esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methods  
### Data Sources and Processing  
The data involved in the study were as follows: relief amplitude data were obtained from the Chinese Academy of Sciences Resource and Environmental Science Data Center ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)) (accessed on 10 June 2022)); Land-use diversity data were obtained from the Chinese Academy of Sciences Resource and Environmental Science Data Center with the spatial resolution of 1 km \(\times\) 1 km; Rainfall and temperature data were obtained from the meteorological station data provided by the China Meteorological Information Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 10 June 2022)). The data were mainly based on the ArcGIS software platform, and kriging interpolation was used to spatially interpolate the station data with the spatial resolution of 1 km \(\times\) 1 km; then averaging was used to obtain the spatial distribution data of the annual average temperature and annual average rainfall of county units in Henan Province in 2010, 2015, and 2020. Population and GDP data were obtained from the statistical yearbooks of the cities in Henan Province, including the resident population and gross domestic product of each county and city at the end of 2010, 2015, and 2020. Forestry project data were gathered from the official website of the Henan Provincial Forestry Bureau and Henan Provincial People's Government, and they were used to count the number of continuously implemented national forestry projects in Henan Province's county units in 2010, 2015, and 2020, including turning farmland into forests, natural forest protection projects, and shelter forest projects. The data on fractional vegetation cover, leaf area index, and gross primary productivity were from the Institute of Remote Sensing, Chinese Academy of Sciences, with the spatial resolution of 1 km \(\times\) 1 km; they were used to calculate the ecosystem quality index (_EQI_) of county units in Henan Province. From 2010 to 2020, there were a few changes in the county administrative units in Henan Province. Therefore, the population, GDP, and regional area data of the county units in 2010 and 2015 were reprocessed according to the division standards of the county administrative units in Henan Province in 2020. According to the locations of the central coordinates of each county administrative unit, we determined the domain to which each belonged.
# Sliding Mode Control of Servo Feed System Based on Fuzzy Reaching Law
## 2 Flexible Dynamics Modeling of the Feed System
### Fuzzy Convergence Law Sliding Mode Controller Design
#### 3.2.2. Design of a New Fuzzy Convergence Law Sliding Mode Controller  
A new fuzzy convergence law is introduced for the sliding mode controller designed in Section 3.1, and the expression of the controller is obtained by associating (7) and Equation (14) as:  
\[u=m_{1}\left[\begin{array}{l}\lambda_{1}(\overset{\bullet}{x_{1r}}- \overset{\bullet}{x_{1}})+\lambda_{3}(\overset{\bullet}{x_{2r}}-\overset{ \bullet}{x_{2}})+\lambda_{2}(\overset{\bullet\bullet}{x_{1r}}+\frac{k}{m_{2}} x_{1}-\frac{k}{m_{2}}x_{2}+\frac{b_{2r}+c}{m_{2}}\overset{\bullet}{x_{1}}-\frac{c}{m_{2}} \overset{\bullet}{x_{2}})\\ +(\overset{\bullet\bullet}{x_{2r}}-\frac{k}{m_{1}}x_{1}+\frac{k}{m_{1}}x_{2}+ \frac{b_{1}+c}{m_{1}}\overset{\bullet}{x_{2}}-\frac{c}{m_{1}}\overset{ \bullet}{x_{1}})+D+fuzzy_{n}(\sigma,\overset{\bullet}{\sigma})\sigma\\ +fuzzy_{n}(\sigma,\overset{\bullet}{\sigma})\tanh(\sigma)\end{array}\right] \tag{15}\]  
**Proof.** Define the Lyapunov function  
\[V_{2}=\frac{1}{2}\sigma^{2} \tag{16}\]  
Combining (7) and Equation (14), we can see that  
