esa-sceva/satcom-chunk-collection huggingface dataset:
# Antioxidant, Antimicrobial, Antidiabetic and Cytotoxic Activity of _Crocus sativus_ L. Petals
## 4 Results
### Antidiabetic Study  
The \(\alpha\)-glucosidase inhibition of SPH, SPD and SPE of _C. sativus_ L. petals are given in Figure 2. All the extracts revealed dose dependent \(\alpha\)-glucosidase inhibition with IC\({}_{50}\) value 89.94 \(\pm\) 1.09, 63.32 \(\pm\) 0.89 and 31.83 \(\pm\) 1.39 respectively. The IC\({}_{50}\) value of Acarbose used as standard was found to be 19.83 \(\pm\) 0.89.  
Figure 2: Bars demonstrate inhibition of \(\alpha\)-glucosidase by 5 mg/mL of crude _C sativus_ L. petal extract in n-hexane, dichloromethane and ethanol. Acarbose (1 mg/mL) has been used as a standard drug. The bars are the mean \(\pm\) standard deviation of three observations. The bars of different alphabets are significantly different (\(p<0.05\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Extracts**} & \multirow{2}{*}{**Conc. \(\mu\)g/mL**} & \multicolumn{4}{c}{**Growth Inhibition Zone (mm)**} \\ \cline{3-6}  & & **Gram-Positive Bacteria** & **Gram-Negative Bacteria** & **Fungi** \\ \cline{3-6}  & & _S. aureus_ & _P. aeruginosa_ & _E. coli_ & _C. albicans_ \\ \hline \multirow{6}{*}{**SPH**} & 500 & \(10.6\pm 0.90\) & \(12.8\pm 0.64\) & \(5.7\pm 0.49\) & \(2.6\pm 0.36\) \\  & 250 & \(8.9\pm 0.67\) & \(10.2\pm-0.88\) & \(1.8\pm 0.93\) & \(0\pm 00\) \\  & 125 & \(6.1\pm 0.83\) & \(7.9\pm 0.96\) & \(0\pm 00\) & \(0\pm 00\) \\  & 62.5 & \(3.7\pm 0.70\) & \(5.3\pm 0.93\) & \(0\pm 00\) & \(0\pm 00\) \\  & 31.25 & \(0\pm 00\) & \(0\pm 00\) & \(0\pm 00\) & \(0\pm 00\) \\  & 15.63 & \(0\pm 00\) & \(0\pm 00\) & \(0\pm 00\) & \(0\pm 00\) \\ \hline \multirow{6}{*}{**SPD**} & 500 & \(15.4\pm 0.54\) & \(13.3\pm 0.90\) & \(6.5\pm 0.69\) & \(5.4\pm 0.65\) \\  & 250 & \(13.8\pm 0.98\) & \(10.7\pm 0.67\) & \(3.0\pm 0.35\) & \(1.9\pm 0.90\) \\  & 125 & \(10.5\pm 0.77\) & \(7.8\pm 0.83\) & \(0\pm 00\) & \(0\pm 00\) \\  & 62.5 & \(8.9\pm 0.34\) & \(4.5\pm 0.70\) & \(0\pm 00\) & \(0\pm 00\) \\  & 31.25 & \(5.3\pm 0.80\) & \(2.4\pm 0.80\) & \(0\pm 00\) & \(0\pm 00\) \\  & 15.63 & \(0\pm 00\) & \(0\pm 00\) & \(0\pm 00\) & \(0\pm 00\) \\ \hline \multirow{6}{*}{**SPE**} & 500 & \(18.2\pm 0.98\) & \(14.8\pm 0.64\) & \(9.7\pm 0.90\) & \(8.7\pm 0.54\) \\  & 250 & \(16.5\pm 0.53\) & \(11.2\pm-0.88\) & \(7.4\pm 0.64\) & \(3.6\pm 0.78\) \\ \cline{1-1}  & 125 & \(12.2\pm 0.89\) & \(9.3\pm 0.96\) & \(2.0\pm 0.90\) & \(0\pm 00\) \\ \cline{1-1}  & 62.5 & \(11.5\pm 0.43\) & \(7.9\pm 0.93\) & \(0\pm 00\) & \(0\pm 00\) \\ \cline{1-1}  & 31.25 & \(10.8\pm 0.48\) & \(4.6\pm 0.67\) & \(0\pm 00\) & \(0\pm 00\) \\ \cline{1-1}  & 15.63 & \(8.3\pm 0.77\) & \(2.0\pm 0.74\) & \(0\pm 00\) & \(0\pm 00\) \\ \hline Gentamicin & 10 & \(22.19\pm 0.55\) & \(16.56\pm 0.60\) & \(12.43\pm 0.89\) & – \\ \hline Fluuconazolezole & 10 & – & – & – & \(11.3\pm 0.54\) \\ \hline \hline \end{tabular} Data are means of three replicates (n = 3) \(\pm\) standard error.  
