esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Physicochemical and Microbiological Analysis of Wastewater  
The efficiency of wastewater treatment of the CW has been previously assessed [1; 25; 28], however in order to compare to past campaigns and to relate to changes on microbial communities overtime, wastewater samples (n = 4 per season) were collected from the inlet and outlet zones of the CW during the cold (autumn/winter--C) and hot seasons (spring/summer--H), in three consecutive years (1, 2, 3). Physicochemical parameters for the wastewater samples were determined based on Standard Methods [29]: chemical oxygen demand (COD), biochemical oxygen demand (BOD\({}_{5}\)), total suspended solids (TSS), ammonia nitrogen (NH\({}_{3}\)-N), phosphates (PO\({}_{4}\)\({}^{3-}\)), nitrate (NO\({}_{3}\)\({}^{-}\)); nitrite (NO\({}_{2}\)\({}^{-}\)); pH, electric conductivity (EC), and temperature. The air temperature and relative humidity was recorded with a logger (OH503 logger--Greutor, Barcelona, Spain), at each sampling time.  
Simultaneously with physicochemical analysis, the enumeration of fecal indicators, namely total coliforms and _E. coli_, present in wastewater was also determined by plate counting using the selective media DIFCO\({}^{\text{TM}}\) mFC Agar and ChromoCult\({}^{\text{\textregistered}}\) Coliform Agar (Merck, Darmstadt, Germany), respectively, as followed in [PERSON] et al. [1].  
Enumeration of Total Coliforms, _E. coli_, and Heterotrophic Bacteria Associated to Substrate and Plant Roots  
Eight subsamples of substrate (from a depth between 10 and 15 cm) and of roots of plants colonizing the inlet and outlet zones were pooled to form a composite sample (10 g). A sampling area of 6 m\({}^{2}\) was considered at the inlet and outlet zones. This analysis was performed only once at the end of cold (autumn/winter--C) and hot seasons (spring/summer--H), of each year (1, 2, 3), to assess the effect of the season on microbiological parameters.  
Each composite sample was placed separately in sterile tubes with 10 mL of saline solution (0.85% NaCl) and shaken on a vortex mixer for 1 min at room temperature, based on a previous approach [16]. Serial dilutions were made in duplicate, and 0.1 mL of each dilution was spread onto ChromoCult, mFC, and nutrient agar media for _E. coli_, total coliforms, and heterotrophic bacteria enumeration, respectively. Plates were incubated at 25 \({}^{\circ}\)C (heterotrophic bacteria) and 37 \({}^{\circ}\)C (total coliforms and _E. coli_) for 4 days after which colony forming units (CFU) were counted.
# Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network
## 4 Experiment  
The evaluation and comparison of the matching performance of PCA-LMN with the MTM [21], MI [28], HOPC [9], CFOG [35], and deep homography estimation (DHE) [26] are presented in this section. MTM, MI, HOPC, and CFOG are traditional image-matching methods. MTM and MI match images directly based on image gray, while HOPC and CFOG match images based on handcraft features. PCA-LMN is also based on the handcrafted feature, but it employs a learnable matching network to measure the similarity and regress the transformation parameters. DHE is a deep learning-based end to end-trainable method.  
For the ablation study, PCA-LMN is also compared with the PCA-SAD, which uses the similarity measurement based on the SAD, and LGO-LMN, which directly estimates the structure orientation based on the log-Gabor filters.
## 4 Simulation Verification of Transferring Pressure Across Goal Effect
### Arrangement of Monitoring Points  
#### 4.2.1 Arrangement of Monitoring Points for Vertical Displacement of KS in Overlying Strata  
1. For the comprehensive assessment of overburden behavior adjacent to BHR 1513, seven vertical displacement monitoring lines (designated as Line A) were installed--one for each key stratum (KS1-KS7). These measurement arrays spanned from the intact coal rib of BHR 1513 to the boundary of Coal Pillar A, with spatial distribution illustrated in Figure 6(a) (using KS1 as representative case);
2. Simultaneously, a dedicated 10 m monitoring line (Line B) was deployed in the immediate roof strata above Coal Pillar B to capture its deformation characteristics during panel 1511 extraction. This instrumentation extended from the pillar's left edge to a position 5 m inward from its right boundary (Figure 6b).  
