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At least 19 records

ASF archive issues: Current status, past history, and questions for the future

The Alaska SAR Facility (ASF) collects, processes, archives, and distributes data from synthetic aperture radar (SAR) satellites in support of scientific research. ASF has been in operation since 1991 and presently has an archive of over 100 terabytes of data. ASF is performing an analysis of its magnetic tape storage system to ensure long-term preservation of this archive. Future satellite missions have the possibility of doubling to tripling the amounts of data that ASF acquires. ASF is examining the current data systems and the high volume storage, and exploring future concerns and solutions.

Goula, Crystal A.

RADARSAT Processing System at ASF

This paper outlines the ASF (Alaska SAR[synthetic aperture radar] Facility) Radarsat data processing requirements as driven by the science users and describes the Radarsat processing system design and implementaiton approach to meet the challenge of providing ASF with and integrated operational SAR image production facility. Design and implementation attributes that facilitate syswtem growth in handling future SAR missions such as Envisat and HIROS are also addressed.

ASF

ASF Design Considerations for Radarsat/ERS-2

This paper examines the requirements and the design considerations of the Synthetic Aperture Radar (SAR) ground data system for the Alaska SAR Facility (ASF) at the University of Alaska in Fairbanks (UAF) for the new era of Radarsat/ERS-2 missions. These include a new data acquisition planning capability to manage more satellites with global planning and to manage more than one instrument mode; a new archive strategy that is cheaper, faster, and better; a product generation system to produce data on demand and to produce data for the varied instruments and modes; and a product verification ability for the new and old products. In response to these new functional requirements, JPL is using a design approach that emphasizes an open systems, client/server architecture based on industry standards and commercial off-the-shelf technology.

Radarsat/ERS-2 ground data system upgrade

Radarsat Processing System at ASF

Radarsat is a Canadian polar orbiting remote sensing satellite scheduled for launch in September 1995. Its lone instrument on-board is a synthetic aperture radar (SAR) that is capable of operating in a number of imaging modes including the first operational ScanSAR mode. As one of the data reception, processing and archive facility for Radarsat data, Alaska SAR Facility (ASF) has responded to its Science users by establishing a Radarsat processing system to handle the data processing of all Radarsat modes. This task involves enhancements to the high throughput hardware based Alaska SAR Processor (ASP) to handle standard mode Radarsat data in addition to its existing ERS and JERS capabilities, the addition of the new ScanSAR Processor (SSP) to process the Radarsat ScanSAR mode data, and the introduction of a Precision Processor (PP) to accommodate the special Radarsat modes such as fine resolution and wide swath. For raw data ingestion and distribution to the appropriate SAR processor, a new Control Processor (CP) and Raw Data Scanner (RDS) subsystem is also incorporated.

Radarsat

Comparison of the nonlinear dynamic characteristics of Barber S-2 and ASF ride control freight trucks

The results of an experimental and analytical program to define the load deflection characteristics of a Barber S-2 freight truck and to compare these characteristics to those of an ASF freight truck of the same load capacity are presented. The comparison of the two trucks is made on a parameter basis and on the basis of the wheel/rail loads induced by track misalignment. The results indicate there is very little difference in the parameters and the response of the two trucks. This is of course qualified by the assumptions required in the development of the mathematical models.

Abbott, P.

ASF RADARSAT Processor System Performance Summary

The Alaska SAR Facility (ASF) located at the University of Alaska Fairbanks (UAF) has been in operations since 1991 serving as a key data acquisition, processing, archive and distribution center for a number of polar orbiting SAR (synthetic aperture radar) satelittes including ERS 1/2 and JERS-1.

synthetic aperture radar RADARSAT Mapping

Full-Length ASFV B646L Gene Sequencing by Nanopore Offers a Simple and Rapid Approach for Identifying ASFV Genotypes

African swine fever (ASF) is an acute, highly hemorrhagic viral disease in domestic pigs and wild boars. The disease is caused by African swine fever virus, a double stranded DNA virus of the Asfarviridae family. ASF can be classified into 25 different genotypes, based on a 478 bp fragment corresponding to the C-terminal sequence of the B646L gene, which is highly conserved among strains and encodes the major capsid protein p72. The C-terminal end of p72 has been used as a PCR target for quick diagnosis of ASF, and its characterization remains the first approach for epidemiological tracking and identification of the origin of ASF in outbreak investigations. Recently, a new classification of ASF, based on the complete sequence of p72, reduced the 25 genotypes into only six genotypes; therefore, it is necessary to have the capability to sequence the full-length B646L gene (p72) in a rapid manner for quick genotype characterization. Here, we evaluate the use of an amplicon approach targeting the whole B646L gene, coupled with nanopore sequencing in a multiplex format using Flongle flow cells, as an easy, low cost, and rapid method for the characterization and genotyping of ASF in real-time.

