We describe an experiment, located in south-east Colorado, USA, that measured aerosol optical depth profiles using two L techniques. Two independent detectors measured scattered light from a vertical UV laser beam. One detector, located at the laser site, measured light via the inelastic Raman backscattering process. This is a common method used in atmospheric science for measuring aerosol optical depth profiles. The other detector, located approximately 40 km distant, viewed the laser beam from the side. This detector featured a 3.5 m 2 mirror and measured elastically scattered light in a bistatic L configuration following the method used at the Pierre Auger cosmic ray observatory. The goal of this experiment was to assess and improve methods to measure atmospheric clarity, specifically aerosol optical depth profiles, for cosmic ray UV fluorescence detectors that use the atmosphere as a giant calorimeter. The experiment collected data from September 2010 to July 2011 under varying conditions of aerosol loading. We describe the instruments and techniques and compare the aerosol optical depth profiles measured by the Raman and bistatic L detectors.

Automated image processing techniques have the potential to aid in the performance evaluation of nuclear fuels by eliminating judgment calls that may vary from person-to-person or sample-to-sample. Analysis of in-core fuel performance is required for design and safety evaluations related to almost every aspect of the nuclear fuel cycle. This study presents a methodology for assessing the quality of uranium-molybdenum fuel images and describes image analysis routines designed for the characterization of several important microstructural properties. The analyses are performed in CellProfiler, an open-source program designed to enable biologists without training in computer vision or programming to automatically extract cellular measurements from large image sets. The quality metric scores an image based on three parameters: the illumination gradient across the image, the overall focus of the image, and the fraction of the image that contains scratches. The metric presents the user with the ability to 'pass' or 'fail' an image based on a reproducible quality score. Passable images may then be characterized through a separate CellProfiler pipeline, which enlists a variety of common image analysis techniques. The results demonstrate the ability to reliably pass or fail images based on the illumination, focus, and scratch fraction of the image, followed by automatic extraction of morphological data with respect to fission gas voids, interaction layers, and grain boundaries.