Real-Time Cubesat Thermal Simulation using Artificial Neural Networks

Junior, Jose; Ambrosio, Ana María; Sousa, Fabiano Luís de

doi:10.6062/jcis.2017.08.02.0126

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Further, the ANN technique is widely used for highly accurate predictions and/or optimization due to its simplicity and efficiency [7]. e authors in [8][9][10][11] are representative examples of related work that considered ANN for optimization and enhancement.…”

Section: Introductionmentioning

confidence: 99%

Predicting Joint Effects on CubeSats to Enhance Internet of Things in GCC Region Using Artificial Neural Network

Almalki

Soufiene

2021

Mobile Information Systems

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Satellite telecommunication systems promise to bridge digital gaps and deliver wireless communication services to any corner of the world. However, despite satellites’ global connectivity and wide footprint, still atmospheric and dust impairments are open challenges that face satellite systems, especially at high-frequency bands in arid and semiarid regions. Therefore, this paper aims to predict joint effects of atmospheric and dust attenuations in Gulf Cooperation Council (GCC) countries on CubeSat communications using Artificial Neural Network (ANN). The prediction model has been carried out using a massive Multiple-Input Multiple-Output (MIMO) antenna payload at K-frequency Bands. Consider these joint effects have positive relations in calculating satellites link margin, which leads to obtaining efficient communication system, delivering better quality of service (QoS), and enhancing Internet of Things (IoT) connectivity, or even Internet of Space Things (IoST). Predicated results infer that the ANN attenuation predictions, along with the 5G MIMO antenna on-board the CubeSat, offer much promise channel model for satellite communications, which in turn leads to not only supporting IoT connectivity but also reducing power consumption, thus enhancing lifetime of CubeSat. Also, this study can provide a reference for CubeSat engineers to guarantee large-capacity communication.

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Section: Introductionmentioning

confidence: 99%

Predicting Joint Effects on CubeSats to Enhance Internet of Things in GCC Region Using Artificial Neural Network

Almalki

Soufiene

2021

Mobile Information Systems

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Spacecraft real-time thermal simulation using artificial neural networks

Junior

Ambrosio

Sousa

et al. 2021

J Braz. Soc. Mech. Sci. Eng.

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Spacecraft Operational Simulators are mainly used for training satellite operators, to test the ground control system, and the evaluation of operational and onboard procedures before their execution in the real satellite. To achieve these objectives, all the internal models of the Operational Simulator must provide information in real time. Traditionally, the thermal simulation in these simulators is accomplished through interpolation on a set of pre-calculated scenarios or by the integration of a very simplified mathematical model. Both approaches, however, have limitations in both fidelity and runtime. In order to overcome these limitations, in this work it is proposed to build the thermal model of a Spacecraft Operational Simulator using artificial neural networks. This approach was applied to the Amazonia-1, a medium size satellite currently being developed at the Brazilian National Institute for Space Research. The obtained results show increased fidelity and an extremely short execution time, evidencing the potential of the approach to simulate satellite thermal behavior in Operational Simulators.

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Simulation and Prediction for a Satellite Temperature Sensors Based on Artificial Neural Network

Abdelkhalek

Medhat

Ziedan

et al. 2019

J.Aerosp. Technol. Manag.

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Spacecrafts in space environment are exposed to several kinds of thermal sources such as radiation, albedo and emitted IR from the earth. The thermal control subsystem in spacecraft is used to keep all parts operating within allowable temperature ranges. A failure in one or many temperature sensors could lead to abnormal operation. Consequently, a prediction process must be performed to replace the missing data with estimated values to prevent abnormal behavior. The goal of the proposed model is to predict the failed or missing sensor readings based on artificial neural networks (ANN). It has been applied to EgyptSat-1 satellite. A backpropagation algorithm called Levenberg-Marquardt is used to train the neural networks (NN). The proposed model has been tested by one and two hidden layers. Practical metrics such as mean square error, mean absolute error and the maximum error are used to measure the performance of the proposed network. The results showed that the proposed model predicted the values of one failed sensor with adequate accuracy. It has been employed for predicting the values of two failed sensors with an acceptable mean square and mean absolute errors; whereas the maximum error for the two failed sensors exceeded the acceptable limits.

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Real-Time Cubesat Thermal Simulation using Artificial Neural Networks

Cited by 3 publications

References 12 publications

Predicting Joint Effects on CubeSats to Enhance Internet of Things in GCC Region Using Artificial Neural Network

Predicting Joint Effects on CubeSats to Enhance Internet of Things in GCC Region Using Artificial Neural Network

Spacecraft real-time thermal simulation using artificial neural networks

Simulation and Prediction for a Satellite Temperature Sensors Based on Artificial Neural Network

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