Profile Curvature Research Articles

Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area and “0” a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions.

Revisiting the time-integrated polarizations of gamma-ray burst prompt phase

ABSTRACT In previous studies, the time evolution information is missed in deducing the time-integrated polarizations of gamma-ray burst prompt emission. Here, it is considered and the time-integrated polarizations is investigated through the summation of the time-resolved ones. The statistical properties of the distribution of the time-integrated polarization degree (Π) can be read from the q–Π curve, where q ≡ θV/θj. θV and θj are the observational and jet half-opening angles, respectively. Hence, only the q–Π curves are studied. In addition to a toroidal magnetic field in the radiation region, an aligned field is also discussed. We found the predicted time-integrated polarization degree is around $(40\!-\!50)\,\, \mathrm{ per\,cent}$ for High-energy Polarimetry Detector (HPD) onboard POLAR-2 and is roughly $(30\!-\!40)\,\, \mathrm{ per\,cent}$ for its Low-energy Polarimetry Detector (LPD). Therefore, Π value detected by the HPD will be larger than that of the LPD in statistics and the result of the former estimations will underestimate the value of Π in an ordered field. There are mainly two types of the q–Π curve profiles, corresponding to two ordered magnetic field configurations.

Prediction of Atmospheric Profiles With Machine Learning Using the Signature Method

AbstractAn array of atmospheric profile observations consists of three‐dimensional vectors representing pressure, temperature, and humidity, with each profile forming a continuous curve in this three‐dimensional space. In this paper, the Signature method, which can quantify a profile's curve, was adopted for the atmospheric profiles, and the accuracy of profile representations was investigated. The description of profiles by the signature was confirmed with adequate accuracy. The machine‐learning‐based model, developed using the signature, exhibited a high level of annual accuracy with minimal absolute mean differences in temperature and water vapor mixing ratio (<2.0 K or g kg−1). Notably, the model successfully captured the vertical structure and atmospheric instability, encompassing drastic variations in water vapor and temperature, even during intense rainfall. These results indicate the Signature method can comprehensively describe the vertical profile with information on how ordered values are correlated. This concept would potentially improve the representation of the atmospheric vertical structure.

Numerical analysis of thermal characteristics for micropolar fluid flux close to an isothermal vertical plate inward porous region

The merit of this manuscript is to examine numerically a heated vertical plate with uniform temperature subjected to a steady, viscous, incompressible micro-polar fluid flow that is situated inside a saturated porous medium with permeability and porosity. Similarity variables have been applied to convert coupled of constrained partial differential equations that describe physical problem into coupled of constrained nonlinear ordinary differential equations, which in turn allows for the reliable numerical solution of the problems. Finite difference and shooting methods coupled with Runge-Kutta scheme have been implemented by Matlab function bvp4c to solve equations numerically. The graphical findings showed the impact of emerging dimensionless physical parameters for micro- polar liquid on non-dimensional velocity, temperature, and micro-rotation distributions. The latest findings exhibit a high degree of agreement with the findings from earlier research. Elevating parameter values drive to an increase in the velocity profile values, but a decrease in the temperature distribution values. The benchmarks parameters of this research are modified vortex viscosity and micro-inertia density. The high porosity in the range of 0.7–0.9 is considered the noteworthy limitation of the current study. Furthermore, increasing the material's physical characteristics cause the micro-rotation vorticity profile's curvature to flip.

Application of potential machine learning models in landslide susceptibility assessment: A case study of Van Yen district, Yen Bai province, Vietnam

Landslides are natural hazards that cause significant damage to both property and human lives. This study employs potential machine learning models such as Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) to assess landslide susceptibility in Van Yen District, Yen Bai Province, Vietnam, that experiences a higher frequency of landslides compared to other localities in the region. The study incorporates thirteen input variables, including elevation, slope angle, aspect, plan curvature, profile curvature, Topographic Wetness Index (TWI), distance to faults, lithology, distance to roads, distance to rivers, land cover, rainfall, and Normalized Difference Vegetation Index (NDVI). To construct the models, landslide statistics reports were utilized, consisting of 302 landslide points collected through field surveys and 52 landslide points determined using Radar Sentinel-1 images. The Google Earth Engine cloud computing platform is utilized for constructing the landslide susceptibility models. The outcome of the research is a landslide susceptibility map with five levels: very low, low, moderate, high, and very high. The Area Under the Curve (AUC) is used as a metric to evaluate the performance of all three models. The findings indicate that, besides similarities observed in landslide susceptibility maps for previously occurred landslides, the Random Forest model demonstrates a favorable performance compared to the other models, with an AUC of 0.883.

