esa-sceva/satcom-chunk-collection huggingface dataset:
# CFD-Based Metamodeling of the Propagation Distribution of Styrene Spilled from a Ship
## 3 Results and Discussion
### Estimation of Styrene Distribution  
As stated above, saving time costs and getting accurate solutions are the ultimate objectives of metamodeling. Hence, we numerically simulated 80 cases and stored the acquired data for different variables, such as local velocity, kinetic energy, pressure, concentration, and so on. A metamodel connects inputs (scenario) and outputs (prediction) mathematically. Thus, we need to find out new parameters that are useful for responding to an accident. The present study suggested two parameters, as illustrated in Figure 5, which were the change in propagation distribution with time and the propagation velocity of the styrene interface.  
First, the two parameters were estimated from the CFD results, and then those parameters were implemented in the kriging model. In Figure 5, a criterion regarding styrene concentration was taken  
Figure 4: Propagation behavior of the styrene with different surface current velocities (\(v_{s}\)), spilled from the (**a**) side crack and (**b**) bottom crack (\(t=1000\) s, \(v_{d}/v_{s}=0.5\), and \(d_{s}=30\) m).  
Figure 5: Schematic showing the interface of styrene and the interface propagation velocity.  
as 100 ppm, corresponding to the exposure limit of styrene specified by the Occupational Safety and Health Administration [21]. Based on this criterion, the propagation velocity of styrene interface could be calculated as follows: first, the center point was selected as a middle point of the styrene interface at \(t\) = 300 s after the spill, and the interface area was then divided into intervals of 10\({}^{\circ}\) based on the center point, as shown in Figure 5. At a certain time, a two-dimensional interface contour line and the lines formed every 10\({}^{\circ}\) intersected each other at some points; the propagation velocity of the styrene interface and the propagation distribution were then estimated at these points. Finally, all data successively stored with time were used to make the metamodel.  
Figure 6 shows the predicted styrene interface location at \(t\) = 1000 s according to the surface current velocity, which was predicted using the present metamodel in the bottom crack case with \(v_{d}/v_{s}\) = 0.5 and \(d_{s}\) = 30 m. The blue dot represents the styrene interface. It shows that at a low surface current velocity, the diffusion effect became dominant, and the styrene interface had an almost circular shape. As the surface current velocity increased, the shape of the styrene interface was closer to an ellipse due to the large advection effect. The goodness of fit was evaluated using \(R^{2}\) and RMSE (root mean square error). As listed in Table 1, the \(R^{2}\) values were greater than 0.9, which means that the metamodel provided a good fit.
# Comparison of Water Pretreatment Devices for the Measurement of Polar Odorous Compounds
###### Abstract  
A major problem of measuring polar compounds in the air is water vapor. Therefore, it is important to use a water pretreatment device prior to sampling and analysis. However, many studies have reported the occurrence of many problems following the application of an existing water pretreatment device. Accordingly, the performance of a Desolvator-K that was developed by the authors and two commercial coolers were investigated and compared in this study. Water vapor removal efficiency, recovery rate, and reproducibility of polar odorous compounds (i.e., methyl ethyl ketone (MEK), isobutyl alcohol (i-BuAl), methyl isobutyl ketone (MIBK), butyl acetate (BuAC), styrene) in air were taken into account. It was found that the Desolvator-K, the Cooler-G, and the Cooler-K showed 91.6%, 67.2%, and 62.1% water vapor removal efficiency, respectively, at the relative humidity of 90%. In terms of recovery rate, after water vapor removal devices, the Desolvator-K, the Cooler-G, and the Cooler-K revealed average recoveries of 96.6-103%, 81-101%, and 88.6-100%, respectively. Reproducibility of odorous compounds under all conditions of the Desolvator-K, the Cooler-G, and the Cooler-K were 5.94%, 31.2%, and 8.14% of relative standard deviation (RSD), respectively. Therefore, it is suggested that the Desolvator-K should be established as a water pretreatment device for the MEK, i-BuAl, MIBK, and BuAc compounds in the air.  
