Institutt for fysikk og teknologi
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Studentaktiv læring med store studentgrupper: flervalgsoppgaver i sentrum
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-02-16)To viktige prinsipper for å øke studentenes læring i undervisning er aktiv deltagelse og formativ vurdering. Innføring av disse prinsippene i undervisning med store studentgrupper kan imidlertid by på utfordringer, særlig i nettbasert undervisning. En mulig løsning kan være bruk av flervalgsoppgaver som diagnostisk testing med innsamling av svarene og påfølgende formativ vurdering – en undervisningsform ... -
Bringing it all together: Science priorities for improved understanding of Earth system change and to support international climate policy
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-10-18)We review how the international modelling community, encompassing integrated assessment models, global and regional Earth system and climate models, and impact models, has worked together over the past few decades to advance understanding of Earth system change and its impacts on society and the environment and thereby support international climate policy. We go on to recommend a number of priority ... -
Sea ice mass balance during the MOSAiC drift experiment: Results from manual ice and snow thickness gauges
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-07-09)Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ... -
Point-cloud clustering and tracking algorithm for radar interferometry
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-10-22)In data mining, density-based clustering, which entails classifying datapoints according to their distributions in some space, is an essential method to extract information from large datasets. With the advent of software-based radio, ionospheric radars are capable of producing unprecedentedly large datasets of plasma turbulence backscatter observations, and new automatic techniques are needed to ... -
Polar mesospheric summer echo (PMSE) multilayer properties during the solar maximum and solar minimum
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-11-11)Polar mesospheric summer echoes (PMSEs) are radar echoes that are measured in the upper atmosphere during the summer months and that can occur in several layers. In this study, we aimed to investigate the relationship between PMSE layers ranging from 80 to 90 km altitude and the solar cycle. We investigated 230 h of observations from the EISCAT very high frequency (VHF) radar located near Tromsø, ... -
Deriving the Ionospheric Electric Field From the Bulk Motion of Radar Aurora in the E-Region
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-10-28)In the auroral E‐region strong electric fields can create an environment characterized by fast plasma drifts. These fields lead to strong Hall currents which trigger small‐scale plasma instabilities that evolve into turbulence. Radio waves transmitted by radars are scattered off of this turbulence, giving rise to the ‘radar aurora’. However, the Doppler shift from the scattered signal does not ... -
Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced Arctic spring sea ice surface classification
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-07-10)The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution Ku-band radar altimetry data over the polar regions up to 81° North. The combination of synthetic aperture radar (SAR) mode altimetry (SRAL instrument) from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging ... -
Cross-modality sub-image retrieval using contrastive multimodal image representations
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-08-13)In tissue characterization and cancer diagnostics, multimodal imaging has emerged as a powerful technique. Thanks to computational advances, large datasets can be exploited to discover patterns in pathologies and improve diagnosis. However, this requires efficient and scalable image retrieval methods. Cross-modality image retrieval is particularly challenging, since images of similar (or even the ... -
Overview of the First Pathloss Radio Map Prediction Challenge
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-06-26)Pathloss quantifies the reduction in power density of a signal radiated from a transmitter. The attenuation is due to large-scale effects such as free-space propagation loss and interactions (e.g., penetration, reflection, and diffraction) of the signal with objects such as buildings, vehicles, trees, and pedestrians in the propagation environment. Many current or planned wireless communications ... -
Automated Sentinel-1 ice type mapping and in-situ validation during the CIRFA-22 cruise
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-05-13)We present a fully-automated workflow to map sea ice types from Sentinel-1 data and transfer the results in near real-time to the research vessel Kronprins Haakon (KPH) in order to support tactical navigation and decision-making during a research cruise conducted towards Belgica Bank in April and May 2022. We used overlapping SAR and optical imagery to train a pixel-wise classifier for the required ... -
A three-point velocity estimation method for two-dimensional coarse-grained imaging data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-09-04)Time delay and velocity estimation methods have been widely studied subjects in the context of signal processing, with applications in many different fields of physics. The velocity of waves or coherent fluctuation structures is commonly estimated as the distance between two measurement points divided by the time lag that maximizes the cross correlation function between the measured signals, but ... -
Waves and Instabilities in Saturn's Magnetosheath: 1 Mirror Mode Waves and Their Impact on Magnetopause Reconnection
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-10-03) -
Waves and Instabilities in Saturn’s magnetosheath: 2 Dispersion relation analysis
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-10-03) -
Multi-Instrument and SAMI3-TIDAS Data Assimilation Analysis of Three-Dimensional Ionospheric Electron Density Variations During the April 2024 Total Solar Eclipse
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-08-31)This paper conducts a multi-instrument and data assimilation analysis of the three-dimensional ionospheric electron density responses to the total solar eclipse on 08 April 2024. The altitude-resolved electron density variations over the continental US and adjacent regions are analyzed using the Millstone Hill incoherent scatter radar data, ionosonde observations, Swarm in situ measurements, and a ... -
Characterising the magnetic and plasma environment upstream of Ganymede
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-10-17)We present an application of the latest UCL‐AGA magnetodisc model (MDISC) to the study of the magnetic and plasma conditions in the near‐Ganymede space. By doing this, we provide a comparison with measurements from Juno's most recent flyby of the Jovian moon, perijove 34 (PJ34). We find good agreement between the model results and the magnetometer data, pointing toward a hot plasma index value ... -
Defending Against Poisoning Attacks in Federated Learning with Blockchain
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-03-18)In the era of deep learning, federated learning (FL) presents a promising approach that allows multiinstitutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy. However, most existing FL approaches rely on a centralized server for global model aggregation, leading to a single point of failure. This makes the system vulnerable to ... -
Machine learning for gap-filling in greenhouse gas emissions databases
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-07-10)Greenhouse gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies aiming to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use three datasets of increasing complexity ... -
Turbulence Around Auroral Arcs
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-08-18)The spectacular visual displays from the aurora come from curtains of excited atoms and molecules, impacted by energetic charged particles. These particles are accelerated from great distances in Earth's magnetotail, causing them to precipitate into the ionosphere. Energetic particle precipitation is associated with currents that generate electric fields, and the end result is a dissipation of the ... -
Turbulence Embedded Into the Ionosphere by Electromagnetic Waves
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-08-19)When charged particles are accelerated from Earth's magnetosphere and precipitate into the atmosphere, their impact with neutral gas creates the aurora. Structured electric fields drive the acceleration processes but they are also passed down to the ionosphere, meaning that turbulence can in part be embedded into the ionosphere rather than emerge through instability processes locally. Applying a ... -
Incipient fault detection and isolation with Cauchy–Schwarz divergence: A probabilistic approach
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-08-02)To monitor the dynamics and non-stationarity inherent in industrial processes, we propose a novel incipient fault detection and isolation scheme grounded in a probabilistic perspective, using the Cauchy–Schwarz (CS) divergence. Our innovation lies in the utilization of marginal CS divergence for incipient fault detection and the conditional CS divergence for fault isolation. This approach neither ...