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Under an SBIR contract with Stennis Space Center, Baltimore-based StormCenter Communications Inc. developed an improved interoperable platform for sharing geospatial data over the Internet in real time-information that is critical for decision makers in emergency situations.
We previously developed an algorithm named Tafkaa for atmospheric correction of remote sensing ocean color data from aircraft and satellite platforms. The algorithm allows quick atmospheric correction of hyperspectral data using lookup tables generated with a modified version of Ahmad & Fraser s vector radiative transfer code. During the past few years we have extended the capabilities of the code. Current modifications include the ability to account for within scene variation in solar geometry (important for very long scenes) and view geometries (important for wide fields of view). Additionally, versions of Tafkaa have been made for a variety of multi-spectral sensors, including SeaWiFS and MODIS. In this proceeding we present some initial results of atmospheric correction of AVIRIS data from the 2001 July Hyperspectral Coastal Ocean Dynamics Experiment (HyCODE) at LEO-15.
Monitoring services play a crucial role in the day-to-day operation of distributed computing systems. The ATLAS Experiment at LHC uses the Production and Distributed Analysis workload management system (PanDA WMS), which allows a million computational jobs to run daily at over 170 computing centers of the WLCG and opportunistic resources, utilizing 600k cores simultaneously on average. The BigPanDA monitor is an essential part of the monitoring infrastructure for the ATLAS Experiment that provides a wide range of views, from top-level summaries to a single computational job and its logs. Over the past few years of the PanDA WMS advancement in the ATLAS Experiment, several new components were developed, such as Harvester, iDDS, Data Carousel, and Global Shares. Due to its modular architecture, the BigPanDA monitor naturally grew into a platform where the relevant data from all PanDA WMS components and accompanying services are accumulated and displayed in the form of interactive charts and tables. Moreover the system has been adopted by other experiments beyond HEP. In this paper we describe the evolution of the BigPanDA monitor system, the development of new modules, and the integration process into other experiments.
To detect long-term climate trends, it is essential to produce long-term and consistent data sets from a variety of different satellite platforms. With current global cloud climatology data sets, such as the International Satellite Cloud Climatology Experiment (ISCCP) or CLAVR (Clouds from Advanced Very High Resolution Radiometer), one of the first processing steps is to determine whether an imager pixel is obstructed between the satellite and the surface, i.e., determine a cloud 'mask.' A cloud mask is essential to studies monitoring changes over ocean, land, or snow-covered surfaces. As part of the Earth Observing System (EOS) program, a series of platforms will be flown beginning in 1997 with the Tropical Rainfall Measurement Mission (TRMM) and subsequently the EOS-AM and EOS-PM platforms in following years. The cloud imager on TRMM is the Visible/Infrared Sensor (VIRS), while the Moderate Resolution Imaging Spectroradiometer (MODIS) is the imager on the EOS platforms. To be useful for long term studies, a cloud masking algorithm should produce consistent results between existing (AVHRR) data, and future VIRS and MODIS data. The present work outlines both existing and proposed approaches to detecting cloud using multispectral narrowband radiance data. Clouds generally are characterized by higher albedos and lower temperatures than the underlying surface. However, there are numerous conditions when this characterization is inappropriate, most notably over snow and ice of the cloud types, cirrus, stratocumulus and cumulus are the most difficult to detect. Other problems arise when analyzing data from sun-glint areas over oceans or lakes over deserts or over regions containing numerous fires and smoke. The cloud mask effort builds upon operational experience of several groups that will now be discussed.
NASA requires timely on-demand data and analysis capabilities to enable practical benefits of Earth science observations. However, a significant challenge exists in accessing and integrating data from multiple sensors or platforms to address Earth science problems because of the large data volumes, varying sensor scan characteristics, unique orbital coverage, and the steep learning curve associated with each sensor and data type. The development of sensor web capabilities to autonomously process these data streams (whether real-time or archived) provides an opportunity to overcome these obstacles and facilitate the integration and synthesis of Earth science data and weather model output. A three year project, entitled Sensor Management for Applied Research Technologies (SMART) - On Demand Modeling (ODM), will develop and demonstrate the readiness of Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities that integrate both Earth observations and forecast model output into new data acquisition and assimilation strategies. The advancement of SWE-enabled systems (i.e., use of SensorML, sensor planning services - SPS, sensor observation services - SOS, sensor alert services - SAS and common observation model protocols) will have practical and efficient uses in the Earth science community for enhanced data set generation, real-time data assimilation with operational applications, and for autonomous sensor tasking for unique data collection.
NASA seeks on-demand data processing and analysis of Earth science observations to facilitate timely decision-making that can lead to the realization of the practical benefits of satellite instruments, airborne and surface remote sensing systems. However, a significant challenge exists in accessing and integrating data from multiple sensors or platforms to address Earth science problems because of the large data volumes, varying sensor scan characteristics, unique orbital coverage, and the steep "learning curve" associated with each sensor, data type, and associated products. The development of sensor web capabilities to autonomously process these data streams (whether real-time or archived) provides an opportunity to overcome these obstacles and facilitate the integration and synthesis of Earth science data and weather model output.
