esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Cyclic Shear Performance of the Four-Way Geogrid Reinforcement-Soil Interface
## 4 Conclusions  
This paper investigates the effects of normal stress and shear displacement amplitude on the cyclic shear performance of the geogrid-soil interface through cyclic shear tests. The main conclusions can be drawn as follows.  
1. The results indicate that the amplitude of shear displacement is a key factor affecting the development of hysteresis loops. When the shear displacement is less than 1.5 mm, the hysteresis loops have a long pike shape throughout the cyclic process. However, when the shear displacement is greater than or equal to 1.5 mm, the shape of the hysteresis loops at the geogrid-soil interface changes significantly with an increase in cyclic cycles. At the early stage of the test, the shape is similar to a long ellipse. As the number of cycles increases, the hysteresis loop bulges out in the middle, taking on a shuttle-like shape with two sharp ends and a wide center. As the number of cycles continues to increase, the middle bulging section becomes fuller, resulting in a hysteresis loop that resembles a parallelogram.
2. The curves depicting the change in shear stress and shear displacement at the interface between the geogrid and soil can be divided into three stages based on their characteristics: strain hardening, hardening and softening differentiation, and stabilization. It is observed that the number of cycles corresponding to the peak average shear stress at the interface decreases with an increase in the normal stress. Under different rules of change, the peak average shear stress, shear stiffness, and damping ratio were measured in high and low normal stress conditions.
3. The area of the hysteresis loop increases as the shear displacement amplitude increases. Under the same number of cycles, a larger shear displacement amplitude corresponds to a smaller shear stiffness and a larger damping ratio.  
Due to the limitation of time and experimental conditions, a more in-depth study of the four-way geogrid reinforced soil interface is not carried out in this paper. Future scholars can also study the interaction between large particle sizes and four-way geogrids, the characteristics of the reinforcement-soil interface under vertical cyclic loading, and the influence of the grid node structure on the characteristics of the reinforcement-soil interface.  
Methodology, M.Z.; Investigation, X.R.; Writing--original draft, X.R.; Supervision, L.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data are contained within the article. Conflicts of Interest: Authors [PERSON] and [PERSON] were employed by the company Shanghai Road and Bridge Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
## 3 Results  
### Model Parameters for Chlorophyll a  
Based on the measurements of the concentration of chlorophyll a C\({}_{\rm chl}\), turbidity C, lake depth h, and the reflectance \(R\)... for wavelengths: 665, 705, 740, and 842 nm, the model parameters were determined (14). Due to the large number of parameters:  
\[a_{0}\,\ a_{3}\,\ a_{b,\ 665}\,\ k_{665}\,\ a_{4}\,\ a_{b,\ 705}\,\ k_{705}\,\ a_{5}\,\ a_{7}\,\ a_{11}\,\ a_{12}\,\ a_{13}\,\ a_{15}\]  
the two-stage procedure was introduced. At first, a preliminary estimate of the parameters: \(a_{0}\,\ a_{3}\,\ a_{4}\,\ a_{5}\,\ a_{7}\,\ a_{11}\,\ a_{12}\,\ a_{13}\,\ a_{15}\) was produced for the logarithm of \(r\) (4) using the least square method, i.e., minimizing the sum of squares of deviations between the logarithm of the measured chlorophyll concentration C\({}_{\rm chl}\) and the logarithm of the concentration calculated from model (4). Then, other model parameters were found, assuming the parameter values previously determined for model (4) for the calculations. All parameters were determined by minimizing the sum of squared deviations between the measured C\({}_{\rm chl}\) concentration and the one calculated from model (14); the correlation coefficient was 0.944. The values of the model parameters (14) are summarized in Table 2, col. 2. The model fit is shown in Figure 14. If turbidity was calculated from model (17), the parameters of model (14) would have been provided, as shown in Table 2, col. 3. The correlation coefficient was 0.925. The fit of model (14), while using model (17), is shown in Figure 15. In both cases, the model's fit to the measurements was good. If there is no detailed data on turbidity and the parameter shows little variability, the average value of turbidity can be used in the calculations.
