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In this research, the strategic deconfliction of unmanned aircraft systems for an urban package delivery environment with two depots and multiple drop-off locations is studied. This research aims to formulate a mathematical model to compute both the departure sequence and scheduled time of departure for each unmanned aircraft system at a depot, considering temporal constraints at en-route crossing waypoints and depots for strategic deconfliction. However, the problem formulation results in an NP-hard mixed-integer nonlinear programming problem for the global optimal solution, so instead, a "rolling horizon with𝑘-position search"heuristic method is developed. The simulation studies show that an increase in the value of𝑘(the parameter used to determine the size of the local neighborhood) reduces the average ground delay at the cost of an increase in the computation time for a given problem size. The study also shows an order of magnitude increase in the maximum number of flights scheduled with the integration of rolling horizon (time decomposition) compared to those without the integration of rolling horizon in the heuristic algorithm for a given computation time cut off.
Water vapor has been detected in the Martian atmosphere by multiple orbiting instruments. The Atmospheric Chemistry Suite (ACS) on the ExoMars Trace Gas Orbiter (TGO) observed H2O mixing ratios reaching up to 50 ppmv at altitudes of 100-120 km during global dust storms, while levels remained low (<2 ppmv) during other seasons. The Neutral Gas and Ion Mass Spectrometer (NGIMS) on the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft revealed that water transported to the upper atmosphere is dissociated by ions, producing atomic hydrogen that escapes into space, contributing to Mars’ water loss. This transport is seasonal, peaking in southern summer and intensifying during dust storms. Additionally, the Mars Reconnaissance Orbiter’s (MRO) imaging spectrometer detected hydrated minerals on slopes, suggesting that liquid water may intermittently flow on present-day Mars. However, an observational gap exists between high-altitude water vapor and surface water due to limitations in spatial resolution and a lack of measurements in the lower atmosphere. To address this gap, we propose using an airborne differential absorption lidar (DIAL) to search for water sources. DIAL provides high-resolution measurements both day and night, bridging the observational gap between high-altitude water vapor and surface water, thus enhancing our understanding of water transport and loss on Mars. Absorption lines of water vapor in the 2.7 μm and 1.8 μm bands have been selected in this study. Simulations show that both lines are capable of detecting water vapor sources with reasonable system parameters.
NASA’s Goddard Earth Science (GES) Data and Information Services Center (DISC) is one of twelve data centers in NASA's Science Mission Directorate (SMD), providing vital earth science data to a diverse user base. To enhance the discoverability of this data, GES DISC employs a keyword search system, which leverages scientific keywords embedded in dataset metadata. However, the evolving nature of scientific applications of our data necessitates regular review and augmentation of these keywords. To address this, we developed a service to automatically predict missing science keywords in the metadata. This service constructs a knowledge graph from the latest GES DISC metadata within NASA’s Common Metadata Repository (CMR). Using an open-source library, we trained a machine learning model to predict absent science keywords in the metadata. Our preliminary results indicate that the model has high levels of accuracy at predicting science keywords in the dataset metadata when exposed to data not included in its training. These predicted keywords were then evaluated by GES DISC data curation scientists and compared against other AI tools for metadata augmentation. We aim to enhance the overall usability and accessibility of NASA’s earth science data by implementing this tool in our data curation processes.
Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given to augmenting existing processes for mining these safety reporting system databases. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety database mining activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using publicly available NASA’s ASRS narratives and another using publicly available C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.
Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given in augmenting existing processes of safety reporting systems. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety reporting systems activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using all public NASA's ASRS narratives and another using all public C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.
Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given in augmenting existing processes of safety reporting systems. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety reporting systems activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using all public NASA's ASRS narratives and another using all public C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.
We introduce the Swath Segment Selection problem (SSSP). The SSSP consists of a constrained geometric covering problem and a capacitated resource problem. It comes from the real-life problem of scheduling on- and off-times for air or space-borne instruments that image a target by flying over and collecting a swath of information.
Current mission activities and future strategic plans will be surveyed in this talk.
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Science missions and instruments continue to produce volumes of useful data and scientists depend on the data systems and tools that archive this data as a means to access and analyze it.
Optimization of expected values in a stochastic domain is common in real world applications.
Optimization of expected values in a stochastic domain is common in real world applications.
While the general class of most scheduling problems is NP-hard in worst-case complexity, in practice, for specific distributions of problems and constraints, domain-specific solutions have been shown to perform in much better than exponential time.
Ground-based, equal-arm-length laser interferometers are being built to measure high-frequency astrophysical graviatational waves. Because of the arm-length equality, laser light experiences the same delay in each arm and thus phase or frequency noise from the laser itself precisely cancels at the photodetector.
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The goal of this plan is to briefly describe new technologies available to us in the arenas of information discovery and discuss the strategic value they have for the NASA enterprise with some considerations and suggestions for near term implementations using the NASA Engineering Network (NEN) as a delivery venue.
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