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Register a new userRevisiting the orbital tracking problem
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Consider a space object in an orbit about the earth. An uncertain initial state can be represented as a point cloud which can be propagated to later times by the laws of Newtonian motion. If the state of the object is represented in Cartesian earth centered inertial (Cartesian-ECI) coordinates, then even if initial uncertainty is Gaussian in this coordinate system, the distribution quickly becomes non-Gaussian as the propagation time increases. Similar problems arise in other standard fixed coordinate systems in astrodynamics, e.g. Keplerian and to some extent equinoctial. To address these problems, a local Adapted STructural (AST) coordinate system has been developed in which uncertainty is represented in terms of deviations from a central state. Given a sequence of angles-only measurements, the iterated nonlinear extended (IEKF) and unscented (IUKF) Kalman filters are often the most appropriate variants to use. In particular, they can be much more accurate than the more commonly used non-iterat
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As is widely-known, the eigen-functions of the Landau problem in the symmetric gauge are specified by two quantum numbers. The first is the familiar Landau quantum number $n$, whereas the second is the magnetic quantum number $m$, which is the eigen-value of the canonical orbital angular momentum (OAM) operator of the electron. The eigen-energies of the system depend only on the first quantum number $n$, and the second quantum number $m$ does not correspond to any direct observables. This seems natural since the canonical OAM is generally believed to be a {it gauge-variant} quantity, and observation of a gauge-variant quantity would contradict a fundamental principle of physics called the {it gauge principle}. In recent researches, however, Bliohk et al. analyzed the motion of helical electron beam along the direction of a uniform magnetic field, which was mostly neglected in past analyses of the Landau states. Their analyses revealed highly non-trivial $m$-dependent rotational dynamics of the Landau electron, but the problem is that their papers give an impression that the quantum number $m$ in the Landau eigen-states corresponds to a genuine observable. This compatibility problem between the gauge principle and the observability of the quantum number $m$ in the Landau eigen-states was attacked in our previous letter paper. In the present paper, we try to give more convincing answer to this delicate problem of physics, especially by paying attention not only to the {it particle-like} aspect but also to the {it wave-like} aspect of the Landau electron.
The interaction of two colliding Alfven wave packets is here described by means of magnetohydrodynamics (MHD) and hybrid kinetic numerical simulations. The MHD evolution revisits the theoretical insights described by Moffatt, Parker, Kraichnan, Chandrasekhar and Elsasser in which the oppositely propagating large amplitude wave packets interact for a finite time, initiating turbulence. However, the extension to include compressive and kinetic effects, while maintaining the gross characteristics of the simpler classic formulation, also reveals intriguing features which go beyond the pure MHD treatment.
The MAYA detector is a Time-Charge Projection Chamber based on the concept of active target. These type of devices use a part of the detection system, the filling gas in this case, in the role of reaction target. The MAYA detector performs three-dimensional tracking, in order to determine physical observables of the reactions occurring inside the detector. The reconstruction algorithms of the tracking use the information from a two-dimensional projection on the segmented cathode, and, in general, they need to be adapted for the different experimental settings of the detector. This work presents some of the most relevant solutions developed for the MAYA detector.
Polarimetric Synthetic Aperture Radar (PolSAR) images are an important source of information. Speckle noise gives SAR images a granular appearance that makes interpretation and analysis hard tasks. A major issue is the assessment of information content in these kind of images, and how it is affected by usual processing techniques. Previous works have resulted in various approaches for quantifying image information content. As Narayanan, Desetty, and Reichenbach(2002) we study this problem from the classification accuracy viewpoint, focusing in the filtering and the classification stages. Thus, through classified images we verify how changing properties of the input data affects their quality. Our input is an actual PolSAR image, the control parameters are the filter (Local Mean or Model Based PolSAR, MBPolSAR), the size of them and the classification method (Maximum Likelihood, ML, or Support Vector Machine, SVM), and the output are the classification precision obtained applying the classification algorithm to the filtered data. To expand the conclusions, this study deals not only with Classification Accuracy, but also with Kappa and Overall Accuracy as measures of map precision. Experiments were conducted on two airborne PolSAR images. Unless Narayanan, Desetty, and Reichenbach(2002) almost all measure values are good and increase with degradation, i.e. the filtering algorithm that we used always improves the classification results at least up to 7x7.
Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.
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