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All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Evaluation of Cloud Physical Properties of ECMWF Analysis and Re-Analysis (ERA-40 and ERA Interim) against CERES Tropical Deep Convective Cloud Object Observations

This study presents an approach that converts the vertical profiles of grid-averaged cloud properties from large-scale models to probability density functions (pdfs) of subgrid-cell cloud physical properties measured at satellite footprints. Cloud physical and radiative properties, rather than just cloud and precipitation occurrences, of assimilated cloud systems by the European Center for Medium-range Weather Forecasts (ECMWF) operational analysis (EOA) and ECMWF Re-Analyses (ERA-40 and ERA Interim) are validated against those obtained from Earth Observing System satellite cloud object data for January-August 1998 and March 2000 periods. These properties include ice water path (IWP), cloud-top height and temperature, cloud optical depth and solar and infrared radiative fluxes. Each cloud object, a contiguous region with similar cloud physical properties, is temporally and spatially matched with EOA and ERA-40 data. Results indicate that most pdfs of EOA and ERA-40 cloud physical and radiative properties agree with those of satellite observations of the tropical deep convective cloud-object type for the January-August 1998 period. There are, however, significant discrepancies in selected ranges of the cloud property pdfs such as the upper range of EOA cloud top height. A major discrepancy is that the dependence of the pdfs on the cloud object size for both EOA and ERA-40 is not as strong as in the observations. Modifications to the cloud parameterization in ECMWF that occurred in October 1999 eliminate the clouds near the tropopause but shift power of the pdf to lower cloud-top heights and greatly reduce the ranges of IWP and cloud optical depth pdfs. These features persist in ERA-40 due to the use of the same cloud parameterizations. The downgrade of data assimilation technique and the lack of snow water content information in ERA-40, not the coarser horizontal grid resolution, are also responsible for the disagreements with observed pdfs of cloud physical properties although the detection rates of cloud object occurrence are improved for small size categories. A possible improvement to the convective parameterization is to introduce a stronger dependence of updraft penetration heights with grid-cell dynamics. These conclusions will be rechecked using the ERA Interim data, due to recent changes in the ECMWF convective parameterization (Bechtold et al. 2004, 2008). Results from the ERA Interim will be presented at the meeting.

Xu, Kuan-Man↗
NASA NTRS2018

Robotic External Leak Locator (RELL) Leak Plume Field Detection on the International Space Station (ISS)

The Robotic External Leak Locator (RELL) was deployed to the International Space Station (ISS) with the objective of demonstrating the ability to detect and locate small leaks. On-orbit operations began in late November 2016 and following scanning activities to characterize the natural and induced environment of the ISS, RELL focused on the United States External Active Thermal Control System (EATCS). RELL successfully detected ammonia related to a known small ammonia leak in the port-side EATCS, with the highest pressure values around the inboard Radiator Beam Valve Module 1 (RBVM 1). An additional day of scanning was subsequently performed in December 2017 to focus on RBVM 1. RELL was approved for additional external operations in February 2017 with the goal of fine tuning the location of the leak. Using grid scanning patterns, RELL detected ammonia around RBVM 1 and located the approximate source of the leak. The potential leak site was inspected by a crew member during an Extravehicular Activity (EVA) in March 2017, and the suspected radiator-side lines were isolated from the port-side EATCS coolant loop in April 2017. Subsequent monitoring of the system pressures showed that the leak has stopped, indicating RELL accurately located the source of the EATCS leak. These activities verify that RELL enhances the ISS Program's ability to not only locate small leaks, but isolate the source with minimal impact to the entire ISS system.

External Active Thermal Control System↗
NASA NTRS1993

Satellite situation report, volume 33, number 4

The Satellite Situation Report is a listing of those satellites (objects) currently in orbit and those which have previously orbited the Earth. Some objects are too small or too far from the Earth's surface to be detected; therefore, the Satellite Situation Report does not include all manmade objects orbiting the Earth. Generally, satellites are classified as follows: (1) Payloads may contain one or more functioning or nonfunctioning experiments. Usually only the owners of the satellites know if the experiments are functioning, and there is no one source which indicates the operational status of all payloads and/or experiments. Payloads are normally the first listed in the Satellite Situation Report, i.e., 1982 087A, unless there are multiple payloads for the launch. In which case, the first objects cataloged are usually all payloads, unless a subsequent payload is later identified after objects other than payloads have been cataloged. (2) Platforms are used to support a payload while it is being placed into orbit. A platform may remain in orbit long after its purpose is served, usually longer than rocket bodies. It is usually the first object identified in the Satellite Situation Report listing after the payload(s), i.e., 1982 087B (when a platform is not used, the first object after the payload(s) is usually the rocket body). (3) Rocket bodies are used to place the payload and platform (if one is used) into orbit. Some launches may have more than one rocket body because of the payload weight or the type of orbit or experiment. Most rocket bodies decay within a short time after the payload (and platform) have achieved orbit. Rocket bodies are usually the third object listed in the Satellite Situation Report after the payload(s), i.e., 1982 087C. (4) Debris in orbit occurs when parts (nose cone shrouds, lens or hatch covers) are separated from the payload, when rocket bodies or payloads disintegrate or explode, or when objects are placed into free space from manned orbiting spacecraft during operations. Debris is detected by its size and distance from the Earth. Debris objects are the last objects after payload(s), platform, and rocket body(s) listed in the Satellite Situation Report, i.e., 1982 087D, 1982 087E, 1982 087F.

