esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Comparison
### Verification of the Problem in the General Closing Method  
For the sample bridge, the reaction force prior to the set back is examined. To compare the remaining reaction forces, the model for a self-anchored cable-stayed bridge (L-SE-1) is also analyzed. Table 4 shows the results of comparison. The remaining reaction forces are relatively small in bridge models with a self-anchored cable system (L-SE-1). However, the bridge model with a partially earth-anchored cable system shows relatively large reactions owing to the difference of axial forces between the left and right side girders of the restraint device (L-EA-6). During the construction sequence, in the bridge model with a partially earth-anchored cable system, the accumulated axial forces in the side span girder occurring via the inclined cables are different from those in the main span girders since some cables are anchored to the outside of the girders.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Stage** & **Description of Stage Actions** \\ \hline
1 &
\begin{tabular}{l} \(\Delta L\) = \(\alpha\times\Delta T\times L\); \(\alpha\) = thermal coefficient; \(\Delta T\) = temperature rise; \(L\) = heating length; \(\Delta L\) = elongation length). \\ \end{tabular} \\
2 &
\begin{tabular}{l} \(\Delta L\) = elongation length \\ \end{tabular} \\
3 &
\begin{tabular}{l} \(\Delta L\) = elongation length \\ \end{tabular} \\
4 &
\begin{tabular}{l} \(\Delta L\) = elongation length \\ \end{tabular} \\
5 &
\begin{tabular}{l} \(\Delta L\) = elongation length \\ \end{tabular} \\
6 &
\begin{tabular}{l} \(\Delta L\) = elongation length \\ \end{tabular} \\
7 &
\begin{tabular}{l} \(\Delta L\) = elongation length \\ \end{tabular} \\
10 &
\begin{tabular}{l} \(\Delta L\) = elongation length \\ \end{tabular} \\
11 &
\begin{tabular}{l} \(\Delta L\) = elongation length \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Construction sequence.  
The movements caused by the removal of longitudinal restraints are compared in Table 5. Owing to the release of the reaction forces, large movements occur in the bridge model with the partially earth-anchored cable system. Owing to tremendous movement, the process of reset back is impossible. In contrast, the bridge model with the self-anchored cable system shows relatively small movements.  
Therefore, it is difficult to apply the set back process for the partially earth-anchored cable system owing to the large movements in the process of set back. Thus, the classical closing method of the set back and reset back processes is not valid. In contrast, the new method does not remove the longitudinal restraints in a cantilever state but rather only after the connection of the key segment, which is in a continuous bridge state.
## 5 Discussion
### The Importance of Remote Sensing in Large-Scale NSP Intensity Mapping  
Because of the randomness, lag, and fuzziness of NSP, there are many problems in its mapping and quantification. Considering that the geographical databases of many countries and regions worldwide are imperfect and cannot meet the parameter requirements of the NSP estimation model, they cannot be applied to large-scale NSP intensity estimation [69]. Remote sensing technology can provide high precision geographic data for NSP intensity mapping. With the development of precision agriculture, cultivated land management is becoming more information-intensive, and the cultivation mode is becoming more complex [70]. To maximize the income of cultivated land, multiple cropping generally exists in countries and regions with less cultivated land per capita, which complicates the calculation of NSP intensity. Particularly among large-scale areas, the methods of cultivated land management differ substantially. The field measurement of NSP intensity is both time- and labor-intensive. With the characteristics of high speed, timeliness, and reliability, remote sensing technology has great advantages in large-scale target identification and mapping. The use of remote sensing technology can realize the  
Figure 13: Comparison of the areas in red and green squares demonstrates that the distributions of results calculated by the traditional and proposed method are obviously different. The values of the same grid position are also different. There are various crops in the same grid, but they are generally classified into one category by traditional method, resulting in NSP estimation errors (the grids are the NSP intensity calculation cells, and their dimension is 1 km\({}^{2}\)).  
fine classification and information mining of crops [71]. Remote sensing makes it possible to accurately estimate NSP intensity in larger scale areas.
