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See detail5G-SpaceLab
Querol, Jorge UL; Abdalla, Abdelrahman UL; Bokal, Zhanna UL et al

Poster (2021, April 19)

The new phase of space exploration involves a growing number of human and robotic missions with varying communication and service requirements. Continuous, maximum coverage of areas where activities are ... [more ▼]

The new phase of space exploration involves a growing number of human and robotic missions with varying communication and service requirements. Continuous, maximum coverage of areas where activities are concentrated and orbiting missions (single spacecraft or constellations) around the Earth, Moon or Mars will be particularly challenging. The standardization of the 5G Non-Terrestrial Networks (NTN) has already begun [1], and nothing prevents 5G from becoming a common communications standard supporting space resource missions [2]. The 5G Space Communications Lab (5G-SpaceLab) is an interdisciplinary experimental platform, funded by the Luxembourg Space Agency and is part of the Space Research Program of SnT. The lab allows users to design and emulate realistic space communications and control scenarios for the next-generation of space applications. The capabilities of the 5G-SpaceLab testbed combine the experience of different disciplines including space communications, space and satellite mission design, and space robotics. The most relevant include the demonstration of SDR 5G NTN terminals including NB-IoT, emulation of space communications channel scenarios (e.g. link budget, delay, Doppler…), small satellite platform and payload design and testing, satellite swarm flight formation, lunar rover and robotic arm control and AI-powered telerobotics. Earth-Moon communications is one of the scenarios demonstrated in the 5G-SpaceLab. Bidirectional communication for the teleoperation of lunar rovers for near real-time operations including data collection and sensors feedback will be tested. AI-based approaches for perception and control will be developed to overcome communication delays and to provide safer, trustworthy, and efficient remote control of the rovers. [1] 3GPP Release 17 Timeline. [Online]. Available: https://www.3gpp.org/release-17 [2] Nokia, Nokia selected by NASA to build first ever cellular network on the Moon. [Online]. Available: https://www.nokia.com/about-us/news/releases/2020/10/19/nokia-selected-by-nasa-to-build-first-ever-cellular-network-on-the-moon/ [less ▲]

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See detailA warmer world
van Dam, Tonie UL; Weigelt, Matthias UL; Jäggi, Adrian

in Pan European Networks (2015), (14), 58-59

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See detailSingular spectrum analysis for modeling seasonal signals from GPS time series
Chen, Qiang; van Dam, Tonie UL; Sneeuw, Nico et al

in Journal of Geodynamics (2013), 72

Seasonal signals in GPS time series are of great importance for understanding the evolution of regional mass fluctuations, i.e., ice, hydrology, and ocean mass. Conventionally these signals quasi-annual ... [more ▼]

Seasonal signals in GPS time series are of great importance for understanding the evolution of regional mass fluctuations, i.e., ice, hydrology, and ocean mass. Conventionally these signals quasi-annual and semi-annual signals are modeled by least-squares fitting harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e., they will have a time-variable amplitude and phase. Recently, Davis et al.(2012) proposed a Kalman filter based approach to capture the stochastic seasonal behavior of geodetic time series. Singular Spectrum Analysis (SSA) is a non-parametric method, which uses time domain data to extract information from short and noisy time series without a priori knowledge of the dynamics affecting the time series. A prominent benefit is that trends obtained in this way are not necessarily linear. Further, true oscillations can be amplitude and phase modulated. In this work, we will assess the value of SSA for extracting time-variable seasonal signals from GPS time series. We compare our SSA-based results to those obtained using 1) least-squares analysis and 2) Kalman filtering. Our results demonstrate that SSA is a viable and complementary tool for extracting modulated oscillations from GPS time series. [less ▲]

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