arXiv subjects

Emiliano Fiorenza

Publications and source records attributed to Emiliano Fiorenza.

5 recordsLinked to original sources

arXiv2026

A general framework for disturbance compensation in airborne and seaborne gravimetry supported by machine learning

High-accuracy measurement systems operating in complex operational environments, such as airborne and seaborne gravimetry, are severely affected by interacting external influences (disturbances) including temperature variations, inertial accelerations, and platform rotations. This work aims to develop and prove a general method for compensating such disturbances beyond the limits of conventional approaches. We present a general framework based on three pillars: a multi-sensor system, supervised machine learning, and a dedicated laboratory -training platform-. This measurement technique was investigated through two experimental case studies relevant to airborne and seaborne gravimetry: (i) temperature and thermal-gradient rejection in a high-sensitivity tri-axial accelerometer, and (ii) pitch and roll rejection for vertical acceleration estimation. The experiments highlighted that, although the general framework provides a useful guideline, its application to airborne and seaborne gravimetry is challenging and not straightforward, requiring the development of dedicated and innovative experimental solutions. This difficulty arises from the specific nature of the measurand-gravity-which cannot be easily varied under controlled conditions. In this work, we present a dedicated approach to addressing these challenges.

physics.ins-det↗

arXiv2024

Italian Spring Accelerometer measurements of unexpected Non Gravitational Perturbation during BepiColombo second Venus swing-by

The Italian Spring Accelerometer (ISA) is a three axis mass-spring accelerometer, one of the payloads of the BepiColombo joint space mission between the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA). At launch in October 2018, BepiColombo started its seven-year cruise as a stack of three different modules, overall named Mercury Composite Spacecraft (MCS). The spacecraft will provide BepiColombo the necessary Delta V to reach Mercury with its electric thrusters and along with one, two and six gravity assists, respectively with Earth, Venus and Mercury. The accelerometer is accommodated on the Mercury Planetary Orbiter (MPO) module and, jointly with the Ka-band Transponder (KaT) tracking data, will primarily serve the BepiColombo Radio Science Experiment (BC-RSE). During the second Venus swing-by, strong tidal effect and external perturbations was expected to act on the spacecraft and to become detectable by ISA. The swing-by had a closest approach of about 550 km and the gravity gradient expected on the IDA sensing elements was perfectly measured. Hence, in this paper, the first direct Gravity Gradient effect detection generated by an extraterrestrial body is shown. Nevertheless, around the closest approach, the measurements evidenced a spurious acceleration event lasting for several minutes. This work, exploiting information on the Attitude and Orbit Control System (AOCS) commanded torques, focuses and analyses this ISA acceleration signal, ascribing it to a net force really acting on the MCS spacecraft. Furthermore, using an estimation method, the application point of the force is confined to an area close to the MPO radiator.

astro-ph.IM↗

arXiv2024

Sea wave data reconstruction using micro-seismic measurements and machine learning methods

Sea wave monitoring is key in many applications in oceanography such as the validation of weather and wave models. Conventional in situ solutions are based on moored buoys whose measurements are often recognized as a standard. However, being exposed to a harsh environment, they are not reliable, need frequent maintenance, and the datasets feature many gaps. To overcome the previous limitations, we propose a system including a buoy, a micro-seismic measuring station, and a machine learning algorithm. The working principle is based on measuring the micro-seismic signals generated by the sea waves. Thus, the machine learning algorithm will be trained to reconstruct the missing buoy data from the micro-seismic data. As the micro-seismic station can be installed indoor, it assures high reliability while the machine learning algorithm provides accurate reconstruction of the missing buoy data. In this work, we present the methods to process the data, develop and train the machine learning algorithm, and assess the reconstruction accuracy. As a case of study, we used experimental data collected in 2014 from the Northern Tyrrhenian Sea demonstrating that the data reconstruction can be done both for significant wave height and wave period. The proposed approach was inspired from Data Science, whose methods were the foundation for the new solutions presented in this work. For example, estimating the period of the sea waves, often not discussed in previous works, was relatively simple with machine learning. In conclusion, the experimental results demonstrated that the new system can overcome the reliability issues of the buoy keeping the same accuracy.

physics.ins-det↗

arXiv2024

Temperature compensation in high accuracy accelerometers using multi-sensor and machine learning methods

Temperature is a major source of inaccuracy in high-sensitivity accelerometers and gravimeters. Active thermal control systems require power and may not be ideal in some contexts such as airborne or spaceborne applications. We propose a solution that relies on multiple thermometers placed within the accelerometer to measure temperature and thermal gradient variations. Machine Learning algorithms are used to relate the temperatures to their effect on the accelerometer readings. However, obtaining labeled data for training these algorithms can be difficult. Therefore, we also developed a training platform capable of replicating temperature variations in a laboratory setting. Our experiments revealed that thermal gradients had a significant effect on accelerometer readings, emphasizing the importance of multiple thermometers. The proposed method was experimentally tested and revealed a great potential to be extended to other sources of inaccuracy, such as rotations, as well as to other types of measuring systems, such as magnetometers or gyroscopes.

physics.ins-det↗

arXiv2023

Gamma-Ray Burst observations by the high-energy charged particle detector on board the CSES-01 satellite between 2019 and 2021

In this paper we report the detection of five strong Gamma-Ray Bursts (GRBs) by the High-Energy Particle Detector (HEPD-01) mounted on board the China Seismo-Electromagnetic Satellite (CSES-01), operational since 2018 on a Sun-synchronous polar orbit at a $\sim$ 507 km altitude and 97$^\circ$ inclination. HEPD-01 was designed to detect high-energy electrons in the energy range 3 - 100 MeV, protons in the range 30 - 300 MeV, and light nuclei in the range 30 - 300 MeV/n. Nonetheless, Monte Carlo simulations have shown HEPD-01 is sensitive to gamma-ray photons in the energy range 300 keV - 50 MeV, even if with a moderate effective area above $\sim$ 5 MeV. A dedicated time correlation analysis between GRBs reported in literature and signals from a set of HEPD-01 trigger configuration masks has confirmed the anticipated detector sensitivity to high-energy photons. A comparison between the simultaneous time profiles of HEPD-01 electron fluxes and photons from GRB190114C, GRB190305A, GRB190928A, GRB200826B and GRB211211A has shown a remarkable similarity, in spite of the different energy ranges. The high-energy response, with peak sensitivity at about 2 MeV, and moderate effective area of the detector in the actual flight configuration explain why these five GRBs, characterised by a fluence above $\sim$ 3 $\times$ 10$^{-5}$ erg cm$^{-2}$ in the energy interval 300 keV - 50 MeV, have been detected.

astro-ph.HE↗