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Report describes program for creating and editing telemetry frames on personal computer. Menu-driven and creates or edits digital telemetry manually or automatically by occurrence of event. Stream of telemetric data used to test operation of hardware and software parts of telemetry-receiving station. Program useful in satellite-data-processing facilities like remote-sensing stations.
As part of an effort by the Lunar Data Node (LDN) we are restoring data returned by the Apollo Dust, Thermal, and Radiation Engineering Measurements (DTREM) packages emplaced on the lunar surface by the crews of Apollo 11, 12, 14, and 15. Also commonly known as the Dust Detector experiments, the DTREM packages measured the outputs of exposed solar cells and thermistors over time. They operated on the surface for up to nearly 8 years, returning data every 54 seconds. The Apollo 11 DTREM was part of the Early Apollo Surface Experiments Package (EASEP), and operated for a few months as planned following emplacement in July 1969. The Apollo 12, 14, and 15 DTREMs were mounted on the central station as part of the Apollo Lunar Surface Experiments Package (ALSEP) and operated from deployment until ALSEP shutdown in September 1977. The objective of the DTREM experiments was to determine the effects of lunar and meteoric dust, thermal stresses, and radiation exposure on solar cells. The LDN, part of the Geosciences Node of the Planetary Data System (PDS), operates out of the National Space Science Data Center (NSSDC) at Goddard Space Flight Center. The goal of the LDN is to extract lunar data stored on older media and/or in obsolete formats, restore the data into a usable digital format, and archive the data with PDS and NSSDC. For the DTREM data we plan to recover the raw telemetry, translate the raw counts into appropriate output units, and then apply calibrations. The final archived data will include the raw, translated, and calibrated data and the associated conversion tables produced from the microfilm, as well as ancillary supporting data (metadata) packaged in PDS format.
The Juno Mission to Jupiter is the second mission selected by the NASA New Frontiers Program. Juno launched August 2011 and will reach Jupiter July 2016. Juno's payload system is composed of nine instruments plus a gravity science experiment. One of the primary functions of the Juno Ground Data System (GDS) is the assembly and distribution of the CFDP (CCSDS File Delivery Protocol) product telemetry, also referred to as raw science data, for eight out of the nine instruments. The GDS accomplishes this with the Instrument Data Pipeline (IDP). During payload integration, the first attempt to exercise the IDP in a flight like manner revealed that although the functional requirements were well understood, the system was unable to meet latency requirements with the as-is heritage design. A systems engineering gap emerged between Juno instrument data delivery requirements and the assumptions behind the heritage flight-ground interactions. This paper describes the use of quality attributes to measure and overcome this gap by introducing a new systems engineering activity, and a new monitoring service architecture that successfully delivered the performance metrics needed to validate Juno IDP.
The Geostationary Operational Environmental Satellite R-Series Program (GOES-R, S, T, and U) mission is a joint program between National Oceanic & Atmospheric Administration (NOAA) and National Aeronautics & Space Administration (NASA) Goddard Space Flight Center (GSFC). SpaceWire was selected as the science data bus as well as command and telemetry for the GOES instruments. GOES-R, S, T, and U spacecraft have a mission data loss requirement for all data transfers between the instruments and spacecraft requiring error detection and correction at the packet level. The GOES-R Reliable Data Delivery Protocol (GRDDP) [1] was developed in house to provide a means of reliably delivering data among various on board sources and sinks. The GRDDP was presented to and accepted by the European Cooperation for Space Standardization (ECSS) and is part of the ECSS Protocol Identification Standard [2]. GOES-R development and integration is complete and the observatory is scheduled for launch November 2016. Now that instrument to spacecraft integration is complete, GOES-R Project reviewed lessons learned to determine how the GRDDP could be revised to improve the integration process. Based on knowledge gained during the instrument to spacecraft integration process the following is presented to help potential GRDDP users improve their system designs and implementation.
In photo above, the electrocardiogram of a hospitalized patient is being transmitted by telemetry. Widely employed in space operations, telemetry is a process wherein instrument data is converted to electrical signals and sent to a receiver where the signals are reconverted to usable information. In this instance, heart readings are picked up by the electrode attached to the patient's body and delivered by wire to the small box shown, which is a telemetry transmitter. The signals are relayed wirelessly to the console in the background, which converts them to EKG data. The data is displayed visually and recorded on a printout; at the same time, it is transmitted to a central control station (upper photo) where a nurse can monitor the condition of several patients simultaneously. The Patient Monitoring System was developed by SCI Systems, Inc., Huntsville, Alabama, in conjunction with Abbott Medical Electronics, Houston, Texas. In developing the system, SCI drew upon its extensive experience as a NASA contractor. The company applied telemetry technology developed for the Saturn launch vehicle and the Apollo spacecraft; instrumentation technology developed for heart, blood pressure and sleep monitoring of astronauts aboard NASA's Skylab long duration space station; and communications technology developed for the Space Shuttle.