Source record↗
NASA NTRS2021

TOI-1518b: A Misaligned Ultra-hot Jupiter with Iron in Its Atmosphere

We present the discovery of TOI-1518b—an ultra-hot Jupiter orbiting a bright star (V = 8.95). The transiting planet is confirmed using high-resolution optical transmission spectra from EXPRES. It is inflated, with Rp = 1.875 ± 0.053 RJ, and exhibits several interesting properties, including a misaligned orbit (240.34 (+0.93, -0.98) degrees) and nearly grazing transit (b=0.9036 (+0.0061, -0.0053)). The planet orbits a fast-rotating F0 host star (Teff ≃ 7300 K) in 1.9 days and experiences intense irradiation. Notably, the TESS data show a clear secondary eclipse with a depth of 364 ± 28 ppm and a significant phase-curve signal, from which we obtain a relative day–night planetary flux difference of roughly 320 ppm and a 5.2σ detection of ellipsoidal distortion on the host star. Prompted by recent detections of atomic and ionized species in ultra-hot Jupiter atmospheres, we conduct an atmospheric cross-correlation analysis. We detect neutral iron (5.2σ), at K(p) = 157 (+68, -44) km/s and V(sys) = -16 (+2, -4), adding another object to the small sample of highly irradiated gas-giant planets with Fe detections in transmission. Detections so far favor particularly inflated gas giants with radii ≳1.78 R(J), which may be due to observational bias. With an equilibrium temperature of T(eq) = 2492 ± 38 K and a measured dayside brightness temperature of 3237 ± 59 K (assuming zero geometric albedo), TOI-1518b is a promising candidate for future emission spectroscopy to probe for a thermal inversion.

Samuel H. C. Cabot↗
NASA NTRS2010

Modeling of LEO Orbital Debris Populations in Centimeter and Millimeter Size Regimes

The building of the NASA Orbital Debris Engineering Model, whether ORDEM2000 or its recently updated version ORDEM2010, uses as its foundation a number of model debris populations, each truncated at a minimum object-size ranging from 10 micron to 1 m. This paper discusses the development of the ORDEM2010 model debris populations in LEO (low Earth orbit), focusing on centimeter (smaller than 10 cm) and millimeter size regimes. Primary data sets used in the statistical derivation of the cm- and mm-size model populations are from the Haystack radar operated in a staring mode. Unlike cataloged objects of sizes greater than approximately 10 cm, ground-based radars monitor smaller-size debris only in a statistical manner instead of tracking every piece. The mono-static Haystack radar can detect debris as small as approximately 5 mm at moderate LEO altitudes. Estimation of millimeter debris populations (for objects smaller than approximately 6 mm) rests largely on Goldstone radar measurements. The bi-static Goldstone radar can detect 2- to 3-mm objects. The modeling of the cm- and mm-debris populations follows the general approach to developing other ORDEM2010-required model populations for various components and types of debris. It relies on appropriate reference populations to provide necessary prior information on the orbital structures and other important characteristics of the debris objects. NASA's LEO-to-GEO Environment Debris (LEGEND) model is capable of furnishing such reference populations in the desired size range. A Bayesian statistical inference process, commonly adopted in ORDEM2010 model-population derivations, changes a priori distribution into a posteriori distribution and thus refines the reference populations in terms of data. This paper describes key elements and major steps in the statistical derivations of the cm- and mm-size debris populations and presents results. Due to lack of data for near 1-mm sizes, the model populations of 1- to 3.16-mm objects are an empirical extension from larger debris. The extension takes into account the results of micro-debris (from 10 micron to 1 mm) population modeling that is based on shuttle impact data, in the hope of making a smooth transition between micron and millimeter size regimes. This paper also includes a brief discussion on issues and potential future work concerning the analysis and interpretation of Goldstone radar data.