\[\begin{array}{lcl}\overset{\bullet}{V}_{2}&=&\sigma\overset{\bullet}{ \sigma}\\ &=&\sigma[\lambda_{1}(\overset{\bullet}{x_{1r}}-\overset{\bullet}{x_{1}})+ \lambda_{2}(\overset{\bullet\bullet}{x_{1r}}+\frac{k}{m_{2}}x_{1}-\frac{k}{m_{ 2}}x_{2}+\frac{b_{2r}+c}{m_{2}}\overset{\bullet}{x_{1}}-\frac{c}{m_{2}} \overset{\bullet}{x_{2}}-\frac{1}{m_{2}}d_{2})\\ &&+\lambda_{3}(\overset{\bullet}{x_{2r}}-\overset{\bullet}{x_{2}})+(\overset{ \bullet\bullet}{x_{2r}}-\frac{k}{m_{1}}x_{1}+\frac{k}{m_{1}}x_{2}+\frac{b_{1} +c}{m_{1}}x_{2}-\frac{c}{m_{1}}x_{1}-\frac{1}{m_{1}}d_{1}-\frac{1}{m_{1}}u)] \\ &=&\sigma[-D_{\text{sup}}\tanh(\sigma)+D-fuzzy_{n}\Big{(}\sigma,\overset{ \bullet}{\sigma}\Big{)}\sigma-fuzzy_{n}\Big{(}\sigma,\overset{\bullet}{ \sigma}\Big{)}\tanh(\sigma)]\\ &=&-D_{\text{sup}}\left|\sigma\right|+D\sigma-fuzzy_{n}\Big{(}\sigma, \overset{\bullet}{\sigma}\Big{)}\sigma^{2}-fuzzy_{n}\Big{(}\sigma,\overset{ \bullet}{\sigma}\Big{)}\left|\sigma\right|\\ &\leq&\left(\left|D\right|-D_{\text{sup}}\left|\sigma\right|-fuzzy_{n}\Big{(} \sigma,\overset{\bullet}{\sigma}\Big{)}\sigma^{2}-fuzzy_{n}\Big{(}\sigma, \overset{\bullet}{\sigma}\Big{)}\left|\sigma\right|\right.\\ &\leq&0\end{array} \tag{17}\]  
In summary, \(V_{2}\geq 0\), \(\overset{\bullet}{V_{2}}\leq 0\), Lyapunov stability condition is satisfied, thus proving that the new fuzzy convergence law sliding mode controller designed in this paper is stable. \(\square\)
## 3 Results and Discussion
### Effect of Thermal-Bonded Temperature on Yarn-Out Force  
In order to deeply study on effect of thermal-bonded temperature on the stab resistance and burst property, the fracture planes of sandwich composites after knife damage are observed in Figure 11 using Tabletop Microscope 1000 (Hitachi, Japan). It can be seen from Figure 11a that when composites were thermally bonded at 100 \({}^{\circ}\)C, most of the fiber ends generated cut deformation and remained in the fiber state. As thermal-bonding temperature increased from 120 \({}^{\circ}\)C to 140 \({}^{\circ}\)C, thermal-bonding area became bigger and thermal-bonding points distributed uniformly, as seen in the red circles in Figure 11b,c. When composites were thermal-bonded at 160 \({}^{\circ}\)C, LPET lost their fiber shape, and conversely, a relatively bigger void was found, which can explain the decreased tendency of knife resistance and spike resistance as function of thermal-bonded temperature in Figures 6b and 8b. Thermal-bonding points can increase the fibers cohesion and decrease the inter-fiber slippage, which is beneficial to improve the stab resistances and their stability [26]. However, when surpassing the melting point, the composites' excessive shrinkage conversely produced a large number of voids after hot-baking, which in turn decreases the stab resistance.  
The tendency of yarn pull-out force also follows the same with varying temperatures as shown in Figure 12. Compared to pure basalt fabric, the yarn pull-out force at 140 \({}^{\circ}\)C is increased by one time. Using an approach similar to Coulomb friction, \(f=\mu F\), the pull-out force (\(f\)) is proportional to coefficient of friction (\(\mu\)) at the same applied pre-load perpendicular to the pull-out force (\(F\)). Therefore, higher pull-out force corresponded to the bigger coefficient of friction. Figure 12 shows the static and dynamic forces of friction as related to displacement. Figure 12a displays significant static and kinetic friction forces, but Figure 12a reveals that the kinetic friction force becomes insignificant because vertical needle-bonded points and thermal-bonded points limited the pull-out displacement of basalt yarns. The slopes of pull-out force after thermal-bonding at 100 \({}^{\circ}\)C, 120 \({}^{\circ}\)C, 140 \({}^{\circ}\)C, and 160 \({}^{\circ}\)C are 5.63, 4.36, 9.19, and 4.25, respectively, which shows the bonding effects from vertical tufts and thermal-bonding points and thus explains the tendency of spike and knife resistance in relation to thermal-bonded temperature.  
Figure 10: (**a**) Burst force and (**b**) energy of PET–BF sandwich composites after thermal-bonding at different time and temperature.
