esa-sceva/satcom-chunk-collection huggingface dataset:
# Distinct Bacterial Consortia Established in ETBE-Degrading Enrichments from a Polluted Aquifer
## 3 Results  
### Bacterial Communities in Polluted and Upstream Well Water  
MDS (Figure 1) and ANOSIM on microarray data showed that bacterial communities in UW and PW water significantly differed (\(P<0.05\)) (the four sampling dates were grouped together). There were 99 positive probes in common between UW and PW water, 99 probes were positive only in UW water and 41 were positive only in PW water. SIMPER analysis together with Wilcoxon rank sum tests showed which probes significantly (\(P<0.05\)) contributed to up to 50% of the differences in bacterial community composition between UW and PW (Figure S2). Notably, the signal intensities from probes targeting sequences affiliated to Spirochates (trepncln), alphaproteobacterial genus _Azoarcus_ (Arcus1, Arcus2, AZA463), betaproteobacterial genera _Nitrosospira_ (Nit1A, Nit1C), _Acidovorax_ (AcidP2 cln, Acave3, Acave4), _Polaromonas_ (Polarcln), and family Comamonadaceae (Rhodofcln; with highest identities to _Albidiferax_), and delaproteobacterial genus _Desulfoibrio_ (DVLVT222f) were higher in PW water than UW water. In contrast, signals from probes affiliated to the family Rhizobiaceae (Rzbc1247, Alphaproteobacteria), family Oxalobacteraceae (Dugacln, Betaproteobacteria), betaproteobacterial genera _Ralstonia/Cupriavidus/Wautersia_ (Ralstcln2) and _Janthinobacterium_ (Janaga3), _Pseudomonas_ (PseuD; Gammaproteobacteria) and _Nitrospira_ (NSR1156 and NSR826) were higher in UW water than in PW water.  
Bacterial communities from PW and UW water sampled in June 2008 were analyzed by 16S rRNA gene sequencing to confirm the presence of the distinguishing bacterial taxa between the two wells. The bacterial community in UW water was more diverse (Shannon index 2.97, 48 OTUs estimated with Chao1 at 97% level of sequence identity) than in PW water (Shannon index 2.61, 34 OTUs estimated) (Figure S3). The only known genus shared by UW and PW water, based on 16S rRNA gene sequencing, was _Opittuus_ (Verrucomicrobia) (Figure S4). The UW water was dominated by Alphaproteobacteria (mainly _Splingomonas_ and _Methylocystis_), while sequences related to known anaerobes (e.g., epsilonproteobacteria _Aroobacter_) and Betaproteobacteriaobia (unclassified Betaproteobacteria, Rhodocyclaceae and Commamonadaceae) prevailed in PW. This is concordant with microarray results. Microarray probes targeting these groups, that is, Sphingo4 and Sphingo5A for Sphingomonadaceae, Methylocyst1 for Methylocytaceae, Rhodofcln for sequences related to Comamonadaceae and Arco1 for _Aroobacter_ were always positive when these taxa were found with the 16S rRNA sequencing approach, although they were not always revealed by SIMPER as the most contributing to the differences between UW and PW.
## 7 Fault Location Method Based on Similarity of Voltage Signals
### Fault Analysis Based on Symmetrical Components
#### 8.1.2 Pre-Double Pole to Ground Fault  
Let us consider the grounding resistances \(R_{ga}\) and \(R_{gb}\) for pole \(a\) to ground and pole \(b\) to ground, respectively. Figure 12 shows circuit for pole \(a\) to ground and its respective instantaneous boundary conditions are:  
\[u_{fa}=\;i_{fa}R_{ga}\;u_{fb}=\;i_{fb}R_{gb} \tag{63}\]  
and sequence components are:  
\[\left\{\begin{array}{c}u_{fa0}+u_{fa1}=\left(i_{fa0}+i_{fa1}\right)R_{ga}\\ u_{fa0}+u_{fa1}=\left(i_{fa0}-i_{fa1}\right)R_{gb}\end{array}\right. \tag{64}\]  
Figure 11: Sequence network connections for pole to ground fault.  
