esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Gastrointestinal parasitism is a problem that has a significant economic impact on sheep production systems in the tropics. Its effects translate into mortality, decreased growth, and low quality of products (meat, milk, and wool), which in turn depend on factors such as the intensification of production, resistance of parasites to chemical synthesis products, climate change, stressors of various origins, and nutritional deficits. The use of forage resources with recognized nutritional quality and anthellintic activity (AA) is a viable, sustainable, and easy-to-apply option in sheep production schemes, particularly for small and medium-sized producers who require effective low-cost alternatives. There is growing scientific interest in exploring the effect of plants on anthellintic biological activity, especially in tropical environmental conditions, and in the context of the use and benefit of local forage resources. Gastrointestinal parasitism in sheep production systems, especially that caused by _Haemonchus contortus_ (\(H\). _contortus_), is of great interest due to its productive effect and epidemiological importance.  
There is evidence of the effect of plant extracts obtained with solvents of different polarity on AA in gastrointestinal parasites [1; 2; 3; 4; 5; 6; 7]. Most studies do not report significant differences between these extracts; however, a greater AA effect has been found with those extract partitions of greater polarity, with some exceptions such as that observed by [8], who identified caffeoyl and coumaroyl, caffeic acid, p-coumaric acid, ferulic acid, methyl caffeine, methyl-p-coumarate, methyl ferulate, and quercetin in the fractions of lower polarity with higher AAs. Concerning _Gliricidia. sepium_ (_G. sepium_), ref. [9] identified two subfractions with complete inhibition of _Cooperia punctata_ hatching, in one of which the compound 2H-chromen-2-one was identified. Reference [6] tested 4 fractions of _L. leucocphala_, finding that the fraction containing flavonoids and tannins produced greater inhibition of the development and viability of larvae of gastrointestinal nematodes.  
Numerous compounds have been identified in _G. sepium_[10; 11; 12; 13; 14], including _Leucaaen leucocphala_ (_L. leucocphala_) [15; 16; 17; 18; 19] and _Pithecellobium dulce (P. dulce_) [20; 21; 22; 23]. However, the anthelmittic effect on _H. contortus_ and the composition of the most active extract partitions of the species _G. sepium_, _L. leucocphala_, and _P. dulce_, which grow in a tropical dry forest (TDF) environment, are not known. The objective of the present investigation was to evaluate the in vitro ovicidal activity of extract partitions in _H. contortus_ of the species _G. sepium_, _L. leucocphala_, and _P. dulce_ and to identify the compounds in those with the highest activity by employing UHPLC-Q/ Orbitrap/MS/MS.
## 3 Results and Discussion
### Phytochemical Screening  
Conducting a qualitative analysis of phytochemicals is an essential step in gathering scientific knowledge regarding the presence of pharmacologically important secondary metabolites in plants. This examination reveals valuable information about the plants' physiological and medicinal properties, including their potential roles in activities such as anticancer, antidiabetic, antiviral, antihypertensive, antioxidant, immune modulation, and antimicrobial effects [52; 53].  
The phytochemical screening of _C. axillaris_ fruit samples using different solvents (ethyl acetate, acetone, methanol, and water) revealed the presence of various compounds, including carbohydrates, terpenoids, tannins, cardiac glycosides, flavonoids, resins, proteins/amino acids, and steroids. However, in our study, all the extracts were found to be free of alkaloids, phytosterols, and anthaquintones. Additionally, saponins, tannins, and steroids were absent in the aqueous extract. Interestingly, when reviewing an article related to phytochemical screening, we observed similar results in terms of the presence of carbohydrates and phenols, but opposite findings in relation to the presence of alkaloids in the aqueous extract [54]. Furthermore, the article reported the presence of saponins and tannins, while the absence of terpenoids in the aqueous extract, which contradicts our study's findings. The results for the phytochemical screening are presented on Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Extracts** & **Wt. of Crude** & **Wt. of Dry Extract** & **Extraction Yield** \\  & **Sample (g)** & **(g)** & **(\%)** \\ \hline Ethyl acetate extract & 40 & 5.56 & 13.90 \\ \hline Acetone extract & 40 & 8.58 & 21.45 \\ \hline Methanol extract & 50 & 18.00 & 36.00 \\ \hline Water extract & 50 & 25.10 & 50.20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Extractive yield values of _C. axillaris_ dried fruit obtained from ethyl acetate, acetone, methanol, and aqueous extracts.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S.N** & **Test for** & **Test Used** & **Ethyl Acetate** & **Acetone** & **Methanol** & **Water Extract** \\  & & & **Extract** & **Extract** & **Extract** & **Water Extract** \\ \hline
1 & Alkaloids &
\begin{tabular}{c} [PERSON]’s, [PERSON]’s, and \\ [PERSON]’s test \\ \end{tabular} & \(-\) & \(-\) & \(-\) & \(-\) \\ \hline
2 & Carbohydrates & [PERSON]’s test & \(-\) & \(+\) & \(+\) & \(+\) \\ \hline
3 & Terpenoids & [PERSON] test & \(+\) & \(+\) & \(+\) & \(+\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result for the phytochemical screening of fruit extract of _C. axillaris_ in ethyl acetate, acetone, methanol, and aqueous solvents.
