VLBI DSN Current Status and Prospects
A review of the current status of very long baseline interferometry in the Deep Space Network.
evaluation VLBI scheduling procedures↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Human System Risk Communication: Directed Acyclic Graphs
- The Human System Risk Board (HSRB) is responsible for the management of a portfolio of 30 human system risks that NASA tracks and configuration manages to mitigate for future crewed exploration missions. - The HSRB has been exploring the concept of causal diagrams (in the form of Directed Acyclic Graphs or DAGs) as an approach to creating knowledge graphs for each risk to enable shared mental models of causal flow from spaceflight hazards to mission outcomes among HSRB Stakeholders. - These diagrams are intended to improve insight and communication of risk across the myriad subject matter experts and management interested in human system risk reduction. This includes program managers, systems engineers, and operators in addition to the Human Health and Performance Directorate. - The DAG project was intended to create the foundation for composition of the 30 baselined DAGs into a single risk network and software is being developed in parallel to enable this forward work.
directed acrylic graph↗
Resource Scheduling for a Network of Communications Antennas
This paper describes tha Demand Access Network Scheduler (DANS) system for automatically scheduling and rescheduling resources for a network of communication antennas. DANS accepts a baseline schedule and supports rescheduling of antenna and subsystem resources to satisfy tracking goals in the event of changing track requests, equipment outages, and inclement weather.
Deep↗
Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM)
Four state-of-the-art Deep Learning-based Convolutional Neural Networks (DCNN) were applied to automate the semantic segmentation of a 3D Transmission x-ray Microscopy (TXM) nanotomography image data. The standard U-Net architecture as baseline along with UNet++, PSPNet, and DeepLab v3+ networks were trained to segment the microstructural features of an AA7075 micropillar. A workflow was established to evaluate and compare the DCNN prediction dataset with the manually segmented features using the Intersection of Union (IoU) scores, time of training, confusion matrix, and visual assessment. Comparing all model segmentation accuracy metrics, it was found that using pre-trained models as a backbone along with appropriate training encoder-decoder architecture of the Unet++ can robustly handle large volumes of x-ray radiographic images in a reasonable amount of time. This opens a new window for handling accurate and efficient image segmentation of in situ time-dependent 4D x-ray microscopy experimental datasets.
36 MATERIALS SCIENCE↗
Use of Very Long Baseline Array Interferometric Data for Spacecraft Navigation
The main VLBI technique that is used at JPL is known as the Delta Differential One-way Ranging ((Delta)DOR). Two DSN antennas simultaneously track a source, and alternate between sources. The signals recorded at the antennas from each source are correlated to obtain the delay in arrival to the two antennas, and the delays are differenced to remove common-source errors. An alternative technique is to use carrier phase differences between antennas. This is routinely done by the Very Large Baseline Array (VLBA) as part of source imaging. The VLBA capabilities are used for scientific research, but also have the potential to be used for navigation. Two main experiments were performed with the VLBA and JPL spacecraft. This paper describes and analyzes these experiments and discusses the possible uses of VLBA tracking for spacecraft navigation.
Very Large Baseline Array (VLBA)↗
Active Learning‐Driven Inkless Additive Nanomanufacturing for Printed Electronics
Inkless additive nanomanufacturing for printed electronics promises broad material and substrate versatility, yet the high-dimensional print parameter space makes tuning print parameters time-intensive. We present a Bayesian optimization study that constructs a digital twin from printed-silver data to benchmark surrogate models, acquisition functions, and batch sizes head-to-head to achieve user-specified target resistance. Tested surrogate models included Gaussian process, random forest, and Bayesian neural network surrogates with expected improvement and confidence bound acquisition functions. In total, we evaluate 48 unique model configurations alongside a random sampling baseline for comparison. For printed silver, the Bayesian neural network with a batch size of one achieved the lowest average cumulative regret, approximately four times more efficient on average than random sampling. To balance performance and substrate space, a random forest model with expected improvement and a batch size of four was chosen as the model for validation testing. Applying this chosen configuration to copper with an additional print parameter, the model achieved a resistance within 0.15 Ω of a 1 Ω target in fewer than 30 printed lines across five validation sets. Altogether, the workflow yields a tuned and validated model that efficiently guides experiments toward the target while simultaneously learning the parameter space.
