esa-sceva/satcom-chunk-collection huggingface dataset:
# Native Plant Capacity for Gentle Remediation in Heavily Polluted Mines
## 3 Results
### Nutrient Pool in Leaf and Root of Dittrichia Viscosa  
Significant differences between contaminated and uncontaminated soils were found in the pool of Zn and Pb in leaves and roots (Table 4). For Pb, the pool in the roots was higher in individuals that grew in uncontaminated soils compared with the pool in leaves and the concentrations in leaves and roots (Tables 2 and 3). Moreover, the Mn and Fe pools \begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{6}{c}{**Trace-Element Concentration in Leaves (ppm)**} \\ \hline  & **Zn** & **Pb** & **Cu** & **Mn** & **Fe** \\ \hline
**CC** & 290.2 \(\pm\) 61.4a & 179.5 \(\pm\) 3.5a & 19.5 \(\pm\) 1.7a & 105.1 \(\pm\) 16.3a & 3570.8 \(\pm\) 171.4a \\  & (167.9-361.6) & (173.1-185.2) & (16.3-22.1) & (83.6-137.2) & (3229.5-3769.8) \\
**CN** & 126.4 \(\pm\) 36.9a & 109.9\(\pm\)10.9b & 19.4 \(\pm\) 1.7a & 52.6 \(\pm\) 6.1b & 2238.0 \(\pm\) 184.7b \\  & (72.7-197.1) & (88.0-122.3) & (17.2-22.8) & (40.4-59.9) & (1971.5-2592.9) \\ \hline
**p-value** & 0.126 & **0.049** & 0.82 & **0.049** & **0.049** \\ \hline
**DC** & 1807.2 \(\pm\) 239.6a & 592.2 \(\pm\) 104.4a & 40.1 \(\pm\) 2.3a & 224.7 \(\pm\) 78.0a & 6016.5 \(\pm\) 969.5a \\  & (1336.6-2121.1) & (458.2-798.0) & (36.6-44.5) & (109.9-373.7) & (4178.9-4771.3) \\
**DN** & 188.7 \(\pm\) 31.9b & 163.0 \(\pm\) 19.3b & 29.8 \(\pm\) 7.3a & 156.3 \(\pm\) 29.9a & 3656.7\(\pm\)543.4a \\  & (152.5-252.4) & (124.4-183.5) & (16.0-41.2) & (120.8-215.9) & (2978.7-4731.3) \\ \hline
**p-value** & **0.021** & **0.049** & 0.275 & 0.827 & 0.126 \\ \hline
**EC** & 644.2 \(\pm\) 325.4a & 168.1 \(\pm\) 19.8a & 7.1 \(\pm\) 0.7a & 95.7 \(\pm\) 44.7a & 271.6 \(\pm\) 40.3a \\  & (223.2-1284.5) & (147.9-207.8) & (6.2-8.5) & (43.8-184.7) & (190.9-614.1) \\
**EN** & 21.3 \(\pm\) 6.6b & 144.9 \(\pm\) 12.7a & 16.3 \(\pm\) 1.5b & 84.4 \(\pm\) 12.4a & 3884.9 \(\pm\) 201.0b \\  & (10.8-33.5) & (126.4-169.3) & (13.3-17.9) & (59.8-100.1) & (3486.1-4128.0) \\ \hline
**p-value** & **0.049** & 0.275 & **0.049** & 0.512 & **0.049** \\ \hline \end{tabular}
\end{table} 
Table 2: Trace-element concentration in leaves. Mean \(\pm\) SE value is indicated, and minimum and maximum values are shown in brackets. The non-parametric Kruskal–Wallis test was applied. Different letters indicate significant differences at a significance level \(<\) 0.05005 within the same element and plant species. CC = contaminated soil under _C. salviofilius_, CN = uncontaminated soil under _C. salviofilius_, DC = contaminated soil under _D. viscosa_, DN = uncontaminated soil under _D. viscosa_, EC = contaminated soil under _E. pithyusa_.
# Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks
## 4 Numerical Results
### Simulation Results
#### 4.1.3 Performance of SA-FRL Relay Selection
In summary, the SA-FRL UWA relay selection scheme proposed in this paper can select the relay node with high channel link quality while considering the access delay of the relay node, and finally achieves the optimum data rate.
## 3 Factors Affecting Accuracy and Accuracy-Improvement Methods  
Since the LD is adopted as the laser source of the measurement system, it is essential to compensate and eliminate the measurement errors caused by the elliptical laser spot and the low stability in the output beam of the LD.
## 2 Results and Discussion
### Photoreduction of CO\({}_{2}\)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Catalyst** & **HS Conv.** & **HCOOH** & **HCOOH** & **Total Stored** \\  & **(\%)** & **(mol/kgcat h)** & **(g/kgcat h)** & **Energy (J) in 6 h** \\ \hline P25 & 47 & 16.9 & 778 & 195 \\ FSP TiO\({}_{2}\) & 40 & 13.9 & 640 & 160 \\ FSP WO\({}_{3}\) & 43 & 14.7 & 677 & 169 \\ FSP TiO\({}_{2}\)/WO\({}_{3}\) & 77 & 36.5 & 1680 & 406 \\
60/40 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Productivity and stored energy of tested catalysts—18 bar—pH 14. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Catalyst** & **Reaction Medium** & **Light Source** & **Productivity** (mol/kgcat h)** & **Reference** \\ \hline \multirow{3}{*}{Cu\({}_{2}\)O} & Aqueous suspension in & \multirow{3}{*}{Arc Lamp} & \multirow{3}{*}{2.76} & \multirow{3}{*}{[36]} \\  & 0.3 M NaHCO\({}_{3}\), 2 M & & \\ \cline{1-1}  & glycerol as HS & & & \\ \hline \multirow{2}{*}{Ag/TiO\({}_{2}\)} & Aqueous suspension in & \multirow{2}{*}{Arc Lamp} & \multirow{2}{*}{3.89} & \multirow{2}{*}{[37]} \\  & 0.2 M NaHCO\({}_{3}\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Literature comparison—cataly, reaction medium, light source and productivity. The performances of both of the bare oxides prepared by FSP were comparable, with high productivity compared to what has been reported in the literature, although it is worth noticing that different conditions, such as lamps and hole scavengers, were used. Authors have reported a Ag/TiO\({}_{2}\) catalyst which showed a remarkable productivity of 3.89 mol/kg\({}_{\text{cat}}\cdot\)h when tested in an aqueous suspension (0.2 M NaHCO\({}_{3}\)) under irradiation by a 1000 W Xenon arc lamp [37]. As previously said, the hybrid oxide being prepared through FSP synthesis allowed it to obtain an astonishing productivity of 36.5 mol/kg\({}_{\text{cat}}\cdot\)h, emphasizing that FSP represents a fundamental tool for the preparation of oxide-based catalysts, and all the more so in the field of photocatalysis, where the intimate contact obtained through this technique allows for simple and fast preparations of type-II heterojunctions.
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 4 Discussion  
### Related Work  
#### 4.1.1 Gait Recognition Based on Sensor  
In traditional research based on gait recognition, sensors are often used to obtain movement information. [PERSON] et al. [16] proposed a real-time motion-tracking system based on tri-axis microelectromechanical accelerometers, rate gyros, and magnetometers by the Kalman algorithm. The results showed that the standard deviation of the heading angle was about 1.5\({}^{\circ}\), and validated the effectiveness of the proposed method. [PERSON] et al. [17] proposed a human gait analysis method by using a wavelet transform of the signal obtained from six inertial ProMove mini sensors. [PERSON] et al. [18] proposed a method for three-dimensional gait analysis using wearable sensors and quaternion calculations. The results showed that the average RMSE and correlation coefficient (CC) were 10.14 deg and 0.98, 7.88 deg and 0.97, 9.75 deg and 0.78 for the hip, knee and ankle flexion angles, respectively. However, these devices are susceptible to smoke and obstacles, and have great limitations in the harsh mine environment.
