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A description of the attitude control support being supplied by the Mission and Data Operations Directorate is presented. Descriptions of the computer programs being used to support the mission for attitude determination, prediction, control, and definitive attitude processing are included. In addition, descriptions of the operating procedures which will be used to accomplish mission objectives are provided.
Natural environment criteria are established and interpreted for aeronautical vehicle design and engineering operations. Data summaries, computer formats, frequency distributions, and composite listings of the temperature profiles acquisition program are included.
This requirements and analyses of commercial operations (RACO) study data release reflects the current status of research activities of the Microgravity and Materials Processing Facility under Modification No. 21 to NASA/MSFC Contract NAS8-36122. Section 1 includes 65 commercial space processing projects suitable for deployment aboard the Space Station. Section 2 contains reports of the R:BASE (TM) electronic data base being used in the study, synopses of the experiments, and a summary of data on the experimental facilities. Section 3 is a discussion of video and data compression techniques used as well as a mission timeline analysis.
A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.
This project leverages the Concept Exploration Laboratory (CEL) to collect, warehouse, and augment data relevant to human lunar exploration as a platform for NA (S&MA) to develop operational data integration techniques. The project capitalizes on 16+ years of CEL experience applied to NASA, DoD, the City of Houston, the State of Texas, and private industry. The integrated data will be utilized in the two scenarios described in a definition of concept for development of a full scale data analysis suite and storage solution, useful to all JSC organizations engaged in real time operations and safety tasks, and may be useful as pathfinders for the Digital Transformation Program.
Mission, launch operations, communications, and test operation are discussed for the Intelsat 4 satellite. These include mission objective and plan, launch vehicle and spacecraft descriptions, postlaunch operations, data acquisition, meteorological plan, and F-1 and F-0 day operations.
Recent advances at the Global Modeling and Assimilation Office (GMAO) have focused on the assimilation of additional radio occultation (RO) bending angle measurements in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system. These efforts targeted the GEOS near-real-time forward processing system and may serve as the backbone for the next atmospheric reanalysis of the 21st century. In the most recent system upgrade, the assimilated RO counts increased by a factor of four with the addition of the COSMIC-2 constellation. Furthermore, with the advent of near-real-time commercial RO measurements, even more growth is expected. This presentation will quantify the impacts of new public and commercially-sourced data available to the GEOS system. The RO data assimilated in these experiments are from both the routinely acquired operational data streams as well as those from Spire Global, Inc. available via the Commercial SmallSat Data Acquisition (CSDA) Program.
Satellite observations are vital for the initialization of Numerical Weather Prediction models, and very important for climate monitoring and prediction, as well as other applications such as hydrology and flood awareness prediction. Knowledge of radiative contributions from the Earth's surface is needed to sound the lower troposphere from space. The lack of a reference quality ocean emission and backscatter model is a major gap in our ability to provide absolute calibration of the satellite based observing system. Uncertainty in emissivity models is not well characterized and different models are used for different spectral bands, for active and passive instruments. An International Space Science Institute (ISSI) team was put together [4] to address these issues. The objectives of the team are to provide a reference model as a community software (i.e., documented and freely available code), that is maintained and supported, has traceable uncertainty estimations, and that enables new science from microwaves to infrared with bidirectional reflectance distribution function (BRDF) capability. We will present the model and its various components, discussing the choices between various parameterizations, building on the LOCEAN model of [2]. The model predictions will be evaluated at various frequencies, including comparisons to radiometric observations by SMAP, AMSR2 and GMI (e.g., [5]). We will discuss early model evaluation in the infrared and for active microwave sensors. Areas of ongoing research include improving the foam parametrization (coverage and emissivity) to provide consistent performances across frequencies, building on [1], and the azimuthal dependence of the active and passive signals. The model will be used to generate training data for fast models e.g., Fastem, [3], that are used in operational data assimilation and climate re-analysis.
Aircraft deicing and anti-icing on airport ground in winter weather conditions is important to ensure safe departure takeoff and enroute flying. At the same time, it imposes additional workload to controllers and airport operators, and causes delays to airlines and passengers.This presentation shows the initial results of analyzing the deicing related operations at Incheon International Airport (ICN). It describes the deicing operations concept, procedures, and deicing making processes at ICN. It also looks into the three-month ICN operation data from Dec 2015 to Feb 2016 to identify the characteristics of the current day deicing operation.This work is under the joint collaboration between Korea Aerospace Research Institute and NASA. The objective is to develop a deicing operation simulation capability in NASA's fast time simulation environment to study and improve deicing operations efficiency.
