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We present an independent investigation of heavy-ion single event effect data for the Microsemi RTG4 field programmable gate array (FPGA).
The performance of electronic devices in a space radiation environment is often limited by susceptibility to single-event effects (SEE), total ionizing dose (TID), and displacement damage (DD). Interpreting the results of SEE, TID, and DD testing of complex devices is quite difficult given the rapidly changing nature of both technology and the related radiation issues. Radiation testing is performed to establish the sensitivities of candidate spacecraft electronics to single-event upset (SEU), single-event latchup (SEL), single-event gate rupture (SEGR), single-event burnout (SEB), single-event transients (SETs), TID, and DD effects. Knowing where to search for these test results is a valuable resource for the aerospace engineer or spacecraft design engineer. This poster is intended to be a resource tool for finding radiation test data.
This presentation discusses two efforts of the NARI AI/ML Intern team during the Fall 2021 OSTEM Internship term. For Letters of Agreement (LoA), we have studied how LoAs are structured and explored the question ‘What is an LoA constraint?’ To do this, our approach is data-driven, iterative, and assisted by machine learning when available. In this presentation, we will walk through our tasks of manually scanning through documents, performing a preliminary entity labelling task, and our unsupervised analysis on LoA procedures sections. After this research phase, we define the smallest constraint unit in an LoA, and start to perform entity extraction. Looking towards constraint extraction, we are also exploring the use of a one-class support vector machine (OneClassSVM) model to identify patterns within the data. The second effort of our team this term is focused on Air Traffic Control System Command Center (ATCSCC) advisory meetings, and the subsequent advisory documents that get published from their content. These advisory documents are important to give readily accessible summaries of daily operations, so that data centers, airline officials, and other stakeholders can easily understand the context of these meetings in real time. In applying machine learning to this scenario, two natural language processing tasks are used. First is developing machine learning models to convert the meeting speech data into text. With this text, use of extractive and abstractive text summarization models are used to automatically generate preliminary versions of the advisory documents.
Through established, highly-accurate ab initio quartic force fields (QFFs), a complete set of fundamental vibrational frequencies, rotational constants, and rovibrational coupling and centrifugal distortion constants have been determined for both the cyclic 1(sup 1) 1A' and bent 2(sup 1)A' DCCN, H(C13)CCN, HC(C-13)N, and HCC(N-15) isotopologues of HCCN. Spectroscopic constants are computed for all isotopologues using second-order vibrational perturbation theory (VPT2), and the fundamental vibrational frequencies are computed with VPT2 and vibrational configuration interaction (VCI) theory. Agreement between VPT2 and VCI results is quite good with the fundamental vibrational frequencies of the bent isomer isotopologues in accord to within a 0.1 to 3.2 / cm range. Similar accuracies are present for the cyclic isomer isotopologues. The data generated here serve as a reference for astronomical observations of these closed-shell, highly-dipolar molecules using new, high-resolution telescopes and as reference for laboratory studies where isotopic labeling may lead to elucidation of the formation mechanism for the known interstellar molecule: X 3A0 HCCN.
Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale datasets, as well as standardized evaluation tasks and metrics. Furthermore, the specialized knowledge and software typically required to process particle physics data pose significant barriers to interdisciplinary collaboration with the broader machine learning community. This work introduces a large, openly accessible dataset of 10 million simulated proton-proton collisions, designed to support self-supervised training of foundation models. To facilitate ease of use, the dataset is provided in a common NumPy format. In addition, it includes 70,000 labeled examples spanning three well defined downstream tasks: track finding, particle identification, and noise tagging, to enable systematic evaluation of the foundation model's adaptability. The simulated data are generated using the Pythia Monte Carlo event generator at a center of mass energy of $\sqrt{s}$ = 200 GeV and processed with Geant4 to include realistic detector conditions and signal emulation in the sPHENIX Time Projection Chamber at the Relativistic Heavy Ion Collider, located at Brookhaven National Laboratory. This dataset resource establishes a common ground for interdisciplinary research, enabling machine learning scientists and physicists alike to explore scaling behaviors, assess transferability, and accelerate progress toward foundation models in nuclear and high energy physics. The complete simulation and reconstruction chain is reproducible with the sPHENIX software stack. All data and code locations are provided under Data Accessibility.