## 3 Proposed Scheme
### Communication Round of the Proposed Scheme
#### 3.1.3 GTS Announcement Period  
The WPAN coordinator, after successfully receiving all the GTS requests, informs nodes about their slot allocation in the GTS announcement period (\(GAP\)). In addition, the PAN coordinator also informs about the total number of GTS assigned to the nodes, which helps nodes to know about the start of the next beacon frame. Each node is informed about its allocated slot by providing information about the starting slot and the number of requested slots assigned to the node. The duration of the GAP varies in each session and depends upon the number of GTS assigned to GTS-requesting nodes in each session. A complete GTS announcement frame is shown in Figure 3.
## Materials and Methods
### Imagery Data and Processing  
#### 2.2.1 Sentinel-2 Data  
Sentinel-2 satellites have high-resolution and multi-spectral information, and include 13 multi-spectral bands. Among them are four 10 m resolution bands, six 20 m bands, and three 60 m bands, with an orbital width of 290 km [38]. Because Sentinel-2 has multiple narrow bands in the visible and near-infrared ranges, it plays an important role in land use/cover [39], vegetation growth [40], water cover [41], and crop mapping [22].  
This study utilized GEE to obtain Sentinel-2 Level 1C archived data. When selecting images, it is important to consider the specific phenological period of winter wheat, and to shorten the interval time of different orbit images as much as possible. Therefore, in order to avoid the influence of snow cover on wheat spectrum, images were selected from March to May. In this stage, the leaves and stems grow rapidly after turning green, and the chlorophyll concentration of leaves increases significantly [42]. Therefore, the spectral information of winter wheat is more prominent, which is more conducive to the identification of winter wheat (Figure 2).  
After selecting the image collection with the smallest cloud cover from February to May, the quality band (QA60) (Table 1) can be used for the cloud mask to remove pixels with cloud cover, and finally to composite high-quality images without clouds. Considering the spectral difference and variation of different land covers in different bands, and to more accurately distinguish the difference between winter wheat and other vegetation, urban, water and other classes, all bands were input to SVM, RF, CART classification algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Band** & **Center Wavelength (nm)** & **Resolution (m)** & **Spectral Region** \\ \hline B1 & 443 & 60 & Coastal Aerosol \\ B2 & 490 & 10 & Blue \\ B3 & 560 & 10 & Green \\ B4 & 665 & 10 & Red \\ B5 & 705 & 20 & Red edge1 \\ B6 & 740 & 20 & Red edge2 \\ B7 & 783 & 20 & Red edge3 \\ B8 & 842 & 10 & Near infrared (NIR) \\ B8A & 865 & 20 & Red edge4 \\ B9 & 940 & 60 & Water vapor \\ B10 & 1375 & 60 & Cirrus \\ B11 & 1610 & 20 & Short -wave infrared spectral range 1 (SWIR1) \\ B12 & 2190 & 20 & Short -wave infrared spectral range 2 (SWIR2) \\ OA10 & — & 10 & — \\ QA20 & — & 20 & — \\ QA60 & — & 60 & Cloud mask \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spectral bands of the Sentinel-2 imagery.  
Figure 2: Wheat phenology and image acquisition date.
###### Abstract  
The objective was to compare pain relief in alveolar osteeitis achieved by the application of platelet-rich fibrin (PRF) and aspirin cones, and to assess the influence of both treatments on bacterial concentrations in post-extraction wounds. A split-mouth, single-center, non-randomized controlled trial was conducted in 30 patients. Pain relief was assessed using the visual analog scale at three time points (before, 24 h after, and 48 h after intervention). Bacterial concentrations were evaluated from cultures of swab samples collected at the three time points. A Wilcoxon matched-pairs signed-rank test was performed to detect differences in distributions of scalar variables between treatment with PRF and aspirin cones. Pain relief at 24 h and 48 h after treatment was significantly higher (\(p\) = 0.003) with PRF application than with aspirin cone placement. Additionally, PRF application caused a significantly higher reduction in aerobic bacteria concentration, an average increase, instead of a decrease, in lactic acid bacilli concentration in the first 24 h, and a significantly greater increase in streptococci concentration at 48 h follow-up. PRF provides better pain relief than aspirin cones in alveolar osteeitis. Bacterial concentrations in the extraction wounds are consistent with the pain relief achieved after PRF or aspirin application.  
