velocity in each mesh [33]. The CO\({}_{2}\) concentration outside of the greenhouse (400 ppm) is lower than the initial CO\({}_{2}\) concentration inside the greenhouse (450 ppm), which may cause the CO\({}_{2}\) concentration near the ventilated wall to be lower. Furthermore, the position of the plants in the greenhouse was asymmetric (tend to the right side).  
The effects of open side vents on CO\({}_{2}\) distribution were slightly significant, especially near the wall. In comparison to the case of open side vents, the case with closed side vents showed a slightly more uniform CO\({}_{2}\) concentration within the greenhouse. Although the  
Figure 12: The CO\({}_{2}\) distribution inside of the greenhouse in case the side ventilations open and closed (image taken at cross-section 6 m from south wall).  
side vents are closed, air circulators supported moving large volumes of air to provide even distribution. The quantitative evaluation of these results using CV shown in Table 5 is 18.2% for the open and 15.6% for the closed side vents case for the whole greenhouse.  
However, focusing on the variability of CO\({}_{2}\) concentration inside the plant, open (8.8%) and closed (8.7%) side vents produced essentially no substantial contribution to even the variation of CO\({}_{2}\) concentration in the plant canopy because no initial wind velocity was considered at both side vents in our case. Therefore, virtually no air exchange occurred between outside and inside the greenhouse.  
Figure 12 (side ventilation open) showed the CO\({}_{2}\) concentration around the plant (rectangular shapes) was still maintained higher than ambient CO\({}_{2}\) concentration (approximately 400 ppm). This suggests that practical CO\({}_{2}\) enrichment was still effective even if side vent was open (in case, no outside wind).
## 4 Experimentation and Results
### Results for the Steady State  
For diagnosing the SCT fault in steady state, a segment of the vibration signal during the steady state is extracted. In Figure 10, the red rectangle depicts the selected signal.  
Figure 8: Time–frequency representation for the vibration signals with the comb filter: **(a)** 0 SCTs, **(b)** 5 SCTs, **(c)** 10 SCTs, **(d)** 15 SCTs, **(e)** 20 SCTs, **(f)** 25 SCTs, and **(g)** 30 SCTs.  
Figure 9: Boxplots for the _ENE_ index in: **(a)** Vx, **(b)** Vy, and **(c)** Vz.  
Once the signal has been selected, the FT with a Gaussian window, applied to minimize the leakage problem, is used to obtain its spectrum. Figure 11 shows the resulting spectra for all of the SCTs conditions in the three axes (Vx, Vy, and Vz). As can be observed, there are different changes in the frequency components for each SCT condition. At this point, it is not important to visually distinguish among spectra since they will be differentiated through a quantitative index. In order to do so, the _THD_ index, following Equation (6), is used. Figure 12 shows the normalized _THD_ values, with each boxplot summarizing the results from 100 tests for each SCT condition. Each subfigure corresponds to one axis (Vx, Vy, and Vz). Similar to the _ENE_ index, the values are also normalized, with the 0 SCTs condition serving as the baseline, leading to an index value of 1 for that condition. This strategy can be helpful for establishing a reference in different transformers. In Figure 12, it is observed that the _THD_ index can also increase in accordance with the severity of damage in the three vibration axes (see the red arrows). From a visual inspection, the results for both the X-axis and the X-axis exhibit proportional trends relative to the fault severity; however, the trend of the Z-axis appears more consistent, as indicated by the narrower boxplots. Table 2 presents the average values of the _THD_ index for each axis.
## 4 Implementation and Results  
In order to show how our proposed framework works, we gathered images from the Sentinel satellites of the European Space Agency (ESA) and the Landsat satellites from NASA. Then, we transformed these images into useful information that can be used to predict deforestation. This transformation includes atmospheric correction and cloud masking.
###### Abstract  
In order to comprehensively understand the content, source, speciation characteristics, and risk of heavy metals in cultivated soil of Pingshui Village, Zhaoping County, Hezhou City, this study conducted measurements on the total amounts of Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg in 34 soil samples within the study area. Correlation analysis and principal component analysis were employed to investigate their sources. An improved BCR sequential extraction procedure was utilized to analyze the occurrence forms of eight heavy metals in soil samples. Ecological risks were evaluated using the geo-accumulation index (\(I_{geo}\)), potential ecological risk index (\(RI\)), and risk assessment code (RAC). The findings revealed that: (1) The soil heavy metals in the study area exhibited varying degrees of enrichment, primarily attributed to anthropogenic activities. (2) There was no significant difference in the speciation characteristics of the eight heavy metals in the soil of each sampling site in the study area, and the main components were all residual fraction, and the mild acid-soluble fraction of Cd and Zn accounted for a relatively high proportion in individual sampling sites, which should be paid attention to. (3) Through the results of three risk assessment methods, it is concluded that the heavy metal pollution of soil in the study area is serious, and continuous attention should be paid to the corresponding pollution prevention measures.  
Zhaoping County; soil; heavy metals; speciation analysis; risk assessment Article