Changes in classification detail of forest species descriptions were made for Landsat data on 2.2 million acres in northwestern California. Because basic forest canopy structures may exhibit very similar E-M energy reflectance patterns in different environmental regions, classification labels based on Landsat spectral signatures alone become very generalized when mapping large heterogeneous ecological regions. By adding a seven ecological zone stratification, a 167% improvement in classification detail was made over the results achieved without it. The seven zone stratification is a less costly alternative to the inclusion of complex collateral information, such as terrain data and soil type, into the Landsat data base when making inventories of areas greater than 500,000 acres.
A brief review of the purposes and the results from the Viking Biology experiments is presented, in the expectation that the lessons learned from this mission will be useful in planning future approaches to the biological exploration of Mars. Since so little was then known about potential micro-environments on Mars, three different experiments were included in the Viking mission, each one based on different assumptions about what Martian organisms might be like. In addition to the Viking Biology Instrument (VBI), important corollary information was obtained from the Viking lander imaging system and from the molecular analysis experiments that were conducted using the gas chromatograph-mass spectrometer (GCMS) instrument. No biological objects were noted by the lander imaging instrument. The GCMS did not detect any organic compounds. A description of the tests conducted by the Gas Exchange Experiment, the Labeled Release experiment, and the Pyrolytic Release experiment is given. Results are discussed. Taken as a whole, the Viking data yielded no unequivocal evidence for a Martian biota at either landing site. The results also revealed the presence of one or more reactive oxidants in the surface material and these need to be further characterized, as does the range of micro-environments, before embarking upon future searches for extant life on Mars.
High resolution images of the 12 micron and 25 micron IRAS survey data from each HCON crossing the Galactic Plane are being created for those regions that the original IRAS processing labeled as confused. This encompasses the area within 100 deg longitude of the Galactic Center and within 3 deg to 10 deg of the Plane. The procedures used to create the images preserve the spatial resolution inherent in the IRAS instrument. The images are separated into diffuse and point source components and candidate sources are extracted from the point source image after non-linear spatial sharpening. Fluxes are estimated by convolving the candidate sources with the point response function and cross-correlating with the original point source image. A source is considered real if it is seen on at least two HCON's with a rather generous flux match but a stringent position criterion. A number of fields spanning a range of source densities from low to high have been examined. Initial analysis indicates that the imaging and extraction works quite well up to a source density of about 100 sources per square degree or down to roughly 0.8 Janskys.
Quantitative autoradiographic analysis of receptors for GABA and acetylcholine in the forebrain of rats flown on COSMOS 2044 was undertaken as part of a joint US-Soviet study to determine the effects of microgravity on the central nervous system, and in particular on the sensory and motor portions of the forebrain. Changes in binding of these receptors in tissue from animals exposed to microgravity would provide evidence for possible changes in neural processing as a result of exposure to microgravity. Tritium-labelled diazepam and Quinuclidinyl-benzilate (QNB) were used to visualize GABA (benzodiazepine) and muscarinic (cholinergic) receptors, respectively. The density of tritium-labelled radioligands bound to various regions in the forebrain of both flight and control animals were measured from autoradiograms. Data from rats flown in space and from ground-based control animals that were not exposed to microgravity were compared.
