esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, based on meteorological, forest survey, soil texture, remote sensing, and other auxiliary data, we simulated the vegetation NPP in Northeast China between 2003 and 2012 with the BEPS model and verified it with tree ring data. The results indicated the simulation to be accurate and the tree ring data to be a reliable means for verifying the time series vegetation productivity on a regional scale, with \(R^{2}=0.84\) and \(RMSE=42.73\) gC/m\({}^{2}\)-a.  
The annual NPP over Northeast China showed evident spatial gradients, where they were generally higher in the northeast than in the southwest part of the study area, which could be explained by spatial variabilities in the vegetation types and climate. During the study period, the annual NPP showed a significant increasing trend over Northeast China, particularly in forest lands, whereas the areas showing a reducing trend were mainly in farmlands and grasslands.  
Partial correlation analysis suggested that the NPP was more sensitive to temperature and solar radiation. Further analysis of the time lag in the response of the NPP to precipitation changes suggested that the lag period of the forests was substantially longer than that of the grasslands and farmlands. This was because forests have a developed root system and, consequently, present a stronger soil and water conservation function than other vegetation types do. Alternatively, vegetation growth in grasslands and farmlands not only relies on the precipitation of the current month but also on the soil moisture conditions. Since soil moisture is the result of cumulative precipitation over multiple months, grasslands and farmlands have a longer cumulative number of lag months than forests do. The authors are confident that the results of this study can provide scientific references for vegetation management under changing climate conditions.  
**Author Contributions:** Conceptualization, M.Y.; methodology, M.X.; software, M.X.; validation, B.C.; formal analysis, M.Y.; investigation, B.C.; data curation, Y.D.; writing--original draft preparation, M.Y.; writing--review and editing, X.T.; supervision, L.Z.; funding acquisition, M.Y. and X.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by the National Natural Science Foundation of China (grant number 41901364 and 41871279).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not available.  
**Acknowledgments:** The authors thank Beijing Normal University for providing the GLASS products and soil texture data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Analysis of Existing Cases
### Result of System Analysis from Case Study  
Table 8 shows the summary of the simulation parameters used in the economic evaluation.  
Figure 1: Concept of charge and discharge schedule for peak reduction.  
Figure 2: Reduced Base Rates by Charge/Discharge schedule.  
Table 9 shows the result of economic analysis based on the data discussed so far. The charge and discharge efficiency of ESS was set to 6%. Basic rate discounts, discounts for reducing the peak, and reduced power use saving were separately applied for before 2020 and after 2021. As can be seen in Table 9, the energy savings for 10 years is about $210,000. Reducing the ESS battery capacity ratio could save approximately $100,000, and reducing power-based funds could save about $11,000. In total, savings of about $330,000 resulted in 10 years. The installation fee of the system was estimated to be around $540,000 with a $86 [kWh] battery and a PCS of 250 kW. Under these assumptions, the ROI period will be about 17.55 years. Table 10 details investment costs for each condition.
# Study on the Influence of Groundwater Variation on the Bearing Capacity of Sandy Shallow Foundation
## 2 Experimental Tests
### Experimental Setup  
In order to investigate the effect of water-level change on the foundation bearing capacity, the model box foundation bearing capacity test was carried out. In Figure 2, the experimental equipment is shown, which includes a transparent glass model box, a pneumatic control system, a water pump, a loading system, a PIV shooting system and a data-acquisition system. The purposes of each section are as follows:  
1. The model box with transparent glass: Its purpose is to hold the foundation soil. It has an inlet at the bottom to let water in and out. The size of the model box is 1.2 m \(\times\) 0.6 m \(\times\) 0.6 m. The bottom of the model is covered with 0.1 m gravel, and the layer above is 0.35 m thick. The water level in the soil can be seen through clear glass because saturated soil is darker than unsaturated soil. The glass is marked with a scale to measure the height from the bottom of the model box to the water level.
2. Air-pressure control system: It controls the air pressure in the pump to move water in and out of the model box 3. Water pump: Its purpose is to simulate groundwater.
4. Loading system. It can apply vertical load to the foundation soil through the loading plate. The loads can be controlled by the loading system.
