esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Evaluation Criteria and Statistical Indices  
In [117], the author proposed a method for evaluating the performance of obtained results, introducing a performance score (\(\varphi\)) to quantify the effectiveness of a model. This score provides a standardized metric for assessing model accuracy and reliability in predicting and forecasting PM concentrations. The mathematical formulation of \(\varphi\) is defined by Equation (4). Higher values of \(\varphi\) signify poor model performance. The metrics utilized in this Equation include Mean Bias Error (MBE), Root Mean Square Error (RMSE), T-Statistic (TS) and Standard Deviation (\(\sigma\)), Coefficient of Determination (R\({}^{2}\)), Willmott's Index of Agreement (WIA), and Slope of Best-Fit line (SBF), which are represented by Equations (5)-(10).  
\[\varphi=\text{rank}(\text{MBE})+\text{rank}(\text{RMSE})+\text{rank}(\text{ TS})+\text{rank}\Big{(}\text{R}^{2}\Big{)}+\text{rank}(\text{WIA})+\text{ rank}(\text{SBF}) \tag{4}\]  
\[\text{MBE}=\frac{1}{\text{K}}\text{$\sum$}\Big{(}\text{v}_{\text{p}}^{\text{ i}}-\text{v}_{\text{m}}^{\text{i}}\Big{)} \tag{5}\]  
\[\text{RMSE}=\bigg{(}\frac{1}{\text{K}}\text{$\sum$}\Big{(}\text{v}_{\text{p}} ^{\text{i}}-\text{v}_{\text{m}}^{\text{i}}\Big{)}^{2}\bigg{)}^{\frac{1}{2}} \tag{6}\]  
\[\text{TS}=\Bigg{[}\frac{(\text{K}-1)\text{MBE}^{2}}{\Big{(}\text{RMSE}^{2}- \text{MBE}^{2}\Big{)}}\Bigg{]}^{\frac{1}{2}} \tag{7}\]  
\[\text{R}^{2}=1-\frac{\text{$\sum$}\Big{(}\text{v}_{\text{p}}^{\text{i}}-\text {v}_{\text{m}}^{\text{i}}\Big{)}^{2}$}{\sum$}\Big{(}\text{v}_{\text{m}}^{ \text{i}}-\overline{\text{v}_{\text{m}}}\Big{)}^{2} \tag{8}\]  
\[\text{SBF}=\frac{\left[\text{$\sum$}\Big{(}\text{v}_{\text{p}}^{\text{i}}- \overline{\text{H}_{\text{p}}}\Big{)}\big{(}\text{v}_{\text{m}}^{\text{i}}- \overline{\text{v}_{\text{m}}}\big{)}\right]$}{\sum$} \tag{9}\]  
\[\text{WIA}=1-\frac{\left[\text{$\sum$}\Big{(}\text{v}_{\text{p}}^{\text{i}}- \text{v}_{\text{m}}^{\text{i}}\Big{)}^{2}\right]$}{\sum$} \tag{10}\]Additional analysis can be performed using the Standard Deviation \(\sigma\) and Mean Absolute Percentage Error (MAPE), which are expressed by Equations (11) and (12), respectively.  
\[\sigma=\left[\frac{\mathrm{K}\left(\mathrm{RMSE}^{2}-\mathrm{MBE}^{2}\right)}{ \left(\mathrm{K}-1\right)}\right]^{\frac{1}{2}} \tag{11}\]  
\[\mathrm{MAPE}=\frac{100}{\mathrm{K}}\Sigma\Bigg{|}\frac{\left(\mathrm{v}_{ \mathrm{P}}^{\mathrm{i}}-\mathrm{v}_{\mathrm{m}}^{\mathrm{i}}\right)}{\mathrm{ v}_{\mathrm{m}}^{\mathrm{i}}}\Bigg{|} \tag{12}\]  
The equations involve the parameters \(\mathrm{v}_{\mathrm{P}}^{\mathrm{i}}\), \(\mathrm{v}_{\mathrm{m}}^{\mathrm{i}}\), and \(\overline{\mathrm{v}}\). Here, \(\mathrm{K}\) denotes the total number of measurements, and \(\mathrm{v}_{\mathrm{P}}^{\mathrm{I}}\), \(\mathrm{v}_{\mathrm{m}}^{\mathrm{I}}\), and \(\overline{\mathrm{v}}\) correspond to the ith predicted value, ith measured value, and mean value of ozone.