## 2 Description of the Experiment
### Rate-Based Model Description  
The rate-based model consists of a set of well-accepted equations, which are modelled to calculate the mass and energy transfer across the interface using rate equation and mass transfer coefficients ([PERSON] and [PERSON], 2001). For the calculation of the mass transfer coefficient and interfacial area, the correlation by [PERSON] and [PERSON] (2001) can be applied.
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 4 Performance Evaluation of Improved ACA
### Performance Evaluation
#### 4.2.2 Simulation Experiments  
On the generalized barrier grid maps for the two selected areas, we conducted path-planning simulation experiments using the traditional ACA that does not consider trafficability (T-ACA-NT), the traditional ACA that considers trafficability (T-ACA-T), and our improved ACA that considers trafficability (I-ACA-T). The simulation results are shown in Figures 11 and 12. Given the randomness of the experiments, each set of simulations ran 10 times to obtain the average value, and the results are shown in Tables 3 and 4.  
Figure 8: Influence of \(m\) on the algorithm’s performance. (**a**) The change trend of maneuvering time with \(m\). (**b**) The change trend of iterations time with \(m\).  
Figure 11: Path-planning diagram in Beishan Town (selected region). (**a**) Path planning diagram of T-ACA-NT. (**b**) Path planning diagram of T-ACA-T. (**c**) Path planning diagram of I-ACA-T (our algorithm).  
Figure 9: Maps of Beishan Town (selected area). (**a**) Original remote sensing image map. (**b**) 01 map. (**c**) Generalized obstacle grid map.  
Analyses of Figures 11 and 12 and Tables 3 and 4 revealed the following findings:  
1. In terms of convergence speed, the T-ACA-T reduced the iterations by 20.91% and 49.18% in comparison with T-ACA-NT; our improved ACA that considers trificability (I-ACA-T) reduced the number of iterations by 56.25% and 66.82% in comparison with T-ACA-NT. To sum up, our improved ACA significantly improved the convergence and computing efficiency.
2. In terms of the quality of solutions, T-ACA-T reduced the traveling time by 16.93% and 22.21% despite a slight increase in the route length; our I-ACA-T reduced the traveling time by 17.48% and 22.25%. Our improved ACA showed no significant improvement on the traveling time in comparison with T-ACA-T. Nonetheless, as Figures 11b,c and 12b,c show, our algorithm was more likely to find the global optimum, whereas T-ACA-T was susceptible to the local optimum.
3. In terms of the stability of path planning, our algorithm achieved quick convergence on two maps of different barrier rates; furthermore, variance analysis showed that the algebraic difference in convergence between the different  
vehicles ranges from several miles to several hundred miles; therefore, it is challenging to balance precision and breadth in path planning. In this study, a multilevel adaptive path-planning model was proposed to meet the requirements for vehicle trafficability, off-road trafficability, rough planning, and accurate planning.
## 2 Materials and Methods
### Data Types and Preprocessing  
The analyses were based on four Landsat images, including Thematic Mapper (TM, Landsat 5), Enhanced Thematic Mapper Plus (ETM+, Landsat 7), and Operational Land Imager and Thermal Infrared Sensor (OLI and TIRS, Landsat 8), recorded in 1990, 2000, 2010, and 2020, respectively (Table 1). All images were freely obtained from the US Geological Survey (USGS) Earth Explorer website ([[http://earthexplorar.usgs.gov/](http://earthexplorar.usgs.gov/)]([http://earthexplorar.usgs.gov/](http://earthexplorar.usgs.gov/)) (accessed on 5 February 2022)).  
All images were carefully collected during the dry season (July) to reduce the impact of scene variations and illumination conditions. The scenes covered path 164 and row 042, had a spatial resolution of 30 m for optical bands and 60 to 100 m for thermal bands, and were cloud-free. Landsat images were selected because of their availability and their potential to provide reflective and thermal infrared bands that can be used to estimate different elements, including the normalized difference built-up index (NDBI), land use/land cover (LULC), normalized difference vegetation index (INDVI), and land surface temperature (LST).  
