Time domain radio astronomy observing campaigns frequently generate large volumes of data. Our goal is to develop automated methods that can identify events of interest buried within the larger data stream. The V-FASTR fast transient system was designed to detect rare fast radio bursts (FRBs) within data collected by the Very Long Baseline Array. The resulting event candidates constitute a significant burden in terms of subsequent human reviewing time. We have trained and deployed a machine learning classifier that marks each candidate detection as a pulse from a known pulsar, an artifact due to radio frequency interference, or a potential new discovery. The classifier maintains high reliability by restricting its predictions to those with at least 90% confidence. We have also implemented several efficiency and usability improvements to the V-FASTR web-based candidate review system. Overall, we found that time spent reviewing decreased and the fraction of interesting candidates increased. The classifier now classifies (and therefore filters) 80-90% of the candidates, with an accuracy greater than 98%, leaving only the 10-20% most promising candidates to be reviewed by humans.

The use of small Unmanned Aircraft Systems (sUAS) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on a four-year, community-based investigation into the tools, data practices, and challenges that currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data—as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases—is characterized as both sharing many traits with traditional remote sensing data and also as exhibiting—as common across the spectrum of disciplines and use cases—novel characteristics that require novel data support infrastructure; and (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost.

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In the context of the high-luminosity upgrade of the LHC and ATLAS, the microstrip-tracking detector will be redesigned. The main building blocks are substructures with multiple sensors and their electronics. Each substructure will have a single interface to the off-detector system, the so-called End-of-Substructure (EoS) card. Its physical realisation is a set of printed circuit boards (PCBs). The PCB integrates ASICs and hybrids, which multiplex or demultipex the data and transmit with a rate up to 10 Gb/s or receive with a rate up to 2.5 Gb/s on optical fibres. These active parts are developed at CERN and are known as lpGBT and VTRx+. The EoS card integrates the active parts with the required electronics for the specified operation and within the mechanical constraints of the detector. In this paper critical design aspects such as the low-impedance powering scheme and the PCB setup are described. The EoS card has reached its final state for a series production, including the required setups for quality control. The achieved transmission quality on the 10 Gb/s links is presented.