# Real-Time 3D PET Image with Pseudoinverse Reconstruction
### Theory
#### Projection over Planes  
In many cases, real-time images are used to follow radiotracer distribution along the body of a patient for which the sum of the image collapsed in specific directions instead of a 3D presentation of the full image may be enough. This way of presenting real-time PET images is also convenient as short frame images would typically contain a very low number of counts, and 2D projections will be less noisy in this case.  
In general, to obtain images collapsed over planes, a 3D image is needed, which is later projected. However, using the pseudoinverse, this process can be reduced to one single step, if the pseudoinverse, instead of the image, is collapsed once and for all. This leads to much smaller PINV matrices, and thus much faster matrix products.  
In this work, we used 2D-PINV matrices collapsed over the X and Y directions. These matrices, combined with PINV\({}_{z}\), produces projections in the XZ and YZ planes, respectively. Note that if we sum over all the slices obtained from the rebinning step, and then reconstruct the resulting slice, we obtain the equivalent of the 3D image collapsed into the XY plane.
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
## 2 Problem Formulation  
This study aims to determine the optimal design, number, and placement of single tuned harmonic filters in distorted distribution systems. The harmonic spectrum of the DG units in the system is considered. The proposed method aims to minimize THD, power loss, and filter investment cost, and improve the system voltage through the following objective functions.
## 3 Experiment Verification
### Experimental Description  
In this section, the effectiveness of the proposed method will be demonstrated through two comparative validation experiments: standard spatiotemporal prediction learning and translator replacement. Both experiments will be conducted using the same dataset and experimental environment, thereby validating the proposed model from two different perspectives.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Platform** & **Configuration** \\ \hline Integrated Development Environment & Visual Studio Code \\ Programming Language & Python 3.8 \\ Operating System & Ubuntu 18.04 \\ CPU & Intel(R) Xeon(R) Gold 6130 \\ GPU & Tesla V100-32 GB \\ GPU Accelerator & CUDA 10.1 \\ Memory & 25 GB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Software environment and hardware parameters.  
Figure 11: Evaluation index processing.  
Standard Spatiotemporal Prediction Learning Comparison: The prediction model characterized by an equal number of input and output frames is regarded as the benchmark for spatiotemporal prediction learning. This study conducts a comparative analysis of the proposed model against contemporary spatiotemporal prediction models and conventional prediction techniques, including optical flow [36], under consistent datasets and experimental conditions. Various evaluation metrics are employed to assess the predictive performance of the proposed model in relation to the standard spatiotemporal prediction model.  
Translator Replacement Validation Experiment: In this model, the translator module is responsible for learning temporal evolution. To verify the temporal attention unit's ability to learn temporal evolution, several representative modules are used to replace TAU as translators. This will help assess which module is most suitable for spatiotemporal prediction modeling within this framework. For CNNs, ConvNext [37], a modern convolutional module trained using a vision transformer (ViT)-based strategy, is selected. For transformers, the recently notable hierarchical transformer [38] is chosen. Additionally, the visual attention network (VAN) [39] module, which is based on large-kernel convolutional attention, as well as the vision transformer (ViT) [40] module and the gated spatiotemporal attention (gSTA) [41] module, are included.  
Comparison and Validation Experiments on the SEVIR Dataset: The SEVIR (storm event imagery) dataset [42] is a meticulously labeled and curated collection of spatiotemporally aligned data sampled across the continental United States. It includes weather images sourced from both radar (NEXRAD weather radars) and satellite (GOES-16), comprising over 10,000 documented weather events. Each event features a sequence of images with a resolution of 384 km \(\times\) 384 km, taken at five-minute intervals over a period of four hours. To validate our model's applicability in various meteorological contexts, we conducted comparative experiments using the standard SEVIR dataset alongside other spatiotemporal prediction models. These experiments aimed to explore the broad potential applications of our model under diverse weather conditions.
## 6 Conclusions  
We calculated eleven different normalized difference water indexes (NDWIs) based on the green, near-infrared (NIR), and shortwave-infrared (SWIR) bands of Earth Observation-1 (EO-1) Advanced Land Imager (ALI), Landsat Thematic Mapper (TM), and Landsat Enhanced Thematic Mapper Plus (ETM+) sensors. The results of this quantitative analysis show that (1) the NDWI model based on the green band (Band 4: 0.520-0.605 \(\upmu\)m) and the SWIR band (Band 9: 1.550-1.750 \(\upmu\)m) of the ALI sensor, namely NDWI\({}_{\text{A4},9}\), is the best indicator for land surface water (LSW) mapping; (2) using Bands 4 and 9 of the ALI sensor to produce an LSW map will obtain the best effect, followed by Bands 4 and 8 (1.200-1.300 \(\upmu\)m), Bands 4 and 10 (2.080-2.350 \(\upmu\)m), Bands 4 and 7 (0.845-0.890 \(\upmu\)m), and Bands 4 and 6 (0.775-0.805 \(\upmu\)m).  
