Who’s responsible for these Blues?: Reflecting on the murder of Armstrong Todd in Bebe Moore Campbell\u27s YOUR BLUES AIN\u27T LIKE MINE

This article is featured in the journal Tapestries: Interwoven voices of local and global identities, volume 4

Airplane Pitch Response to Rapid Configuration Change: Flight Test and Safety Assessment

This paper examines airplane response to rapid flap extension on seven general aviation airplanes. The scenario involves a pilot flying in the traffic pattern becoming distracted, abruptly extending flaps while looking outside the airplane, and failing to notice airspeed and pitch-attitude changes. The airplanes tested reached pitch forces of up to 36 lbf, meeting FAA requirements but exceeding the capability of 55% of the population. Flight data showed a pitch-up to more than 30˚ in 5 s after flap extension, causing airspeed to drop below stall speed for four of the airplanes. At traffic pattern altitudes, stalling an airplane can be fatal. The NTSB lists over 1000 accidents caused by loss of control in the traffic pattern between 1982 and 2017. As general aviation airplanes do not carry flight data recorders, it is unknown how many of those accidents may have involved stalls caused by uncommanded response after flap extension. From the data gathered in flight, it seems possible some were. To improve safety, flight training should prepare students to anticipate rapid pitch changes during flap extension and retraction. In addition, airplane developers could interconnect flaps with the elevator, reduce horizontal tail size, or use a T-tail. The FAA should consider reducing the maximum pitch stick and wheel forces in 14 CFR §23.143 to 20 lbf or less

Real-Time Satellite Component Recognition with YOLO-V5

With the increasing risk of collisions with space debris and the growing interest in on-orbit servicing, the ability to autonomously capture non-cooperative, tumbling target objects remains an unresolved challenge. To accomplish this task, characterizing and classifying satellite components is critical to the success of the mission. This paper focuses on using machine vision by a small satellite to perform image classification based on locating and identifying satellite components such as satellite bodies, solar panels or antennas. The classification and component detection approach is based on “You Only Look Once” (YOLO) V5, which uses Neural Networks to identify the satellite components. The training dataset includes images of real and virtual satellites and additional preprocessed images to increase the effectiveness of the algorithm. The weights obtained from the algorithm are then used in a spacecraft motion dynamics and orbital lighting simulator to test classification and detection performance. Each test case entails a different approach path of the chaser satellite to the target satellite, a different attitude motion of the target satellite, and different lighting conditions to mimic that of the Sun. Initial results indicate that once trained, the YOLO V5 approach is able to effectively process an input camera feed to solve satellite classification and component detection problems in real-time within the limitations of flight computers

Resource-constrained FPGA Design for Satellite Component Feature Extraction

The effective use of computer vision and machine learning for on-orbit applications has been hampered by limited computing capabilities, and therefore limited performance. While embedded systems utilizing ARM processors have been shown to meet acceptable but low performance standards, the recent availability of larger space-grade field programmable gate arrays (FPGAs) show potential to exceed the performance of microcomputer systems. This work proposes use of neural network-based object detection algorithm that can be deployed on a comparably resource-constrained FPGA to automatically detect components of non-cooperative, satellites on orbit. Hardware-in-the-loop experiments were performed on the ORION Maneuver Kinematics Simulator at Florida Tech to compare the performance of the new model deployed on a small, resource-constrained FPGA to an equivalent algorithm on a microcomputer system. Results show the FPGA implementation increases the throughput and decreases latency while maintaining comparable accuracy. These findings suggest future missions should consider deploying computer vision algorithms on space-grade FPGAs.Comment: 9 pages, 7 figures, 4 tables, Accepted at IEEE Aerospace Conference 202

SPH Simulations of Direct Impact Accretion in the Ultracompact AM CVn Binaries

The ultracompact binary systems V407 Vul (RX J1914.4+2456) and HM Cnc (RX J0806.3+1527) - a two-member subclass of the AM CVn stars - continue to pique interest because they defy unambiguous classification. Three proposed models remain viable at this time, but none of the three is significantly more compelling than the remaining two, and all three can satisfy the observational constraints if parameters in the models are tuned. One of the three proposed models is the direct impact model of Marsh & Steeghs (2002), in which the accretion stream impacts the surface of a rapidly-rotating primary white dwarf directly but at a near-glancing angle. One requirement of this model is that the accretion stream have a high enough density to advect its specific kinetic energy below the photosphere for progressively more-thermalized emission downstream, a constraint that requires an accretion spot size of roughly 1.2x10^5 km^2 or smaller. Having at hand a smoothed particle hydrodynamics code optimized for cataclysmic variable accretion disk simulations, it was relatively straightforward for us to adapt it to calculate the footprint of the accretion stream at the nominal radius of the primary white dwarf, and thus to test this constraint of the direct impact model. We find that the mass flux at the impact spot can be approximated by a bivariate Gaussian with standard deviation \sigma_{\phi} = 164 km in the orbital plane and \sigma_{\theta} = 23 km in the perpendicular direction. The area of the the 2\sigma ellipse into which 86% of the mass flux occurs is roughly 47,400 km^2, or roughly half the size estimated by Marsh & Steeghs (2002). We discuss the necessary parameters of a simple model of the luminosity distribution in the post-impact emission region.Comment: 24 pages, 5 figures, Accepted for publication in Ap

Comparison of Tracking-By-Detection Algorithms for Real-Time Satellite Component Tracking

