The Langton Ultimate Cosmic ray Intensity Detector (LUCID) is a payload onboard the satellite TechDemoSat-1, used to study the radiation environment in Low Earth Orbit (∼635km). LUCID operated from 2014 to 2017, collecting over 2.1 million frames of radiation data from its five Timepix detectors on board. LUCID is one of the first uses of the Timepix detector technology in open space, with the data providing useful insight into the performance of this technology in new environments. It provides high-sensitivity imaging measurements of the mixed radiation field, with a wide dynamic range in terms of spectral response, particle type and direction. The data has been analysed using computing resources provided by GridPP, with a new machine learning algorithm that uses the Tensorflow framework. This algorithm provides a new approach to processing Medipix data, using a training set of human labelled tracks, providing greater particle classification accuracy than other algorithms. For managing the LUCID data, we have developed an online platform called Timepix Analysis Platform at School (TAPAS). This provides a swift and simple way for users to analyse data that they collect using Timepix detectors from both LUCID and other experiments. We also present some possible future uses of the LUCID data and Medipix detectors in space.

Peter Hatfield and students from the Institute for Research in Schools describe their latest results and describe how school students are contributing to real research.

The Langton Ultimate Cosmic ray Intensity Detector (LUCID) is a payload onboard the satellite TechDemoSat-1, used to study the radiation environment in Low Earth Orbit (∼635km). LUCID operated from 2014 to 2017, collecting over 2.1 million frames of radiation data from its five Timepix detectors on board. LUCID is one of the first uses of the Timepix detector technology in open space, with the data providing useful insight into the performance of this technology in new environments. The data has been analysed using computing resources provided by GridPP, with a novel machine learning algorithm. For managing the LUCID data, we have developed an online platform called Timepix Analysis Platform at School (TAPAS). This provides a swift and simple way for users to analyse data that they collect using Timepix detectors from both LUCID and other school based Timepix projects. These projects constitute a secondary school programme 'CERN@school' that give a framework for novel implementations of conventional classroom experiments using Timepix, as well as letting students contribute to large international scientific collaborations and devise their own research projects. K : Space instrumentation; Charged particle detection; Radiation monitoring; Analysis and statistical methods 1Corresponding author.

This paper describes the design and preliminary test results of a 360-degree scanning, multispectral intrusion detection sensor. This moderate-resolution, panoramic imaging sensor is intended for exterior use at ranges from 50 to 1500 meters. This Advanced Exterior Sensor (AES) uses three sensing technologies (infrared, visible, and radar), separate track processors and sensor fusion to provide low false-alarm intrusion detection, tracking, and immediate visual assessment. The images from the infrared and visible detector sets and the radar range data are updated as the sensors rotate about once per second. The radar provides range data with onemeter resolution. This sensor has been designed for low-cost, easy use and rapid deployment to cover wide areas beyond, or in place of, typical perimeters, and tactical applications around fixed or temporary high-value assets. A prototype AES has been developed and preliminary test results are presented. This sensor represents a growing trend to use low-cost thermal imaging sensors, combined with other devices and advanced processing, for protection of U.S. military forces and other national assets.