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NASA + Pratt X-HAB: Oasis Habitat
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TOI-674b: An oasis in the desert of exo-Neptunes transiting a nearby M dwarf
Context. The NASA mission TESS is currently doing an all-sky survey from space to detect transiting planets around bright stars. As part of the validation process, the most promising planet candidates need to be confirmed and characterized using follow-up observations. Aims. In this article, our aim is to confirm the planetary nature of the transiting planet candidate TOI-674b using spectroscopic and photometric observations. Methods. We use TESS, Spitzer, ground-based light curves, and HARPS spectrograph radial velocity measurements to establish the physical properties of the transiting exoplanet candidate TOI-674b. We perform a joint fit of the light curves and radial velocity time series to measure the mass, radius, and orbital parameters of the candidate. Results. We confirm and characterize TOI-674b, a low-density super-Neptune transiting a nearby M dwarf. The host star (TIC 158588995, V = 14.2 mag, J = 10.3 mag) is characterized by its M2V spectral type with M⋆ = 0.420 ± 0.010 Mꙩ, R⋆ = 0.420 ± 0.013 Rꙩ, and T(eff) = 3514 ± 57 K; it is located at a distance d = 46.16 ± 0.03 pc. Combining the available transit light curves plus radial velocity measurements and jointly fitting a circular orbit model, we find an orbital period of 1.977143 ± 3 × 10^(−6) days, a planetary radius of 5.25 ± 0.17 Rꚛ, and a mass of 23.6 ± 3.3 Mꚛ implying a mean density of ρp =0.91 ± 0.15 g/cu. cm. A non-circular orbit model fit delivers similar planetary mass and radius values within the uncertainties. Given the measured planetary radius and mass, TOI-674b is one of the largest and most massive super-Neptune class planets discovered around an M-type star to date. It is found in the Neptunian desert, and is a promising candidate for atmospheric characterization using the James Webb Space Telescope.
ASCEND: On-orbit/On-surface Servicing, Assembly, and Manufacturing (OSAM) Architecture Simulation System (OASiS)
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Mars OASIS (Mars Operational Agricultural System for In-Situ Specialization) System Reference Manual
In order to enable long term habitation on planetary surfaces, a means of sustainable food production must be developed. Addressing this need for surface habitats on Mars, the MarsOASIS team has developed a concept for a Martian surface greenhouse for unmanned crop production research as a proof of concept for larger scale food production facilities for manned surface missions. Utilizing in-situ resources such as the Martian atmosphere, sunlight, and UV-C radiation, the greenhouse aims to provide a sustainable method of long-term food production requiring minimal consumable resources. The MarsOASIS system is capable of growing a full life cycle of Outredgeous lettuce with its autonomous control system designed for a unmanned environment, only requiring teleoperation in extreme circumstances. A reduced-scope prototype of MarsOASIS is being developed to test technologies such as a natural/artificial hybrid lighting system, a closed water recycling system, remote teleoperation, and fully autonomous monitoring and control of the greenhouse. The prototype is currently in the final stages of design, with a full demonstration of plant life cycle testing set to occur in summer 2015. Results from this prototype demonstration will help quantify the feasibility of the innovative approaches seen in the MarsOASIS design.
Quaternary history of the Kiseiba Oasis region, southern Egypt
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Telescience Testbed Program: A study of software for SIRTF instrument control
As a continued element in the Telescience Testbed Program (TTP), the University of Arizona Steward Observatory and the Electrical and Computer Engineering Department (ECE) jointly developed a testbed to evaluate the Operations and Science Instrument System (OASIS) software package for remote control of an instrument for the Space Infrared Telescope Facility (SIRTF). SIRTF is a cryogenically-cooled telescope with three focal plane instruments that will be the infrared element of NASA's Great Observatory series. The anticipated launch date for SIRTF is currently 2001. Because of the complexity of the SIRTF mission, it was not expected that the OASIS package would be suitable for instrument control in the flight situation, however, its possible use as a common interface during the early development and ground test phases of the project was considered. The OASIS package, developed at the University of Colorado for control of the Solar Mesosphere Explorer (SME) satellite, serves as an interface between the operator and the remote instrument which is connected via a network. OASIS provides a rudimentary windowing system as well as support for standard spacecraft communications protocols. The experiment performed all of the functions required of the MIPS simulation program. Remote control of the instrument was demonstrated but found to be inappropriate for SIRTF at this time for the following reasons: (1) programming interface is too difficult; (2) significant computer resources were required to run OASIS; (3) the communications interface is too complicated; (4) response time was slow; and (5) quicklook of image data was not possible.
