esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This research presents a multi-criteria decision support system (MCDSS) designed to optimise the selection of photovoltaic (PV) sets for microgrid installations. By integrating two robust multi-criteria decision-making (MCDM) methods, AHP and TOPSIS, the MCDSS provides a comprehensive framework for evaluating PV sets based on efficiency, cost, and return on investment. Applying this system in case studies from Barreiras and Curitiba demonstrates its effectiveness, yielding global efficiencies of 14.4% and 14.8% and internal rates of return (IRR) of 56.0% and 52.0%, respectively.  
The findings highlight significant analytical, methodological, and managerial contributions. Analytically, the study offers a detailed assessment model for PV set selection, addressing the critical factors impacting energy generation. Methodologically, it showcases the integration of AHP and TOPSIS in renewable energy applications, enhancing decision-making processes. Managerially, the MCDSS serves as a practical tool for decision-makers, improving the feasibility and attractiveness of microgeneration projects.  
Challenges like low energy conversion efficiency and shading effects remain despite technological advancements and reduced costs. The proposed MCDSS addresses these issues, facilitating more efficient and accessible solar energy generation. This research underscores the potential of MCDSSs to support the broader adoption of renewable energy sources, contributing to a sustainable energy future.  
Implementing this system can reduce the complexity of selecting the most appropriate PV components, making it easier for experts and non-experts to make informed decisions. The MCDSS ensures that the selected PV sets are tailored to maximise energy output and economic viability by considering various factors such as climatic conditions, geographic location, and specific installation requirements. Additionally, the research highlights the importance of considering local environmental factors and specific installation conditions in the selection process. By incorporating these variables into the MCDSS, the system can  
Figure 11: Analysis of the alternatives selected by the MCDSS for photovoltaic set definition of Curitiba City.  
provide more accurate and context-sensitive recommendations, ultimately leading to better performance and higher satisfaction for end-users.  
Therefore, the MCDSS is a method for assessing and selecting PV sets based on efficiency, cost, and return on investment. Methodologically, it integrates multiple MCDM techniques, demonstrating their applicability in renewable energy. Managerially, it offers a practical tool for decision-makers in the energy sector to enhance the feasibility and attractiveness of microgeneration projects. The MCDSS can potentially improve the efficiency and accessibility of solar energy generation, promoting the adoption of renewable energy sources and supporting a transition to a sustainable energy infrastructure.
## 3 Results and Discussion
### Comparison of Observed H with SEBS and MDDI-Integrated SEBS
The red circles in Figure 6 represent samples with relatively high vegetation (LAI > 4) but low soil moisture conditions (MPDI > 0.5), where the pixels are wheat parcels, orchard, and vegetables, which is consistent with the planting structure as shown in Figure 1. There was a moderate increase in these areas, with about 22.5 W m\({}^{-2}\) of H. Regarding the wetland or open water area (e.g., river) where adequate water was available (MPDI < 0.2), the MPDI-integrated SEBS model showed little impact on H. The implementation of the MPDI-integrated SEBS better depicts H rising under water stress conditions through an increase in z\({}_{\text{th}}\). As explained in Section 2.2, in areas under water stress, low soil moisture availability decreases the kB\({}^{-1}\) value, thereby increasing the surface roughness of heat transport (z\({}_{\text{th}}\)). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**H Estimation Under Different**} & \multicolumn{4}{c}{**Sensible Heat Flux (H)**} \\ \cline{2-5}  & **Root Mean Square Error (RMSE) (W/m\({}^{2}\))** & **Mean Absolute Error (MAE) (W/m\({}^{2}\))** \\ \hline SM (m\({}^{3}\)/m\({}^{3}\)) & SM \(\leq\) 0.30 & SM \(>\) 0.30 & SM \(\leq\) 0.35 & SM \(>\) 0.30 \\ SEBS & 59.78 & 35.94 & 58.17 & 26.01 \\ MPDI-integrated SEBS & 15.28 & 26.53 & 7.06 & 19.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Error evaluations of the estimated H from the original SEBS and the MPDI-integrated SEBS at Daman and Huazhaizi stations. Figure 5: Comparison of time series of H\({}_{\text{EC}}\) with H\({}_{\text{SEBS}}\) and H\({}_{\text{SEBS-MPDI}}\) together with soil moisture and LAI at Daman station (**a**) and Huazhaizi station (**b**). Figure 6: _Cont._