Figure 5: Step-by-step excavation process of working face 1511.  
Figure 6: _Cont._  
2.2 Arrangement of Monitoring Points for Vertical Displacement and Vertical Stress Above Coal Pillar  
To assess the load-bearing behavior of roof strata above coal pillars A and B during the extraction of working face 1511, two monitoring arrays were installed:  
Five measurement stations (M1-M5) positioned 2 m above coal pillar A (Figure 6c) to record vertical stress and displacement variations;  
An identical configuration (M6-M10) installed at the same elevation above coal pillar B (Figure 6d) for comparative analysis.
## 4 Results and Discussion
### Coefficient of Performance  
Equation (10) defines the COP and Figure 11a,b show the simulation results for the COP, based on Equation (10). Less regeneration heat is required under hot and dry conditions (Figure 9a,b), and the cooling capacity is high under hot and humid conditions (Figure 10a,b). In the 39 simulated scenarios, the regeneration heat ranges from 0 to 6.14 kW, and the cooling capacity ranges from approximately 4 to 7.5 kW. The COP is high under hot and dry conditions since regeneration heat is more sensitive to ambient conditions. In the actual system, the cooling capacity was more sensitive to the outdoor air conditions than to regeneration heat (seeFigures 9c and 10c). Therefore, the COP tended to behave in a similar manner to the cooling capacity: it was high under low temperature and high humidity conditions (see Figure 11c).  
\[\text{COP}=\frac{q_{Coal}}{q_{Reg}+W_{Elc}} \tag{10}\]  
Figure 11: Coefficient of performance (COP) for (**a**) Unit 1 and (**b**) Unit 2. (**c**) Field test data [10] of the COP of the existing system.  
Figure 10: Cooling capacity of (**a**) Unit 1 and (**b**) Unit 2. (**c**) Field test data [10] of cooling capacity for the existing system.  
In the calculation of the COP of the HDCS, which used the exhaust heat of the gas engine, the COP was determined by correcting the values of the driving energy sources, heat and electricity. A definition of the COP reflecting a heat-to-electricity ratio of 1:2 as provided in the Public Utility Regulatory Policies Act (PURPA) is provided in Equation (11) below [26].  
\[\text{COP}_{\text{PURPA}}=\frac{q_{Cool}}{0.5q_{Reg}+W_{Elc}} \tag{11}\]  
Even if the heat value is recalculated (Figure 12a,b), the trend of the COP does not change significantly with respect to the ambient conditions. While the overall distribution is similar to those in Figure 11a,b, the slopes of the contours in Figure 12a,b are steeper because of the reduced influence of the regeneration heat, which is sensitive to the outdoor humidity ratio. As the level of regenerative heat is considered half that of electricity, \(\text{COP}_{\text{PURPA}}\) has a larger value compared with the COP under identical ambient conditions. The change in the distribution according to the COP definition in the actual system is similar (Figures 11c and 12c).
## 6 Biocompatibility Analysis  
Biocompatibility is a key factor for PCB systems intended for biological applications. A few research groups have reported the biocompatibility of several PCB systems for in vitro cell cultures [41; 42]. The proposed system has been implemented in a 24-well cell culture plate with Human Dermal Fibroblast (HDF) cell culture to explore biocompatibility. Two 24-well culture plates were taken, where four wells from each plate were seeded with  
Figure 7: Bipolar and tetrapolar impedance measurement of DMEM using the multi–electrode system.  