# A 3D Object Detection Based on Multi-Modality Sensors of USV
## 3 Method
### Proposal Generation Network
#### 3.2.3 Proposal Generating  
* **3D Box Description:** In two-stage 2D object detection, the proposal is generated from a series of prior boxes. In [6; 12], an encoding method of 3D prior boxes is proposed. Each 3D prior box is parameterized to (x,y,z,l,w,h,r), where (x,y,z) is the center of the mass coordinate of box, (l,w,h) is the size of box, and (r) is the direction of the box. The prior box is generated from BEV. The sampling interval (x, y) is 0.5 m, while the ground height of the sensor determines parameter z.
* **Crop and Resize Instead of Roi Pooling:** We chose to use crop and resize [12; 29] to extract the box's corresponding feature map from a particular view instead of the ROI-pooling. Because ROI-pooling uses nearest neighbor interpolation, it may lose spatial symmetry. nearest neighbor interpolation means that ROI-pooling adopts rounding, which is equivalent to selecting the Nearest point to the target point when the scaled coordinate cannot be an exact integer. To keep the symmetry of the space, crop and resize uses bilinear interpolation to resample an image to a fixed size.
* **Drop Path:** In the fusion of RPN stage, we added the drop path method. The extracted feature channel is randomly discarded. Then, the elements were evenly fused. fractalnet used drop path method to normalize collaborative adaptiveneutron paths in fractal structures [PERSON] et al. [26]. Through this normalization, it is able to prove that the answer given by the shallow subnetwork is faster, while the answer given by the deep subnetwork is more accurate.
* **3D Proposal Generating:** We used \(1\times 1\) convolution instead of the full connection layer and then used a multi-task loss to classify the object/background and computed the regression of proposal boxes. To sort out the background/object, we used cross-entropy loss, while, for the regression loss of the proposal boxes, we selected smooth L1 loss. When computing boxes regression, we ignored the background anchor point. The background anchor point is determined by the IoU overlap between ground truth and the anchor point in BEV. Overlap above 0.7 is considered as the background. If the overlap is less than 0.5, it is considered as the target. To to redundancy, Non-Maximum Suppression (NMS) is applied in BEV with a threshold of 0.8.
## 5 Dynamics of Jack-Up Platform and Seabed Foundation  
### Initial State  
In the long geological history, the original seabed must have undergone a consolidation settlement process under the self-weight and the hydrostatic pressure. After the jack-up platform is installed on the seabed foundation, under the gravity of the jack-up platform structure, the structure-seabed foundation system will reach a new equilibrium state. This equilibrium state should be used as the initial state to analyze the dynamic response of the jack-up offshore platform and its seabed foundation under environmental loading.  
In order to display the distribution of pore pressure and effective stress in the initial state more clearly, the computational results are shown on several sections in the 3D computation domain here. Figure 11 shows the distribution of pore pressure, effective  
Figure 10: Time history of the wave-induced resultant force on the No. 1 and No. 2 pile legs of the jack-up platform under different wave conditions.  
Figure 9: Time history of the wave impact on the different positions on the pile legs 2 m above the static water surface in Case 1 (\(H=7\) m, \(d=50\) m, \(T=15\) s).  
stress, and shear stress in the jack-up platform and its seabed foundation in the initial state considering the self-weight and hydrostatic pressure. It is observed that the distribution of pore pressure is layered in the seabed foundation. There is only hydrostatic pressure and no excess pore pressure at all. Since the offshore platform is made of completely impermeable steel, the pore pressure in the platform and pile legs is zero. From the distribution of \(\sigma_{x}^{\prime}\) and \(\sigma_{z}^{\prime}\), it can be seen that the influence of the dead weight of the jack-up platform structure on the distribution of effective stress in the seabed foundation is limited in a small region around the pile legs, and the effective stress is also distributed in layers in the seabed foundation far away from the platform structure. From the distribution of the shear stress \(\tau_{xz}\) and \(\tau_{yz}\), it can be seen that the shear stress in the seabed foundation near the pile legs is significant, and there are shear stress-concentrated zones with a maximum magnitude exceeding 20 kPa.  
Figure 11: Distribution of pore pressure, effective stress, and shear stress in the offshore platform and its seabed foundation in the initial equilibrium state.  
Figure 12 shows the displacement distribution of the jack-up platform and seabed foundation in the initial state. It is observed that there is only the horizontal displacement with the magnitude of millimeter inside the seabed foundation under the gravity of the platform structure. This magnitude of horizontal displacement is actually very small. For such a large-scale platform structure, it basically could be ignored. However, in the vertical direction, the initial settlement of the platform structure is significant, reaching 120 mm.