## 4 Discussion  
According to the samples, the degradation characteristics for the batteries in case 1 and case 4 are basically smooth and do not change dramatically with time, but the individual differences of each battery in each case are clear: one's capacity is constantly higher than the other through the whole degradation process. Thus, such conditions are suitable for the use of the degradation rate to describe batteries' individual differences. It is demonstrated in Table 2 that the proposed method has an obviously higher prediction accuracy in cases 1 and 4 compared with the other four methods. In case 1-2, the prediction error of the HTL method is a bit lower than the proposed method, but under overall consideration, the error of the proposed method is significantly lower than the others. When it comes to cases 2 and 3, the capacity of the two batteries in each group has a cross-point, which means the degradation rate fluctuates and has more complex individual differences. As a result, higher errors can be witnessed among all methods, but our proposed method still controls the MAPE under 10%; meanwhile, other methods have errors as high as 30%.  
Overall, according to the figure and numerical analysis, the proposed method has a better prediction performance and can be applied to overcome errors caused by individual differences efficiently.
###### Abstract  
The RST (Robust Satellite Techniques) approach is a multi-temporal scheme of satellite data analysis widely used to investigate and monitor thermal volcanic activity from space through high temporal resolution data from sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In this work, we present the results of the preliminary RST algorithm implementation to thermal infrared (TIR) data, at 90 m spatial resolution, from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Results achieved under the Google Earth Engine (GEE) environment, by analyzing 20 years of satellite observations over three active volcanoes (i.e., Etna, Shishaldin and Shinmoedake) located in different geographic areas, show that the RST-based system, hereafter named RASTer, detected a higher (around 25% more) number of thermal anomalies than the well-established ASTER Volcano Archive (AVA). Despite the availability of a less populated dataset than other sensors, the RST implementation on ASTER data guarantees an efficient identification and mapping of volcanic thermal features even of a low intensity level. To improve the temporal continuity of the active volcanoes monitoring, the possibility of exploiting RASTer is here addressed, in the perspective of an operational multi-satellite observing system. The latter could include mid-high spatial resolution satellite data (e.g., Sentinel-2/MSI, Landsat-8/OLI), as well as those at higher-temporal (lower-spatial) resolution (e.g., EOS/MODIS, Suomi-NIP/VIRS, Sentinel-3/SLSTR), for which RASTer could provide useful algorithm's validation and training dataset.  
volcanoes; ASTER; Robust Satellite Techniques; Google Earth Engine Article
## 2 Materials and Methods
### Experimental Setup  
The experimental setup for optoacoustic wave signal generation and characterization is illustrated in Figure 1. A Q-switched Nd: YAG laser (STL-5000Q, Stratek, Anyang-si, Korea) was employed as an optical source with a wavelength of 532 nm and a pulse repetition frequency of 2 Hz. The diameter of the laser beam was initially 6 mm. It expanded up to 80 mm, increasing the distance from the composite transducer after passing through the beam expander. The composite transducer of interest was mounted inside a degassed water tank and aligned with the laser system. The laser beam has a spatial beam profile of Gaussian distribution with a much higher acoustic pressure at the center than the off-axis location [8]. A 0.2 mm needle hydrophone (NH0200, Precision Acoustics Ltd., Dorchester, UK) was aligned with an acoustic wave generation setup and immersed inside the degassed water tank for the acoustic signal measurement. The needle hydrophone was fixed on an XYZ base with adjustable handwheels to find the focal point and range of the acoustic pressure. The distance of the needle hydrophone from the composite transducer was kept to the focal length of the respective composite transducer. The acoustic wave signal recorded through the needle hydrophone was amplified using a preamplifier (HP Precision Acoustics. Ltd., Dorchester, UK) and fed to an oscilloscope (LT354, Lecroy, New York, NY, USA), which was connected to a personal computer to record the signal data. An external trigger from the laser system was employed to synchronize the time series of laser excitation with the data collected from the oscilloscope. Ultrasound wave signals received by the needle hydrophone were postprocessed to calculate the acoustic pressure and investigate for further characterization.  
Figure 1: (**a**) Experimental setup for shock wave generation and measurement. The CNT-PDMS composite transducer was mounted inside a degassed water tank and aligned with the laser system (Q-switched Nd: YAG laser (STL-5000Q, Stratek, Anyang-si, Korea). The needle hydrophone (NH0200, Precision Acoustics Ltd., Dorchester, UK) collected the shock wave signal generated from the CNT-PDMS composite transducer, which was then amplified by a pre-amplifier (HP Precision Acoustics. Ltd., Dorchester, UK) and sent to the oscilloscope (LT354, Lecroy, New York, NY, USA). The signal data from the oscilloscope were fed to a PC for analysis. (**b**) CNT-PDMS composite transducers of different sizes were used in the experiment. The top view of 2 cm, 5 cm, and 8 cm transducers, along with their side view, are presented.
