Exact(28)
Otherwise, multiple reflection is needed and the sample point is in NLOS severe state.
Secondly, a larger-scale noise estimator derived from activity measured at the geographically closest magnetic observatories to the sample point.
However, this does not help to produce the boundedness of Θ with respect to the sample point ω, either.
Our approach characterizes the manifold structure of each sample point using the geodesic distances between the sample point and given correspondences.
Secondly, we employ a larger-scale noise estimator (the 'LAVA' index) derived from the activity measured at the geographically closest magnetic observatories to the sample point.
Here, the two function values are the integral of geodesic distance over the polygonal shape [9] and surface curvature, where the integral of geodesic distance represents how much the sample point is far away from the object center.
Similar(32)
The sample points are distributed according to different strategies.
In the final step, the sample points are calculated.
The sample points are evaluated based on model (5).
Selected habitat characteristics were estimated for stands containing the sample points and within buffers with radii of 250, 500 m, and 1 km surrounding the sample points.
The EM algorithm utilizes an attraction repulsion mechanism to move the sample points towards the optimum.
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