esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.3.3 Spatiotemporal Analysis of VMFDI  
The spatiotemporal distribution of the VMFDI at grid cells was analyzed using univariate linear regression to identify interannual trends. It can be expressed as Equation (4):  
\[\text{VMFDI}_{\text{slope}}=\frac{\left(\text{n}\sum\limits_{\text{j}=1}^{ \text{n}}\text{j}\times\text{VMFDI}_{\text{i}\text{j}}-\sum\limits_{\text{j}= 1}^{\text{n}}\text{j}\sum\limits_{\text{j}=1}^{\text{n}}\text{VMFDI}_{\text{ i}\text{j}}\right)}}{\text{n}\sum\limits_{\text{j}=1}^{\text{n}}\text{i}^{2}-(\sum \limits_{\text{j}=1}^{\text{n}}\text{i})^{2}} \tag{4}\]  
where \(\text{VMFDI}_{\text{slope}}\) is the trend in the change, n is the length of the research period, i is the month (\(1\leq\text{i}\leq 12\)), j is the year (\(1\leq\text{j}\leq\text{n}\)), and \(\text{VMFDI}_{\text{i}\text{j}}\) is the value of the VMFDI in the \(i^{th}\) month of the \(j^{th}\) year. \(\text{VMFDI}_{\text{slope}}\) > 0 indicates a decreasing trend in drought; \(\text{VMFDI}_{\text{slope}}\) < 0 suggests an increasing trend.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Degree of Drought** & **VMFDI** & **SPEI** & **GRACE-DSI** \\ \hline Drought & (\(-\infty\), \(\text{VMFDI}_{\text{mean}}\)) & (\(-\infty\), \(-0.5\)) & (\(-\infty\), \(-0.5\)) \\ \hline No drought & [\(\text{VMFDI}_{\text{mean}}\), \(+\infty\)) & (\(-0.5\), \(+\infty\)) & (\(-0.5\), \(+\infty\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Drought classification based on the VMFDI, SPEI, and GRACE-DSI.  
To assess the significance of the trends, the Theil-Sen median test and Mann-Kendall (M-K) test were conducted [59; 60]. These tests are widely used to analyze trends in climate and hydrological data, and they provide a nonparametric approach to determining whether the observed trends are statistically significant at a 95% confidence level.  
The center of gravity model and standard deviation ellipse were used to identify changes in the drought center in this study, which have been widely used in meteorological disasters [61; 62]. The standard deviational ellipse, which can reflect the direction of the overall dominant distribution of spatial elements and the dispersion of each direction, is generally used to analyze the directional distribution of spatial elements [61; 62]. In this study, the spatial aggregation characteristics and trends in changes in drought in the Yellow River Basin were analyzed by plotting the standard deviational ellipse of drought in different periods.
# Towards a Multi-Perspective Time of Flight Laser Ranging Device Based on Mirrors and Prisms
## 2 Materials and Methods
### Measurement Setup
#### 2.4.3 Measurement Procedure  
For each FOV redirection optic and its configuration, we conducted the same experimental procedure. The ranging device was mounted on the furthermost position (55 cm) and the target on the linear actuator was set to its furthermost distance as well. Optics, mounted onto a stand, were placed into the ranging device's FOV such that the laser spot was redirected onto a correct spot on the target. It was ensured that all the transmitted light was hitting either the mirror or the prism, and that all reflected light was hitting the target. Following that, autonomous ranging commenced.  
The linear actuator was instructed to move to the home position (5 cm from the optics) and the system started ranging. The target moved one centimeter further away each time, for the total of 80 set distances with 128 raw samples collected at each one.  
When data for the entire range were collected, the ToF ranging device was manually moved one position (5 cm) closer to the redirection optics, and the procedure was repeated for a total capture of eleven datasets per redirection optics configuration.  
For the experiments with clean optics, they were thoroughly cleaned using nonwoven cotton wipes (Webril Handi-Pads) damped in isopropanol. When the solvent had evaporated, an optical inspection was conducted. If any streaking or imperfections were observed, the cleaning process was repeated.  
Figure 6: Measurement setup as used in the experiments. Photographed configuration depicts a state during testing of effects of a clean first surface mirror on ranging where distance between the ranging device and the mirror was 40 cm.
# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 3 Methods
### Generate NDVI Profiles at Different Spatial Resolutions  
Satellite image time series at different spatial resolutions (i.e., from 30 m to 8 km) are needed to conduct this study. Although several satellite sensors, such as MODIS, SPOT-VGT, and AVHRR, provide NDVI products at different spatial resolutions, they are not sufficient to conduct a comprehensive study. This is due to these available products providing limited spatial resolutions, such as MODIS 250 m, SPOT-VGT and AVHRR 1 km and different sensors having differences in other aspects besides spatial resolution, such as, sensor configurations, dates of data acquisition, and atmospheric condition, which will make it harder to explore the effect of spatial resolution exclusively. Therefore, image time series with different spatial resolutions, including 30 m, 60 m, 90 m, 120 m, 150 m, 180 m, 210 m, 250 m, 500 m, 1 km, and 8 km, were simulated from the 10 m time series by the Pixel Aggregate method. To be specific, a pixel value at 30 m is generated by averaging the values of 3\(\times\)3 pixels at 10 m. Figure 4 shows the appearance of an 8\(\times\)8 km\({}^{2}\) sub-region in images with different spatial resolutions from 10 m to 8 km. The Pixel Aggregate method is a widely used strategy to explore the scale effect of remote sensing data on land surface monitoring; for example, surface temperature [46], land cover characterization [47], and land change [48]. After generating image time series to other spatial resolutions from the original 10 m time series, NDVI profiles at each spatial resolution were calculated by Equation (1):  
\[\text{NDVI}=(\text{NIR - RED})/(\text{NIR + RED}), \tag{1}\]  
where NIR and RED mean the surface reflectance of NIR and RED bands, respectively.
## 6 Experiment and Result
### Monitoring Service
#### 6.3.3 Dashboard for Monitoring Vital Signs  
In the monitoring process, the vital signs of the patient are transferred from the elderly person's smartphone to the messaging subsystem and Kafka Connect collects the data from Kafka to record them in the database. The interface collects the data in a set of variables and points out the level of these variables. Figure 19 shows the instant when the elderly person has \"prehypertension\", a temperature level classified as \"fever\", and a heart rate considered as \"normal\".
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 2 Materials and Methods
### Experiment and Data
#### 2.1.2 Data Description  
A controlled testbed experiment was conducted to capture the vibration data at different unbalance levels and rotational speeds for model training and evaluation [22]. The dataset contains recordings of the input voltages of the motor controller, rotation speeds of the motor, and signals from the first vibration sensor. The recordings were performed in five configurations, as detailed in Table 1. Four levels of unbalance were introduced by adjusting the mass and radius of the inserted weight, and a configuration without added weight served as the balanced case.  
Two separate measurements were conducted for each configuration, forming development (D) and evaluation (E) datasets. Although both datasets originate from the same test conditions, they serve distinct purposes. The development dataset is intended to be used for training the models, while the evaluation dataset is designated for testing. The unbalance setup was fully dismantled and reassembled between these two recordings to introduce additional variation and ensure a more realistic assessment of the model generalization.  
Table 1 provides detailed information on the parameters of the introduced unbalance levels, including the added mass and installation radii for all recordings. Each row represents a specific setting, with the ID column indicating unbalance severity (0 = no added unbalance, 1-4 = increasing unbalance levels, with 4 being the highest). The accompanying letter specifies the intended use of the dataset: D for development and E for evaluation.  
The dataset contains a continuous recording of the acceleration signal during two cycles of stepwise rotational speed increases achieved by adjusting the voltage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Radius (mm)**} & \multirow{2}{*}{**Mass (g)**} & \multicolumn{2}{c}{**Dataset Recording Duration (s)**} \\ \cline{3-5}  & & & **Development** & **Evaluation** \\ \hline
0D/0E & – & 0 & 6438 & 1670 \\
1D/1E & \(14\pm 0.1\) & 3.281 \(\pm\) 0.003 & 6434 & 1673 \\
2D/2E & \(18.5\pm 0.1\) & 3.281 \(\pm\) 0.003 & 6434 & 1669 \\
3D/3E & \(23\pm 0.1\) & 3.281 \(\pm\) 0.003 & 6430 & 1672 \\
4D/4E & \(23\pm 0.1\) & 6.614 \(\pm\) 0.007 & 6340 & 1675 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of recorded datasets, adapted from [22].  
Figure 1: Experimental setup [22].  
The same speed level was maintained for 20 s during each recording before switching. More details on the data acquisition settings and dataset descriptions can be found in [22].
