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Payload Operations Support Team Tools is a software system that assists in (1) development and testing of software for payloads to be flown aboard the space shuttles and (2) training of payload customers, flight controllers, and flight crews in payload operations
We review here the progress of the Variability of the American MOnsoon Systems (VAMOS) extremes working group since it was formed in February of 2010. The goals of the working group are to 1) develop an atlas of warm-season extremes over the Americas, 2) evaluate existing and planned simulations, and 3) suggest new model runs to address mechanisms and predictability of extremes. Substantial progress has been made in the development of an extremes atlas based on gridded observations and several reanalysis products including Modern Era Retrospective-Analysis for Research and Applications (MERRA) and Climate Forecast System Reanalysis (CFSR). The status of the atlas, remaining issues and plans for its expansion to include model data will be discussed. This includes the possibility of adding a companion atlas based on station observations based on the software developed under the World Climate Research Programme (WCRP) Expert Team on Climate Change. Detection and Indices (ETCCDI) activity. We will also review progress on relevant research and plans for the use and validation of the atlas results.
The Measurement Technology Center of the Instrumentation Section configures automated data acquisition systems to meet the diverse needs of JPL's experimental research community. These systems are based on personal computers or workstations (Apple, IBM/Compatible, Hewlett-Packard, and Sun Microsystems) and often include integrated data analysis, visualization and experiment control functions in addition to data acquisition capabilities. These integrated systems may include sensors, signal conditioning, data acquisition interface cards, software, and a user interface. Graphical programming is used to simplify configuration of such systems. Employment of a graphical programming language is the most important factor in enabling the implementation of data acquisition, analysis, display and visualization systems at low cost. Other important factors are the use of commercial software packages and off-the-shelf data acquisition hardware where possible. Understanding the experimenter's needs is also critical. An interactive approach to user interface construction and training of operators is also important. One application was created as a result of a competative effort between a graphical programming language team and a text-based C language programming team to verify the advantages of using a graphical programming language approach. With approximately eight weeks of funding over a period of three months, the text-based programming team accomplished about 10% of the basic requirements, while the Macintosh/LabVIEW team accomplished about 150%, having gone beyond the original requirements to simulate a telemetry stream and provide utility programs. This application verified that using graphical programming can significantly reduce software development time. As a result of this initial effort, additional follow-on work was awarded to the graphical programming team.
NASA's Integrated Demand Management research activity developed a concept for a novel use of the FAA's Collaborative Trajectory Options Program (CTOP) decision support software to support a particular type of traffic management use case. The research team used an emulation of CTOP software to prototype and test the concept, and developed several enhancements to the CTOP user interface that were well received by the stakeholder community. This document provides a detailed description of these enhancements and their operation that could be used to support their incorporation into the official CTOP software.
Misaligned incentives lead to reduced interaction between scientists and programmers on modern NASA science data-product development teams. Typically, situations arise where the scientist is not incentivized to learn modern coding practices and the programmer does not understand the science algorithms in the code. A programmer is responsible for the deliverable code thus setting a tradeoff between the desire for code improvement versus fear of compromising the integrity of data-product while the scientist continues to rely on their legacy codebases owing to the complexity of using the delivered code outside the processing environment and lack of validation modules. The NASA/CERES-TISA project has adopted a collaborative approach, with scientists and programmers both utilizing the same software repository with multiple branches, some optimized for delivery to a processing datacenter and others for scientific product development and validation. A team of scientists and programmers jointly review any new science code updates for integration into the codebase and strive to improve practices through promoting algorithm understanding, better institutional knowledge exchange and documentation, modularization, and developing data processing flow-dictated validation and debugging methods. This leads to a reduction in the personnel single point failures and reduced development time for creation of new science data-products.
The Mars Science Laboratory (MSL) team is sending the rover, Curiosity, to Mars, and therefore is physically and technically complex. During my stay, I have assisted the MSL Flight Software (FSW) team in implementing functional test scripts to ensure that the FSW performs to the best of its abilities. There are a large number of FSW requirements that have been written up for implementation; however I have only been assigned a few sections of these requirements. There are many stages within testing; one of the early stages is FSW Internal Testing (FIT). The FIT team can accomplish this with simulation software and the MSL Test Automation Kit (MTAK). MTAK has the ability to integrate with the Software Simulation Equipment (SSE) and the Mission Processing and Control System (MPCS) software which makes it a powerful tool within the MSL FSW development process. The MSL team must ensure that the rover accomplishes all stages of the mission successfully. Due to the natural complexity of this project there is a strong emphasis on testing, as failure is not an option. The entire mission could be jeopardized if something is overlooked.