The Autonomous Landing and Hazard Avoidance Technology (ALHAT) program is building a sensor that enables a spacecraft to evaluate autonomously a potential landing area to generate a list of hazardous and safe landing sites. It will also provide navigation inputs relative to those safe sites. The Hazard Detection System Compute Element (HDS-CE) box combines a field-programmable gate array (FPGA) board for sensor integration and timing, with a multicore computer board for processing. The FPGA does system-level timing and data aggregation, and acts as a go-between, removing the real-time requirements from the processor and labeling events with a high resolution time. The processor manages the behavior of the system, controls the instruments connected to the HDS-CE, and services the "heavy lifting" computational requirements for analyzing the potential landing spots.
Inaccurate prediction of aircraft trajectory by ground-based decision support tools (DST) is a major concern in air traffic management (ATM). Aircraft trajectory prediction tools rely on a simplified point-mass aircraft performance model (APM) to make their predictions. Even though the performance coefficients and weight of an aircraft are a vital part of the APM’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to DSTs. Current ATM research focuses on improving the estimate of some APM parameters by freezing all other coefficients. This simplified approach introduces unwanted sources of bias and negatively impacts the accuracy of the performance model. In this paper, we apply machine learning (ML) techniques for the simultaneous prediction of three key APM parameters (two drag coefficients and the initial aircraft weight). To accomplish this, we employ an ordinary differential equation (ODE) fitting approach to generate optimized APM parameter labels customized to each individual flight record. Subsequently, we train ML models to capture the relationship between the historical data and the optimized APM parameters. Two different ML model solutions are applied and APM coefficients are predicted for unseen flights. The results indicate that the ML models are able to capture the relationship between APM parameters and flight-related features with good accuracy.
This paper presents Single Event Effect (SEE) in-flight data on Solid State Recorders (SSR) that have been collected over a long period of time for two NASA spacecraft: Orbview-2 and XTE. SEE flight data on solid-state memories give an opportunity to study the behavior in space of SEE sensitive commercial devices. The actual Single Event Upset (SEU) rates can be compared with the calculated rates based on environment models and ground test data. The SEE mitigation schemes can also be evaluated in actual implementation. A significant amount of data has already been published concerning observed SEE effects on memories in space. However, most of the data presented cover either a short period of time or a small number of devices. The data presented here has been collected on a large number of devices during several years. This allows statistically significant information about the effect of space weather fluctuations on SEU rates, and the effectiveness of SEE countermeasures used to be analyzed. Only Orbview-2 data is presented in this summary. XTE data will be included in the final paper.
We present an independent investigation of heavy-ion single event effect data for the Microsemi RTG4 field programmable gate array (FPGA).
Object detection in remote sensing demands extensive, high-quality annotations—a process that is both labor-intensive and time-consuming. In this work, we introduce a real-time active learning and semi-automated labeling framework that leverages foundation models to streamline dataset annotation for object detection in remote sensing imagery. For example, by integrating a Segment Anything Model (SAM), our approach generates mask-based bounding boxes that serve as the basis for dual sampling: (a) uncertainty estimation to pinpoint challenging samples, and (b) diversity assessment to ensure broad data coverage. Furthermore, our Dynamic Box Switching Module (DBS) addresses the well-known cold start problem for object detection models by replacing its suboptimal initial predictions with SAM-derived masks, thereby enhancing early-stage localization accuracy. Extensive evaluations on multiple remote sensing datasets plus a real-world user study, demonstrate that our framework not only reduces annotation effort, but also significantly boosts detection performance compared to traditional active learning sampling methods. The code for training and the user interface will be made available.
The author has identified the following significant results. Barley showed significant separability from spring wheat, both multitemporally and on a single date chosen near the turning time for barley. Oats showed occasional multitemporal separability from barley and spring wheat; however, the cause of this separability was not well understood. Oats showed no significant separability from spring wheat on any single date during the growing season. By pooling data from segments having an acquisition near the turning time for barley, a fixed unitemporal projection for aiding in the labeling of barley versus spring wheat and oats was constructed. This projection has about the same separability of barley from spring wheat and oats as the unitemporal greeness versus brightness plot. The new fixed projection has the advantage that barley occurs consistently in the same general location on the plot with respect to spring wheat and oats. Attempts to construct a fixed multitemporal or a segment-dependent multitemporal projection for aiding in the labeling of spring wheat versus other small grains were unsuccessful due to segment availability and the fact that each segment has a unique acquisition history.