Similarly, one can write for pole \(b\) to ground. However, if \(R_{ga}\
eq R_{gb}\), then:  
\[\left\{\begin{array}{c}u_{fa0}=\frac{\sqrt{2}U_{dc+}(R_{gb}-R_{ga})Z_{0,\Sigma} }{\Sigma}\\ u_{fa1}=\frac{\sqrt{2}U_{dc-}[(R_{gb}+R_{gb})Z_{0,\Sigma}+2R_{ga}R_{gb}}{\Sigma} \end{array}\right. \tag{65}\]  
where  
\[Z_{\Sigma}=2\Big{(}R_{ga}R_{gb}+Z_{0,\Sigma}Z_{1,\Sigma}\Big{)}+\Big{(}R_{ga}+ R_{gb}\Big{)}(Z_{1,\Sigma}+Z_{0,\Sigma}) \tag{66}\]
## 3 Multi-Objective Optimization
### Multi-Objective Optimization Based on NSGA-II Algorithm  
There are two main types of multi-objective optimization algorithms: ordinary gradient methods and gradient-free direct methods. The first type of method relies on the quality of the initial guess, which is easy to fall into local extremes and is only applicable to continuous smooth functions. The gradient-free direct method is more suitable for the study of nonlinear phenomena. Among them, genetic algorithms are the most widely used [37; 38]. Such algorithms are insensitive to the discontinuity of the objective function, do not get trapped in local optima, and are suitable for parallel processing.  
NSGA-II is a kind of genetic algorithm that can efficiently order the nondominated solutions while providing a set of Pareto optimal solutions well distributed along the Pareto frontier and considering an elite strategy of accelerating convergence [39]. The algorithm is widely used to minimize or maximize two or more objective functions under given constraints and boundary conditions. The result of its optimization represents the set of solutions with the best compromise between the objective functions.  
NSGA-II generates a random population in the initial state, and then the population individuals undergo crossover (parents produce offspring) and mutation (small random changes in offspring). The algorithm then sorts individuals based on non-dominance rank and crowding degree, and selects higher quality individuals to form the next generation. The population is driven towards the optimal Pareto frontier while maintaining the diversity of the population [40]. The algorithm runs until a predefined number of generations is reached.  
In this study, NSGA-II was used to optimize three conflicting objective functions (heat transfer quantity, oil pressure drop, oil outlet temperature). The population size, crossover probability, mutation probability, and maximum number of generations are set to 2000, 0.9, 0.2, and 5000, respectively. The boundary conditions are:  
1. \(30^{\circ}\leq\) Fin angle \(\leq 90^{\circ}\);
2. \(\mathrm{L}/\mathrm{min}\leq\mathrm{Oil}\) flow rate \(\leq 15\) L/min;
3. \(\mathrm{L}/\mathrm{min}\leq\mathrm{Water}\) flow rate \(\leq 15\) L/min.
## 3 Materials and Methods
### EIM Data Processing and Classification  
Figure 2 presents a schematic representation of the analysis and classification process followed for EIM images. The process began with the generation of a variability map from the input EIM package using integration or fusion methods. This variability map captured essential information regarding the conductivity index values across all the EIM layers. Next, the extraction of features for classification was conducted using a convolutional neural network based on the VGG19 architecture. Ultimately, each variability map was labeled as either low suspicion (BI-RADS grades 1-3) or high suspicion (BI-RADS grades 4-5) of a breast lesion based on the density information derived from the EIM images.
## 3 Results and Discussion
### Economic and Resilience Analysis of Microgrid Systems
#### 3.7.4 Business Models for Resilient Microgrids and Financing Mechanisms
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Topic** & **References** & **Research Challenges and Future Directions** \\ \hline \multirow{9}{*}{Integration of Renewable Energy Sources and Hybrid Microgrid Configurations} & S-037 highlights PV-battery integration with hybrid inverters. S-059 evaluates control in hybrid AC/DC topologies. S-299 discusses energy sector coupling. Challenges involve establishing protection and interoperability standards for AC/DC microgrids; expanding sector coupling with heating/cooling and EVs; and developing blockchain-enabled peer-to-peer markets. [34; 48; 6; 83] \\ \cline{2-3}  & S-169 presents optimization of hybrid battery-supercapacitor ESS. S-232 reviews lifecycle models for storage. S-105 investigates predictive control for ESS dispatch. Challenges include improving cost/lifecycle of hybrid systems; adopting circular economy principles; and applying multi-objective optimization frameworks for storage sizing and location. [22; 43; 47; 48; 61; 70] \\ \hline \multirow{9}{*}{Application of Artificial Intelligence, Predictive Analytics, and Digital Twins} & S-124 implements federated learning in EMSs. S-136 integrates predictive maintenance with ML. S-192 explores digital twin platforms. Challenges include enhancing privacy through federated AI; standardizing open-access digital twin models; and improving anomaly detection with deep learning. [21; 27; 49] \\ \cline{2-3}  & S-232 outlines a multi-layer cybersecurity framework. WoS-086 applies blockchain for secure data exchange. WoS-066 discusses regulatory gaps in data privacy. Challenges include real-time threat detection using AI; harmonizing data standards and legal compliance; and integrating cybersecurity into all microgrid layers. [15; 31; 32] \\ \cline{2-3}  & S-209 models resilience in grid-tied microgrids. S-127 evaluates cost-benefit of microgrid expansion. S-270 introduces Energy-as-a-Service models. Challenges include robust financial models under uncertainty; innovative financing mechanisms like green bonds; and embedding resilience indicators in economic assessments. [30; 37; 39] \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 3 SCUC with HVDC Constraints
### UC Constraints  
#### 3.2.1 Generating Unit Constraints  
1. Generating unit capacity  
Constraints (18)-(21) ensure that generators operate between their minimum and maximum allowed outputs, while the overall output is dependent on both rated minimum power output and ramp-down limit of generating unit.  