With space becoming more and more crowded, there is a growing demand for increasing satellite lifetimes and performing on-orbit servicing (OOS) at a scale that calls for autonomous missions. Many such missions would require chaser satellites to autonomously execute safe and effective flightpath to dock with a non-cooperative target satellite on orbit. Performing this autonomously requires the chaser to be aware of hazards to route around and safe capture points through time, i.e., by first identifying and tracking key components of the target satellite. State-of-the-art object detection algorithms are effective at detecting such objects on a frame-by-frame basis. However, implementing them on a real-time video feed often results in poor performance at tracking objects over time, making errors which could be easily corrected by rejecting non-physical predictions or by exploiting temporal patterns. On the other hand, dedicated object tracking algorithms can be far too computationally expensive for spaceflight computers. Considering this, the paradigm of tracking-by-detection works by incorporating patterns of prior-frame detections and the corresponding physics in tandem with a base object detector. This paper focuses on comparing the performance of object tracking-by-detection algorithms with a YOLOv8 base object detector: namely, BoTSORT and ByteTrack. These algorithms are hardware-in-the-loop tested for autonomous spacecraft component detection for a simulated tumbling target satellite. This will emulate mission conditions, including motion and lighting, with a focus on operating under spaceflight computational and power limitations, providing an experimental comparison of performance. Results demonstrate lightweight tracking-by-detection can improve the reliability of autonomous vision-based navigation

Autonomous Rendezvous with Non-cooperative Target Objects with Swarm Chasers and Observers

Space debris is on the rise due to the increasing demand for spacecraft for com-munication, navigation, and other applications. The Space Surveillance Network (SSN) tracks over 27,000 large pieces of debris and estimates the number of small, un-trackable fragments at over 1,00,000. To control the growth of debris, the for-mation of further debris must be reduced. Some solutions include deorbiting larger non-cooperative resident space objects (RSOs) or servicing satellites in or-bit. Both require rendezvous with RSOs, and the scale of the problem calls for autonomous missions. This paper introduces the Multipurpose Autonomous Ren-dezvous Vision-Integrated Navigation system (MARVIN) developed and tested at the ORION Facility at Florida Institution of Technology. MARVIN consists of two sub-systems: a machine vision-aided navigation system and an artificial po-tential field (APF) guidance algorithm which work together to command a swarm of chasers to safely rendezvous with the RSO. We present the MARVIN architec-ture and hardware-in-the-loop experiments demonstrating autonomous, collabo-rative swarm satellite operations successfully guiding three drones to rendezvous with a physical mockup of a non-cooperative satellite in motion.Comment: Presented at AAS/AIAA Spaceflight Mechanics Meeting 2023, 17 pages, 9 figures, 3 table

Performance Study of YOLOv5 and Faster R-CNN for Autonomous Navigation around Non-Cooperative Targets

Autonomous navigation and path-planning around non-cooperative space objects is an enabling technology for on-orbit servicing and space debris removal systems. The navigation task includes the determination of target object motion, the identification of target object features suitable for grasping, and the identification of collision hazards and other keep-out zones. Given this knowledge, chaser spacecraft can be guided towards capture locations without damaging the target object or without unduly the operations of a servicing target by covering up solar arrays or communication antennas. One way to autonomously achieve target identification, characterization and feature recognition is by use of artificial intelligence algorithms. This paper discusses how the combination of cameras and machine learning algorithms can achieve the relative navigation task. The performance of two deep learning-based object detection algorithms, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLOv5), is tested using experimental data obtained in formation flight simulations in the ORION Lab at Florida Institute of Technology. The simulation scenarios vary the yaw motion of the target object, the chaser approach trajectory, and the lighting conditions in order to test the algorithms in a wide range of realistic and performance limiting situations. The data analyzed include the mean average precision metrics in order to compare the performance of the object detectors. The paper discusses the path to implementing the feature recognition algorithms and towards integrating them into the spacecraft Guidance Navigation and Control system.Comment: 12 pages, 10 figures, 9 tables, IEEE Aerospace Conference 202

Individual variability in cardiac biomarker release after 30 min of high-intensity rowing in elite and amateur athletes

This study had two objectives: (i) to examine individual variation in the pattern of cardiac troponin I (cTnI) and N-terminal pro-brain natriuretic peptide (NT-proBNP) release in response to high-intensity rowing exercise, and (ii) to establish whether individual heterogeneity in biomarker appearance was influenced by athletic status (elite vs. amateur). We examined cTnI and NT-proBNP in 18 elite and 14 amateur rowers before and 5 min, 1, 3, 6, 12, and 24 h after a 30-min maximal rowing test. Compared with pre-exercise levels, peak postexercise cTnI (pre: 0.014 ± 0.030 μg·L–1; peak post: 0.058 ± 0.091 μg·L–1; p = 0.000) and NT-proBNP (pre: 15 ± 11 ng·L–1; peak post: 31 ± 19 ng·L–1; p = 0.000) were elevated. Substantial individual heterogeneity in peak and time-course data was noted for cTnI. Peak cTnI exceeded the upper reference limit (URL) in 9 elite and 3 amateur rowers. No rower exceeded the URL for NT-proBNP. Elite rowers had higher baseline (0.019 ± 0.038 vs. 0.008 ± 0.015 μg·L–1; p = 0.003) and peak postexercise cTnI (0.080 ± 0.115 vs. 0.030 ± 0.029 μg·L–1; p = 0.022) than amateur rowers, but the change with exercise was similar between groups. There were no significant differences in baseline and peak postexercise NT-proBNP between groups. In summary, marked individuality in the cTnI response to a short but high-intensity rowing bout was observed. Athletic status did not seem to affect the change in cardiac biomarkers in response to high-intensity exercise