esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
They verified the reliability of the FE analysis by comparing the predictions with those measured from a pilot cold rolling test that used a 25-mm wide 3.0 wt% silicon strip being rolled. However, the Johnson-Cook DF criterion, which has been mainly applied when the stress triaxialities are high (above 1/3), was used in the FE analysis. In previous studies [4; 5; 7; 8], the width of the strip used in the rolling test was limited to 25-100 mm. These widths are too small for the results of their research to be applied to actual cold rolling scenarios. In addition, in the actual cold rolling, different stress states, ranging from axisymmetric tension to axisymmetric compression and passing through in-plane strain tension, exist in the strip. Therefore, the Lode angle parameter which reflects those stress states should be included in the DF criteria to predict edge cracking more accurately. In this study, the modified Mohr-Coulomb (MMC) DF criterion [10] including both stress triaxiality and the Lode angle parameter, was adopted to accurately predict the edge cracking behavior in cold rolling. For this purpose, a 3D fracture locus was constructed  
Figure 1: Scheme of change in the cross section of the strip due to roll bending. (**a**) Central wrinkle (center buckle) by positive roll bending; (**b**) Edge waviness by negative roll bending. through a series of fracture tests with specimens of various shapes and corresponding FE simulations of the fracture tests. The developed 3D fracture locus was implemented into a VUMAT user-defined subroutine of Abaqus. A series of FE analyses was then conducted to investigate the effect of the secondary roll-bending ratio (L\({}_{2}\)/R\({}_{2}\)) and the initial notch length on edge cracking in a wide (300 mm) strip under a positive roll bending condition. L\({}_{2}\) and R\({}_{2}\), respectively, denote the secondary work roll barrel length and the radius of the convex curvature of the work roll surface profile emulating positive roll bending. We also investigated the difference in edge cracking when 2D and 3D fracture loci were used.
# Coupling of Modes in Step-Index Plastic Optical Fibers by Using D-Shape Technique
## 4 Experimental Results
Can we say that the D-shape technique significantly reduces the Zs value, thereby eliminating the need to incorporate supplementary elements, intentional curvatures, or bending deformation into the POF normally used for this task? On the other hand, when using the technique known as Strained on the POF [23], a considerable reduction in the Zs value is also observed. However, it i  
Figure 10: Normalized output light power distribution for the ESKA CK30 POF for various input angles and z = 5 m using the D shape, obtained experimentally (\(\hat{\pi}=+-20^{\circ}\), \(\hat{\Diamond}=+-15^{\circ}\), \(\Delta=+-10^{\circ}\), o = \(+-5^{\circ}\), and \(\ast=0^{\circ}\)). Figure 9: Output light power distribution for the ESKA CK30 POF for various input angles and z = 10 m using the D shape, obtained experimentally (\(\hat{\pi}=+-20^{\circ}\), \(\hat{\Diamond}=+-15^{\circ}\), \(\Delta=+-10^{\circ}\), o = \(+-5^{\circ}\), and \(\ast=0^{\circ}\)). this approach requires a significant area of 300 mm \(\times\) 200 mm and twelve 10 mm diameter cylinders to induce bending deformations in the POF and achieve SSD. The Zs values obtained experimentally were compared with the calculated values. It can be seen that in the reference experiment [25], there is an 88% divergence between the experimental value of Zs (49 m) and the calculated value (26 m) without using the Strain technique. When the Strain technique is applied, the difference between the experimental value (2.5 m) and the calculated value (1.46 m) drops to 71%. In this work, the experimental Zs value for POF ESKA SK40 without the application of the D-shape technique was 27 m, and the calculated value was 25.94 m, a difference of 4%; the experimental Zs value for POF ESKA CK30 without the D shape was 20 m, and the calculated value was 19.3 m, a difference of 3.6%.