Xu, Y.-L.↗
NASA NTRS2019

Small target detection for search and rescue operations using distributed deep learning and synthetic data generation

It is important to find the target as soon as possible for search and rescue operations. Surveillance camera systems and unmanned aerial vehicles (UAVs) are used to support search and rescue. Automatic object detection is important because a person cannot monitor multiple surveillance screens simultaneously for 24 hours. Also, the object is often too small to be recognized by the human eye on the surveillance screen. This study used UAVs around the Port of Houston and fixed surveillance cameras to build an automatic target detection system that supports the US Coast Guard (USCG) to help find targets (e.g., person overboard). We combined image segmentation, enhancement, and convolution neural networks to reduce detection time to detect small targets. We compared the performance between the auto-detection system and the human eye. Our system detected the target within 8 seconds, but the human eye detected the target within 25 seconds. Our systems also used synthetic data generation and data augmentation techniques to improve target detection accuracy. This solution may help the search and rescue operations of the first responders in a timely manner.

Chow, Edward↗
DOE OSTI2021

Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working on Content-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the search query results should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. It is pertinent to design systems that retrieve images with these nuanced relationships in addition to providing more traditional results, such as duplicates and near-duplicates — and to do so with enough efficiency at large scale. In this work, we propose a new approach for spatial verification that aims at modeling object-level regions using image keypoints retrieved from an image index, which is then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method the Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations, which can run quickly on either CPUs or GPUs. It performs comparably to state-of-the-art methods on classic CBIR problems (Oxford 5K, Paris 6K, and Google-Landmarks), and outperforms them in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style imagery from Reddit.

42 ENGINEERING↗
NASA NTRS1993

Bright near-infrared sources within 1 deg of the Galactic center. I - Survey and 1-20 micron photometry

Results of a near-IR survey of 0.55 sq deg toward the Galactic center are reported. Additional IR photometry of 50 objects found in this survey was made in order to investigate the nature of luminous stars in the central region of the Milky Way including all sources with K less than 7.6 and H-K not less than 1.4. In addition to candidates for normal M-type stars and long-period variables, four objects whose energy spectra peak at about 5 microns have been detected in a small region around (l, b) = (0.15, 0.0 deg), near the crossing of the 6-cm radio arc with the Galactic plane. These four might be young stellar objects near the Galactic center. It is suggested that the relative depth change in the silicate absorption is localized to a fairly small region around the Galactic center.

Nagata, Tetsuya↗
NASA NTRS2010

An High Resolution Near-Earth Objects Population Enabling Next-Generation Search Strategies

Over the past decade, the dedicated search for kilometer-size near-Earth objects (NEOs), potentially hazardous objects (PHOs), and potential Earth impactors has led to a boost in the rate of discoveries of these objects. The catalog of known NEOs is the fundamental ingredient used to develop a model for the NEOs population, either by assessing and correcting for the observational bias (Jedicke et al., 2002), or by evaluating the migration rates from the NEOs source regions (Bottke et al., 2002). The modeled NEOs population is a necessary tool used to track the progress in the search of large NEOs (Jedicke et al., 2003) and to try to predict the distribution of the ones still undiscovered, as well as to study the sky distribution of potential Earth impactors (Chesley & Spahr, 2004). We present a method to model the NEOs population in all six orbital elements, on a finely grained grid, allowing us the design and test of targeted and optimized search strategies. This method relies on the observational data routinely reported to the Minor Planet Center (MPC) by the Catalina Sky Survey (CSS) and by other active NEO surveys over the past decade, to determine on a nightly basis the efficiency in detecting moving objects as a function of observable quantities including apparent magnitude, rate of motion, airmass, and galactic latitude. The cumulative detection probability is then be computed for objects within a small range in orbital elements and absolute magnitude, and the comparison with the number of know NEOs within the same range allows us to model the population. When propagated to the present epoch and projected on the sky plane, this provides the distribution of the missing large NEOs, PHOs, and potential impactors.

Tricaico, Pasquale↗
NASA NTRS1994

The symbiosis of photometry and radial-velocity measurements

The FRESIP mission is optimized to detect the inner planets of a planetary system. According to the current paradigm of planet formation, these planets will probably be small Earth-sized objects. Ground-based radial-velocity programs now have the sensitivity to detect Jovian-mass planets in orbit around bright solar-type stars. We expect the more massive planets to form in the outer regions of a proto-stellar nebula. These two types of measurements will very nicely complement each other, as they have highest detection probability for very different types of planets. The combination of FRESIP photometry and ground-based spectra will provide independent confirmation of the existence of planetary systems in orbit around other stars. Such detection of both terrestrial and Jovian planets in orbit around the same star is essential to test our understanding of planet formation.