In concert with the NASA-wide Intelligent Synthesis Environment Program, the Exploration Office at the Johnson Space Center has assembled an Advanced Design Team. The purpose of this team is two-fold. The first is to identify, use, and develop software applications, tools, and design processes that streamline and enhance a collaborative engineering environment. The second is to use this collaborative engineering environment to produce conceptual, system-level-of-detail designs in a relatively short turnaround time, using a standing team of systems and integration experts. This includes running rapid trade studies on varying mission architectures, as well as producing vehicle and/or subsystem designs. The standing core team is made up of experts from all of the relevant engineering divisions (e.g. Power, Thermal, Structures, etc.) as well as representatives from Risk and Safety, Mission Operations, and Crew Life Sciences among others. The Team works together during 2- hour sessions in the same specially enhanced room to ensure real-time integration/identification of cross-disciplinary issues and solutions. All subsystem designs are collectively reviewed and approved during these same sessions. In addition there is an Information sub-team that captures and formats all data and makes it accessible for use by the following day. The result is Team Expo: an Advanced Design Team that is leading the change from a philosophy of "over the fence" design to one of collaborative engineering that pushes the envelope to achieve the next-generation analysis and design environment.
The Space Shuttle Ascent Flight Design team is responsible for defining a launch to orbit trajectory profile that satisfies all programmatic mission objectives and defines the ground and onboard reconfiguration requirements for this high-speed and demanding flight phase. This design, verification and reconfiguration process ensures that all applicable mission scenarios are enveloped within integrated vehicle and spacecraft certification constraints and criteria, and includes the design of the nominal ascent profile and trajectory profiles for both uphill and ground-to-ground aborts. The team also develops a wide array of associated training, avionics flight software verification, onboard crew and operations facility products. These key ground and onboard products provide the ultimate users and operators the necessary insight and situational awareness for trajectory dynamics, performance and event sequences, abort mode boundaries and moding, flight performance and impact predictions for launch vehicle stages for use in range safety, and flight software performance. These products also provide the necessary insight to or reconfiguration of communications and tracking systems, launch collision avoidance requirements, and day of launch crew targeting and onboard guidance, navigation and flight control updates that incorporate the final vehicle configuration and environment conditions for the mission. Over the course of the Space Shuttle Program, ascent trajectory design and mission planning has evolved in order to improve program flexibility and reduce cost, while maintaining outstanding data quality. Along the way, the team has implemented innovative solutions and technologies in order to overcome significant challenges. A number of these solutions may have applicability to future human spaceflight programs.
The Jet Propulsion Laboratory’s Innovation Foundry is an enterprise tasked with shepherding space mission concepts through the formulation lifecycle. It oversees a number of “virtual teams” for the various stages of formulation. Among these is Team X, which has had considerable success over its more than 20-year history. In a Team X study, domain experts (including engineers devoted to the various spacecraft subsystems) work concurrently and collaboratively over several days to arrive at a feasible point design with a reasonable cost estimate. They use a set of linked Excel workbooks, each developed and approved by a responsible “line organization” within JPL. This toolset has served Team X well over the years, and has evolved since its inception. At the same time, the Innovation Foundry’s portfolio of formulation teams has expanded, and so has the scope of the design challenges they face. The A-Team runs workshop-like architecture studies to focus science investigations, generate mission concepts, assess feasibility, and explore trade spaces. Team Xc performs rapid point design in the style of Team X, but for CubeSats and small spacecraft, using a different toolset. Proposal teams further mature concepts to the point where they can be proposed. Recognizing the importance of trade space modeling combined with new IT services for providing and integrating data, JPL is developing the Foundry Furnace web-based software infrastructure. It will support A-Team, Team Xc, and Team X, providing study management, a catalog of hardware components, a library of re-usable analyses, and a design environment. It is a modernization of JPL’s concurrent engineering infrastructure, embracing the core concepts of Model-Based Systems Engineering, and built with modern software design philosophies.
