arXiv subjects

Eun-Jung Choi

Publications and source records attributed to Eun-Jung Choi.

3 recordsLinked to original sources

arXiv2026

Korean Space Collision Environment Assessment Framework Based on 3D-Cell Model

Space situational awareness (SSA) requires purpose-matched models across spatial, temporal, and fidelity scales. Building on our previously reported three-dimensional (3D) cell formulation and implementation, this study establishes a reproducible, resolution-aware, catalog-conditioned framework for macroscopic assessment of the low Earth orbit (LEO) collision environment. The framework maps supplied catalog or scenario populations to time-averaged spatial density and target-specific impact metrics while retaining individual-object information. Using a 2025 Space-Track snapshot, we evaluate radial, declination, and right-ascension resolution sensitivity and computational performance for six targets, including two Korean space assets. Normalized expected impact counts range from 0.615 to 1.599 and vary nonmonotonically; for a synthetic 500-km circular target, the result at a 0.25-km radial width is 38.5\% below the 10-km reference. Runtime and memory show direction-dependent trade-offs. Ten annual snapshots show catalog growth from 15,723 objects in 2016 to 28,540 in 2025 and a 7.86-fold increase in the 500-km target metric, driven primarily by Starlink, other payloads, and unknown/TBA records. In a conditional stress test of the proposed 998,240-satellite SpaceX Orbital Data Center population, exact annual probabilities of at least one impact reach $3.75\times10^{-3}$ and $1.48\times10^{-3}$ for the 700-km and 1,000-km targets. The framework provides a reproducible, resolution-aware basis for catalog-conditioned environment monitoring, comparative scenario assessment, and prioritization of cases for higher-fidelity follow-up analysis.

astro-ph.EP↗

arXiv2021

DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues

In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-value that are the same as the training data. In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values. For generalization, diffusion signals are normalized in a q-space and then projected and quantized, producing a matrix (Qmatrix) as an input for the network. To demonstrate the validity of this approach, DIFFnet is evaluated for diffusion tensor imaging (DIFFnetDTI) and for neurite orientation dispersion and density imaging (DIFFnetNODDI). In each model, two datasets with different gradient schemes and b-values are tested. The results demonstrate accurate reconstruction of the diffusion parameters at substantially reduced processing time (approximately 8.7 times and 2240 times faster processing time than conventional methods in DTI and NODDI, respectively; less than 4% mean normalized root-mean-square errors (NRMSE) in DTI and less than 8% in NODDI). The generalization capability of the networks was further validated using reduced numbers of diffusion signals from the datasets. Different from previously proposed deep neural networks, DIFFnet does not require any specific gradient scheme and b-value for its input. As a result, it can be adopted as an online reconstruction tool for various complex diffusion imaging.

eess.IV↗

arXiv2018

Optical observations of NEA 3200 Phaethon (1983 TB) during the 2017 apparition

The near-Earth asteroid 3200 Phaethon (1983 TB) is an attractive object not only from a scientific viewpoint but also because of JAXA's DESTINY+ target. The rotational lightcurve and spin properties were investigated based on the data obtained in the ground-based observation campaign of Phaethon. We aim to refine the lightcurves and shape model of Phaethon using all available lightcurve datasets obtained via optical observation, as well as our time-series observation data from the 2017 apparition. Using eight 1-2-m telescopes and an optical imager, we acquired the optical lightcurves and derived the spin parameters of Phaethon. We applied the lightcurve inversion method and SAGE algorithm to deduce the convex and non-convex shape model and pole orientations. We analysed the optical lightcurve of Phaethon and derived a synodic and a sidereal rotational period of 3.6039 h, with an axis ratio of a/b = 1.07. The ecliptic longitude (lambda) and latitude (beta) of the pole orientation were determined as (308, -52) and (322, -40) via two independent methods. A non-convex model from the SAGE method, which exhibits a concavity feature, is also presented.

astro-ph.EP↗