esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The authors employed a two-step approach, starting with the segmentation of context semantics followed by contrast enhancement. This enabled the model to learn and distinguish between different dental structures while improving the visualization and definition of tooth boundaries. To evaluate the performance of the Teeth U-Net model, the researchers compared the results obtained from their model with other existing segmentation methods, demonstrating the superiority of the Teeth U-Net in terms of accuracy and efficiency. The findings of the study highlight the effectiveness of the Teeth U-Net model in accurately segmenting teeth in dental panoramic X-ray images. The accuracy, precision, recall, Dice, Volumetric Overlap Error, and Relative Volume Difference were 98.53%, 95.62%, 94.51%, 94.28%, 88.92%, and 95.97%, respectively. The model's ability to capture context semantics and enhance the contrast improved the quality and reliability of tooth segmentation, thereby aiding in dental diagnosis, treatment planning, and research applications. In a recent review, [PERSON] et al. [36] explored the advantages and challenges associated with the automatic segmentation of various dental structures from OPG from a database of 8138 images by reviewing and comparing various automatic segmentation methods proposed in the literature. The images were converted into PNG files and transferred to the segmentation tool's database, Computer Vision Annotation Tool, for the segmentation process. The authors included in the study, not only the segmentation of teeth but also the analysis of caries, restorations, implants, and residual roots. The calculated DSC values were 0.85 for the teeth, 0.88 for dental caries, 0.87 for dental restorations, 0.93 for crown-bridge restorations, 0.94 for dental implants, 0.78 for root canal fillings, and 0.78 for residual roots. The authors stated that the automatic segmentation of OPT significantly reduces the time and effort required for the procedure, allowing dentists to focus on other aspects of diagnosis and treatment planning. Additionally, automatic segmentation can enhance accuracy and consistency compared to manual segmentation, potentially improving the quality of dental care. The ability to segment multiple dental structures simultaneously, such as teeth, restorations, implants, and caries, further adds to the convenience of these methods. Despite the advantages, there are several pitfalls and challenges in automatic segmentation. OPG images can vary in quality, resolution, and artifacts, which can affect the accuracy of segmentation algorithms. The presence of overlapping structures, such as adjacent teeth or restorations, poses a significant challenge to accurate segmentation. Moreover, the diversity of anatomical variations and pathologies among individuals can make it difficult to achieve a one-size-fits-all segmentation model. Limited annotated training data and a lack of standardized segmentation protocols also contribute to the challenges.
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 2 System Description  
### Korean Power System  
In South Korea, 40% of the total load is concentrated in the metropolitan area, so the system is composed of multi-looped types for improving system stability. This system is advantageous for most aspects of stability, but it makes the fault current problem more serious. In order to solve the fault current problem, current limiting reactor installation, increasing circuit breakers (CB), grid segmentation, and other suggestions have been proposed. However, none of the methods can solve the fault current problem ultimately. Recently, as an alternative to these, a grid segmentation using BTB HVDC has been proposed. In this aspect, fault current reduction can be cited as a criterion for deciding on the HVDC position in the meshed power system. Currently, two long-distance HVDCs are in operation in the Korean power system, and four HVDCs will be added in the future. One of them is an embedded-type HVDC, which will be installed in the Seoul metropolitan area to solve the fault current problem. Studies on the installation of BTB HVDC for resolving the fault current problem in the metropolitan area have been actively conducted, as shown in [22; 23].
