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- API opendata.utah.gov | Last Updated 2019-02-11T21:20:14.000Z
This data set contains daily discharge for the Jordan river from 2000 - 2009. Discharge is measured in daily discharge CUBIC FEET PER SECOND.
- API data.texas.gov | Last Updated 2019-04-26T16:59:48.000Z
Residential Child Care Licensing (RCCL) was transferred from DFPS to HHSC September 1, 2018. This data set includes Fiscal Years 2008-2017 only. Check with HHSC for more recent data.
An Estimation Algorithm for Detecting and Reconstructing Optimal Maneuvers from Measurement Residualsdata.nasa.gov | Last Updated 2018-07-19T09:18:30.000Z
This proposed research addresses the problem of optimal maneuver detection and reconstruction with regards to an astrodynamics application. Maneuver detection and reconstruction are processes by which an independent observer may determine when and how a body is moving in a dynamical system under the influence of unmodeled active control. This research shall specifically address the problem of how one can determine and reconstruct orbital maneuvers assuming they are performed optimally with respect to fuel expenditure. This assumption is valid due to limited availability of fuel for space-based missions and how important fuel is to a successful mission. The inevitable goal of this research is produce an estimation algorithm that accounts for the influences of unmodeled optimal control. Essentially, this research is uniting the fields of estimation and optimal control in an algorithm that is applicable to various disciplines. Algorithm development will involve merging current research in maneuver detection with lessons from various fields of study including astrodynamics, estimation, optimal control, information fusion, and Space Situational Awareness. The work will primarily be simulation-based, and actual mission data would likely be used to help quantify the algorithms accuracy. Initial work will start by investigating linear estimator algorithms, but eventually the work will be pushed to higher order, non-Gaussian filters. Understanding the dynamics of Earths orbital environment is of paramount concern. With the increase in orbital debris from recent collisions, and the increase in launch activity this environment is becoming increasingly crowded. Overcrowding in this environment increases the hazards associated with it due to increased potential for collisions. By gaining a better understanding of the dynamics (both natural and active) associated with this environment we can better pinpoint these potential hazards. Algorithms such as the one proposed in this paper help reduce the hazards associated with space-based missions, which is imperative to ensuring continued access to Earth-orbit and beyond.
- API data.nasa.gov | Last Updated 2018-08-02T15:26:14.000Z
Current and emerging spaceflight processors are leveraging heterogeneous multicore/co-processor architectures to satisfy the ever increasing onboard processing demands required by planned NASA missions. These architectures can provide increased processing bandwidth, power efficiency, and fault tolerance for onboard processing applications. However, these advantages come at the cost of increased hardware and software complexity. As software development is a major cost driver for missions, this increased complexity has the potential to significantly increase cost for future missions. To address this risk, Troxel Aerospace Industries, Inc. proposes to develop a robust middleware management technology for spacecraft-focused multicore/co-processor architectures. The proposed middleware technology will enable a fault tolerant computing environment that is agnostic to the underlying hardware and is largely transparent to mission applications executing upon the middleware to provide a standardized, intelligent resource, fault, and power management interface.
- API stat.cityofgainesville.org | Last Updated 2018-02-02T14:49:36.000Z
- API data.nasa.gov | Last Updated 2018-07-20T07:18:10.000Z
Physical Sciences Inc. and Advanced Solutions, Inc. propose a novel approach for on-orbit assembly of a modular spacecraft using a unique universal, intelligent, electromechanical interface (AUTOCONNECT) on surfaces of individual modules. AUTOCONNECT not only provides mechanical fastening between modules (irrespective of precise alignments and orientations), but also automatically configures electrical connections among modules. Mechanical attachment occurs due to docking and physical contact between modules with sufficient initial momenta. The mass properties of the assembly are determined on orbit and the entire assembly functions as a spacecraft unit. In Phase I we simulated spacecraft assembly in two dimensions using instrumented hexagonal modules supported on air bearings with yaw control provided by a reaction wheel on each module. We demonstrated the feasibility of attachment via AUTOCONNECT, power and data transfer across the interface, and angular orientation control of the assembly. In Phase II, we propose to simulate orbital assembly of a spacecraft configuration as an AUTOCONNECTed assembly of multiple instrumented modules, where each module functions as a spacecraft subsystem or payload, and demonstrate command and control of the entire assembly. Additionally, we will address the system level design issues for AUTOCONNECT-equipped spacecraft modules and the concept of operations for their on-orbit assembly.
