esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Novel Combined Extraction Techniques
### Cavitation and Enzyme Assisted Extraction  
Enzymatic treatment of plant cells is prevalent, due to their capability to enhance hydrolysis, as well as degradation of impenetrable cell walls, thereby facilitating the release of intracellular contents, such as bioactive compounds. However, the combination of enzyme and CE could bring better outcomes in the extraction. The extraction yield of polysaccharides was reported to enhance via the application of US, combined with enzymes. The enzymatic reaction with the substrate is said to be increased via the introduction of ultrasound that could enhance the collision frequency between the enzyme and the matrix. [PERSON] et al. [84] reported an enhancement in the recovery of polysaccharide from pumpkin (_Cucurbita moschata_) via combined US/enzyme extraction as compared to SLE or US alone. Evaluation of the properties of polysaccharides extracted from pumpkin suggested their potential application in food and medicinal industries as a natural antioxidant because of their radical scavenging activity and reducing power. [PERSON] [85] reported an increase in the yield of phytochemical compounds from mulberry (_Morus nigra_) must via enzyme assisted ultrasound extraction. The process has considerably enhanced the extraction of phytochemicals (phenol, flavonoid, and anthocyanin). The combined effect of US/enzyme on pectin extraction from sisal waste was evaluated by [PERSON] et al. [86]. Both sequential, as well as combined, treatment of enzyme (Celluclast 1.5 L) and ultrasound resulted in appreciable higher pectin yield (31.1%) than any of the individual processes (9.4%). Moreover, the recovered pectin via US/enzyme extraction demonstrated higher galacturonic acid content and a higher extent of esterification than conventional acidic extraction. [PERSON] et al. [87] successfully utilized polyethylene glycol (PEG) as the environment friendly solvent for the US/enzyme-based extraction of polysaccharides from Gingko biloba leaves (GBLP) and have demonstrated that the combined method is far better than the traditional hot water extraction. During the extraction of oil from Pomegranate seed, the low extraction yield using water as a solvent was successfully mitigated by combining enzyme (_Peclyve V_) with US, which reduced the extraction time by 91.7% as compared to individual methods [88]. The low extraction yield during aqueous cavitation-based extraction of oils could be attributed to the resultant stable emulsion obtained from the combination of water and oil, which is either difficult to break apart or laborious to isolate the oil fraction from the solution.
## 3 Results and Discussion
### Classification Models
Moreover, the classification accuracy of SVM models and PLS-DA models was also close. These results show that HSI systems at different spectral ranges could both be used to identify the geographical origins of single Longjing tea leaves with SVM and PLS-DA models. Various studies using near-infrared spectroscopy and hyperspectral imaging to identify tea varieties and geographical origins have been reported. [PERSON] et al. (2018) used hyperspectral imaging (spectral range: 370-1042 nm) to classify six different varieties of Loingjing fresh tea. The classification accuracy could reach over 94%. [PERSON] et al. (2019) used hyperspectral imaging (spectral range: 400.68-1001.612 nm) to identify oolong tea varieties, and the classification accuracy was over 97%. [PERSON] et al. (2019) used hyperspectral imaging (spectral range: 390-1050 nm) to identify green tea varieties, and the classification accuracy was over 91%. [PERSON] et al. (2020) used near-infrared spectroscopy (spectral range: 1350-1800 nm) to identify the growing area of Longjing tea, and the classification accuracy of two growing areas was 97.3%. [PERSON] et al. (2020) used near-infrared spectroscopy to identify grades, varieties, and geographical origins of green tea. The classification accuracy all reached 100%. The results in our study were similar to or a little worse than the results in these studies. The reason might be that the number of samples in our study was much more than the number of samples in these studies. On the other hand, a single tea leaf, rather than a batch of tea leaves or tea powders, was studied in our manuscript. A single tea leaf is the basis of all tea products. The identification of the geographical origins based on a single leaf is of great importance for protecting Longjing tea from adulteration. By using hyperspectral imaging, spectral information of the entire single tea leaf was extracted, providing more comprehensive information than non-imaging near-infrared spectroscopy. Moreover, the PCA score images obtained from hyperspectral images provided an alternative to visually analyze the differences among samples. The overall results in this study illustrated the feasibility of using hyperspectral imaging to identify the geographical origins of a single tea leaf.
