Systems Engineering Lessons Learned for Class D Missions

One of NASA's goals within human exploration is to determine how to get humans to Mars safely and to live and work on the Martian surface. To accomplish this goal, several smaller missions act as stepping-stones to the larger end goal. NASA uses these smaller missions to develop new technologies and learn about how to survive outside of Low Earth Orbit for long periods. Additionally, keeping a cadence of these missions allows the team to maintain proficiency in the complex art of bringing spacecraft to fruition. Many of these smaller missions are robotic in nature and have smaller timescales, whereas there are others that involve crew and have longer mission timelines. Given the timelines associated with these various missions, different levels of risk and rigor need to be implemented to be more in line with what is appropriate for the mission. Thus, NASA has four different classifications that range from Class A to Class D based on the mission details. One of these projects is the Resource Prospector (RP) Mission, which is a multi-center and multi-institution collaborative project to search for volatiles in the polar regions of the Moon. The RP mission is classified as a Class D mission and as such, has the opportunity to more tightly manage, and therefore accept, greater levels of risk. The requirements for Class D missions were at the forefront of the design and thus presented unique challenges in vehicle development and systems engineering processes. This paper will discuss the systems engineering process at NASA and how that process is tailored for Class D missions, specifically the RP mission

A search for rapidly pulsating hot subdwarf stars in the GALEX survey

NASA's Galaxy Evolution Explorer (GALEX) provided near- and far-UV observations for approximately 77 percent of the sky over a ten-year period; however, the data reduction pipeline initially only released single NUV and FUV images to the community. The recently released Python module gPhoton changes this, allowing calibrated time-series aperture photometry to be extracted easily from the raw GALEX data set. Here we use gPhoton to generate light curves for all hot subdwarf B (sdB) stars that were observed by GALEX, with the intention of identifying short-period, p-mode pulsations. We find that the spacecraft's short visit durations, uneven gaps between visits, and dither pattern make the detection of hot subdwarf pulsations difficult. Nonetheless, we detect UV variations in four previously known pulsating targets and report their UV pulsation amplitudes and frequencies. Additionally, we find that several other sdB targets not previously known to vary show promising signals in their periodograms. Using optical follow-up photometry with the Skynet Robotic Telescope Network, we confirm p-mode pulsations in one of these targets, LAMOST J082517.99+113106.3, and report it as the most recent addition to the sdBVr class of variable stars.Comment: 11 Pages, 8 Figures, Accepted for publication in the Astrophysical Journa

Durrington Walls to West Amesbury by way of Stonehenge: a major transformation of the Holocene landscape

A new sequence of Holocene landscape change has been discovered through an investigation of sediment sequences, palaeosols, pollen and molluscan data discovered during the Stonehenge Riverside Project. The early post-glacial vegetational succession in the Avon valley at Durrington Walls was apparently slow and partial, with intermittent woodland modification and the opening-up of this landscape in the later Mesolithic and earlier Neolithic, though a strong element of pine lingered into the third millennium BC. There appears to have been a major hiatus around 2900 cal BC, coincident with the beginnings of demonstrable human activities at Durrington Walls, but slightly after activity started at Stonehenge. This was reflected in episodic increases in channel sedimentation and tree and shrub clearance, leading to a more open downland, with greater indications of anthropogenic activity, and an increasingly wet floodplain with sedges and alder along the river’s edge. Nonetheless, a localized woodland cover remained in the vicinity of DurringtonWalls throughout the third and second millennia BC, perhaps on the higher parts of the downs, while stable grassland, with rendzina soils, predominated on the downland slopes, and alder–hazel carr woodland and sedges continued to fringe the wet floodplain. This evidence is strongly indicative of a stable and managed landscape in Neolithic and Bronze Age times. It is not until c 800–500 cal BC that this landscape was completely cleared, except for the marshy-sedge fringe of the floodplain, and that colluvial sedimentation began in earnest associated with increased arable agriculture, a situation that continued through Roman and historic times

Antennas Embedded in the Front Frame of Smart Glasses

Ergonomic head-up display is enabled by reliable, low latency, radio frequency (RF) communication between smart glasses and smartphones. The form factor of antennas on head-worn frames is impacted by the proximity of the frame to the human head and by cross-body interference. In addition, the large bandwidth required on the glass-to-phone link entails the use of multiple antennas, limiting the space available to route signals. The presence of antennas also restricts the choice of material for the front frames. This disclosure describes a multi-element, front-frame structure for smart glasses that enables metallic front-frame materials that enable superior cosmetic, structural, and communication design options. The described structure reduces the space occupied by antennas and reduces the complexity of assembly of multiple discrete antennas. The propagation loss presented by the human head is nullified, enabling lower radiated power, lower power consumption, and smaller and/or lighter smart glasses

Zarr: A Cloud-Optimized Storage for Interactive Access of Large Arrays

For decades, the sharing of large N-dimensional datasets has posed issues across multiple domains. Interactively accessing terabyte-scale data has previously required significant server resources to properly prepare cropped or down-sampled representations on the fly. Now, a cloud-native chunked format easing this burden has been adopted in the bioimaging domain for standardization. The format — Zarr — is potentially of interest for other consortia and sections of NFDI

Wildcats never trail, Clobber Morehead State 79-54

Article published in the Lexington Herald-Leader on November 22, 2019 on Morehead State vs. University of Kentucky women\u27s basketball game.https://scholarworks.moreheadstate.edu/college_histories/1259/thumbnail.jp

Session 5: \u3cem\u3eEquipment Finance Credit Risk Modeling - A Case Study in Creative Model Development & Nimble Data Engineering\u3c/em\u3e

This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it is integrated into our other systems for decision purposes. We will also talk about how the data is transformed from a raw, sometimes unstructured state, to something more usable by a data science team – and demonstrate how this data was harnessed to help guide model enhancements as key opportunity areas have been identified

Publishing and sharing multi-dimensional image data with OMERO

Imaging data are used in the life and biomedical sciences to measure the molecular and structural composition and dynamics of cells, tissues, and organisms. Datasets range in size from megabytes to terabytes and usually contain a combination of binary pixel data and metadata that describe the acquisition process and any derived results. The OMERO image data management platform allows users to securely share image datasets according to specific permissions levels: data can be held privately, shared with a set of colleagues, or made available via a public URL. Users control access by assigning data to specific Groups with defined membership and access rights. OMERO’s Permission system supports simple data sharing in a lab, collaborative data analysis, and even teaching environments. OMERO software is open source and released by the OME Consortium at www.openmicroscopy.org