Solid state, S-band telemetry receiver having special demodulation verification
Exascale data environments are fast approaching, driven by diverse structured and unstructured data such as system and application telemetry streams, open-source information capture, and on-demand simulation output. Storage costs having plummeted, the question is now one of converting vast stores of data to actionable information. Complicating this problem are the low degrees of awareness across domain boundaries about what potentially useful data may exist, and write-once- read-never issues (data generation/collection rates outpacing data analysis and integration rates). Increasingly, technologists and researchers need to correlate previously unrelated data sources and artifacts to produce fused data views for domain-specific purposes. New tools and approaches for creating such views from vast amounts of data are vitally important to maintaining research and operational momentum. We propose to research and develop tools and services to assist in the creation, refinement, discovery and reuse of fused data views over large, diverse collections of heterogeneously structured data. We innovate in the following ways. First, we enable and encourage end-users to introduce customized index methods selected for local benefit rather than for global interaction (flexible multi-indexing). We envision rich combinations of such views on application data: views that span backing stores with different semantics, that introduce analytic methods of indexing, and that define multiple views on individual data items. We specifically decline to build a big fused database of everything providing a centralized index over all data, or to export a rigid schema to all comers as in federated query approaches. Second, we proactively advertise these application-specific views so that they may be programmatically reused and extended (data proactivity). Through this mechanism, both changes in state (new data in existing view collected) and changes in structure (new or derived view exists) are made known. Lastly, we embrace found data heterogeneity by coupling multi-indexing to backing stores with appropriate semantics (as opposed to a single store or schema).
Exascale data environments are fast approaching, driven by diverse structured and unstructured data such as system and application telemetry streams, open-source information capture, and on-demand simulation output. Storage costs having plummeted, the question is now one of converting vast stores of data to actionable information. Complicating this problem are the low degrees of awareness across domain boundaries about what potentially useful data may exist, and write-once-read- never issues (data generation/collection rates outpacing data analysis and integration rates). Increasingly, technologists and researchers need to correlate previously unrelated data sources and artifacts to produce fused data views for domain-specific purposes. New tools and approaches for creating such views from vast amounts of data are vitally important to maintaining research and operational momentum. We propose to research and develop tools and services to assist in the creation, refinement, discovery and reuse of fused data views over large, diverse collections of heterogeneously structured data. We innovate in the following ways. First, we enable and encourage end-users to introduce customized index methods selected for local benefit rather than for global interaction (flexible multi-indexing). We envision rich combinations of such views on application data: views that span backing stores with different semantics, that introduce analytic methods of indexing, and that define multiple views on individual data items. We specifically decline to build a big fused database of everything providing a centralized index over all data, or to export a rigid schema to all comers as in federated query approaches. Second, we proactively advertise these application-specific views so that they may be programmatically reused and extended (data proactivity). Through this mechanism, both changes in state (new data in existing view collected) and changes in structure (new or derived view exists) are made known. Lastly, we embrace found data heterogeneity by coupling multi-indexing to backing stores with appropriate semantics (as opposed to a single store or schema).
The Lunar Atmosphere and Dust Environment Explorer (LADEE) mission launched on September 7, 2013 with a one month cruise before lunar insertion. The LADEE spacecraft is a power limited, octagonal, composite bus structure with solar panels on all eight sides with four vertical segments per side and 2 panels dedicated to instruments. One of these panels has the Lunar Laser Communications Demonstration (LLCD), which represents a furthering of the laser communications technology demonstration proved out by the Lunar Reconnaissance Orbiter (LRO). LLCD increases the bandwidth of communication to and from the moon with less mass and power than LROs technology demonstrator. The LLCD Modem and Controller boxes are mounted to an internal cruciform composite panel and have no dedicated radiator. The thermal design relies on power cycling of the boxes and radiation of waste heat to the inside of the panels, which then reject the heat when facing cold space. The LADEE mission includes a slow roll and numerous attitudes to accommodate the challenging thermal requirements for all the instruments on board. During the cruise phase, the internal Modem and Controller avionics for LLCD were warmer than predicted by more than modeling uncertainty would suggest. This caused concern that if the boxes were considerably warmer than expected while off, they would also be warmer when operating and could limit the operational time when in lunar orbit. The thermal group at Goddard Space Flight Center evaluated the models and design for these critical avionics for LLCD. Upon receipt of the spacecraft models and audit was performed and data was collected from the flight telemetry to perform a sanity check of the models and to correlate to flight where possible. This paper describes the efforts to correlate the model to flight data and to predict the thermal performance when in lunar orbit and presents some lessons learned.
