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TEC

About: TEC is a research topic. Over the lifetime, 5119 publications have been published within this topic receiving 84696 citations.


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Proceedings ArticleDOI
09 Jul 2017
TL;DR: A long short-term memory (LSTM) based model is proposed to predict ionospheric vertical TEC of Beijing and shows the root of mean square (RMS) error of test data can reach 3.50 and RMS error is less than this number during the period of low solar activity.
Abstract: Ionosphere is an important space environment near the earth. Its disturbance would result in severe propagation effects to radio information system, thus causing bad influences on communication, navigation, radar and so on. The total electron content (TEC) is an important parameter to present the disturbance of ionosphere, so TEC forecast is very meaningful in scientific research field. In this paper, we propose a long short-term memory (LSTM) based model to predict ionospheric vertical TEC of Beijing. The input of our model is a time sequence consisting of the vector of daily TECs and other closely related parameters. The output is TECs of future 24 hours. The result shows the root of mean square (RMS) error of test data can reach 3.50 and RMS error is less than this number during the period of low solar activity. Compared to multilayer perceptron network, LSTM is more promising and reliable to forecast ionospheric TEC.

60 citations

Journal ArticleDOI
TL;DR: The recovery and viability of TEC, notably the rare cortical subsets, were significantly enhanced with Liberase products containing medium to high levels of thermolysin, and improved stromal dissociation led to numerically increased TEC yield and total TEC RNA isolated from pooled digests of adult thymus.

60 citations

Journal ArticleDOI
TL;DR: In this article, the authors used a variety of ground and space-based instruments, including ionosonde, ground-based GPS receivers, magnetometers, and solar wind data from the Advanced Composition Explorer (ACE), to examine the response of the ionospheric F2-layer height during the November 2003 superstorm.
Abstract: [1] We use observations from a variety of different ground- and space-based instruments, including ionosonde, ground- and space-based Global Positioning System (GPS) receivers, magnetometers, and solar wind data from the Advanced Composition Explorer (ACE), to examine the response of the ionospheric F2-layer height during the November 2003 superstorm. We found that the topside ionosphere responded unusually to the 20 November 2003 severe storm compared to behavior observed in a number of previous storms. While ground-based GPS receivers observed a large enhancement in dayside TEC, the low-Earth orbiting (∼400 km) CHAMP satellite did not show any sign of dayside TEC enhancement. The real-time vertical density profiles, constructed from ground-based GPS TEC using a tomographic reconstruction technique, clearly revealed that the ionospheric F2-layer peak height had been depressed down to lower altitudes. Ionospheric F-layer peak height (hmF2) from the nearby ionosonde stations over Europe also showed that the dayside F2-layer peak height was below 350 km, which is below the orbiting height of CHAMP. The vertical E × B drift (estimated from ground-based magnetometer equatorial electrojet delta H) showed strong dayside downward drifts, which may be due to the ionospheric disturbance dynamo electric field produced by the large amount of energy dissipation into high-latitude regions. This storm demonstrates that data from LEO satellites varies widely among different superstorms.

60 citations

Journal ArticleDOI
TL;DR: In this paper, the authors applied the neural network (NN) for the prediction of the total electron content (TEC) over Chumphon, an equatorial latitude station in Thailand, based on the available data during the low-solar activity period from 2005 to 2009.
Abstract: This paper describes the neural network (NN) application for the prediction of the total electron content (TEC) over Chumphon, an equatorial latitude station in Thailand. The studied period is based on the available data during the low-solar-activity period from 2005 to 2009. The single hidden layer feed-forward network with a back propagation algorithm is applied in this work. The input space of the NN includes the day number, hour number and sunspot number. An analysis was made by comparing the TEC from the neural network prediction (NN TEC), the TEC from an observation (GPS TEC) and the TEC from the IRI-2007 model (IRI-2007 TEC). To obtain the optimum NN for the TEC prediction, the root-mean-square error (RMSE) is taken into account. In order to measure the effectiveness of the NN, the normalized RMSE of the NN TEC computed from the difference between the NN TEC and the GPS TEC is investigated. The RMSE, and normalized RMSE, comparisons for both the NN model and the IRI-2007 model are described. Even with the constraint of a limited amount of available data, the results show that the proposed NN can predict the GPS TEC quite well over the equatorial latitude station.

60 citations

Journal ArticleDOI
TL;DR: Yan et al. as mentioned in this paper presented a light use efficiency-based terrestrial gross primary production model called the terrestrial ecosystem carbon flux model (TEC) driven by MODIS FPAR and climate data coupled with a precipitation-driven evapotranspiration (E) model.

59 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023303
2022578
2021284
2020321
2019293
2018272