No abstract available.

This paper presents a summary of findings related to some current developments and emerging applications of the space edge-computing and onboard artificial intelligence (AI) as discussed at a workshop by the IEEE Future Directions Low- Earth Orbit Satellites and Systems (LEO SatS) Initiative. It focuses on the state-of-the-art AI techniques across various layers of the space communication link, benchmarking of deep-learning models and flight software applications evaluated on embedded processors on board the ISS, a mission for in-orbit experiments of AI and edge computing, and AI safety and security with applications in the context of Dataspaces and satellites for Earth observations.

No abstract available.

No abstract available.

No abstract available.

Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a primary radar intelligently targets ice storms based on information collected by a lookahead radiometer. Critical to the intelligent targeting is accurate identification of storm/cloud types from eight bands of radiance collected by the radiometer. The cloud types of interest are: clear sky, thin cirrus, cirrus, rainy anvil, and convection core. We describe multi-step use of Machine Learning and Digital Twin of the Earth's atmosphere to derive such a classifier. First, a digital twin of Earth's atmosphere called a Weather Research Forecast (WRF) is used generate simulated lookahead radiometer data as well as deeper "science" hidden variables. The datasets simulate a tropical region over the Caribbean and a non-tropical region over the Atlantic coast of the United States. A K-means clustering over the scientific hidden variables was utilized by human experts to generate an automatic labelling of the data - mapping each physical data point to cloud types by scientists informed by mean/centroids of hidden variables of the clusters. Next, classifiers were trained with the inputs of the simulated radiometer data and its corresponding label. The classifiers of a random decision forest (RDF), support vector machine (SVM), Gaussian naïve bayes, feed forward artificial neural network (ANN), and a convolutional neural network (CNN) were trained. Over the tropical dataset, the best performing classifier was able to identify non-storm and storm clouds with over 80% accuracy in each class for a held-out test set. Over the non-tropical dataset, the best performing classifier was able to classify non-storm clouds with over 90% accuracy and storm clouds with over 40% accuracy. Additionally both sets of classifiers were shown to be resilient to instrument noise.

No abstract available.

Future space missions will process and analyze imagery onboard and plan and act more autonomously placing greater demands on flight computing. Traditional flight hardware provides modest compute, even when compared to laptop and desktop computers. A new generation of commercial off the shelf (COTS) processors, such as the Qualcomm Snapdragon, deliver significant compute in a small Size Weight and Power (SWaP) and offer direct hardware acceleration in the form of Graphics Processing Units (GPU) and Digital Signal Processors (DSP). We benchmark a variety of instrument processing and mission planning software on a Qualcomm Snapdragon SoC currently hosted by HPE’s Spaceborne Computer-2 (SBC-2) onboard the International Space Station.

Future space missions can benefit from processing imagery onboard to detect science events, create insights, and respond autonomously. This capability can enable the discovery of new science. One of the challenges to this mission concept is that traditional space flight hardware has limited capabilities and is derived from much older computing in order to ensure reliable performance in the extreme environments of space, particularly radiation. Modern Commercial Off The Shelf (COTS) processors, such as the Movidius Myriad X and the Qualcomm Snapdragon, provide significant improvements in small Size Weight and Power (SWaP) packaging. They offer direct hardware acceleration for deep neural networks, which are state-of-the art in computer vision. We deploy neural network models on these processors hosted by Hewlett Packard Enterprise’s Spaceborne Computer-2 onboard the International Space Station (ISS). We benchmark a variety of algorithms on these processors. The models are run multiple times on the ISS to see if any errors develop. In addition, we run a memory checker to detect radiation effects on the embedded processors.

No abstract available.