5. PIV filming system: It can capture the displacements of foundation soil by using PIV technology.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Soil Sample** & **Density, \(\rho\) (g/cm\({}^{3}\))** & **Cohesive Force, \(\sigma\) (kPa)** & **Angle of Internal \(\sigma\) (kPa)** & **Young’s** & **Poisson’s** \\  & & **Force, \(\sigma\) (kPa)** & **Friction, \(\sigma\) (\({}^{\circ}\))** & **Modulus, \(\sigma\) (MPa)** & **Ratio, \(\sigma\) (MPa)** & **Ratio, \(\sigma\)** \\ \hline Silty sand (natural) & 1.92 & 12.7 & 20.1 & 15.0 & 0.25 \\ Silty sand (saturated) & 2.05 & 8.4 & 18.3 & 9.4 & 0.25 \\ Gravel & 2.20 & 3.2 & 38.7 & 150 & 0.3 \\ \hline \end{tabular}
\end{table}
Table 1: Related parameters of test soil.  
Figure 1: Flowchart of the overall research.
## 3 Results  
Due to the high volume of data, results are presented in aggregated form. The overall discriminability of the insert textures across VOI volumes for the different scanners was obtained by contemplating the discriminability of inserts 1 vs. 2, 1 vs. 3, and 3 vs. 2 for all filters and feature classes. The percentage of discriminative features for each scanner at different VOI volumes is shown in Figure 3.  
The level of aggregation was subsequently decreased to obtain the percentage of discriminative features separated by feature class at different volumes for each scanner, still aggregating the different filters and including the comparisons among all texture couples. The corresponding coefficients of determination to assess the association between the percentage of discriminative features and VOI volume were also determined. Theseresults are shown in Figure 4. Additionally, in the Supplementary Material, Figures S2-S10 show, for each scanner, the discriminability of each radiomic feature for all filters, for different VOI volumes, this time separating for each pair of inserts. Detailed examples and comparisons are also provided in Figure S11. Similarly, Figure S12 reports the percentage of discriminative features at different volumes separated by the filter for each scanner, aggregating the different feature classes and all texture couples.  
Figure 4: Overall percentage of features discriminative at different volumes for each feature class in scanners A (top left), B (top right), and C (bottom left). At bottom right, coefficient of determination, R-squared, of the percentage of discriminative features explained by the volume for each scanner.  
Figure 3: Overall percentage of features discriminative at different volumes for each scanner.  
In Figure 5, the same plot as Figure 4 is reported, this time considering only the features extracted from original images without filtering. This was performed in consideration of the fact that different filters act differently on image texture (Figure S13). A similar analysis was performed for each of the applied filters, and results are shown in Supplementary Material Figures S14-S22.
## 4 Discussion
### MRONJ and Route of Administration  
Several studies have focused on the risk factors for MRONJ development with the treatment of IV BPs (nitrogenated) and performing tooth extractions (identified as important risk factors) [10]. There is scientific evidence showing that drugs (Pamidronate and Zolendronate) whose route of administration is exclusively IV have been strongly associated with cases of MRONJ [19]. This can be explained because these drugs are more potent and have greater bioavailability due to the type of administration (IV).  
For this purpose, [PERSON] et al. [25] described a case of a 54-year-old woman with gum ulceration, bone exposure, and intense spontaneous pain around implants. The patient in question had undergone IV therapy with BPs (Pamidronate and Zolendronate) for 2 years to treat bone metastases from breast cancer. The authors reported MRONJ related to BPs, with symptoms of necrotic bone for more than 8 weeks; the patient did not undergo radiotherapy in the maxillofacial area.  
Drugs, such as Alendronate and Risedronate, are administered exclusively orally. It has been reported that these drugs are safer and have a lower risk of MRONJ [16]. This was observed in the [PERSON]'s study [15] of a 56-year-old woman who underwent rehabilitation of two missing molars in the maxilla. She was taking oral Alendronate and, during the time of osteoporosis treatment with Alendronate, there were no episodes of MRONJ.  
Upon analyzing the studies included in this systematic review, it was not possible to be precise in presenting the failure rates for both routes of administration, due to the lack of data provided by the studies. Nevertheless, there was a consensus among authors that the IV route of administration results in a high number of failure cases. Thus, the oral route of administration still seems to be the safest route.