HDF and supplementary growth media. One culture plate was exposed to the electrode setup, referred to as _experimental_ setup, whereas the other one was kept as the _control_ setup. The electrodes were placed in the experimental setup such that they were submerged in the wells, reaching near the bottom of each well. Research by [PERSON] et al. suggests that fibroblasts were stimulated for 4 h to achieve targeted biomodulation [43]; hence, the electrodes were placed in the HDF cell culture for 4 h. Both culture plates were placed in an incubator with the following conditions: 37 \({}^{\circ}\)C, 5% CO\({}_{2}\), and 95% humidity.  
Live cell count was performed on the culture plates at 48 h, and those wells in which the electrodes had been placed for 4 h (_experimental setup_) were compared against those that were not exposed to the electrodes (_control setup_). The proliferation rate of HDF after 48 h was (\(-\)3.9%) in the experimental setup when compared to the control setup. There was a standard deviation of 4.9%, indicating overlapping data points between the two setups. There were no statistically significant differences between the two groups (\(p=0.91\)), as determined by a Mann\(-\)Whitney U test (\(n=\) four per group).  
The microscope images of the HDF cell cultures are shown in Figure 8. Figure 8a shows the cell culture at the time of placement, noted as 0 h. Subsequent images of the culture plates were taken 24 h after placement (Figure 8b) and 48 h after placement (Figure 8c). A natural proliferation of HDF over a period of 48 h was observed. No evidence of any corrosion or adverse effect on the cells was observed due to the presence of the electrodes. It can be concluded that the proposed electrodes provide good biocompatibility for in vitro cell cultures.
## 3 Results and Discussion
### Discussion  
From the test results, it can be observed that in the first test stage, the actual model ratio coefficient \(k\) changed slightly from its original level. Further, the relative deviation of the cumulative application rate was less than 3%, which indicated that the overall control accuracy was excellent, and the calibration relationship model was relatively stable for a short period (in one day of work in our tests). This relative stability is the main reason for the ongoing wide usage of the current open-looped model, as users tend to ignore the fact that the control error gradually increases with the operating time.  
In the second and third test stages, the change of the ratio coefficient \(k\) was much larger than in the first test stage, as the original parameters of relationship model had been deliberately changed and the relative deviation of the ratio coefficient exhibited a gradual upward trend with the operating time. Since the control parameters were not updated in the second test stage, the relative deviation of the cumulative application rate was significantly higher than in the first test stage. This indicated that the preset control model was quite different from the actual control object model. Because of the essential open-loop of the control system, the control system could not correct the control deviation itself. With the increase of working time, the control effect of the control system weakened or even lost effect.  
In the third test stage, the relative deviation of the ratio coefficient \(k\) further increased. Deformation or damage in the brushes appeared relatively more frequently than in the first two test stages. Further, the increased moisture absorption by the fertilizer resulted in partial caking and bonding in the rollers and brushes. These phenomena can potentially be the reason for the deviation from the original calibration relationship model. However, the relative deviation of the cumulative application rate did not increase with the increase in the ratio coefficient \(k\). Conversely, it decreased to a relatively lower level, similar to the initial test stage. This is because the parameters of the self-calibrating system was in action and the closed-looped control effectively counteracted the negative effects of the parameter change on the control object. This indicates that the dynamic calibration on the parameters of the control model had been realized.  
The overall test results showed that the calibration relationship model had a relatively higher stability for a short period, but the parameters of the calibration relationship model gradually deteriorated with the operating time. The relationship model needs an appropriate way to calibrate to support the control model in practice to continue with better performance. Our parameters self-calibrating method provides a feasible way to solve this problem.  
Figure 12: Relative deviation of the cumulative application rate in different test stages.