alveolar osteeitis; dry socket; platelet rich fibrin; antimicrobial effect Article
## 4 Results
### Within-Case Analysis
#### 4.1.6 Case 6  
The warehouse considered was the one located in Landriano (PV) in the north of Italy (for further information on the 3 PL and warehouse features, see Table 3). Going forward into the interview, the warehouse features are discussed as follows:  
1. Location. This is considered the most crucial area in the interviewee's mind since it highly affects the operative cost (rated \"A\") but not the quality aspects of the logistics service provider nor the performance (rated, on the contrary, \"1\").
2. External Areas. This feature is not considered very attractive, as it is considered a \"must be\" element. The interviewee affirmed that wide parking areas could have a significant impact, but it is not comparable with the other features' relevance. Additionally, the external spaces, mainly consisting of loading/unloading bays, can play a fundamental role in increasing the productivity of the 3 PL asset (the only indicators that reach the rate \"O\").
3. Building. This category is considered important as the warehouse layout and size can profoundly affect 3 PLs' productivity, lead times, and costs (all rated as \"O\"). For example, a regular layout can facilitate the timing of processing and fulfilment of the order itself. Moreover, the importance of the building in shaping the 3 PL performance is also highlighted by its impact on flexibility (\"O\"). Indeed, according to the interviewee, the possibility of expanding or quickly adapting the warehouse layout could help to meet the changing customer needs, significantly improving warehouse utilisation (\"A\").
4. Internal Areas. The interviewee considered utilities very important; he pointed out how the costs concerning the electricity, water, and heating/freezing systems have enormous relevance regarding cost efficiency (\"M\"). Therefore, an excellent logistics building must have good equipment, contributing heavily to the business's financial sustainability.
# Numerical Simulation on the Leakage-Induced Collapse of Segmental Tunnels
## 1 Introduction
[23] conducted a numerical simulation to analyze the complete process of seepage erosion after tunnel joint leakage using a coupled CFD-DEM method. The influence of burial depth and soil size distribution on seepage erosion was investigated. [PERSON] et al. [24] investigated the ground subsidence due to pipeline leakage. In that model, the strata were simulated by DEM and the fluids were investigated by the Darcy flow formulas. From the above discussion, it can be seen that the DEM-based coupled fluid-solid analysis method is suitable for solving the seepage erosion problem and the subsequent soil deformation problem caused by it. Therefore, it can be applied to the simulation of the stratum in this research. In the process of structural collapse induced by leakage in shield tunnels, the deformation of tunnels is relatively small compared to the deformation of the strata in terms of scale. The main role of the tunnel is to provide an outlet for the soil, while the deformation of the structure itself has a small effect on the seepage erosion of the soil due to the stiffness. Therefore, when simulating the stratum response during a tunnel collapse, the details of the tunnel structure can be simplified without considering the effect of its deformation. For the tunnel, the response of the shield tunnel can be accurately analyzed by building an FEM tunnel model and applying the loads extracted from the strata model to analyze the tunnel collapse mechanism. In conclusion, decoupling the large deformation of strata and tunnel collapse is a more feasible simulation idea under the consideration of the strata-tunnel interaction. Based on the above discussion, a coupled fluid-solid strata model and a nonlinear FEM tunnel model were established for simulating the tunnel collapse process based on model test results [25]. After experimental validation, the strata response during tunnel collapse and the process of tunnel collapse were analyzed.