Non-constant geometric curvature for tailored spin–orbit coupling and chirality in superconductor-magnet heterostructures

We show that tailoring the geometric curvature profile of magnets can be used for bespoke design of an effective non-relativistic spin–orbit coupling, which may be used to control proximity effects if the magnet is coupled to a superconductor. We consider proximity-coupled one-dimensional magnetic wires with variable curvatures, specifically three distinct shapes classified as J-, C-, and S-type. We demonstrate a chirality-dependent spin polarization of the superconducting correlations, and show the role of curvature in determining the ground state of mixed-chirality junctions. We speculate on how this may be implemented in novel device design, and include analysis of its usage in a spin-triplet SQUID.

Keystone Flap Type IV in Breast Reconstruction: A Case Report.

The objective of this paper is to present and document a specific case of breast reconstruction using an adapted Type IV Keystone Flap technique, with a droplet-shaped design with a reduced flap ratio, and to identify the qualities of this method. A 41-year-old woman, with a history of myocardial infarction and low ejection fraction, underwent a lumpectomy, resulting in a lower medial quadrant deficit in her left breast. After she developed skin and tissue necrosis and infection, implementing the Type IV Keystone Flap effectively addressed the deficit, ensuring sufficient coverage. The flap extended dropwise beneath the deficit, progressing anteriorly towards the upper rectus abdominis, with a ratio of 2.5:1. The flap's novel droplet shape allowed for the utilization of fewer perforators, while ensuring adequate blood supply and tissue coverage, leading to improved perfusion and aesthetic outcome. The application of the adapted Type IV Keystone Flap highlights its capacity as a versatile and effective method for breast reconstruction post-lumpectomy. With the advantages of a short learning curve, easy execution, and acceptable risk profile, it offers a valuable alternative for patients who may not be suitable for more complex surgeries. Further research is recommended to confirm its broader applicability and to conduct a comparative analysis with other techniques.

Denoising parameter dependence of coronary artery depictability in compressed sensing magnetic resonance angiography.

Using numerical indices and visual evaluation, we evaluated the dependence of coronary-artery depictability on the denoising parameter in compressed sensing magnetic resonance angiography (CS-MRA). This study was conducted to clarify the acceleration factor (AF) and denoising factor (DF) dependence of CS-MRA image quality. Vascular phantoms and clinical images were acquired using three-dimensional CS-MRA on a clinical 1.5T system. For the phantom measurements, we compared the full width at half maximum (FWHM), sharpness, and contrast ratio of the vascular profile curves for various AFs and DFs. In the clinical cases, the FWHM, sharpness, contrast ratio, signal-to-noise ratio, noise level values, and visual evaluation results were compared for various DFs. Phantom image analyses demonstrated that the respective measurements of the FWHM, sharpness, and contrast ratios did not significantly change with an increase in AF. The FWHM and sharpness measurements slightly changed with the DF level. However, the contrast ratio tended to increase with an increase in the DF level. In the clinical cases, the FWHM and sharpness showed no significant differences, even when the DF level was changed. However, the contrast ratio tended to decrease as the DF level increased. When the DF levels of the clinical cases increased, the background signals of the myocardium, fat, and noise levels decreased. We investigated the dependence of the coronary-artery depictability on AF and DF using CS-MRA. Analysis of the coronary-artery profile curves indicated that a better image quality was achieved with a stronger DF on coronary CS-MRA.

