From miniature satellites to giant sun shields – the extreme technology transforming space engineering

Scientists are hoping to turn tiny spacecraft into starships by coupling them with large solar sails

Development of a 10g Femto-satellite with Active Attitude Control

This paper describes the initial design of a Femto-satellite with active attitude control. It has a structure with a matchbox form factor, measures 3.5 x 3.5 x 0.5cm, and uses three miniaturized orthogonal magnetorquers as actuators for attitude control. It also features short range RF communication at 868 MHz for demonstrating satellite networking and swarming around a CubeSat as a host. Preliminary test results obtained from a prototype device weighing 10g are given, including IMU based attitude determination, miniaturized magnetorquers, a custom-built Helmholtz coil system, active attitude control and RF communications

Laboratory-Scale Test Platforms for Femto-Satellite Attitude Control Systems

Femto-satellites have attracted growing attention over the past few years, with various universities taking up the challenge declared by the N-Prize. Many novel designs have been proposed, some of which are equipped with active attitude control based on either magnetic or electro-chromic control systems. However, conventional test platforms are unsuitable for testing control systems on such small length-scales and as such, developing suitable test platforms to be used for testing femto-satellite attitude control algorithms becomes important. This paper proposes six different concepts of laboratory-scale test platforms for femto-satellite attitude control testing, based on various physical laws. These platforms are manufactured using SLA 3D printing technology and then assembled. Qualitative observations are recorded and an experimental setup is devised in order to quantify the total resistive torque that appears in each platform. Results show that two of the designs display a maximum resistive torque on the order of 10^(-6) when spinning very rapidly, decreasing to the order of 10^(-7) at low angular velocities, which makes these designs suitable for their intended purpose. Finally, future work is proposed towards assessing the suitability of the other four platforms

Development of a 10g femto-satellite with active attitude control

This paper describes the initial design of a femto-satellite with active attitude control. It has a structure with a matchbox form factor, measures 3.5 x 3.5 x 0.5cm, and uses three miniaturized orthogonal magnetorquers as actuators for attitude control. It also features short range RF communication at 868 MHz for demonstrating satellite networking and swarming around a CubeSat as a host. Preliminary test results obtained from a prototype device weighing 10g are given, including IMU based attitude determination, miniaturized magnetorquers, a custom-built Helmholtz coil system, active attitude control and RF communications

Driver anomaly quantification for intelligent vehicles: a contrastive learning approach with representation clustering

Driver anomaly quantification is a fundamental capability to support human-centric driving systems of intelligent vehicles. Existing studies usually treat it as a classification task and obtain discrete levels for abnormalities. Meanwhile, the existing data-driven approaches depend on the quality of dataset and provide limited recognition capability for unknown activities. To overcome these challenges, this paper proposes a contrastive learning approach with the aim of building a model that can quantify driver anomalies with a continuous variable. In addition, a novel clustering supervised contrastive loss is proposed to optimize the distribution of the extracted representation vectors to improve the model performance. Compared with the typical contrastive loss, the proposed loss can better cluster normal representations while separating abnormal ones. The abnormality of driver activity can be quantified by calculating the distance to a set of representations of normal activities rather than being produced as the direct output of the model. The experiment results with datasets under different modes demonstrate that the proposed approach is more accurate and robust than existing ones in terms of recognition and quantification of unknown abnormal activities