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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Planting a Flag in the Tropics: The Essential Tropical Geometric Background for Networking Applications

This Technical Memorandum serves to guide future NASA researchers and the public at large in terms of how Tropical Geometry can be applied to optimization problems, namely in networking. We give a basic overview of Tropical Geometry through a collection of resources we have summarized. Lastly, we include a discussion of power diagrams viewed as stratified spaces which may be a useful tool to study tropical varieties.

Tropical Geometry↗
NASA NTRS2020

TESS Data Release Notes:Reprocessing of Sectors 14–19, DR30 & DR33

TESS data release 30 (DR30) provides reprocessed data products of Sector 14 to 19. The updated data products were generated using version 4.0 of the science processing pipeline and conform to the final set of data anomaly flags defined over the last two years of TESS data analysis and pipeline development. Data release 33 (DR33) corresponds to a multisector search for transiting planets in the same reprocessed data. A detailed description of the changes in the data products in DR30 and DR33 is discussed in§2, and a brief list of changes is summarized here: The timestamps for 2 minute cadence and FFI data are more accurate. The differences between reprocessed data and previous data releases are less than 2.0 seconds in all cases. Photometric apertures were increased in size for targets with T mag<11. Three new Data Anomaly Flags were added to mitigate the effects of scattered light:–Cadences with strong scattered light signals or saturation effects that corrupt the calibration data are flagged and removed from analysis (bit 15, value 16384, “Bad Calibration Exclude”).–Scattered light data anomaly flags are customized for each target, and flagged automatically based on the local background level (bit 13, value 4096, ”Scattered light flag”).–Cadences with insufficient targets to derive cotrending basis vectors are flagged and the PDCSAP FLUX light curves are set to NULL at these times (bit 16, value 32768, “Insufficient Targets for Error Correction Exclude”). The planet search of the reprocessed light curves produced a different set of TCEs from the original processed data. Although there is a high degree of overlap between the original and reprocessed data (∼83% of targets produced TCEs in common), new TCEs were produced in DR30 and not every TCE from previous data releases was recovered. The same is true of the multisector search results from DR33 compared to DR28

TESS'↗
NASA NTRS2021

PURE mRNA display and cDNA display provide rapid detection of core epitope motif via high‐throughput sequencing

The reconstructed in vitro translation system known as the PURE system has been used in a variety of cell‐free experiments such as the expression of native and de novo proteins as well as various display methods to select for functional polypeptides. We developed a refined PURE‐based display method for the preparation of stable messenger RNA (mRNA) and complementary DNA (cDNA)‐peptide conjugates and validated its utility for in vitro selection. Our conjugate formation efficiency exceeded 40%, followed by gel purification to allow minimum carry‐over of components from the translation system to the downstream assay enabling clean and efficient random peptide sequence screening. We chose the commercially available anti‐FLAG M2 antibody as a target molecule for validation. Starting from approximately 1.7 × 10(exp 12) random sequences, a round‐by‐round high‐throughput sequencing showed clear enrichment of the FLAG epitope DYKDDD as well as revealing consensus FLAG epitope motif DYK(D/L/N)(L/Y/D/N/F)D. Enrichment of core FLAG motifs lacking one of the four key residues (DYKxxD) indicates that Tyr(Y) and Lys (K) appear as the two key residues essential for binding. Furthermore, the comparison between mRNA display and cDNA display method resulted in overall similar performance with slightly higher enrichment for mRNA display. We also show that gel purification steps in the refined PURE‐based display method improve conjugate formation efficiency and enhance the enrichment rate of FLAG epitope motifs in later rounds of selection especially for mRNA display. Overall, the generalized procedure and consistent performance of two different display methods achieved by the commercially available PURE system will be useful for future studies to explore the sequence and functional space of diverse polypeptides.

cDNA display, FLAG epitope, mRNA display, peptide ↗
NASA NTRS2020

Development of a Display Tool to Quality Control Weather Balloon Data for Space Launch Vehicles Using Python

Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.

Jessica K Headley↗
NASA NTRS2020

Development of a Display Tool to Quality Control Weather Balloon Data for Space Launch Vehicles

Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.

Jessica K Headley↗
NASA NTRS1989

Identification of atmospheric fronts over the ocean with microwave measurements of water vapor and rain

This paper describes some basic research techniques and algorithms developed to diagnose fronts in cyclonic storms over the ocean with data from satellite-borne microwave radiometers. Methods are developed for flagging strong gradients in integrated atmospheric water vapor and the presence of rain by using data from the SSMR on board the polar orbiting Seasat and Nimbus-7 satellites. Examination of 65 frontal systems showed that the water vapor gradient flag correctly identified 86 percent of the fronts, while the precipitation flagged 91 percent. The two types of flags emphasize different portions of the cyclone and are therefore complementary. Ultimately, these techniques are intended for operational use with data from the Special Sensor Microwave Imager which was launched in June 1987 on a satellite in the Defense Meteorological Satellite Program (DMSP).

