Update README.md

README.md

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 SDOML-lite is a lightweight alternative to the [SDOML](https://registry.opendata.aws/sdoml-fdl/) dataset specifically designed for machine learning applications in solar physics, providing continuous full-disk images of the Sun with magnetic field and extreme ultraviolet data in several wavelengths. The data source is the [Solar Dynamics Observatory (SDO)](https://sdo.gsfc.nasa.gov/) space telescope, a NASA mission that has been in operation since 2010.
 
-NASA’s SDO mission has generated over 20 petabytes of high-resolution solar imagery since its launch in 2010, representing one of the most comprehensive records of science data ever collected. This dataset offers an extraordinary opportunity for scientific discovery and machine learning, enabling advances in space weather forecasting, climate and heliophysics modeling, spatiotemporal prediction of solar dynamics, image-to-image translation, the study of active region evolution, and solar flare forecasting. Yet, the sheer scale and complexity of the raw archive have made it largely inaccessible to the broader ML community. SDOML-lite bridges this gap by delivering a curated, normalized, and temporally consistent subset of SDO data, specifically designed for seamless integration into large-scale machine learning pipelines.
+NASA’s SDO mission has generated over 20 petabytes of high-resolution solar imagery since its launch in 2010, representing one of the most comprehensive records of science data ever collected. This dataset offers an extraordinary opportunity for scientific discovery and machine learning, enabling advances in space weather forecasting, climate modeling, solar flare and coronal mass ejection (CME) prediction, image-to-image translation, and unsupervised representation learning of solar dynamics. Yet, the sheer scale and complexity of the raw archive have made it largely inaccessible to the broader ML community. SDOML-lite bridges this gap by delivering a curated, normalized, and temporally consistent subset of SDO data, specifically designed for seamless integration into large-scale machine learning pipelines.
 
 _IMPORTANT: SDOML and SDOML-lite datasets are different in structure and data distributions. SDOML-lite is inspired by SDOML, but it is not based on SDOML data and there is no compatibility between the two formats._