- API data.nasa.gov | Last Updated 2018-07-19T17:47:39.000Z
UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH SPATIOTEMPORAL RELATIONAL RANDOM FORESTS AMY MCGOVERN, TIMOTHY SUPINIE, DAVID JOHN GAGNE II, NATHANIEL TROUTMAN, MATTHEW COLLIER, RODGER A. BROWN, JEFFREY BASARA, AND JOHN K. WILLIAMS Abstract. Major severe weather events can cause a significant loss of life and property. We seek to revolutionize our understanding of and ability to predict such events through the mining of severe weather data. Because weather is inherently a spatiotemporal phenomenon, mining such data requires a model capable of representing and reasoning about complex spatiotemporal dynamics, including temporally and spatially varying attributes and relationships. We introduce an augmented version of the Spatiotemporal Relational Random Forest, which is a Random Forest that learns with spatiotemporally varying relational data. Our algorithm maintains the strength and performance of Random Forests but extends their applicability, including the estimation of variable importance, to complex spatiotemporal relational domains. We apply the augmented Spatiotemporal Relational Random Forest to three severe weather data sets. These are: predicting atmospheric turbulence across the continental United States, examining the formation of tornadoes near strong frontal boundaries, and understanding the translation of drought across the southern plains of the United States. The results on such a wide variety of real-world domains demonstrate the extensive applicability of the Spatiotemporal Relational Random Forest. Our long-term goal is to significantly improve the ability to predict and warn about severe weather events.
- API data.nasa.gov | Last Updated 2018-07-19T08:52:25.000Z
<p>We want to look at the concept of combining small, passive windows with replaceable cameras to improve viewing capabilities for habitable modules of future spacecraft. A more modular concept than currently used for spacecraft windows, this concept would reduce the overall window square footage for a vehicle or habitat, yet increase the viewing capabilities around it, and be available for both passive and electronic (video camera, IR sensors, etc) methods. This project would evaluate configurations of small portal windows with internally placed imaging sensors, pointed in various directions, and integrated views of near real-time video.<p/><p>Windows in habitable modules represent significant design and operations impacts to future spacecraft, yet viewing requirements, both electronic and passive, continue to mature. With longer missions over greater distances, it becomes harder to accommodate both size and modularity of viewing. The Hybrid Window Portal would allow easier design, more locations for direct viewing, and easier maintenance and represent less of an impact to a vehicle's structural integrity than traditional windows, and applies to aluminum, composite and inflatable structures. This project intends to build on an on-going IR&D effort and a 2011 ICA investigation of virtual window technology, and will explore options to provide adequate viewing and sensing through arrangements of multiple, small portals that can accommodate cameras and sensors. Smaller portals accommodate more cameras and sensors than ever before, and offer different pointing directions so optimum viewing angles can be selected, while an integrated view (Mosaic Video) provides perspective. </p>
- API datahub.transportation.gov | Last Updated 2018-12-19T00:12:21.000Z
Contains data on compliance reviews and new entrant safety audits performed by FMCSA and State grantees.
Intelligent Transportation Systems Research Data Exchange - Pasadena - 05b City of Pasadena Link and Turn Volume Data (Text format)datahub.transportation.gov | Last Updated 2018-12-19T00:13:44.000Z
The Pasadena data environment covers the diverse roadway network in and around the City of Pasadena, California. The data was collected in 2011 during the months of September and October. The data environment includes a variety of data sets including network data (highway network file), demand data (trip tables), network performance data (link volumes, turn volumes, speeds and capacity), work zone data, weather data, Closed Circuit Television (CCTV) camera data, and Changeable Message Sign (CMS) data. Data from simulations are included where there are no sensors, and to provide forecasts.