- API data.nasa.gov | Last Updated 2018-07-19T08:42:09.000Z
<p>To provide economical, reliable and safe access to space, design weaknesses should be identified earlier in the engineering life cycle, using model-based systems engineering. The slow manual approach to performing Failure Modes and Effects Analysis (FMEA) is a barrier to early identification of weaknesses. To semi-automate the identification of failure modes and causes use a prototype FMEA Assistant, including a library with standard terminology, to classify components associated with failure modes and automatically identify candidate functions, infrastructure and failure modes. This automation will reduce cost and increase coverage, standardization and reuse. Early identification of design weaknesses can substantially reduce rework costs later in the life cycle, which are all too common in the testing phase. Use of SysML will closely link safety analysis with the overall engineering process, resulting in smoother collaboration and safer vehicles and missions. The resulting reusable model would become part of the model-based system engineering process.<p/><p>This project was a small proof-of-concept case study, generating SysML model information as a side effect of safety analysis. A prototype FMEA Assistant was used to semi-automate safety analysis that identifies failure modes and causes, using a library with standard SysML-compatible terminology to classify components associated with failure modes and to automatically identify candidate functions, infrastructure and failure modes. FMEA analysts select from standard functions and failures to systematically narrow down failure mode selection (presented in automatically created pick lists). Standard terminology from an existing Aerospace Ontology is used to classify components and automatically identify candidate functions and failure modes. With automatically created pick lists, analysts can easily and correctly select standard functions and failures for a SysML architecture model as a side effect of using FMEA Assistant. A white paper reports on a concept for using SysML profiles for safety analysis, to standardize FMEA-related terminology for reuse in several types of safety analysis (hazard analyses, fault trees, reliability block diagrams). See related project: Failure Modes and Effects Analysis (FMEA) Simulation Tool</p>
- API data.nasa.gov | Last Updated 2019-04-29T15:22:30.000Z
NARSTO EPA Supersite in Pittsburgh Gac Conc and PM Physical Properties Data
- API data.nasa.gov | Last Updated 2018-07-19T07:14:38.000Z
<p>The AES Autonomous Mission Operations project will develop understanding of the impacts of increasing communication time delays on mission operations and develop automation technologies to mitigate the impacts. The technologies are expected to reduce operations costs as well. This will be tested on ISS in FY14. The results of this project are being incorporated and built upon in the Autonomous Systems and Operations project. <p/><p>Future human spaceflight missions will occur with crews and spacecraft at large distances, with long communication delays to the Earth. The one-way light-time delay to the Moon is 1.3 seconds, which is sufficient to make some scenarios (e.g. landing) difficult or impossible to conduct from Earth. One-way communication delays to human exploration destinations such as Near Earth Asteroids (NEA) at close approach range from seconds to minutes. The one-way light-time delay to Mars ranges from 3 minutes (at conjunction) to 22 minutes (at opposition). As the communication delays increase, the crews in the spacecraft must execute, and manage, much of the mission themselves. Throughout the course of a mission, as distances increase, NASA must continue to migrate operations functionality from the Mission Control Center flight control room to the vehicle for use by the crew. The role of the ground control teams and systems will evolve away from real-time support to more long-range planning, diagnosis, analysis and prognostics support role. While the vehicle systems and crew must take on the role of onboard daily schedule execution, planning, and systems management. Both ground and vehicle systems will require automation to maximize crew functionality, minimize unnecessary overhead, and reduce operating costs. This project is to understand the impacts of increasing communications time delays on operations and to develop technologies to mitigate the impacts.</p>
- API data.nasa.gov | Last Updated 2018-07-19T17:54:09.000Z
Consider a scenario in which the data owner has some private or sensitive data and wants a data miner to access them for studying important patterns without revealing the sensitive information. Privacy-preserving data mining aims to solve this problem by randomly transforming the data prior to their release to the data miners. Previous works only considered the case of linear data perturbations - additive, multiplicative, or a combination of both - for studying the usefulness of the perturbed output. In this paper, we discuss nonlinear data distortion using potentially nonlinear random data transformation and show how it can be useful for privacy-preserving anomaly detection from sensitive data sets. We develop bounds on the expected accuracy of the nonlinear distortion and also quantify privacy by using standard definitions. The highlight of this approach is to allow a user to control the amount of privacy by varying the degree of nonlinearity. We show how our general transformation can be used for anomaly detection in practice for two specific problem instances: a linear model and a popular nonlinear model using the sigmoid function. We also analyze the proposed nonlinear transformation in full generality and then show that, for specific cases, it is distance preserving. A main contribution of this paper is the discussion between the invertibility of a transformation and privacy preservation and the application of these techniques to outlier detection. The experiments conducted on real-life data sets demonstrate the effectiveness of the approach.