Data concerning a fast X ray transient observed by the HEAO 1 on March 17, 1978 are discussed. The object, labeled H0547-14, was located in Lepus, with galactic coordinates 219.37 deg longitude, b = -20.12 deg in the anticenter direction. A 10 sec scan of the transient was obtained with proportional counter detectors. An X ray luminosity of more than 5.1 x 10 to the 31st ergs/sec was measured as a lower limit, with the decay taking 17 min. A lower limit to the flare was set at 30 sec. The flux intensity resided in the range 4.4/1 billion-4/100 million erg/sq cm per sec, with a rise time of a factor of 10 in about 5 sec. Possible characterizations of the object, as either a gamma ray burst or as an M dwarf star, are discussed.
The goal of my project was to create a graphical user interface for a prototype crop scheduler. The crop scheduler was developed by Dr. Jorge Leon and Laura Whitaker for the ALSS (Advanced Life Support System) program. The addition of a system-independent graphical user interface to the crop planning tool will make the application more accessible to a wider range of users and enhance its value as an analysis, design, and planning tool. My presentation will demonstrate the form and functionality of this interface. This graphical user interface allows users to edit system parameters stored in the file system. Data on the interaction of the crew, crops, and waste processing system with the available system resources is organized and labeled. Program output, which is stored in the file system, is also presented to the user in performance-time plots and organized charts. The menu system is designed to guide the user through analysis and decision making tasks, providing some help if necessary. The Java programming language was used to develop this interface in hopes of providing portability and remote operation.
Establishing space communication between ground facilities and other satellites is a painstaking task that requires many precise calculations dealing with relay time, atmospheric conditions, and satellite positions, to name a few. The Space Communications Emulation Facility (SCEF) team here at NASA is developing a facility that will approximately emulate the conditions in space that impact space communication. The emulation facility is comprised of a 32 node distributed cluster of computers; each node representing a satellite or ground station. The objective of the satellites is to observe the topography of the Earth (water, vegetation, land, and ice) and relay this information back to the ground stations. Software originally designed by the University of Kansas, labeled the Emulation Manager, controls the interaction of the satellites and ground stations, as well as handling the recording of data. The Emulation Manager is installed on a Linux Operating System, employing both Java and C++ programming codes. The emulation scenarios are written in extensible Markup Language, XML. XML documents are designed to store, carry, and exchange data. With XML documents data can be exchanged between incompatible systems, which makes it ideal for this project because Linux, MAC and Windows Operating Systems are all used. Unfortunately, XML documents cannot display data like HTML documents. Therefore, the SCEF team uses XML Schema Definition (XSD) or just schema to describe the structure of an XML document. Schemas are very important because they have the capability to validate the correctness of data, define restrictions on data, define data formats, and convert data between different data types, among other things. At this time, in order for the Emulation Manager to open and run an XML emulation scenario file, the user must first establish a link between the schema file and the directory under which the XML scenario files are saved. This procedure takes place on the command line on the Linux Operating System. Once this link has been established the Emulation manager validates all the XML files in that directory against the schema file, before the actual scenario is run. Using some very sophisticated commercial software called the Satellite Tool Kit (STK) installed on the Linux box, the Emulation Manager is able to display the data and graphics generated by the execution of a XML emulation scenario file. The Emulation Manager software is written in JAVA programming code. Since the SCEF project is in the developmental stage, the source code for this type of software is being modified to better fit the requirements of the SCEF project. Some parameters for the emulation are hard coded, set at fixed values. Members of the SCEF team are altering the code to allow the user to choose the values of these hard coded parameters by inserting a toolbar onto the preexisting GUI.