Bevel, Colton [Auburn University, AL (United State↗
Distribution of centrality measures on undirected random networks via the cavity method
The Katz centrality of a node in a complex network is a measure of the node’s importance as far as the flow of information across the network is concerned. For ensembles of locally tree-like undirected random graphs, this observable is a random variable. Its full probability distribution is of interest but difficult to handle analytically because of its “global” character and its definition in terms of a matrix inverse. Leveraging a fast Gaussian Belief Propagation-Cavity algorithm to solve linear systems on tree-like structures, we show that i) the Katz centrality of a single instance can be computed recursively in a very fast way, and ii) the probability P ( K ) that a random node in the ensemble of undirected random graphs has centrality K satisfies a set of recursive distributional equations, which can be analytically characterized and efficiently solved using a population dynamics algorithm. We test our solution on ensembles of Erdős-Rényi and Scale Free networks in the locally tree-like regime, with excellent agreement. The analytical distribution of centrality for the configuration model conditioned on the degree of each node can be employed as a benchmark to identify nodes of empirical networks with over- and underexpressed centrality relative to a null baseline. We also provide an approximate formula based on a rank- 1 projection that works well if the network is not too sparse, and we argue that an extension of our method could be efficiently extended to tackle analytical distributions of other centrality measures such as PageRank for directed networks in a transparent and user-friendly way.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Data-Driven Validation of NOvA's Convolutional Neural Network for Electron (Anti)Neutrino Selection
NOvA is a long-baseline neutrino oscillation experiment, designed to make measurements of several oscillation parameters using muon neutrino disappearance and electron neutrino appearance. It consists of two functionally equivalent detectors and utilizes the Fermilab NuMI neutrino beam. NOvA uses a convolutional neural network for particle identification of electron neutrino events with a validation process that includes several data-driven techniques. These Muon Removed studies ensure that our classifier performs the same on data as it does on simulation. In particular, Muon Removed Electron-Added studies involve selecting muon neutrino charged current events from both data and simulation and replacing the muon with a simulated electron of similar energy. For Muon Removed Bremsstrahlung and Muon Removed Decay-in-Flight studies, we remove muonic hits from cosmic muons that have either experienced Bremsstrahlung radiation or decayed in flight, producing samples of pure electromagnetic showers. Each of these electron neutrino-like samples are then evaluated by our classifier to obtain selection efficiencies. Our most recent analysis showed good agreement in the electron selection efficiency between data and simulation using these techniques. Furthermore, these cross-checks can be extended to corrections to our predicted electron neutrino signal. The impact of one such set of corrections on the overall analysis results were also evaluated in thesis.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
The global tracking networks for crustal dynamics
Highly accurate Satellite Laser Ranging (SLR) and Very-Long-Baseline Interferometry (VLBI) have been implemented by the NASA Crustal Dynamics Project and many cooperating groups in many countries to form global SLR and VLBI networks for geodetic measurements of global plate motion, plate deformation, regional deformations in areas of high earthquake activity, and accurate measurements of the earth's polar motion and changes in rotation rate. These systems are measuring vector baselines between stations to an accuracy of 2-5 cm. New improvements being implemented will improve the accuracy to about 1 cm.
Coates, R. J.↗
Differentially Private Adaptive Noise Injection (DP-ANI) v1.0
Location data is collected from users continuously to understand their mobility patterns. Releasing the user trajectories may compromise user privacy. Therefore, the general practice is to release aggregated location datasets. However, private information may still be inferred from an aggregated version of location trajectories. Differential privacy (DP) protects the query output against inference attacks regardless of background knowledge. This software implements a differential privacy-based privacy model that protects the user's origins and destinations from being inferred from aggregated mobility datasets. This is achieved by injecting Planar Laplace noise to the user origin and destination GPS points. The noisy GPS points are then transformed into a link representation using a link-matching algorithm. Finally, the link trajectories form an aggregated mobility network. The injected noise level is selected using the Sparse Vector Mechanism. This DP selection mechanism considers the link density of the location and the functional category of the localized links. Compared to the different baseline models, including a k-anonymity method, our differential privacy-based aggregation model offers query responses that are close to the raw data in terms of aggregate statistics at both the network and trajectory-levels with maximum 9% deviation from the baseline in terms of network length.
