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NASA NTRS2004

Science Opportunity Analyzer (SOA): Science Planning Made Simple

.For the first time at JPL, the Cassini mission to Saturn is using distributed science operations for developing their experiments. Remote scientists needed the ability to: a) Identify observation opportunities; b) Create accurate, detailed designs for their observations; c) Verify that their designs meet their objectives; d) Check their observations against project flight rules and constraints; e) Communicate their observations to other scientists. Many existing tools provide one or more of these functions, but Science Opportunity Analyzer (SOA) has been built to unify these tasks into a single application. Accurate: Utilizes JPL Navigation and Ancillary Information Facility (NAIF) SPICE* software tool kit - Provides high fidelity modeling. - Facilitates rapid adaptation to other flight projects. Portable: Available in Unix, Windows and Linux. Adaptable: Designed to be a multi-mission tool so it can be readily adapted to other flight projects. Implemented in Java, Java 3D and other innovative technologies. Conclusion: SOA is easy to use. It only requires 6 simple steps. SOA's ability to show the same accurate information in multiple ways (multiple visualization formats, data plots, listings and file output) is essential to meet the needs of a diverse, distributed science operations environment.

observation planning operations↗
NASA NTRS2004

Science Opportunity Analyzer (SOA): Not Just Another Pretty Face

This viewgraph presentation reviews the Science Opportunity Analyzer (SOA). For the first time at JPL, the Cassini mission to Saturn is using distributed science operations for sequence generation. This means that scientist at other institutions has more responsibility to build the spacecraft sequence. Tools are required to support the sequence development. JPL tools required a complete configuration behind a firewall, and the tools that the user community had developed did not interface with the JPL tools. Therefore the SOA was created to bridge the gap between the remote scientists and the JPL operations teams. The presentation reviews the development of the SOA, and what was required of the system. The presentation reviews the functions that the SOA performed.

mission planning↗
NASA NTRS2004

Observation Planning Made Simple with Science Opportunity Analyzer (SOA)

As NASA undertakes the exploration of the Moon and Mars as well as the rest of the Solar System while continuing to investigate Earth's oceans, winds, atmosphere, weather, etc., the ever-existing need to allow operations users to easily define their observations increases. Operation teams need to be able to determine the best time to perform an observation, as well as its duration and other parameters such as the observation target. In addition, operations teams need to be able to check the observation for validity against objectives and intent as well as spacecraft constraints such as turn rates and acceleration or pointing exclusion zones. Science Opportunity Analyzer (SOA), in development for the last six years, is a multi-mission toolset that has been built to meet those needs. The operations team can follow six simple steps and define his/her observation without having to know the complexities of orbital mechanics, coordinate transformations, or the spacecraft itself.

observation planning operations↗
NASA NTRS2013

Science Opportunity Analyzer (SOA) Version 8

SOA allows scientists to plan spacecraft observations. It facilitates the identification of geometrically interesting times in a spacecraft s orbit that a user can use to plan observations or instrument-driven spacecraft maneuvers. These observations can then be visualized multiple ways in both two- and three-dimensional views. When observations have been optimized within a spacecraft's flight rules, the resulting plans can be output for use by other JPL uplink tools. Now in its eighth major version, SOA improves on these capabilities in a modern and integrated fashion. SOA consists of five major functions: Opportunity Search, Visualization, Observation Design, Constraint Checking, and Data Output. Opportunity Search is a GUI-driven interface to existing search engines that can be used to identify times when a spacecraft is in a specific geometrical relationship with other bodies in the solar system. This function can be used for advanced mission planning as well as for making last-minute adjustments to mission sequences in response to trajectory modifications. Visualization is a key aspect of SOA. The user can view observation opportunities in either a 3D representation or as a 2D map projection. Observation Design allows the user to orient the spacecraft and visualize the projection of the instrument field of view for that orientation using the same views as Opportunity Search. Constraint Checking is provided to validate various geometrical and physical aspects of an observation design. The user has the ability to easily create custom rules or to use official project-generated flight rules. This capability may also allow scientists to easily assess the cost to science if flight rule changes occur. Data Output allows the user to compute ancillary data related to an observation or to a given position of the spacecraft along its trajectory. The data can be saved as a tab-delimited text file or viewed as a graph. SOA combines science planning functionality unique to both JPL and the sponsoring spacecraft. SOA is able to ingest JPL SPICE Kernels that are used to drive the tool and its computations. A Percy search engine is then included that identifies interesting time periods for the user to build observations. When observations are then built, flight-like orientation algorithms replicate spacecraft dynamics to closely simulate the flight spacecraft s dynamics. SOA v8 represents large steps forward from SOA v7 in terms of quality, reliability, maintainability, efficiency, and user experience. A tailored agile development environment has been built around SOA that provides automated unit testing, continuous build and integration, a consolidated Web-based code and documentation storage environment, modern Java enhancements, and a focus on usability