# Optical Chirality Determined from Mueller Matrices
## Appendix A Conventions
### Relation between \(C_{\text{D}}\) and Ellipticity Angle \(\epsilon\)  
For elliptically polarized light, the ellipticity is defined as \(e=b/a\) where \(a\) and \(b\) are the major and minor axes of the polarization ellipse [14]. The ellipticity angle \(\epsilon=\arctan e\) is also in use. The relation between \(C_{\text{D}}\) and \(e\) can be found by using Jones formalism. If an incident linearly \(x\)-polarized light with a Jones vector \(\mathbf{E}=[1,0]^{\text{T}}\) propagates through a circular dichroic sample with a Jones matrix from Equation (10), the emerging Jones vector will be  
\[\mathbf{J}^{\text{out}}=\left[\begin{array}{c}E_{x}\\ E_{y}\end{array}\right]=e^{-\frac{2\pi d}{\lambda}\frac{(\lambda+k_{\text{r}}) }{2}}\left[\begin{array}{cc}\cosh C_{\text{D}}/2&-\mathrm{i}\sinh C_{\text{ D}}/2\\ \mathrm{i}\sinh C_{\text{D}}/2&\cosh C_{\text{D}}/2\end{array}\right]\left[ \begin{array}{c}1\\ 0\end{array}\right]=\\ e^{-\frac{2\pi d}{\lambda}\frac{(\lambda+k_{\text{r}})}{2}}\left[\begin{array} []{c}\cosh C_{\text{D}}/2\\ \mathrm{i}\sinh C_{\text{D}}/2\end{array}\right]. \tag{11}\]  
The ellipticity is calculated from \(e=\Im[E_{y}]/\Re[E_{x}]\) and is  
\[e=\tan\epsilon=\tanh C_{\text{D}}/2. \tag{12}\]  
In CD-spectroscopy, the difference in absorbance \(\Delta A=A_{1}-A_{\text{r}}\) is measured. Here, \(A_{1}\) and \(A_{\text{r}}\) are the absorbance for left-handed and right-handed polarization, respectively. The Jones vector in Equation (10) can be reformulated as  
\[\left[\begin{array}{c}E_{x}\\ E_{y}\end{array}\right]=\frac{1}{2}\left[\begin{array}{c}e^{-\frac{2\pi d}{ \lambda}h_{1}}+e^{-\frac{2\pi d}{\lambda}k_{\text{r}}}\\ -i(e^{-\frac{2\pi d}{\lambda}k_{\text{r}}}-e^{-\frac{2\pi d}{\lambda}k_{\text{ r}}})\end{array}\right]=\frac{1}{2}\left[\begin{array}{c}\sqrt{T_{1}}+\sqrt{T_{ \text{r}}}\\ -i(\sqrt{T_{\text{i}}}-\sqrt{T_{\text{r}}})\end{array}\right], \tag{13}\]  
and the definition of ellipticity used in CD-spectroscopy follows  
\[e=\tan\epsilon=\frac{\sqrt{T_{\text{r}}}-\sqrt{T_{\text{i}}}}{\sqrt{T_{\text{ r}}}+\sqrt{T_{\text{i}}}}. \tag{14}\]  
[PERSON] law \(T=10^{-A}=e^{-A\ln 10}\) implies that \(\sqrt{T}=e^{-\frac{A}{2}\ln 10}\) and  
\[\left[\begin{array}{c}E_{x}\\ E_{y}\end{array}\right]=\frac{1}{2}\left[\begin{array}{c}e^{-\frac{A_{1}  
In CD-spectroscopy, it is customary to assume small \(\Delta A\) (small \(e=\tan e\approx\epsilon\)) and Equation (A9) reduces to the following relation in practical use  
\[e\approx\epsilon\approx\frac{\Delta A}{4}\ln 10\frac{180}{\pi}=32.98\Delta A\quad \text{[deg]}\] (A11)  
where the factor \(180/\pi\) is a conversion from rad to degrees. Finally, we also find, from Equation (A5), that \(e\approx C_{\text{D}}/2\) for small \(e\) and, thus,  
\[C_{\text{D}}\approx 2\epsilon.\] (A12)