Peisert, Sean [Lawrence Berkeley National Laborato↗
Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network
Transverse cirrus bands (TCBs) are bands of upper-level clouds that are regularly seen in mesoscale and synoptic-scale weather systems. In tropical cyclones, their appearance has been subjectively linked to intensification and the diurnal cycle, but these hypothesized relationships have not been rigorously tested due to the difficulty of objectively identifying TCBs in satellite images. This presentation describes a machine learning technique that successfully identifies TCBs in imagery from the GOES-16 Advanced Baseline Imager. The technique uses a convolutional neural network (CNN) that assigns a probability of each pixel in the image being associated with a TCB. Using the CNN, a database of TCBs from 2018 to 2022 was developed for the Atlantic basin. Statistics using this database will be presented, including the relationship between TCBs and deep-layer vertical wind shear, intensity change, and the TC diurnal cycle.
John Mark Mayhall↗
Beyond interpolation: Physics-inspired gating transformers for extrapolating irradiation conditions to novel nuclear fuels
The qualification of advanced nuclear fuels relies on irradiation experiments in test reactors that emulate commercial conditions. Designing these tests requires accurate prediction of key irradiation quantities, particularly heat generation rate and burnup, yet obtaining them typically involves computationally expensive multi-step simulation workflows. We propose a physics-inspired gating transformer (PIGT) that integrates an inverse-square, distance-based attenuation into the encoder representation to bias attention toward physically relevant spatial relationships while retaining data-driven flexibility. Using MiniFuel irradiation data from the High Flux Isotope Reactor at Oak Ridge National Laboratory, we benchmark against ensemble methods, feedforward and recurrent networks, convolutional models, and standard transformers. While baseline models perform well under interpolation, they exhibit a pronounced generalization gap when evaluated on fuels not included in the training set. The proposed model consistently improves extrapolative accuracy and stability, yielding the strongest performance on unseen fuel configurations. These results indicate that a lightweight physics structure embedded within attention mechanisms can substantially improve robustness, enabling more reliable surrogate predictions to accelerate the design of nuclear fuel irradiation experiments.
Fuel qualification↗
Inverse deep learning methods and benchmarks for artificial electromagnetic material design
In this work we investigate the use of deep inverse models (DIMs) for designing artificial electromagnetic materials (AEMs) – such as metamaterials, photonic crystals, and plasmonics – to achieve some desired scattering properties (e.g., transmission or reflection spectrum). DIMs are deep neural networks (i.e., deep learning models) that are specially-designed to solve ill-posed inverse problems. There has recently been tremendous growth in the use of DIMs for solving AEM design problems however there has been little comparison of these approaches to examine their absolute and relative performance capabilities. In this work we compare eight state-of-the-art DIMs on three unique AEM design problems, including two models that are novel to the AEM community. Our results indicate that DIMs can rapidly produce accurate designs to achieve a custom desired scattering on all three problems. Although no single model always performs best, the Neural-Adjoint approach achieves the best overall performance across all problem settings. As a final contribution we show that not all AEM design problems are ill-posed, and in such cases a conventional deep neural network can perform better than DIMs. We recommend that a deep neural network is always employed as a simple baseline approach when addressing AEM design problems. Furthermore, we publish python code for our AEM simulators and our DIMs to enable easy replication of our results, and benchmarking of new DIMs by the AEM community.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Gravitational wave measurement in the mid-band with atom interferometers
Abstract Gravitational Waves (GWs) have been detected in the ∼ 100 Hz and nHz bands, but most of the gravitational spectrum remains unobserved. A variety of detector concepts have been proposed to expand the range of observable frequencies. In this work, we study the capability of GW detectors in the “mid-band”, the ∼ 30 mHz– 10 Hz range between LISA and LIGO, to measure the signals from and constrain the properties of ∼ 1 – 100M ⊙ compact binaries. We focus on atom-interferometer-based detectors. We describe a Fisher matrix code,AIMforGW, which we created to evaluate their capabilities, and present numerical results for two benchmarks: terrestrial km-scale detectors, and satellite-borne detectors in medium Earth orbit. Mid-band GW detectors are particularly well-suited to pinpointing the location of GW sources on the sky. We demonstrate that a satellite-borne detector could achieve sub-degree sky localization for any detectable source with chirp mass ℳ c ≲ 50M ⊙ . We also compare different detector configurations, including different locations of terrestrial detectors and various choices of the orbit of a satellite-borne detector. As we show, a network of only two terrestrial single-baseline detectors or one single-baseline satellite-borne detector would each provide close-to-uniform sky-coverage, with signal-to-noise ratios varying by less than a factor of two across the entire sky. We hope that this work contributes to the efforts of the GW community to assess the merits of different detector proposals.