\end{table}
Table 3: Minimum (or minimal) inhibitory concentration (MIC) of extracts against various microbes.
## 3 Results and Discussion
### Forecasting Performance for Each Provincial-Level Administrative Region in 2023
The higher the R\({}^{2}\) value is, the better the linear relationship between the actual values and the forecasting values of the model is, and the better the forecasting performance of the model is. The results are shown in Figure 8. The red lines in the figure represent the linear trend between the actual values and the forecasting values. Figure 6: The relative error (RE) of the established models for each provincial-level administrative region in 2023. Figure 7: The number of provincial-level administrative regions at various levels of relative error (RE) in 2023. Compared to the TNLI-power exponential and LPQI-power exponential models, the ELM model achieves the better overall forecasting performance with MRE for 42.74% in 2023. This indicates that the ELM model based on multiple NTL indices can better reflect the potential relationship between NTL data and the TOVAFAF compared to the power exponential models based on single NTL index. Moreover, among the four models, the MRE values of the PSO-ELM model with 32.20% in 2023 are lowest, and the R\({}^{2}\) values of the linear relationship between the true values and the forecasting values for the PSO-ELM model with 0.6460 in 2023 are highest. Therefore, the PSO-ELM model achieves the best overall forecasting performance in 2023, which indicates that the PSO-ELM model can further effectively explore and characterize the potential relationship between NTL data and the TOVAFAF.
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
## 3 Performance Analysis  
In this section, we analyze the outage probability and ergodic capacity for the RIS-DH case and the RIS-T case in the presence of IQI. To provide more insight into the RIS-aided scheme, an asymptotic analysis is presented, and a benchmark scheme is also mentioned in Figure 3.  
Figure 2: System model for RIS acting as a transmitter.