Cochran, William D.↗
NASA NTRS2021

Compositional Study of Trans-Neptunian Objects at λ > 2.2 μm

Using data from the Infrared Array Camera on the Spitzer Space Telescope, we present photometric observations of a sample of 100 trans-Neptunian objects (TNOs) beyond 2.2 μm. These observations, collected with two broadband filters centered at 3.6 and 4.5 μm, were done in order to study the surface composition of TNOs, which are too faint to obtain spectroscopic measurements. With this aim, we have developed a method for the identification of different materials that are found on the surfaces of TNOs. In our sample, we detected objects with colors that are consistent with the presence of small amounts of water, and we were able to distinguish between surfaces that are predominantly composed of complex organics and amorphous silicates. We found that 86% of our sample have characteristics that are consistent with a certain amount of water ice, and the most common composition (73% of the objects) is a mixture of water ice, amorphous silicates, and complex organics. Twenty-three percent of our sample may include other ices, such as carbon monoxide, carbon dioxide, methane, or methanol. Additionally, only small objects seem to have surfaces dominated by silicates. This method is a unique tool for the identification of complex organics and to obtain the surface composition of extremely faint objects. Furthermore, this method will be beneficial when using the James Webb Space Telescope for differentiating groups within the trans-Neptunian population.

Trans-Neptunian objects↗
NASA NTRS2020

Object and Gas Source Detection with Robotic Platforms in Perceptually-Degraded Environments

In exploration-oriented robotic missions for disaster relief in unknown subterranean environments, it is of prime importance for a human supervisor to rapidly gain situational awareness of salient objects within the environment. In this paper we present an automated object detection pipeline that is adaptable to heterogeneous robots with arbitrary sensor configurations. It has been deployed in time-critical scenarios with multiple collaborative robots in a variety of demanding underground environments. For visually observable objects, detections are made in both the visible and thermal spectra using a state-of-the-art machine learning framework for object detection and classification. Our pipeline can be rapidly adapted to a specific task by using a small, structured dataset to fine-tune a pre-trained convolutional neural network (CNN). Relative localization is separated from the CNN for speed of operation. A robust architecture for localization is used with outlier rejection and a hierarchy of fall-back distance measurement methods. Point-source objects such as gas and WiFi hotspots can also be detected, by tracking signal strength over time and presenting an intuitive visualization on a map. Observations of each object types are presented to the operator in ranked confidence order for final evaluation.

Agha-mohammadi, Ali-akbar↗
NASA NTRS1993

JAPE 91: Influence of terrain masking of the acoustic propagation of helicopter noise

The acoustic propagation in the case of a noise source masked by a small element of terrain has been investigated experimentally. These data have been measured during the 'terrain masking' experiment of the NATO JAPE 91 experimental campaign. The main objective of that experiment was to study the acoustic detection of a helicopter masked by a small hill. Microphones have been placed at different locations on the shadow zone of the hill to study the effect of the terrain obstruction on sound propagation. The results presented come from data measured by Atlas Elektronik and by ISL, and have been processed together. The terrain obstruction causes an excess attenuation of the SPL (Sound Pressure Level) for all the frequencies, but this attenuation is more effective for the high frequencies than for the low frequencies. Results typical of diffraction phenomena have been observed; the SPL is minimal at the foot of the hill and is relatively constant beyond it.

Naz, P.↗
NASA NTRS1995

Studying the Sky/Planets Can Drown You in Images: Machine Learning Solutions at JPL/Caltech

JPL is working to develop a domain-independent system capable of small-scale object recognition in large image databases for science analysis. Two applications discussed are the cataloging of three billion sky objects in the Sky Image Cataloging and Analysis Tool (SKICAT) and the detection of possibly one million small volcanoes visible in the Magellan synthetic aperture radar images of Venus (JPL Adaptive Recognition Tool, JARTool).

geomorphology↗
DOE OSTI2025

Orbital Debris IC Report

We started a simulation campaign in support of our ongoing plasma signatures of small orbital debris project SINTRA program funded by IARPA. This project aims to identify plasma signatures generated by the interaction of small orbital debris (>10cm) with space plasma that can be used for detection, tracking and eventually, develop technologies to remove these small but lethal objects. The small orbital debris problem is increasing exponentially threaten the current and future use of space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2001

Kuiper Belt Mapping Radar

Since their initial discovery in 1992, to date only a relatively small number of Kuiper Belt Objects (KBO's) have been discovered. Current detection techniques rely on frame-to-frame comparisons of images collected by optical telescopes such as Hubble, to detect KBO's as they move against the background stellar field. Another technique involving studies of KBO's through occultation of known stars has been proposed. Such techniques are serendipitous, not systematic, and may lead to an inadequate understanding of the size, range, and distribution of KBO's. In this paper, a future Kuiper Belt Mapping Radar is proposed as a solution to the problem of mapping the size distribution, extent, and range of KBO's. This approach can also be used to recover radar albedo and object rotation rates. Additional information is contained in the original extended abstract.

Freeman, A.↗
DOE OSTI2022

High–throughput measurement of plant fitness traits with an object detection method using Faster R–CNN

Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r 2 = 0.9996) and highly accurate fruit counts (r 2 = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

59 BASIC BIOLOGICAL SCIENCES↗