\[\underline{P}_{i,\min}\leq P_{G,i}^{t}+r_{i}^{t}\leq\overline{P}_{i,\max} \tag{18}\]  
\[\underline{P}_{i,\min}=\max(\underline{P}_{G,i,\min},P_{G,i}^{t-1}-60\times DR _{i}) \tag{19}\]  
\[\overline{P}_{i,\max}=\min(\overline{P}_{G,i,\max},P_{G,i}^{t-1}+60\times UR_{ i}) \tag{20}\]  
\[\underline{Q}_{G,i,\min}\leq Q_{G,i}^{t}\leq\overline{Q}_{G,i,\max} \tag{21}\]  
2. Minimum ON/OFF status constraints  
The next constraints are minimum up and down time constraints of generators, which use different start-up states, depending on the time a generator had been ON or OFF before being started.  
\[\left\{\begin{array}{c}u_{i}^{t}=1,\;\;if\;\;\;T_{i,\text{on}}^{t-1}<T_{i, \text{up}}\\ u_{i}^{t}=0,\;\;if\;\;\;T_{i,\text{off}}^{t-1}<T_{i,\text{down}}\\ u_{i}^{t}=0\,\text{or}\,1\;\;\text{others}\end{array}\right. \tag{22}\]  
3. Generator start-up cost function  
The start cost is modeled by the following constraint (23). It is divided into hot-start and cold-start, depending on OFF time and minimum OFF time of the generator.  
\[\left\{\begin{array}{c}ST_{i}^{t}=HST_{i},\,i\,\,\text{if}\,\,T_{i,\text{ down}}\leq T_{i,\text{off}}^{t}\leq T_{i,\text{cold}}+T_{i,\text{down}}\\ ST_{i}^{t}=CST_{i},\,i\,\,\text{if}\,\,T_{i,\text{off}}^{t}\geq T_{i,\text{ cold}}+T_{i,\text{down}}\end{array}\right. \tag{23}\]\[i\in N_{G},\,t\in T\]
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Satellite Data
#### 3.1.2 Sentinel-2 Time Series Data  
For the entire study region, we used all available Sentinel-2 L2A Surface Reflectance data (more than 47,000), which have been acquired by its wide-swath, high-resolution sensor--the multi-spectral Imager (MSI). The L2A data accessible via the Google Earth Engine that was downloaded from the Copernicus Open Access Hub ([[https://scihub.copernicus.eu](https://scihub.copernicus.eu)]([https://scihub.copernicus.eu](https://scihub.copernicus.eu)), accessed on 10 November 2021) and atmospherically corrected using the Sen2 Cor processor [46]. The Sentinel-2 data contains 12 spectral bands ranging from the visible to the near-infrared and spatial resolution of 10 m to 60 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sentinel-1A/B**} & \multicolumn{4}{c}{**Number of Scenes**} \\ \cline{2-5}  & **Ascending Orbit** & **Descending Orbit** & **Ascending Orbit** & **Descending Orbit** \\ \hline Quarter I 2019 & 1 January–31 March & 2959 & 3168 & 6127 \\ Quarter II 2019 & 1 April–30 June & 2940 & 3088 & 6028 \\ Quarter III 2019 & 1 July–30 September & 3160 & 3229 & 6389 \\ Quarter IV 2019 & 1 October–31 December & 3144 & 3746 & 6890 \\ \hline Total & & 12,203 & 13,231 & 25,434 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Available Sentinel-1 IW GRDH scenes for the study region for the complete year 2019.  
Figure 4: Coverage frequency of VH-polarized Sentinel-1 scenes (1 January 2019–31 December 2019) for the coastal zone of Asia acquired in ascending and descending orbit.