## 4 Experiment and Results
### Feature Extraction  
We first performed a dynamic analysis to identify the structural and functional differences between benign and malicious documents in a virtual machine. We found metadata, encoding patterns, and the differences in functions and keywords used in JavaScript between benign and malicious documents. We developed a comprehensive feature set encompassing the structure feature, metadata, and JavaScript content features. Below, we summarized the types of features used in our model and the characteristics of malicious document files, benign document files, and benign files containing JavaScript and adversarial examples. Analysis results of malware and benign samples exhibits differences in the size and the existence of JavaScript. Benign PDFs generally have big size and do not include JavaScript in them.
# Effect of Preheating Treatment before Defatting on the Flavor Quality of Skim Milk
## 2 Results and Discussion
### Relationships Between Sensory Attributes and Volatile Compounds  
There were some correlations between flavor compounds and sensory attributes in milk. It could further explain the effect of different treatments on the flavor quality of skim milk. Compared with our previous work [20], the correlations between flavor compounds and sensory attributes were further analyzed in this study. The multivariate data analysis (partial least squares regression, PLSR) was applied to display the correlation between flavor compounds and sensory descriptors [37]. The flavor compounds (X variables) and sensory attributes (Y variables) on the first two factors are presented in Figure 6. The inner and outer ellipse revealed 50% and 100%, respectively, which explained the variance. The attributes \"milk flavor\", \"milk aroma\", and \"caramelized\" were correlated with dodecane, tetradecane, 2-ethyl-1-hexanol, 6-pentyloxan-2-one, 2-heptanone, 2-nonanone, and 2-undecanone. It has been reported that 2-heptanone and 2-nonanone were closely related with milk flavor, which possessed an odor of cream and sweetness [17; 18; 19]. Additionally, the correlation between decanal and \"residual mouthfeel\" was observed. Notably, the concentrations of these related flavor compounds were significantly the highest in the USM. It was shown that the preheating treatment at 137-141 \({}^{\circ}\)C could improve the sensory quality of skim milk. The attributes \"fishy\" was correlated with naphthalene, 2-methyl naphthalene, and nonanoic acid. It could be the reason for the poor flavor of the RSM. Briefly, although some flavor compounds could not be explained by sensory attributes, the relationship between volatile compounds and sensory attributes was obvious.  
Figure 5: Classification of volatile compounds from the three kinds of skim milk samples: (**a**) Score plot after PCA of the samples. (**b**) Dendrogram from CA of the samples.
# Analysis Strategies for MHz XPCS at the European XFEL
## 1 Introduction
The ability to detect bursts of extremely intense X-rays is its main feature, but under the appropriate conditions it can achieve single photon sensitivities in high-gain mode [16]. Recently, we demonstrated the measurement of sub-microsecond dynamics in soft matter systems using XPCS at the European XFEL [17; 18]. We used the AGIPD that is installed at both the SPB/SFX [19] and the MID instrument, the latter is the preferred instrument for XPCS and XSVS experiments due to the long sample-detector distance of up to 8 m [20]. The tools that have been developed to analyze XPCS data at synchrotron radiation sources with commercial detectors like the Eiger series [21; 22] are not capable of analyzing the complex datasets produced by the AGIPD that can reach volumes of up to Petabytes per week. The burst mode operation scheme of the AGIPD was found to yield several sources of noise and correlations within the noise that can produce severe artifacts in the XPCS analysis. Therefore, specialized analysis strategies and methods have to be developed, which account for these additional effects. In this work we present the XPCS data pipeline developed by us and explain the various steps that are required to perform an XPCS experiment with the AGIPD at the European XFEL instruments. We distinguish between two different aspects of the data treatment. First, we will explain how the raw data should be calibrated to provide the best possible data quality of single speckle images. Second, we will discuss how the standard XPCS analysis (cf. Equation (1)) needs to be modified to correct detector burst mode characteristics. Eventually, we compare different data calibration and analysis methodsto benchmark the different approaches. A summary of our data pipeline is sketched in Figure 1. A detailed explanation of the individual steps follows in the next sections.