## 5 Conclusions  
This study presents an innovative approach that divides China's vegetation area into 10 zones, forming a novel product termed China Vegetation Functional Zones (CVFZ). The primary findings of this study are summarized below:  
* Firstly, CVFZ outperforms MCD12Q1 and CLCD, exhibiting superior performance in distinguishing vegetation with varying functions. Even in smaller zones, CVFZ can also well distinguish vegetation with different functions from the angle of LAI mean or the LAI time series. Although this study does not cover further measured tests, it indicates that the use of the VCA-MLC algorithm provides some taxonomic value in vegetation studies. The resulting CVFZ derivative product offers valuable information for climate and ecological monitoring and management.
* Secondly, the speed of greening of vegetation ranges from 9.02 \(\times\) 10\({}^{-4}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\) in shrubland subregions to 2.34 \(\times\) 10\({}^{-2}\) m\({}^{2}\)m\({}^{-2}\)yr\({}^{-1}\) in savanna subregions. In relative terms, the average greening speed of forests is moderate, and savannas tend to have the fastest average greening speed. The average greening speed of grasslands and crops with different functions varies widely. In contrast, the average greening speed of shrulands is the smallest.
* Thirdly, as the location of CVFZ shifts to a higher type within the same vegetation, the greening speed generally increases and then decreases, with the slope peaking around types 4-6, and the phenological cycle generally lags but tends to increase; for example, CVFZ-detected grasslands with one or two phenological cycles, broadleaf crops with one or two phenological cycles, and shrulands with one or not-so-obvious phenological cycles.  
T.X.: formal analysis and writing--original draft preparation. K.Y. ([PERSON]): conceptualization, methodology, writing--review and editing, funding acquisition, supervision, and project administration. Y.H.: validation and software. S.G.: visualization. K.Y. ([PERSON]): formal analysis. J.W.: investigation. J.L.: editing. Z.L.: resources. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China Major Program (42192580) and the National Natural Science Foundation of China (42271356).  
The data used in the study is publicly available.  
We thank the MODIS LAI & FPAR team for all their help and the team of [PERSON] and [PERSON] of Wuhan University for support with the CLCD dataset. We also appreciate the fruitful suggestions from the anonymous reviewers which made the work better.  
The authors declare no conflict of interest.
## 3 Model Characteristics and Calibration  
The hydro-mechanical coupling behavior of unsaturated geotechnical materials can be described using the granular temperature motion equation (Equation (10)), mean effective stress equation (Equation (17)), shear stress equation (Equation (18)), strain evolution equation (Equations (22) and (23)), and SWCC equation (Equation (24)). This model does not involve yield criterion, flow rule, hardening criterion, or other classical concepts in elastoplastic mechanics. Instead, thermodynamic concepts, such as granular temperature, migration coefficients, and energy functions, are replaced. The dissipation system and migration coefficients are determined theoretically, especially the dissipation at the mesoscopic level. The constitutive relationship of non-elastic deformation is obtained by thermodynamic identity, and through the migration coefficients and energy functions, the dissipation mechanism is connected with macroscopic mechanical behavior. These mean that the model can reflect the energy dissipation mechanism and the macroscopic mechanical properties induced by it, and the multi field coupling characteristics of unsaturated geotechnical materials in a complex environment can be described in a unified thermodynamic theoretical framework.  
The model involves some thermodynamic parameters, including \(\eta_{\
u}\), \(\eta_{s}\), \(\lambda_{sg}\), \(\lambda_{sg}\), \(b\), and \(\gamma\). However, it is not necessary to determine the value of each of the above coefficients, because the parameters required for the calculation of the model are \(c_{1}\), \(c_{2}\), \(c_{3}\), \(c_{4}\), and \(c_{5}\), and they can be calibrated through laboratory tests. For example, \(c_{5}\) can be calibrated through the stress relaxation test, that is, the stress applied to the sample decreases under the condition of zero strain, and the value of \(c_{5}\) can be calibrated by the time of stress relaxation; \(c_{2}\) and \(c\) can be determined by the stress-strain relationship at the critical state; \(c_{1}\) can be determined by the stress paths and stress-strain relationship of the undrained shear test; \(c_{3}\) is related to the volume deformation of soil, which can be calibrated through the isotropic compression test, and the value of \(B_{0}\) can also be calibrated; \(c_{4}\) can be determined by the wetting or drying test.