# Microbiota \"Fingerprint\" of Greek Feta Cheese through Ripening
###### Abstract
The secondary microflora consist of non-starter lactic acid bacteria (NSLAB) which grow internally and are often unique to specific cheese varieties. Specifically, the microbial ecology of cheese is based on the complex interaction among SLAB and NSLAB [11]. The screening of microbiota which affects Feta's unique organoleptic properties and reflects product quality, authenticity, and safety is critical, as this is the Greek cheese with the higher consumption worldwide. Cheese microbiota diversity and overall safety is affected by production regions, traditional techniques, and ripening time. This diversity can also determine the sensorial appeal and nutritional value for the consumer. The microbial profile of Feta PDO has been reported in a very limited number of scientific papers. [PERSON] et al. studied the microbiota in four Feta cheese samples from pasteurized milk obtained from retail shops by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and identified LAB and yeast species [12]. Additionally, [PERSON] et al. studied the NSLAB, by molecular methods such as random amplified polymorphic DNA-polymerase chain reaction (RAPD-PCR) and pulsed-field gel electrophoresis (PFGE) on fresh and mature artisanal Feta cheese samples at three mountainous areas of Greece [13]. Furthermore, [PERSON] et al. used 16s rRNA gene sequencing to examine the effect of container material on fresh and mature Feta cheese bacterial diversity [14]. The main goal of our work was to define the standard core microbiota of Feta PDO cheeses through the identification of the bacteria by the culture-dependent technique MALDI-TOF MS Biotyper. Moreover, this study aimed to identify and report the effect of the ripening period before consumption, which is often a period of three to six months (i.e., 90 to 180 days) post-production, on the microbial diversity and chemical composition of Feta PDO cheese.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 2 Materials and Methods
### Discriminant Factor
#### 2.3.3 Fuzzy Entropy (FE)  
Fuzzy entropy introduces fuzzy membership function, which calculates the fuzzy membership degree of the distance value between each series. Thus, the algorithm is designed more scientifically. Then, the fuzzy membership degrees are tabulated to calculate the ratio, which is a similar process to SampEn.
###### Abstract  
A two-mode network is a type of network in which nodes can be divided into two sets in such a way that links can be established between different types of nodes. The relationship between two separate sets of entities can be modeled as a bipartite network. In computer networks data is transmitted in form of packets between source to destination. Such packet-switched networks rely on routing protocols to select the best path. Configurations of these protocols depends on the network acquirements; that is why one routing protocol might be efficient for one network and may be inefficient for a other. Because some protocols deal with hop-count (number of nodes in the path) while others deal with distance vector. This paper investigates the minimum transmission in two-mode networks. Based on some parameters, we obtained the minimum transmission between the class of all connected n-nodes in bipartite networks. These parameters are helpful to modify or change the path of a given network. Furthermore, by using least squares fit, we discussed some numerical results of the regression model of the boiling point in benzenoid hydrocarbons. The results show that the correlation of the boiling point in benzenoid hydrocarbons of the first Zagreb eccentricity index gives better result as compare to the correlation of second Zagreb eccentricity index. In case of a connected network, the first Zagreb eccentricity index \(\zeta_{1}(\mathbb{N})\) is defined as the sum of the square of eccentricities of the nodes, and the second Zagreb eccentricity index \(\zeta_{2}(\mathbb{N})\) is defined as the sum of the product of eccentricities of the adjacent nodes. This article deals with the minimum transmission with respect to \(\zeta_{i}(\mathbb{N})\), for \(i=1,2\) among all \(n\)-node extremal bipartite networks with given matching number, diameter, node connectivity and link connectivity.  
