esa-sceva/satcom-chunk-collection huggingface dataset:
# Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis
## 3 Metal-Organic Frameworks
### MOFs as SPME Coatings for Environmental Applications
#### 3.3.4 Extraction of Poly- and Perfluoroalkyl Substances
The presence of capped nanocrystals (NH\({}_{2}\)- UiO-66(Zr)-hp) resulted in higher hydrophobicity, faster extraction kinetics and higher enrichment capacity compared to the uncapped coating. The DI-SPME-HPLC-MS/MS method was validated, obtaining LOQs in the range of 0.21-1.8 ng/L, and applied for the analysis of tap, river, and pond water samples detecting 2 PFASs of 29.3, 58.7, and 45.0 ng/L, respectively. A similar approach was proposed by [PERSON] et al., who developed an SPME coating based on MIL-101(Cr) functionalized with amino and fluorinated groups to enhance the interaction towards PFASs [73]. As depicted in Figure 5, the bifunctional MOF was synthesized by the hydrothermal method, aminated by diethylenetriamine (DETA), and fluorinated using perfluorocanoyl chloride. Finally, the coating was obtained by dipping the SS needle coated with epoxy resin in the developed material. The dual-functionalized MIL-101(Cr) (MIL-101-DETA-F) SPME coating was applied for the extraction of nine model PFASs, obtaining EFs in the 70-112 range: these values were higher than those obtained by using UiO-66(Zr), MIL-101(Cr), the mono-amino-functionalized MOF, and the commercial PDMS/DVB coatings. In addition, the pore size of MIL-101-DETA-F was close to the molecular size of perfluorododecanoic acid, allowing for the size exclusion of interfering compounds. Compared to the monofunctionalized MOF, fluorination enabled F\(-\)F interactions, improving both the selectivity and extraction capability of SPME. Finally, the DI-SPME-ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was validated for PFAS extraction in aqueous samples, achieving extremely low LOQ values, in the range of 0.01\(-\)0.40 ng/L, a three order of magnitude linear range, and RSDs \(\leq\) 12%. The \(\Sigma\)PFASs was 5.5 and 27.2 ng/L for tap and river water, respectively, and in the 263.8-2790.7 ng/L range for municipal, dyeing, and mining wastewater samples.
# Satellite Requirements for Observation of Close Proximity Celestial Bodies +
###### Abstract  
Celestial bodies of our solar system remain as a major unexplored and unexploited reserve of natural resources available to humans. Furthermore, those constitute a valuable source of information about the origins and evolution of the solar system and an alternative to establish human settlements in the future. Observation and understanding of the land conditions of those celestial bodies is vital to learn more about those celestial bodies, to generate accurate maps of them, to look for natural resources of interest, and to evaluate the feasibility and help in the preparation of future land missions. A satellite constellation constitutes an important infrastructure element to observe those celestial bodies and to transmit the retrieved information back to Earth. Nonetheless, the operation of sensing satellites in other planets needs understanding of the requirements to perform such observations. In this paper, we discuss those sensing requirements from the point of view of orbits and payload requirements for one of our closest neighbors of the solar system (Moon, Mars). To analyze the orbit of the sensing satellite, we discuss the required altitude to facilitate ground observation, the orbit's conditions (such as radiation levels and orbital perturbations, among others), suitable orbit configurations, required number of satellites, and ways to estimate the required time to perform full observation of the celestial body. To evaluate suitable payloads, we discuss available information in the literature (such as known atmospheric and land conditions) to determine the best observation frequencies and determine the best kind of payload (such as sensors, a camera, or a lower frequency observation payload) to study that celestial body. Finally, we discuss some important considerations such as the requirements of satellite communication link to transmit the retrieved information back to Earth.  