# Hybrid Renewable Systems for Small Energy Communities: What Is the Best Solution?
## 2 Methods and Developments
### Model Deployment
#### 2.2.2 Microgrid System
Regarding the economic aspect, the cash flow can be calculated for every year using the following Equation (9). \[Cash\ Flow(n)=\sum_{i=1}^{k}Profits(i)-\sum_{i=1}^{k}Costs\ (i) \tag{9}\]  
The cash flow (n) corresponds to the total annual difference between profits and costs, where \"n\" represents the year; profits(i) and costs(i) are the hourly profits and costs, respectively; and \"k\" is the total number of hours in the studied year. Additionally, the levelized cost of energy (LCOE) can be computed. This metric is crucial to analyze the viability of a project for an extended period. The formula to obtain the LCOE is listed by Equation (10). \[LCOE=\frac{Total\ Costs}{Total\ Generated\ Energy} \tag{10}\]  
Accordingly, the net present value of the cash flow is computed by Equation (11). The total project's NPV and LCOE are obtained from the following:  
\[NPV_{CF}=\text{Cash\ Flow}\cdot\frac{1-(1+r)^{-25}}{r} \tag{11}\] \begin{table}
\begin{tabular}{l l} \hline
**Pumped Hydropower Storage parameters**[53; 54; 55] \\ \hline C\({}_{inv}\)-Hydro & 1500 \(\epsilon\)/kW \\ \hline O\&M\_Hydro & 20 \(\epsilon\)/kW/year \\ \hline C\({}_{inv}\)-Pump & 950 \(\epsilon\)/kW \\ \hline O\&M\_Pump & 9.5 \(\epsilon\)/kW/year \\ \hline
**BESS (Battery Energy Storage System)**[56] \\ \hline C\({}_{inv}\)-BESS & 300 \(\epsilon\)/kW \\ \hline O\&M\_BESS & 15 \(\epsilon\)/kW/year \\ \hline
**Additional parameters**[53] \\ \hline Lifetime of the project & 25 years \\ \hline Interest rate—r & 10\% \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Hence, after defining all equations to support the model's best solution decision, the smart hybrid energy algorithm is ready to be applied to a small energy community to find the finest system energy source combination depending on the system constraints and objectives.
## 4 Principle and Implementation
### Calculation of Water Level Values  
After the location of the water level line is determined, the mapping relationship described in the previous section is utilized to obtain the current water level. Firstly, the perspective transformation matrix \(M_{3\times 3}\) is employed to convert the water level line from the pixel coordinate system to the virtual gauge coordinate system. The coordinates \((u_{i},v_{i})\) of the water level line in the virtual gauge coordinate system can then be computed. To obtain a stable water level, the outliers are removed from all the vertical coordinates of the water level in the virtual gauge coordinate system, and the average value is calculated. Finally, the average value is linearly converted to the water level value, according to Equation (5).  
\[h=\frac{\sum_{i=0}^{n}v_{i}}{n}k+b, \tag{5}\]  
where \(h\) is the water level value, \(n\) is the total number of pixel points, \(v_{i}\) is the vertical coordinate value of the \(i\)-th point, and \(k\) and \(b\) are the linear variation scale and offset coefficients, the values of which depend on the precise size of the water gauge in the virtual gauge space chosen. Because the length of a single standard scale in virtual space is 1000 pixels, \(k\) and \(b\) in this work are \(-0.1\) and \(100\), respectively. Considering the real-world scenario where two water gauges are joined together vertically, the virtual space water gauge needs to be adjusted accordingly: in this case, as the total length of the virtual space water gauge has been adjusted to 2000 pixels, and the values of \(k\) and \(b\) in Equation (5) should be set to \(-0.1\) and 200, respectively. After the water level line is converted to the virtual water gauge, assuming its vertical coordinate in the virtual water gauge is \(v\), the water level value can be easily calculated based on \(v\), as shown in Figure 8.  
Figure 7: Water level line detection.
