61 resultados para Santorini


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Bottom pressure, tilt and seawater physical-properties were monitored for a year using two instruments within the immerged Santorini caldera (Greece). Piggy-backed on the CALDERA2012 cruise, this geodetic experiment was designed to monitor evolution of the 2011-2012 Santorini unrest. Conducted during a quiescent period, it allowed us to study oceanographic and atmospheric signal in our data series. We observe periodic oceanographic signals associated with tides, and seiches that are likely linked to both the caldera and Cretan basin geometries. In winter, the caldera witnesses sudden cooling events that tilt an instrument towards the Southeast, indicating cold-water influx likely originating from the north-western passage between Thirasia and Oia. We do not obtain evidence of long-term vertical seafloor deformation from the pressure signal, although it may be masked by instrumental drift. However, tilt data suggests a local seafloor tilt event ~1 year after the end of the unrest period which could be consistent with inflation under or near Nea Kameni. Seafloor geodetic data recorded at the bottom of the Santorini caldera illustrates that the oceanographic signature is an important part of the signal, which needs to be considered for monitoring volcanic or geological seafloor deformation in shallow-water and/or nearshore areas.

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Detailed description of a silk reeling machine invented by Santorini.

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Mode of access: Internet.

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The problem of sensor-network-based distributed intrusion detection in the presence of clutter is considered. It is argued that sensing is best regarded as a local phenomenon in that only sensors in the immediate vicinity of an intruder are triggered. In such a setting, lack of knowledge of intruder location gives rise to correlated sensor readings. A signal-space viewpoint is introduced in which the noise-free sensor readings associated to intruder and clutter appear as surfaces $\mathcal{S_I}$ and $\mathcal{S_C}$ and the problem reduces to one of determining in distributed fashion, whether the current noisy sensor reading is best classified as intruder or clutter. Two approaches to distributed detection are pursued. In the first, a decision surface separating $\mathcal{S_I}$ and $\mathcal{S_C}$ is identified using Neyman-Pearson criteria. Thereafter, the individual sensor nodes interactively exchange bits to determine whether the sensor readings are on one side or the other of the decision surface. Bounds on the number of bits needed to be exchanged are derived, based on communication complexity (CC) theory. A lower bound derived for the two-party average case CC of general functions is compared against the performance of a greedy algorithm. The average case CC of the relevant greater-than (GT) function is characterized within two bits. In the second approach, each sensor node broadcasts a single bit arising from appropriate two-level quantization of its own sensor reading, keeping in mind the fusion rule to be subsequently applied at a local fusion center. The optimality of a threshold test as a quantization rule is proved under simplifying assumptions. Finally, results from a QualNet simulation of the algorithms are presented that include intruder tracking using a naive polynomial-regression algorithm.

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We present a distributed algorithm that finds a maximal edge packing in O(Δ + log* W) synchronous communication rounds in a weighted graph, independent of the number of nodes in the network; here Δ is the maximum degree of the graph and W is the maximum weight. As a direct application, we have a distributed 2-approximation algorithm for minimum-weight vertex cover, with the same running time. We also show how to find an f-approximation of minimum-weight set cover in O(f2k2 + fk log* W) rounds; here k is the maximum size of a subset in the set cover instance, f is the maximum frequency of an element, and W is the maximum weight of a subset. The algorithms are deterministic, and they can be applied in anonymous networks.