## 4 Results and Discussion  
### Disengaging Duration  
The variation of gaps in the disengaging process with different ATF temperatures is shown in Figure 5a-e. It can be found that the friction pair gaps first increase slowly, then increase rapidly and finally stabilize after fluctuation during the disengaging process. In addition, the first friction pair gap \(\delta_{1}\) fluctuates most dramatically and lasts the longest. Accordingly, the disengaging process is completed after the stabilization of the first friction pair gap. The disengaging moment \(t_{3}\) with different ATF temperatures is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(A_{\text{red}}\) & 0.68 & \(m_{0}/\text{(kg)}\) & 3 & \(\beta/\text{(m)}\) & \(8\times 10^{-4}\) \\ \(c_{s}/\text{(N\cdot s/m)}\) & 0.0714 & \(m_{1}/\text{(kg)}\) & 0.45 & \(\mu_{\text{spline}}\) & 0.1 \\ \(d_{m}/\text{(m)}\) & \(6\times 10^{-4}\) & \(m_{2}/\text{(kg)}\) & 0.6 & \(\sigma/\text{(m)}\) & \(8.4\times 10^{-6}\) \\ \(E^{\prime}/\text{(Pa)}\) & \(4.84\times 10^{9}\) & \(M_{R}/\text{(N\cdot m)}\) & 240 & \(\Psi/\text{(m^{2})}\) & \(2\times 10^{-12}\) \\ \(H_{fd}/\text{(m)}\) & \(2\times 10^{-3}\) & \(N/\text{(m^{-2})}\) & \(7\times 10^{7}\) & \(\kappa\) & 1.2332 \\ \(H_{sd}/\text{(m)}\) & \(3.2\times 10^{-3}\) & \(R_{i}/\text{(m)}\) & 0.086 & \(\rho/\text{(kg/m^{3})}\) & 875 \\ \(l_{f1}/\text{(kg\cdot m^{2})}\) & 2 & \(R_{o}/\text{(m)}\) & 0.124 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input data of the simulation.  
Figure 4: Variations of the hydraulic pressure and rotating speeds.  
Figure 5: Variations of gaps and the NUC in the disengaging process with different ATF temperatures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **T1** & **T2** & **T3** & **T4** & **T5** \\ \hline _T/_(\({}^{\circ}\)C)_ & **60** & **80** & **100** & **120** & **140** \\ \hline \(\delta_{1}\)/(mm) & 1.6330 & 1.5822 & 1.5950 & 1.6034 & 1.6006 \\ \(\delta_{2}\)/(mm) & 0.3852 & 0.3962 & 0.3926 & 0.3926 & 0.3956 \\ \(\delta_{3}\)/(mm) & 0.3094 & 0.3200 & 0.3173 & 0.3162 & 0.3179 \\ \(\delta_{4}\)/(mm) & 0.2467 & 0.2572 & 0.2548 & 0.2527 & 0.2526 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation results with different ATF temperatures.  
As the ATF temperature increases from 60 \({}^{\circ}\)C to 140 \({}^{\circ}\)C, the disengaging moment is advanced from 1.745 s to 1.610 s, and the corresponding disengaging durations are 0.245 s, 0.174 s, 0.14 s, 0.121 s, and 0.11 s, respectively. Therefore, the increase of ATF temperature significantly shortens the disengaging duration (shortened by 55.1%), but its influence is getting weaker and weaker.
# Evaluation of Different Chemical Mechanisms on O3 and PM2.5 Predictions in Alberta, Canada
## 3 Results and Discussion
### Summer  
When the CB6R3 case was compared to the SAPRC07 and RACM2 cases, both SAPRC07 and RACM2 produced 1%-3% less O3 in low NOx and low VOC areas (the most-eastern selected region is shown in Figure 2a). In high NOx and VOCs areas such Grande Prairie (the most-western selected region), SAPRC07 and RACM2 produced up to 6% less O3 than CB6R3, as observed in Figure 5b,c. CB6R3 does seem to be the better performer in this region (Grand Prairie, Beaverledge), as seen in Figure 6. In regions of low NOx/moderate VOCs (the second-most-western selected region), there is a similar but reduced discrepancy in predicted O3 by both SAPRC07 and RACM2 relative to CB6R3, while regions with moderate NOx/low VOCs (continuing eastward to the next selected region) show a greater reduction in this discrepancy. This implies that CB6R3 produces higher levels of O3 in the presence of higher VOCs and NOx levels, relative to the SAPRC07 and RACM2 mechanisms, while producing similar levels of O3 amongst all mechanisms in the presence of low NOx and VOC levels. The heightened sensitivity to VOC levels does support the argument that the additional ketone chemistry in the CB6R3 model is an important pathway for O3 production.