## 3 Results and Discussion
### Comparison with Chemical Precipitation and Adsorption Processes
In the adsorption process, generated sludge needs to be separated from the solution and requires regeneration or labelling as a hazardous waste due to the strong possibility of leaching out of metals ions in the environment, while needing post-treatment sludge management. Van der Waal forces are very weak to strong for different adsorbents, due to which the process is unable to deliver promising results [62, 63, 64]. NF process lies between ultrafiltration (UF) and reverse osmosis (RO). Designed to separate contaminants smaller than 10 nm, it emerges as one of the exemplary processes for eliminating dissolved metals ions from wastewater. The leading gains of this process are higher removal efficiency, reliability and easy operation, lesser space requirement, and relatively lower energy requirement [60,63,65]. Table 9 shows an outstanding rejection rate for metals ions from this study. The EC process is also widely recognized as an effective treatment method for eliminating heavy metal ions from industrial wastewater. It does not require any additional chemicals because the electron is a crucial reagent in the process. EC is considered a rapid and well-controlled technique, provides good reduction yield, produces less sludge, has the potential of metal recovery, requires less labor, can save significant energy, and is ecosfriendly [57,59]. Table 9 shows an excellent metal ion reduction in the present experimental work.
## 3 Materials and Methods
### Determination of NO, IL-6, and TNF-\(\alpha\) Release  
Before conducting anti-inflammatory tests, the toxicity of the investigated samples was assessed using RAW 264.7 macrophages. The cells were seeded in 96-well plates at a density of 1.5 \(\times\) 10\({}^{5}\) cells per well and exposed to varying concentrations of fruit wines (0-100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) for 24 h. The viability of the cells was then evaluated using the MTT assay, with all tests conducted in triplicate. Results were presented as the percentage of cell viability, expressed as the mean \(\pm\) standard deviation. Based on these results, concentrations of 25 and 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of the freeze-dried wines were selected as non-toxic and used in subsequent anti-inflammatory tests.  
In the anti-inflammatory experiments, RAW 264.7 cells were seeded in 96-well plates at the same density and pre-treated with the wine samples of the selected concentrations or dexamethasone (reference drug, 0.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) for one hour. This was followed by exposure to 10 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of LPS to initiate inflammation, consistent with previous methods [35]. The cells were incubated for 24 h and the supernatants were then analyzed to measure nitric oxide production using a Griess Reagent Kit (Promega Corporation, Madison, Winooski, VT, USA), and levels of cytokines such as TNF-\(\alpha\) and IL-6 were analyzed using Human ELISA kits (Bioassay Technology Laboratory, Shanghai, China) as per the supplier's guidelines. These assays were also performed in triplicate, and absorbance was read on a microplate reader (SynergyTM HT--BioTek Instruments Inc., Winooski, VT, USA). The outcome of these assays was expressed as a percentage of the control values.
## 2 Results
### Key Residues and Site-Directed Mutagenesis of ApTPS  
By comparing genomic information, we found that ApTPS16, ApTPS17, ApTPS18 and ApTPS19 (ApTPS19 did not have enzymatic activity and was ignored in this study)  
Figure 4: GC-MS analysis of the products from the extractions of _A. paniculata_ TPS-a/b/g subfamily perpene synthase (in BY-T15) fermentation products (*: peak of the main products; peaks 1–13: the byproducts (Figure S4)).  
were located on the same scaffold 2 and showed high homology according to phylogenetic analysis and protein homology (Figure 1, Tables S1 and S3). The crystal structures of 5-_epi_-aristolochene synthase from Nicotiana tabacum (tobacco) (PDB IDs: 4 rnq, 4 di5 and 5 eat) were selected as suitable templates for ApTPS16, ApTPS17 and ApTPS18 in view of the high GMQE values (0.76, 0.75 and 0.75, respectively) of these enzymes and their high amino acid identities. Multiple sequence alignment analysis, homology modelling and molecular docking analysis of these three sesquiterpene synthases (Figure 5A,B) showed that ApTPS17 had a nonpolar amino acid (Val) at position 409, unlike ApTPS16 (and ApTPS18), which had a polar amino acid [30] at the same position (408). Consistent with these data, it was found that the ApTPS17 V409T mutant tended to make a single product compared with those made by WT ApTPS17, with the main product accounting for approximately 100% of the total products (Figure 5C), which suggests that V409 may be a key site for the diversification of ApTPS17 products. On the other hand, ApTPS16 (and ApTPS18) had a nonpolar amino acid (Val) at position 533, unlike ApTPS17, which had a polar amino acid (Tyr) at position 534; the ApTPS17 Y534V mutation caused the main product to disappear so that only one byproduct could be detected, compared with multiple products made by WT ApTPS17. Moreover, compared with the WT ApTPS16, the ApTPS16 V533Y mutation resulted in multiple main products.  
Figure 5: Key residues and site-directed mutants of ApTPS. (**A**) Docking of FPP in the active site of ApTPS16. (**B**) Docking of FPP in the active site of ApTPS17. (**C**) Conservation of select target residues  
in ApTPS16/17/18, as discussed in the text. (**D**) GC-MS analysis of extracted fermentation products of ApTP16 and their mutants (in BY-T15). (**E**) GC-MS analysis of extracted fermentation products of ApTP17 and their mutants (in BY-T15) (* peak of the main products; the ApTPS16 or ApTPS17 model is shown in green colour, the FPP ligand is shown as yellow sticks, and the target amino acids are shown as blue sticks).
