This technical note describes how to interpret those parameters to produce an electron density profile at any place and time by defining the ten parameters in use, and the rules used to construct six quasi-parabolic segments (QPS) that combine to produce a robust, complete, and flexible representation of the overhead electron density profile, eN(z).
Our scientific and technical publications are an important vehicle for the dissemination of our work.
We have several goals for our publications:
- To communicate the results of the research program to clients and fulfil our reporting responsibilities to the Department of Defence.
- To record the results of our research program.
- To communicate the results of scientific research and technical information to Australian industry and academia.
- To increase awareness of and enhance the reputation of DST Group.
For enquiries about science and technical publications, or to request a publication please contact Choyvpngvbaf@qfg.qrsrapr.tbi.nh
Latest scientific publications
This paper describes a new fully automatic data driven real-time model of the morphology of Es, and describes the associated expected propagation characteristic that should reduce the need for manual intervention. It has been adapted to work with JORN ionospheric sounder data in real time and tested with years of data.
This report contains details of the use of the Principal Components Analysis method to select a representative selection of body scans from the Australian Army Anthropometric survey and manufacture them into physical manikins.
This paper uses a real life non-controlled scenario to examine verification performance gains possible when fusing low quality face and voice samples at the matching score level.
This report is a review of the application of an Evidential Network, with the objective to develop a tactical decision aid for use in real-time above water warfare (AWW).
This user manual provides critical information on how to use and update the C-27J Global FEM.
In this report the use of Machine Learning (ML) techniques in Software Vulnerability Research (SVR) is investigated, discussing previous and current efforts to illustrate how ML is utilised by academia and industry in this area.
This technical note considers processes that alternate randomly between 'working' and 'broken' over an interval of time.