The United States Navy established 8 Maritime Operations Centers (MOC) to enhance the command and control of forces at the operational level of warfare. Each MOC is a headquarters manned by qualified joint operational-level staffs, and enabled by globally interoperable C41 systems. To assess and refine MOC staffing, equipment, and schedules, a dynamic software model was developed. The model leverages pre-existing operational process architecture, joint military task lists that define activities and their precedence relations, as well as Navy documents that specify manning and roles per activity. The software model serves as a "computational wind-tunnel" in which to test a MOC on a mission, and to refine its structure, staffing, processes, and schedules. More generally, the model supports resource allocation decisions concerning Doctrine, Organization, Training, Material, Leadership, Personnel and Facilities (DOTMLPF) at MOCs around the world. A rapid prototype effort efficiently produced this software in less than five months, using an integrated process team consisting of MOC military and civilian staff, modeling experts, and software developers. The work reported here was conducted for Commander, United States Fleet Forces Command in Norfolk, Virginia, code N5-0LW (Operational Level of War) that facilitates the identification, consolidation, and prioritization of MOC capabilities requirements, and implementation and delivery of MOC solutions.
The Mars2020 project, developed and operated by the Jet Propulsion Laboratory (JPL), successfully landed the Perseverance rover and its flying companion Ingenuity on the surface of Mars on February 18th 2021. Perseverance combines heritage and cutting-edge flight software and hardware to accomplish crucial mission requirements related to Martian surface sampling. The design, development, and operation of NASA’s large strategic science missions require the ability to communicate spacecraft capabilities to hundreds of engineers across multiple disciplines. The interaction between flight and ground software development, Verification and Validation (V&V), Assembly, Test, and Launch Operations (ATLO), and management each demand quick understanding of unique slices of information for each discipline. This information includes the current capabilities of the flight system as well as future capabilities and their status as they are developed and tested. Despite the fundamental and critical nature of this information, the flight software dictionaries used to track it are a stumbling block for many projects. These dictionaries provide the cornerstone for the interpretation of data sent from the spacecraft, allowing for quick comprehension by engineers on the ground. During both spacecraft development and operations, flight software dictionary management includes significant challenges due to the large number of interfacing systems and the subtle yet distinct needs of each.The engineering of flight software dictionaries for Mars2020 had numerous challenges, most-notably: parallel dictionary development to support simultaneous separate flight software build campaigns for each mission phase (cruise and surface), managing requests for operations-enabling information without perturbing the heritage interface with the rover, and the introduction of new tools by the dictionary stakeholders that forced the dictionary team to innovate and redesign the heritage tool chain. These challenges generated guiding principles for the dictionary development effort: emphasize coding best practices and unit testing in the dictionary code development tool chain, use institutionally provided COTS (commercial-off-the-shelf) tools whenever possible, and maintain the heritage flight-ground interface all while advancing operations-enabling information via a loosely coupled interface.Throughout development and operations, the Mars2020 dictionary toolchain included IBM DOORS Next Generation, GitHub, Microsoft Excel, Docker, Jenkins, and a significant custom-built Python codebase. Significant interfaces included JPL’s command and control software, heritage flight software team tools and processes, and the many cloud-based ground tools developed for the mission.This paper will discuss the requirements for the Mars2020 dictionary development, the development team’s response to those requirements, lessons learned throughout the process, steps taken towards automated deliveries and continuous integration of stakeholder inputs, potential toolchain improvements for Mars2020, and key takeaways that could be applied to future missions.
This paper presents an analysis of the unit testing approach developed and used by the Core Flight Software (CFS) product line team at the NASA GSFC. The goal of the analysis is to understand, review, and reconunend strategies for improving the existing unit testing infrastructure as well as to capture lessons learned and best practices that can be used by other product line teams for their unit testing. The CFS unit testing framework is designed and implemented as a set of variation points, and thus testing support is built into the product line architecture. The analysis found that the CFS unit testing approach has many practical and good solutions that are worth considering when deciding how to design the testing architecture for a product line, which are documented in this paper along with some suggested innprovennents.
The International Space Station (ISS) began payload operations in earnest in 2000 with the arrival of the Expedition 1. To date, ISS has offered Principal Investigators (PIs) a reliable platform for microgravity research, having hosted thousands of onboard science experiments. Most of this research is supported by experiment hardware and software and many include a crew‐operated Graphical User Interface (GUI). The purpose of this article is to share information with PIs and Payload Developer (PD) teams about the processes, standards, and guidelines applicable to crew GUI design that must be complied with when planning payload software. The goal is not to enumerate all of the ISS display standards, but rather to highlight design guidelines and the ISS Program milestones for verification and approval of onboard crew displays.