Witoff, Robert J.↗
NASA NTRS2003

Science opportunity analyzer - a multi-mission approach to science planning

In the past Science Planning for space missions has been comprised of using ad-hoc software toolscollected or reconstructed from previous missions, tools used by other groups who often speak a different 'technical' language or even 'the backs of envelopes'. In addition to the tools being rough, the work done with these tools often has had to be redone or at least re-entered when it came time to determine actual observations. Science Opportunity Analyzer (SOA), a Java-based application, has been built for scientists to enable them to identify/analyze observation opportunities and then, to create corresponding observation designs.

Science opportunity analyzer science planning SOA ↗
NASA NTRS2003

Create your own science planning tool in 3 days with SOA

Scientific discovery and advancement of knowledge has been, and continues to be, the goal for space missions at Jet Propulsion Laboratory. Scientist must plan their observation/experiments to get the maximum data return in order to make those discoveries. However, each mission has different science objectives, a different spacecraft and different instrument payloads, as well as, different routes to different destinations with different spacecraft restrictions and characteristics. In the current reduced cost environment, manageable cost for mission planning software is a must. Science Opportunity Analyzer (SOA), a planning tool for scientists and mission planners, utilizes a simple approach to reduce cost and promote reusability.

Science Opportunity Analyzer (SOA).↗
NASA NTRS2002

Science opportunity analyzer - a multi-mission tool for planning

For many years the diverse scientific community that supports JPL's wide variety ofinterplanetary space missions has needed a tool in order to plan and develop their experiments. The tool needs to be easily adapted to various mission types and portable to the user community. The Science Opportunity Analyzer, SOA, now in its third year of development, is intended to meet this need. SOA is a java-based application that is designed to enable scientists to identify and analyze opportunities for science observations from spacecraft. It differs from other planning tools in that it does not require an in-depth knowledge of the spacecraft command system or operation modes to begin high level planning. Users can, however, develop increasingly detailed levels of design. SOA consists of six major functions: Opportunity Search, Visualization, Observation Design, Constraint Checking, Data Output and Communications. Opportunity Search is a GUI driven interface to existing search engines that can be used to identify times when a spacecraft is in a specific geometrical relationship with other bodies in the solar system. This function can be used for advanced mission planning as well as for making last minute adjustments to mission sequences in response to trajectory modifications. Visualization is a key aspect of SOA. The user can view observation opportunities in either a 3D representation or as a 2D map projection. The user is given extensive flexibility to customize what is displayed in the view. Observation Design allows the user to orient the spacecraft and visualize the projection of the instrument field of view for that orientation using the same views as Opportunity Search. Constraint Checking is provided to validate various geometrical and physical aspects of an observation design. The user has the ability to easily create custom rules or to use official project-generated flight rules. This capability may also allow scientists to easily impact the cost to science if flight rule changes occur. Data Output generates information based on the spacecraft's trajectory, opportunity search results or based on a created observation. The data can be viewed either in tabular format or as a graph. Finally, SOA is unique in that it is designed to be able to communicate with a variety of existing planning and sequencing tools. From the very beginning SOA was designed with the user in mind. Extensive surveys of the potential user community were conducted in order to develop the software requirements. Throughout the development period, close ties have been maintained with the science community to insure that the tool maintains its user focus. Although development is still in its early stages, SOA is already developing a user community on the Cassini project, which is depending on this tool for their science planning. There are other tools at JPL that do various pieces of what SOA can do; however, there is no other tool which combines all these functions and presents them to the user in such a convenient, cohesive, and easy to use fashion.