## 2 Mechanisms and Factors Affecting the Inactivation of Vegetative Microorganisms in Foods  
Alterations in the cellular structure or physiological functions of microorganisms with exposure to high pressure results in microbial cell death. This can be seen by observing the structural damage of the cell membrane and envelopes due to membrane phase transition and fluidity changes ([PERSON], 2013; [PERSON] et al., 2017) [19; 20]. Microbial inactivation can occur due to protein denaturation and the disruption of cell membranes, with subsequent solute loss (Figure 2) ([PERSON] and [PERSON], 2021) [21]. Research also suggests that microbial inactivation by HPP involves the perforation of the cell membrane, the formation of dimples and swelling, or the overall shrinkage of the volume of the cells ([PERSON] and [PERSON], 2008; [PERSON] et al., 2017) [20; 22].  
The major factors affecting the inactivation of vegetative spoilage or pathogenic bacteria in foods and beverages are the HPP pressure and treatment time, in which higher pressure and/or time cause higher microbial inactivation ([PERSON] and [PERSON], 2017; [PERSON] and [PERSON], 2018) [34]. When the aim of treatment is the inactivation of spore forms of the microorganisms, the initial temperature of the food can be increased before the treatment. In heat-assisted HPP (also referred as high-pressure thermal processing, HPTP), the higher the temperature, the higher the inactivation ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2017; [PERSON] and [PERSON], 2018) [23; 24; 25; 26]. HPP combined with heat has been the focus of many research studies but is not currently employed at commercial scale by the industry (EFSA, 2022) [5].  
Other important aspects affecting the microbial inactivation are the type of microbe (bacteria, mold, yeast), the species and strain, and the composition of the food medium being treated. More complex vegetative cells of molds and yeasts (eukariotic) are more susceptible to inactivation than vegetative cells of bacteria (prokaviotic), and parasites are usually inactivated at relatively low pressures (100-400 MPa) ([PERSON] et al., 2011) [27].  
With respect to water activity (a\({}_{w}\)) [PERSON] et al. (2015, 2017) [28; 29] demonstrated that it was more difficult to inactivate pathogens with HPP (_Listeria monocytogenes, Salmonella enterica_) in dry-cured ham formulated with lower water activity (a\({}_{w}\)). Regarding pH, although low pH (<4.6) prevents pathogens' growth, it seems not to have such an important role for HPP microbial inactivation ([PERSON] and [PERSON], 2017) [14].
## 3 Results and Discussion
### Selectivity of ITEC for Metal Ions  
In view of the above easy way that ITEC response to Al\({}^{3+}\), a further optical investigation was carried out to evaluate potential application of ITEC for Al\({}^{3+}\) detection. The absorption and fluorescence spectra of ITEC was investigated after adding different metal ions (Al\({}^{3+}\), Ag\({}^{+}\), Ca\({}^{2+}\), Cd\({}^{2+}\), Co\({}^{2+}\), Cu\({}^{2+}\), Fe\({}^{3+}\), Hg\({}^{2+}\), Na\({}^{+}\), Ni\({}^{2+}\), Pb\({}^{2+}\), Zn\({}^{2+}\)). In Figure 2a, a new absorption band at 475 nm was found after adding Cu\({}^{2+}\), Fe\({}^{3+}\), Hg\({}^{2+}\), and Al\({}^{3+}\). In Figure 2b, compared with other metal ions, the fluorescence of ITEC exhibited remarkable enhancements in the presence of Al\({}^{3+}\). Under the excitation wavelength of 365 nm, though the fluorescence of ITEC was enhanced in the presence of Fe\({}^{3+}\), the fluorescent intensity was far lower than that in the presence of Al\({}^{3+}\). Moreover, the two spectral shapes were absolutely difference. The possible reason for the enhanced fluorescence was the blocking of the photo-induced electron transfer process in the presence of Al\({}^{3+}\)[35; 36].  