water pretreatment device; desolvator; KPASS-Odor; cooler; odorous compounds; water soluble compounds; polar compounds +
Footnote †: journal: Applied
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 4 Mitigation of Corrosion
The chemical resistance of the SSI fibers is linked to the robust treatment process, which involves treatment with acetic acid and NaOH [26]. In particular, SSI has a moisture absorption property of 11%, elongation at failure of 2-3%, modulus of elasticity, and tensile strength of 38 and 600-700 MPa, respectively, as shown in Table 11. Following the comparison of the material properties of various fibers, flax has the most suitable material properties except for the percentage elongation at failure (1.2-1.6%) [24]. Additionally, the properties of FRPs are augmented by the incorporation of nanoparticles to improve the surface area. Therefore, the durability of FRPs in farm structures is influenced by additives such as nanoparticles, type of fiber, and polymer properties. \begin{table}
\begin{tabular}{c c c c} \hline
**Natural Fibers** & **\(\alpha\)-Cellulose (\%)** & **Lignin (\%)** & **Pentosans (\%)** \\ \hline Sisal & 47–62 & 8–9 & 21–24 \\ Pineapple & 70 & 4 & 18 \\ Wheatgrass & 21.4 & – & – \\ Bamboo & 26–43 & 21–30 & 15–26 \\ Rice & 28–48 & 12–16 & 23–28 \\ Ryegrass & 33–50 & 16–19 & 27–30 \\ Sugar grass & 32–48 & 19–24 & 27–32 \\ Wheat straw & 29–51 & 16–21 & 26–32 \\ Kenaf bast & 44–57 & 15–19 & 22–23 \\ Jute & 45–63 & 21–26 & 18–21 \\ Oat straw & 31–48 & 16–19 & 27–38 \\ Esparto grass & 33–48 & 17–19 & 27–32 \\ Hemp bast & 57–77 & 9–13 & 14–17 \\ Barley & 31–45 & 14–15 & 24–29 \\ Wood grass & 40–45 & 26–34 & 7–14 \\ Marsh grass & 25–27 & – & – \\ Sorghum & 27–31 & 12–16 & 23–28 \\ Cornstalk & 32–34 & 5–34 & 20–41 \\ Coconut coir & 28–29 & 16–45 & 7–23 \\ Rush grass & 26–29 & – & – \\ Peanut hulls & 35–36 & 32–33 & 19–20 \\ Flaxseed & 43–47 & 21–23 & 24–26 \\ Okra & 23–35 & – & – \\ Ramie bast & 87–91 & 0.5–0.7 & 5–8 \\ Sabai & – & 22–23 & 24–25 \\ Cotton linter seed hull & 90–95 & 0.7–1.6 & 1–3 \\ Reed fiber & 44–46 & 22–24 & 20 \\ Silver maple & 42.0 & 21.0 & 19.0 \\ (Hardwood) & & 30.6 & 11.1 \\ Groundnut husk & – & 23.0 & 25.0 \\ \hline \end{tabular}
\end{table} 
Table 10: Composition of selected natural fibers used in FRPs [24].
## 2 Theoretical Model
### Governing Equations
The Laplace equation was adopted to describe the heat convection of solid rings, and the heat convection equation for the stationary seal ring in the polar coordinates is shown as:  
\[\frac{\partial^{2}T_{s}}{r^{2}\partial\theta^{2}}+\frac{\partial}{r\partial r }\left(r\frac{\partial T_{S}}{\partial r}\right)+\frac{\partial^{2}T_{s}}{ \partial z^{2}}=0, \tag{3}\]  
Owing to the existence of the interfacial velocity, the heat convection equation for the rotating seal ring in the polar coordinates is shown as:  
\[\frac{k_{c2}}{\rho_{s2}c_{s2}}\left[\frac{\partial^{2}T_{s}}{r^{2}\partial \theta^{2}}+\frac{1}{r}\frac{\partial}{\partial r}\left(r\frac{\partial T_{s} }{\partial r}\right)+\frac{\partial^{2}T_{s}}{\partial z^{2}}\right]=w\frac{ \partial T_{s}}{\partial t}, \tag{4}\]  
where \(k_{c}\) is the coefficient of heat conduction of seal rings, \(r_{s}\) is the density of seal rings, and \(c_{s}\) is specific heat capacity of seal rings. Additionally, a forced convection heat transfer exists between the gas film and the interfaces, owing to the high-speed flow of the sealing medium on the interfaces of the seal rings, which satisfies the following equations. \[-k_{c1}\left(\frac{\partial T_{s}}{\partial n}\right)_{s}=k_{s1}(T_{s1}-T), \tag{5}\]  
\[-k_{c2}\left(\frac{\partial T_{s}}{\partial n}\right)_{s}=k_{s2}(T_{s2}-T), \tag{6}\]  
where \(k_{s}\) is the coefficient of the convection heat transfer at the friction surfaces. Considering the compressibility of the sealing medium, the pressure involved in the governing equations satisfies the following relationship:  
\[p=\varepsilon c_{p}\rho i_{d}E_{m}, \tag{7}\]and the temperature of the gas film expresses as:  
\[T=\frac{i_{d}E_{m}}{c_{p}}, \tag{8}\]  