## 2 Theoretical Background
As discussed earlier, selecting accurate wireless communication is critical for overcoming the problems of power consumption and transmission range. A study of various communication techniques is tabulated below according to frequency band, network size, network topology, etc. Table 1 discusses three emerging LPWAN technologies, namely LoRa, narrow band-IoT (NB-IoT), and SigFox, regarding the requirements of the IoT for a wide range of applications. Of these LPWAN technologies, LoRa is considered to be an independent network and can utilize the frequency bands without any cost [27]. NB-IoT is a licensed band, and is also a dependent network that charges for the use of the bands. The Sigfox network is deployed by network operators, and users are subscription-based. LoRa is one of the best candidates for long distance and low power transmissions [28].
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 3 Phenology of Croplands
### Mapping of Crops Using Time Series Data
Though the authors did not find significantly different numbers of cloud-free images from Landsat and Sentinel-2, they identified the presence of additional red-edge bands in Sentinel-2 as being critical to the higher classification accuracies. In this study, from an analysis of variable importance in classification models based on machine learning approaches, such as random forests, it was found that Sentinel-2 images from May (emergence of infloresence in winter crops) and July (senescence of winter crops and leafing of summer crop) as well as near infra-red (NIR), red-edge (RE) and short wave infra-red (SWIR) bands were the most important among temporal and spectral variables. The plant senescence reflectance index (PSRI) calculated from the red, green and NIR bands also appeared to be of particular value compared to NDVI and NDWI. The importance of including information on key phenological stages for mapping sugarcane crops was also raised by [PERSON] et al. (2019) [100], who identified seedling, elongation and the harvest periods to be critical for maximum separability from other classes including forests. In comparison, use of single Sentinel-2 NDVI images provided sub-par performance, ranging from 23% for the seedling stage to 70% for the elongation stage. To maximize accuracy, cloud cover limitations were overcome by compositing images in each key stage. Similarly, [PERSON] et al. (2019) [101] could discriminate winter and spring crops using NDVI time series information with a focus on the peak and harvest seasons. The peak of season was particularly important in identifying weed-infested pixels, where peaks after crop harvest (in a unimodal distribution) indicated weed growth. Additionally, they unmixed 10 m Sentinel-2 pixels with the CORINE land cover labelled as \"non-irrigated and arable land\" into seven phenologically distinct classes. In addition to phenological information from the two-band EVI (i.e., EVI2), [PERSON] and [PERSON] (2019) [102] found that inclusion of other spectral band ratios improved accuracies for mapping crops such as wheat and maize. Although the EVI2 index performs similarly to the three band EVI, it lacks the blue band and is calculated using the red and NIR bands only [103]. It was found that the dates of green-up and the peak of season were most important in achieving high accuracies for all crop types. [PERSON] et al. (2019) [104] also suggested using temporal information to discriminate rice from other crops due to its distinctly higher reflectance in the red spectrum at the harvest stage and lower NIR reflectance in the transplanting period. The use of such temporally distinct phenological information led to mapping accuracies ranging from 88% to 98% for different rice cropping systems.
