841 resultados para Hausdorff Distance


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Context. The ESA Rosetta spacecraft, currently orbiting around cornet 67P/Churyumov-Gerasimenko, has already provided in situ measurements of the dust grain properties from several instruments, particularly OSIRIS and GIADA. We propose adding value to those measurements by combining them with ground-based observations of the dust tail to monitor the overall, time-dependent dust-production rate and size distribution. Aims. To constrain the dust grain properties, we take Rosetta OSIRIS and GIADA results into account, and combine OSIRIS data during the approach phase (from late April to early June 2014) with a large data set of ground-based images that were acquired with the ESO Very Large Telescope (VLT) from February to November 2014. Methods. A Monte Carlo dust tail code, which has already been used to characterise the dust environments of several comets and active asteroids, has been applied to retrieve the dust parameters. Key properties of the grains (density, velocity, and size distribution) were obtained from. Rosetta observations: these parameters were used as input of the code to considerably reduce the number of free parameters. In this way, the overall dust mass-loss rate and its dependence on the heliocentric distance could be obtained accurately. Results. The dust parameters derived from the inner coma measurements by OSIRIS and GIADA and from distant imaging using VLT data are consistent, except for the power index of the size-distribution function, which is alpha = -3, instead of alpha = -2, for grains smaller than 1 mm. This is possibly linked to the presence of fluffy aggregates in the coma. The onset of cometary activity occurs at approximately 4.3 AU, with a dust production rate of 0.5 kg/s, increasing up to 15 kg/s at 2.9 AU. This implies a dust-to-gas mass ratio varying between 3.8 and 6.5 for the best-fit model when combined with water-production rates from the MIRO experiment.

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This paper presents an empirical investigation of the appropriateness of distance as a determinant of international transport costs by using Philippine import data. This study addresses three specific questions. First, does distance really matter in the determination of transport costs? Second, if distance is a significant factor, what is the magnitude of its impact? Third, does the impact of distance on transport costs vary by commodity? Results indicate that while distance is important in determining transport costs, using distance alone as the proxy of international transport costs is insufficient, and such use underestimates the impact of distance on international transport costs. Results also indicate that the impact of distance varies across commodity groups, but it is difficult to precisely determine the direction and the magnitude of this impact.

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Geographic distance is a standard proxy for transport costs under the simple assumption that freight fees increase monotonically over space. Using the Japanese Census of Logistics, this paper examines the extent to which transport distance and time affect freight costs across shipping modes, commodity groups, and prefecture pairs. The results show substantial heterogeneity in transport costs and time across shipping modes. Consistent with an iceberg formulation of transport costs, distance has a significantly positive effect on freight costs by air transportation. However, I find the puzzling results that business enterprises are likely to pay more for short-distance shipments by truck, ship, and railroad transportation. As a plausible explanation, I discuss aggregation bias arising from freight-specific premiums for timely, frequent, and small-batch shipments.

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The need for the use of another surveillance system when radar cannot be used is the reason for the development of the Multilateration (MLT) Systems. However, there are many systems that operate in the L-Band (960-1215MHz) that could produce interference between systems. At airports, some interference has been detected between transmissions of MLT systems (1030MHz and 1090MHz) and Distance Measuring Equipment (DME) (960-1215MHz).

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Many existing engineering works model the statistical characteristics of the entities under study as normal distributions. These models are eventually used for decision making, requiring in practice the definition of the classification region corresponding to the desired confidence level. Surprisingly enough, however, a great amount of computer vision works using multidimensional normal models leave unspecified or fail to establish correct confidence regions due to misconceptions on the features of Gaussian functions or to wrong analogies with the unidimensional case. The resulting regions incur in deviations that can be unacceptable in high-dimensional models. Here we provide a comprehensive derivation of the optimal confidence regions for multivariate normal distributions of arbitrary dimensionality. To this end, firstly we derive the condition for region optimality of general continuous multidimensional distributions, and then we apply it to the widespread case of the normal probability density function. The obtained results are used to analyze the confidence error incurred by previous works related to vision research, showing that deviations caused by wrong regions may turn into unacceptable as dimensionality increases. To support the theoretical analysis, a quantitative example in the context of moving object detection by means of background modeling is given.

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High temperatures and relative humidity can compromise animal welfare on the farm level, but less is known about those changes during long distance transport of domestic animals to slaughter. Although upper temperature limits have been established to transport pigs in Europe, few indices include relative or absolute humidity maxima or mention appropriate enthalpy ranges.