## 2 Development of the Numerical Model
### Governing Equations
Momentum conservation equation \[\frac{\partial(\rho u_{i})}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{i}u_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{ \partial x_{j}}\Bigg{(}\mu_{t}\frac{\partial u_{i}}{\partial x_{j}}\Bigg{)}+ \frac{\partial}{\partial x_{j}}\bigg{(}\mu_{t}\frac{\partial u_{j}}{\partial x _{i}}\bigg{)}+(\rho-\rho_{a})g_{i}\] (2) where \(p\) is the absolute pressure (Pa); \(\mu_{t}\) is the turbulent viscosity (Pa \(\cdot\) s); \(g_{i}\) is acceleration due to gravity (m/s\({}^{2}\)). Figure 1: Schematic diagram of the gas leakage process in the improved model (Arrows represent refrigerant gas movement) [19]. (3)  
Energy conservation equation  
\[\frac{\partial(\rho T)}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(}\rho u_{ j}T\big{)}=\frac{1}{c_{p}}\frac{\partial}{\partial x_{j}}\left(k_{t}\frac{ \partial T}{\partial x_{j}}\right)+\frac{c_{pv}-c_{pa}}{c_{p}}\left[\left(\frac{ \mu_{t}}{\sigma_{c}}\right)\frac{\partial J}{\partial x_{j}}\right]\frac{ \partial T}{\partial x_{j}} \tag{3}\]  
where \(T\) is the time - averaged temperature of the fluid(K); \(k_{t}\) is the turbulent thermal conductivity (W/(m\(\cdot\)K)); \(c_{pv}\) is the constant pressure specific heat capacity of the leaking refrigerant (J/(kg\(\cdot\)K)); \(c_{pa}\) is the constant pressure specific heat capacity of air (J/(kg\(\cdot\)K)); \(\sigma_{c}\) is the turbulent Schmidt number. (4)  
Component transport equation  
\[\frac{\partial(\rho J)}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(}\rho u _{j}J\big{)}=\frac{\partial}{\partial x_{j}}\left(\rho D_{\text{t}}\frac{ \partial J}{\partial x_{j}}\right) \tag{4}\]  
where \(J\) is the time-averaged mass fraction of the component; \(D_{\text{t}}\) is the turbulent diffusion coefficient of the fluid (m\({}^{2}\)/s). (5)  
Turbulence models  
Turbulent kinetic energy k equation:  
\[\frac{\partial(\rho\ k)}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(} \rho\ u_{j}k\big{)}=\frac{\partial}{\partial x_{i}}\bigg{(}\Gamma_{k}\frac{ \partial k}{\partial x_{i}}\bigg{)}+G_{k}+G_{b}-Y_{k} \tag{5}\]  
Turbulence dissipation rate \(\varepsilon\) equation:  
\[\frac{\partial(\rho\ \varepsilon)}{\partial t}+\frac{\partial}{\partial x_{j}} \big{(}\rho\ u_{j}\varepsilon\big{)}=\frac{\partial}{\partial x_{i}}\bigg{(} \Gamma_{\varepsilon}\frac{\partial\varepsilon}{\partial x_{i}}\bigg{)}+G_{ \varepsilon}+G_{\varepsilon b}-Y_{\varepsilon} \tag{6}\]  
where \(G_{k}\) represents the turbulent kinetic energy generated by the time-averaged velocity; \(\Gamma_{k}\) and \(\Gamma_{\varepsilon}\) represent the effective diffusivity of \(k\) and \(\varepsilon\), respectively; \(Y_{k}\) and \(Y_{\varepsilon}\) represent the dissipation of \(k\) and \(\varepsilon\) under the action of turbulence, respectively. \(G_{b}\) and \(G_{\varepsilon b}\) represent the turbulent kinetic energy and turbulent dissipation rate generated by buoyancy, respectively.
# Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping
## 2 Materials and Methods
### Classification  
#### 2.4.1 Predictors Selection  
The Pearson correlation coefficient and the variance inflation factor (VIF) among all variables were performed to test for multi-collinearity to reduce the high number of predictors and improve model accuracy removing redundancy. All of the predictors with a Pearson correlation coefficient higher than 0.7 and a VIF higher than 10 [91] were removed. R-package 'Boruta' [92] was used to test whether the remaining variables were correlated. Boruta is a wrapper algorithm around a Random Forest (RF) classification model, in which several runs of RF are performed to generate shadow features and test the significance of each variable, in order to confirm or reject them. Finally, RF [93] was used to investigate the importance of predictors and assess their contribution to the prediction of the response  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectral Index** & **Equation** & **Reference** \\ \hline Enhanced Vegetation Index EVI & \(\frac{2.5\times(88-84)}{(88+6\times 84-7.5\times 2+1)}\) & [82] \\ Normalized Difference Yellow Index NDVI & \(\frac{(83-82)}{(83-82)}\) & [83] \\ Normalized Difference Red/Green Redness Index RI & \(\frac{(84-83)}{(84-83)}\) & [84] \\ Carotenoid Reflectance Index CRI1 & \(\frac{(1-82)}{(1+83)}\) & [85] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spectral indices adopted, equation with Sentinel-2 Multispectral Instrument (MSI) bands, and bibliographic references.  
variable, through the calculation of the importance value and the Gini index. RF was executed with a default set of hyperparameters using the 'ranger' R-package [94], and selecting the variables on an expert-based decision. All of these steps were done for each RF model (see the following section). Finally, the variable selection procedure was amended, based on expert knowledge and scientific reference [29; 50; 95], in order to reconsider a few variables that have a high significance for the habitat detection (see Figure 4).