## 4 Results and Empirical Approach
### Hierarchical Regression Testing
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **RSC** & **RSC** & **RSC** & **ISI** & **ISI** & **ISI** & **ISI** & **ISI** \\  & **Model 1** & **Model 2** & **Model 3** & **Model 4** & **Model 5** & **Model 6** & **Model 7** & **Model 8** \\ \hline \multicolumn{10}{c}{Control variables} \\ \hline Sex & \(-\)0.096 & \(-\)0.098 & \(-\)0.081 & \(-\)0.060 & \(-\)0.072 & \(-\)0.053 & \(-\)0.066 & \(-\)0.063 \\ \hline Age & 0.045 & 0.049 & 0.043 & 0.052 & 0.123 ** & 0.069 & 0.080 & 0.068 \\ \hline Main effects & & & & & & & & \\ \hline ISM & \(-\)0.217 *** & \(-\)0.175 ** & 0.438 *** & 0.482 *** & 0.474 *** & 0.413 *** \\ \hline RSC & & & & \(-\)0.238 *** & \(-\)0.246 *** & \(-\)0.234 *** & \(-\)0.220 *** \\ \hline ISA & \(-\)0.116 ** & \(-\)0.101 & \(-\)0.064 & \(-\)0.049 & \(-\)0.068 & \(-\)0.061 \\ \hline ISC & & & & 0.021 & 0.013 & 0.019 & 0.027 \\ \hline \multicolumn{10}{c}{Moderating effects} \\ \hline ISM x ISA & & & 0.166 ** & & 0.157 ** & & & \\ \hline ISM x ISC & & & & & & & \\ \hline RSC x ISC & & & & & & & 0.156 ** \\ \hline \(F\)-value & 1.592 & 5.707 *** & 6.238 *** & 0.884 & 17.640 *** & 18.652 *** & 17.543 *** & 18.779 *** \\ \hline Adjust R\({}^{2}\) & 0.004 & 0.062 & 0.084 & \(-\)0.001 & 0.289 & 0.303 & 0.290 & 0.305 \\ \hline Durbin-Watson & 0.840 & 0.877 & 0.882 & 1.696 & 1.576 & 1.602 & 1.551 & 1.642 \\ \hline \hline \end{tabular}  
**Note: *** Coefficient is significant at the 0.01 level; ** coefficient is significant at the 0.05 level.**  
\end{table} 
Table 5: Summary of moderating test results.
## 2 Materials and Methods
### Characterization of the Synthesized AgNPs  
The formation of AgNPs was observed through the color change of _T. erecta_ extracts after being mixed with AgNO\({}_{3}\). The synthesized AgNPs were characterized using UV-visible spectroscopy, with the UV-Vis spectra being recorded in the 250-600 nm range to distinguish the maximum surface plasmon resonance (SPR). For this determination, a Jasco V-530 UV-Vis double beam spectrophotometer (Jasco, Tokyo, Japan) was used.  
In order to investigate the different functional groups of the extract and those involved in the AgNPs synthesis, the FTIR spectra of both extracts and AgNPs were recorded using a Vertex 70 Spectrometer (Bruker, Billerica, MA, USA) by potassium bromide tableting, in a scan interval of 4000-310 cm\({}^{-1}\).  
EDX qualitative analysis was performed using a Quanta 200 Environmental Scanning Electron Microscope (ESEM) with EDX (FEI Company, Brno, Czech Republic). The employed detector allowed rapid determination of the elemental composition of the biosynthesized nanoparticles.  
The DLS analysis was carried out using a Delsa Nano Submicron Particle Size Analyzer (Beckman Coulter Inc., Fullerton, CA, USA), that measured the average diameter of AgNPs and the Zeta potential. Additionally, the morphology and dimensions of nanoparticles were investigated by TEM analysis, which was performed using a High-Tech HT 7700 Transmission Electron Microscope (Hitachi High-Technologies Corporation, Tokyo, Japan).  
Furthermore, prior to the purification of AgNPs, the plant extract and the corresponding supernatant were evaluated regarding the total phenolic content using a modified spectrophotometric Folin-Ciocalteu method [27]. The standard was represented by gallic acid. The tests were performed in triplicate and the results were expressed in mg gallic acid equivalents (GAE)/mL sample.