where \(\varepsilon\) is the compressibility coefficient of the gas, \(c_{p}\) is the constant coefficient of pressure, \(i_{d}\) is the freedom degree of gas molecules motion, \(E_{m}\) is the energy of gas molecular per freedom. The leakage and opening force are the main parameters used to measure the sealing performance, and they can be defined as follows, respectively. \[Q=\frac{h^{3}r\rho}{12\eta}\int_{0}^{2r}\frac{\partial p}{\partial r}d\theta \tag{9}\]  
\[F=\int_{0}^{2\pi}\int_{r_{i}}^{r_{0}}prdrd\theta \tag{10}\]
## 3 Results and Discussion
### Comparison of PT and LST through CNR Analysis
Considering PT results for defect 3 (first 8 columns in Figure 9), as was the case for defect 1(Figure 8), signal processing and filtering have a major impact on improving defect contrast, as can be verified from the comparison of unfiltered raw (CNRmax = 0.4) vs. Gaussian filtered (\(\sigma=3\)) second time derivative (CNRmax = 7.7) results. In the case of LST, contrary to defect 1 (Figure 8), defect 3 (Figure 9) cannot be clearly detected without  
Figure 8: Pulsed thermography vs. line scan thermography at 10 mm/s. Impact of advanced signal processing and Gaussian filtering on **single layer porosity at intermediate depth**: sample S48, front face, defect 1 (insert at depths = 1.69 mm, thickness = 0.16 mm). Figure 9: Pulsed thermography vs. line scan thermography at 25 mm/s. Impact of advanced signal processing and Gaussian filtering on **multilayer porosity at intermediate depth**: sample S48, front face, defect 3 (inserts at depths = 1.41, 1.69, and 1.98 mm, thickness = 0.16 mm). signal processing as shown in the last column in Figure 9, which corresponds to the 2 nd time derivative. Figure 10 presents a notable example of the impact of signal processing and LST capabilities to detect deep features. As seen in Table 2, defect 3 in sample S45 is not detected by PT. On the other hand, it is possible to fairly (CNR around 3) detect this defect by LST but only after applying advanced signal processing. Figure 10 shows the results of PCT, which presented the highest CNR\({}_{\text{max}}\) values. It is not clear though why in this case the PCT (from raw data) shows the best CNR instead of other techniques, TSR 1 st or 2 nd time derivatives in particular. Interestingly, as can be observed from Table 2, a slower scanning speed does not necessarily translates into an increase in defect contrast. For sample S48, CNR results for three scanning speeds (25, 10, and 5 mm/s) are listed. Although some improvement can be observed on defects 1 and 2 in sample S48 when slowing down from 25 to 10 mm/s, defect 3 is no longer detected at 10 mm/s. Further reducing the scanning speed (to 5 mm/s), worsen defect contrast in all cases, only defect 1 can be detected and with lesser contrast.
# Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
## 4 Experimental Results  
### Experimental Setup  
Figure 6a shows the experimental setup in which the clinical sleep data were obtained from the PSG test subjects. Clinical sleep data were collected from 85 participants in the Polysomnographic Laboratory at Pusan National University Yangsan Hospital. The radar was installed 0.4 m above the headboard facing the subject's chest and used for an average of more than 6 h simultaneously with PSG. The PSG readings provided by clinical specialists were used as the reference ground truth and were compared with the results obtained from the proposed sleep-stage detection algorithm. A single-channel FMCW radar (BitSensing Inc., Seoul, Korea) [34], as shown in Figure 6b, was used to collect and record the biometric information of the PSG subjects. The radar specifications are listed in Table 1. This radar, a 3-channel radar comprising one transmitting antenna and three receiving antennas, which transmits millimeter waves with an intermediate frequency of 60 GHz, is an indoor-sensing radar capable of detecting the distance and Doppler information of a target object located within the range of 0-0.75 m before the radar. We used only the data received through the single channel with the best SNR.  
Figure 5: FMCW radar-based wake stage detection results. (**a**) Subject 1. (**b**) Subject 2. (**c**) Subject 3. (**d**) Subject 4.  