## 2 Data and Methods
### SAR Data and Methods
In total, we studied 48 CSK synthetic aperture radar (SAR) amplitude images. The descending images (Table S1) were acquired between 25 September and 14 December 2021 (track H4-08) with a look angle of \(\sim\)38\({}^{\circ}\). In the ascending orbit (track H4-06, Table S2), we have 11 images acquired between 25 September and 6 December 2021. These images have a look angle of \(\sim\)35.5\({}^{\circ}\). Lastly, the remaining 20 images of the ascending orbit (track H4-12, Table S3) were acquired between 30 December and 9 December 2021, with a look angle of \(\sim\)44\({}^{\circ}\). The resulting database allows us to monitor the 85-day-long crater evolution at an unprecedented level of temporal detail. The SAR amplitude images were co-registered to a corresponding reference image for each of these three tracks to create three series of aligned and stacked images [39]. Next, each SAR amplitude image stack was terrain corrected and projected onto a common geographic reference system [37]. Both registration and terrain correction were performed with a 5 m LiDAR DEM generated in 2009 before the eruption by the Centro Nacional de Informacion Geografica of Spain. Lastly, the images were speckle-filtered and normalized to reduce scatters and radiometric noise. Toward the end of the eruption, a new DEM created from a UAS survey became available and was used for the terrain correction of the descending SAR data acquired on 6 December 2021 (See Section 2.3). To trace the evolution and locations of the craters, we studied the SAR data stack in different ways. First, we generated composite maps (Figure S1) to identify general changes in the amplitude from one image to the next. Secondly, we extracted NW-SE amplitude profiles (see Figure 2c) and traced the crater centers as points with latitude and longitude through time. Finally, a post-eruption UAS DEM and orthophoto were used to validate the results of the SAR by comparing them with the geocoded SAR image on 6 December 2021. The crater center points are hence manually identified from a characteristic brightness pattern: The crater rims and slopes facing the satellite line of sight (LOS) appear brighter. The deep crater floors and shadowed crater walls are shown as darker (Figure 2a,b). Considering the amplitude profiles (Figure 2c), we see that the peaks and troughs of the profile allow for the identification of a crater, where its center point is inferred near the transition from dark to bright regions. Due to the geometric distortion, the crater walls facing the satellite are foreshortened, while the opposite walls and bottoms of the craters appear shadowed and wider (Figure 2b), which is why the brightness pattern appears non-circular and crescentic. Using this approach, we quantified and analyzed all the crater locations through time.
# Indoor Carrier Phase Positioning Technology Based on OFDM System
###### Abstract  
Carrier phase measurement is a ranging technique that uses the receiver to determine the phase difference between the received signal and the transmitted signal. Carrier phase ranging has a high resolution; thus, it is an important research direction for high precision positioning. It is widely used in global navigation satellite systems (GNSS) systems but is not yet commonly used inwireless orthogonal frequency division multiplex (OFDM) systems. Applying carrier phase technology to OFDM systems can significantly improve positioning accuracy. Like GNSS carrier phase positioning, using the OFDM carrier phase for positioning has the following two problems. First, multipath and non-line-of-sight (NLOS) propagation have severe effects on carrier phase measurements. Secondly, ambiguity resolution is also a primary issue in the carrier phase positioning. This paper presents a ranging scheme based on the carrier phase in a multipath environment. Moreover, an algorithm based on the extended Kalman filter (EKF) is developed for fast integer ambiguity resolution and NLOS error mitigation. The simulation results show that the EKF algorithm proposed in this paper solves the integer ambiguity quickly. Further, the high-resolution carrier phase measurements combined with the accurately estimated integer ambiguity lead to less than 30-centimeter positioning error for 90% of the terminals. In conclusion, the presented methods gain excellent performance, even when NLOS error occur.  
extended Kalman filter; localization; time of arrival; carrier phase; ambiguity resolution 2021216731  
2  
[PERSON], [PERSON], X. Indoor Carrier Phase Positioning Technology Based on OFDM System.  
Sensors2021216731 https:////doi.org/10.3390/s21206731
## 2 Methodology
### MIC-FCBF Combined Feature Screening  
In the context of the intricate relationship between energy consumption data and features in large public buildings, employing a single feature selection method is insufficient even after signal decomposition. Therefore, this study utilizes the Maximum Mutual Information Coefficient (MIC) method to capture intricate interconnections between features effectively. Initially, MIC ensures high-information features, reducing dataset dimensionality and enhancing computational efficiency. Combining MIC with the Fast Correlation-Based Filter (FCBF) method further refines feature selection, considering symmetric uncertainty for more accurate correlation and redundancy information. This integrated feature screening method comprehensively assesses feature importance, improving model generalization and reducing overfitting risks compared to single methods.
