Article
Geochemistry & Geophysics
D. Kudin, A. A. Soloviev, R. Sidorov, V. Starostenko, Yu P. Sumaruk, O. Legostaeva
Summary: The system for preparing quasi-definitive data of the INTERMAGNET standard is described, providing data one day after the last series of absolute observations. These data are used for modeling fast variations in the Earth's main magnetic field and calculating geomagnetic activity indices. The quality of the data obtained through automated algorithms using this system is comparable to traditional approaches for preparing final INTERMAGNET standard data.
GEOMAGNETISM AND AERONOMY
(2021)
Article
Environmental Sciences
Mauro Regi, Antonio Guarnieri, Stefania Lepidi, Domenico Di Mauro
Summary: In this study, the authors analyze geomagnetic field measurements collected from Italian observatories and investigate the influence of tidal sea water level changes on local magnetic variations. They find evidence of geomagnetic power spectral peaks at solar and lunar tidal frequencies, and the effect of these variations differs between the two observatories. Furthermore, the study reveals different behaviors of single-station induction arrows and a close relationship between induction arrows and sea level variations at Lampedusa.
Article
Engineering, Multidisciplinary
Prasanna Mahavarkar, Debkumar Bhadra, Jacob John, Varun Dongre, Anil Iype, Shyamoli Mukherjee, B. Veenadhari, K. Vijaykumar, Nitin Sharma, Pradeep Kumar Birthare
Summary: The Indian Institute of Geomagnetism is operating Multiparametric Geophysical Observatory (MPGO) in Port Blair in South Andaman, collecting data and monitoring earthquakes with various magnetometer equipment. The observatory is currently upgrading and planning more experiments for the future.
Article
Chemistry, Multidisciplinary
Manjula Lingala, Phani Chandrasekhar Nelapatla, Kusumita Arora
Summary: The study found that the noise levels in the geomagnetic observatory data from Hyderabad significantly decreased during the COVID-19 lockdown, especially the noise caused by vehicular traffic. There was also a decrease in scatter observed in the absolute values of the geomagnetic field components, indicating an improvement in data quality in the absence of traffic-generated noise sources.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Electrical & Electronic
Xiaolin Li, Wenjun Zhang
Summary: This paper presents a multi-baseline VESPA approach for direction finding, utilizing cumulant matrices and a refinement step for accurate estimates. Two refinement approaches are introduced, with the method being simple, closed-form, search-free, applicable to irregularly linear arrays, and not affected by sensor gain uncertainties.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Geosciences, Multidisciplinary
F. Javier Pavon-Carrasco, Santiago Marsal, Saioa A. Campuzano, J. Miquel Torta
Summary: Research indicates that a new geomagnetic jerk occurred around 2019-2020, with global characteristics observed in both ground and satellite magnetic data. The most recent geomagnetic field model confirms the worldwide nature of this new jerk, while a new pulse at the core-mantle boundary appears to mark the onset of this event.
EARTH PLANETS AND SPACE
(2021)
Article
Materials Science, Multidisciplinary
Lirong Shen, Guang Sun, Wei Zhai
Summary: This study proposed a new geomagnetic assimilation method based on direction, distance, and parameter k, which can significantly improve the accuracy of vector geomagnetic data, especially for data from marginal areas.
RESULTS IN PHYSICS
(2021)
Article
Astronomy & Astrophysics
V Haberle, A. Marchaudon, A. Chambodut, P-l Blelly
Summary: This study applies signal filtering techniques on geomagnetic field measurements to determine baselines and extract quiet variations. It confirms the effectiveness of the filter approach in capturing quiet sources and shows good agreement with other baseline methods. This research provides an important foundation for real-time space weather applications.
JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS
(2022)
Article
Multidisciplinary Sciences
Alessandro Bemporad, Silvano Fineschi, Lucia Abbo, Carlo Benna, Ruggero Biondo, Gerardo Capobianco, Francesco Carella, Alberto Cora, Federica Frassati, Silvio Giordano, Herve Haudemand, Federico Landini, Davide Loreggia, Salvatore Mancuso, Andrea Mignone, Gianalfredo Nicolini, Maurizio Pancrazzi, Francesco Salvati, Roberto Susino, Daniele Telloni, Luca Zangrilli
Summary: The Solar Physics Group at the INAF-Turin Astrophysical Observatory is actively involved in various Space Weather projects and missions, providing data analysis tools and contributing to the development of future applications. Their research focuses on the origin and acceleration of solar wind and Coronal Mass Ejections, as well as their interplanetary propagation and evolution.
RENDICONTI LINCEI-SCIENZE FISICHE E NATURALI
(2023)
Article
Engineering, Electrical & Electronic
Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho So, Qing Ling
Summary: This work investigates stochastic quasi-Newton methods for minimizing a finite sum of cost functions over a decentralized network. A general algorithmic framework is developed where each node constructs a local, inexact quasi-Newton direction that approaches the global, exact one asymptotically. Two fully decentralized stochastic quasi-Newton methods are designed to construct Hessian inverse approximations with uniformly bounded positive eigenvalues, without requiring extra sampling or communication. Numerical results demonstrate that the proposed methods are much faster than existing decentralized stochastic first-order algorithms.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Filip Karisik, Mathias Baumert
Summary: In this study, a framework for adapting arbitrary quasi-periodic time series using traditional free form deformations is proposed. The method follows an alternating approach inspired by the robust point matching algorithm, updating a correspondence matrix to obtain subsequent deformation, thus adapting template data to target data. Performance of the algorithm is demonstrated across various ECG and PPG databases and compared to previous works.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Qingsong Zhang, Feihu Huang, Cheng Deng, Heng Huang
Summary: Stochastic optimization methods are popular in machine learning, with second-order methods offering better solutions. The SpiderSQN method achieves the best known SFO complexity, improving practical performance and matching existing best results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Aerospace
N. A. Zakaria, S. H. M. Yusoff, N. A. M. Rizal, N. S. A. Hamid, M. H. Hashim, Z. Mohd Radzi, M. H. Jusoh, A. Yoshikawa, T. Uozumi, S. Abe
Summary: MAGDAS PEN was established at Universiti Sains Malaysia as part of the MAGDAS observatory arrays, with the goal of monitoring global electromagnetic and ambient plasma density in the geospace environment. A one-month study of geomagnetic field data provided insights into daily variations and the influence of eastward electric field effects during peak solar hours. Overall, the observed trends indicated a solar quiet field with no solar-terrestrial disturbances.
ADVANCES IN SPACE RESEARCH
(2021)
Article
Computer Science, Theory & Methods
Robin Richter, Shankar Bhamidi, Sach Mukherjee
Summary: Causal structure learning (CSL) is the process of estimating causal graphs from data. This paper proposes a new baseline method called graph-based predictors (GBPs), which leverage the known graph structure to provide improved baselines for comparing CSL methods. Experimental results show that GBPs outperform random baselines in practice.
STATISTICS AND COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Andrei Vorobev, Anatoly Soloviev, Vyacheslav Pilipenko, Gulnara Vorobeva, Yaroslav Sakharov
Summary: This paper focuses on the research of diagnostic methods for GIC in power transmission lines in northwestern Russia based on IMAGE magnetometer data. By statistical and correlation analysis, the features that best characterize the target variable are identified. A relationship for GIC diagnostics is established using machine learning methods, with regression and artificial neural networks found to be the best solutions.
APPLIED SCIENCES-BASEL
(2022)
Article
Geochemistry & Geophysics
Alan W. P. Thomson, Brian Hamilton, Susan Macmillan, Sarah J. Reay
GEOPHYSICAL JOURNAL INTERNATIONAL
(2010)
Article
Astronomy & Astrophysics
Alan W. P. Thomson, Ewan B. Dawson, Sarah J. Reay
SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
(2011)
Article
Astronomy & Astrophysics
AWP Thomson, AJ McKay, E Clarke, SJ Reay
SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
(2005)
Article
Astronomy & Astrophysics
SJ Reay, W Allen, O Baillie, J Bowe, E Clarke, V Lesur, S Macmillan
ANNALES GEOPHYSICAE
(2005)