# Improved Performance in the Detection of ACO-OFDM Modulated Signals Using Deep Learning Modules
## 2 ACO-OFDM System Model  
### Turbulent Channel Characteristics  
In FSO communication, the transmitted signals propagate in free space through atmospheric turbulence. The turbulent channel includes changes in the refractive index structure parameter caused by inhomogeneities in the temperature and pressure of the atmosphere [1; 2]. At the receiver, the atmospheric turbulence causes random fluctuations in the amplitude and the phase of the received signal, which leads to signal scintillation and affects the performance of the FSO system model. The fluctuations and the changes in the temperature through the turbulent channel can be described by the parameter \(C_{\rm n}^{2}\), which is the refractive index structure parameter. Different statistical models have been proposed over the years to model the atmospheric turbulent channel, depending on the strength of the turbulence, which ranges from weak to strong [24]. The Rytov variance \(\sigma_{\rm R}^{2}\) is the parameter that determines the strength of the turbulence. It depends on the value of \(C_{\rm n}^{2}\)  and can be calculated by  
\[\sigma_{\rm R}^{2}=1.23C_{\rm n}^{2}\frac{\sqrt{2}}{\rm L}^{\frac{11}{6}}, \tag{1}\]where L is the distance between the transmitter and the receiver, \(\mathrm{k}=2\pi/\lambda\) is the wave number, and \(\lambda\) is the wavelength. In the case of weak turbulence \(\sigma_{\mathrm{R}}^{2}\ll 1\), the intensity of the received signal that passes through weak turbulence is lognormally distributed, and the probability density function (PDF) can be described by  
\[\mathrm{f_{I}(I)}=\frac{1}{2\mathrm{I}}\frac{1}{\sqrt{2\pi\frac{\sigma_{\mathrm{ R}}^{2}}{4}}}\exp\left[-\frac{(\ln(I)-\ln(I_{0}))^{2}}{2\sigma_{\mathrm{R}}^{2}} \right], \tag{2}\]  
where I is the received signal intensity at the receiver, \(\sigma_{\mathrm{R}}^{2}\) is the Rytov variance and refers to the variance of the log amplitude of the received signal, and \(\ln(I_{0})\) is the mean of the log intensity of the received signal. When the fluctuations of the turbulence are strong, we have \(\sigma_{\mathrm{R}}^{2}>1\). The distribution of the received signal in this case is Gamma-Gamma with a PDF described in Equation (3) [25]:  
\[\mathrm{f_{I}(I)}=\frac{2(\alpha\beta)^{\frac{(\alpha+\beta)}{2}}}{\mathrm{r( \alpha)r(\beta)}}\cdot\mathrm{I}^{\frac{(\alpha+\beta)}{2}-1}\cdot\mathrm{k}_ {\alpha-\beta}\left(2\sqrt{\alpha\beta}\right), \tag{3}\]  
\[\alpha=\frac{1}{\exp\left[\frac{0.49\sigma_{\mathrm{R}}^{2}}{\left(1+1.11 \sigma_{\mathrm{R}}^{12/5}\right)^{7/6}}\right]-1}, \tag{4}\]  
\[\beta=\frac{1}{\exp\left[\frac{0.51-\sigma_{\mathrm{R}}^{2}}{\left(1+0.6\sigma 9\sigma_{\mathrm{R}}^{12/5}\right)^{5/6}}\right]-1}, \tag{5}\]  
where \(\mathrm{r(.)}\) is the Gamma function, \(\alpha\) and \(\beta\), respectively, represent the effective number of the large and small scales of the scattering process calculated by Equations (4) and (5), and \(\mathrm{k}_{\alpha-\beta}\) is the order of the Bessel function of the second kind.
## 2 Materials and Methods
### Procedures  
Each subject visited the laboratory three times (with a week wash-out period) on separate days at the same time of day (Figure 1).  
Upon arrival at the laboratory for the first time, subjects read and signed the informed consent form. At each visit, subjects were fitted with a heart rate monitor, and asked to lie down on a treatment table for a 10-min rest to achieve cardiovascular stability before measuring their resting heart rate.  