## 3 Results
### Spatial Pattern of NPP  
The spatial pattern of mean annual NPP derived from MODIS for the period 2001-2014 is shown in Figure 2a. The spatial pattern of annual NPP was uneven, which showed gradients decreasing from the south to the north and from the east to the west. The highest value occurred in Southern China, with values generally higher than 0.6 kg C m\({}^{-2}\) in most of that area. In contrast, annual NPP was usually lower than 0.2 kg C m\({}^{-2}\) in the Tibetan Plateau. For the remaining regions, annual NPP ranged between 0.2 and 0.6 kg C m\({}^{-2}\). The standard deviation (Figure 2b) shows a similar spatial pattern with mean annual NPP. The highest value (more than 0.05 kg C m\({}^{-2}\)) was mostly distributed in Southern China. The values in Northern China ranged from 0.02-0.05 kg C m\({}^{-2}\), with the exception of the northern and eastern parts of Northeast China, which was more than 0.05 kg C m\({}^{-2}\). The lowest standard deviation value was found in the Tibetan Plateau, with values mostly less than 0.02 kg C m\({}^{-2}\)Figure 1: Comparison between the in situ Net Primary Productivity (NPP) site observations and NPP derived from MODIS **(a)**; Miami model **(b)**; and Thornthwaite Memorial model (**c**).  
Figure 2: Spatial distribution of mean Net Primary Productivity (NPP) (**a**) and standard deviation (**b**) in China between 2001–2014.  
At the biome level, NPP differs in terms of vegetation types (Figure 3). Generally, forest ecosystems have a higher annual NPP than grassland (\(p\) < 0.001). Among the forest ecosystems, evergreen forests show a higher NPP than deciduous forests (\(p\) < 0.001). The highest NPP was found in EBF, with an average annual value of 0.975 \(\pm\) 0.362 kg C m\({}^{-2}\), which was almost twice that of the DBF (0.542 \(\pm\) 0.301 kg C m\({}^{-2}\)) and DNF (0.329 \(\pm\) 0.103 kg C m\({}^{-2}\)). ENF had the second largest NPP (0.605 \(\pm\) 0.277 kg C m\({}^{-2}\)), followed by Farmland (0.422 \(\pm\) 0.159 kg C m\({}^{-2}\)) and Shrub (0.317 \(\pm\) 0.273 kg C m\({}^{-2}\)), respectively. Grassland had the lowest annual NPP, with an average value of 0.162 \(\pm\) 0.196 kg C m\({}^{-2}\).
# Optimization and Application of Drilling Parameters Based on Gas-Solid Coupling Simulation
## 5 Engineering Practice  
### Mine Overview and Drill Layout  
Fengtai Coal Mine in Pingding County, Yangquan City, Shanxi Province, is currently mining the No. 15 coal seam, with an average dip angle of 5\({}^{\circ}\), an average seam thickness of 6.06 m, and a maximum gas pressure of 1.0 MPa. According to the mine's gas outflow prediction report, the gas content of the coal seam is 5.04 m\({}^{3}\)/t, and the maximum absolute gas outflow of the 15101 working face is 27.07 m\({}^{3}\)/min, which is the largest gas outflow of a mine in the world. Due to the existence of the 12#, 13#, and 14# unmineable neighboring coal seams in the 15101 working face, the mine is in danger of gas overlimit and coal and gas protrusion.  
To address the aforementioned challenges, four directional high-level long boreholes were constructed in the Fengtai Coal Mine to mitigate the gas overlimit risks and reduce the coal and gas outburst potential. Integrated analysis of theoretical research and field conditions determined the optimal drainage parameters: a negative pressure of 25 kPa, borehole diameter of 203 mm, and extraction duration of 90 days, achieving an effective drainage radius of approximately 4.8 m. To ensure operational reliability while accounting for permeability variations in overlying strata, fault influences, and inter-borehole interference, the borehole spacing was finalized at 4 m, informed by empirical data from adjacent mines and theoretical safety margins.  
The drilling arrangement is shown in plan view, and the sections are shown in Figures 8 and 9. Table 3 shows the actual drilling parameters of the directional high-level long boreholes.