SOA science planning mission operations sequence s↗
NASA NTRS2021

Psyche Science Planning with the Science Opportunity Analyzer

Psyche is a NASA Discovery mission intent on solving mysteries surrounding early planetary formation. The mission spacecraft will journey to (16) Psyche: a metallic asteroid believed to be the exposed core of an early planetesimal. Despite the unique nature of the target asteroid and the mission’s novel science goals, a significant portion of the project’s science planning needs may be met through the use of legacy tools. In particular, the adoption of the Jet Propulsion Laboratory’s (JPL) Science Opportunity Analyzer (SOA) promises to satisfy mission requirements with minimal development effort. Originally developed as a multi-mission science planning tool, SOA can design and visualize observations, perform geometric flight rule checking, model physical phenomena, and much more. The software has been used by the mission since its proposal phase and is expected to continue providing valuable capabilities throughout operations.

Elkins-Tanton, Linda T.↗
NASA NTRS2013

NASA Tech Briefs, July 2013

Dielectrophoresis-Based Particle Sensor Using Nanoelectrode Arrays; Multi-Dimensional Damage Detection for Surfaces and Structures; ULTRA: Underwater Localization for Transit and Reconnaissance Autonomy; Autonomous Cryogenic Leak Detector for Improving Launch Site Operations; Submillimeter Planetary Atmospheric Chemistry Exploration Sounder; Method for Reduction of Silver Biocide Plating on Metal Surfaces; Silicon Micromachined Microlens Array for THz Antennas; Forward-Looking IED Detector Ground Penetrating Radar; Fully Printed, Flexible, Phased Array Antenna for Lunar Surface Communication, Battery Charge Equalizer with Transformer Array; An Efficient, Highly Flexible Multi-Channel Digital Downconverter Architecture; Dimmable Electronic Ballast for a Gas Discharge Lamp; Conductive Carbon Nanotube Inks for Use with Desktop Inkjet Printing Technology; Enhanced Schapery Theory Software Development for Modeling Failure of Fiber-Reinforced Laminates; High-Performance, Low-Temperature-Operating, Long-Lifetime Aerospace Lubricants; Carbon Nanotube Microarrays Grown on Nanoflake Substrates; Differential Muon Tomography to Continuously Monitor Changes in the Composition of Subsurface Fluids; Microgravity Drill and Anchor System; 20 Granular Media-Based Tunable Passive Vibration Suppressor; 21 Miga Aero Actuator and 2D Machined Mechanical Binary Latch; Micro-XRF for In Situ Geological Exploration of Other Planets; Hydrogen-Enhanced Lunar Oxygen Extraction and Storage Using Only Solar Power; Uplift of Ionospheric Oxygen Ions During Extreme Magnetic Storms; Miniaturized, High-Speed, Modulated X-Ray Source; Hollow-Fiber Spacesuit Water Membrane Evaporator 25 High-Power Single-Mode 2.65-micrometers InGaAsSb/AlInGaAsSb Diode Lasers; Optical Device for Converting a Laser Beam Into Two Co-aligned but Oppositely Directed Beams; A Hybrid Fiber/Solid-State Regenerative Amplifier with Tunable Pulse Widths for Satellite Laser Ranging; X-Ray Diffractive Optics; SynGenics Optimization System (SynOptSys); 29 CFD Script for Rapid TPS Damage Assessment; radEq Add-On Module for CFD Solver Loci-CHEM; Science Opportunity Analyzer (SOA) Version 8; 30 Autonomous Byte Stream Randomizer; Distributed Engine Control Empirical/Analytical Verification Tools; Dynamic Server-Based KML Code Generator Method for Level-of-Detail Traversal of Geospatial Data; Automated Planning of Science Products Based on Nadir Overflights and Alerts for Onboard and Ground Processing; Linked Autonomous Interplanetary Satellite Orbit Navigation; Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing; Scheduling Operations for Massive Heterogeneous Clusters; Deepak Condenser Model (DeCoM); Flight Software Math Library; Recirculating 1-K-Pot for Pulse-Tube Cryostats; 35 Method for Processing Lunar Regolith Using Microwaves; Wells for In Situ Extraction of Volatiles from Regolith (WIEVR); and Estimating the Backup Reaction Wheel Orientation Using Reaction Wheel Spin Rates Flight Telemetry from a Spacecraft.