## 6 Conclusions  
Thus far, there have been efforts to introduce payment systems with greater efficiency and safety. As a consequence, offline payment functions utilizing e-cash as national legal tender have been emerging with the prospects suggested by several research studies throughout multiple countries.  
CBDC, which is currently considered attractive to several national financial institutions, can also be utilized as national legal tender when offline payments can proceed safely. However, in offline transactions, there are still strong possibilities that transactional information cannot be stored on the server. Moreover, even if the transaction records are forged or falsified, they cannot be recognized. Consequently, currently available digital payment methods that satisfy these security requirements have limitations with regard to the introduction of digital payment methods in offline payment functions.  
Accordingly, in this paper, current methods, such as blockchain technology, trusted third party involvement, supply of certificate to transactions members, tracking the flow of currency, and cryptographic technique were reviewed to improve the security of offline payment functions.  
Moreover, considering the eventual purpose of the TEE device proposed by [PERSON] et al. [95] for offline transactions, it is possible to see that the criteria for the security requirements of a transaction are still not satisfied. Hence, in this review, possible methods  
Figure 6: Flowchart of the method to limit the number of transactions to satisfy the security requirements to prevent DDoS attacks.  
of modifying the system security against DDoS attacks and unforgeability during a transaction made as an offline payment via a TEE device proposed by [PERSON] et al. [95] were reviewed.  
In addition to the security methods reviewed, it was possible to confirm the fact that the payment system through CBDC has been gaining attention recently as an alternative to national legal tender, and the research studies thereupon are now active. However, the issues related to the security requirements remain to be resolved due to its technical nature; the offline environment in which the participants transact cannot be fully monitored. Thus, it is critical for countries planning to introduce payment methods based on currently available CBDC in an offline environment to further investigate and research thereon to improve the security of transaction systems.  
Conceptualization, Y.C. and S.K.; methodology, S.K.; validation, Y.C., J.L. and S.K.; investigation, Y.C.; resources, Y.Y.; writing--original draft preparation, Y.C. and S.K.; writing--review and editing, H.K. and H.C.; visualization, Y.C.; supervision, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Seoul National University Electric Power ResearchInstitute (SEPRI).  
Not applicable.  
The authors declare no conflict or interest.
## 4 Discussion  
The high accuracy achieved by all the tested models, exceeding a \(R^{2}\) score of 0.97, indicates the potential of machine learning for oil recovery forecasting. However, the relative performance and suitability of different algorithms varied. While the linear and polynomial regression were surprisingly effective, the success of the linear models suggests that the underlying relationships in the synthetic dataset, while potentially nonlinear, might be well approximated by simpler models. This finding warrants further investigation with real-world data, which often exhibits greater complexity.  
Tree-based ensembles demonstrated an excellent performance with their ability to capture nonlinear interactions among the reservoir parameters. The significant improvement observed after the hyperparameter tuning demonstrates the importance of careful model configuration for the optimal results. The neural network also achieved high accuracy, suggesting its potential for uncovering subtle patterns and interactions within the reservoir data that might be missed by other methods.  
Despite their promise, it is crucial to acknowledge the limitations of these models. All machine learning models are inherently limited by the data they are trained on. While our synthetic dataset encompassed a wide range of reservoir conditions, it may not fully represent the complexities and heterogeneity of real-world reservoirs. Moreover, machine learning models are generally poor at extrapolating beyond the range of data they were  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**GB** & **DT** & **RF** & **LR** & **PR** & **NN** \\ \hline
0.9978 & 0.9884 & 0.9981 & 0.9999 & 0.9995 & 0.9991 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Kendall concordance coefficient of the forecast on eight processes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GB** & **DT** & **RF** & **LR** & **NN** \\ \hline
0.28 & 0.68 & 0.66 & 0.013 & 15.8 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Time to train regression models on GPU, seconds.  
trained on. If the model encounters reservoir conditions significantly different from those represented in the training data, its predictions may be unreliable.  
Another crucial aspect to note is how parallelization has expedited training. Although the results indicate that parallel execution across multiple CPUs significantly accelerates the training, the speedup is not comparable to that achieved with GPUs. Utilizing GPUs allows for processing large datasets and accelerating computations, which is critical for real-time decision-making in oil reservoir management. However, the first type of parallelization remains vital when training on large datasets and cluster systems.  
In future research, it might be beneficial to combine these approaches by segmenting the dataset into several parts and training each model on multiple GPUs. Such strategies are planned for our forthcoming studies.