- API data.nasa.gov | Last Updated 2018-07-19T07:16:27.000Z
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> <p>Lunar Flashlight (LF) is an innovative cubesat mission sponsored by NASA’s Advanced Exploration Systems (AES) division to be launched on the Space Launch System (SLS) EM-1 flight as a secondary payload. LF is dedicated to locating water ice in the permanently shadowed regions of the lunar south pole by measuring surface reflectance at multiple wavelengths. LF will be one of the first cubesats performing science measurements beyond low Earth orbit and the first planetary mission to use multi-band active reflectometry from orbit.</p> <p>The Lunar Flashlight (LF) mission is a NASA 6U (6 Units of approximately 10×10×10 cm each) cubesat mission dedicated to mapping water ice in the permanently shadowed regions within 10° latitude of the lunar south pole. The measurement approach utilizes a multi-band reflectometer in orbit at the Moon. Planned to launch on the SLS EM-1 flight, this innovative secondary payload concept will begin to map the lunar south pole for volatiles and demonstrate several technological firsts, including being the first cubesat to orbit the Moon, the first planetary cubesat mission to use green propulsion, and the first mission to use lasers to look for water ice.</p> <p>Locating ice deposits in the Moon’s permanently shadowed craters addresses one of NASA’s Strategic Knowledge Gaps (SKGs) to detect composition, quantity, distribution, form of water/H species and other volatiles associated with lunar cold traps. The scientific and economic importance of lunar volatiles extends far beyond the question “is there water on the Moon?” Volatile materials including water come from sources central to NASA’s strategic plans, including comets, asteroids, interplanetary dust particles, interstellar molecular clouds, solar wind, and lunar volcanic and radiogenic gases. The volatile inventory, distribution, and state (bound or free, evenly distributed or blocky, on the surface or at depth, etc.) are crucial for understanding how these molecules interact with the lunar surface, and for utilization potential.</p> <p>The LF science goal is to identify locations where water ice may be present at concentrations ≥ 0.5wt% (0.5 weight %) on the lunar surface with a mapping resolution of 1-2 km (10 km for the minimum success criteria). During the planned 2-month primary mission, LF will pulse the lasers for several minutes from each of 11 near-rectilinear orbits, at altitudes of 12.6-52.4 km within 10° latitude of the lunar south pole.</p> <p>Lunar Flashlight is an exciting new mission concept that was recently selected by NASA’s Advanced Exploration Systems (AES) by a team from the Jet Propulsion Laboratory, UCLA, and Marshall Space Flight Center. Planned to launch on the SLS EM-1 flight, this innovative, low-cost secondary payload concept will map the lunar south pole for volatiles and demonstrate several technological firsts, including being the first CubeSat to reach the Moon, the first planetary cubesat mission to use green propulsion, and the first mission to use lasers to look for water ice.</p> <p>Locating ice deposits in the Moon’s permanently shadowed craters addresses one of NASA’s Strategic Knowledge Gaps (SKGs) to detect composition, quantity, distribution, form of water/H species and other volatiles associated with lunar cold traps. The scientific and economic importance of lunar volatiles extends far beyond the question “is there water on the Moon?” Volatile materials including water come from sources central to NASA’s strategic plans, including comets, asteroids, interplanetary dust particles, interstellar molecular clouds, solar wind, and lunar volcanic and radiogenic gases. The volatile inventory, distribution, and state (bound or free, evenly distributed or blocky, on the surface
- API data.nasa.gov | Last Updated 2018-07-20T07:13:30.000Z
Airport configuration is a primary factor in various airport characteristics such as arrival and departure capacities and terminal area traffic patterns. These characteristics, in turn, are central to a variety of Air Traffic Management (ATM) decisions that in turn affect delays and efficiency in the National Airspace System (NAS). There is presently poor knowledge about the airport characteristics at each airport and even less information available about how those characteristics are expected to change in the future. This lack of knowledge about airports results in inefficient local and national traffic management decisions. Metron Aviation will develop and test a set of models that generate information about airport configuration and related airport characteristics. This information, which is not currently available or is of poor quality, will improve traffic management decisions, and automation to support those decisions, both at the airports and on a national scale. This work addresses existing gaps in air traffic management automation technologies. The proposed technology development promise immediate improvements to the NAS. Rather than requiring a new tool or system to be deployed, the information generated by this work could be absorbed into existing systems and information networks to transparently improve traffic management on a local and national scale.