An Analytical Model for Nonlinear-Elastic Compliant Mechanisms with Tension-Compression Asymmetry

Abstract While nonlinear-elastic materials demonstrate potential in enhancing the performance of compliant mechanisms, their behavior still needs to be captured in a generalized mechanical model. To inform new designs and functionality of compliant mechanisms, a better understanding of nonlinear-elastic materials is necessary and, in particular, their mechanical properties that often differ in tension and compression. In the current work, a beam-based analytical model incorporating nonlinear-elastic material behavior is defined for a folding compliant mechanism geometry. Exact equations are derived capturing the nonlinear curvature profile and shift in the neutral axis due to the material asymmetry. The deflection and curvature profile are compared with finite element analysis along with stress-distribution across the beam thickness. The analytical model is shown to be a good approximation of the behavior of nonlinear-elastic materials with tension-compression asymmetry under the assumptions of the von Kármán strain theory. Through a segmentation approach, the geometries of a semicircular arc and folding compliant mechanism design are defined. The deflection of the folding compliant mechanism due to an applied tip load is then evaluated against finite element analysis and experimental results. The generalized methods presented highlight the utility of the model for designing and predicting the behavior of other compliant mechanism geometries and different nonlinear-elastic materials.

Geometric localization of waves on thin elastic structures.

We consider the localization of elastic waves in thin elastic structures with spatially varying curvature profiles, using a curved rod and a singly curved shell as concrete examples. Previous studies on related problems have broadly focused on the localization of flexural waves on such structures. Here, using the semiclassical WKB approximation for multicomponent waves, we show that in addition to flexural waves, extensional and shear waves also form localized, bound states around points where the absolute curvature of the structure has a minimum. We also see excellent agreement between our numerical experiments and the semiclassical results, which hinges on the vanishing of two extra phases that arise in the semiclassical quantization rule. Our findings open up novel ways to fine-tune the acoustic and vibrational properties of thin elastic structures and raise the possibility of introducing new phenomena not easily captured by effective models of flexural waves alone.

Comparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkey

Turkey’s Artvin province is prone to landslides due to its geological structure, rugged topography, and climatic characteristics with intense rainfall. In this study, landslide susceptibility maps (LSMs) of Murgul district in Artvin province were produced. The study employed tree-based ensemble learning algorithms, namely Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and eXtreme Gradient Boosting (XGBoost). LSM was performed using 13 factors, including altitude, aspect, distance to drainage, distance to faults, distance to roads, land cover, lithology, plan curvature, profile curvature, slope, slope length, topographic position index (TPI), and topographic wetness index (TWI). The study utilized a landslide inventory consisting of 54 landslide polygons. Landslide inventory dataset contained 92,446 pixels with a spatial resolution of 10 m. Consistent with the literature, the majority of landslide pixels (70% – 64,712 pixels) were used for model training, and the remaining portion (30% – 27,734 pixels) was used for model validation. Overall accuracy, precision, recall, F1-score, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC-ROC) were considered as validation metrics. LightGBM and XGBoost were found to have better performance in all validation metrics compared to other algorithms. Additionally, SHapley Additive exPlanations (SHAP) were utilized to explain and interpret the model outputs. As per the LightGBM algorithm, the most influential factors in the occurrence of landslide in the study area were determined to be altitude, lithology, distance to faults, and aspect, whereas TWI, plan and profile curvature were identified as the least influential factors. Finally, it was concluded that the produced LSMs would provide significant contributions to decision makers in reducing the damages caused by landslides in the study area.

Derivative Spectroscopy and its Application at Detecting the Weak Emission/Absorption Lines

The development of spectroscopic survey telescopes like Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), Apache Point Observatory Galactic Evolution Experiment and Sloan Digital Sky Survey has opened up unprecedented opportunities for stellar classification. Specific types of stars, such as early-type emission-line stars and those with stellar winds, can be distinguished by the profiles of their spectral lines. In this paper, we introduce a method based on derivative spectroscopy (DS) designed to detect signals within complex backgrounds and provide a preliminary estimation of curve profiles. This method exhibits a unique advantage in identifying weak signals and unusual spectral line profiles when compared to other popular line detection methods. We validated our approach using synthesis spectra, demonstrating that DS can detect emission signals three times fainter than Gaussian fitting. Furthermore, we applied our method to 579,680 co-added spectra from LAMOST Medium-Resolution Spectroscopic Survey, identifying 16,629 spectra with emission peaks around the Hα line from 10,963 stars. These spectra were classified into three distinct morphological groups, resulting in nine subclasses as follows. (1) Emission peak above the pseudo-continuum line (single peak, double peaks, emission peak situated within an absorption line, P Cygni profile, Inverse P Cygni profile); (2) Emission peak below the pseudo-continuum line (sharp emission peak, double absorption peaks, emission peak shifted to one side of the absorption line); (3) Emission peak between the pseudo-continuum line.

Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method

The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies.

Impacts of Resampling and Downscaling Digital Elevation Model and Its Morphometric Factors: A Comparison of Hopfield Neural Network, Bilinear, Bicubic, and Kriging Interpolations

The digital elevation model (DEM) and its derived morphometric factors, i.e., slope, aspect, profile and plan curvatures, and topographic wetness index (TWI), are essential for natural hazard modeling and prediction as they provide critical information about the terrain’s characteristics that can influence the likelihood and severity of natural hazards. Therefore, increasing the accuracy of the DEM and its derived factors plays a critical role. The primary aim of this study is to evaluate and compare the effects of resampling and downscaling the DEM from low to medium resolution and from medium to high resolutions using four methods: namely the Hopfield Neural Network (HNN), Bilinear, Bicubic, and Kriging, on five morphometric factors derived from it. A geospatial database was established, comprising five DEMs with different resolutions: specifically, a SRTM DEM with 30 m resolution, a 20 m resolution DEM derived from topographic maps at a scale of 50,000, a 10 m resolution DEM generated from topographic maps at a scale of 10,000, a 5 m resolution DEM created using surveying points with total stations, and a 5 m resolution DEM constructed through drone photogrammetry. The accuracy of the resampling and downscaling was assessed using Root Mean Square Error (RMSE) and mean absolute error (MAE) as statistical metrics. The results indicate that, in the case of downscaling from low to medium resolution, all four methods—HNN, Bilinear, Bicubic, and Kriging—significantly improve the accuracy of slope, aspect, profile and plan curvatures, and TWI. However, for the case of medium to high resolutions, further investigations are needed as the improvement in accuracy observed in the DEMs does not necessarily translate to the improvement of the second derivative morphometric factors such as plan and profile curvatures and TWI. While RMSEs of the first derivatives of DEMs, such as slope and aspect, reduced in a range of 8% to 55% in all five datasets, the RMSEs of curvatures and TWI slightly increased in cases of downscaling and resampling of Dataset 4. Among the four methods, the HNN method provides the highest accuracy, followed by the bicubic method. The statistics showed that in all five cases of the experiment, the HNN downscaling reduced the RMSE and MAE by 55% for the best case and 10% for the worst case for slope, and it reduced the RMSE by 50% for the best case of aspect. Both the HNN and the bicubic methods outperform the Kriging and bilinear methods. Therefore, we highly recommend using the HNN method for downscaling DEMs to produce more accurate morphometric factors, slope, aspect, profile and plan curvatures, and TWI.

Radial force analysis of long span bridge with trapezoidally corrugated steel webs

The study numerically and theoretically investigates the radial force effect of long span prestressed concrete (PC) box girders with trapezoidally corrugated steel webs (TCSWs). It reveals that under the influence of radial forces, the curved prestressing tendons in the bottom slab in long span box girder bridges with TCSWs exhibited higher levels of stress and deflection compared to conventional concrete box girder bridges due to the accordion effect of the TCSWs. Furthermore, this study simplifies the radial forces as concentrated forces of PC box girder with TCSWs. Based on this simplification, a formula is derived to accurately calculate the magnitude of these radial forces. Through parameter analysis of the power exponent in the equation defining the curve profile of the bottom slab, it was observed that lower power exponents result in more significant radial force effects. Finally, to eliminate the radial force effect, the study innovatively proposed an optimized structural scheme by adopting horizontal bottom slab at midspan and arranging horizontal diaphragms uniformly across the entire longitudinal span of the bridge. A contrastive study suggested that the horizontal arrangement of internal prestressing tendons in the bottom slab at midspan could effectively eliminate the negative impact of the radial force effects and greatly improve the overall bearing capacity of the box girders with TCSWs.

Staircase resiliency in a fluctuating cellular array.