## 2 Mathematical Model
### Collection Coefficient Calculation  
The ice crystal trajectories are calculated to obtain the initial position of each ice crystal and its corresponding impact point. As shown in Figure 2, the initial position of a series of ice crystals can be determined as \((y_{0},y_{1},y_{2},\cdots y_{n})\), with the corresponding impact position of these ice crystals as \((s_{0},s_{1},s_{2},\cdots s_{n})\). The distance between the initial positions of the two ice crystal trajectories is \(S_{0}\), the distance between the adjacent impact positions is \(S_{i}\), and the mass flow of the ice crystals at the initial position is  
\[m_{0}=V_{\infty}\cdot IWC\cdot S_{0}\cdot\cos\alpha, \tag{16}\]  
The mass flow of ice crystals at the impact location is  
\[m_{i}=V_{\infty}\cdot IWC\cdot S_{i}, \tag{17}\]  
According to the definition of local collection coefficient, the calculation equation for the collection coefficient of ice crystals can be expressed as  
\[\beta=\frac{m_{0}}{m_{i}}=\frac{S_{0}\cdot cos\alpha}{S_{i}}, \tag{18}\]  
The collection coefficient of the object surface can be obtained by calculating a large number of ice crystal trajectories. In the initial state, the large initial spacing of the ice crystals can be used to determine the approximate range of impact. The ice crystals outside this area will not impact on the object surface, while the ice crystals trajectories inside this area will be calculated with a smaller spacing, to accurately determine the impact limitation of the ice crystals. The internal area of the impact limitation is the ice crystal impact area, and the collection coefficients within this area can be obtained using the above collection coefficient calculation method.  
Figure 1: Flowchart of ice crystal motion trajectory calculation.  
Figure 2: Calculation of the collection coefficient based on the Lagrangian method.
# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 4 Results and Discussion
### Comparison with Other Methods
The ECI results are relatively dispersed, potentially leading to the exclusion of many bare coal patches during subsequent morphological processing, which reduces recognition accuracy. Additionally, ECI is not very effective in removing impermeable surfaces and water areas, resulting in confusion between these features and bare coal in the calculation results for all eight areas. The solar azimuth angle of the images for the eight regions is between 130\({}^{\circ}\) and 160\({}^{\circ}\), causing terrain and building shadows to appear in the northwest direction. The results indicate that these shadows do not affect the recognition capabilities of the four methods, likely due to the resolution of the Landsat images, where shadows and other land cover types are merged within a few pixels. In future research, higher-resolution images (e.g., Sentinel-2, etc. ), can be used for more in-depth exploration of this issue. Overall, the BCI demonstrates stronger applicability, yielding smooth identification results and effectively recognizing bare coal in various complex environments.
# A Comprehensive Survey for Deep-Learning-Based
## 3 Abnormal Detection with Images over Short Distances
### Infrared Image
#### 3.2.2 Infrared-Image-Based Anomaly Detection for Intelligent Sensor Technology in Smart Grids
To meet this functional gap, remote operation requires one to actively confirm the actions taken in response to the appropriate commands issued by the dispatching room. The intelligent sensor introduced here actually evaluates the alignment degree of the two parts of high-voltage disconnectors through local measurement. In addition, when the circuit breaker is closed, continuous condition monitoring is very important to detect abnormal bus heating caused by poor contact in real time, to avoid hot spots. Therefore, [PERSON] et al. [26] proposed a new solution. A set of integrated miniaturized intelligent detection equipment is used to collect data from a centralized power grid environment, and an independent and wireless multivariable intelligent sensor is used. The sensor can collect and verify the temperature data in the high-voltage disconnector and transmit the data obtained by the sensor to the instrument system of the remote terminal for analysis; that is, it can determine the working state of the high-voltage disconnector and diagnose the faults that are occurring. This autonomous, wireless multivariable intelligent sensor is directly integrated with the workshop interconnection and integrated intelligent sensor as the bottom layer of what is now called the \"instrument cloud\". Because the equipment is widely distributed in the power grid, we need to use the equipment to detect each centralized power grid environment [26; 28; 29; 30]. We need to transmit the data obtained by the sensor to the instrument system of the remote terminal for analysis to find out the cause of the temperature in the high-voltage disconnector. Figure 2: Abnormality detection with wireless multivariable intelligent sensor.