## 4 Expressway PTV Detection Algorithm for Over-the-Horizon
### Data Preprocessing  
#### 4.3.1 Cleaning of Abnormal Data  
When a vehicle passes through the ETC gantry, a transaction record will be generated. Therefore, the ETC gantry system can generate a large amount of transaction data in a short period of time. However, due to various reasons, such as equipment failures, network issues, and so on, the transaction data may appear abnormal, which affects the accuracy of data analysis and decision-making results. In order to reduce the impact of abnormal data, the following situations need to be addressed:  
Figure 3: Schematic diagram of the section.  
Figure 2: Schematic diagram of the expressway network.  
1. Missing data: Vehicle transaction data is not captured effectively. For example, fields such as entry and exit station, transaction time, and vehicle model are missing.
2. Data redundancy: Multiple sets of data duplicate each other. For example, there are multiple sets of records of the same vehicle passing through the same ETC gantry.
3. Data Error: Data records that do not match the normal rules of the road, such as ETC gantries in different directions of travel, while capturing the crossing records of the same vehicle.  
Therefore, in order to mine the effective information implied in the transaction data, it is necessary to carry out data cleansing, solve the outlier in the data, ensure the accuracy of the data, and support subsequent decision making and PTV detection.
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 2 New Orthogonal Transforms  
The transforms analyzed in this paper were first proposed in [16]. The main characteristic of these transforms is that their matrices are orthogonal and symmetric. This makes it possible to use only one matrix for both forward and inverse transform operation. Another key feature is the parameterization in matrix generation process. This allows for the creation of an infinite number of transform matrices and at the same time can result in achieving various spectra depending on the set of selected parameter values. In addition, some parameters may be used as a security feature. For instance, in order to create a sample transform matrix, the user only needs to define its size and first three elements. These three values can be treated as a security mechanism that can prevent unauthorized persons from retrieving or getting access to secret information--for instance, in the form of a watermark embedded in a spectrum of an image. In addition, there are a few methods for matrix generation. The five major ones will be briefly introduced in the below subsections.
## 4 Results
### Overall Error Estimation  
The errors above are simulated independently, keeping the rest of the parameters ideal. The overall error can be calculated by using the sum of the squared errors. R-J approximation is not included, since this error can be avoided by the Planck unit. Figure 21 shows the overall errors of the four radiometers. The errors of temperature are \(<\) 3 K from 27 to 57 km. The instrument uncertainties play the most important role above 45 km, while the calibration errors dominate below.  
Figure 21: The overall retrieval errors estimation: **(a)** 118 GHz temperature retrieval; **(b)** 190 GHz H\({}_{2}\)O retrieval; **(c)** 240 GHz O\({}_{3}\) retrieval; **(d)** 643 GHz HCl retrieval. The errors are calculated by using the sum of squared errors.  
Figure 20: The HCl retrieval error induced by external error source: **(a)** absolute and **(b)** relative retrieval errors. The uncertainties assumed are summarized in Table 5.  
The errors of H\({}_{2}\)O are \(<\) 10% from 16 to 58 km. The instrument uncertainties mainly affect the retrieval above 20 km. The errors of O\({}_{3}\) are \(<\) 10% from 20 to 58 km, which are similar to that of H\({}_{2}\)O. The errors of HCl are \(<\) 20% from 15 to 59 km which are controlled by instrument uncertainties, while the calibration errors have little impact.
# Research and Design of the RF Cavity for an 11 MeV Superconducting Cyclotron
## 5 Calculation and Verification of the RF Cavity for 11 MeV Superconducting Cyclotrons
### Analysis of the RF Cavity Based on Multiple Linear Regression Models
After determining the resonant frequency adjustment range, the regression equation can be used to accurately calculate the available range for moving the short-circuit end. In addition, the resonant frequency fine-tuning structure design of the RF cavity can refer to the influence of the outer conductor inner radius \(r_{\rm oc}\) on the resonant frequency. Figure 13 shows the effects of the round cavity length \(L\) on the resonant frequency when \(r_{\rm ic}\) = 20 mm and \(r_{\rm oc}\) = 240 mm. It illustrates a high degree of fit between numerical simulation and equivalent circuit calculations. The errors in the prediction results at the target frequency for both models do not surpass 1%. They remain under 0.3%, satisfying the engineering design's predictive needs.