Katsaros, Kristina B.↗
NASA NTRS1997

[MODIS Investigation]

We are responsible for the delivery of two at-launch products for AM-1: Fluorescence line height (FLH) and chlorophyll fluorescence efficiency (CFE). In our last report we had planned to combine the two separate algorithms into a single piece of code. However, after discussions with Bob Evans, it was decided that it was best to leave the two algorithms separate. They have been integrated into the MOCEAN processing system, and given their low computational requirements, it easier to keep them separate. In addition, there remain questions concerning the specific chlorophyll product that will be used for the CFE calculation. Presently, the CFE algorithm relies on the chlorophyll product produced by Ken Carder. This product is based on a reflectance model, and is theoretically different than the chlorophyll product being provided by Dennis Clark (NOAA). These two products will be compared systematically in the coming months. If we decide to switch to the Clark product, then it will be simpler to modify the CFE algorithm if it remains separate from the FLH algorithm. Our focus for the next six months is to refine the quality flags that were delivered as part of the algorithm last summer. A description of these flags was provided to Evans for the MOCEAN processing system. A summary was included in the revised ATBD. Some of the flags depend on flags produced by the input products so coordination will be required.

Abbott, Mark R.↗
NASA NTRS2003

Algorithm Updates for the Fourth SeaWiFS Data Reprocessing

The efforts to improve the data quality for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data products have continued, following the third reprocessing of the global data set in May 2000. Analyses have been ongoing to address all aspects of the processing algorithms, particularly the calibration methodologies, atmospheric correction, and data flagging and masking. All proposed changes were subjected to rigorous testing, evaluation and validation. The results of these activities culminated in the fourth reprocessing, which was completed in July 2002. The algorithm changes, which were implemented for this reprocessing, are described in the chapters of this volume. Chapter 1 presents an overview of the activities leading up to the fourth reprocessing, and summarizes the effects of the changes. Chapter 2 describes the modifications to the on-orbit calibration, specifically the focal plane temperature correction and the temporal dependence. Chapter 3 describes the changes to the vicarious calibration, including the stray light correction to the Marine Optical Buoy (MOBY) data and improved data screening procedures. Chapter 4 describes improvements to the near-infrared (NIR) band correction algorithm. Chapter 5 describes changes to the atmospheric correction and the oceanic property retrieval algorithms, including out-of-band corrections, NIR noise reduction, and handling of unusual conditions. Chapter 6 describes various changes to the flags and masks, to increase the number of valid retrievals, improve the detection of the flag conditions, and add new flags. Chapter 7 describes modifications to the level-la and level-3 algorithms, to improve the navigation accuracy, correct certain types of spacecraft time anomalies, and correct a binning logic error. Chapter 8 describes the algorithm used to generate the SeaWiFS photosynthetically available radiation (PAR) product. Chapter 9 describes a coupled ocean-atmosphere model, which is used in one of the changes described in Chapter 4. Finally, Chapter 10 describes a comparison of results from the third and fourth reprocessings along the US. Northeast coast.

Hooker, Stanford, B.↗
NASA NTRS2014

Near-Cloud Aerosol Properties from the 1 Km Resolution MODIS Ocean Product

This study examines aerosol properties in the vicinity of clouds by analyzing high-resolution atmospheric correction parameters provided in the MODIS (Moderate Resolution Imaging Spectroradiometer) ocean color product. The study analyzes data from a 2 week long period of September in 10 years, covering a large area in the northeast Atlantic Ocean. The results indicate that on the one hand, the Quality Assessment (QA) flags of the ocean color product successfully eliminate cloud-related uncertainties in ocean parameters such as chlorophyll content, but on the other hand, using the flags introduces a sampling bias in atmospheric products such as aerosol optical thickness (AOT) and Angstrom exponent. Therefore, researchers need to select QA flags by balancing the risks of increased retrieval uncertainties and sampling biases. Using an optimal set of QA flags, the results reveal substantial increases in optical thickness near clouds-on average the increase is 50% for the roughly half of pixels within 5 km from clouds and is accompanied by a roughly matching increase in particle size. Theoretical simulations show that the 50% increase in 550nm AOT changes instantaneous direct aerosol radiative forcing by up to 8W/m2 and that the radiative impact is significantly larger if observed near-cloud changes are attributed to aerosol particles as opposed to undetected cloud particles. These results underline that accounting for near-cloud areas and understanding the causes of near-cloud particle changes are critical for accurate calculations of direct aerosol radiative forcing.

near-cloud aerosols↗
NASA NTRS1972

Inexpensive programmable clock for a 12-bit computer

An inexpensive programmable clock was built for a digital PDP-12 computer. The instruction list includes skip on flag; clear the flag, clear the clock, and stop the clock; and preset the counter with the contents of the accumulator and start the clock. The clock counts at a rate determined by an external oscillator and causes an interrupt and sets a flag when a 12-bit overflow occurs. An overflow can occur after 1 to 4096 counts. The clock can be built for a total parts cost of less than $100 including power supply and I/O connector. Slight modification can be made to permit its use on larger machines (16 bit, 24 bit, etc.) and logic level shifting can be made to make it compatible with any computer.