The Flight Research Services Directorate at the NASA Langley Research Center (LaRC) provides development and operations services associated with three general aviation (GA) aircraft used for research experiments. The GA aircraft includes a Cessna 206X Stationair, a Lancair Colombia 300X, and a Cirrus SR22X. Since 2004, the GA Data Framework software was designed and implemented to gather data from a varying set of hardware and software sources as well as enable transfer of the data to other computers or devices. The key requirements for the GA Data Framework software include platform independence, the ability to reuse the framework for different projects without changing the framework code, graphics display capabilities, and the ability to vary the interfaces and their performance. Data received from the various devices is stored in shared memory. This paper concentrates on the object oriented software design patterns within the General Aviation Data Framework, and how they enable the construction of project specific software without changing the base classes. The issues of platform independence and multi-threading which enable interfaces to run at different frame rates are also discussed in this paper.
This article introduces an interactive parallel programming environment (IPPE) that simplifies the generation and execution of parallel programs. One of the tasks of the environment is to generate message-passing parallel programs for homogeneous and heterogeneous computing platforms. The parallel programs are represented by using visual objects. This is accomplished with the help of a graphical programming editor that is implemented in Java and enables portability to a wide variety of computer platforms. In contrast to other graphical programming systems, reusable parts of the programs can be stored in a program library to support rapid prototyping. In addition, runtime performance data on different computing platforms is collected in a database. A selection process determines dynamically the software and the hardware platform to be used to solve the problem in minimal wall-clock time. The environment is currently being tested on a Grand Challenge problem, the NASA four-dimensional data assimilation system.
The design and preliminary testing of a system for isolating microgravity sensitive payloads from spacecraft vibrational and impulsive disturbances is discussed. The Microgravity Isolation Mount (MGIM) concept consists of a platform which floats almost freely within a limited volume inside the spacecraft, but which is constrained to follow the spacecraft in the long term by means of very weak springs. The springs are realized magnetically and form part of a six degree of freedom active magnetic suspension system. The latter operates without any physical contact between the spacecraft and the platform itself. Power and data transfer is also performed by contactless means. Specifications are given for the expected level of input disturbances and the tolerable level of platform acceleration. The structural configuration of the mount is discussed and the design of the principal elements, i.e., actuators, sensors, control loops and power/data transfer devices are described. Finally, the construction of a hardware model that is being used to verify the predicted performance of the MGIM is described.
The Laser Vegetation Imaging Sensor (LVIS) is an airborne, scanning laser altimeter designed and developed at NASA's Goddard Space Flight Center. LVIS operates at altitudes up to 10 km above ground, and is capable of producing a data swath up to 1000 m wide nominally with 25 m wide footprints. The entire time history of the outgoing and return pulses is digitized, allowing unambiguous determination of range and return pulse structure. Combined with aircraft position and attitude knowledge, this instrument produces topographic maps with decimeter accuracy and vertical height and structure measurements of vegetation. The laser transmitter is a diode-pumped Nd:YAG oscillator producing 1064 nm, 10 nsec, 5 mJ pulses at repetition rates up to 500 Hz. LVIS has recently demonstrated its ability to determine topography (including sub-canopy) and vegetation height and structure on flight missions to various forested regions in the U.S. and Central America. The LVIS system is the airborne simulator for the Vegetation Canopy Lidar (VCL) mission (a NASA Earth remote sensing satellite due for launch in 2000), providing simulated data sets and a platform for instrument proof-of-concept studies. The topography maps and return waveforms produced by LVIS provide Earth scientists with a unique data set allowing studies of topography, hydrology, and vegetation with unmatched accuracy and coverage.
This prototype seeks to demonstrate the utility of repurposing a Micro-ElectroMechancical (MEMS) Inertial measurement Unit (IMU) to perform surface gravimetry on a rover. Gravimetry is a common analytical tool used for probing density distributions in the subsurface of a planetary body. Historically, extraterrestrial gravimetry has been confined to orbital platforms. While orbital surveys allow for the construction of global gravity models, the spatial resolution of the data is constrained by the platform’s orbital altitude and high inherent speed. Data collected at or near the surface would increase spatial resolution and allow finer-scale crustal structure to be resolved. To date, there have been only two extraterrestrial surface gravity surveys: the Apollo 17 Traverse Gravimeter Experiment and a survey using the MEMS accelerometers contained within the Curiosity rover’s IMUs. The Curiosity survey highlighted the potential of using MEMS technology to perform planetary gravimetry, albeit with lower sensitivities than traditional surface gravimeters. MEMS accelerometers are included on every rover platform as part of the IMU navigation systems. MEMS accelerometers have low mass, cost, and power requirements while being robust across a range of environments, whereas traditional gravimeters are fragile, costly, and relatively massive (≥8kg versus ≈50g for MEMS IMUs). Thus, the emergence of MEMS gravimeters provides a low-risk and cost-effective method for performing planetary surface gravimetry [3,4]. Here, we present a method to recalibrate the MEMS accelerometers in rover IMUs to collect gravimetric measurements. Such measurements could assist current and future rover missions and support wider efforts to mature MEMS gravimeters.