Zagreb eccentricity indices; bipartite networks; matching number; diameter; node connectivity; link connectivity 05C09; 05C92
# Effect of Seawater Irrigation on the Sugars, Organic Acids, and Volatiles in 'Reliance' Grape
## 3 Results
### Effect of 10% Seawater Treatment on Soil Salinization  
The concentration of soluble salt, Na\({}^{+}\), Cl\({}^{-}\), SO\({}^{2-}\), moisture content, and the pH of soil were determined to investigate the effect of 10% seawater irrigation on the soil. Table 4 shows seawater treatment significantly increased the soil pH, especially in the S1 and S2 groups. The soil pH in S1, S2, and S3 treatment groups was 0.21, 0.09, and 0.10 higher than that in the control group. The soluble salt content in the soil after 10% seawater irrigation was also significantly higher than that in the control, but far from the standard measure of soil salinization (1 g kg\({}^{-1}\)). Furthermore, a slight increment in soil Na\({}^{+}\), Cl\({}^{-}\), SO\(4^{2-}\), and moisture content were observed between the control and treatment groups, but the differences were not statistically significant.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Control** & **S1** & **S2** & **S3** \\ \hline pH & 6.80 \(\pm\) 0.06 c & 7.01 \(\pm\) 0.05 a & 6.89 \(\pm\) 0.02 b & 6.90 \(\pm\) 0.04 b \\ Soluble salt content (g kg\({}^{-1}\)) & 0.189 \(\pm\) 0.12 d & 0.704 \(\pm\) 0.04 a & 0.542 \(\pm\) 0.08 b & 0.403 \(\pm\) 0.14 c \\ Na\({}^{+}\) content (g kg\({}^{-1}\)) & 0.0158 \(\pm\) 0.0007 b & 0.0176 \(\pm\) 0.0008 a & 0.0169 \(\pm\) 0.0004 ab & 0.0159 \(\pm\) 0.002 b \\ Cl\({}^{-}\) content (g kg\({}^{-1}\)) & 0.0235 \(\pm\) 0.0005 b & 0.0268 \(\pm\) 0.0008 a & 0.0261 \(\pm\) 0.0002 a & 0.0244 \(\pm\) 0.0001 a \\ SO4\({}^{2-}\) content (g kg\({}^{-1}\)) & 0.0230 \(\pm\) 0.0002 b & 0.0270 \(\pm\) 0.0010 a & 0.0259 \(\pm\) 0.0005 a & 0.0246 \(\pm\) 0.0006 a \\ Soil moisture content (\%) & 16.13 \(\pm\) 0.6 a & 17.10 \(\pm\) 1.3 a & 16.15 \(\pm\) 0.9 a & 16.55 \(\pm\) 0.1 a \\ \hline \hline \end{tabular} Values represent the means \(\pm\) SD of three replicates. Different lowercase letters indicate significant differences, according to [PERSON] multiple comparison post-hoc test at \(p\) < 0.05.  
\end{table}
Table 4: The concentration of soluble salt, Na\({}^{+}\), Cl\({}^{-}\), SO\(4^{2-}\), moisture content, and pH of soil.  
Figure 6: Relative expression levels of key genes responsible for volatiles in the fatty acid pathway. (**a**) _VvLoxA_. (**b**) _VvHPL_. (**c**) _VvADH_. (**d**) _VvAAT_. Values represent the means \(\pm\) SD of three replicates. Different lowercase letters indicate significant differences of samples in one developmental phase, according to [PERSON] multiple comparison post-hoc test at \(p\) < 0.05.
## 2 Results
### Engineering of a Gardnerella Endolysin with Enhanced Bactericidal Activity  
To increase the lytic activity of the endolysins identified above, we used domain shuffling [42; 43] to systematically recombine ten of the EADs encoded by the newly identified endolysin genes EL 1-7 and EL 10-12 (annotated as H1-7 and H10-12) with nine CBDs encoded by EL1-5, EL7 and EL10-12 (annotated as B1-5, B7, and B10-12; since EL6 was inactive, we did not use its truncated CBD). All of the 81 resulting chimeric proteins were expressed in _E. coli_ and purified by means of their N-terminal tags. We tested these chimeric endolysins for their bactericidal activity on the abovementioned _Gardnerella_ strains and compared their activity to that of the 10 wild-type endolysins (Table 1). Overall, _G. swidsinskii_ GS10234 was most sensitive to most endolysins--wild-type and chimeric. Two of the chimeric endolysins, H2B10 and H2B11, were more active than any wild-type enzyme against all four _Gardnerella_ strains, whereas others were more active than any wild-type endolysin against some strains (e.g., H2B12, H5B3, H5B4, H7B3, and H7B11) (Table 1). Overall, chimeric endolysins combining domains H2, H7, or H10 with domains B10, B11, or B12 showed the highest bactericidal activity. The combination of H2 and B10 was most effective against any of the four strains and reduced their viability by an average of 4.3 \(\log_{10}\) CFU/mL. We designated this construct PM-477 and chose it for further development as a candidate for the treatment of BV.  