The results of this paper also show that [PERSON]'s NDWI performs better than [PERSON]'s NDWI on water body information enhancement and LSW mapping, confirming previous results [34,38]. The indexes using the green band (0.520-0.605 \(\upmu\)m) and the SWIR band (1.550-1.750 \(\upmu\)m), that is [PERSON]'s NDWI, are the most efficient indices for detecting water body information and for mapping water bodies. Specifically for the EO-1 ALI sensors, we recommend NDWI\({}_{\text{A4},9}\), for detection of LSW body features, such as open water body information extracting, flood disaster monitoring, flood disaster risk assessing, wetland mapping, LSW mapping, and identification of other water body features.  
This paper is financially supported by the National Natural Science Foundation of China (No: 41101516, 41201413, 61203373, and 91024008) and the Industry development special project of China's National Development and Reform Commission (2012 satellite and applications industry development project). We are indebted to U.S. Geological Survey server ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/))) for preprocessing and providing the EO-1 ALI, Landsat TM, and Landsat ETM+ data used in this manuscript. We also thank [PERSON] (Guizhou University of Finance and Economics) for embellishing figures in the manuscript. The authors wish to thank anonymous reviewers for their very constructive suggestions and comments.  
The authors declare no conflict of interest.
# Influence of Microbial Treatments on Vine Growth and Must Quality: Preliminary Results
## 2 Results and Discussion
### Aroma Composition
#### 2.4.3 Grape Juice  
Table 6 shows the complete volatile composition of the headspaces of the investigated grape juices. Analogously to the pressed grape berries, juice aroma was mainly characterized by aldehydes, with particular reference to hexanal and (_E_)-2-hexenal. For the grape juice, however, the differences in the relative content of those molecules between the control and the Bioma samples were milder. Indeed, although (_E_)-2-hexenal in this case was also higher in the Bioma than in the control sample, the discrepancy in their relative content was of 3%. Conversely, no significant differences were revealed for hexanal. Besides aldehydes, non-negligible amounts of alcohols were found and only represented by (_E_)-2-hexen-1-ol and 1-hexanol, both significantly lower in Bioma-treated grape juice. The overall volatile profile of the grape juice evidences slight modification of the plant volatile metabolism, with a mild shift towards the biosynthesis of important aroma precursors that could influence the flavor and aroma during fermentation, with probable differences in the final wine product. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**Lri. 1**} & \multirow{2}{*}{**Class**} & **Relative Abundance (\%) \(\pm\) Standard Deviation** & \multirow{2}{*}{**Significance Level**} \\ \cline{3-3} \cline{5-5}  & & & **Control Grape Juice** & & **Bioma Grape Juice** \\ \hline Acetic acid & 610 & nt & 0.2 \(\pm\) 0.05 & 0.2 \(\pm\) 0.04 & n.s. \\ \hline Heptane & 700 & nt & 0.2 \(\pm\) 0.02 \({}^{\text{A}}\) & 0.1 \(\pm\) 0.02 \({}^{\text{B}}\) & ** \\ \hline Hexanal & 800 & nt & 30.7 \(\pm\) 0.22 & 31.2 \(\pm\) 0.24 & n.s. \\ \hline Hex-2-enal & 851 & nt & 0.8 \(\pm\) 0.07 & 0.9 \(\pm\) 0.06 & n.s. \\ \hline (_E_)-2-Hexenal & 854 & nt & 56.2 \(\pm\) 0.14 \({}^{\text{B}}\) & 59.2 \(\pm\) 0.16 \({}^{\text{A}}\) & *** \\ \hline (_E_)-2-Hexen-1-ol & 862 & nt & 6.2 \(\pm\) 0.36 \({}^{\text{A}}\) & 3.3 \(\pm\) 0.21 \({}^{\text{B}}\) & ** \\ \hline
1-Hexanol & 871 & nt & 5.5 \(\pm\) 0.09 \({}^{\text{A}}\) & 4.7 \(\pm\) 0.2 \({}^{\text{B}}\) & * \\ \hline Nonanal & 1104 & nt & 0.1 \(\pm\) 0.01 \({}^{\text{B}}\) & 0.3 \(\pm\) 0.03 \({}^{\text{A}}\) & ** \\ \hline Other non-terpene derivatives (nt) & & 100 \(\pm\) 0.01 & 100 \(\pm\) 0.00 & n.s. \\ \hline Total identified (\%) & & 100 \(\pm\) 0.01 & 100 \(\pm\) 0.00 & \\ \hline \hline \end{tabular}  
\({}^{1}\) Linear retention index on a HP 5-MS capillary column. Superscript uppercase letters (A–B) indicate statistically significant differences between Bioma and control samples. The asterisks in the “Significant level” column indicates **_p_ \(<\) 0.05, **_p_ \(<\) 0.01, **_p_ \(<\) 0.0001, while n.s. means not significant.  