Astronomy & Astrophysics↗
A Southern Hemisphere VLBI Survey on a 275-km Baseline
A very long base interferometry (VLBI) survey at 2.29 GHz was conducted using a 275 km baseline consisting of the NASA Deep Space Network tracking site in Tidbinbilla, Australia, and the 64 m antenna located at Parkes, Australia. The purpose of the survey was to identify sources in the southern sky possessing strong compact cores ( 0.1 arcseconds). Such sources will be used to form a reference frame for conducting VLBI geodesy experiments in the Southern Hemisphere. The 70 candidate sources were chosen to be south of -39 degrees declination, and only four had been previously observed from the northern hemisphere. Of the observed sources, 49 were found to have compact structure. In addition to determining correlated flux densities, the delay and delay rate observables of several detected sources were used to determine an estimate of the three dimensional location of the Parkes antenna relative to the Tidbinbilla site with a 1-sigma accuracy of 10 meters.
Morabito, D. D.↗
Study of tracking and data acquisition system for the 1990's. Volume 4: TDAS space segment architecture
Tracking and data acquisition system (TDAS) requirements, TDAS architectural goals, enhanced TDAS subsystems, constellation and networking options, TDAS spacecraft options, crosslink implementation, baseline TDAS space segment architecture, and treat model development/security analysis are addressed.
Orr, R. S.↗
Global positioning system network analysis with phase ambiguity resolution applied to crustal deformation studies in California
An efficient algorithm is developed for multisession adjustment of GPS data with simultaneous orbit determination and ambiguity resolution. Application of the algorithm to the analysis of data from a five-year campaign in progress in southern and central California to monitor tectonic motions using observations by GPS satellites, demonstrates improvements in estimates of station position and satellite orbits when the phase ambiguities are resolved. Most of the phase ambiguities in the GPS network were resolved, particularly for all the baselines of geophysical interest in California.
Dong, Da-Nan↗
A demonstration of TDRS orbit determination using differential tracking observables from GPS ground receivers
A Global Positioning System (GPS) flight receiver provides a means of precisely determining orbits for satellites in low to moderate altitude orbits. Above 5000 km altitude, however, relatively few GPS satellites are visible. We present a new approach to orbit determination for satellites at higher altitudes. Modification of GPS ground receivers enables a beacon from the orbiter to be tracked simultaneously with GPS data. The orbit accuracy expected from this GPS-like tracking (GLT) technique in principle could be comparable to accuracies of GPS orbits. Present-day GPS orbit quality from a daily semi-automated analysis system at the Jet Propulsion Laboratory is at the 30 - 50 cm level: expected accuracies for orbiters with GLT are calculated to be in the few-meter range for altitudes up to 100000 km. For geosynchronous satellites, however, there are unique challenges due to geometrical limitations and to the lack of strong dynamical signature in tracking data. We examine two approaches for tracking Tracking and Data Relay Satellites (TDRS) geostationary orbiters. One uses GLT with a global network; the other relies on a small `connected element' ground network with a distributed clock for short baseline differential carrier phase (SB Delta Phi). In priciple, both could meet 50-m TDRS operational requirements. However there are practical difficulties with either pure GLT or SB Delta Phi tracking schemes for present-day TDRS satellites. We describe an experiment planned for late 1993 which will combine aspects of both GLT and SB Delta Phi to demonstrate a new approach for tracking TDRS which offers a number of operationally convenient and attractive features. The TDRS demo will in effect ba a proof of concept experiment for both the GLT and SB Delta Phi approaches to tracking spacecraft.
Lichten, Stephen M.↗