Figure 2: Wild-type endolysins are highly active against four different _Gardnerella_ species. Suspensions of _G. vaginalis_ (ATCC 14018T), _G. leopoldi_ (UGent 09.48), _G. piotii_ (UGent 18.01T), and _G. swidsinskii_ (GS10234) were treated with recombinant endolysins prepared by expression of the naturally occurring genes (EL1–EL7 and EL10–EL12) (see Methods) and the \(\log_{10}\) reduction in viable CFU/mL was determined. The \(\log_{10}\) reduction was calculated by comparing the CFU/mL after endolysin treatment with that of the buffer control (_w/o_). LOD indicates the limit of detection. _p_-values were calculated by one-way ANOVA with multiple comparison to the buffer treated control (_w/o_) on log-normalized data. \({}^{\star}\)_p_\(\leq\) 0.05; \({}^{\star\star}\)_p_\(\leq\) 0.01; ns, not significant.
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## Materials and Methods
### Classification Methods and Feature Extraction  
#### 2.3.1 Random Forest  
The RF classifier developed by [PERSON] [37] performs classification by extending decision trees to multiple trees rather than a single tree. Its classification performance is superior to a single decision tree due to its ability to maximize diversity through tree ensembles. It also demonstrates greater stability due to the synthesis of classification results from a large number of trees and the determination of final class labels through majority voting. In addition, RF requires a few parameters (i.e., the number of variables for node partitioning and the number of trees to be grown) to be set, unlike other machine learning algorithms.  
The RF classifier applies bootstrap aggregating (bagging) to tree learners. Bagging repeatedly selects a random sample to replace the training data and fits trees to these samples. The remaining training data, the out-of-bag (OOB) data, are used to validate trees [37]. The OOB error that is the error rate of the OOB classifiers is often used as a measure of the generalization error on the training data [37]. To avoid overfitting the training data, each node of the trees determines the partitioning  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No.** & **Acquisition Date** \\ \hline
1 & 29 June \\
2 & 12 July \\
3 & 27 July \\
4 & 25 August \\
5 & 13 September \\
6 & 21 September \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of unmanned aerial vehicle (UAV) image mosaics acquired in the study area in 2017.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classes** & **Total Area (ha)** & **Average Area per Parcel (ha)** \\ \hline Highland Kimchi Cabbage & 22.38 & 0.59 \\ Cabbage & 8.35 & 0.59 \\ Potato & 8.65 & 0.86 \\ Fallow & 3.08 & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Crop classes and their respective area information in the study area.  
condition, and each tree chooses the random predictor variable and divide node using a genie index, as a measure of heterogeneity. An additional function of the RF classifier is to compute quantitative measures for variable importance using mean decrease impurity (MDI) and mean decrease accuracy (MDA) [28]. When constructing a large number of trees, MDI and MDA can be calculated by averaging the weighted impurity of each tree and the degree of accuracy improvement, respectively, by randomly changing the variable. In this study, the variable importance was used to quantify how useful texture information is for crop classification.