Pre-run measurements were recorded in the order of heart rate, energy expenditure, fatigue perception, circumference of the thighs and calves, and blood lactate (measurements were recorded in the same order at all other time points). Subjects then performed an exercise protocol on the treadmill (Jog Forma, Technogym S.p.A, Gambettola, Italy) where the run began at 4 km/h without treadmill inclination and the speed and inclination increased by 1 km/h and 0.5% every minute. The exercise protocol was ended when the subjects reached the target heart rate (90% of the maximal heart rate calculated using the Karvonen formula) [24]. The post-run 0-min measurements were recorded immediately. After a set of measurements, the researcher left the laboratory and a different researcher came into the laboratory to guide the cool-down conditions.  
Subjects executed the three cool-down conditions (IBP, active, and passive) in a counterbalanced order. For the IBP condition, subjects performed five sets of continuous alternating positions on the inversion table. Each set was defined as a position change from A to B for 30 s (Figure 2A,B) followed by a position change from B to A (Figure 2A,B). The duration of inversion was determined to prevent discomfort due to increased intracranial pressure [25]. To change positions, the researcher asked a subject to press a button on the inversion table. For the active condition, subjects walked on the treadmill at speeds of 5 and 6 km/h for females and males, respectively, for 5 min. The walking treadmill speed was determined in our pilot study where we wanted it to be neither too fast (to avoid additional fatigue) nor too slow (no effect on active recovery). For the passive condition, subjects remained seated on a chair for 5 min. After the next set of measurements (post-run 5 min), a 5-min static stretch of the hip adductors, extensors, abductors, and flexors, and knee extensors with a 30-s hold on each side (the same protocol was applied for each condition) was performed. After completion of the static stretch, the post-run 10-, 20-, and 30-min measurements were recorded.  
Figure 1: Testing procedures, IBP: inverted body position. Heart rate, energy expenditure, fatigue perception, circumference of lower-extremity, and blood lactate were recorded at each measurement time.
## 3 Results and Discussion
### Influence of Discharge Area
#### 3.2.4 Comparison between Large and Small Atomizers  
In Figure 9, the extinguishing time, peak surface temperature, and minimum oxygen concentration for large and small atomizers are compared under the same experimental conditions. The differences in all parameters are defined as \"Large atomizer-Small atomizer\". As shown in Figure 9, the differences in the extinguishing times and peak surface temperatures are mostly positive (Figure 9a,b), and the differences in the minimum oxygen concentrations are mostly negative (Figure 9c). This implies that the large atomizer exhibits a longer extinguishing time, higher peak surface temperature, and lower minimum oxygen concentration than the small atomizer. Given that the discharge areas of the small atomizer are smaller than those of the large atomizer, the small atomizer achieves higher velocities of discharged flows than the large atomizer under the same flow rate conditions, and enhances the entrainment. Additionally, the droplet size of the water mist for the small atomizer is smaller than that for the large atomizer, as shown in Figure 3. The high velocity of the discharged flow and small droplet size of the water mist can result in shortening of the extinguishing time, cooling of the flame and fuel surface, and preventing of fuel vapor retention in the reaction region. The early fire-extinguishing time implies that the duration of oxygen consumption in the enclosed chamber is reduced. Hence, the small atomizer showed shorter extinguishing times, lower peak surface temperatures, and higher minimum oxygen concentrations when compared with those of the large atomizer. Meanwhile, in Figure 9, at a water flow rate of 85 g/min, the differences in all measurement parameters between both twin-fluid atomizers are close to zero. This implies that under these conditions, the effect of the discharge area in the twin-fluid atomizer on fire-extinguishing performance is neglected. Based on Figure 9, it was observed that there are certain flow rate conditions in which the effect of the discharge area on the fire-extinguishing performance is dominant.
## 4 Future Studies for Scaffolds in GTR and GBR  
Until now, various biomanufacturing techniques enabled the formation of more advanced scaffolds for regeneration therapy. Although many options were introduced, there are still some limitations due to the biomaterials themselves and their combination of the manufacturing process. To obtain an ideal 3D scaffold suitable for periodontal tissue, scaffolds with cell sheets, pre-vascularized scaffolds, and scaffolds with nano-design and bioprinting should be studied further.  
