Ground‐based indices, such as the Dst, ap, and AE, have been used for decades to describe the interplay of the terrestrial magnetosphere with the solar wind and provide quantifiable indications of the state of geomagnetic activity in general. These indices have been traditionally derived from ground‐based observations from magnetometer stations all around the Earth. In the last 7 years though, the highly successful satellite mission Swarm has provided the scientific community with an abundance of high quality magnetic measurements at Low Earth Orbit, which can be used to produce the space‐based counterparts of these indices, such the Swarm‐Dst, Swarm‐ap, and Swarm‐AE indices. In this work, we present the first results from this endeavor, with comparisons against traditionally used parameters. We postulate on the possible usefulness of these Swarm‐based products for a more accurate monitoring of the dynamics of the magnetosphere and thus, for providing a better diagnosis of space weather conditions.

The Phi Books have used the house as a metaphor for interdisciplinary collaboration by using narrative, making and performance to explore how borders, walls and doors facilitate collaboration. This has lead to the production of books and interactive material produced by the authors and the participants, which are both fictional and imaginative while also being methodologically reflective. We would like to present the development of the Phi Books Project, showing its different stages, from the initial formulation of algorithmic fictions to technologically mediated and embodied systems for collaboration.The Phi Books use the house as a metaphor for interdisciplinary collaboration. The two researcher-artists use narrative, making and performance to explore how borders, walls and doors facilitate collaboration. This has led to the production of two books and interactive material produced by the authors and the participants, which are both fictional and imaginative while also Keywords collaboration participation performance narrative fiction algorithms

<p>Ultra-low frequency (ULF) magnetospheric plasma waves play a key role in the dynamics of the Earth’s magnetosphere and, therefore, their importance in Space Weather studies is indisputable. Magnetic field measurements from recent multi-satellite missions (e.g. Cluster, THEMIS, Van Allen Probes and Swarm) are currently advancing our knowledge on the physics of ULF waves. In particular, Swarm satellites, one of the most successful mission for the study of the near-Earth electromagnetic environment, have contributed to the expansion of data availability in the topside ionosphere, stimulating much recent progress in this area. Coupled with the new successful developments in artificial intelligence (AI), we are now able to use more robust approaches devoted to automated ULF wave event identification and classification. The goal of this effort is to use a deep learning method in order to classify ULF wave events using magnetic field data from Swarm. We construct a Convolutional Neural Network (CNN) that takes as input the wavelet spectra of the Earth’s magnetic field variations per track, as measured by each one of the three Swarm satellites, and whose building blocks consist of two convolution layers, two pooling layers and a fully connected (dense) layer, aiming to classify ULF wave events in four different categories: 1) Pc3 wave events (i.e., frequency range 20-100 MHz), 2) non-events, 3) false positives, and 4) plasma instabilities. Our primary experiments show promising results, yielding successful identification of more than 95% accuracy. We are currently working on producing larger training/test datasets, by analyzing Swarm data from the mid-2014 onwards, when the final constellation was formed, aiming to construct a dataset comprising of more than 50000 wavelet image inputs for our network.</p>

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