Ionospheric structure usually changes dramatically during a strong geomagnetic storm period, which will significantly affect the short-wave communication and satellite navigation systems. It is critically important to make accurate ionospheric predictions under extreme space weather conditions. However, prediction always challenge, pure physical methods often fail get satisfactory result since behavior varies greatly with different storms. In this paper, in order find an effective method,...

Abstract Observations with a global coverage are very important for space physics research and weather monitoring. However, due to the technical limitations, it would be expensive or even impossible achieve seamless advanced observational devices. It useful fill missing data gaps create map from available data, but up until now this has been challenging. Fortunately, deep learning method, recent breakthrough in artificial intelligence, may provide an effective way solve problem by making...

Abstract The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), communications and other space applications. In this study, a model IGS‐TEC maps are established based on testing several different long short‐term memory (LSTM) network (LSTM)‐based algorithms to explore direction that can effectively alleviate the increasing error with time. We find Multi‐step auxiliary algorithm performs best. It predict in next 6 days...

Geoscience research has generated vast amounts of data, creating a need for effective extraction and integration knowledge to address global-change challenges, promote sustainable development, accelerate scientific discovery. Foundation language models, trained through extensive pre-training instruction tuning on large text corpora, can facilitate this process. However, when foundational models lack sufficient geoscience expertise, with relevant data generate content that is inconsistent...

Deep learning methods have great potential to predict tumor characterization, such as histological diagnosis and genetic aberration. The objective of this study was evaluate validate the predictive performance multimodality imaging-derived models using computer-aided diagnostic (CAD) for prediction MDM2 gene amplification identify well-differentiated liposarcoma (WDLPS) lipoma.All 127 patients from two institutions were included with 89 in one institution model training 38 other external...

[1] The ionospheric responses to the solar eclipse of 15 January 2010 in equatorial anomaly region have been investigated by three vertical-incidence and seven oblique-incidence ionosondes arranged along meridian from geomagnetic latitudes 18°N 30°N eastern China. Though occurred later evening, effect on electron density reflection height F2 layer was clearly observed. study lag (the time between occurrence maximum obscuration depletion foF2) with latitude indicates it increased altitude....

Abstract Solar flare formation mechanisms and their corresponding predictions have commonly been difficult topics in solar physics for decades. The traditional forecasting method manually constructs a statistical relationship between the measured values of active regions flares that cannot fully utilize information related to contained observational data. In this article, we first used neural-network methods driven by magnetogram magnetic characteristic parameters sunspot group learn...

There have been many studies on the sub-terahertz (sub-THz) wave transmission in reentry plasma sheaths. However, only some of them paid attention to sub-THz waves magnetized In this paper, both unmagnetized and sheaths was investigated. The impacts temporal evolution sheath were studied. “atmospheric window” frequencies a discussed detail. According study, power rates (Tp) for left hand circular (LHC) right modes are obviously higher lower than those sheath, respectively. Tp LHC mode...

Abstract The accurate prediction of storm‐time thermospheric mass density is always critically important and also a challenge. In this paper, an available model established by Long Short‐Term Memory (LSTM)‐based ensemble learning algorithms. However, the generalization ability deep often suspicious since training data testing are from same set in conventional method. Therefore, order to objectively validate performance model, we utilize GOCE for SWARM‐C verify its mainly during geomagnetic...

Abstract Geomagnetic storms induce ionospheric disturbances, affecting short‐wave radio communication systems. Accurate total electron content (TEC) prediction is vital for accurately describing the environment of ionosphere. We use Multi‐Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi‐LSTM networks, an auxiliary and convolutional processes spatiotemporal modeling. Our validation shows outperforms...

With the rapid development of information technology, demand for digital agriculture is increasing. As an important agricultural production topic, crop yield has always attracted much attention. Currently, artificial intelligence, particularly machine learning, become leading approach prediction. a result, developing learning method that accurately predicts one central challenges in agriculture. Unlike traditional regression prediction problems, significant time correlation. For example,...

Introduction The accurate determination of the ocean sound speed profile (SSP) is essential for oceanographic research and marine engineering. Traditional methods acquiring SSP data are often time-consuming costly. Machine learning techniques provide a more efficient alternative inversion, effectively addressing limitations conventional approaches. Methods This study proposes novel inversion model based on grouped dilated convolution (GDC) Informer architecture. By replacing standard...

Abstract Due to the scarcity of in situ measurements thermosphere, retrieval thermospheric mass density primarily relies on model simulations or inversion satellite accelerometers and orbital data. Density derived from is more accurate, often reflecting actual at altitude. However, due challenges comprehensive spacecraft information as well its accessibility, complexity methods, real‐time data are generally not immediately available. Atmospheric can introduce errors, particularly exacerbated...

Plasmaspheric hiss plays an important role for the electron precipitation and formation of slot in radiation belts. It is easy waves to resonate with scatter energetic electrons at higher L shells, as frequencies decrease distance away from earth. Recent studies show whistler-mode can be guided density irregularities, performing parallel propagation experiencing little Landau damping. Therefore, resonance between ducted expand latitudes, then drive strong scattering. In this study, we report...

Abstract With rapid development of artificial intelligence technology, machine learning has been widely applied to the thermospheric mass density (TMD) modeling. In this study we propose a machine‐learning approach, bidirectional gated recurrent unit with multi‐head attention mechanism (BGMA), for modeling and predicting TMD, based on Gravity Recovery Climate Experiment (GRACE) satellite data. GRACE data spanning over one solar cycle provide valuable opportunity explore altitude activity...

Abstract Plasmaspheric hiss plays an important role in the electron precipitation and slot formation radiation belts. Recent studies show whistler‐mode waves can be guided density irregularities, performing parallel propagation. Therefore, resonance between ducted energetic electrons expand to higher latitudes, then drive strong scattering. In this study, we report a conjugate observation using data from Van Allen Probe A POES. Through analysis of quantification quasi‐linear diffusion...

Vertical sounding of the ionosphere yields ionograms that reflects relationship between virtual height and frequency, electron density profiles can be obtained by inversion ionogram. The existing vertical ionogram is mainly based on model method, but this method has poor stability. Influenced geomagnetic field, electric wave splits into O-wave X-wave when it propagates in ionosphere, study considers introducing inversion, adopts data assimilation to improve stability adding more physical...

Intracranial aneurysm is a common clinical disease that seriously endangers the health of patients. In view shortcomings existing intracranial recognition methods in dealing with complex morphologies, varying sizes, as well multi-scale feature extraction and lightweight deployment, this study introduces an detection framework, AS-YOLO, which designed to enhance precision while ensuring compatibility device deployment. Built on YOLOv8n backbone, approach incorporates cascaded enhancement...

This study proposes a deep learning-based vertical decomposition model for ionospheric Total Electron Content (TEC), which establishes nonlinear mapping from macroscale TEC data to vertically layered electron density (Ne) spanning 60–800 km by integrating geomagnetic indices (AE, SYM-H) and solar activity parameters (F10.7). Utilizing global grid (spatiotemporal resolution: 1 h/5.625° × 2.8125°) provided the International GNSS Service (IGS), Multilayer Perceptron (MLP) was developed, taking...

Abstract Solar eruptions and the solar wind are sources of space weather disturbances, extreme-ultraviolet (EUV) observations widely used to research activity forecasts. Fengyun-3E is equipped with X-ray Extreme Ultraviolet Imager, which can observe EUV imaging data. Limited by lower resolution, however, we super-resolution techniques improve data quality. Traditional image interpolation methods have limited expressive ability, while deep-learning learn reconstruct high-quality images...