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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.
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
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 da
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 tidall
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
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,