Tele-Supervised Adaptive Ocean Sensor Fleet
The Tele-supervised Adaptive Ocean Sensor Fleet (TAOSF) is a multi-robot science exploration architecture and system that uses a group of robotic boats (the Ocean-Atmosphere Sensor Integration System, or OASIS) to enable in-situ study of ocean surface and subsurface characteristics and the dynamics of such ocean phenomena as coastal pollutants, oil spills, hurricanes, or harmful algal blooms (HABs). The OASIS boats are extended- deployment, autonomous ocean surface vehicles. The TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy. One feature of TAOSF is the adaptive re-planning of the activities of the OASIS vessels based on sensor input ( smart sensing) and sensorial coordination among multiple assets. The architecture also incorporates Web-based communications that permit control of the assets over long distances and the sharing of data with remote experts. Autonomous hazard and assistance detection allows the automatic identification of hazards that require human intervention to ensure the safety and integrity of the robotic vehicles, or of science data that require human interpretation and response. Also, the architecture is designed for science analysis of acquired data in order to perform an initial onboard assessment of the presence of specific science signatures of immediate interest. TAOSF integrates and extends five subsystems developed by the participating institutions: Emergent Space Tech - nol ogies, Wallops Flight Facility, NASA s Goddard Space Flight Center (GSFC), Carnegie Mellon University, and Jet Propulsion Laboratory (JPL). The OASIS Autonomous Surface Vehicle (ASV) system, which includes the vessels as well as the land-based control and communications infrastructure developed for them, controls the hardware of each platform (sensors, actuators, etc.), and also provides a low-level waypoint navigation capability. The Multi-Platform Simulation Environment from GSFC is a surrogate for the OASIS ASV system and allows for independent development and testing of higher-level software components. The Platform Communicator acts as a proxy for both actual and simulated platforms. It translates platform-independent messages from the higher control systems to the device-dependent communication protocols. This enables the higher-level control systems to interact identically with heterogeneous actual or simulated platforms.
Intensity-based rock detection for acquiring onboard rover science
Missions to Mars already under study call for very long autonomous traverses (on the order of hundreds of meters per sol) during which there will be limited contact with Earth. The onboard Autonomous Science Investigation System (OASIS) is a technology for increasing science return during rover traverses by prioritizing science opportunities. By prioritizing data for downlink onboard, it is expected that the set of images selected for downlink by OASIS will have a consistently higher scientific interest then any set of equal number of images of the same traverse obtained using random or periodic sampling. Thus, OASIS can be used to increase the science returned from a long traverse. OASIS prioritizes data and identifies science opportunities by extracting information from image data and then using this information to prioritize the data and identify potential new science opportunities. Currently, the information extracted from the images is the location of the rocks in the image and several properties of these rocks such as size, albedo, texture and shape, In this paper we give an overview of the role of rock detection in the OASIS system and describe the functionality of the rock detector. We then present results on representative images
Measures for simulator evaluation of a helicopter obstacle avoidance system
The U.S. Army Aeroflightdynamics Directorate (AFDD) has developed a high-fidelity, full-mission simulation facility for the demonstration and evaluation of advanced helicopter mission equipment. The Crew Station Research and Development Facility (CSRDF) provides the capability to conduct one- or two-crew full-mission simulations in a state-of-the-art helicopter simulator. The CSRDF provides a realistic, full field-of-regard visual environment with simulation of state-of-the-art weapons, sensors, and flight control systems. We are using the CSRDF to evaluate the ability of an obstacle avoidance system (OASYS) to support low altitude flight in cluttered terrain using night vision goggles (NVG). The OASYS uses a laser radar to locate obstacles to safe flight in the aircraft's flight path. A major concern is the detection of wires, which can be difficult to see with NVG, but other obstacles--such as trees, poles or the ground--are also a concern. The OASYS symbology is presented to the pilot on a head-up display mounted on the NVG (NVG-HUD). The NVG-HUD presents head-stabilized symbology to the pilot while allowing him to view the image intensified, out-the-window scene through the HUD. Since interference with viewing through the display is a major concern, OASYS symbology must be designed to present usable obstacle clearance information with a minimum of clutter.
