Computer Sciences Corporation

About: Computer Sciences Corporation is a based out in . It is known for research contribution in the topics: Spacecraft & Software development. The organization has 1916 authors who have published 2389 publications receiving 62729 citations. The organization is also known as: CSC.

Mars Global Reference Atmospheric Model 2001 Version (Mars-GRAM 2001): Users Guide

Abstract: This document presents Mars Global Reference Atmospheric Model 2001 Version (Mars-GRAM 2001) and its new features. As with the previous version (mars-2000), all parameterizations fro temperature, pressure, density, and winds versus height, latitude, longitude, time of day, and season (Ls) use input data tables from NASA Ames Mars General Circulation Model (MGCM) for the surface through 80-km altitude and the University of Arizona Mars Thermospheric General Circulation Model (MTGCM) for 80 to 70 km. Mars-GRAM 2001 is based on topography from the Mars Orbiter Laser Altimeter (MOLA) and includes new MGCM data at the topographic surface. A new auxiliary program allows Mars-GRAM output to be used to compute shortwave (solar) and longwave (thermal) radiation at the surface and top of atmosphere. This memorandum includes instructions on obtaining Mars-GRAN source code and data files and for running the program. It also provides sample input and output and an example for incorporating Mars-GRAM as an atmospheric subroutine in a trajectory code.

Condition component framework for reinsurance

Abstract: An improved method and system for configuring components of a reinsurance contract to process reinsurance business transactions. Reinsurance users may use the improved method and system for configuring a multi-dimensional reinsurance contract to process reinsurance business transactions with increased customization, re-use of existing functionality and decreased development time. An object-oriented, multi-dimensional reinsurance contract framework may represent a reinsurance contract. An object-oriented condition component framework may represent a condition dimension of a reinsurance contract. The condition component framework may permit the addition and/or modification of condition components of the reinsurance contract. These components may comprise, for example, configuring or changing the premium limits, consolidation conditions, and other conditions for a reinsurance contract. The condition components may be implemented as reusable objects. The multi-dimensional reinsurance contract framework and the condition component framework may also include an object-oriented graphical user interface. The graphical user interface may include a plurality of displays, along with a navigational tool to enable users to navigate through a hierarchy of displays related to the reinsurance contract.

Dual-Level Parallel Analysis of Harbor Wave Response Using MPI and OpenMP

Abstract: The authors describe their experiences converting an existing serial production code to a parallel code combining both MPI and OpenMP. Such dual-level parallel codes will be able to take full advantage of the emerging class of high performance computer architectures using small clusters of shared-memory processors connected via a message-passing network. While the focus is restricted to a harbor response simulation code, the techniques presented herein are appropriate for a broad class of applications that explore a parameter space. The code modifications reduced the execution time of one test case from 3100 minutes on a single CPU to just over 12 minutes on 256 CPUs. Results demonstrate that dual-level parallelism allows substantial increases in model resolution combined with improvements in simulation turnaround time but, contrary to conventional wisdom, requires very little source code alteration.

Comparison of level I land cover classification accuracy for MSS and AVHRR data

Abstract: The capabilities of the Advanced Very-High-Resolution Radiometer (AVHRR) for land-cover mapping were investigated by comparing the accuracy of land-cover information for the Washington, DC area derived from NOAA-7 AVHRR data with that from Landsat Multispectral Scanner Subsystem (MSS) data. Unsupervised level I land-cover classifications were performed for MSS and AVHRR data sets collected on July 11, 1981. A detailed accuracy assessment was conducted based on ground data delineated on 12 U.S. Geological Survey 7-5 min series topographic maps. These results produced overall land-cover classification accuracies of 71.9 and 76.8 per cent for AVHRR and MSS, respectively. While the accuracies for predominant categories were similar for both sensors, land-cover discrimination for less commonly occurring and/or spatially heterogeneous categories was improved with the MSS data set. The AVHRR, however, performed as well as or better than the MSS in classifying large homogeneous areas. The application of AVHRR data with its lower processing cost and more frequent worldwide coverage appears promising for regional land-cover mapping.