\end{table} 
Table 6: Complete volatile profile of the grape juices.
## 5 Discussion
### Classification of Pore in Es4 Interval  
The classification of the porosity in the Es4 is presented in Figure 11 and Table 2. The classification of the conglomerate reservoir pores in the northern Dongying subdepression is summarized based on the genesis and features of the pore type in both retrogradation and progradation sequences. The Es4 comprises three genetic pores: primary, secondary, and mixed pores. The secondary pore consists of intergranular (Figure 11A-C), fracture pores (Figure 11D-G), intragranular (Figure 11H,I), moldic pores (Figure 11J), and intracrystal (Figure 11K,L). The intergranular secondary dissolution pore comprises the enlarged, intergranular normal secondary pore (cement dissolution). The fracture pore consists of rock fracture pores, grain fracture pores, and enlarged fracture pores (Figure 11D-G).  
Figure 10: Secondary pore development. (**A**) Remaining clay rims, (**B**) interconnected intragranular and intergranular pores, (**C**,**E**) primary pore during deposition, (**D**,**F**) enlarged mixed pore after dissolution. PP-primary pore, EP-enlarged pore, EMP-enlarged mixed pore.  
Among the pores type, the intergranular and enlarged pores are the main contributors to good reservoir quality.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Pore Type**} & \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{**Distribution Area**} & \multirow{2}{*}{**Effect on the Reservoir Quality**} \\ \cline{5-6}  & & & **HST** & **TST** & **HST** & **TST** \\ \hline Primary & Intergranular & Low and isolated & Yes & Yes & small to moderate & moderate \\ pore & intragranular & non & No & No & No & No \\  & Intergranular pore & moderate to large & Yes & Yes & moderate to large & Moderate \\  & Enlarged pore & moderate to large & Yes & Yes & moderate to large & Moderate \\  & Matrix clay dissolution pore & moderate to large & Yes & Yes & small to moderate & very small \\  & Grain fracture pore & common & Yes & Yes & small to moderate & small to moderate \\  & Grain-enlarged fracture pore & Low to moderate & Yes & Yes & small to moderate & small to moderate \\ Secondary & Rock fracture pore & Low and isolated & Yes & Yes & small & small \\  & Rock enlarged fracture pore & Low and isolated & Yes & Yes & small to moderate & small \\  & Intragranular pore & moderate to large & Yes & Yes & small to moderate & small to moderate \\  & mixed intragranular- & Low to moderate & Yes & Yes & small to moderate & small \\  & intergranular pore & Low to moderate & Yes & Yes & small to moderate & small \\  & Moldic pore & Low to moderate & Yes & Yes & small to moderate & small \\  & Kaolinite intercrystal pore & low to moderate & Yes & Yes & small to moderate & small \\  & Carbonate cement & low & Yes & Yes & small & small \\  & intercrystal pore & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of porosity in conglomerate.  
Figure 11: Schematic figures of the main type of pore in the Es4 of the northern Dongying Depression.