The phase of tissue regeneration depends on the infiltration of mesenchymal cells from surrounding tissue and their differentiation into correctly specified cells such as osteoblasts, cementoblasts, and PDL fibroblasts. A biphasic scaffold with porous structure was already invented, and additional approaches for promoting cell reaction are expected soon. As re-vascularization is another crucial factor of fast regeneration, seeding pro-angiogenic factors into the scaffold before implantation should be discussed, in addition to other approaches such as harvesting vascular bundles for the defect or vascularizing sheets of cells before insertion [120; 121; 122]. To construct a vascularized cell sheet, human mesenchymal cells and human umbilical vascular endothelial cells are added. For example, one such sheet was combined with a \(\beta\)-TCP scaffold, and it showed enhanced angiogenesis and bone matrix apposition [122]. Further studies are needed to determine promising results with a controlled procedure.  
More advanced regeneration targets having original biology of tissue after implantation of biomaterials. The polymers used in TE have both advantageous and disadvantageous features. Therefore, a precise coupling of synthetic polymers with a biologic component could be the future trend of biomaterials. However, the combination method is yet to be thoroughly tested. In the future, optimal manufacturing procedures with a reproducible manual should be completed. Bioprinting is a technology utilizing 3D printing to combine cells, growth factors, and biomaterials to imitate natural tissue. Generally, they use a layer-by-layer method to deposit materials such as bioinks [123]. Although they have high similarity with the original tissue, this technique should be approved by governments and the protocol should be more standardized. Finally, bioprinting could accomplish patient-customized tissue for ideal regeneration.
## 2 Methodology
### Finite Element Method and the Computational Modelling  
The objective of the finite element method is to obtain information about the system from its geometric model, working by means of the subdivision of the domain into smaller and simpler elements. These elements are defined by nodes, where each one is associated with a set of shape functions that allow us to interpolate data between nodes. According to the model's dimensions (1D, 2D or 3D), each shape function is defined with certain degrees of freedom. Subsequently, the Galerkin approach can be used, and the solution field is constructed from the solution vector and the set of the basic shape functions in all elements [45].  
The geometric model considered in this work consists of a three-dimensional structure defined by the skin, fat, gland, and tumor domains. It was based on the breast model reported in [PERSON] [46]. In that work, the model was validated by means of a comparative analysis with the results obtained by [17], obtaining satisfactory results. The breast was modeled as a hemisphere with a diameter of 11.0 cm and height of 7.2 cm; it contains a glandular tissue composed of 16 lobes, with each one connected (Figure 2b) to a galactophore conduct. The domains corresponding to the epidermis and dermis layers were merged to simulate the skin (Figure 2a). This model allowed to obtain internal thermal gradients close to those of a real situation, as was demonstrated in [46], due to the geometry being nearly attached to the anatomy of the mammary gland. This is unlike other works which use layers to represent different domains, making it impossible to obtain gradients related to a tumor's location and size inside the breast. Another fact is that all parameter values used in the simulation were obtained from previous works, where the used data were taken from real cases or estimated from the computation of data extracted from real tissues.  
The geometric design of the model was produced in SolidWorks(r) and imported by COMSOL Multiphysics(r) in order to simulate the thermal phenomenon. The mesh of the model is depicted in Figure 2. The model was solved for different cases, such as for three tumor sizes (5 mm, 6 mm, and 8 mm) and a case without a tumor (healthy). The features of each simulated model were the number of elements--1540413, 1537284, 1541649, and 1521349; degrees of freedom--2072848 (internal: 759749), 2068621 (internal: 758977), 2074458 (internal: 760733), and 2047357 (internal: 747933); physical memory--7.7 GB, 6.46 GB, 6.49 GB, and 6.44 GB; virtual memory--11.38 GB, 8.98 GB, 9.23 GB, and 9.18 GB; and number of nodes--2072848 (internal: 759749), 2068621 (internal: 758977), 2074458 (internal: 760733), and 2047357 (internal: 747933). This was performed for tumor sizes of 5 mm, 6 mm, and 8 mm and for healthy tumors, respectively.  
Figure 2: Geometric 3D model of the female breast and mesh. (**a**) Complete model. (**b**) Detail of the internal geometry. (**c**) Tumor mesh and location in the model.