In this paper we describe the concepts behind and architecture of a communication protocol family, which was designed to fulfill the communication requirements of ESOC's new distributed spacecraft control system SCOS 2. A distributed spacecraft control system needs a data delivery subsystem to be used for telemetry (TLM) distribution, telecommand (TLC) dispatch and inter-application communication, characterized by the following properties: reliability, so that any operational workstation is guaranteed to receive the data it needs to accomplish its role; efficiency, so that the telemetry distribution, even for missions with high telemetry rates, does not cause a degradation of the overall control system performance; scalability, so that the network is not the bottleneck both in terms of bandwidth and reconfiguration; flexibility, so that it can be efficiently used in many different situations. The new protocol family which satisfies the above requirements is built on top of widely used communication protocols (UDP and TCP), provides reliable point-to-point and broadcast communication (UDP+) and is implemented in C++. Reliability is achieved using a retransmission mechanism based on a sequence numbering scheme. Such a scheme allows to have cost-effective performances compared to the traditional protocols, because retransmission is only triggered by applications which explicitly need reliability. This flexibility enables applications with different profiles to take advantage of the available protocols, so that the best rate between sped and reliability can be achieved case by case.
The overall data link commences with the lens of the spacecraft camera, followed by the telemetry format, and then the passing of the data onto a spacecraft tape recorder for later transmission at a slower rate. The properties of the individual pieces of information are described, and rate distortion theory is discussed. The video channel from the standpoint of information theory, and fidelity criteria for planetary video are considered. Areas for further research are indicated.
The Very Large Array (VLA) was proposed for use as a supplement to the Deep Space Network (DSN) for telemetry reception at Voyager 2 Uranus and Neptune encounters. The main problem with the use of VLA for telemetry is that it is not capable of producing a continuous stream of data. Gaps of one millisecond follow every 51 milliseconds of data. The effect of these millisecond gaps on coded telemetry is investigated. An upgapped system of the same aperture as the VLA would be capable of handling data rates of 38.4 kbps at Uranus encounter and 19.2 kbps at Neptune encounter. It is shown that VLA with (7, 1/2) convolutional coding (the baseline coding scheme for Voyager) will support a data rate of 10.8 kbps but not 19.2 kbps at both Uranus and Neptune. It is also shown that by implementing Voyager's concatenated Reed-Solomon/convolutional coding capability, data rates of 38.4 kbps and 19.2 kbps would be achievable at Uranus and Neptune respectively. Concatenation also offers a factor of 2 improvement in overall throughput.
Novel chemical sensors that improve detection and quantification of CO 2 are critical to ensuring safe and cost-effective monitoring of carbon storage sites. Fiber optic (FO) based chemical sensor systems are promising field-deployable systems for real-time monitoring of CO 2 in geological formations for long-range distributed sensing. Here, a mixed-matrix composite integrated FO sensor system was developed with a purely optical readout that reliably operates as a detector for gas-phase and dissolved CO 2 . A mixed-matrix composite sensor coating consisting of plasmonic nanocrystals and hydrophobic zeolite embedded in a polymer matrix was integrated on the FO sensor. The mixed-matrix composite FO sensor showed excellent reversibility/stability in a high humidity environment and sensitivity to gas-phase CO 2 over a large concentration range. This remarkable sensing performance was enabled by using plasmonic nanocrystals to significantly enhance the sensitivity and a hydrophobic zeolite to effectively mitigate interference from water vapor. The sensor exhibited the ability to sense CO 2 in the presence of other geologically relevant gases, which is of importance for applications in geological formations. A prototype FO sensor configuration which possesses a robust sensing capability for monitoring dissolved CO 2 in natural water was demonstrated. Reproducibility was confirmed over many cycles, both in a laboratory setting and in the field. More importantly, we demonstrated on-line monitoring capabilities with a wireless telemetry system, which transferred the data from the field to a website. The combination of outstanding CO 2 sensing properties and facile coating processability makes this mixed-matrix composite FO sensor a good candidate suitable for practical carbon storage applications.
Despite the close linkage between extreme floods and snowmelt, particularly through rain-on-snow (ROS), hydrologic infrastructure is mostly designed based on standard precipitation Intensity-Duration-Frequency curves (PREC-IDF) that neglect snow processes in runoff generation. For snow-dominated regions, such simplification could result in substantial errors in estimating extreme events and infrastructure design risk. To address this long-standing problem, we applied the Next Generation IDF (NG-IDF) technique to estimate design basis extreme events for different durations and return periods in the conterminous United States (CONUS) to distinctly represent the contribution of rain, snowmelt, and ROS events to the amount of water reaching the land surface. A suite of datasets were developed to characterize the magnitude, trend, seasonality, and dominant mechanism of extreme events for over 200,000 locations. Infrastructure design risk associated with the use of PREC-IDF was estimated. Accuracy of the model simulations used in the analyses was confirmed by long-term snow data at over 200 Snowpack Telemetry stations. The presented spatially continuous datasets are readily usable and instrumental for supporting site-specific infrastructure design.
Video data from spacecraft photographic mission telemetry is scanned to generate digital tape computer program which prints out intensity points, cleans noise and telemetry drop-out, enhances contrast, modifies the picture, and calculates contour lines. The output is converted into new photographic film.
Manned spaceflight RF radiation communications systems including voice, telemetry, tracking and ranging, up data and TV
Phase nonlinearities in telemetry receiving systems, discussing various data degrading effects
Grand Tour missions centralized data handling, describing computer aided telemetry system and self testing and repairing control computer