## 4 Tropospheric profiling with neural networks
### Early stopping technique with Principal Component Analysis  
In the second approach, we have applied the same early stopping technique as described above, with the same division of the available data set, but we have processed the input and target matrices by the Principal Component Analysis (PCA). The PCA decomposes the 56-level and 54-level profiles, for desert and vegetation area respectively, on a basis of empirical orthogonal functions called principal components [14]. The PCA permits a reduction of the number of descriptive profile levels by exploiting the correlation among values at different altitudes, ensuring a faster processing and a reduction of computer memory requirements in comparison with the original data (full profiles). We have chosen to employ a number of principal components representing the 99.9% of the total variance of the original data [11], leading to the use of only 15 and 14 principal components for the total refractivity instead of the original 56 and 54 levels, over desert and vegetation lands respectively. Concerning the neural network targets, the number of components for dry refractivity, wet refractivity and dry pressure profiles are 11, 14 and 8 for the desert area. The need of a bigger number of wet refractivity principal components with respect to dry refractivity and dry pressure is an evidence of a greater variability in the troposphere of the wet parameters with respect to the dry ones, particularly over desert zones with respect to vegetation ones. In fact, concerning the neural network targets of the vegetation area, the number of components for dry and wet refractivity and dry pressure profiles are 11, 11 and 7 respectively, maybe sign of the perseverance of wet conditions over these sub-tropical zones, mainly for those ones closest to the equator.
## 2 Materials and Methods
### Methods
#### 2.2.3 SEM-EDS Analyses  
Scanning electron microscopy (SEM) images of the cement samples were obtained using a Hitachi S-4300 E/N (Hitachi, Santa Clara, CA, USA), considering their micropore structure. A variable pressure backscattered electron detector operating at a 10 kV accelerating voltage is featured in the Hitachi S/N 4300(Hitachi, Santa Clara, CA, USA). To reduce charging on the sample surface, the variable pressure is adjusted. The microstructural characteristics of the cement samples' matrix (grain) were described using the SEM images. Measurements of the surface morphology, grain exposure of the mineral, and microfractures between grains were made on designated sections of all the samples before and after they were exposed to hydrogen. Each sample had three designated sections where SEM analyses were performed. SEM magnifications of 40, 100, 300, and 500 were used.  
The energy dispersive spectroscopy (EDS) method can yield semi-quantitative data but is primarily utilized for qualitative material examination. An EDS system is typically included with SEM instrumentation to enable the chemical analysis of features seen in the SEM display. A simultaneous SEM and EDS analysis is beneficial when conducting a failure analysis since a spot analysis becomes vital to reaching a reliable result. The signals generated by an SEM/EDS system comprise X-rays utilized for chemical identification and quantification at measurable quantities and secondary and backscattered electrons used in image formation for a morphological analysis. The sample surface conditionsaffect the EDS detection limit; the smoother the surface, the lower the detection limit. Major and minor elements with concentrations greater than ten weight percent (major) and minor elements with concentrations between 1 and 10 weight percent can be detected using EDS. Since bulk materials have a 0.1 weight percent detection limit, trace elements (concentrations less than 0.01 weight percent) cannot be detected by EDS. For the three designated sections of each cement sample, EDS mapping was used to determine the changes in the elemental composition of the designated sections. Figure 4 represents the flowchart of the methodology used in this study.
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 3 Modern Internet Transport Protocols
### Quick UDP Internet Connection
Also, QUIC encrypts each segment individually which is slower than TCP+TLS which encrypts a group of segments (chunk) together [18; 19].