## 3 Results
### Accuracy Evaluation  
Data of the 10 experiments were described in Table 2. The mean absolute deviation between the planned and achieved positions for upper incisor point was 0.10 \(\pm\) 0.15 mm in the medio-lateral direction, 0.05 \(\pm\) 0.07 mm in the antero-posterior direction, and 0.12 \(\pm\) 0.15 mm in the supero-inferior direction. For right and left upper 1 st molars, mean absolute deviations were 0.09 \(\pm\) 0.10 mm and 0.09 \(\pm\) 0.10 mm in the medio-lateral direction, 0.10 \(\pm\) 0.11 mm and 0.09 \(\pm\) 0.12 mm in the antero-posterior direction, and 0.06 \(\pm\) 0.04 mm and 0.10 \(\pm\) 0.11 mm in the supero-inferior direction, respectively. The mean RMSD between the planned and achieved positions for five dental landmarks was 0.18 \(\pm\) 0.16 mm, and the maximum and minimum RMSDs were 0.54 mm (in the experiment #1) and 0.05 mm (in the experiment #10) (Table 3). With respect to the maxillary repositioning accuracy between the anterior and posterior maxilla, there were no statistically significant differences in the absolute deviations for medio-lateral (\(P\) = 0.513), antero-posterior (\(P\) = 0.058), and supero-inferior (\(P\) = 0.959) directions between the anterior and posterior landmarks.  
\begin{table}
\begin{tabular}{c c} \hline
**Experiment** & **Surgical Plan** \\ \hline \#1 & Advancement by 3 mm and downward by 2 mm \\ \#2 & Advancement by 3 mm and downward by 2 mm \\ \#3 & Cant correction by 4 mm (Right, 2 mm upward; Left, 2 mm downward) \\ \#4 & Cant correction by 4 mm (Right, 2 mm upward; Left, 2 mm downward) \\ \#5 & Posterior impaction at the upper first molar by 3 mm \\ \#6 & Posterior impaction at the upper first molar by 3 mm \\ \#7 & Bodily shift to right side by 3 mm \\ \#8 & Bodily shift to right side by 3 mm \\ \#9 & Advancement by 3 mm and downward by 2 mm \\ \#10 & Cant correction by 4 mm (Right, 2 mm upward; Left, 2 mm downward) \\ \hline \end{tabular}
\end{table}
Table 1: The surgical plans implemented in this study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Upper Incisor**} & \multicolumn{3}{c}{**Right Upper**} & \multicolumn{3}{c}{**Left Upper**} & \multicolumn{3}{c}{**Right Upper 1 st**} & \multicolumn{3}{c}{**Left Upper 1 st**} \\  & \multicolumn{3}{c}{**Point**} & \multicolumn{3}{c}{**Canine**} & \multicolumn{3}{c}{**Canine**} & \multicolumn{3}{c}{**Molar**} & \multicolumn{3}{c}{**Molar**} \\ \cline{2-13}
**Experiment** & **x** & **y** & **z** & **x** & **y** & **z** & **x** & **y** & **z** & **x** & **y** & **z** & **x** & **y** & **z** \\ \hline \#1 & 0.51 & 0.03 & 0.41 & 0.43 & 0.14 & 0.31 & 0.42 & 0.18 & 0.14 & 0.27 & 0.07 & 0.12 & 0.25 & 0.43 & 0.39 \\ \#2 & 0.19 &
## 2 Finite Element Analysis for Bridge Structures with Damage and Deterioration
### Detecting Pier Scour by Vibration-Based MEH  
Figure 13 also implies that detecting the pier scour is possible if the power generated by the MEH devices are monitored in the x- and y-directions. Especially monitoring that at point No. 2, an obvious change in generated power could be observed when scour occurred.  
Practically, as it is nearly impossible to obtain data from the perfectly scouring-free state, a scouring detection method that requires no reference state data (reference-free method) is of great importance. In Figure 13b, it is observed that when uneven scouring occurred, an unequal power was generated at the three observation points in the y-direction. Although the generated energy is small for the 3 rd mode, this observation is also true as supported by a closer look in Figure 14 (subject to normalization with respect to the maximum out of the three observation points). When no uneven scouring occurred (the riverbed is flat), the power generated by the MEH at the three observations is almost equal.  
Based on the observations above, a reference-free method could be proposed for detecting uneven pier scour using MEMS vibrational power generator devices as follows. (1) To install MEMS vibrational power generator devices at multiple observation points of equal height at the pier cap. The observation points are suggested to be one at the upstream side, one at the downstream side, and one in the middle; the selection of other observation points is not fully investigated yet and could be an open  
Figure 14: Power generation in the y-direction, 3 rd mode.  