Source record↗
NASA NTRS2020

Integrating the Science Opportunity Analyzer with a Reusable Opportunity Search Framework

In our interactions with the Science Opportunity Analyzer (SOA) software, we recognized how its ability to search for geometric events in space is a need for robotic space missions in general. To satisfy this need, we propose the Tychonis framework, which is built upon the principles of: (1) separations of concerns, (2) extensibility, (3) reusability, and (4) independent verification and validation. Tychonis’ separation of concerns results in the availability of different constructs to model geometric events and search for them. These constructs can be extended by users as needed and reused across missions without changes. Given the low coupling between concerns, and the fact Tychonis can be augmented in isolation, its constructs can be validated independently from other pieces of software. This paper elaborates on these topics and presents an integration case study between Tychonis and SOA that relies on the concept of dynamic integration. Dynamic integration entails that augmentations of the framework are reflected automatically in the host application without any changes to the host application’s code. The SOA-Tychonis integration case study can be extrapolated to other tools that need to search for geometric events as the pattern repeats across implementations: (i) the manipulation of a user interface to model opportunities, (ii) the execution of algorithms to search for opportunities, and (iii) the presentation of search results to users. Overall, Tychonis’ is a story about how the application of proven software principles and good design choices can reduce risk and cost to space missions

Soria, Manel↗
DOE OSTI2025

Advancing Aerosol Chemical Characterization and Vertical Profiling over the Southern Great Plains Using Uncrewed Aerial Sampling and Offline Aerosol Mass Spectrometry

Recent advancements in uncrewed aerial systems (UASs) and particulate matter (PM) analytical techniques have provided opportunities for atmospheric research. In this study, we deployed the Department of Energy’s fixed-wing ArcticShark UAS to examine PM 2.5 composition at varying altitudes─within and above the planetary boundary layer (PBL)─over the Southern Great Plains atmospheric observatory (SGP). A total of 22 flights were conducted across March, June, and August 2023. Composite filter samples were collected during each flight and analyzed with offline aerosol mass spectrometry (AMS), complemented by on-board real-time sensors and ground-based instrumentation, to provide a comprehensive view of regional aerosol characteristics. Results show clear vertical and seasonal differences in the aerosol composition. Relative to ground-level measurements, aloft samples exhibited shifts in the distribution of organic and inorganic PM, with the organic composition varying distinctly across seasons. Particulate organic nitrogen (ON) was elevated, with bulk compositions similar in March and June but strongly altered in August, likely driven by biomass burning and enhanced photochemical activity. Combined AMS and chemical ionization mass spectrometry analyses detected amines, amides, and amino acids. PM above the planetary boundary layer was enriched in oxidized organic aerosols, while ground-level PM contained higher nitrate and sulfate. Seasonal differences in aqueous-phase processing were also observed, which were strongest in March during persistent cloud cover and weaker in the drier August period, suggesting a shift from aqueous- to gas-phase SOA formation. In conclusion, these findings highlight the value of UAS in advancing PM measurements and vertical profiling of aerosol composition.

54 ENVIRONMENTAL SCIENCES↗