We geo-rectified the images to the WGS84/UTM, zone 39 N. Radiometric calibration, atmospheric correction, and a gap-filling process were performed for the July 2010 image using data-specific utilities, the dark object subtraction (DOS) technique, and the gap-filling tool, utilizing ENVI 5.1 image processing software. All other images were atmospherically corrected and radiometrically calibrated by employing a Semi-Automatic Classification Plugin (SCP) [37] of the open-source QGIS software program (version 3.16). Images with pixel values ranging from 0 to 1 were obtained from the pre-processing operations; then, a subset covering the pilot area (Figure 1) was delineated and clipped.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Sensor-ID** & **Spacecraft-ID** & **Acquired Date** & **No. of Bands** & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} \\ \hline TM & Landsat-5 & 15 July 1990 & \begin{tabular}{c} 6(reflective), \\ 1(thermal) \\ \end{tabular} & \begin{tabular}{c} 30 m (reflective), \\ 120 m (thermal) \\ \end{tabular} \\ \hline ETM+ & Landsat-7 & \begin{tabular}{c} 2 July 2000 \\ 14 July 2010 \\ \end{tabular} & \begin{tabular}{c} 6 (reflective), \\ 2 (thermal) \\ \end{tabular} & \begin{tabular}{c} 30 m (reflective), \\ 120 m (thermal) \\ \end{tabular} \\ \hline OLI and TIRS & Landsat-8 & 17 July 2020 & \
\begin{tabular}{c} 8 (reflective), \\ 2 (thermal) \\ \end{tabular} &
\begin{tabular}{c} 30 m (reflective), \\ 100 m (thermal) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Landsat image characteristics.
## 2 Materials and Methods for Coupled Thermal-Electrical Modeling of the Grounding Electrode
### Assembling the Thermal Model with the Electric Calculations  
In practice, the model for determining the heating of a conductor is applied to grounding electrodes in electrical installations. These electrodes consist of thin conductive rods that can be segmented into small sections, to which the described model is individually applied. The times involved in the Joule heating process are so short that applying the temperature and resistivity calculation algorithm to each segment individually does not introduce significant error. Since the conductor piece to which the described thermal model is applied undergoes changes in internal resistivity with temperature \(p_{c}(T_{c})\), in addition to changes in the resistivity of the surrounding soil \(p_{s}(r,T_{s})\), these variations result in a modification of the current values \(I_{c}\) and \(I_{s}\), which in turn alters the temperature profiles of the conductor and its surroundings. For this reason, the time interval considered has been divided into several stages, during which these currents are calculated using an electrical calculation software developed by the authors. Subsequently, the internal and external temperatures of the conductor are determined using the described model, in order to evaluate the updated resistivity of the materials involved. These updated values are then integrated into the electrical calculation of the next stage. The aforementioned electrical calculation software [23] considers the internal resistance of the conductors and the modified radii, according to Equation (20), to recalculate the currents \(I_{c}\) and \(I_{s}\). The flowchart in Figure 4 illustrates the calculation procedure for the complete model.  
Figure 4: Flowchart for the calculation of the final temperature and resistivity profiles.  
During the calculation process, the grounding potential of the electrode is also obtained, which depends on the current injection point, as well as the data required for calculating the step and touch potentials of the grounding system to which the conductive electrode belongs.