## 1 Introduction
Other ground motion parameters, such as ground motion duration [21], cumulative absolute velocity [22], and Arias intensity [23], were also introduced to generate relationships with MSI. Each ground motion parameter has its own advantage over others in certain cases. For example, PGA and acceleration spectrum intensity are better parameters for estimating lower MSIs, and PGV and velocity spectrum intensity are better parameters for estimating higher MSIs. It should be noted that the above findings are based on the linear regression method. If another, nonlinear regression method is used to establish the relationship, the findings could be different. The linear regression method handles multiple variables using ordinary least square or weighted least square, but the regression results show large scatter due to two main problems. One main problem is that the true relationship is not just linearly related to each variable, and cross-terms of multiple variables may appear. For example, [PERSON] [24] added quadratic terms in regression equation. The true relationship is complicated and nonlinear, and linear regression is not good for handling the hidden nonlinear cross-terms. For example, [PERSON] et al. [25] used neural networks to set up the complicated and nonlinear relationship. The other main problem is that MSI is a discrete integer, while the ground motion parameters are continuous real variables and are not consistent with one another. There should be some steps to handle this inconsistency. One possible choice is to round the function of the continuous real variables, so that the regression value is correct if it is within MSI \(\pm\) 0.5. For example, if the output of regression function is 7.3, then round\((7.3)=7\) (VII). In this case, the loss between regression output and this MSI value should be zero, but linear regression counts 0.3 as a loss. The nonlinear regression method is a choice to handle the above two problems.
# Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis
## 2 Business Integrity Management and Analytics Framework
### Big Data Processing and Analysis Services Offered by BIMA Framework
#### 2.4.2 Black Scholes Merton (BSM) Model  
The Black-Scholes-Merton (BSM) option pricing model [41] is used in the BIMA framework for estimating the probability of bankruptcy of suppliers by extracting and examining the riskiness in the stock market price of the supplier. The model assumes that the maturity of liabilities equals one year; the dividend rate is based on the sum of ordinary dividends, preferred dividends, and interest expense; and the call option equation has been modified to account for the fact that shareholders receive ordinary dividends [41].  
Under the BSM model, the probability of bankruptcy is simply the probability that the market value of assets, \(V_{0}\), is less than the face value of the liabilities, \(D\), at time T (i.e., \(V_{0}(T)<D\)). The BSM model assumes that the natural log of future asset values is normally distributed. The probability of bankruptcy is a function of the distance between the current value of the firm's assets and the face value of its liabilities, which is adjusted for the expected growth in asset values relative to asset volatility.
## 2 Study Area and Data Sources
### Data Sources  
#### 2.2.1 Satellite Data  
To ensure multi-sensor data interoperability, this study implemented a synergistic remote sensing framework integrating optical and SAR observations: Landsat 8/9 Operational Land Imager (Collection 2, Level-2 surface reflectance), Sentinel-2A/B Multi-Spectral Instrument (Level-2A), and Sentinel-1 SAR data (Table 1). Within Google Earth Engine (GEE), Landsat series underwent cloud/shadow masking using the mask algorithm followed by least squares regression for temporal smoothing. Sentinel-2 Level-2A products, which have already undergone radiometric calibration, atmospheric correction, and band resampling to a unified resolution by the data generation (the European Space Agency), were further cloud masked using the QA60 band.  
Figure 1: Study area and topography, crop distribution and sample observations in Zhangjiakou City, Hebei Province, China (2023). (**a**) Location of the farming–pastoral ecotone of China (FPEC) and the position of Zhangjiakou in FPEC; (**b**) administrative boundaries of Zhangjiakou and elevation (DEM, meters); (**c**) spatial distribution of ground samples for major crop types; (**d**) number of samples per crop type in 2021–2023; (**e**) surveyed crops: oats, potato, maize, and sesame.  
Although Landsat has a coarser spatial resolution (30 m), it provided supplementary observations during cloud-obstructed periods and was merged with Sentinel-2 to improve temporal continuity. Vegetation indices were then calculated from the combined optical dataset to construct classification features. Complementing optical limitations, Sentinel-1 C-band Ground Range-Detected data (VV + VH polarization) were processed through \(\sigma^{0}\) calibration, terrain correction, and multi-temporal filtering to mitigate speckle noise, effectively bypassing cloud obstruction challenges in agricultural monitoring [5]. The final classification model and all outputs were implemented at 10 m resolution, aligning with Sentinel-2 spatial granularity. This multi-modal integration capitalizes on SAR's all-weather capability and optical sensors' spectral richness, establishing a robust feature extraction baseline.