The purpose of this document is to provide program/project teams necessary instruction and guidance in the best practices for WBS and WBS dictionary development and use for project implementation and management control. This handbook can be used for all types of NASA projects and work activities including research, development, construction, test and evaluation, and operations. The products of these work efforts may be hardware, software, data, or service elements (alone or in combination). The aim of this document is to assist project teams in the development of effective work breakdown structures that provide a framework of common reference for all project elements. The WBS and WBS dictionary are effective management processes for planning, organizing, and administering NASA programs and projects. The guidance contained in this document is applicable to both in-house, NASA-led effort and contracted effort. It assists management teams from both entities in fulfilling necessary responsibilities for successful accomplishment of project cost, schedule, and technical goals. Benefits resulting from the use of an effective WBS include, but are not limited to: providing a basis for assigned project responsibilities, providing a basis for project schedule and budget development, simplifying a project by dividing the total work scope into manageable units, and providing a common reference for all project communication.
This technique enhances the detection capability of the autonomous Real-Nose system from MIT to detect odorants and their concentrations in noisy and transient environments. The lowcost, portable system with low power consumption will operate at high speed and is suited for unmanned and remotely operated long-life applications. A deterministic mathematical model was developed to detect odorants and calculate their concentration in noisy environments. Real data from MIT's NanoNose was examined, from which a signal conditioning technique was proposed to enable robust odorant detection for the RealNose system. Its sensitivity can reach to sub-part-per-billion (sub-ppb). A Space Invariant Independent Component Analysis (SPICA) algorithm was developed to deal with non-linear mixing that is an over-complete case, and it is used as a preprocessing step to recover the original odorant sources for detection. This approach, combined with the Cascade Error Projection (CEP) Neural Network algorithm, was used to perform odorant identification. Signal conditioning is used to identify potential processing windows to enable robust detection for autonomous systems. So far, the software has been developed and evaluated with current data sets provided by the MIT team. However, continuous data streams are made available where even the occurrence of a new odorant is unannounced and needs to be noticed by the system autonomously before its unambiguous detection. The challenge for the software is to be able to separate the potential valid signal from the odorant and from the noisy transition region when the odorant is just introduced.
Although the carnivorous plant Venus flytrap (Dionaea muscipula) is recognized globally, its native range is restricted to a small portion of the North and South Carolina Coastal Plain and Sandhills. Within this limited range, Venus flytrap populations are threatened by habitat loss, fire suppression, and poaching. NASA DEVELOP partnered with the North Carolina Botanical Garden (NCBG), the University of North Carolina Herbarium (NCU), and the North Carolina Natural Heritage Program (NCNHP) to support Venus flytrap conservation. The team developed models based on species presence data and environmental variables using the Software for Assisted Habitat Modeling (SAHM) to create a 2021 habitat suitability map for Venus flytrap. These models incorporated Earth observations collected by Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), and Sentinel-2 Multispectral Instrument (MSI). To predict areas at high risk of development, the team produced a 2050 land-use change map using TerrSet Land Change Modeler. The team found potential areas of conflict between predicted habitat and forecasted future development. Many areas of suitable habitat were concentrated along the coast where development was likely to occur, placing populations there at risk of extirpation. Overlaying suitable habitat with forecasted land change also identified suitable habitats with minimal risk of development, which may serve as lasting Venus flytrap habitat. These results can inform the NCBG and NCNHP’s conservation decision-making, including targeted seed banking, reintroduction, and prioritization of enduring habitats for protection and management.
Although the carnivorous plant Venus flytrap (Dionaea muscipula) is recognized globally, its native range is restricted to a small portion of the North and South Carolina Coastal Plain and Sandhills. Within this limited range, Venus flytrap populations are threatened by habitat loss, fire suppression, and poaching. NASA DEVELOP partnered with the North Carolina Botanical Garden (NCBG), the University of North Carolina Herbarium (NCU), and the North Carolina Natural Heritage Program (NCNHP) to support Venus flytrap conservation. The team developed models based on species presence data and environmental variables using the Software for Assisted Habitat Modeling (SAHM) to create a 2021 habitat suitability map for Venus flytrap. These models incorporated Earth observations collected by Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), and Sentinel-2 Multispectral Instrument (MSI). To predict areas at high risk of development, the team produced a 2050 land-use change map using TerrSet Land Change Modeler. The team found potential areas of conflict between predicted habitat and forecasted future development. Many areas of suitable habitat were concentrated along the coast where development was likely to occur, placing populations there at risk of extirpation. Overlaying suitable habitat with forecasted land change also identified suitable habitats with minimal risk of development, which may serve as lasting Venus flytrap habitat. These results can inform the NCBG and NCNHP’s conservation decision-making, including targeted seed banking, reintroduction, and prioritization of enduring habitats for protection and management.