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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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Spot: A Programming Language for Verified Flight Software

The C programming language is widely used for programming space flight software and other safety-critical real time systems. C, however, is far from ideal for this purpose: as is well known, it is both low-level and unsafe. This paper describes Spot, a language derived from C for programming space flight systems. Spot aims to maintain compatibility with existing C code while improving the language and supporting verification with the SPIN model checker. The major features of Spot include actor-based concurrency, distributed state with message passing and transactional updates, and annotations for testing and verification. Spot also supports domain-specific annotations for managing spacecraft state, e.g., communicating telemetry information to the ground. We describe the motivation and design rationale for Spot, give an overview of the design, provide examples of Spot's capabilities, and discuss the current status of the implementation.

validation

Contextualizing Air Traffic Management Conversations using Natural Language Understanding

Efficient management of air traffic and mitigation of delays depend on extracting actionable information from unstructured data, such as dialogues from the Federal Aviation Administration’s (FAA’s) Air Traffic Control System Command Center (ATCSCC) telecons. This study presents a pipeline utilizing Natural Language Processing (NLP) methods for Intent Classification (IC) and Slot Filling (SF) to identify and extract Traffic Management Initiatives (TMIs) from aviation-specific dialogues. We leveraged DeBERTa, a pre-trained transformer model, and fine-tuned it to the nuances of the aviation domain. Despite challenges posed by annotation complexities, the IC model achieved promising results with a weighted average F1-score of 0.81. Our results are close to those of human annotators, which demonstrates the model’s strong alignment with human-level performance. The SF model also showed strong performance, achieving a weighted F1-score of 0.97, which demonstrates its effectiveness in accurately predicting key slots. Our analysis revealed limitations in handling less frequent intents and slot labels due to data sparsity, motivating future efforts to adopt joint IC-SF modeling and data augmentation strategies. This research highlights the potential of domain-specific NLP to streamline decision-making in the aviation industry and improve the management of TMIs.

Air Traffic Control Management

Quantity, Revisited: An Object-Oriented Reusable Class

"Quantity", a prototype implementation of an object-oriented class, was developed for two reasons: to help engineers and scientists manipulate the many types of quantities encountered during routine analysis, and to create a reusable software component to for large domain-specific applications. From being used as a stand-alone application to being incorporated into an existing computational mechanics toolkit, "Quantity" appears to be a useful and powerful object. "Quantity" has been designed to maintain the full engineering meaning of values with respect to units and coordinate systems. A value is a scalar, vector, tensor, or matrix, each of which is composed of Value Components, each of which may be an integer, floating point number, fuzzy number, etc., and its associated physical unit. Operations such as coordinate transformation and arithmetic operations are handled by member functions of "Quantity". The prototype has successfully tested such characteristics as maintaining a numeric value, an associated unit, and an annotation. In this paper we further explore the design of "Quantity", with particular attention to coordinate systems.

Funston, Monica Gayle