###### Abstract  
Angiogenesis and osteogenesis coupling processes are essential for bone regeneration, and human bone marrow stromal cells (hBMSCs) along with endothelial cells (ECs) are crucial participants. Deferoxamine (DFO), a hypoxia-mimetic agent, could activate the hypoxia-inducible factor (HIF)-1\(\alpha\) signaling pathway and trigger angiogenic and osteogenic effects in these cells. However, the lifetime of DFO is very short, thus a suitable delivery system is urgently needed. In this study, we encapsulated DFO in Poly(lactide-_co_-glycolide)-Poly(ethylene glycol)-Poly(lactide-_co_-glycolide) (PLGA-PEG-PLGA) nanoparticles (DFO-loaded NPs) to realize its long-term angiogenic and osteogenic bioactivities. Surface morphology, size, size distribution of DFO-loaded NPs as well as DFO loading content (LC), encapsulation efficiency (EE) and release profile were systematically evaluated. When hBMSCs were exposed to the vehicle with DFO concentration of 100 \(\upmu\)M, cells showed good viability, increased HIF-1\(\alpha\) expression and enhanced vascular endothelial growth factor (VEGF) secretion. The transcriptional levels of the angiogenic and osteogenic genes were also upregulated. Moreover, promoted alkaline phosphatase (ALP) activity further confirmed better osteogenic differentiation. Similarly, angiogenic activity of human umbilical vein endothelial cells (HUVECs) were enhanced after the addition of DFO-loaded NPs, evidenced by increased angiogenic genes expressions and tube formation. Taken together, DFO-loaded NPs could provide a sustained supply of DFO, with its angiogenic and osteogenic coupling effects preserved, which extends the potential of this system for bone defect repair.  
P 2016 6290 1
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 4 Case Analysis  
This paper made an empirical analysis of the grid load change in A region of southwest China from the first quarter of 2004 to the first quarter of 2019 (as project requirements, the data were processed). The original total load demand and multi-energy coupling supply are shown in Figure 7. It can be seen that the self-supporting amount of multi-energy coupling energy accounts for a relatively small proportion of the total quarterly load data, but it has developed rapidly in recent years, showing the characteristics of rapid development speed and drastic changes within the year. With the popularization and rapid development of multi-energy coupling, the stability of supply and demand will have higher and higher requirements on the planning and dispatching of the power grid.
## 4 HSI in Fresh-Cut Product Quality Assessment
### Fresh-Cut Fruits
#### 4.3.4 Apple  
Fresh-cut apple processing can lead to browning reactions that strongly influence the purchase of the end consumer. Browning is the consequence of a series of biochemical  
Figure 10: Fresh-cut potato starch distribution maps of the content in **(a)** 11.8 g kg\({}^{-1}\); **(b)** 13.5 g kg\({}^{-1}\); (c) 19.0 g kg\({}^{-1}\); and **(d)** 21.6 g kg\({}^{-1}\)([PERSON] et al., 2021). Reproduced from Ref. [52] with permission from The Royal Society of Chemistry.  
reactions under the polyphenol-oxidase enzyme's activity. In this case, HSI can be used to detect these damages beforehand [77]. The development of brown pigments is common in fresh-cut and dried apple slices. [PERSON] et al. (2020) demonstrated that polyphen-oxidase activity was mainly detectable around 677 nm, proposing a robust detection algorithm independent from cultivars and treatments [50].  
Moreover, nutritional features are another important indicator of acceptability, especially in fresh-cut apples. NIR-HSI (1000-2500 nm) was applied by [PERSON] et al. (2021) to evaluate the contents of dry matter, total sugar content, fructose, glucose, sucrose, malic acid, and polyphenols. NIR-HSI images were acquired on the surface of apple slices, and PLS models were developed and successfully used to describe the dry matter (R\({}^{2}\)CV = 0.83, RMSE = 9.7 g kg\({}^{-1}\)) and total sugar content (R\({}^{2}\) = 0.81, RMSE = 8.4 g kg\({}^{-1}\)) distribution, as shown in Figure 11[54].
