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Shao Mingfu

Publications and source records attributed to Shao Mingfu.

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JW-FD: A Long Horizon Multimodal Solar Flare Forecasting Dataset

Solar flares drive severe space weather hazards, and forecasting their occurrence remains a central challenge for both heliophysics and operational space weather services. Data driven methods require long horizon datasets in which images, magnetic features, and flare labels are coregistered in space and time. We present JW-FD (JW-Flare Dataset), a 15 year multimodal release spanning 1 January 2011 through 31 December 2025, constructed from SDO/HMI line of sight magnetograms, NOAA Solar Region Summary reports, and NOAA X-ray flare event lists. The dataset comprises 3,064 independent active regions and 1,991,247 coregistered magnetogram crops, together with FITS, PNG, CSV, and MP4 modalities. Each sample provides 29 magnetic features linked to configurable flare labels under a strict pre-eruption window spanning seven forecast horizons and four GOES intensity thresholds. An 8:1:1 split at the active region level is adopted to prevent temporal leakage between partitions. PNG branches are released at six magnetic saturation thresholds, and internal Transformer experiments on >=C1.0 forecasting suggest Bth=1000 G as a preliminary default, although the optimal saturation is model and task dependent. The open source construction pipeline is available at https://github.com/Xiaoxuan-1/JW-FD.

astro-ph.SR

Scientific-Intention Driven Embodied Intelligent Solar Telescope: Conceptual Design

Artificial Intelligence (AI) is profoundly transforming the paradigms of scientific research. Cutting-edge technologies such as Large Language Models (LLMs) and embodied intelligence are continuously pushing the boundaries of scientific instrumentation. Against this backdrop, this paper proposes a novel conceptual system: the Scientific-Intention Driven Embodied Intelligent Solar Telescope (SIDEST). The system is designed with three core layers to achieve three types of intelligent scientific research closed loops. First, the Scientific Intent Research and Demonstration Layer parses the research objectives and intents of scientists (e.g., solar physicists) through natural language interaction, achieving a closed loop for the generation and optimization of executable observation plans aligned with scientific intent via in-depth research. Subsequently, the Observation Realization Layer schedules embodied intelligent solar telescopes to implement a closed loop for the execution of scientific observation plans. Finally, the Evaluation and Evolution Layer coordinates intelligent agents for data processing and scientific analysis to analyze observation data, generate research reports, and iteratively optimize observation strategies and model methods based on results, thereby realizing a self-evolving closed loop for the entire system. During the research process, we constructed a minimal prototype system based on a precision temperature control device for solar telescope birefringent filters to validate the core principles of SIDEST. This prototype successfully implemented all key steps of intention-driven automated research, demonstrating the feasibility of the technical pathways for the three types of intelligent research closed loops. SIDEST redefines telescopes through cutting-edge AI methods.

astro-ph.IM