## 4 Analysis of Zero Carbon Performance in Production Stage
### Establishment of EPD Database of Major Materials  
In Korea, the EPD certification system has been effective in evaluating environmental impacts across all product stages and granting certifications by KEITI. EPD is categorized into EPD-certified products (Type I) and low-carbon certified products (Type II). A total of 1832 cases have obtained certification, including 1372 EPD-certified cases and 460 low-carbon-certified cases [17]. A database for EPD-certified products (hereafter, EPD DB) was created for 797 cases of essential construction materials used in building construction. For the created EPD DB, A1-A3 CE was determined by converting the product specifications into weight (tons) according to the building LCA preparation guideline. The EPD DB for major construction materials comprises 797 cases, including 632 EPD-certified products and 165 low-carbon-certified products, with low-carbon-certified products accounting for approximately 20%. Out of the 797 cases in the EPD DB, ready-mixed concrete accounts for 770 cases, or about 96% of the total. Notably, aggregates and stones, key materials in construction, lack certified products. Therefore, diversifying certified products is considered crucial for reducing A1-A3 CE. Table 4 shows the details of the EPD DB of major construction materials established in this study. Tables 5 and 6 were developed and analyzed based on the eight major materials and fifteen product categories identified as key construction materials in the reference building established in Section 5. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Ready-Mixed** & \multirow{2}{*}{**Repar**} & **Masony** & **Insulation** & **Aggregate** & **Stone** & **Cement** & **Gypsum** & **Total** \\  & **Concrete** & & & & & & & & **Board** & \\ \hline Total certified products & 770 & 2 & 4 & 11 & 0 & 0 & 7 & 3 & 797 \\ \hline EPD-certified & 610 & 2 & 4 & 8 & 0 & 0 & 7 & 1 & 632 \\ \hline Low-carbon certified & 160 & 0 & 0 & 3 & 0 & 0 & 0 & 2 & 165 \\ \hline Unit conversion & m2 \textgreater{} ton & kg \textgreater{} ton & m2 \textgreater{} ton & m2 \textgreater{} ton & - & - & kg \textgreater{} ton & m2 \textgreater{} ton & ton \\ \hline \hline \end{tabular}
\end{table} 
Table 4: EPD DB development information for major construction materials. The EPD DB showed varying CE for products within the same product groups, depending on the manufacturer and model. To assist the decision-making process for stakeholders in construction concerning construction materials, it is essential to analyze the range of CE across different products. Thus, the EPD DB was organized based on the lowest, average, and highest CE of each product. As Hyundai Steel products are the only ones used for
## 2 Numerical Model and Simulations  
### Mesh Generation  
A one-dimensional transient model of the regenerator under oscillating flow is developed. Considering the use of Lagrangian representation, it is necessary to describe the mesh generation before discussing the governing equations. Using Lagrangian representation, the fluid in the regenerator is divided into parcels, and the governing equations of gas are set up to track the motion, deformation and heat transfer processes of gas parcels in the regenerator. A \"staggered grid\" scheme is implemented for the pressure and velocity, as shown in Figure 1 for gas parcel \(i\). It should be noted that the staggered grid of the parcels is different from that of control volumes. In the Lagrangian representation, the velocity and the deformation are defined at the parcel boundary, while the pressure, temperature and other properties are defined at the center of the parcels. The parcel boundaries move forward and backward in the simulation. However, the staggered grid is fixed in the control volume scheme. The flow direction from the warm end to the cold end in the regenerator is defined as positive, and the orders of parcels along the positive direction at the boundary are defined as their indices. The Eulerian representation is used for the mesh generation of the solid matrix in the regenerator, and the energy equations for control volumes are solved using the finite volume method.  