## 4 Experimental Results
### Implementation Details  
The maximum length of the language was 15, the number of candidate windows \(K\) was five, the lengths of windows were \(\left\lfloor\frac{1}{4},\frac{3}{4}\right\rfloor\), \(\left\lfloor\frac{1}{3},\frac{2}{3}\right\rfloor\), \(\left\lfloor\frac{2}{3},1\right\rfloor\), \(\left\lfloor\frac{1}{3},\frac{1}{2}\right\rfloor\), \(\left\lfloor\frac{2}{3},\frac{5}{6}\right\rfloor\), \(d\), the epoch threshold \(E\) in Equation (6) was 10, the length of video \(L\) was 100, and the length of word \(M\) was 20. Following the settings of CPL [39], the feature dimensions of language and video \(D_{q}\) and \(D_{v}\) were 300 and 1024, respectively, the number of Transformer layers was three, and the number of attention heads was two. The dimension of score vector \(d\) was 50. In the loss function, the margin \(\delta\) in Equation (4) was 0.2, and the threshold \(thr\) in Equation (13) was 0.8. In addition, there are several common hyper-parameters for RL. The discount coefficient of reward \(\gamma\) in Equation (14) was 0.4, the maximum number of steps was 12, the parameter \(\epsilon\) in Equation (19) was 0.2, and the loss coefficients \(\lambda_{1}\) and \(\lambda_{2}\) were 0.002, 0.2, respectively.  
We conducted all experiments on the PyTorch (v.1.12.1) platform using a GTX 3090 GPU (NVIDIA, Santa Clara, CA, USA). The batch size was four, and the learning rate was 0.0004. We employed the Adam algorithm to optimize the network. We trained the network for 20 epochs. Ours (C3D) refers to the baseline for comparing feature extraction by replacing the Transformer with C3D.
# A Location-Time-Aware Factorization Machine Based on Fuzzy Set Theory for Game Perception
## 4 Experiments and Results in Discussion
### Comparison Algorithm  
In our experiments, we consider the diversity of influencing factors and want the model to find out the rules from the data as much as possible, so we randomly divide the dataset into 80% as the training set and 20% as the test set. We demonstrate the predictive performance of LTFM and baseline models for game user perception prediction. In view of the fact that there are few types of research on game perception (most of them are to explore network factors and finally obtain relevant formulas) when choosing the baseline model, we chose the algorithm that is homogeneous with FM and the comparison of popular classification algorithms on our dataset, including random forest (RF), decision tree (DTree), multilayer perceptron (MLP), light gradient-boosting machine (Lightgbm), Extreme Gradient Boosting (Xgboost), Catboost, FM [9], NFM [14], AFM [15], IFM [16], and LDFM [13]. In our experiments, we design three kinds of LTFM, import the projections of location and time information, and add the membership calculation to finally obtain the best performance of LTFM.