Vrancik, J. E.↗
NASA NTRS1984

Memory-based parallel data output controller

A memory-based parallel data output controller employs associative memories and memory mapping to decommutate multiple channels of telemetry data. The output controller contains a random access memory (RAM) which has at least as many address locations as there are channels. A word counter addresses the RAM which provides as it outputs an encoded peripheral device number and a MSB/LSB-first flag. The encoded device number and a bit counter address a second RAM which contains START and STOP flags to pick out the required bits from the specified word number. The LSB/MSB, START and STOP flags, along with the serial input digital data go to a control block which selectively fills a shift register used to drive the parallel data output bus.

Stattel, R. J.↗
NASA NTRS1989

Southern Durchmusterung (Schoenfeld 1886): Documentation for the machine-readable version

The machine-readable version of the catalog, as it is currently being distributed from the Astronomical Data Center, is described. The Southern Durchmusterung (SD) was computerized at the Centre de Donnees Astronomiques de Strasbourg and at the Astronomical Data Center at the National Space Science Data Center, NASA/Goddard Space Flight Center. Corrigenda listed in the original SD volume and published by Kuenster and Sticker were incorporated into the machine file. In addition, one star indicated to be missing in a published list, and later verified, is flagged so that it can be omitted from computer plotted charts if desired. Stars deleted in the various errata lists were similarly flagged, while those with revised data are flagged and listed in a separate table. This catalog covers the zones -02 to -23 degrees; zones +89 to -01 degrees (the Bonner Durchmusterung) are included in a separate catalog available in machine-readable form.

Warren, Wayne H., Jr.↗
NASA NTRS1989

Bonner Durchmusterung (Argelander 1859-1862): Documentation for the machine-readable version

The machine-readable version of the catalog, as it is currently being distributed from the Astronomical Data Center, is described. The entire Bonner Durchmusterung (BD) was computerized through the collaborative efforts of the Centre de Donnees Astronomiques de Strasbourg, l'Observatoire de Nice, and the Astronomical Data Center at the NASA/Goddard Space Flight Center. All corrigenda published in the original BD volumes were incorporated into the machine file, along with changes published following the 1903 edition. In addition, stars indicated to be missing in published lists and verified by various techniques are flagged so that they can be omitted from computer plotted charts if desired. Stars deleted in the various errata lists were similarly flagged, while those with revised data are flagged and listed in a separate table.

Warren, Wayne H., Jr.↗
NASA NTRS1994

Telecommunications end-to-end systems monitoring on TOPEX/Poseidon: Tools and techniques

The TOPEX/Poseidon Project Satellite Performance Analysis Team's (SPAT) roles and responsibilities have grown to include functions that are typically performed by other teams on JPL Flight Projects. In particular, SPAT Telecommunication's role has expanded beyond the nominal function of monitoring, assessing, characterizing, and trending the spacecraft (S/C) RF/Telecom subsystem to one of End-to-End Information Systems (EEIS) monitoring. This has been accomplished by taking advantage of the spacecraft and ground data system structures and protocols. By processing both the received spacecraft telemetry minor frame ground generated CRC flags and NASCOM block poly error flags, bit error rates (BER) for each link segment can be determined. This provides the capability to characterize the separate link segments, determine science data recovery, and perform fault/anomaly detection and isolation. By monitoring and managing the links, TOPEX has successfully recovered approximately 99.9 percent of the science data with an integrity (BER) of better than 1 x 10(exp 8). This paper presents the algorithms used to process the above flags and the techniques used for EEIS monitoring.

Calanche, Bruno J.↗
NASA NTRS1999

Space Architecture: Building The Future

There's been a good deal of flag-waving over the last five years about technology-or rather, a certain terror of technology that underlies almost all recent talk of the global avant-garde. Don't be fooled: the cool, clinical praise of the cyborg and the virtual realm is no more than critical bravado. It's an existential machismo in the world of semiotics which forces the contemporary philosopher to ante up, to get theoretically comfortable with an anti-sensual world of possibilities to which we all respond-let's be frank-with profound discomfort. Does this flag-waving about media, Y2K, robotics and biotechnology serve to cover a pervasive, cross-cultural mesh of fear? Or are we waving our surrender to a process we ourselves have set in motion? Let's look at the medium of a flag-the image and its underlying message.

Adams, Constance↗
NASA NTRS2000

Atmospheric Boundary Layer Wind Data During the Period January 1, 1998 Through January 21, 1999 at the Dallas-Fort Worth Airport: Data and Processing - Volume 2

The NASA Langley Research Center's Aircraft Vortex Spacing System (AVOSS) requires accurate winds and turbulence to determine aircraft wake vortex behavior near the ground. Volume 1 described the wind input and quality analysis process. This volume documents the data available during the period January 1998 through January 1999 and the partitioning and concatenation of files for time of day, turbulence, non duplication, cross wind profile quality and ceiling and visibility. It provides the resultant filtered files for the first three partitions as well as identification of ceiling/visibility conditions when they were below 5000 feet and 5 miles respectively. It also includes the wind profile quality flags to permit automatic selection of files for AVOSS application using selected ceiling/visibility and wind profile quality values and flags (or no flags).

Zak, J. Allen↗