Embry–Riddle Aeronautical University

Abstract Foreshock ultralow frequency (ULF) waves are a major contributor to magnetospheric Pc3–4 waves (7–100 mHz), but their transmission through the magnetosheath is not well understood. Using 109 THEMIS traversals from the bow shock to the magnetopause, in conjunction with ground magnetometer (GMAG) measurements, we performed a statistical analysis of magnetosheath ULF waves and their relationship to ground‐based Pc3–4 waves. Our findings reveal that in quasi‐parallel regions, magnetic and dynamic pressure wave power are correlated with ground‐based magnetic wave power at periods of ∼30s, with correlation coefficients reaching up to ∼0.7. The correlation depends on the THEMIS spacecraft's position in the magnetosheath as well as the magnetic local time and latitude of the ground stations. In contrast, in quasi‐perpendicular regions, the correlation is weaker (up to ∼0.3) and increases with decreasing frequency. Additionally, in quasi‐perpendicular regions, wave power increases from the bow shock to the magnetopause, consistent with local excitation, whereas waves in quasi‐parallel regions are less compressive with power relatively stable, consistent with a foreshock origin. Our results suggest that foreshock‐originated waves in quasi‐parallel regions contribute more effectively to magnetospheric ULF waves than those in quasi‐perpendicular regions, through both magnetic field and dynamic pressure oscillations, with a preferential propagation from the dawnside magnetosheath to both sides of the magnetosphere.

As Machine Learning (ML) becomes ever more ubiquitous, it is critical to increase the rigor of design and testing. In traditional software, this is done using the Requirements Engineering (RE) process, but RE looks different for ML because it is data-centric. There are different Non-Functional Requirements (NFRs) and standard testing techniques do not apply. While there are standards for verifying NFRs in traditional software, there is no standard measurement for ML NFRs, e.g. how is a model verified to meet an explainability NFR? In traditional software, NFRs are decomposed into Functional Requirements (FRs), but without clear measurements for ML NFRs, their decomposition into FRs is nearly impossible. However, recently, research has shown that provenance can help improve model transparency and reproducibility. This work builds on such literature and suggests provenance as a lower-level NFR to connect high-level NFRs, e.g. explainability and transparency, and FRs, thereby enabling concrete model verification based on requirement specifications. This work examines types of ML provenance and their use in decomposing model NFRs into verifiable FRs, thereby better aligning ML development with RE and increasing the rigor of ML testing. This paper aggregates current literature on provenance for ML and provides a method of measurement for otherwise unquantifiable NFRs. This work also analyzes trends in how provenance can decompose various ML NFRs and future directions for the field.

Data transmission from superconducting digital electronics such as single flux quantum (SFQ) logic to semiconductor (CMOS) circuits is subject to bit errors due to, e.g., flux trapping, process parameter variations (PPV), and fabrication defects. In this paper, a lightweight hardware-efficient error-correction code encoder is designed and analyzed. Particularly, a Reed-Muller code RM(1,3) encoder is implemented with SFQ digital logic. The proposed RM(1,3) encoder converts a 4-bit message into an 8-bit codeword and can detect and correct up to 3- and 1-bit errors, respectively. This encoder circuit is designed using MIT-LL SFQ5ee process and SuperTools/ColdFlux RSFQ cell library. A simulation framework integrating JoSIM simulator and MATLAB script for automated data collection and analysis, is proposed to study the performance of RM(1,3) encoder. The proposed encoder improves the probability of having no bit errors by 6.7% as compared to an encoder-less design under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>20% PPV. With <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>15% and lower PPV, the proposed encoder could correct all errors with at least 99.1% probability. The impact of fabrication defects such as open circuit faults on the encoder circuit is also studied using the proposed framework.

Machine learning (ML) engineering increasingly incorporates principles from software and requirements engineering to improve development rigor; however, key non-functional requirements (NFRs) such as interpretability and explainability remain difficult to specify and verify using traditional requirements practices. Although prior work defines these qualities conceptually, their lack of measurable criteria prevents systematic verification. This paper presents a novel provenance-driven approach that decomposes ML interpretability and explainability NFRs into verifiable functional requirements (FRs) by leveraging model and data provenance to make model behavior transparent. The approach identifies the specific provenance artifacts required to validate each FR and demonstrates how their verification collectively establishes compliance with interpretability and explainability NFRs. The results show that ML provenance can operationalize otherwise abstract NFRs, transforming interpretability and explainability into quantifiable, testable properties and enabling more rigorous, requirements-based ML engineering.

The safe and efficient integration of small unmanned aircraft systems (sUAS) into the National Airspace System (NAS) requires a systems-based understanding of the interrelations among human, technological, and regulatory components. Existing Federal Aviation Administration (FAA) guidelines restrict most operations to visual line of sight (VLOS), which constrains operational scalability and underscores the need for system-level innovations supporting beyond-visual-line-of-sight (BVLOS) operations. This study adopted a socio-technical systems approach to evaluate how first-person view (FPV) technologies influence operator workload and situational awareness (SA), key human performance elements within the broader sUAS safety system. Participants meeting FAA Part 107 eligibility criteria were assigned to one of three visual configurations: (a) traditional VLOS, (b) FPV using a 21-inch monitor, or (c) FPV with immersive goggles. Workload was measured with the NASA Task Load Index (NASA-TLX), and Level 1 SA was assessed via post-task recall. ANOVA results revealed no statistically significant differences across visual conditions, indicating no evidence that FPV integration either increased cognitive load or impaired perceptual awareness compared to traditional methods. Complementary analysis of NASA’s Aviation Safety Reporting System (ASRS) identified SA as the most recurrent human-factor issue, suggesting system-level implications for human–machine interaction and training design. These findings contribute to the systemic understanding of human factors in UAS operations, supporting FPV’s potential as a viable subsystem for achieving safe and effective BVLOS integration within complex socio-technical aviation systems.

This work presents an adaptive Unscented Kalman Filter (UKF) navigation architecture for rendezvous and proximity operations (RPO) with the International Space Station (ISS). The filter dynamically adjusts process and measurement covariances using residual-based trust metrics, enabling resilient state estimation under thrust dispersion, initial injection offsets, and orbital perturbations including drag, J2, and solar radiation pressure. This Monte-Carlo campaign evaluates navigation robustness across varied maneuver-execution thresholds, revealing three distinct regimes of behavior: high-gain continuous correction, under-actuated sparse correction, and a mid-range operational corridor. Within this corridor, ∼100-400 maneuver events yield consistent 97-98% 3σ relative-navigation accuracy with ΔV tightly clustered at 0.324-0.329 km/s. The study identifies a scenario (~214 maneuvers) as the most realistic cold-gas-like operational configuration, balancing correction frequency, estimator stability, and fuel usage. These results demonstrate that adaptive UKF navigation can maintain performance across a wide control spectrum while exposing a tunable region suitable for autonomous ISS-class RPO.