Inhomogeneous mixing by stationary convective cells set in a fixed array is a particularly simple route to layering. Layered profile structures, or staircases, have been observed in many systems, including drift-wave turbulence in magnetic confinement devices. The simplest type of staircase occurs in passive-scalar advection, due to the existence and interplay of two disparate timescales, the cell turn-over (τ_{H}), and the cell diffusion (τ_{D}) time. In this simple system, we study the resiliency of the staircase structure in the presence of global transverse shear and weak vortex scattering. The fixed cellular array is then generalized to a fluctuating vortex array in a series of numerical experiments. The focus is on regimes of low-modest effective Reynolds numbers, as found in magnetic fusion devices. By systematically perturbing the elements of the vortex array, we learn that staircases form and are resilient (although steps become less regular, due to cell mergers) over a broad range of Reynolds numbers. The criteria for resiliency are (a) τ_{D}≫τ_{H} and (b) a sufficiently high profile curvature (κ≥1.5). We learn that scalar concentration travels along regions of shear, thus staircase barriers form first, and scalar concentration "homogenizes" in vortices later. The scattering of vortices induces a lower effective speed of scalar concentration front propagation. The paths are those of the least time. We observe that if background diffusion is kept fixed, the cell geometric properties can be used to derive an approximation for the effective diffusivity of the scalar. The effective diffusivity of the fluctuating vortex array does not deviate significantly from that of the fixed cellular array.

Geometry of Enumerable Class of Surfaces Associated with Mylar Balloons

In this paper, the very fundamental geometrical characteristics of the Mylar balloon like the profile curve, height, volume, arclength, surface area, crimping factor, etc. are recognized as geometrical moments In(x) and In and this observation has been used to introduce an infinite family of surfaces Sn specified by the natural numbers n=0,1,2,…. These surfaces are presented via explicit formulas (through the incomplete Euler’s beta function) and can be identified as an interesting family of balloons. Their parameterizations is achieved relying on the well-known relationships among elliptic integrals, beta and gamma functions. The final results are expressed via the fundamental mathematical constants, such as π and the lemniscate constant ϖ. Quite interesting formulas for recursive calculations of various quantities related to associated figures modulo four are derived. The most principal results are summarized in a table, illustrated via a few graphics, and some direct relationships with other fundamental areas in mathematics, physics, and geometry are pointed out.

Enhanced Monofocal Intraocular Lenses in Fuchs' Endothelial Dystrophy Patients: Results from Triple Descemet Membrane Endothelial Keratoplasty Procedure.

Descemet membrane endothelial keratoplasty (DMEK) is currently regarded as the most effective surgical procedure for addressing Fuchs Endothelial Corneal Dystrophy (FECD), frequently performed in conjunction with cataract surgery. In this retrospective study, we present a comparison of visual performance, clinical outcomes, and optical quality between two types of monofocal Intraocular Lenses (IOLs): one standard and one enhanced intermediate vision model, implanted in patients who underwent combined phacoemulsification and DMEK surgery. This single center comparative retrospective study was conducted at the Eye Clinic of the University of Florence (Italy) and included a total of 48 eyes of 48 patients affected by FECD and cataract. All patients underwent combined DMEK with phacoemulsification procedures. The patients' data were analyzed and divided into two groups: one group (standard group) consisted of 24 eyes that underwent phaco-DMEK with implantation of a monofocal IOL, and the second group (enhanced monofocal group) included 24 eyes that underwent phaco-DMEK with implantation of an enhanced monofocal IOL. In both groups, the following monocular visual outcomes were evaluated 6 months after surgery: Uncorrected Distance Visual Acuity (UDVA) and Best-Corrected Distance Visual Acuity (BCDVA) at 4 mts; Uncorrected Intermediate Visual Acuity (UIVA), Distance-Corrected Intermediate Visual Acuity (DCIVA) at 66 cm; Uncorrected Near Visual Acuity (UNVA) and Best Corrected Near Visual Acuity (BCNVA). Monocular defocus curves were also assessed. Furthermore, optical quality in terms of Contrast Sensitivity (CS) in photopic conditions, Higher-Order Aberrations (HOAs) at a pupil size of 5 mm. Modulation Transfer Function (MTF), Objective Scatter Index (OSI), and Strehl ratio, were also analyzed. A Patient-Reported Spectacle Independence Questionnaire was revised to evaluate spectacle independence outcomes. the two groups did not exhibit statistically significant differences in terms of UDVA, BCDVA, UNVA and BCNVA, photopic CS, HOAs, OSI, Strehl ratio, and MTF. However, in the phaco-DMEK enhanced monofocal IOL group, significantly better results were observed in terms of UIVA and DCIVA as well as a different defocus curve profile at 1.50 D, providing better defocus results at intermediate distance compared with the ZCB00 IOL. In our study, we found that enhanced monofocal lens performed favorable visual outcomes, even in cases of FECD, compared to standard monofocal IOLs. Comparable optical quality observed in the Eyhance group could allow surgeons to consider these lenses as a viable option for selected patients with FECD.