Figure 1: Velocity and thermodynamic parameters of gas parcels in the regenerator model: \(T_{\mathrm{g}}\)—temperature of gas parcel; \(P_{\mathrm{g}}\)—pressure of gas parcel; \(\rho_{\mathrm{g}}\)—density of gas parcel; \(u_{\mathrm{g}}\)—velocity of gas parcel; \(t\)—instant time; \(i\)—number of gas parcel.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Feature Selection and Preprocessing  
#### 2.2.1 Computation of Vegetation Indices  
Using the aforementioned spectral reflectance data, the following 13 VIs were calculated based on the related literature [25; 27; 33; 51; 52; 53; 54]:  
\[\text{Enhanced Vegetation Index}:\;EVI=2.5\cdot(B8A-B04)/(B8A+6\cdot B04-7.5\cdot B02+1), \tag{1}\]  
\[\text{Green Difference Vegetation Index}:\;GDVI=B8A-B03, \tag{2}\]  
\[\text{Green Normalized Difference Vegetation}:\;GNDVI=(B8A-B03)/(B8A+B03), \tag{3}\]  
\[\text{Green-Red Vegetation Index}:\;GRVI=(B03-B04)/(B03+B04), \tag{4}\]  
\[\text{Modified Soil-Adjusted Vegetation Index}:\;MSAVI=2\cdot B8A+1-\sqrt{(2\cdot B8A+1)^{2}-8 \cdot(B8A-B04)}/\;2, \tag{5}\]  
\[\text{Normalized Difference Vegetation Index}:\;NDVI=(B8A-B04)/(B8A+B04), \tag{6}\]  
\[\text{Normalized Difference Water Index}:\;NDWI=(B03-B8A)/(B03+B8A), \tag{7}\]  
\[\text{Optimized Soil-Adjusted Vegetation Index}:\;OSAVI=(B8A-B04)/(B8A+B04+0.16), \tag{8}\]  
\[\text{Soil-Adjusted Vegetation Index}:\;SAVI=((B8A-B04)\cdot(1+0.5))/(B8A+B04+0.5), \tag{9}\]  
\[\text{Simple Band Index}:\;SBI=B12/B03, \tag{10}\]  
\[\text{Simple Ratio}:\;SR=B8A/B04, \tag{11}\]  
\[\text{Water Index}:\;WI=(B03+B04)/(B8A+B11), \tag{12}\]  
\[\text{Wide Dynamic Range Vegetation Index}:\;WDRVI=(0.1\cdot B8A-B04)/(0.1\cdot B8A+B04). \tag{13}\]  
Each index targets specific aspects of vegetation and contributes to monitor different factors that affect crop yield. In total, the above VIs provide a detailed analysis of crop health, vegetation cover, water stress, and chlorophyll content. Occasionally, they are utilized in conjunction with some of the nine spectral bands listed above, which are frequently employed in the field of agricultural yield prediction, as highlighted in the recent review paper of [PERSON] et al. [55].
## 5 Conclusions  
In order to eliminate noise elements from the sound signal for the acoustic-based diagnosis system, an adaptive threshold denoising method-based EMD and an improved FOA was designed in this paper. Improved strategies on the basis of introducing a variation coefficient and a disturbance coefficient into the basic FOA were applied in the noise cancellation process. The modified FOA is introduced into the threshold determination process of the traditional EMD denoising approach to replace the threshold selection based on statistics estimation and numerous trials. The proposed method can represent an important pretreatment on field sound signals for an acoustic-based diagnosis system. To demonstrate the effectiveness and superiority of the launched WTD-IFOA, a series of simulations were conducted and some comparisons were presented. The performance of the proposed and other four denoising methods in 0 dB, 5 dB, 10 dB, 15 dB, and 20 dB noisy conditions were presented. Overall, the EMD-IFOA had the best results in all noisy conditions. The MSE and PRD of the denoised signal decreased 10.01% and 5.17% respectively even in the worst conditions, and the \(\text{SNR}_{\text{imp}}\) increased from 0.0151 dB to 0.4767 dB compared with traditional EMD solution. The simulations and comparison results proved that the automatic denoising method could effectively eliminate noise and outperform the others. Finally, an industrial application for the shearer in a fully-mechanized coal  
Figure 11: (**a**) The arrangement of the shearer; (**b**) the arrangement of the cutting sound collection system.  
Figure 12: (**a**) The amplitude of the original and the denoised signal; (**b**) the frequency spectrum of the original and the denoised signal.  
Figure 13: The zoom on the range of [0, 2000 Hz] of the frequency spectrum.  
mining face was performed to validate field effect. In future studies, the authors plan to investigate some improvements in determining the variation coefficient and disturbance coefficient automatically with the proposed approach.  
