esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Influence of Plant Roots on Dynamic Characteristics of Clay
## 4 Conclusions  
Prior studies have primarily concentrated on investigating the dynamic impact of root distribution patterns on soil. In this study, resonance column tests were conducted on clay with different root distribution patterns and root mass densities, leading to the following key conclusions:  
1. The upper root distribution significantly increased the dynamic shear modulus of the soil under confining pressures of 100-200 kPa, demonstrating greater stiffness and strength, which enhanced dynamic soil properties and reduced dynamic strain amplitude.
2. Plant roots improved the dynamic shear modulus of soil, with the effect varying according to root mass density. The dynamic shear modulus peaked at a root mass density of 1.5% g \(\cdot\) cm\({}^{-3}\), then stabilized. Increased confining pressure reduced the influence of high root mass density, with the \(G\sim\gamma\) relationship curve gradually converging. This indicates that optimizing root distribution and density can effectively enhance soil dynamic properties.
3. The upper root distribution significantly increased the soil damping ratio, enhancing energy dissipation capacity due to increased upper stiffness and accelerated vibration response. In contrast, the lower root distribution exhibited a lower damping ratio due to confinement effects. This suggests that placing roots in the upper soil layer can improve dynamic soil stability, consistent with the root distribution characteristics on embankment surfaces.
4. The damping ratio decreased initially and then increased with rising root mass density, with critical values observed at each confining pressure (1.5% g \(\cdot\) cm\({}^{-3}\) for 100 kPa, 2% g \(\cdot\) cm\({}^{-3}\) for 200 kPa, and 2.5% g \(\cdot\) cm\({}^{-3}\) for 300 kPa). At a low density, roots enhanced soil compaction and reduced the damping ratio, while at a high density, they improved ductility and increased energy dissipation. Optimizing root mass density can help improve the dynamic properties of slope soils.  
Methodology, Q.S.; validation, Q.S., C.T. and C.Z.; formal analysis, Q.S., C.T. and C.Z.; investigation Y.M.; resources, Q.S.; data curation, C.T. and C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, C.T. and Q.S.; supervision, Y.M.; project administration, Q.S. and Y.M.; funding acquisition, Q.S. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Science Foundation of China (No. 51878185; No. 2019 YFC1509805) and the Scientific Research Project of Hunan Provincial Department of Education (24C0978).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Dental Hydrogels with Essential Oils with Potential Activity in Periodontitis
## 2 Results and Discussion  
### GC-MS Analysis of Gels Containing Essential Oils  
By using the gas chromatography-mass spectrometry (GC-MS) technology, the essential oils were examined. Due to the volatile and semi-volatile components (monoterpenes and sesquiterpenes) of essential oils being easily separated, recognized, and quantified, GC-MS is the most popular analytical approach for the examination of essential oils. The GC method is trustworthy because it can identify analytes in minute quantities (traces)recovered from gels and because its mass spectrometry (MS) coupling provides structural information about the detected compounds [17; 18; 19]. GC-MS chromatogram analysis shows the volatile compounds specific to experimental gels: Oregon(r) essential oil (O), Thieves(r) essential oil (T), and Frankincense(r) essential oil (F).
## 7 Results and Analysis
### Sensitivity Analysis
#### 7.2.2 Bus Passenger Capacity  
A total of 178 sets of examples were classified according to the passenger capacity of the buses on the arterial road, and the average values of per capita delay, per bus delay, and per car delay of each type of example were calculated, respectively. Based on the average value of the first type of examples, the reduced value of per bus delay, the increased value of per car delay, and the difference between the two were calculated. The results are shown in Figure 18.  
Within the range of 5 to 35, with the increase in bus passenger capacity, the per capita delay and bus average delay decreased significantly, with a decrease of 14.3% and 15.2%, respectively, and the average delay of cars increased, with an increase of 7.7%. The difference between the decrease in the average delay of buses and the increase in the average delay of cars was greater than 0 and showed an increasing trend, indicating that the decrease in the average delay of buses was greater than the increase in the average delay of small cars, and, as the bus passenger capacity increases, the model in this paper was more effective in reducing the average delay of buses.