# Scaling of FAPAR from the Field to the Satellite
## 2 Study Area and Data
### Measurement and Data Preparation
#### 2.2.2 Satellite Data and Preprocessing  
MODIS FAPAR product is among the most popular biophysical products in environmental studies [38]. It is generated through a look-up-table algorithm to retrieve FAPAR parameter from a three-dimensional radiative transfer model [8]. Numerous efforts have been exerted on validating MODIS FAPAR product (e.g., [13; 27]) or comparing it with other global products (e.g., [5; 29]). The selected MODIS FAPAR products in Table 1 were extracted, scaled and rectified to UTM projection, WGS-84 coordinate system using MODIS re-projection tool (MRT). To facilitate the multi-scale analysis starting from ASTER data at 15 m resolution, the MODIS data were resampled to 960 m resolution using nearest neighborhood algorithm.  
ASTER L1B data at 15 m resolution provides the bridge between field measurements with small support area and coarse-resolution data with large pixels. By extending the field measurements to the ASTER data at 15 m resolution, the ASTER FAPAR can be aggregated to the resolution of 960 m that are spatially comparable to MODIS data. A series of processing were performed to ASTER L1B images sequentially, including radiometric calibration, atmospheric correction and geometric correction. The software of the second simulation of a satellite signal in the solar spectrum (6S) [39] and MODIS aerosol products were used for atmospheric correction, as well as solar zenith angle and azimuth angle data in ASTER metadata file.  
Geometric registrations were performed on ASTER data and further MODIS product, to ensure the geometric comparability among the multi-scale satellite data. The SPOT-5 image acted as the base map, while the classification data at 1 m resolution [40] was used to testify the geometric co-registration of the multi-source data. By selecting control points, e.g., crossovers of roads and rivers, the registration accuracy of ASTER data was carefully controlled within 2 m, while that of MODIS products was within 15 m.
## 5 Experimental Results
### Analysis of Results
#### 5.3.2 Compared with Original Scheduling Program  
The port adopts a manual scheduling mode based on the principle of \"first come, first served\", which results in a berth deviation cost of CNY 3820, a total scheduling time of 311,655 min, and a berth utilization rate of 26.94% for the scheduling scenario in the experimental planning period.  
The comparison between the optimized NSGA-III and the original scheduling program results is shown in Figure 4. The optimized NSGA-III program output the following optimal result: for \(f_{1}\), the berth preference cost was CNY 2643; for \(f_{2}\), the total scheduling time was 268,867 min; for \(f_{3}\), the berth utilization rate was 0.0292 (approximately 34.25%). This comparison indicates that the optimized NSGA-III \(f_{1}\), \(f_{2}\), and \(f_{3}\) values were 30.81%, 13.73%, and 12.11% better, respectively, than those of the original program.
## 2 Results
### Decay Control of VOCs Produced by BCN2 and BCN10 in Natural Postharvest Storage of Loquat Fruits  
As shown in Table 3, the VOCs generated by both _B. methylotrophic_ BCN2 and _B. thuringiensis_ BCN10 had a strong inhibitory effect on the natural decay of loquat fruit. The treated loquats began to rot on the fifth day while the first rotten loquat found in control groups appeared on the second day, which means the VOCs from the two _Bacillus_ strains enabled the loquat to keep fresh for more than 3 days at 25 \({}^{\circ}\)C and 85% RH. And on the tenth day, the disease incidence of the combined _Bacillus_ group was 20.19%, which would still be low, compared to 54.17% of the control group. Overall, VOCs produced by BCN2 and BCN10 had an obvious inhibitory effect, while combined strains performed slightly better than BCN2 or BCN10 individually. Compared to the single bacteria treatment, the disease spots on the fruit were smaller, and the fruit was harder in the treatment group of the combined strains. Until now, there have been few good methods available to preserve loquat fruit. Several plant essential oil components have been reported to be a potential natural preservative for fruits or other foodstuffs [20]. A previous study [21] showed that essential oil components like citral and carvacrol at 250 ppm could exhibit a fungistatic activity and inhibit mycelium growth. However, in our attempt to use essential oil as a positive control, some turned out to be not effective enough in controlling postharvest pathogens from loquat fruit. Figure 2: Disease incidence (percent of rotten fruits) of loquat fruit with the treatment of volatile organic compounds produced by two _Bacillus_ strains. The loquats were artificially inoculated with _Fusarium oxysporum_ (**a**), _Botryosphaeria_ sp. (**b**), _Penicillium expansum_ (**c**), _Trichoderma atroviride_ (**d**), and _Colletotrichum gloeosporoides_ (**e**) at 25 \({}^{\circ}\)C and 85% RH. * _p_\(<\)0.05, **_p_\(<\)0.01, ***_p_\(<\)0.001 vs control, according to [PERSON]’s HSD test.