This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.
This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.
Radar data collected during the 1991 NASA flight tests have been selectively analyzed to support research directed at developing both improved as well as new algorithms for detecting hazardous low-altitude windshear. Analysis of aircraft attitude data from several flights indicated that platform stability bandwidths were small compared to the data rate bandwidths which should support an assumption that radar returns can be treated as short time stationary. Various approaches at detection of weather returns in the presence of ground clutter are being investigated. Non-coventional clutter rejection through spectrum mode tracking and classification algorithms is a subject of continuing research. Based upon autoregressive modeling of the radar return time sequence, this approach may offer an alternative to overcome errors in conventional pulse-pair estimates. Adaptive filtering is being evaluated as a means of rejecting clutter with emphasis on low signal-to-clutter ratio situations, particularly in the presence of discrete clutter interference. An analysis of out-of-range clutter returns is included to illustrate effects of ground clutter interference due to range aliasing for aircraft on final approach. Data are presented to indicate how aircraft groundspeed might be corrected from the radar data as well as point to an observed problem of groundspeed estimate bias variation with radar antenna scan angle. A description of how recorded clutter return data are mixed with simulated weather returns is included. This enables the researcher to run controlled experiments to test signal processing algorithms. In the summary research efforts involving improved modelling of radar ground clutter returns and a Bayesian approach at hazard factor estimation are mentioned.
Perovskite photovoltaic technologies are approaching commercial deployment, yet single junction and tandem architectures both still have significant room to improve power conversion efficiency and stability. The ability to perform rapid screening of material quality after altering processing conditions is critical to accelerating the optimization and commercialization of perovskite-based technologies. Currently, researchers utilize a wide range of stand-alone metrology tools to isolate sources of power loss throughout a device stack, which can be slow and labor intensive. Here, we demonstrate the use of a multimodal metrology approach to rapidly determine the maximum achievable and predicted open circuit voltages of >100 perovskite devices during fabrication. Acquisition of these different data is facilitated by combining them into a single integrated measurement platform. We show that these data and automated analysis can be used to rapidly understand and ultimately predict quantitative trends in open circuit voltages of state-of-the-art device architectures. The data and automated analysis workflow presented provides a reliable approach to quickly identify absorber and charge transport layer combinations that can lead to improved open circuit voltages.
For improved understanding of chaotic processes and the diurnal cycle, an advanced GOES imager must also have the multi-spectral spectral bands used by low earth orbit (LEO) imagers, with on-orbit calibration for all bands. A synergy between GEO and LEO radiometry would enable earth system scientists to fuse the remote sensing data from all the spaceborne platforms. These additional radiometric capabilities are designed to observe important physical processes that vary rapidly and unpredicably: smoke, fires, precipitation, ozone, volcanic ash, cloud phase and height, and surface temperature. We believe the technology now exists to develop an imaging system that can meet future weather reporting and earth system science needs. To meet this need, we propose a design for a comprehensive geosynchronous atmospheric imager. This imager is envisioned to fly on a GOES-N class spacecraft, within the volume, weight and power constraints of a platform similar to GOES-N while delivering 100 times more data and radiometric quality than the GOES-N imager. The higher data rate probably requires its own ground station, which could serve as a systems prototype for NOAA's next generation of operational satellites. For operational compatibility, our proposed advanced GOES imaging system contains the GOES-R requirements as a subset, and the GOES-N imager capabilities (and the sounder's imaging channels) as a further subset.
An experiment was conducted to characterize the effects of HIRF-induced upsets on a prototype onboard data network. The experiment was conducted at the NASA Langley Research Center s High Intensity Radiation Field Laboratory and used a generic distributed system prototyping platform to realize the data network. This report presents the results of the hardware susceptibility threshold characterization which examined the dependence of measured susceptibility on factors like the frequency and modulation of the radiation, layout of the physical nodes and position of the nodes in the test chamber. The report also includes lessons learned during the development and execution of the experiment.
The TES-8 was jettisoned from the International Space Station on January 31, 2019. As an orbital laboratory and 8th in on-going series, the design makes use of a standard set of interfaces and safety features that permit rapid re-flight. On this flight, an advanced Exo-Brake is flown with de-orbit targeting capability that will engender sample return capability from LEO platforms. A Virtual Reality data recording system uses stereo imaging and efficient data-compression with an NVIDIA GPU (Graphics Processing Unit) to permit compression and transmission of very large data files. An SDR (Software Defined Radio) will download data to the NEN (Near Earth Network) for the first time - demonstrating potential use in cis-lunar space using S-band. For the first time, a comparison will be made regarding the functionality of the Iridium and Globalstar short burst data modems - as essential communication tools for future nano-sat projects. Lastly, the 7 micro-processors and 4 cameras provide an excellent learning platform for university students and NASA young professionals.