Figure 6: Experimental setup. (**a**) Clinical data acquisition environment. (**b**) Single-channel FMCW radar.
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 3 Methods
### Rapid Update of Cropland Extent Based on LUILC
#### 3.1.2 Mapping the Cropland Using Time-Series Data  
Cropland is inherently a composite feature encompassing various crop types [6]. Single-phase imagery, however, can only capture the state of cropland at a specific time point, which may lead to misclassification across different phenological stages [41]. As such, utilizing time-series data to capture the phenological characteristics of cropland is more advantageous for accurate cropland information extraction. Since Shanwei is in a cloudy and rainy climatic environment, by searching in GEE, the cloud cover in Shanwei City has a very serious effect on optical imaging in summer and autumn. In contrast, SAR data remain unaffected by clouds and rain, providing consistent ground observations with 2-3 full coverages of Shanwei per month. Consequently, this study uses time-series SAR data from 2019, with monthly SAR imagery synthesized using the mean synthesis method. Additionally, one cloud-free optical image was generated through median synthesis. Optical features such as the Enhanced Vegetation Index (EVI) and the Normalized Difference Water Index (NDWI) were calculated. These data sources were then combined, and cropland mapping was conducted using the Random Forest (RF, [[https://developers.google.cn/earth-engine/apidocs/ee-classifier-smilerandomforest](https://developers.google.cn/earth-engine/apidocs/ee-classifier-smilerandomforest)]([https://developers.google.cn/earth-engine/apidocs/ee-classifier-smilerandomforest](https://developers.google.cn/earth-engine/apidocs/ee-classifier-smilerandomforest)), accessed on 10 October 2024) model available in GEE. The specific input features used in the RF model are detailed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Bands** & **Number of Features** \\ \hline Sentinel-1 & VH, VV, VH/VV & 36 \\ Sentinel2 & B2, B3, B4, EUI, NDWI & 5 \\ SRTM V1 & DEM & 1 \\ Total & & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of characteristic bands.  
Figure 3: Screening high-quality sample points.
# Acoustic-Based Engine Fault Diagnosis Using WPT, PCA and Bayesian Optimization
## 3 Methodology  
The main objective was to develop and experimentally validate an adaptive fault detection and classification system for vehicles that is robust to different speeds and operating conditions. The acoustic  
Figure 3: Ignition timing variation—healthy and faulty: (**a**) signals; and (**b**) spectrogram.  
Figure 2: Engine misfire—healthy and faulty: (**a**) signals; and (**b**) spectrogram.  
signals were collected from the engine and preprocessed, where the signal was decomposed using WPT and the feature vectors were using statistical measures of the wavelet coefficients. Three models were analyzed and compared--from standard classifiers (Model 1) to Bayesian optimized classification model (Model 2) and finally to PCA combined with Bayesian optimized model (Model 3). A generalized workflow is demonstrated in Figure 4.  
Model 1 uses standard classification models. Model 2 uses Bayesian optimization for auto classification model selection and hyperparameter optimization. Finally, Model 3 uses PCA alongside Bayesian optimization to reduce the dimensionality of the large feature matrix and ensure good accuracy with reduced computational complexity and evaluation time.
# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## 2 Related Work
### Large-Scale Multi-Objective Evolutionary Algorithms
LMEA [48] builds on MOEA/DVA by using the K-means clustering method to further subdivide variables in the MV set into CV and DV, optimizing convergence and diversity within each variable group during each algorithm iteration. LMEA effectively handles multi-objective optimization problems with decision variable scales of up to 5000 and objective variable dimensions of 5-10. In LSMOEA/D, reference vector guidance is introduced into DVA control, and these grouped variables are optimized through an adaptive scalarization strategy. The local DVA method measures each decision variable's contribution to convergence using a projection-based detection method. LSMOEA/D [28] has been experimentally compared with a group of mainstream algorithms on a set of test problems with 2-10 objectives and 200-1000 variables. The results show that LSMOEA/D effectively handles LSMOPs. Based on this, DVA-TPCEA [49], as a dual-population cooperative evolutionary algorithm grounded in decision variable analysis, enhances the overall algorithm performance by improving search efficiency through the utilization of decision variable characteristics. Additionally, SPEA2 [50] analyzes the effectiveness of decision variables by evaluating individual density and dominance relationships in the objective space, providing a reference for further optimizing the decision-making process. As illustrated in Table 1, this positioning table summarizes the key strategies and contributions of related studies in the field of large-scale multi-objective evolutionary algorithms. This comparison highlights how our research aligns with existing studies and underscores its contributions to the field. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Study** & **Key Strategies** & **Focus/Contribution** \\ \hline Cooperative Co-evolution (CC) & Groups complex decision variables & Optimizes groups independently and integrates results; explores effective grouping methods. \\ \hline Problem Transformation (WOF/LSMOF) & Reduces dimensionality through transformation functions & Reconfigures problems to low-dimensional space; improves search directions. \\ \hline Decision Variable Analysis (DVA) & Identifies characteristics of decision variables & Divides variables into CV, DV, and MV; enhances convergence and diversity in optimization. \\ \hline MOEA/DVA & Samples convergence and divergence properties & Optimizes in groups based on dependency relationships among variables. \\ \hline LMEA & Uses K-means clustering for variable subdivision & Handles large decision variable scales (up to 5000) effectively. \\ \hline LSMOEA/D & Introduces reference vector guidance & Optimizes grouped variables using adaptive scalarization; demonstrates effectiveness in LSMOPs. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Positioning table of related studies.
