Exploring the Solar Poles: The Last Great Frontier of the Sun

Nandy, Dibyendu; Banerjee, Dipankar; Bhowmik, Prantika; BRUN, Allan Sacha; Cameron, Robert H.; Gibson, S. E.; Hanasoge, Shravan; Harra, Louise; Hassler, Donald M.; Jain, Rekha; Jiang, Jie; Jouve, Laurène; Mackay, Duncan H.; Mahajan, Sushant S.; Mandrini, Cristina H.; Owens, Mathew; Pal, Shaonwita; Pinto, Rui F.; Saha, Chitradeep; Sun, Xudong; Tripathi, Durgesh; Usoskin, Ilya G.

doi:10.3847/25c2cfeb.1160b0ef

Cited by 1 publication

(1 citation statement)

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“…The solar polar regions are vital in controlling solar activity and driving space weather, but remain as the least-known mysterious territory on the Sun. So far, some polar exploration activities have been carried out, such as Ulysses (Wenzel et al 1992) and Solar Orbiter (Müller et al 2020), but there have been no frontal imaging observations on the solar polar regions (Nandy et al 2023). The Solar Polar-orbit Observatory (SPO) proposed by Chinese scientists will directly image the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle (80°) and a small ellipticity.…”

Section: Introductionmentioning

confidence: 99%

Lossless Compression Method for the Magnetic and Helioseismic Imager (MHI) Payload

Tong,

Lin,

Deng

et al. 2024

Res. Astron. Astrophys.

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The Solar Polar-orbit Observatory (SPO), proposed by Chinese scientists, is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity. However, one of the most significant challenges lies in ultra-long-distance data transmission, particularly for the Magnetic and Helioseismic Imager (MHI), which is the most important payload and generates the largest volume of data in SPO. In this paper, we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data. The background out of solar disk is removed to decrease the pixel number of image under compression. And multiple predictive coding methods are combined to eliminate the redundance utilizing the correlation (space, spectrum, and polarization) in dataset, improving the compression ratio. Experimental results demonstrate that our method achieves an average compression ratio of 3.67. The compression time is also less than the general observation period. The method exhibits strong feasibility and can be easily adapted to MHI.

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Section: Introductionmentioning

confidence: 99%