n 2022, 27, 68. [[https://doi.org/10.3390/ecsa-9-13329](https://doi.org/10.3390/ecsa-9-13329)]([https://doi.org/10.3390/ecsa-9-13329](https://doi.org/10.3390/ecsa-9-13329))
## 3 Results and Discussion
### Testing of Mechanical Properties of Composite Materials
#### 3.2.4 Friction and Wear Experiment  
According to the \"Test Method for Sliding Friction and Wear of Plastics\", a ring-block-type sliding friction and wear test was conducted using a friction and wear tester. In the test, a rotating steel ring and the MWCNT/HA/CF/PEEK composite specimen block form the friction vice. The MWCNT/HA/CF/PEEK composite specimen had dimensions of 30 mm \(\times\) 7 mm \(\times\) 6 mm, with a working surface size of 30 mm \(\times\) 7 mm. It underwent processing using a grinding machine to ensure surface flatness and roughness. The friction vice was made of quenched 45# steel, with heat-treated hardness between HRC40 and 45, a surface roughness (Ra) of not more than 0.4, and dimensions of \(\Phi\)40 mm \(\times\)\(\Phi\)16 mm \(\times\) 10 mm. The surface was also processed using a grinder. Both specimens and steel rings were cleaned with alcohol and dried for testing. The test conditions included a load of 196 N, a steel ring speed of 200 r/min, a test time of 2 h, and a test environment at room temperature under dry friction without a lubricating medium. The coefficient of friction obtained was 0.233, which is low and makes the material suitable for manufacturing friction components, such as the preparation of gears and bearings, and reduces energy loss and wear.
## 2 Materials and Methods
### Meteorological Data Collection Utilizing IoT Sensor  
Meteorological data were collected using an IoT-based meteorological sensor (Boreas Ltd., EcoLogger Boreas datalogger unit BEL-06, BHS-06 for temperature and humidity, BWS/s-06 Wind Speed Sensor, BIS-06 global solar radiation, and BPD-06 for precipitation) (Figure 2A). The manufacturer claims an accuracy of \(\pm\)0.2 \({}^{\circ}\)C for temperature, \(\pm\)2% for humidity, and \(\pm\)0.1 mm for precipitation. To minimize the measurement error that could affect SMC prediction, the meteorological sensor was calibrated to ensure the accuracy of data. The data were collected at 10-15-min intervals using LoRaWAN, a low power consumption device with the capability of transmitting sensor signals up to 30-40 km away on level terrain. Meteorological parameters, including precipitation (mm), temperature (\({}^{\circ}\)C), humidity (%), wind speed (km/h), and solar radiation (W/m\({}^{2}\)), were used to examine how these meteorological factors influence variations in soil moisture content, considering the effects of both soil depth and soil texture. These features were selected based on their influence on soil moisture dynamics, where precipitation is considered the primary source of soil water and SMC increased with increasing precipitation [32]. Humidity, on the other hand, reflects the air moisture content, influencing the evaporation rate from the surface soil where increasing the humidity reduces the moisture losses by evaporation [33]. Solar radiation also has a significant impact on SMC depletion by its effect on available energy for evaporation and transpiration. Temperature also plays a crucial role in SMC variation; evaporation increases at higher temperatures. Also, wind speed has a role in enhancing moisture losses in the soil surface due to the increase in air movement [34; 35]. The data collected represent the average of the field and were assigned to all sampling locations. Also, the data were synchronized with the dates of soil sampling to ensure accurate pairing of meteorological and SMC observations.
# Intelligent Reception of Frequency Hopping Signals Based on CVDP
## 3 Algorithm Simulation and Performance Analysis
### Estimation Performance of Hopping Frequency  
The accuracy of the CVDP network's hopping frequency estimation in the one-time slot determines its performance.
## 3 Results
### Correlation of NEP, WUE, and CUE with Climate Variables
#### 3.4.2 Correlation with Temperature  
As shown in Figure 10, the correlations of the NEP, WUE, and CUE with temperature in the MRYR show obvious regional differences. The correlation coefficients of the NEP and TEM range from \(-\)0.67 to 0.78, with a mean value of 0.12. The positive correlation area between the NEP and TEM accounts for 74.75% of the study area, which is widely distributed in the MRYR. The south-central part of the MRYR has a wide distribution of negative correlations. The correlation coefficients of WUE, CUE, and TEM range from \(-\)0.80 to 0.70 and from \(-\)0.75 to 0.67, with mean values of 0.04 and 0.01, respectively. In the distribution of their correlation with temperature, both are similar to the NEP. However, the distribution of negative correlations between WUE and CUE is larger. The correlation between the NEP and temperature is predominantly positive for the four vegetation types. In contrast, WUE and CUE are predominantly positively correlated in the grasslands and croplands, and negatively correlated in the shrubs and forests.
# Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms
## 6 Skin Biomarkers Discovery through Metabolomics Applications
### Sampling Procedures and Analysis of Skin Metabolites  
The quality of skin metabolomic analysis is directly influenced by the efficiency of metabolite collection and extraction techniques. Studies on drug metabolism through skin are done in vitro with the employment of skin cells, skin tissue fluid, and skin microsomal and cytoplasmic homogenates, because it is difficult to distinguish skin metabolites from systemic metabolism. On the other hand, studies on drug metabolism through the liver is done in vivo, after the collection of blood or/and urine samples, due to the higher viability of metabolic enzymes in liver, compared to the skin [38].  
Recently, various procedures in vivo and in situ have been developed for collecting skin specimens and are used in different studies for skin metabolome and biomarker identification. These procedures are either invasive, minimally invasive, or even non-invasive. Skin is the most exposed, accessible, and largest organ in the human body, and, therefore, there is an interest in the progress of these procedures. Therefore, skin sampling methods have been advanced to address different applications.  
Conventional techniques that use matrices from skin layers are utilized to sample skin tissues and perform skin metabolomic analysis, such as superficial fat, dermis, and epidermis, and they need to be obtained by invasive techniques in the clinical setting, such as suction blistering and biopsy [26]. Other minimally or non-invasive techniques are tape stripping [39], iontophoresis [40], micro dialysis [41], ultrasound [42], and microneedle [43]. Table 1 represents several sample collection methods for skin metabolomic analysis [38].  
Suction blistering is a less invasive procedure compared to a skin biopsy, and are used in the collection of epidermis and interstitial fluid. Other techniques such as open-flow micro perfusion are used for sampling interstitial fluid in human skin by implanting membrane-free probes with macroscopic openings. In contrast, a skin biopsy is an invasive procedure, and depending on specific approaches, samples can be collected from epidermis, or even from the dermis and superficial fat.  
Other non-conventional techniques that collect stratum corneum and skin excretions such as sebum, sweat, and volatile compounds originating from skin are used and considered as non-invasive procedures. A sweat sampling procedure can be done by stimulating the sweat using physical workouts or controlling the environment's temperature, or by inducing it using iontophoresis and pilocarpine, followed by collection via macro duct sweat collector. However, these inducing methods may affect the sample composition. Then, the skin surface can be washed with extraction buffer or solvent to collect sweat and sebum. In addition, they can be collected without contacting the skin, by using a solid-phase microextraction device, or by using different sorbent materials designed within a patch, such as polydimethylsiloxane and hydrogels that directly contact the skin [26].
## 1 Introduction
The first principal component (PC-1) can be used to detect differences between samples because it represents the largest variation from the original data set, while the second principal component (PC-2) explains better the variation shown by the third principal component (PC-3), and so on [29]. Another classification type is partial least squares discriminant analysis (PLS-DA). This algorithm is used as a model for discriminative variable selection, restricting attention mainly to predictive modeling. Theoretically, PLS-DA is a combination of dimensionality reduction and discriminant analysis in one algorithm. Additionally, the algorithm does not necessarily fit a set of data to a particular distribution, making it more flexible than other algorithms. Because of this, this method has countless applications, including food analysis [30; 31; 32]. [PERSON] et al. (2022) used both simulated and experimental vibrational spectra to investigate the ability of PCA-LDA and PLS-DA to discriminate spectra related to different classes. The results showed that in terms of accuracy in classifying FTIR spectra, the PCA-LDA model seemed a little better than the PLS-DA model [33]. In certain cases, an NIR spectrum shows a change in the baseline and the presence of noise. Spectral preprocessing methods have been used to solve these problems--to name a few, standard normal variation (SNV), first-derivative Savitzky-Golay, and detrending methods are used to remove changes in baseline offset, baseline vertical offset, and baseline deviation in a spectral signal, respectively [34; 35; 36; 37; 38; 39]. In one study, a chemometrics analysis was performed to authenticate Andean grain flours using mid-infrared spectroscopy and discriminate flour samples using protein, fatty acid, and fat profiles [40]. In another study, Raman spectroscopy in combination with PCA was used to discriminate cereal flour species; in this study, a PCA loading plot explainedthat changes in starch and protein signals allowed for the discrimination of the samples [41]. Due to the similar physical characteristics of the mentioned foods, they can be easily confused, and this can be used to create adulterated products with lower quality using cheap grains [42; 43]. In another study, a hyperspectral image technique and multivariate statistical process control were useful in detecting adulterations of wheat flour in the presence of different grains [44]. In this context, we proposed to use NIR spectroscopy and chemometrics to classify each type of grain and flour and attempted to find some variable that is spectrally different.
