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Register a new userJovian Dust Streams: A monitor of Ios volcanic plume activity
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Streams of high speed dust particles originate from Jupiters innermost Galilean moon Io. After release from Io, the particles collect electric charges in the Io plasma torus, gain energy from the co-rotating electric field of Jupiters magnetosphere, and leave the Jovian system into interplanetary space with escape speeds over $rm 200 km s^{-1}$. Galileo, which was the first orbiter spacecraft of Jupiter, has continuously monitored the dust streams during 34 revolutions about the planet between 1996 and 2002. The observed dust fluxes exhibit large orbit-to-orbit variability due to systematic and stochastic changes. After removal of the systematic variations, the total dust emission rate of Io has been calculated. It varies between $10^{-3}$ and $mathrm{10} rm kg s^{-1}$, and is typically in the range of 0.1 to $rm 1 kg s^{-1}$. We compare the dust emission rate with other markers of volcanic activity on Io like large-area surface changes caused by volcanic deposits and sightings of volcanic plumes.
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Jupiter was discovered to be a source of high speed dust particles by the Ulysses spacecraft in 1992. These dust particles originate from the volcanic plumes on Io. They collect electrostatic charges from the plasma environment, gain energy from the co-rotating electric field of the magnetosphere, and leave Jupiter with escape speeds over $rm 200 km s^{-1}$. The dust streams were also observed by the Galileo and Cassini spacecraft. While Ulysses and Cassini only had a single encounter with Jupiter, Galileo has continuously monitored the dust streams in the Jovian magnetosphere since 1996. The observed dust fluxes exhibit large orbit-to-orbit variability due to both systematic and stochastic changes. By combining the entire data set, the variability due to stochatic processes can be approximately removed and a strong variation with Jovian local time is found. This result is consistent with theoretical expectations and confirms that the majority of the Jovian dust stream particles originate from Io rather than other potential sources.
Jupiters banded appearance may appear unchanging to the casual observer, but closer inspection reveals a dynamic, ever-changing system of belts and zones with distinct cycles of activity. Identification of these long-term cycles requires access to datasets spanning multiple jovian years, but explaining them requires multi-spectral characterization of the thermal, chemical, and aerosol changes associated with visible color variations. The Earth-based support campaign for Junos exploration of Jupiter has already characterized two upheaval events in the equatorial and temperate belts that are part of long-term jovian cycles, whose underlying sources could be revealed by Junos exploration of Jupiters deep atmosphere.
Much of the geologic activity preserved on Europas icy surface has been attributed to tidal deformation, mainly due to Europas eccentric orbit. Although the surface is geologically young (30 - 80 Myr), there is little information as to whether tidally-driven surface processes are ongoing. However, a recent detection of water vapor near Europas south pole suggests that it may be geologically active. Initial observations indicated that Europas plume eruptions are time-variable and may be linked to its tidal cycle. Saturns moon, Enceladus, which shares many similar traits with Europa, displays tidally-modulated plume eruptions, which bolstered this interpretation. However, additional observations of Europa at the same time in its orbit failed to yield a plume detection, casting doubt on the tidal control hypothesis. The purpose of this study is to analyze the timing of plume eruptions within the context of Europas tidal cycle to determine whether such a link exists and examine the inferred similarities and differences between plume activity on Europa and Enceladus.
Streams of user-generated content in social media exhibit patterns of collective attention across diverse topics, with temporal structures determined both by exogenous factors and endogenous factors. Teasing apart different topics and resolving their individual, concurrent, activity timelines is a key challenge in extracting knowledge from microblog streams. Facing this challenge requires the use of methods that expose latent signals by using term correlations across posts and over time. Here we focus on content posted to Twitter during the London 2012 Olympics, for which a detailed schedule of events is independently available and can be used for reference. We mine the temporal structure of topical activity by using two methods based on non-negative matrix factorization. We show that for events in the Olympics schedule that can be semantically matched to Twitter topics, the extracted Twitter activity timeline closely matches the known timeline from the schedule. Our results show that, given appropriate techniques to detect latent signals, Twitter can be used as a social sensor to extract topical-temporal information on real-world events at high temporal resolution.
We present a framework to discover and characterize different classes of everyday activities from event-streams. We begin by representing activities as bags of event n-grams. This allows us to analyze the global structural information of activities, using their local event statistics. We demonstrate how maximal cliques in an undirected edge-weighted graph of activities, can be used for activity-class discovery in an unsupervised manner. We show how modeling an activity as a variable length Markov process, can be used to discover recurrent event-motifs to characterize the discovered activity-classes. We present results over extensive data-sets, collected from multiple active environments, to show the competence and generalizability of our proposed framework.
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