## 2 Phenolic Compounds Extraction from Renewable Resources within a Biorefinery Scheme
### Techniques for Phenolic Compounds Extraction from Agri-Food By-Products
#### 2.1.1 Natural Deep Eutectic Solvents  
In recent years ionic liquids (IL) and deep eutectic solvents (DES) have been revisited for their capacity to replace organic solvents in the extraction of natural compounds. [PERSON] et al. (2011) [49] hypothesised that some metabolites that occur naturally in cells could act as a third type of liquid. When studied, these metabolites showed similar properties to synthetic IL and DES; these metabolites are referred to as natural deep eutectic solvents (NADES). NADES are formed by mixing two or three hydrogen bond donor metabolites such as amino acids, choline, malic, citric acid, and lactic acid with organic salts that act as hydrogen bond acceptors. This combination results in stable intramolecular hydrogen bonds, and the mix can contain up to 50% water (\(v/v\)). NADES' numerous structural variations and adjustable physicochemical properties such as viscosity, surface tension, and polarity offer a vast opportunity for their use in extracting phenolic compounds [50; 51; 52]. NADES are non-toxic, biodegradable, sustainable, low cost, and reduce oxidative degradation, making them a good and green alternative to conventional and some non-conventional methods [39; 40; 50]. Moreover, NADES, jointly with other non-conventional methods, has been studied to extract phenolic compounds from by-products. A recent study by [PERSON] et al. (2022) [53] on the extraction of anthocyanins from cherry pomace showed that NADES in conjunction with ultra-fast MAE was 62% more efficient than with the conventional solvent (50% EtOH). Another study on grape skins showed that NADES in conjunction with UAE proved to be more effective (47%) than methanol in extracting phenolic compounds and anthocyanins [54]. However, these authors agree that further studies are needed to optimise NADES in the extraction according to the target phenolic compounds, scale up the method, and recover and or recycle NADES after extraction [55; 56; 57].
# Displacement Monitoring of Subway Tracks and Tunnels According to Adjacent Construction
## 3 Displacement Monitoring of Track Structures  
### Measurement Results  
In this study, a track bed settlement sensor was installed at a total distance of 140 m in the train direction, including the adjacent excavation work area. Adjacent excavation work may deform subway track structures. Safety is evaluated using a track bed settlement sensor. Before adjacent excavation work, initial data are obtained by installing a track bed settlement sensor on the subway track structure. Subsequently, the track structure deforma  
Figure 6: Vertical displacement analysis results (Xc + SD): (**a**) Section A; (**b**) Section B; (**c**) Section C.  
tion owing to the adjacent excavation work is measured, and safety is assessed through monitoring until the construction is completed. In this study, long-term measurements were taken for 18 months, with data collected at 60 min intervals.  
Figure 7 shows the measurements during the 1 st and 18 th months of track bed settlement sensor monitoring.  
As shown in Figure 7, the existing subway track structure was monitored simultaneously using monitoring data from all sensors installed onsite. However, the track bed settlement sensor was installed in the direction of train movement, and determining the results at the exact location using existing monitoring techniques is challenging. In addition, the tunnel structure safety evaluation results were numerically evaluated based on the maximum value during the measurement period. Figure 8 shows the results of sensor RM-41 among the track bed settlement sensors monitored during the 18 months of construction.  
Figure 7: Track bed displacement measurements: (**a**) Vertical displacement (before construction); (**b**) Vertical displacement (after construction).  
The vertical displacement measurements of track bed settlement sensor RM-41 revealed a 0.34 mm maximum vertical displacement at the beginning of the adjacent excavation work, as shown in Figure 8. The maximum vertical displacement at 10 months and 1 month after construction was 0.48 mm. The maximum vertical displacement increased by approximately 41.17% 10 months after construction compared with that during the initial construction period. The maximum vertical displacement after 18 months of construction was 0.59 mm. The maximum vertical displacement increased by approximately 22.92% 18 months after construction compared with that at 10 months after construction. Therefore, the vertical displacement of the track structure owing to the adjacent excavation work was approximately 0.25 mm.
## 2 Materials and Methods
### Intracellular Reactive Oxygen Species (ROS) Assay  
Intracellular reactive oxygen species (ROS) levels were quantified using the 2',7'-dichlorohydrofluorescein diacetate (DCFH-DA) assay. HepG2 cells (1.5 \(\times\) 10\({}^{4}\) cells/well) were seeded in a 96-well black polystyrene plate with a clear bottom. After 24 h, cells were treated with DMSO (0.02%), MET70 (100 \(\upmu\)g/mL), doxorubicin (0.5 \(\upmu\)M), or a combination of doxorubicin and MET70 for 72 h. Following treatment, cells were incubated with DCFH-DA (10 \(\upmu\)M in PBS) for 30 min at 37 \({}^{\circ}\)C. Fluorescence intensity was measured using a GloMax(tm) Multi-Detection System (Promega) with a blue filter (excitation: 490 nm; emission: 510-570 nm). To account for signal interference from doxorubicin, which emits fluorescence in the same range, the signal from wells containing both the probe and the drug was adjusted by subtracting the doxorubicin-specific signal. Fluorescence values were normalized to protein content measured using the Bradford assay. Results are presented as a percentage of the negative control (DMSO-treated cells). Each treatment was performed in triplicate.