## 4 Data Analysis
### Partial Least Square-Structural Equation Modeling (PLS-SEM) Analysis  
The Partial Least Square-Structural Equation Modeling (PLS-SEM) analysis tool was employed in this investigation. The researchers utilized SmartPLS version 3.3.6 to examine the data they had collected. PLS-SEM is acceptable for both complicated and elementary models [134]. The adoption and execution of E-procurement technology on green procurement is explained in this study and is later depicted in a path diagram. All factors and indicators that were researched were linked in the path diagram. Following the presentation of the path diagram, an evaluation of the measurement model, i.e., outer model, was performed. This involved the tests used to attain the values for the convergent validity, discriminant validity (cross-loading), validity test for AVE values, composite reliability, as well as the structural model (inner model) evaluation, which incorporated tests for R-square and path coefficient and an assessment of the hypothesis.
## 3 Results and Discussion
### Free Radical Scavenging Activity  
1,1-Diphenyl-2-picrylhydrazzyl (DPPPH) is a stable nitrogen-centered free radical that may convert into 1,1-diphenyl-2-picrylhydrazzine, a diamagnetic molecule, upon one-electron reduction. DPPH has a strong absorption at 516 nm that decreases after the addition of reducing equivalents [37]. Experiments with sample concentrations in the range 5-100 mg/mL showed a decrease of DPPH absorbance (Figure 4); in particular, SLN IDE1.5 had the highest activity at 100 mg/mL that was significantly different from IDE-loaded NLC and SLN IDE1. The test showed greater antioxidant activity of IDE-loaded SLN compared to IDE-loaded NLC, thus confirming the results obtained with the potassium ferricyanide reduction method. The lower antioxidant activity of IDE-loaded NLC in comparison to IDE-loaded SLN could be due to the stronger interaction of IDE with the lipid matrix of NLC, which reduced IDE release from the nanoparticles and hence the antioxidant activity of the resulting colloidal system.  
Figure 4: Antioxidant activity of IDE-loaded SLN and IDE-loaded NLC (5-100 mg/mL) indicated as percent (%) of DPPH radical inhibition. The letters b and c indicate, for each concentration, a significant difference (\(p<0.05\).) between the samples loaded with 1% or 1.5% w/w of idebenone (IDE) and the samples of unloaded SLN and NLC (indicated with the letter a). NLC = unloaded nanostructured lipid carrier; SLN = unloaded solid lipid nanoparticles; NLC-IDE 1 = NLC loaded with IDE 1% w/w; SLN-IDE 1 = SLN loaded with IDE 1% w/w; NLC-IDE 1.5 = SLN loaded with IDE 1.5% w/w.  
Figure 3: Reducing power of SLN, NLC, and samples loaded with 1% and 1.5% w/w of idebenone (IDE). The letters b, c, and d indicate a significant difference (\(p<0.05\)) between samples loaded with different concentrations of IDE and the samples of unloaded NLC and SLN (indicated with the letter a). NLC = unloaded nanostructured lipid carrier; SLN = unloaded solid lipid nanoparticles; NLC-IDE 1 = NLC loaded with IDE 1% w/w; SLN-IDE 1 = SLN loaded with IDE 1% w/w; NLC-IDE 1.5 = NLC loaded with IDE 1.5% w/w; SLN-IDE 1.5 = SLN loaded with IDE 1.5% w/w.