## 2 Study Area and Data
### Data
#### 2.2.2 Spectral Indices  
Various spectral indices were derived from the primary bands of Landsat imagery. These bands in amalgamation with other primary bands were used as training data to produce the LC maps. These multiple indices help us classify images accurately, thereby reducing hillshade and building shadows. The adopted indices are:  
1. NDVI is the normalized difference between the NIR band and the red band. NIR bands are more sensitive to vegetation [58] and are used to perceive the vegetation's richness. It is calculated as shown in Equation (1). \[\text{NDVI}\ =\ \frac{\text{NIR}-\text{Red}}{\text{NIR}+\text{RED}}\] (1)
2. NDBI is the normalized difference between SWIR1 and NIR bands [59; 60]. It separates the built-up area from the background. It is calculated as shown in Equation (2). \[\text{NDBI}\ =\ \frac{\text{SWIR1}-\text{NIR}}{\text{SWIR1}+\text{NIR}}\] (2)
3. MNDWI is the normalized difference between SWIR1 and green bands. It accurately segregates the water body mitigating the built-up noise [61]. It is calculated as shown in Equation (3). \[\text{MNDWI}\ =\ \frac{\text{SWIR1}-\text{Green}}{\text{SWIR1}+\text{ Green}}\] (3)
## 2 Methodology
### Windcube 100S Doppler Wind Lidar  
The mountainous wind field was observed by the Windcube 100S Doppler wind lidar. This type of instrumentation uses the laser light signals back-scattered by aerosols to provide accurate and reliable results, including the horizontal wind (u, v), vertical wind (w), signal-to-noise ratio, mean radial wind speed, and raw spectral data [22; 24; 25]. Windcube 100S commonly adopts the carrier-to-noise ratio (CNR) for data quality control [26; 27], in which the CNR data smaller than a criteria of \(-30\) dBZ were discarded, and those greater  
Figure 1: (**a**) Geophysical location of Shanghuang site marked with a big red dot and (**b**) a photo of the site. The black grids in (**a**) indicate the ERA5 reanalysis gridden data, and the surrounding small red dots are the main cities around the observatory. (**c,d**) The Doppler wind lidar (Windcube 100s, Leosphere, France) and the microwave radiometer (RPG-HATPRO-G5, Radiometer Physics GmbH, Meckenheim, Germany) placed at the Shanghuang site.  
than \(-\)30 dBZ were credible. Windcube 100S emits laser pulse signals into the atmosphere by collecting the backward echo signals scattered by moving particles and uses a specific signal processing algorithm to determine the Doppler shift of the backward-scattered signals, which is analyzed to compute the radial wind speeds and directions along the measurement path. The temporal resolution is 5 s. The unmeasurable area of this lidar is from the ground to a 50 m height, and due to the attenuation of the detection signal, the maximum detection distance is theoretically 3 km. For the details of _Windcube 100S Doppler wind lidar_, please refer to www.leosphere.com (accessed on 7 January 2024). In this paper, we averaged the wind data into hourly scale for ease of comparison.
## 3 Results
### Durability and Cost  
Current users were inquired about various aspects, including the average life span of their sockets (question D1), the frequency of maintenance required (question D2) and, for the electrical prostheses only, how long the battery charge lasted (question D3). These questions were based on questions OPUS-S-6 and QUEST-5. The reported average life span of current users' sockets was 2.30 (\(\pm\)1.72) years (question D1), and the mean reported frequency of required maintenance was 1.39 (\(\pm\)1.77) years (question D2). The mean reported charge duration (for the electrical prostheses only) was 43.94 (\(\pm\)26.21) hours (question D3).  
Subsequently, the participants were asked to rate their possibility of being able to afford the costs of acquiring and maintaining their sockets (question D4) and their prostheses (question D5) on a Likert scale ranging from 1 (with considerable difficulty) to 5 (very easily). These questions were based on question OPUS-S-10. In terms of difficulty affording  
Figure 11: A summary of the results obtained from the semantic differential scale after distinguishing by the degree of amputation (**A**) and gender (**B**).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ID** & **Aspect** \\ \hline A1 & 1 like the socket’s shape (OPUS-S-5) \\ A2 & The socket size is not excessive (OPUS-S-5, QUEST-1) \\ A3 & The socket color is appropriate (OPUS-S-5) \\ A4 & 1 like the socket surface finishing (OPUS-S-5) \\ A5 & The socket design is attractive and does not look like orthopedic (OPUS-S-5) \\ A6 & I had the chance of choosing between different socket designs (OPUS-S-15) \\ A7 & In general, I am satisfied with the socket’s appearance (OPUS-S-5) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The aspects related to the esthetics of their sockets.  
Figure 10: A box-and-whisker plot illustrating the score obtained for each assessed aspect related to socket esthetics. Ratings: 1 = strongly disagree and 5 = strongly agree. The statements are labeled as detailed in Table 5.  
the economical costs of acquisition and maintenance, the participants reported a mean difficulty of 2.00 \(\pm\) 0.94 for affording the overall socket cost (question D4) and 1.80 \(\pm\) 0.79 for affording the overall prosthesis costs (question D5) on a 1-5 Likert scale (1 = with considerable difficulty; 5 = very easily).
