North and South Poles
SeaIceWeather Dataset
This is the SeaIceWeather dataset, collected for training and evaluation of deep learning based de-weathering models. To the best of our knowledge, this is the first such publicly available dataset for the sea ice domain. This dataset is linked to our paper titled: Deep Learning Strategies for Analysis of Weather-Degraded Optical Sea Ice Images. The paper can be accessed at: https://doi.org/10.1109/jsen.2024.3376518
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We present a finite-element-based cohesive zone model for simulating the nonlinear fracture process driving the propagation of water-filled surface crevasses in floating ice tongues. The fracture process is captured using an interface element whose constitutive behavior is described by a bilinear cohesive law, and the bulk rheology of ice is described by a nonlinear elasto-viscoplastic model. The additional loading due to meltwater pressure within the crevasse is incorporated by combining the ideas of poromechanics and damage mechanics.
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SI-STSAR-7 is a labeled spatiotemporal dataset for sea ice classification based on SAR images. The dataset is produced from 80 Sentinel-1 A/B SAR scenes during the two freezing periods of Hudson Bay from October 2019 to May 2020 and from October 2020 to April 2021, which are provided by the Copernicus Open Access Center. The Sentinel-1 SAR images were preprocessed with noise reduction and incidence angle dependence correction before use.
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This dataset is in support of my 3 research papers 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part I', 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part II' and 'Comparative Analysis of 72 Flyback Transformers on 5τ Non-linear Battery with Loss Functions - Part III'.
Preprint :
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