Virology

Automated Ply-By-Ply Lamination and in-Situ Consolidation of Dry Carbon Fiber Non-Crimp Fabrics for High-Rate Aircraft Manufacturing of Structural Aircraft Components

NASA’s Hi-Rate Composites Aircraft Manufacturing (HiCAM) program addresses market needs to advance structural aircraft composite manufacturing technologies to significantly increase production rates. Dry, non-crimp fabric (NCF) carbon materials infused with advanced resin systems offer a promising solution to these manufacturing demands. Northrop Grumman’s Automated Stiffener Forming (ASF) technology has been adapted for ply-by-ply, in-situ processing of NCF materials. The modular ASF process accommodates flexibility in the laminate stacking, while allowing for ply drops, ply additions, and yaw, pitch, and roll in the laminate geometry. To adapt the ASF process for NCF materials, heating technologies and roller compaction processes were designed and tested on representative structural aircraft part geometries. Key success criteria for the ASF process with NCF materials is forming quality and preform compaction. Trials were performed with multiple NCF materials: unidirectional up to quad-axial formats. The NCF constituents, veils, stitching, and binders, were evaluated with the ASF process. The material performance in the ASF process and the resulting preform quality are presented.

dry carbon fiber materials

Automated Ply-By-Ply Lamination and in-Situ Consolidation of Dry Carbon Fiber Non-Crimp Fabrics for High-Rate Aircraft Manufacturing of Structural Aircraft Components

NASA’s Hi-Rate Composites Aircraft Manufacturing (HiCAM) program addresses market needs to advance structural aircraft composite manufacturing technologies to significantly increase production rates. Dry, non-crimp fabric (NCF) carbon materials infused with advanced resin systems offer a promising solution to these manufacturing demands. Northrop Grumman’s Automated Stiffener Forming (ASF) technology has been adapted for ply-by-ply, in-situ processing of NCF materials. The modular ASF process accommodates flexibility in the laminate stacking, while allowing for ply drops, ply additions, and yaw, pitch, and roll in the laminate geometry. To adapt the ASF process for NCF materials, heating technologies and roller compaction processes were designed and tested on representative structural aircraft part geometries. Key success criteria for the ASF process with NCF materials is forming quality and preform compaction. Trials were performed with multiple NCF materials: unidirectional up to quad-axial formats. The NCF constituents, veils, stitching, and binders, were evaluated with the ASF process. The material performance in the ASF process and the resulting preform quality are presented.

dry carbon fiber materials

A Retrospective Analysis Reveals That the 2021 Outbreaks of African Swine Fever Virus in Ghana Were Caused by Two Distinct Genotypes

African swine fever virus (ASFV) is the causative agent of African swine fever (ASF), a highly infectious and lethal disease of domesticated swine. Outbreaks of ASF have been mostly restricted to the continent of Africa. The outbreaks that have occurred outside of Africa were controlled by extensive depopulation of the domesticated pig population. However, in 2007, an outbreak occurred in the country of Georgia, where ASFV infected wild pigs and quickly spread across eastern Europe. Since the reintroduction of ASF into Europe, variants of the current pandemic strain, ASFV Georgia 2007/01 (ASFV-G), which is classified as Genotype 2 based on p72 sequencing, have been reported in countries within western Europe, Asia, and the island of Hispaniola. Additionally, isolates collected in 2020 confirmed the presence of variants of ASFV-G in Nigeria. Recently, we reported similar variants of ASFV-G collected from domestic pigs suspected of dying of ASF in Ghana in 2022. Here, we retroactively report, based on full-length sequencing, that similar variants were present in Ghana in 2021. The SNP analysis revealed derivatives of ASFV with distinct genetic markers. Furthermore, we identified three full-length ASFV genomes as Genotype 1, indicating that there were two genotypes circulating in proximity during the 2021 ASF outbreaks in Ghana.

Virology

ASF3

This reporting period marked a change in the funding configuration from a combination of a grant and a contract from two different National Aeronautics and Space Administration (NASA) sections to one single contract. One year of this reporting was under the grant/kontract configuration with the changeover occurring on 1 April 2003. Much of the work duties remained the same with some exception, notably the removal of the RADARSAT Geophysical Processor System and the removal of the commercialization line item from the contract. We chose this reporting period as a transition from the previous reporting period of 1 April to 31 March, to the current reporting period of 18 November to 17 November. The Alaska Synthetic Aperture Facility s (ASF) mission has been updated to carry us forward into the future congruent with our changed relationship with NASA. ASF will continue to evolve, and NASA will remain our primary customer. To compliment our new mission, we have a new name, the Alaska Satellite Facility (ASF), deeply rooted in the University environment and focused on satellite data products, services, and science support. We have the opportunity to reshape and rebuild ASF; we will continue to honor our heritage and Serve the science community. Our long-term goals include the commitment to continued first-rate service to our user community. This report contains input from ASF as a whole on the three major components of the NASA Contract, namely tasks devoted to the Distributed Active Archive Center (DAAC), the Receiving Ground Station (RGS), and the National Oceanics and Atmospherics Administration (NOAA).

LaBelle-Hamer, Nettie

Path Planning Algorithms for the Adaptive Sensor Fleet

The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.

Stoneking, Eric

Design, test, and applications of the Alaska SAR Facility

The key science requirements, the overall design, and the innovative testing approaches that have been used to ensure the functionality of the Alaska SAR Facility (ASF) are described. The facility is to play an important role in the remote sensing applications of Arctic oceanography, geology, glaciology, hydrology, and ecosystem processes. Attention is given to the ASF's three major components: the Receiving Ground Station, the SAR Processing System, and the Archive and Operations System. The ASF hardware configuration and software support, through extensive design and implementaton reviews, were shown to satisfy the initial memorandum of agreement first initiated by NASA for the establishment of a receiving ground station and image processing facility at the University of Alaska Fairbanks, and also to satisfy the science objectives formulated by the prelaunch Science Working team. The testing strategy and techniques used in the implementation of the ASF to assure functionality is outlined. The test structures, approach, and environment are considered.

Berwin, R. W.