We gratefully acknowledge the support of the National Natural Science Foundation of China (No. U1510117 and 51605477), the National Key Basic Research Program of China (No. 2014 CB046301), the Postgraduate Scientific Research and Innovation Project of Jiangsu Province (KYZZ16_0212) and the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions in carrying out this research. [PERSON] and [PERSON] contributed the new processing method; [PERSON], [PERSON] and [PERSON] designed the simulations and experiments; [PERSON] and [PERSON] performed the simulations and experiments; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Research on a Decoupling Algorithm for the Dual-Deformable-Mirrors Correction System
## 2 Principle of Wavefront Correction with Dual Deformable Mirrors  
### Principle of Dual-Deformable-mirrors Correction System  
The working principle of the dual-deformable-mirrors correction system is shown in Figure 1. The system mainly consists of three parts: wavefront sensor, wavefront controller, and wavefront corrector (two) [10]. When a beam of light propagates in the atmosphere, wavefront distortion is generated due to atmospheric turbulence, and it is corrected using a wavefront corrector. In the dual-deformable-mirrors correction system of this study, wavefront distortion is graded and corrected by two wavefront correctors. Wavefront corrector 1 is generally an FSM or a large stroke, low spatial-frequency DM (commonly referred to as Woofer), responsible for correcting low-order aberrations. Wavefront corrector 2 is generally a small stroke, high spatial-frequency DM (commonly referred to as Tweeter), responsible for correcting high-order aberrations. The system utilizes wavefront sensors to measure the wavefront of the received beam in real-time, feedback the measured wavefront information to the wavefront controller, and then calculate and obtain the control voltage of the wavefront corrector through a certain control algorithm, providing real-time control signals for the wavefront corrector. The wavefront corrector receives real-time control voltage from the wavefront controller, fits the surface shape to correct wavefront distortion, and improves the communication quality of the dual-deformable-mirrors wavefront correction system.
# Chinese Geomagnetic Reference Field 2020 by the Revised Surface Spline Method
## 5 Conclusions  
After improving the surface spline method, we propose the revised surface spline method based on grid lattice distribution positions. Because fundamental wave centers are fixed, it is possible to determine the time variation characteristics of a geomagnetic field by analyzing the time series of coefficients.  
Based on geomagnetic field measurements in mainland China during 2019-2020, we set up Chinese Geomagnetic Reference Field 2020 to describe the geomagnetic field in most of China and surrounding areas. The RMS residual fitted by the RSS values are 0.1318 degrees for D, 0.0857 degrees for I, 93.70 nT for F, 73.94 nT for X, 65.02 nT for Y, and 97.82 nT for Z. The values of F, X, Y, and Z are about 20 nT less than those by the SCH method and IGRF13. So RSS can better reflect the spatial distribution of the geomagnetic field.  
The spatial distribution of the geomagnetic field described by RSS shows that geomagnetic anomalies are spatially related to geological structures. Sichuan Basin and Tarim Basin are with positive geomagnetic anomalies for F, H, X, and Z. Qinling Mountains, Tianshan Mountains, and the south of Tibet are with negative geomagnetic anomalies for F, H, X, and Z.  
Methodology, B.C.; software, B.C.; validation, B.C., J.Y. and Z.N.; data processing, B.C., J.Y. and C.W.; writing--original draft preparation, B.C.; writing--review and editing, B.C. and Z.N.; visualization, B.C.; funding acquisition, B.C. and C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Special Fund of Institute of Geophysics, China Earthquake Administration, \"Study on the lithospheric magnetic field model and its variation based on SWARM satellite and ground magnetic data\" (DQJB20B25), and the monitoring and maintenance project of China Earthquake Administration \"Organization and quality assessment of roving geomagnetic monitoring\" (202006235445).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** You can contact us or the Institute of Geophysics of China Earthquake Administration ([[https://www.cea-igp.ac.cn/](https://www.cea-igp.ac.cn/)]([https://www.cea-igp.ac.cn/](https://www.cea-igp.ac.cn/)), 16 December 2021) to apply for relevant research data.  
**Acknowledgments:** We wish to acknowledge the Geomagnetic Network of China for providing geomagnetic data on geomagnetic observatories. We thank the geomagnetic survey group of China Earthquake Administration for their field and data support. We are also grateful for supporting geomagnetic data and models from the International Association of Geomagnetism and Aeronomy, the International Real-time Magnetic Observatory Network, and National Oceanic and Atmospheric Administration.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Materials and Methods
### Evaluation
#### 3.3.2 Matrix Factorization Model  
The performance of NMF, SVD, and SVD++ has been evaluated in 5-fold cross-validations using two commonly used metrics, namely root mean squared error (RMSE) and mean absolute error (MAE).  