## 3 Results and Discussion
### Analysis of an Optimized Arrangement of Helmholtz Resonators
#### 3.3.2 Analysis of the Effect of the Optimized Solution on the Pressure at the Tunnel Wall  
The high-speed maglev train enters the tunnel at 0.03 s and an initial compression wave is generated at the front of the vehicle and propagates forward at the local speed of sound. The compression wave reaches the measurement point at 0.042 s, causing an initial increase in the pressure measured at the tunnel wall measurement point. At 0.055 s, the head car passes the measurement point, which decreases the measured pressure, whereas the tail car enters the tunnel and generates an expansion wave, which propagates forward at the speed of sound and reaches the measurement point at 0.0668 s. This in turn leads to a second drop in wall pressure values. At 0.0799 s, the tail car passes the measurement point, and thereby, the downward trend of the wall pressure curve ends. The measured value starts to rise until it returns to its initial state and stabilizes after a small oscillation.  
Monitoring and comparison of conventional and various new tunnel wall pressures (as shown in Table 4 and Figure 14) reveal that the new tunnel solution can effectively mitigate the increasing trend in tunnel wall pressure values. The best relief is realized in the 72-cavity new tunnel 1, where the wall pressure increases by only 4.36% when compared to the original tunnel. A comparison of the rise rates of the 64-cavity new tunnel and new 72-cavity tunnel 2 shows that the narrowing of the distance between the two cavities leads to an increase in the tunnel wall pressure and different combinations of cavities affect the tunnel wall pressure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Conventional Tunnel** & **94-Cavity New Tunnel** & **72-Cavity New Tunnel 2** & **64-Cavity New Tunnel** & **72-Cavity New Tunnel 2** \\ \hline
6149.85 Pa & 7176.44 Pa & 6417.83 Pa & 6655.52 Pa & 6779.94 Pa \\  & +16.69\% & +4.36\% & +8.22\% & +10.24\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of pressure variations at the tunnel wall for different scenarios.  
Figure 13: (a) 72-cavity new tunnel arrangement 1; (b) 72-cavity new tunnel arrangement 2; (c) 64-cavity new tunnel arrangement.
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 4 Results and Discussion
### Parameter Setting  
In this study, the optimization of SVM parameters \(c\) and \(g\) was conducted using PSO. By analyzing the data characteristics and testing repeatedly, the population size was finally selected as 25; the position and velocity of the initial population particles were set at \([0,1]\); this study set individual learning factor \(c_{1}\) and social learning factor \(c_{2}\) to 1.6; the maximum number of iterations was selected as 150; the setting range of general inertia weight was  
Figure 5: EWT decomposition results.  
0.3-0.9, this paper set it as 0.8. After several experiments, it was found that the simulation performance was more favorable as the ratio of training set to validation set was 8:2, i.e., the first 396 months was the model training period and the last 96 months was the model validation period. Under these conditions, the runoff series can not only provide the training data required for machine learning to obtain lower errors, but also achieve better accuracy in the forecasting of validation set data. The design values of each parameter are shown in Appendix A.
# Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints
## 2 Positioning Model and Inequality Constraints
#### 2.2.4 Combining Different Types of Constraint  
In some cases, we may obtain multiple types of constraint information that need to be combined to improve position estimation. For example, the previously introduced constraints on direction and vertical velocity can be simultaneously applied to enhance position estimation. In such cases, it is straightforward to combine multiple constraints by constructing matrices based on different information, and performing iteration until all the constraints are satisfied.  
For instance, if we need to simultaneously apply direction and vertical velocity information, we can establish five inequalities using Equations (9) and (17). Then, we can determine and implement coordinate constraints one-by-one to obtain the constrained result.  
Figure 3: A flowchart of direction constraint. Equation (17) (\(i\)) represents the \(i\)th inequality in Equation (17).