相关论文: LSTM-Based Forecasting Model for GRACE Acceleromet…
In this paper we investigate the opportunities offered by the new Earth gravity models from the dedicated CHAMP and, especially, GRACE missions to the project of measuring the general relativistic Lense-Thirring effect with a new Earth's…
The Gravity Recovery And Climate Experiment - Follow On (GRACE-FO) satellite mission (2018-now) hosts the novel Laser Ranging Interferometer (LRI), a technology demonstrator for proving the feasibility of laser interferometry for…
Satellite geodesy uses the measurement of the motion of one or more satellites to infer precise information about the Earth's gravitational field. In this work, we consider the achievable precision limits on such measurements by examining…
Period estimation is an important task in the classification of many variable astrophysical objects. Here we present GRAPE: Genetic Routine for Astronomical Period Estimation, a genetic algorithm optimised for the processing of survey data…
In 2016, we published "A test of general relativity using the LARES and LAGEOS satellites and a GRACE Earth gravity model. Measurement of Earth's dragging of inertial frames [1]", a measurement of frame-dragging, a fundamental prediction of…
Earth observation from satellite sensory data poses challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression has excelled in biophysical parameter estimation tasks from…
Time-dependent gravity data from satellite missions like GRACE-FO reveal mass redistribution in the system Earth at various time scales: long-term climate change signals, inter-annual phenomena like El Nino, seasonal mass transports and…
Context: MATISSE, the mid-infrared spectro-imaging instrument of VLTI, was designed to deliver its advertised performance when paired with an external second generation fringe tracker. Science observation started in 2019, demonstrating…
Accurate climate forecasting is vital for Bangladesh, a region highly susceptible to climate change impacts on temperature and rainfall. Existing models often struggle to capture long-range dependencies and complex temporal patterns in…
We examine how the new forthcoming Earth gravity models from the CHAMP and, especially, GRACE missions could improve the measurement of the general relativistic Lense-Thirring effect according to the various kinds of observables which could…
We introduce a novel deep learning framework based on Long Short-Term Memory (LSTM) networks to predict galactic cosmic-ray spectra on a one-day-ahead basis by leveraging historical solar activity data, overcoming limitations inherent in…
Frequency-domain expressions are found for gradiometer and satellite-to-satellite tracking measurements of a point source on the surface of the Earth. The maximum signal-to-noise ratio as a function of noise in the measurement apparatus is…
In the context of the currently ongoing efforts to improve the accuracy and reliability of the measurement of the Lense-Thirring effect in the gravitational field of the Earth it has recently been proposed to use the data from the existing…
Bilateral teleoperation of low-speed Unmanned Ground Vehicles (UGVs) on soft terrains is crucial for applications like lunar exploration, offering effective control of terrain-induced longitudinal slippage. However, latency arising from…
Recently, Ciufolini et al. reported on a test of the general relativistic gravitomagnetic Lense-Thirring effect by analyzing about 3.5 years of laser ranging data to the LAGEOS, LAGEOS II, LARES geodetic satellites orbiting the Earth. By…
The GRACE Follow-On satellite mission measures distance variations between the two satellites in order to derive monthly gravity field maps, indicating mass variability on Earth on a few 100 km scale due to hydrology, seismology,…
In this paper we will show in detail that the performed attempts aimed at the detection of the general relativistic Lense-Thirring effect in the gravitational field of the Earth with the existing LAGEOS satellites are often presented in an…
This paper introduces an open-source and reproducible implementation of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Networks for time series forecasting. We evaluated LSTM and GRU networks because of their performance…
Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning. Yet most datasets lack one or more characteristics that enhance their actionability, such as accuracy, global coverage, high spatial and…
Accurate power load forecasting is essential for the efficient operation and planning of electrical grids, particularly given the increased variability and complexity introduced by renewable energy sources. This paper introduces GAT-LSTM, a…