## 3 Results and Discussion
### Evaluation of Mechanical Properties Changes
930.1 MPa \(\rightarrow\) 1433.8 MPa) after 2 h due to the increase in the amount of fine secondary \(\alpha\)-phase precipitation caused by the fast diffusion rate at the relatively high temperature, with the highest hardness and compressive yield strength obtained at 24 h, showing increases of 32.7% and 54.1%, respectively. For the 550 \({}^{\circ}\)C case, the hardness and compressive yield strength increased rapidly (304.2 HV \(\rightarrow\) 349.6 HV; 915.0 MPa \(\rightarrow\) 1110.5 MPa) for aging times from 2 h to 16 h and gradually increased to 24 h, with increases of 14.9% and 21.4%, respectively. However, this condition showed lower hardness and compressive yield strength compared to the 500 \({}^{\circ}\)C condition. This was due to the rapid phase separation of \(\beta\rightarrow\beta+\beta^{\prime}\) and \(\beta^{\prime}\rightarrow\) secondary \(\alpha\) phase during the short aging time at 500 \({}^{\circ}\)C and above, as mentioned earlier. Moreover, the higher temperature caused the secondary \(\alpha\) phase to grow, resulting in a softening effect [19]. As a result, as the aging temperature and time increased, the amount of precipitation of the secondary \(\alpha\) phase increased, which improved the mechanical properties; however, in the case of 550 \({}^{\circ}\)C, the secondary \(\alpha\) phase coarsened because of the relatively high temperature, which caused a degradation in the mechanical properties. To analyze the changes in compressive elongation with aging, the compressive stress-strain curves of the aged specimens were obtained and are presented in Figure 8. In the 500 \({}^{\circ}\)C and 550 \({}^{\circ}\)C cases, the elongation decreased somewhat (500 \({}^{\circ}\)C: 36.0% \(\rightarrow\) 31.9%; 550 \({}^{\circ}\)C: 44.0% \(\rightarrow\) 39.5%) with the increase in aging time from 16 h to 24 h. This was due to the precipitation of the secondary \(\alpha\) phase into the \(\beta\) matrix, which reduced the ductility with increasing strength. Under the same aging time of 24 h, the elongation decreased (450 \({}^{\circ}\)C: 39.1% \(\rightarrow\) 500 \({}^{\circ}\)C: 31.9%) because of the increase in the amount of secondary \(\alpha\) precipitation as the aging temperature increased from 450 \({}^{\circ}\)C to 500 \({}^{\circ}\)C. On the other hand, as mentioned earlier, the elongation at 550 \({}^{\circ}\)C actually increased to 41.6% owing to the softening effect caused by the higher temperature. Figure 8: Compressive stress–strain curves of Ti-3 Al-8V-6 Cr-4 Mo-4 Zr alloy according to aging temperatures; (**a**) 400 \({}^{\circ}\)C, (**b**) 450 \({}^{\circ}\)C, (**c**) 500 \({}^{\circ}\)C, and (**d**) 550 \({}^{\circ}\)C.
## 3 Obstacle Avoidance Scenario
### Obstacle Avoidance Techniques
#### 3.4.2 Artificial Potential Field Algorithm  
The artificial potential field algorithm (APF) was proposed by [PERSON] (2005), a unique real-time obstacle avoidance approach for mobile robots. This algorithm sets an artificial potential field to every point of its known area and starts moving towards the lower possible area, where the target point is the lowest possible area. The vehicle is always attracted to the lowest possible area, to eventually reach the target point. In Figure 10, a robot is avoiding an obstacle using the potential field. [PERSON] et al. (2016) used the APF algorithm to avoid other obstacles and forming obstacles. Besides that, the authors used APF in multiple connected vehicles to make a suitable path.  