## 3 Results and Discussion
### Changes in VOCs in millet Huangjiu  
The volatile components of millet Huangjiu samples with varying protein additions were extracted using HS-SPME and analyzed via GC-MS. The volatiles were then identified based on their mass spectrum and KI values. The aroma profile of millet Huangjiu is a complex blend of various flavor components. A total of 56 volatile compounds were identified in the millet Huangjiu samples, comprising 22 esters, 13 alcohols, 10 aldehydes, 6 acids, 3 phenolics, and 2 ketones, as shown in Table 3. These results are generally consistent with previous reports on the volatile composition of other millet Huangjiu samples [4; 10; 12]. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Compound** & **RI (ref) a** & **RI (cal) b** & **CAS** & **Odor Descriptor** & **Identification c** \\ \hline _Alcohols_ & & & & & \\
2-Methyl-1-propanol & 1101 & 1092 & 78-83-1 & Malty & MS, RI \\
3-Methyl-1-butanol & 1209 & 1212 & 123-51-3 & Malty & MS, RI \\
1-Hexanol & 1363 & 1356 & 111-27-3 & Grassy, marzipan-like & MS, RI \\
1-Octen-3-ol & 1430 & 1456 & 3391-86-4 & Mushroom-like & MS, RI \\
1-Heptanol & 1447 & 1465 & 111-70-6 & Fruity, soapy & MS, RI \\
5-Methyl-1-hexanol & 1466 & 1470 & 627-98-5 & Fruity, soapy & MS, RI \\
1-Octanol & 1573 & 1561 & 111-87-5 & Soapy, citrus-like, green & MS, RI \\
2-Furammethanol & 1635 & 1656 & 98-00-0 & Burnt, sweet & MS, RI \\
2-Octen-1-ol & 1645 & 1628 & 22104-78-5 & Soapy & MS, RI \\
1-Nonanol & 1666 & 1640 & 143-08-8 & Soapy, fruity & MS, RI \\
1-Decanol & 1771 & 1763 & 112-30-1 & Soapy & MS, RI \\
2-Butyl-1-octanol & 1853 & 1849 & 3913-02-8 & - & MS, RI \\
2-Phenylethanol & 1902 & 1873 & 60-12-8 & Floral, honey-like & MS, RI \\ \hline \end{tabular}
\end{table} 
Table 3: Identification of volatile organic compounds (VOCs) in millet Huangjiu.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 3 Results and Discussion  
### Structural Characterization of CAA Hydrogels  
Since CAA samples with a degree of substitution (DS) of approximately 0.85 exhibit good water solubility and contain a large number of active acetoacetyl groups, they were selected for the relevant experiments in this study. Fourier transform infrared (FTIR) spectroscopy was used to characterize the chemical structure of the polysaccharide hydrogels with the FTIR spectrum of CAA as shown in Figure 2a. The absorption peak at 3400 cm\({}^{-1}\) corresponds to the stretching vibration of \(-\)OH groups. The absorption peak at 2874 cm\({}^{-1}\) is attributed to the stretching vibration of C\(-\)H bonds. The absorption peaks at 1710 cm\({}^{-1}\) and 1746 cm\({}^{-1}\) are characteristic bicarbonyl peaks of the acetoacetyl groups. In Figure 2b, the characteristic peak at 3.6 ppm in the NMR spectrum belongs to the methylene protons on the acetoacetyl group. The characteristic peak at 1.9-2.4 ppm corresponds to the methyl protons on the acetoacetyl group. The characteristic peak at 3.7-6.0 ppm is associated with the sugar ring of the cellulose backbone. In the FTIR spectrum of chitosan, the absorption peak at 1637 cm\({}^{-1}\) corresponds to the stretching vibration of \(-\)NH\({}_{2}\) groups on the six-membered ring of chitosan. The intensity of this absorption peak in the polysaccharide hydrogel is significantly lower than that in pure chitosan, and it overlaps with the absorption peak of the amide bond at 1645 cm\({}^{-1}\). In the FTIR spectrum of the hydrogels, the characteristic bicarbonyl peaks of the acetoacetyl group at 1710 cm\({}^{-1}\) and 1746 cm\({}^{-1}\) are significantly reduced. New absorption peaks at 1645 cm\({}^{-1}\) and 1612 cm\({}^{-1}\) correspond to the stretching vibrations of enamine bonds as shown in Figure 2a.