- API data.nasa.gov | Last Updated 2018-07-19T05:17:47.000Z
This data set gives the best available values for ion densities, temperatures, and velocities near Neptune derived from data obtained by the Voyager 2 plasma experiment. All parameters are obtained by fitting the observed spectra (current as a function of energy) with Maxwellian plasma distributions, using a non-linear least squares fitting routine to find the plasma parameters which, when coupled with the full instrument response, best simulate the data. The PLS instrument measures energy/charge, so composition is not uniquely determined but can be deduced in some cases by the separation of the observed current peaks in energy (assuming the plasma is co-moving). In the upstream solar wind protons are fit to the M-long data since high energy resolution is needed to obtain accurate plasma parameters. In the magnetosheath the ion flux so low that several L-long spectra (3-5) had to be averaged to increase the signal-to-noise ratio to a level at which the data could be reliably fit. These averaged spectra were fit using 2 proton maxwellians with the same velocity. The values given in the upstream magnetosheath are the total density and the density-weighted temperature. In both the upstream solar wind and magnetosheath full vector velocities, densities and temperatures are derived for each fit component. In the magnetosphere spectra do not contain enough information to obtain full velocity vectors, so flow is assumed to be purely azimuthal. In some cases the azimuthal velocity is a fit parameter, in some cases rigid corotation is assumed. In the 'outer' magnetosphere (L>5) two distinct current peaks appear in the spectra H+ and N+. In the inner magnetosphere the plasma is hot and the composition is ambiguous, although two superimposed Maxwellians are still required to fit the data. These spectra are fit using two compositions, one with H+ and N+ and the second with two H+ components. The N+ composition is preferred by the data provider. All fit values in the magnetosphere come with one sigma errors. It should be noted that no attempt has been made to account for the spacecraft potential, which is probably about -10 V in this region and will effect the density and velocity values. In the outbound magnetosheath and solar wind both moment and fit values are given for velocity, density, and thermal speed. The signal-to-noise ratio in the M-longs is very low, especially near the magnetopause, which can Result in the analysis giving incorrect values. The L-long spectra have too low an energy resolution to permit accurate determinations parameters in many regions temperature and non-radial velocity components may be inaccurate.