## 5 Application in Real-World HDS System
### Results of the Case Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**HUADE Co.,** & & & & \\
**Ltd.** & & & & \\ \hline \multicolumn{5}{c}{Scenarios without obstacles} \\ \hline \(r_{c}\) & 99\% & 99\% & 98\% & 99\% \\ \hline \(\overline{I}_{\text{ship}}\) (s) & 11.6 & 11.6 & 11.6 & 14.3 \\ \hline \(\text{std}_{\text{ship}}\) (s) & 0.15 & 0.25 & 0.35 & 1.20 \\ \hline CI & \((11.56,\,11.64)\) & \((11.54,\,11.66)\) & \((11.51,\,11.69)\) & \((13.99,\,14.61)\) \\ \hline \multicolumn{5}{c}{Scenarios with 5\% obstacles} \\ \hline \(r_{c}\) & 92\% & 91\% & 87\% & 89\% \\ \hline \(\overline{I}_{\text{ship}}\) (s) & 12.1 & 12.9 & 13.4 & 14.8 \\ \hline \(\text{std}_{\text{ship}}\) (s) & 0.2 & 0.4 & 0.6 & 1.8 \\ \hline CI & \((12.05,12.15)\) & \((12.8,13.0)\) & \((13.25,13.55)\) & \((14.34,15.26)\) \\ \hline \multicolumn{5}{c}{Scenarios with 10\% obstacles} \\ \hline \(r_{c}\) & 89\% & 87\% & 73\% & 78\% \\ \hline \(\overline{I}_{\text{ship}}\) (s) & 13.7 & 14.2 & 15.1 & 16.7 \\ \hline \(\text{std}_{\text{ship}}\) (s) & 0.3 & 0.6 & 0.9 & 2.4 \\ \hline CI & \((13.62,13.78)\) & \((14.05,14.35)\) & \((14.87,15.33)\) & \((16.08,17.32)\) \\ \hline \multicolumn{5}{c}{Scenarios with 20\% obstacles} \\ \hline \(r_{c}\) & 84\% & 82\% & 66\% & 67\% \\ \hline \(\overline{I}_{\text{ship}}\) (s) & 15.3 & 16.4 & 17.8 & 19.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The confidence interval for shipping time under the two scenarios. It is also worth mentioning that although the proposed method can perform better than all three benchmark algorithms in both the numerical and case studies, Huade Co., Ltd., the cooperator in our study, has not applied our method in real production. An acceptable 'Completion Rate' for path planning and storage transportation in this company should always be higher than 95% for medium-size HDS systems, given two standard deviations. Therefore, as displayed in Table 3, only under the scenario without obstacles can the proposed algorithm be applied, but this ideal condition seldom happens. In a word, we will continuously improve our method for potential future industrial operations.