## 3 Engineering Applications
### Central Yunnan Water Diversion Project
#### 3.1.2. Slug Test of the Central Yunnan Water Diversion Project
As shown in Figure 8, the standard curve shapes fitted by the three models are basically consistent. Among them, the data of the water level recovery of the boreholes CYWD-2, CYWD-3 and CYWD-4 are relatively stable, and the curve shape is comparatively smooth  
Figure 8: Fitting curve of the CYWD-2–CYWD-5 slug tests with different models. (**a1**) CYWD-2, Kipp model; (**a2**) CYWD-2, CBP model; (**a3**) CYWD-2, HWS model; (**b1**) CYWD-3, Kipp model; (**b2**) CYWD-3, CBP model; (**b3**) CYWD-3, HWS model; (**c1**) CYWD-4, Kipp model; (**c2**) CYWD-4, CBP model; (**c3**) CYWD-4, HWS model; (**d1**) CYWD-5, Kipp model; (**d2**) CYWD-5, CBP model; (**d3**) CYWD-5, HWS model. during the slug test. Meanwhile, the water level recovery time of these three boreholes is relatively long. The qualitative analysis shows that the rock mass permeability around these three boreholes is relatively low. The data of the water level recovery of borehole CYWD-5 have some fluctuations due to some certain disturbances during the test. The speed of the water level recovery of borehole CYWD-5 is fast, and the rock mass permeability around this borehole should be high. According to the analysis of the matching curve, the water level recovery curve of the three boreholes CYWD-2, CYWD-3 and CYWD-4 is basically the same, and all of them fit the standard curve with a damping coefficient of 1 in the Kipp model. The CBP model has a poor fitting to these three groups of data, as the overall shape of the standard curve is relatively gradual, and there is a comparatively large deviation at the beginning and the end. The HWS model can basically fit the three groups of data well. Although the recovery time of the water level of borehole CYWD-5 is relatively short during the slug test, the water level is disturbed to a certain extent, leading to a certain fluctuation effect in the water level recovery data. In general, the Kipp model fits best, the HWS model fits the second best and the CBP model fits relatively poorly.
## 2 Materials and Methods
### Design, Development, and Integration of SRGs
#### 2.2.1 SRG Prototype for SCI Participants  
The initial prototype was designed and developed for the right hand of individuals with SCI. This SRG is designed to monitor, evaluate, and facilitate the challenges faced by SCI patients, including reduced muscle control and sensory deficits. A Qwic Flex Glove Controller [34] attached to the index and middle fingers is connected with the Qwic Flex Glove Controller attached to the ring and little fingers. The Qwic Flex Glove Controller is connected to the microcontroller via the SparkFun Qwic Shield's Qwic connection ports. One of the leads of each pressure sensor is connected to the power (3.3 V) and the other leads to the analog in of the microcontroller while a 10 kOhm pulldown resistor is connected between GND and the analog in. An sEMG Myoware Muscle Sensor's (+) lead is connected to the power (3.3 V), the (\(-\)) lead is connected to ground and the signal lead is connected to the analog in of the microcontroller. In addition to the MSS suite, the system incorporates the SRPAS subsystem based on a PneuNet [32] flexible exoskeleton that applies controlled forces to the hand. The sensor and actuation technologies involved are sewn, attached and wired on the surface of an F.F. Group Antistatic Glove [35] as illustrated in Figure 2.  
Figure 2: SCI SRG prototype architecture: (**a**) MSS subsystem and (**b**) SRPAS subsystem.
## 2 Methodologies and System Description
### Signal Preprocessing
#### 2.3.1 Waveform Processing  
The measurement of RPPG is often affected by ambient light. PSO-ICA is used here to eliminate interference caused by light, as shown in Figure 4. Then, the noise is filtered out and the signal is amplified by a band-pass filter with a range of 0.4-10 Hz, as shown in Figure 5. Finally, peak-to-peak detection is performed, and the time difference between peaks is calculated using the frame rate to determine the data processing time.