The following subject areas are covered: video cloud camera (purpose, design, operation, data); special observing periods (SOP-2, SOP 2.5 - an extension of SOP-2); Garand algorithm; and warm rain.
The dry powder towpreg process overcomes many of the difficulties associated with melt, solution and slurry prepregging of advanced composite materials. In the process, fluidized powder is deposited on spread tow bundles and melted on the fibers by radiant heating to adhere the polymer to the fiber. Bench scale design and operating data have been correlated for use in process scale up to commercial operation. Powdered towpreg has been woven and molded into preform material of good quality. Cost estimates suggest that processing costs are comparable to those of conventional hot melt prepreg. In the future, from a part fabrication point of view, powder coated prepreg tape, woven broad goods and woven and braided preforms may be considered as options to similar materials made by other methods.
The reasons for collecting massive amounts of earth science data in the Earth Observing System (EOS) Project are discussed. A processing hierarchy for handling the data is described, and the prospects for adequate throughput and storage for operational data analysis in the EOS era are addressed. Needs for successful exploratory data analysis are examined, and the policy issues implicated by the large stream of EOS data are considered.
The process for dry powder impregnation of carbon fiber tows being developed at LaRC overcomes many of the difficulties associated with melt, solution, and slurry prepregging. In the process, fluidized powder is deposited on spread tow bundles and fused to the fibers by radiant heating. Impregnated tows have been produced for preform, weaving, and composite materials applications. Design and operating data correlations were developed for scale up of the process to commercial operation. Bench scale single tow experiments at tow speeds up to 50 cm/sec have demonstrated that the process can be controlled to produce weavable towpreg. Samples were woven and molded into preform material of good quality.
Science has entered the era of Big Data with new challenges related to data governance, stewardship, and management. The existing data governance practices must catch up to ensure proper data management. Existing data governance policies and stewardship best practices tend to be disconnected from operational data management practices and enforcement and mainly exist in well-meaning documents or reports. These governance policies are, at best, partially implemented and rarely monitored or audited. In addition, existing governance policies keep adding additional data management steps that require a human, ‘a data steward’, in the loop, and the cost of data management can no longer scale proportionately with the current and future increased data volume and complexity. The goal for developing an updated data governance framework is to modernize scientific data governance to the reality of Big data and align it with the current technology trends such as cloud computing and AI. The goals of this framework are two folds. One is to ensure thoroughness that the governance adequately covers the entire data life cycle. Two, provide a practical approach that offers a consistent and repeatable process for different projects. Three core principles ground this framework. First, focus on just enough governance and prevent data governance from becoming a roadblock toward the scientific process. Remove any unnecessary processes and steps. Second, automate data management steps where possible. Actively remove steps that require ‘human in the loop’ within the management process to be efficient and scale with increasing data. Third, all the processes should continually be optimized using quantified metrics to streamline the monitoring and auditing workflows.
This document is intended to serve as a guide to the use of the data products from the Stratospheric Aerosol Measurement (SAM) 2 experiment for scientific investigations of polar stratospheric aerosols. Included is a detailed description of the Beta and Aerosol Number Density Archive Tape (BANAT), which is the SAM 2 data product containing the aerosol extinction data available for these investigations. Also included are brief descriptions of the instrument operation, data collection, processing and validation, and some of the scientific analyses conducted to date.
A guide for using the data products from the Stratospheric Aerosol and Gas Experiment 1 (SAGE 1) for scientific investigations of stratospheric chemistry related to aerosol, ozone, nitrogen dioxide, dynamics, and climate change is presented. A detailed description of the aerosol profile tape, the ozone profile tape, and the nitrogen dioxide profile tape is included. These tapes are the SAGE 1 data products containing aerosol extinction data and ozone and nitrogen dioxide concentration data for use in the different scientific investigations. Brief descriptions of the instrument operation, data collection, processing, and validation, and some of the scientific analyses that were conducted are also included.
The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.
A family of fans designed with low noise features was acoustically evaluated, and noise results are documented for a 1.6-pressure-ratio, 472-m/sec (155-ft/sec) tip speed fan. The fan is described and some aerodynamic operating data are given. Far field noise around the fan was measured over a range of operating conditions for a variety of configurations having different arrangements of sound absorbing material in the flow ducts. Complete results of 1.3 octave band analysis of the data are presented in tabular form. Included also are acoustic power spectra and sideline perceived noise levels. Representative 1/3 octave band data are presented graphically, and sample graphs of continuous narrow band spectra are also provided.