Securing Federated Learning Against Active Reconstruction Attacks
Federated Learning (FL) has amassed notable attention for its ability to preserve user privacy while emphasizing the retainment of model training efficiency. Due to this potential, FL has been integrated in many domains, such as healthcare, finance, law, and industrial engineering, where data cannot be easily exchanged due to sensitive information and strict privacy laws. However, current research has indicated that FL protocols are easily compromised by active data reconstruction attacks employed by actively dishonest servers. The malicious modification of global model parameters allows an actively dishonest server to obtain a direct copy of users’ private data via gradient inversion. Here, this class of attacks is highly underexplored and continues to be a major challenge due to the intense threat model. In this paper, we propose OASIS as a scalable and modality-agnostic defense based on data augmentation that counteracts active data reconstruction attacks while preserving model performance. To generalize our defense, we uncover the intuition behind gradient inversion that enables these attacks and theoretically establish the conditions by which the defense can be considered robust regardless of attack design. From this, we formulate our defense with data augmentation that illustrates its ability to undermine the attack principle. We evaluate OASIS on five real-world datasets–two image-based (ImageNet and CIFAR100) and three text-based (Wikitext, Stack Overflow, and Shakespeare)–which span diverse uses cases such as vision tasks and language modeling. Comprehensive evaluations on these datasets exhibit the efficacy of OASIS and highlight its feasibility as a solution.
Lessons learned in control center technologies and non-technologies
Information is given in viewgraph form on the Solar Mesosphere Explorer (SME) Control Center and the Oculometer and Automated Space Interface System (OASIS). Topics covered include SME mission operations functions; technical and non-technical features of the SME control center; general tasks and objects within the Space Station Freedom (SSF) ground system nodes; OASIS-Real Time for the control and monitoring of of space systems and subsystems; and OASIS planning, scheduling, and PC architecture.
Cislunar space infrastructure: Lunar technologies
Continuing its emphasis on the creation of a cisluar infrastructure as an appropriate and cost-effective method of space exploration and development, the University of Colorado explores the technologies necessary for the creation of such an infrastructure, namely (1) automation and robotics; (2) life support systems; (3) fluid management; (4) propulsion; and (5) rotating technologes. The technological focal point is on the development of automated and robotic systems for the implementation of a Lunar Oasis produced by automation and robotics (LOARS). Under direction from the NASA Office of Exploration, automation and robotics have been extensively utilized as an initiating stage in the return to the Moon. A pair of autonomous rovers, modular in design and built from interchangeable and specialized components, is proposed. Utilizing a 'buddy system', these rovers will be able to support each other and to enhance their individual capabilities. One rover primarily explores and maps while the second rover tests the feasibility of various materials-processing techniques. The automated missions emphasize availability and potential uses of lunar resources and the deployment and operations of the LOAR program. An experimental bio-volume is put into place as the precursor to a Lunar Environmentally Controlled Life Support System. The bio-volume will determine the reproduction, growth and production characteristics of various life forms housed on the lunar surface. Physiochemical regenerative technologies and stored resources will be used to buffer biological disturbances of the bio-volume environment. The in situ lunar resources will be both tested and used within this bio-volume. Second phase development on the lunar surface calls for manned operations. Repairs and reconfiguration of the initial framework will ensue. An autonomously initiated, manned Lunar Oasis can become an essential component of the United States space program. The Lunar Oasis will provide support to science, technology, and commerce. It will enable more cost-effective space exploration to the planets and beyond.
Navigating the Airways
Through a licensing agreement with JPL, Hughes Aircraft Company is using the Jet Propulsion Laboratory's (JPL) software GIPSY-OASIS for implementation of the Federal Aviation Administration's Wide Area Augmentation System (WAAS). WAAS will provide precision navigation for commercial airliners in U.S. airspace. JPL's GIPSY-OASIS (GIPSY - GPS-Inferred Positioning System; OASIS - Orbit Analysis and Simulation Software) has been used world-wide to produce the most accurate Global Positioning System (GPS) ephemeredes (satellite position) solutions ever achieved for ground and space users, both in scientific and commercial applications.