APF algorithm has some limitations, such as the local minimum point problem and dead point problems. [PERSON] et al. (2017) reconstructed the APF to constrained optimization to solve the traditional dead point problem. Since the traditional algorithm only works for a single-vehicle trajectory, [PERSON] et al. (2017) proposed an optimized APF algorithm for  
Figure 9: Trajectories of the Bug1 (**a**) and Bug2 algorithms (**b**) ([PERSON] and [PERSON], 2017).  
multi-UAV operation in a 3D environment. [PERSON] et al. [299] improved the APF algorithm by solving some inherited problems, such as the local minima and the target's inaccessibility.
## Materials and Methods
### Characterization Techniques  
The prepared materials' X-ray diffraction (XRD) patterns (10-30 mm thick) were obtained at Bragg-Brentano geometry, using a water-cooled Rigaku Ultima+ (company: Rigaku Corporation, Tokyo, Japan) diffractometer with CuKa radiation, a step size of 0.05\({}^{\circ}\) and a step time of 3 s, operating at 40 kV and 30 mA.  
An Instron 3344 dynamometer (company: Instron, Norwood, MA, USA) with a crosshead speed of 50 mm/min was used to perform tensile tests on neat SCB-PE and its nanocomposites, according to ASTM D638-14. The specimens were dumb-bell-shaped (central portions 5 \(\times\) 0.8 mm thick, 22 mm gauge length) and cut in a Wallace cutting press. The extracted results were averaged from at least five samples' measurements to obtain the mean values of the elastic modulus, tensile strength at yield and breakpoint, and elongation at break.  
Fractography studies were conducted via scanning electron microscopy (SEM) on all the prepared materials using the Jeol ISM-7610F Plus scanning microscope (company: JEOL Ltd., Tokyo, Japan), equipped with an AZTEC ENERGY ADVANCED X-act EDS Oxford analytical system (company: Oxford Instruments, Abingdon, UK), operating at 20 kV, probe current 45 nA, and counting time 60 s.  
The influence of ball milling and graphene nanoplatelets on the shear strength of SCB-PE and SCB-PE/M5 specimens was evaluated according to ASTM D 732-17. A universal testing machine (company: Testometric, Rochdale, UK) has been used, equipped with a standard 15 mm flat design cylindrical puncture head. A die cavity with a mean diameter of 15 mm was achieved in each sample by shearing it against two pieces of metal. Prior to each shear test, the puncture head was lubricated. Shear tests were conducted with a crosshead speed of 5 mm/min at ambient temperature. Each test specimen was mounted on a metal fixture and punctured at the center with a punch tool. The shear strength, the energy to shear, and the SCB-PE nanocomposites' ductility with and without ball milling were measured three times.
## 4 Experiments  
### Research Data and Experiment Configuration  
The Shanghai-Nanjing Expressway has one of the busiest traffic flows with complicated conditions in eastern China [37]. Since the trend of \"same city\" in the Yangtze River Delta area is very obvious, this paper selected the Nanjing-Changzhou section of the Shanghai-Nanjing Expressway as the study area and selected the distribution of vehicles departing from Nanjing and passing through 7 exits (Figure 3) during the period from 1 December 2015 to 30 December 2015. To form the intercity expressway traffic, we only selected the vehicles entering directly from Nanjing at each exit. The original data contains information about each vehicle passing through the exit, so the volume is huge. To reduce the data volume, we accumulated the number of vehicles that passed each exit in one hour. Then, we had the hourly time series for each exit. The overall sample points for each exit are \(31(\text{days})\times 24(\text{hours})=744\) (time points).  
Based on the distribution of each exit in the original data and the road topology, the adjacency matrix between each exit was constructed. The quantum walk simulation experiment was conducted based on the adjacency matrix. The control parameters of the quantum walk were mainly \(\Delta k\) and \(\alpha_{i}\), which determined the time interval of the simulated unit time through the exit of the vehicle, thus affecting the time scale characteristics of the simulated time series (length and number of cycles, etc.) over a long time period. To this end, we traversed different \(\Delta k\) ways to obtain the value of the optimal control parameter \(\Delta k\) (for a detailed analysis, see discussion in Section 5). For this, \(\Delta k\) was gradually increased from 0 to 1 at the interval of 0.01, by comparing the statistics of different \(\Delta k\) values, the best value of \(\Delta k\) was determined. The probability sequence obtained by the simulation  
Figure 3: Research area and exit distribution.  
