Related papers: Global 4D Ionospheric STEC Prediction based on Dee…
Real time acquisition of accurate underwater sound velocity profile (SSP) is crucial for tracking the propagation trajectory of underwater acoustic signals, making it play a key role in ocean communication positioning. SSPs can be directly…
Tsunamis can trigger internal gravity waves (IGWs) in the ionosphere, perturbing the Total Electron Content (TEC) - referred to as Traveling Ionospheric Disturbances (TIDs) that are detectable through the Global Navigation Satellite System…
This paper presents the storm time comparative analysis of the performances of latest versions of global ionospheric models: International Reference Ionosphere (IRI) 2016, NeQuick 2 (NeQ) and the IRI extended to Plasmasphere (IRI-P) 2017…
We developed a new technology for global detection of atmospheric disturbances, on the basis of phase measurements of the total electron content (TEC) using an international GPS networks. Temporal dependencies of TEC are obtained for a set…
The ionospheric scattering of pulses emitted by PSR B0950+08 is measured using the 10-m RadioAstron Space Radio Telescope, the 300-m Arecibo Radio Telescope and the 14x25-m Westerbork Synthesis Radio Telescope (WSRT) at a frequency band…
This author suggests the concept of a new technology for global detection (GLOBDET) of atmospheric disturbances of natural and technogenic origin, on the basis of phase measurements of the total electron content (TEC) in the ionosphere…
Radio interferometers, which are designed to observe astrophysical objects in the universe, can also be used to study the Earth's ionosphere. Radio interferometers like the Giant Metrewave Radio Telescope (GMRT) detect variations in…
We present a statistical framework for estimating global navigation satellite system (GNSS) non-ionospheric differential time delay bias. The biases are estimated by examining differences of measured line integrated electron densities (TEC)…
The ionosphere has been normally detected by traditional instruments, such as ionosonde, scatter radars, topside sounders onboard satellites and in situ rocket. However, most instruments are expensive and also restricted to either the…
In this paper, we present SSDNet, a novel deep learning approach for time series forecasting. SSDNet combines the Transformer architecture with state space models to provide probabilistic and interpretable forecasts, including trend and…
The integration of Non-Terrestrial Networks (NTNs) with Low Earth Orbit (LEO) satellite constellations into 5G and Beyond is essential to achieve truly global connectivity. A distinctive characteristic of LEO mega constellations is that…
Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great expressiveness of graph neural networks. However, these models…
In this paper, we explore the feasibility of using communication signals for extended target (ET) tracking in an integrated sensing and communication (ISAC) system. The ET is characterized by its center range, azimuth, orientation, and…
We present Compressible Atmospheric Model-Network (CAM-NET), an AI model designed to predict neutral atmospheric variables from the Earth's surface to the ionosphere with high accuracy and computational efficiency. Accurate modeling of the…
The paper presents an assessment of the performances of the global empirical models: International Reference Ionosphere (IRI)-2016 and the NeQuick2 model derived ionospheric Total Electron Content (TEC) with respect to the Navigation with…
Spatio-temporal area-level datasets play a critical role in official statistics, providing valuable insights for policy-making and regional planning. Accurate modeling and forecasting of these datasets can be extremely useful for…
El Ni\~no-Southern Oscillation (ENSO) exerts global climate and societal impacts, but real-time prediction with lead times beyond one year remains challenging. Dynamical models suffer from large biases and uncertainties, while deep learning…
Detecting pedestrians and predicting future trajectories for them are critical tasks for numerous applications, such as autonomous driving. Previous methods either treat the detection and prediction as separate tasks or simply add a…
One of the key issues facing Direct-to-Cellular (D2C) satellite communication systems is ionospheric scintillation on the uplink and downlink, which can significantly degrade link quality. This work investigates the spatial and temporal…
Weather Forecasting is an attractive challengeable task due to its influence on human life and complexity in atmospheric motion. Supported by massive historical observed time series data, the task is suitable for data-driven approaches,…