The World is Not Enough (WINE): Harvesting Local Resources for Eternal Exploration of Space, Phase Idata.nasa.gov | Last Updated 2018-07-19T10:14:44.000Z
The paradigm of exploration is changing. Smaller, smarter, and more efficient systems are being developed that could do as well as large, expensive, and heavy systems in the past. The 'science' fiction becomes reality fueled by advances in computing, materials, and nano-technology. These new technologies found their way into CubeSats – a booming business in the 21st century. CubeSats are no longer restricted to aerospace companies. Universities and even High Schools can develop them. The World is Not Enough (WINE) is a new generation of CubeSats that take advantage of ISRU to explore space for ever. The WINE takes advantage of existing CubeSat technology and combines it with 3D printing technology and a water extraction system developed under NASA SBIR, called MISWE . 3D printing enables development of cold gas thrusters as well as tanks that fit perfectly within the available space within the CubeSat. The MISWE allows capture and extraction of water, and takes advantage of the heat generated by the CubeSat electronics system. The water is stored in a cold gas thruster's tank and used for propulsion. Thus, the system can use the water that it has just extracted for prospecting to refuel and fly to another location. This replenishing of propellants extends the mission by doing ISRU (living off the land) even during the prospecting phase. In Phase 1, we plan to test and investigate critical technologies such as (1) sample acquisition, (2) volatiles capture, and (3) 3D-printed cold gas thrusters that use water vapor including the organic and particulate contaminants that are inevitable during the early stages of asteroid mining. The engine is similar to a Solar Thermal Engine but scaled for a CubeSat. In Phase 2, we propose to develop a testbed of the critical systems and to demonstrate these onboard the International Space Station (ISS).
- API data.nasa.gov | Last Updated 2018-07-19T09:18:11.000Z
<p>Gravitational waves represent the first new astronomical observing window on the Universe since the introduction of gamma ray telescopes in the 1970's. Many of the most exciting sources are expected in the band from 0.1 to 100 mHz, which is accessible only from space. The European Space Agency (ESA) has selected the "Gravitational Universe" as the science theme of the L3 Cosmic Visions opportunity, and NASA is planning to participate as a junior partner. This proposal requests support to supplement 3 specific strategic technology development tasks that would prepare NASA for participation as a junior partner beginning with the delivery of candidate technologies.</p> <p>The three tasks considered:</p><p><strong>Task 1:</strong> Telescope development has as its objective to investigate a specific design feature, in-field guiding, that has recently been identified by the ESA Gravitational Observatory Advisory Team (GOAT) as the number one priority trade study to be evaluated against a back-link fiber for transporting a laser phase reference from one optical bench to another, and to help settle the question of which approach to take.</p><p><strong>Task 2:</strong> Laser subsystem development has as its goal to begin space qualification testing of the master oscillator laser with vibration and thermal cycling of the external cavity master oscillator laser.</p><p><strong>Task 3</strong>: The optical bench/Gravitational Reference Sensor (GRS) interface development has the objective of restoring the third degree of freedom of motion of the model GRS and the noise studies. The optical bench is the core of the gravitational wave interferometry measurement system and interfaces with the telescope on one side, and the local GRS on the other.</p>
- API data.nasa.gov | Last Updated 2019-08-19T04:49:16.000Z
The ASTER L2 Surface Emissivity is an on-demand product generated using the five thermal infrared (TIR) bands (acquired either during the day or night time) between 8 and 12 µm spectral range. It contains surface emissivity over the land at 90 meters spatial resolution. Estimates of surface emissivity were thus far only derived using surrogates such as land-cover type or vegetation index. The Temperature/Emissivity Separation (TES) algorithm is used to derive both E (emissivity) and T (surface temperature). The main goals of the TES algorithm include: recovering accurate and precise emissivities for mineral substrates, and estimating accurate and precise surface temperatures especially over vegetation, water and snow.The TES algorithm is executed in the ASTER processing chain following generation of ASTER Level-2 Surface Radiance (TIR). The land-leaving radiance and down-welling irradiance vectors for each pixel are taken in account. Emissivity is estimated using the Normalized Emissivity Method (NEM), and is iteratively compensated for reflected sunlight. The emissivity spectrum is normalized using the average emissivity of each pixel. The minimum-maximum difference (MMD) of the normalized spectrum is calculated and estimates of the minimum emissivity derived through regression analysis. These estimates are used to scale the normalized emissivity and compensate for reflected skylight with the derived refinement of emissivity. V003 data set release date: 2002-05-03 Data Set Characteristics Area: ~ 60 km x 60 km Image Dimensions: 700 rows x 830 columns File Size: ~9 Megabytes Units: None Projection: Universal Transverse Mercator (UTM) Data Format: HDF-EOS Data Fields: 5