was subjected to calibration, which was mapped into the total sequence of real observation data; the model performance and statistics were calculated, and the residual analysis was performed. To further demonstrate the performance of the quantum walk, the classical random walk method was used at the same time for comparison. The connection degree of the classical random walk was also determined by the traverse method. In order to further reveal the regularity of the residual part, the autoregressive moving average model was used to fit the residual sequence. Finally, the whole experiment was completed to verify the RQTM.  
For the performance of RQTM, this paper used three statistical indicators to evaluate it, including the mean absolute error (MAE), the root mean square error (RMSE), and the coefficient of determination (R\({}^{2}\)). The definition of these statistical indicators is listed in Table 1. In Table 1, \(y_{i}\) is the real observation data, \(\overline{y_{i}}\) is the average of the real observation data, and \(\hat{y_{i}}\) is the modeling data.
## 3 Results
### Effect of DOM  
The relationship between heavy metals and dissolved organic matter (DOM) is complex and multifaceted [79; 80]. The interaction between soluble organic molecules and metals occurs through processes such as anion exchange, ligand exchange, cationic overlapping, Van der Waals forces, and adsorption [81]. Numerous heavy metals exhibit a pronounced attraction for dissolved organic matter (DOM) and form metal complexes, potentially disrupting the adsorption process. The mobility of metals, their elimination rate, and their ability to adhere to adsorption sites are directly affected by the concentration of dissolved organic matter (DOM) [80]. In order to examine the influence of DOM on the process of lead removal, humic acid doses of 1, 3 and 6 ppm, were employed during the adsorption of lead using DAC. The metal removal rates for these experiments are depicted in Figure 14.  
The examination of dissolved organic matter (DOM) concentration on lead removal efficiency by DAC reveals noteworthy patterns. Generally, as the DOM concentration increases, there is a corresponding rise in lead removal efficiency, especially evident at higher adsorbent doses. Strikingly, the 3 ppm level of DOM consistently yields the highest lead removal efficiency across various doses of DAC. Ninety percent removal was observed across all adsorbent doses considered. Moderate DOM levels enhance the adsorption of lead by forming stable complexes with the lead ions [82], which are subsequently adsorbed onto the surface of the DAC. The DOM levels within this limit could also help remove  
Figure 14: Lead removal efficiency (%) at 1, 3, and 6 ppm of dissolved organic matter by DAC.  
interfering hydroxylated lead species; thus, more Pb\({}^{2+}\) adsorption sites are available. The paired \(t\)-test analysis revealed no significant variation in Pb removal rates between 1 and 6 ppm of DOM (\(p\) = 0.96 \(>\) 0.05). However, the removal rate at 3 ppm of DOM was significantly different from that at 1 and 3 ppm DOM (\(p\) = 5.23 \(\times\) 10\({}^{-6}\) to 0.0003 \(<\) 0.05).  
At a concentration of 1 ppm DOM, removal efficiency was below average and less competitive. It was also anticipated that the DOM concentration at this level would not be sufficient to create stronger complexes with Pb\({}^{2+}\) ions, as there seems to be reduced occupancy in the active sites on the DAC. This is the same phenomenon exhibited at 6 ppm DOM, where the % removal was lower than that of the 3 ppm adsorbent dose. This decline may be attributed to DOM oversaturation, which hinders adsorption and competes for the active adsorbent surfaces, thereby limiting the adsorption of Pb ions. Increasing the adsorbent dose from the range of 50-60 g/L proved to be effective at all concentrations of DOM, indicating an increased availability of active sites and an enhanced surface area. This observation underscores the significance of the 3 ppm DOM concentration in optimizing the effectiveness of these activated carbons for lead removal.