Comparison of different open‐source Digital Elevation Models for landslide susceptibility mapping

AbstractIn this study, the application of open‐source digital elevation model (DEM) is explored for regional landslide susceptibility mapping (LSM), and the potential impact of different DEM choices on the mapping accuracy is also examined. With the advancements in remote sensing technology, an increasing number of global open‐source DEMs have been available, with improvement in the accuracy. However, the latest released data are rarely evaluated in LSM research. In this paper, DEM‐based factors, including elevation, aspect, slope, plan curvature and profile curvature, were generated from seven open‐source DEMs, including Advanced Spaceborne Thermal Emission and Reflection (ASTER) V2, ASTERV3, ALOS World 3D‐30 m (AW3D30), Copernicus DEM 30 m (COP) Forest and Buildings removed Copernicus DEM (FABDEM), NASADEM, and Shuttle Radar Topography Mission (SRTM). DEM‐based factors were coupled with the distance to road, distance to river, land use, lithology, rain and normalized difference vegetation index (NDVI). The significant difference between DEMs is determined by comparing the area proportion. Slope, plane curvature and profile curvature are found to have a maximum difference of 15%–20%. Then, K‐Nearest Neighbours (KNN) and Random Forest (RF) were used to predict landslide susceptibility with two sampling methods, namely, 70% for training and 30% for testing (S1); 67% for training and 33% for testing (S2). For KNN with S1, the prediction rate is range from 0.8299 to 0.8701, with a difference of 0.0402. The difference of prediction rate is decreased to 0.0207 for S2 and 0.0258 for RF. COP has the highest prediction rate of 0.8701, 0.9254 and 0.9461 for KNN with S1 and RF with S1 and S2, respectively. ASTERV2 is the worst with prediction rate of 0.8897 and 0.8996 for KNN with S2 and RF with S1, respectively. The research result provides valuable insights for the selection of open‐source DEMs in future LSM.

Evaluation of the astrometric capability of Multi-Channel Imager and simulated calculation of proper motion of M31

Introduction: The Multi-Channel Imager (MCI), one of the back-end modules of the future China Space Station Telescope (CSST), is designed for high-precision spacebased astronomical observations. This paper evaluates the astrometric capability of the MCI based on simulated observational images and Gaia data: the M31 galaxy is selected as a representative case to validate the astrometric capability by calculating the proper motions (PMs) of the M31 member stars.Method: We analyze the stellar centroids of the simulated images in the R, I and G bands, positional uncertainty of 2.5 mas for brighter foreground reference stars from the Gaia DR3 catalog and of 7.5 mas for the fainter M31 member stars, are adopted respectively. The theoretical PMs are generated from the adopted velocity field model, rotation curve, and stellar surface density profile. And the simulated observed PMs are generated from the aforementioned position uncertainties and theoretical PMs.Result: We conclude that the precision of the MCI derived PMs strongly depends on the number of astrometric epochs per year. Specifically, uncertainty of 10 μas/yr is achievable with 10 epochs per year, and of 5 μas/yr with 50 epochs ignoring possible systematic effects. And symmetrically distributed observed fields yield better M31 kinematic parameters.Discussion: Unknown systematic errors, space environment effects on detectors, dithering strategies, and observation schedules can affect the PMs of M31, the above issues need further analysis and validation in future work.