The RMSE calculates the square root of the average square differences between the predicted (\(s_{ij}\)) and actual ratings (\(r_{ij}\)) (Equation (2). The RMSE provides a measure of the accuracy of the predicted ratings, with lower values indicating better performance [38].  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i,j}\left(s_{i,j}\ -r_{i,j}\right)^{2}} \tag{2}\]  
The MAE calculates the average absolute difference between the predicted (\(y_{i}\)) and actual ratings (\(x_{i}\)) (Equation (3)). The MAE provides a measure of the overall deviation of the predicted ratings from the actual ratings, with lower values indicating better performance.  
\[MAE=\frac{\sum_{i=1}^{n}\left|y_{i}-x_{i}\ \right|}{n} \tag{3}\]  
Table 2 shows the performance comparison of NMF, SVD, and SVD++ in 5-fold cross-validations using the RMSE and MAE.  
Figure 6: Metric of ratio of presence of the recommendations.  
Looking at the RMSE table, we can see that SVD has the lowest average RMSE of 0.6439 among the three methods, followed closely by SVD++ with an average RMSE of 0.6462. NMF has the highest average RMSE of 0.6478. Additionally, we can see that the differences in RMSE values between the methods are relatively small, suggesting that the performance of the methods is comparable.  
For the MAE table, we can see that NMF has the lowest average MAE of 0.455, followed closely by SVD with an average MAE of 0.460. SVD++ has the highest average MAE of 0.462. Again, the differences in MAE values between the methods are relatively small, indicating that the performance of the methods is comparable.  
Based on these results, we can conclude that SVD performs the best in terms of RMSE, while NMF performs the best in terms of MAE. However, the differences in performance between the three methods are relatively small, suggesting that the choice of method may depend on the specific recommendation task and the trade-offs between accuracy and computational efficiency.
# Flavor Profile Evolution of Bottle Aged Rose and White Wines Sealed with Different Closures
## 1 Introduction  
Wine flavor is composed of a wide variety of compounds with different organoleptic properties, which will slightly evolve during bottle aging due to the limited quantities of oxygen penetrating through the closures [1; 2; 3; 4]. The increase of dissolved oxygen (DO) in wines means the replenishment of oxygen is higher than the consumption of oxygen by antioxidants (such as phenolic compounds), while the decrease of DO represents a relatively higher consumption of oxygen [5]. Different types of closures exhibit different abilities in preventing oxygen penetration due to their structural differences [6; 7]. Natural corks are the traditional choice of closure in the wine industry, but other types of closures are also used by wine producers. Agglomerated corks and technical corks are made of of ofcuts of oak wood, thus can be named oak-based corks as opposed to natural corks. In comparison with oak-based corks, polymer synthetic plugs and screw caps are more economical and less dependent on the raw material limitation.  
Bottle aging is an important period when wine's flavor characters must be preserved as much as possible. The main function of closures is to ensure a good seal and to prevent any organoleptic deterioration of wine during storage. However, during bottle aging, various reactions may occur, such as oxidation, hydrolysis, and reactions caused by charge transfer and formation of covalent bonds, which will influence wine flavor evolution [6]. Wine aroma quality like the fruity and floral perception usually decreases due to the diminishment of critical aroma compounds including long-chain aliphatic acid ethyl esters, terpen, and norisprenoids [8; 9]. Wine astringency also decreases because of a decline in the mean polymerization degree of tannins [10], while the hue and color stability usually increase due to the formation of stable orange-yellow pigments such as pyranoanthocyanins [11]. So far, most research observing wine flavor changes during bottle aging has focused on dry red wines [1; 12], or is only concerned about the flavor quality, determined either by volatile compounds or non-volatile compounds [3; 4; 13; 14]; comprehensive investigations monitoring the evolution of rose and dry white wines' flavor profiles and oxidation patterns during bottle aging are quite limited.  
The objectives of our research were: (1) understanding the evolution of flavor compounds of rose and white wines during an 18-month bottle aging; (2) observing the oxidation pattern differences of rose and white wines during an 18-month bottle aging and finding out the possible reasons that caused the differences; (3) comparing the effects of natural cork and its alternatives on the flavor profiles of rose and white wines after an 18-month bottle aging.