## 4 Discussion
### Comparison of Reflectance Factor Conversion Methods  
The orthomosaics produced using different methods do not allow true numerical analysis of the performance of each method but depict clearly the varying types and magnitudes of errors associated with them. Firstly, the orthomosaic with reflectance reference panel method (Figure 7a) shows the problems typically present in UAV data acquired in varying cloud conditions. This method was not able to fix variations in the illumination and thus the parts of the area mapped during clouds show up as dark patches. Based on ASD FieldSpec irradiance data (Figure 6), it can be estimated that even the seemingly uniform areas experience errors of approximately \(\pm\)5% due to variations in illumination. Secondly, the orthomosaic with unstabilized on-board sensor (Figure 7b) was able to mostly fix the clouded areas but suffers from other problems. As the data is noisy due to UAV tilt effects, the orthomosaic continuously shows both overly dark and overly bright patches, especially in cloud-free areas. In addition, as the automated filtering of partially clouded images was not feasible, and manual filtering was considered too subjective for this study, these areas of change show large errors to both positive and negative directions. Thirdly, the orthomosaic with an on-ground spectrometer (Figure 7c) shows visually consistent results. Due to the fact that the spectrometer was not located in the same place as the observed targets, the spectrometer irradiance readings have a temporal offset growing with distance along the direction of cloud movement and uncorrelation growing with distance in cross-wind direction. However, as the mapped area was quite small and the cloud-edge images were filtered out, this was not causing visible problems in this dataset. Fourth, the orthomosaic with AIRS method (Figure 7d) also shows visually consistent results. The mosaic is very similar to the on-ground spectrometer mosaic with only minor variations in the filtering of the partially clouded images.  
The AIRS-approach can be valuable especially in forest applications when it is difficult to find open places for the panels (see e.g., [25]). In addition, it is useful in real-time applications, as the irradiances collected by AIRS can be used directly to transform the image radiances to reflectance factors. AIRS data could also be used as the input to the radiometric block adjustment method [6]; the advantage of this approach is that it could compensate for the remaining uncertainties. Furthermore, the BRF model calculation could be integrated into the same process to generate uniform mosaics.
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
## 4 Results and Analysis  
In the detection process, the ultrasonic transducer was a point focus probe with a frequency of 2.5 MHz, a diameter of 12 mm, a focal length of 20 mm, and a focal spot of 2 mm. The diameter of the thin-walled tube was 37 mm, the length of the detection area was 255 mm, and the rotation speed was 320 rpm. Thus, 0.1875 s was required for the tube to rotate once. Based on the single-channel sampling frequency of 0.75 kHz, the number of points sampled during the rotation of the tube was calculated to be 140. The total sampling length was 20,800 points. For the longitudinal defect with a 10-mm length (i.e., the defect distributed along the axis), the theoretical detection period was 10/1.72 = 5.81 (where 10 is the length of longitudinal defect, in-unit of mm; 1.72 is the pitch; that is, the axial interval of the helix formed by ultrasonic transducer scanning along the circumferential direction of the thin-walled tube, in-unit of mm; 5.81 is the number of defect waves obtained by the ultrasonic transducer scanning the longitudinal artificial defect with 10 mm length, in-unit of cycle). For the transverse defect with a 10-mm length (i.e., the defect distributed along the circumference), the theoretical detection period was 12/1.72 = 6.98 (where 12 is the diameter of the transducer, in-unit of mm; the transverse defect is distributed along the circumference of the thin-walled tube, and the transducer is also spiral-scanned along the circumference of the thin-walled tube, so the number of cycles scanned by the transducer for the transverse defect depends on the ratio of the diameter of the transducer to the pitch; 1.72 is the pitch, in-unit of mm; 6.98 is the number of defect waves obtained by the ultrasonic transducer scanning the transverse artificial defect with a 10-mm length, in-unit of cycle). The actual number of defect detection cycles is shown in Figure 7a-h. The number of transverse defect detection cycles was seven, and the number of longitudinal defect detection cycles was 8-10. The actual detection period of the longitudinal defect was larger than the theoretical value, while that of the transverse defect was equal to the theoretical value. This was due to the different scanning and defect distribution directions.  
Figure 7: _Cont._