## 3 Results and Discussion
### Perforation Diameter Analysis
#### 3.2.2 Impact of Velocity on Perforation Diameter  
According to the simulation results, at impact velocities between 1.5 and 4.5 km/s, the first layer of the steel plate would probably be perforated in a circular pattern that was 1.6-3 times the diameter of the reactive fragment (see the analysis in Figure 10). The perforation diameter of the first layer of steel plate normally increased with increasing impact velocity, reaching a high value at a specific impact velocity and then decreasing, according to an analysis of the cylindrical reactive fragment impact. With a focus on the diameter of the perforation in the first layer of steel plates, the equation was fitted using the quantitative analysis approach.  
Figure 10: The size of the perforation diameter of the first layer of the steel plate under the impact of different velocities and shapes of reactive fragments.  
Figure 9: Propagation process of MIS.STRESS during impact perforation process.
# Multi platform Computer Vision System to Support Physical Fitness Assessments in Schoolchildren
## 2 Materials and Methods
### Techniques and Instruments  
Anthropometric measurements of weight, height, and waist circumference were evaluated following the standards described by [PERSON] and [PERSON] [10]. All variables were measured with the least clothing possible (shorts, t-shirt, and without shoes). Weight (kg) was evaluated with a Seca digital scale with a precision of (100 g). Height (m) was evaluated according to the Frankfurt plane through a Seca brand aluminum stadiometer graduated in millimeters and waist circumference with Seca brand measuring tape (cm). The body mass index (kg/m\({}^{2}\)) was calculated: [BMI = weight (kg)/height (m)\({}^{2}\)].  
The physical tests of the sit and reach test (flexibility), horizontal jump, biceps curl, and abdominal crunches were evaluated during physical education classes according to the following protocols:  
* Sit and reach test (flexibility). The goal is to measure the flexibility of the hamstring muscles. A box is needed to measure flexibility; the dimensions of the box are 35 cm \(\times\) 45 cm \(\times\) 32 cm. The student sits parallel to the floor and must lean forward, extending his arms as far as possible. The distance reached is noted.
* Horizontal jump (explosive force). The objective is to evaluate the explosive force. The student stands behind a line. He must perform preparatory movements with his arms and jump forward as much as possible. The distance from the baseline to the heel of the feet is then recorded. A tape measure records the distance from 0 to 3 m.
* Abdominal muscle resistance (abdominals). The objective is to evaluate the resistance of the abdominal muscles. The test lasts 60 s. The student lies on a mat with knees bent and arms held on the chest. During the 60 s, the student must perform several trunk flexions.
* Arm muscle resistance (biceps curl). The objective is to measure the resistance of the right arm.
* Biceps curl test: The objective is to evaluate the muscular resistance of the arm. Children of both sexes aged 8 to 11 years must repeatedly lift a 1.0 kg weight in 30 s, while those aged 12 to 17 years must lift a 2.0 kg dumbbell in 30 s. The number of repetitions is recorded.