Planning for rover opportunistic science
The Mars Exploration Rover Spirit recently set a record for the furthest distance traveled in a single sol on Mars. Future planetary exploration missions are expected to use even longer drives to position rovers in areas of high scientific interest. This increase provides the potential for a large rise in the number of new science collection opportunities as the rover traverses the Martian surface. In this paper, we describe the OASIS system, which provides autonomous capabilities for dynamically identifying and pursuing these science opportunities during longrange traverses. OASIS uses machine learning and planning and scheduling techniques to address this goal. Machine learning techniques are applied to analyze data as it is collected and quickly determine new science gods and priorities on these goals. Planning and scheduling techniques are used to alter the behavior of the rover so that new science measurements can be performed while still obeying resource and other mission constraints. We will introduce OASIS and describe how planning and scheduling algorithms support opportunistic science.
Current results from a Rover Science Data Analysis System
In this paper, we provide a brief overview of the OASIS system, and then describe our recent successes in integrating with and using rover hardware. OASIS currently works in a closed loop fashion with onboard control software (e.g., navigation and vision) and has the ability to autonomously perform the following sequence of steps: analyze gray scale images to find rocks, extract the properties of the rocks, identify rocks of interest, retask the rover to take additional imagery of the identified target and then allow the rover to continue on its original mission. We also describe the early 2004 ground test validation of specific OASIS components on selected Mars Exploration Rover (MER) images. These components include the rockfinding algorithm, RockIT, and the rock size feature extraction code. Our team also developed the RockIT GUI, an interface that allows users to easily visualize and modify the rock-finder results. This interface has allowed us to conduct preliminary testing and validation of the rockfinder's performance.
Single-Receiver GPS Phase Bias Resolution
Existing software has been modified to yield the benefits of integer fixed double-differenced GPS-phased ambiguities when processing data from a single GPS receiver with no access to any other GPS receiver data. When the double-differenced combination of phase biases can be fixed reliably, a significant improvement in solution accuracy is obtained. This innovation uses a large global set of GPS receivers (40 to 80 receivers) to solve for the GPS satellite orbits and clocks (along with any other parameters). In this process, integer ambiguities are fixed and information on the ambiguity constraints is saved. For each GPS transmitter/receiver pair, the process saves the arc start and stop times, the wide-lane average value for the arc, the standard deviation of the wide lane, and the dual-frequency phase bias after bias fixing for the arc. The second step of the process uses the orbit and clock information, the bias information from the global solution, and only data from the single receiver to resolve double-differenced phase combinations. It is called "resolved" instead of "fixed" because constraints are introduced into the problem with a finite data weight to better account for possible errors. A receiver in orbit has much shorter continuous passes of data than a receiver fixed to the Earth. The method has parameters to account for this. In particular, differences in drifting wide-lane values must be handled differently. The first step of the process is automated, using two JPL software sets, Longarc and Gipsy-Oasis. The resulting orbit/clock and bias information files are posted on anonymous ftp for use by any licensed Gipsy-Oasis user. The second step is implemented in the Gipsy-Oasis executable, gd2p.pl, which automates the entire process, including fetching the information from anonymous ftp
Coarsening Dynamics and Marangoni Effects in Thin Liquid Crystal Bubbles in Microgravity
The Observation and Analysis of Smectic Islands in Space (OASIS) flight hardware was successfully launched on SpaceX-6 on April 15, 2015 and was operated in the Microgravity Science Glovebox (MSG) on board the International Space Station (ISS). The OASIS project comprises a series of experiments that probe the interfacial and hydrodynamic behavior of spherical-bubble freely suspended liquid crystal (FSLC) membranes in space. These are the thinnest known stable condensed phase structures, making them ideal for studies of two-dimensional (2D) coarsening dynamics and thermocapillary phenomena in microgravity. The OASIS experimental investigation was carried out using four different smectic A and C liquid crystal materials in four separate sample chambers housed inside the MSG. In this report, we present the behavior of collective dynamics on 2D bubble surface, including the equilibrium spatial organization and interaction of islands in electric fields and temperature gradients, and the diffusion and coalescence-driven coarsening dynamics of island emulsions in microgravity. We have observed spontaneous bubble thickening behavior caused by gradients between the bubble-blowing needle and ambient air temperatures. A uniform, thicker band forms during coarsening as a result of non-uniform heating by the LED illumination panels. These are proposed to be a result of Marangoni convection on the bubble surface.