Geophysical Monitoring System: The Role of SMEs in System Development.
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Presentation of NNSS Drinking Water and Wastewater 2021 monitoring
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A method of making an optical fiber sensor device for distributed sensing includes generating a laser beam comprising a plurality of ultrafast pulses, and focusing the laser beam into a core of an optical fiber to form a nanograting structure within the core, wherein the nanograting structure includes a plurality of spaced nanograting elements each extending substantially parallel to a longitudinal axis of optical fiber. Also, an optical fiber sensor device for distributed sensing includes an optical fiber having a longitudinal axis, a core, and a nanograting structure within the core, wherein the nanograting structure includes a plurality of spaced nanograting elements each extending substantially parallel to the longitudinal axis of the optical fiber. Also, a distributed sensing method and system and an energy production system that employs such an optical fiber sensor device.
A reference electrode includes a metal body, a lead disposed within the metal body, an insulator disposed between the lead and the metal body, the insulator including a ceramic material, and a porous metal chamber coupled to the metal body, the lead extending into the porous metal chamber. The porous metal chamber is configured to maintain an electrolyte solution within the porous metal chamber to establish a redox couple with the lead.
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The design of a spacecraft monitoring system based on a Neyman-Pearson Detection Criterion is discussed. Each noncatastrophic state of the spacecraft is indicated by the transmission of a specific signal to the ground station. Complete failure of the spacecraft is indicated by the transmission of no signal. The set of signals chosen to represent the spacecraft states consists of a group of orthogonally spaced (in frequency) carriers each with unknown (random) phase.
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.
One of the science goals of NASA's Navigator program is ground-based narrow-angle astrometry for extra-solar planet detection, which could be done as part of the proposed Outrigger Telescopes Project. The narrow-angle measurement process, which would use the outrigger telescopes, starts with the determination of the conventional interferometer astrometric baseline, determined from wide-angle astrometry of Hipparcos stars. A baseline monitor system would be employed at each outrigger telescope. This system monitors the pivot point of each telescope - the end point of the astrometric baseline - to measure telescope imperfections that would cause the baseline to vary with telescope rotation. The baseline monitor includes azimuth and elevation cameras that monitor runout along the azimuth and elevation axes of the telescopes. In conjunction with the baseline monitor system, a pivot monitor camera in the dual-star module is used to register the laser metrology corner-cube reflector to the telescope pivot, tying the narrow-angle baseline, which applies to the narrow-angle astrometric measurement, to the wide-angle baseline. In this paper we present the proposed designs for the baseline monitor and pivot-point camera.