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 4 Results
### Area and Timeseries Comparisons with JRC-GSWE and GLAD GSWD  
The surface water area (km\({}^{2}\)) over mainland China was calculated for each month from 2017 to 2020 and compared to JRC-GSWE and GLAD-GSWD (Figure 6). Our algorithm detects the highest extent in all months except for May and June, where GLAD-GSWD is higher. The higher area detected by GLAD-GSWD is due to a high number of commission errors observed mainly over urban centers with tall buildings, glaciers (particularly when mixed with debris) and cloud shadow over dark, dense forests [12]. The smallest annual variations in surface water area, which are a reflection of seasonality, is in GRAS-SWD and the largest in JRC-GSWE. In general, all datasets show lower SWE's during the winter months than in summer, which is consistent with precipitation patterns and lower runoff due to the upstream freezing of water bodies and winter precipitation being temporarily stored as snow. JRC-GSWE shows the lowest extent in all months, except for February and March where GLAD-GSWD is lower. In the darkest months (December and January), JRC-GSWE reports as little as 20,000 km\({}^{2}\), which is less than 15% of the extent mapped by GRAS-SWD. Albeit to a lesser extent, GLAD-GSWD also returns markedly lower areas during winter. This discrepancy is not a true reflection of the surface water extent, but rather due to the lack of suitable Landsat observations during the winter at higher northern latitudes, where sun angles are low and optical water detection is constrained by light. This inherent issue with Landsat derived SWE makes it difficult to derive meaningful insights into water dynamics across all months.  
Surface water dynamics were investigated through annual comparisons of permanent vs. seasonal water extent. Each algorithm appears to be consistent through time with small deviations in area, and in the proportion to the total water, from year to year (cf. Figure 7). Furthermore, the permanent water estimates are consistent across the algorithms at approximately 100,000 km\({}^{2}\). However, there appears to be significant differences in the seasonal water estimates ranging in area from 200,000 km\({}^{2}\) to 360,000 km\({}^{2}\), with JRC-GSWE estimating the smallest area and GLAD-GSWD the largest. We suspect the difference in seasonal water is due to some key methodological differences where JRC-GSWE uses stricter rules, including a sun-angle threshold, a temporal sliding window to reduce cloud shadows, and building shadow removal, whereas GLAD relies upon a machine learning approach to solve these issues. Seasonal water varies between 64.7% and 79.1% of the total annual maximum surface water extent, whilst permanent water varies between 21.1% and 35.3% depending on the algorithm.  
Figure 6: Monthly surface water area statistics for mainland China, as detected by GRAS-SWD, JRC-GSWE and GLAD-GSWD, respectively.
# Modeling and Analysis of Wearable Antennas
## 2 Sheet-Like Transmission Line
### Information Transmission by a Sheet-Like Transmission Line
Figure 4: Transmission characteristics of the two-dimensional transmission line: The dotted lines show the transmission characteristics in free space. Figure 3: Sheet-like two-dimensional transmission line. The broken line in Figure 4 is the transmission loss which is calculated from the inter-port distance in (a) and (c). Since the cylindrical wave propagates in the two-dimensional transmission line, the transmission loss becomes smaller by 10 to 15 dB or more compared to free space. From the results in (b) and (c), the coupling loss between the dipole and the transmission line can be estimated to be 3 dB. It is only necessary for the coupling antenna to match the resonance frequency with the resonator, and it is a great advantage in practical use that a dedicated coupler for coupling the electromagnetic wave to the transmission line is unnecessary. In addition, the distance of the antenna that can be coupled to the transmission line is about \(\lambda/4\) from the surface of the seat, and electromagnetic waves can be easily excited from the built-in antenna of the small wireless device to the transmission line. The position of the antenna to be coupled can be dealt with at an arbitrary position on the transmission line and is not dependent on the polarization direction of the antenna. Since the coupling is attenuated by more than 30 dB by physically separating the transmission line, even if the transmission lines are adjacent to each other, there is no possibility of mutual coupling, and hence there is an advantage of blocking information between different transmission lines. For use in two frequency bands, it is possible to respond by changing the shape of the lower layer resonator. If a ring resonator as shown in Figure 5 is used as a lower layer, it becomes possible to have dual resonance characteristics and it can be used in two wireless LAN frequency bands of 2.4 GHz and 5 GHz [29].