## 3 Results
### Fluoride Leaching
As the hydration progresses, the leaching curve gradually flattens, and this part of the fluoride immobilization may be  
Figure 6: Leaching results of samples with a (**a**) cement-to-sand ratio of 1:6, (**b**) cement-to-sand ratio of 1:8, and (**c**) cement-to-sand ratio of 1:10. Figure 7: Leaching results of samples (**a**) IFC of 280 mg/L and (**b**) IFC of 20 mg/L. affected by cement hydration. The detailed description of the fluoride fixation mechanism will be expanded in Section 3.6. In the later stages of the reaction in the PCPB cementitious system, fluorine leaching is of primary interest. Therefore, the treatments are focused on two key curing periods: 14 days and 28 days. The fitting formula provided illustrates the relationship between fluoride leaching, the IFC, and the cement-to-sand ratio. These relationships were analyzed and fitted using IBM(r) SPSS27(r) Statistics software. Formula describing the behavior of samples after 14 days and 28 days of curing:  
\[L_{14}=4.132-34.301C-0.006I+101.305C^{2}+8.031\times 10^{-6}I^{2}-0.1 CI,\ \text{R}^{2}=0.911 \tag{3}\]  
\[L_{28}=2.887-30.064C+0.012I+110.26C^{2}+1.298\times 10^{-5}I^{2}-0.048 CI,\ \text{R}^{2}=0.937 \tag{4}\]  
where \(L_{14}\) and \(L_{28}\) are the leaching results of PCPB curing in 14 and 28 days, respectively (mg/L). C is the cement-to-sand ratio (wt%), and \(I\) is the IFC (1 \(\times\) 10\({}^{5}\) wt%). Figure 8a displays the response surface curves and contour graphs based on the equation fitted to fluorine leaching after 14 days of curing. The highest point on the response surface corresponds to a cement-to-sand ratio of 1:6 and an IFC of 0.13 wt% (280 mg/L), while the lowest point corresponds to the same ratio but with an IFC of 0.009 wt% (20 mg/L). The contour graphs indicate that higher cement content significantly impacts fluorine content at this stage, suggesting that fluoride immobilization is primarily influenced by the OPC during hydration. In Figure 8b, the contour graph for 28 days of curing shows a clearer gradient of fluorine leaching. The requirement of 1 mg/L for fluorine leaching can be met for all cement-to-sand ratios within the specified range when the IFC is below 0.009 wt% (20 mg/L). The response surface plot in Figure 8b shows the lowest point with a cement-to-sand ratio of 1:6 and an IFC of 0.009 wt% (20 mg/L), while the highest point has a cement-to-sand ratio of 1:10 and an IFC of 0.13 wt% (280 mg/L). In conclusion, the study demonstrates that both the cement-to-sand ratio and IFC significantly affect fluorine leaching in cemented paste backfill systems. The findings indicate that appropriate adjustments in these parameters can effectively control fluorine leaching, ensuring compliance with environmental standards. The response surface and contour graphs provide a clear visual representation of these relationships, highlighting the importance of optimizing the cementitious system for better fluoride immobilization over time.
## Results  
This screen presents the results of the painting plan generated according to the information from the input spreadsheets, the selected strategy and the simulation configuration. Therefore, the information relates to the systems selected for painting and the platform situation after executing the suggested services. You can view the detailed painting plan and export it to an Excel spreadsheet.  
The painting plan for scheduling maintenance on offshore platforms is divided into three main sections: input data, pre-painting data, and results.  
* Input data: These include system identifiers (ID, module, sector), painting area, productivity, corrosion levels, environmental conditions (temperature, humidity, wind), and regulatory demands (RDs). It comprehensively overviews the platform's initial condition and maintenance requirements.
* Pre-painting data: This section calculates the current and projected corroded areas, estimates corrosion progression using machine learning, determines the required person-hours, assesses the criticality indices, and determines maintenance priorities. It uses the input data to generate projections and prioritise maintenance tasks.
* Results: This section details the optimised painting plan, including waterjet centres' locations, assigned teams, areas to be painted, person-hours used, post-maintenance corrosion status, and remaining RDs. It also shows the anticipated impact of the proposed maintenance plan.  
Figure 8: Results visualisation screen.  
APM integrates these data to generate optimised maintenance plans, enabling efficient resource management and effective prioritisation of maintenance tasks on offshore platforms. After the maintenance plan has been finalised, APM can export the results directly to a 3D CAD/CAE platform model, providing a comprehensive spatial visualisation of the planned maintenance activities (Figure 10). This 3D visualisation capability allows maintenance teams to understand the spatial distribution of the planned interventions better, optimise work sequences based on physical proximity, and identify potential access or logistical challenges. Areas selected for maintenance are highlighted in the 3D model using a color-coded system with two views: a corrosion distribution map (left) and a painting plan (right). In the painting plan view, the blue-coloured areas highlight the specific sections selected for maintenance intervention, allowing for targeted painting work based on the identified corrosion patterns.