## 2 Materials and Methods
### Data Preparation  
The GOCE satellite is equipped with a dual-frequency GPS receiver, which provides GPS observations with a sampling rate of 1 Hz. The on-board GPS raw observations, the attitude information, and the kinematic (KIN) and RD POD solutions are obtained from the released Level 1B and Level 2 products of the GOCE mission [11; 12; 31; 32]. The GPS products (orbits, clock, and ERPs) are provided by the Center for Orbit Determination in Europe (CODE). The phase center offsets (PCOs) and variations (PCVs) for GOCE are provided by the European Space Agency (ESA) [11]. The data of day of year (DOY) 318-332, 2009 (15 days), are used to evaluate the performance of the proposed RD_STP method.
## 2 Methodology
### Application Guidelines
#### 2.3.4 Geolocation Information as Support for Cataloguing Criteria  
Geolocation information represents a data field with special potential from many different points of view. As mentioned before, for each asset included in the catalogue, the GPS coordinates are shown,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Population**} \\  & **2015** & **2016** & **2017** \\ \hline Sevilla & 693,878 & 690,566 & 689,434 \\ Malaga & 569,130 & 569,009 & 569,002 \\ Cordoba & 327,362 & 326,609 & 325,916 \\ Granada & 235,800 & 234,758 & 232,770 \\ Jerez de la Frontera (Cádiz) & 212,876 & 212,830 & 212,915 \\ Almeria & 194,203 & 194,515 & 195,389 \\ Huelva & 146,318 & 145,468 & 145,115 \\ Marbella (Malaga) & 139,537 & 140,744 & 141,172 \\ Dos Hermanas (Sevilla) & 131,317 & 131,855 & 132,551 \\ Algecarias (Cádiz) & 118,920 & 120,601 & 121,133 \\ Cádiz & 120,468 & 118,919 & 118,048 \\ Jaen & 115,395 & 114,658 & 114,238 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cities with the highest population in Andalusia. Source: National Institute of Statistical of Spain [23].  
Figure 6: Guidelines for assets classification by criteria related to the grade of relation with human settlements.  
and when selecting this field for a particular asset, the location of that element is shown in Google Maps. Having every asset of the catalogue linked to Google Maps [23] has an enormous potential and offers many opportunities, both as a promotion tool for this typology and as support information when particular assets must be classified through some of the criteria considered in the catalogue.  
Figure 7 shows some of the main possibilities that this data field offers. Having this information for all of the assets of the catalogue, it is possible to make maps of assets by using Google My Maps. In these maps, it is possible to include a set of assets that has specific characteristics, such as the location in a particular region, the activity sector, or any other aspect included in the catalogue as one of the classification criteria. In addition, for each single asset, it is possible to make a virtual approximation or visit through the functions included in Google Maps [24] such as aerial views, Street View, and the photographs loaded by users.  
Thus, this resource has significant potential applications as a tool for the diffusion and promotion of these assets, as well as for their visitors, so the interest in the field of tourism is considered relevant. However, in addition, during the application of the classification criteria considered in the catalogue the geolocation information also has a key role. In many situations this information will help to apply properly some classification criteria, mainly the scale criterion and the relation with local population. In both cases the geolocation information and the different virtual view modes allowed by it, make possible to evaluate and select the most appropriate category for each analyzed asset.