## 6 Conclusions
By establishing the soil moisture retrieval algorithm within a cloud-processing environment, the SAR-based soil moisture estimation takes a step towards a globally applicable. The scaling of the obtained soil moisture information in terms of adaptable areas, time periods as well as spatial and temporal resolution leads to improvements in its usability within the different operational sectors, e.g., for management decisions in agriculture on a field basis or for drought monitoring on a regional level. Moreover, the temporal resolution of Sentinel-1 satellite recordings will continuously improve with the upcoming launches of Sentinel-1C and Sentinel-1D, despite the failure (23 December 2021) of Sentinel-1B. In this regard, high temporal resolution time series of 1 to 2 days using only single orbit acquisitions as well as sub-daily time series using all available orbits for estimating soil moisture will be possible in the near future. A combination with upcoming launches of L-band SAR missions like NISAR, ALOS-4 and ROSE-L and Tandem-L as well as P-band  
Figure 13: Example of soil moisture map for the Rur catchment for 1 June 2019.  
missions like BIOMASS will offer great potential for improving soil moisture estimation under dense vegetation cover as well as for different soil depths. Conceptualization, D.M., F.M. and H.V. ; methodology, D.M., C.M., T.J. and A.B. ; software, D.M. ; data analysis, D.M., C.M. and T.J.; result interpretation, D.M., C.M., T.J. and A.B. ; writing--original draft preparation, D.M. ; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript. We gratefully acknowledge funding by the German Ministry of Economic Affairs and Climate (BMWK) through the German Aerospace Center for the AssimEO project (50 EE1914A). In situ monitoring was made possible by the Helmholtz Initiative Terrestrial Environmental Observatories (TERENO). Not applicable. Special thanks go to European Copernicus Satellite Program for providing free access to Sentinel-1 SAR data. The authors declare no conflict of interest.
## 2 Physical Modelling and Numerical Methodology
### Numerical Validation Study
#### 2.2.2 Discrete Phase Validation  
Figure 5 shows the concentration profiles at different locations in the horizontal pipe downstream of the \(90^{\circ}\) bend. The particle concentration was normalized by the maximum concentration at the cross section at z/D = 0, following the methodology proposed in [PERSON] et al. (Re = 75,327--Re = 1.8D) with a Rosin-Rammler (RR) particle distribution. The distribution of solid particles with a mass loading of 0.35 and 0.42 are considered (Figure 5a,b, respectively) [6].  
Figure 4: Flow velocities in cross section z/D = \(-1\), \(\varphi=0^{\circ}\), \(\varphi=30^{\circ}\), \(\varphi=60^{\circ}\), \(\varphi=90^{\circ}\), z/D = 2 and z/D = 10. Figures above in color represent the results obtained with the RNG \(k\)-\(\varepsilon\) model, where the numerical values are normalized by the axial velocity of the flow, \(\overline{W}\)/Wa. The lower figures (in black) are the velocity contours given [10]. For each figure, the left side represents the inner area of the curve and the right side the outer area.  
In Figure 5, the deviations between the experimental profiles [6] and the numerical model can be observed, but the concentration levels and the tendency to the formation of the upstream chord can be captured in a good way by the RNG k-e model. The biggest discrepancy that can be observed is mainly that the maximum of the distribution moves faster towards the center of the pipe than those achieved experimentally by [PERSON] et al. [6], this is directly related to the coefficient of restitution of the particles. However, the general trend and particle accumulation lines observed in the experiments can also be identified in the simulation and by using restitution coefficients \(\
eq 1\), it is perceived that this parameter  
Figure 5: Particle concentration profiles in vertical symmetric lines in “y” (\(\theta=90\)\({}^{\circ}\)) for z/D = 0.25, z/D =1, z/D =2. Load condition (**a**) 35% and (**b**) 45%. Experimentally computed values (**line marker**) [6] and estimated with RNG \(k\)-e model (**line only**).  
largely dictates the evolution of the rope of particles upstream of the elbow and not their concentration within the elbow, so it is not relevant in the characterization predicted by the Stokes numbers.