Figure 13: Output power generated by MEH (Pier model).  
question. (2) To monitor the power generated by the MEMS vibrational power generator devices in the longitudinal direction. If the powers generated at the observation points are unequal, it would be detected as uneven pier scour. In considering measurement noises, a certain threshold could be introduced to quantify the unequal powers, which is 5% in this study. The above proposal is limited to detecting uneven pier scours per the assumptions and modeling in this study. Further investigations could be conducted on detecting even pier scour and more complicated scouring states.
## 5 Discussion
### Comparison of Sentinel-1 and LT-1 in Coseismic Surface Monitoring and Analysis  
[PERSON] et al. used 14 Sentinel-1A images to determine glacier deformation velocity, 8 of which were taken in 2020 or 2021 to monitor the deformation characteristics of Hailuogou Glacier under nonseismic effects and the remaining 6 of which were taken before and after the 2022 earthquake to evaluate the degree to which Hailuogou Glacier was affected by the earthquake. They found that the earthquake caused glaciers on the eastern side of Mt. Gongga's peaks to deform sharply, including Hailuogou, Mozigou, Yanzigou, Nanmengou, and their hinterland gullies, while the glaciers on the west side were relatively less affected. The icefall deformation in the Hailuogou catchment area was the largest, with a maximum average movement velocity of approximately 2.5 m/d [35]. The coseismic results from this paper showed that the flow velocity of the Hailuogou Glacier reached 1.5 m/d, which was lower than the values reported by [PERSON] et al. The low value may have been due to potential ice and snow avalanches on the steep slopes leading to a lack of corresponding pixels in the multitemporal SAR images, thus reducing the signal-to-noise ratio for the estimated POT and affecting the results to a certain extent. In addition, the resolution of the Sentinel-1A data used in this paper was 120 m, which could have impacted the accuracy of the values.  
Figure 9: POT deformation results from Sentinel-1 SAR images from different periods. The velocity fields shown in (**a–c**) correspond to period from 12 September 2021 to 26 August 2022. The velocity fields shown in (**d–f**) correspond to the period from 7 September 2022 and 5 January 2023.
# A Novel Measurement Standard for Surface Roughness on Involute Gears
## 2 Methods for the Analysis of Surface Roughness on Involutes
### Roughness Parameters  
The standard ISO 4288 [21] and its successor ISO 21920-3 [22], respectively, specifies the evaluation length \(l_{\rm e}\) of a roughness profile and the value of the cut off wavelength \(\lambda_{\rm c}\), which is the length of a single section, in accordance with the range of values of \(R_{\rm a}\) or \(R_{\rm z}\). The evaluation length \(l_{\rm e}\) is an integer multiple \(n_{\rm sc}\) of sections of a length equal to the cut off wavelength.  
\[l_{\rm e}=n_{\rm sc}\lambda_{\rm c}. \tag{13}\]  
The default value of the number of sections specified in ISO 4288 [21] is \(n_{\rm sc}=5\).  
For the application dealt with in this article, the appropriate value of the cut off wavelength is \(\lambda_{\rm c}=0.8\) mm; hence, \(l_{\rm e}=4.0\) mm.  
The height parameters \(R_{\rm a}\), \(R_{\rm q}\) and \(R_{\rm z}\) characterizing the height distribution are defined in ISO 21920-2 [16], which will soon replace ISO 4287 [23] as follows:  
\[R_{\rm a}=\frac{1}{l_{\rm e}}\int_{0}^{l_{\rm e}}|z(s)|ds\,, \tag{14}\]  
\[R_{\rm q}=\sqrt{\frac{1}{l_{\rm e}}\int_{0}^{l_{\rm e}}z^{2}(s)ds} \tag{15}\]  
and the following is the case.  
\[R_{\rm z}=\frac{1}{n_{\rm sc}}\sum_{i=1}^{n_{\rm sc}}R_{\rm z,i} \tag{16}\]  
\(R_{\rm z,i}\) is the difference between the height value of the summit of the highest peak and the lowest point of the deepest dale within the \(i\)-th of \(n_{\rm sc}\) sections.  
Material distributions provide additional information about the profile. This is why the Abbott-Firestone curve is evaluated and the material ratio parameters \(R_{\rm k}\), \(R_{\rm pk}\), \(R_{\rm vk}\) and Mr1 and Mr2, as defined in ISO 13565-2 [24] which will also be succeeded by ISO 21920-2 [16], are calculated.