FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2966449111> ?p ?o ?g. }

• W2966449111 abstract "The Low Earth Orbit (LEO) region has been attractive to many space agencies and organisations because of its ease of access and the ideal opportunity for remote sensing. Due to the low altitudes, a satellite's orbital state is highly affected by the atmospheric drag force acting on the satellite's body. The largest variation in this drag force is caused by the changes in thermospheric density due to the complex interactions of the Sun with the Earth's thermosphere. In order to properly forecast the orbital state of a LEO satellite, the thermospheric densities need to be predicted as accurately as possible. The thermospheric density values can be estimated by using for example empirical atmospheric density models, such as the DTM2013 (Drag Temperature Model). During this thesis study it has been investigated whether the highly researched field of machine learning models could be used to develop a predictor for the along-track density values for the Swarm satellite constellation. This constellation has an abundant amount of trajectory-based time series of thermospheric density values from Precise Orbit Determination (POD) data. This research has focused on the development of Multi-layer Perceptron (MLP) models which are a type of Feed-Forward Neural Network. These MLP models have been trained and tested on past acceleration and solar activity data sets provided by the Swarm satellite mission and space weather observatories, respectively. The performance of these MLP models was then compared to two baseline models, namely a Calibrated Persistence Model (CPM) and the density values modelled by DTM2013. The results in this research led to the conclusion that a three-layer MLP model performed best when it was trained on data of the same spacecraft like the one it was supposed to perform along-track density forecasts for. The forecasting accuracy increased the most when the model was trained on long periods of training data characterised by high solar and low geomagnetic activity. When trained on these data sets, the MLP model has shown to outperform the baseline models when making predictions up until two days into the future during periods of high solar activity. The DTM2013 seems the best option to forecast density values during low solar activity. As an alternative to the DTM2013, the CPM seems a suitable model when one needs to quickly implement a forecasting model with decent performance irrespective from the presence of geomagnetic storms." @default.
• W2966449111 created "2019-08-13" @default.
• W2966449111 creator A5025393615 @default.
• W2966449111 date "2019-01-01" @default.
• W2966449111 modified "2023-09-27" @default.
• W2966449111 title "Thermosphere Modelling using Machine Learning: Artificial Neural Networks to forecast thermospheric densities and generalise beyond the properties of an acceleration data set using the Swarm satellites as study case" @default.
• W2966449111 hasPublicationYear "2019" @default.
• W2966449111 type Work @default.
• W2966449111 sameAs 2966449111 @default.
• W2966449111 citedByCount "0" @default.
• W2966449111 crossrefType "journal-article" @default.
• W2966449111 hasAuthorship W2966449111A5025393615 @default.
• W2966449111 hasConcept C11413529 @default.
• W2966449111 hasConcept C116403925 @default.
• W2966449111 hasConcept C117896860 @default.
• W2966449111 hasConcept C119857082 @default.
• W2966449111 hasConcept C121332964 @default.
• W2966449111 hasConcept C127413603 @default.
• W2966449111 hasConcept C131980223 @default.
• W2966449111 hasConcept C146978453 @default.
• W2966449111 hasConcept C153294291 @default.
• W2966449111 hasConcept C154945302 @default.
• W2966449111 hasConcept C181335050 @default.
• W2966449111 hasConcept C19269812 @default.
• W2966449111 hasConcept C205649164 @default.
• W2966449111 hasConcept C39432304 @default.
• W2966449111 hasConcept C41008148 @default.
• W2966449111 hasConcept C50644808 @default.
• W2966449111 hasConcept C60908668 @default.
• W2966449111 hasConcept C62649853 @default.
• W2966449111 hasConcept C74650414 @default.
• W2966449111 hasConcept C8058405 @default.
• W2966449111 hasConceptScore W2966449111C11413529 @default.
• W2966449111 hasConceptScore W2966449111C116403925 @default.
• W2966449111 hasConceptScore W2966449111C117896860 @default.
• W2966449111 hasConceptScore W2966449111C119857082 @default.
• W2966449111 hasConceptScore W2966449111C121332964 @default.
• W2966449111 hasConceptScore W2966449111C127413603 @default.
• W2966449111 hasConceptScore W2966449111C131980223 @default.
• W2966449111 hasConceptScore W2966449111C146978453 @default.
• W2966449111 hasConceptScore W2966449111C153294291 @default.
• W2966449111 hasConceptScore W2966449111C154945302 @default.
• W2966449111 hasConceptScore W2966449111C181335050 @default.
• W2966449111 hasConceptScore W2966449111C19269812 @default.
• W2966449111 hasConceptScore W2966449111C205649164 @default.
• W2966449111 hasConceptScore W2966449111C39432304 @default.
• W2966449111 hasConceptScore W2966449111C41008148 @default.
• W2966449111 hasConceptScore W2966449111C50644808 @default.
• W2966449111 hasConceptScore W2966449111C60908668 @default.
• W2966449111 hasConceptScore W2966449111C62649853 @default.
• W2966449111 hasConceptScore W2966449111C74650414 @default.
• W2966449111 hasConceptScore W2966449111C8058405 @default.
• W2966449111 hasLocation W29664491111 @default.
• W2966449111 hasOpenAccess W2966449111 @default.
• W2966449111 hasPrimaryLocation W29664491111 @default.
• W2966449111 hasRelatedWork W1499820507 @default.
• W2966449111 hasRelatedWork W1585396627 @default.
• W2966449111 hasRelatedWork W1965932141 @default.
• W2966449111 hasRelatedWork W2017298136 @default.
• W2966449111 hasRelatedWork W2089111064 @default.
• W2966449111 hasRelatedWork W2098904396 @default.
• W2966449111 hasRelatedWork W2099070676 @default.
• W2966449111 hasRelatedWork W2102965038 @default.
• W2966449111 hasRelatedWork W2313091590 @default.
• W2966449111 hasRelatedWork W2322605680 @default.
• W2966449111 hasRelatedWork W2333132083 @default.
• W2966449111 hasRelatedWork W2775658649 @default.
• W2966449111 hasRelatedWork W2808902576 @default.
• W2966449111 hasRelatedWork W2884133956 @default.
• W2966449111 hasRelatedWork W2954881198 @default.
• W2966449111 hasRelatedWork W3016544363 @default.
• W2966449111 hasRelatedWork W3091089247 @default.
• W2966449111 hasRelatedWork W3118760307 @default.
• W2966449111 hasRelatedWork W3195796723 @default.
• W2966449111 hasRelatedWork W2000839833 @default.
• W2966449111 isParatext "false" @default.
• W2966449111 isRetracted "false" @default.
• W2966449111 magId "2966449111" @default.
• W2966449111 workType "article" @default.