The selective bars of ITEC to various metal ions were shown in Figure 2c. Here, F and F\({}_{0}\) are the fluorescent intensity of ITEC in the presence and absence of metal ions. Compared with other metal ions, the fluorescence intensity of ITEC was obviously improved after adding Al\({}^{3+}\) (at least 6.6 times). The fluorescence quantum yields of ITEC and ITEC-Al\({}^{3+}\) (Mole ratio = 1:2) were calculated to be 0.0023 and 0.016, respectively, by using the solution containing coumarin307 (\(\Phi\) = 0.56, in methanol) standard as the reference [37]. These results clearly showed that ITEC exhibited excellent selectivity for Al\({}^{3+}\).
## 2 Materials and Methods
### Experiment Setup for Dielectric Heating  
The experimental setup was designed to evaluate the performance of the dielectric heating system utilizing a 2.45 GHz horn antenna for delivering controlled microwave energy to fish tissue samples containing liver flukes. The experiment was conducted within an anechoic chamber to minimize electromagnetic interference and ensure precise energy distribution, as shown in Figure 4a. The fish tissue sample was placed on a porcelain plate and positioned precisely beneath the horn antenna. The distance between the antenna and the sample was carefully measured using a tape measure to maintain consistency in energy delivery and optimize heating efficiency, as depicted in Figure 4b. Based on prior computational simulations, the optimal antenna-to-sample distance was determined to be approximately 6-9 cm to achieve efficient microwave absorption while preserving tissue integrity. Figure 4c presents a side view of the setup, providing a clear perspective on the alignment between the horn antenna and the fish tissue sample.  
Figure 4: Experimental setup for dielectric heating: (**a**) Dielectric heating experiment inside an anechoic chamber. (**b**) Measurement of antenna-to-sample distance. (**c**) Side view of the experimental setup. (**d**) Real-time temperature monitoring using a Keysight U5857A (Keysight Technologies Inc., Santa Rosa, CA, USA) infrared thermal imaging camera.  
During the experiment, the horn antenna was activated to emit electromagnetic waves at 2.45 GHz, targeting the dielectric properties of the fish scales and underlying tissues. The energy distribution was designed to ensure uniform heating while preventing localized overheating. Infrared thermal imaging technology (Keysight U5857A, Keysight Technologies Inc., Santa Rosa, CA, USA) was employed for real-time monitoring of the temperature distribution across the sample, as illustrated in Figure 4d. This setup facilitated precise tracking of energy absorption and temperature variations in the fish tissue during microwave exposure.  
This experimental setup provided a controlled and repeatable framework for assessing the feasibility of dielectric heating as a non-chemical approach for liver fluke eradication in aquaculture applications.
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
## 2 Materials and Methods
### Hardware and Software Configuration & Program Execution Time  
The machine learning models in this experiment were run on a computer with an Intel Core i5-1135G7 CPU, Intel Iris Xe Graphics GPU, and 16 GB RAM, using MATLAB R2023b software.  
The model execution time reported in this study refers to the training time of the models, which is the average time taken from running each model five times.
## 3 Tests on the Optimal Mix Proportion of Composite-Cemented Soil  
### Test Methods and Results  
Orthogonal tests were carried out to analyze the influences of three factors, including contents of cement, FA, and WG, on the UCS of the FA-WG-composite-cemented soil and determine its optimal mix proportion. The tests were conducted on cylindrical samples measuring \(\Phi\)50 mm \(\times\)\(H\)50 mm [28], and the orthogonal test design and test results are listed in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compositions** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **CaO** & **SO\({}_{3}\)** & **Moisture Content** & **Loss on Ignition** \\ \hline Content \% & 45.1 & 24.2 & 5.6 & 2.1 & 0.85 & 4.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main compositions of FA (%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Chemical** & & & & & \\
**Compositions** & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **MgO** & **SO\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** \\ \hline Content \% & 51.42 & 24.99 & 8.26 & 3.71 & 2.51 & 4.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main compositions of PO42.5 ordinary Portland cement (%).  
Through range analysis and comprehensive analysis of variance of orthogonal test data, the mixing ratio of cement is crucial to the strength development of the cemented soil after any curing period; the mixing ratio of FA obviously influences the strength development of the cemented soil in the middle and late stages; and the mixing ratio of WG only slightly affects the strength. The theoretical optimal mix proportion is A3B3C2. Whereas considering the engineering application, a cement content of 9% is enough to meet the corresponding engineering requirements for backfilling of the deep foundation pit, that is, the ratio of cement, FA, and WG for FA-WG-composite-cemented soil is 9%:12%:3% under the optimal mix proportion.