## 3 Experimental Section
### GC-TOF-MS analysis  
Briefly, neat urine samples were lyophilized without further pretreatment after our initial finding of severe alterations using urease treatments. Plant tissues were processed as described before [34]. To the dried samples 20 \(\upmu\)L of 40 mg/mL methoxylamine hydrochloride in pyridine was added, and tubes with the samples were agitated at 30 \({}^{\circ}\)C for 30 min. Subsequently, 180 \(\upmu\)L of trimethylsilylating agent \(N\)-methyl-\(N\)-trimethylsilyltrifluoroacetamide (MSTFA) was added, and samples were agitated at 37 \({}^{\circ}\)C for 30 min. GC-TOF-MS analysis was performed using an Agilent 6890 N gas chromatograph (Palo Alto, CA, USA) interfaced to a time-of-flight (TOF) Pegasus III mass spectrometer (Leco, St. Joseph, MI). Automated injections were performed with a programmable robotic Gerstel MPS2 multipurpose sampler (Mulheim an der Ruhr, Germany). The GC was fitted with both an Agilent injector and a Gerstel temperature-programmed injector, cooled injection system (model CIS 4), with a Peltier cooling source. An automated liner exchange (ALEX) designed by Gerstel was used to eliminate cross-contamination from sample matrix occurring between sample runs. Multiple baffled liners for the GC inlet were deactivated with 1-\(\upmu\)L injections of MSTFA. The Agilent injector temperature was held constant at 250 \({}^{\circ}\)C while the Gerstel injector was programmed (initial temperature 50 \({}^{\circ}\)C, hold 0.1 min, and increased at a rate of 10 \({}^{\circ}\)C/s to a final temperature of 330 \({}^{\circ}\)C, hold time 10 min). Injections of 1 \(\upmu\)L were made in split (1:5) mode (purge time 120 s, purge flow 40 ml/min). Chromatography was performed on an Rtx-5 Sil MS column (30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\) film thickness) with an Integra-Guard column (Restek, Bellefonte, PA). Helium carrier gas was used at a constant flow of 1 mL/min. The GC oven temperature program was 50 \({}^{\circ}\)C initial temperature with 1 min hold time and ramping at 20 \({}^{\circ}\)C/min to a final temperature of 330 \({}^{\circ}\)C with 5 min hold time. Both the transfer line and source temperatures were 250 \({}^{\circ}\)C. The Pegasus III TOF (Leco, St. Joseph, MI) mass spectrometer ion source operated at \(-\)70 kV filament voltage with ion source. After a solvent delay of 350 s, mass spectra were acquired at 20 scans per second with a mass range of 50 to 500 \(m/z\).
## 5 Simulation Experiments
### Motion Trajectory Analysis  
In the experimental scenarios above, we set the abscissa x to increase linearly within the range of [0, 10], and the straight-line y was always maintained at 5 as the reference track, as shown in Figure 6. The obstacles in the experimental simulation were regarded as Gaussian white noise, set to 40, and added to the vector signal \(X\). Considering the atmospheric interference at this time, Gaussian white noise was set to 60 and added to the vector signal \(X\).  
Furthermore, the motion position had a certain deviation. Therefore, the following three kinds of data were analyzed: (1) IMU acceleration, velocity, and position data optimized by EKF; (2) for the collected UWB and IMU data, the position data obtained using the tight combination of the improved UWB/IMU were compared with the pure UWB position data; (3) the positioning error data, including the maximum deviation, minimum deviation, and standard deviation, as well as the analysis of the error controllability of the system. The following observations can be seen from Figure 7 and Table 1:  
1. The UWB location had the largest standard deviation, and the improved fusion location had the smallest.  
Figure 5: UWB anchor layout in the 3-D space used in the simulation.  
2. The improved UWB/IMU fusion model could eliminate the cumulative error of IMU at the same period and ensure the accuracy of positioning system.
3. The improved UWB/IMU fusion model made the error meet the controllability requirement of the system better.  
Figure 6: Acceleration, velocity, and position data of IMU.  
Figure 7: Trajectory comparison measured by the improved UWB/IMU method with a pure UWB and conventional UWB/IMU method.
## 4 Performance
### QoD  
QoD is defined as the ratio of the amount of data obtained by the sink node to the amount of data sensed by sensor nodes. The entire sensed data should arrive at the sink node and used by user applications. During congestion, the network should make the best effort to deliver as much data to the sink node as possible. Figure 16 shows the QoD of the CODA, CODA-Comp, CRRT, and ACT. As shown in Figure 14 and mentioned in Section 4.2, CODA exhibits very low network efficiency and therefore has a very low QoD. The network efficiency and the QoD of CODA should be similar because CODA does not use any compression technique. CODA-Comp and CRRT showed similar result at the network efficiency however CODA-Comp shows higher QoD in Figure 16 because with CODA-Comp at the sink received packets are decompressed and could reconstruct much more data than CRRT. In contrast, the ACT has considerably higher QoD than other schemes because the ACT uses compression techniques not only at the source node but also at the relaying node. When the network congestion occurs, ACT tries to compress the packets from the child nodes and thus the less amounts of packets were dropped. With a compression technique, a packet can contain more data than a packet without such a technique. This is the reason why compression techniques are used widely in several networks.  
Figure 16: QoD.