# Corrigendum to “Storm-time atmospheric density modeling using neural networks and its application in orbit propagation” [Adv. Space Res. 53 (2014) 558–567] (Storm-time atmospheric density modeling using neural networks and its application in orbit propagation (2014) 53(3) (558–567), (S0273117713007552), (10.1016/j.asr.2013.11.052))

## 抄録

The authors regret that Eqs. (6) and (8)–(10) in the published article were not correctly presented. Nevertheless, the first author has confirmed that equations used in programs were correct, and therefore, results and information presented in the figures and tables are all correct. Following is the correction to the equations and description in the first half of Section 4.1. A TLE set suggests the mean semi-major axis, [Formula presented] as, [Formula presented] Here, μ is the Earth gravitation constant and [Formula presented] is the mean mean motion indicated in a TLE set. Semi-major axis is related to specific energy, ξ, through the equation, [Formula presented] Since the atmospheric force is the non-conservative force that dominates the orbital decay of LEO objects, it is the only factor taken into account for the change of ξ. Then, Eq. (7) is transformed into [Formula presented] where accdrag denotes the instantaneous drag acceleration vector and v denotes the instantaneous velocity vector of the object. For several revolutions, [Formula presented] of the propagated orbit with time can be integrated and compared to the TLEs at the same epochs. Thus, the density model determining the atmospheric drag can be evaluated. accdrag appearing in Eq. (8) is computed from [Formula presented] Here, the ballistic coefficient BC = Cd∙A/m. A/m denotes the exposed cross-sectional area-to-mass ratio of the object and Cd denotes the drag coefficient. vrel and vrel represent object's velocity relative to the atmosphere in magnitude and vector, respectively.

本文言語 英語 2676 1 Advances in Space Research 68 6 https://doi.org/10.1016/j.asr.2021.06.033 出版済み - 9 15 2021

• 航空宇宙工学
• 天文学と天体物理学
• 地球物理学
• 大気科学
• 宇宙惑星科学
• 地球惑星科学（全般）

## フィンガープリント

「Corrigendum to “Storm-time atmospheric density modeling using neural networks and its application in orbit propagation” [Adv. Space Res. 53 (2014) 558–567] (Storm-time atmospheric density modeling using neural networks and its application in orbit propagation (2014) 53(3) (558–567), (S0273117713007552), (10.1016/j.asr.2013.11.052))」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。