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This paper presents a wideband circularly polarized antenna for small satellites to be used with NASA Near- Earth Networks (NEN). This single-fed stacked antenna utilizes the electromagnetic coupling concept and is usable with a duplex transceiver. The circularly-polarized antenna employs hybrid perturbations on stacked patches and covers NASA NEN’s both uplink and downlink frequencies, thus replacing the conventional requirement of two separate antennas. It provides a notable wide axial ratio (AR) < 3 dB bandwidth of 1.16 GHz from 7.02 GHz to 8.18 GHz (15.3%). The optimized patch dimensions provide 34.6% VSWR ~ 2 bandwidth from 6,525 MHz to 9,253 MHz. The overall antenna size is 17 mm × 17 mm × 6.6 mm, and has a peak gain of 7.9 dBi. This proposed antenna will overcome solar cell space constraint on smallsat’s outer wall by saving at least 50% area required by the conventional two-antenna method. 

A hybrid perturbation scheme is used in this article to achieve wide axial ratio (AR) bandwidth and beamwidth from circularly polarized (CP) microstrip patch–ring antennas using a single probe feed. Perturbations in the diagonal corners of a square ring and a square patch arranged in a stacked configuration are introduced to achieve the circular polarization. First, an enhanced AR bandwidth is obtained when a combination of a square ring and a square patch with negative perturbations is used as parasitic and driven elements, respectively. Next, circular polarization with wider AR bandwidth, wider beamwidth, and lower cross-polarization is obtained when a combination of a driven square patch with positive perturbation and a parasitic square ring with negative perturbations, termed as hybrid perturbations, is used. This antenna has a footprint suitable for small satellite applications (e.g., CubeSats) and its operating frequencies cover the allocated S-band downlink frequencies of NASA Deep Space Network and NASA Near Earth Network. 

Precise and eloquent label information is fundamental for interpreting the underlying data distributions distinctively and training of supervised and semi-supervised learning models adequately. But obtaining large amount of labeled data demands substantial manual effort. This obligation can be mitigated by acquiring labels of most informative data instances using Active Learning. However labels received from humans are not always reliable and poses the risk of introducing noisy class labels which will degrade the efficacy of a model instead of its improvement. In this paper, we address the problem of annotating sensor data instances of various Activities of Daily Living (ADLs) in smart home context. We exploit the interactions between the users and annotators in terms of relationships spanning across spatial and temporal space which accounts for an activity as well. We propose a novel annotator selection model SocialAnnotator which exploits the interactions between the users and annotators and rank the annotators based on their level of correspondence. We also introduce a novel approach to measure this correspondence distance using the spatial and temporal information of interactions, type of the relationships and activities. We validate our proposed SocialAnnotator framework in smart environments achieving ≈ 84% statistical confidence in data annotation