## 5 Conclusions  
In this study, optimization of the aerodynamic shape for NREL S-821 and RAE-2822 airfoils was performed using CST in conjugation with GA. The optimization scheme consists of MATLAB code which further utilizes the XFOIL solver for an iterated process to achieve the resulting optimized airfoils. At the final step, CFD analysis for subsonic airfoil (NREL S-821) has been also conducted to validate the aerodynamic performance enhancement of the optimized airfoil and compared it to the NREL-based experimental data. One of the important motivations of this work was to make the RANS-based aerodynamic shape optimization of the airfoil with rapid changes using high-performance computing resources. Simulations are performed to compute the performance of the optimized airfoil geometry using RANS along with \(k-\omega\) SST turbulence model. The results of the study can be summarized as:  
1. The drag coefficients by XFOIL for optimized NREL S-821 subsonic airfoil and RAE 2822 transonic airfoil are decreased by 10% and 12%, respectively, while the lift to drag ratios are improved by 7.4% and 15.9% at fixed 3\({}^{\circ}\) angle of attack.
2. The proposed methodology in terms of lift to drag ratio, which is the crucial deciding factor in the wind turbine design, exhibits superior characteristics (3.5%) in aerodynamic optimization for subsonic airfoil as compared to the transonic airfoil.
3. The CFD analysis of the baseline airfoil shows the improvements of 2.1% for the lift to drag ratio while comparing the airfoil with the NREL experimental results of the subsonic airfoil (NREL S-821).
4. The CFD result for the lift to drag ratio for an optimized airfoil is about 2.2% higher than the baseline airfoil NREL S-821.
5. The present aerodynamic shape optimization scheme is in close agreement with previous results and further applicability of the CST approach could be deployed alongside other competing optimization techniques with reasonable flexibility.  
Significant reduction of a large number of design parameters offers a fewer number of genes for the candidate solution which further enables the search algorithm to act in comparatively more efficient and time-bound manners. Additionally, it can be also concluded that the aerodynamic shape optimization and the CST method can handle potential airfoils with much fewer parameters in the design space.  
Conceptualization, methodology, formal analysis, and original draft preparation by [PERSON], [PERSON]-H.K. supervised the research and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData is contained within the article.
## 2 Materials and Methods
### Theory
#### 2.1.3. Refractive Index-Based Estimation of Particulate Organic Carbon and Chlorophyll-a  
Research carried out in the 1990s demonstrated that cell volume is not the only parameter that can be used to estimate \(C_{i}\) and \(Chl_{i}\) values. In a series of studies [14; 15; 16; 17; 18], a number of empirical relationships were established for various phytoplankton species between the real refractive index \(n_{r}\) and \(C_{i}\) and between the imaginary refractive index (\(n_{i}\)) and \(Chl_{i}\). Expanding on this premise, [PERSON] [19] established refractive index-based linear models for the estimation of \(C_{i}\) and \(Chl_{i}\) based on data from two phytoplankton species  
\[C_{i}=3441.055n_{r}(660\,\mathrm{nm})-3404.99 \tag{4}\]  
\[Chl_{i}=996.86n_{i}(675\,\mathrm{nm})+1.17. \tag{5}\]  
A follow-up work by [PERSON] et al. [20] established slightly modified relationships with the inclusion of data from additional phytoplankton species  
\[C_{i}=3946n_{r}(650\,\mathrm{nm})-3922 \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**POC \& ChLA Cell Volume Scaling**} \\ \hline
**Particulate Organic Carbon** & **Source** \\ \hline \(y(r)=0.433V(r)^{0.863}\) & [10] \\ \(y(r)=0.109V(r)^{0.991}\) & [11] \\ \(y(r)=0.288V(r)^{0.811}\) & [12] (diatoms) \\ \(y(r)=0.216V(r)^{0.939}\) & [12] (non-diatom mixed protists) \\ \hline
**Chlorophyll-a** & **Source** \\ \hline \(y(r)=0.00429V(r)^{0.917}\) & [11] \\ \(y(r)=0.0398V(r)^{0.863}\) & [13] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Particulate organic carbon and chlorophyll-a cell volume scaling models used in this study \[Chl_{i}=1244n_{i}(675\,\mathrm{nm})-0.32. \tag{7}\]  
These can then be associated with a particle volume model and with particle sizes as provided by PSDs to determine POC and ChlA values. Both sets of equations were employed in this work. The \(n_{r}\) values contained in the PRIDs were originally determined by the FC method for \(\lambda\) = 488 nm, i.e., the wavelength of the laser source used within the flow cytometer [1]; however, \(n_{r}\) values are only weakly dependent on the wavelength (e.g., [21]), and Equations (4) and (6) are thus likely to be usable as is for the UKCW PRIDs as well. The FC method does not provide any information on the \(n_{i}\) values; as will be described in Section 2.2.4, these were derived from literature. The relevant \(n_{i}\) value (i.e., for organic particles at \(\lambda\) = 675 nm) was adapted from [PERSON] et al. [8] as \(n_{i}\) = 1.620 \(\times\) 10\({}^{-3}\).