Lauro Ojeda

Boomless magnetic field measurements represent an enabling technology for CubeSat constellation missions targeting space physics phenomena. The TorqMag concept integrates magnetorquer actuation and search coil sensing within a single hardware unit, achieving dual functionality in a 1U volume by time-multiplexing torque rods between attitude control and AC magnetic field measurement modes. This paper addresses two critical Technology Readiness Level (TRL) challenges inhibiting operational deployment. First, ferromagnetic core remanence persisting after actuation introduces DC offsets 2-3 orders of magnitude above ambient AC signals, degrading sensor sensitivity. Systematic laboratory experiments characterize in-space degaussing sequences across modulation envelope (sinusoidal, ramp, exponential), peak amplitude (±50\% firing voltage), and cycling parameters (1-20 Hz carrier, 7-60 mHz modulation). Results demonstrate exponential decay modulation achieves order-of-magnitude residual field reduction (to <200 nT post-degauss) with minimal energy consumption (<60 mJ), while higher initial amplitudes consistently reduce remanence. Second, spacecraft-generated magnetic interference contaminates measurements across 100 Hz-2 kHz search coil regimes. Monte Carlo simulations evaluate the Wavelet-Adaptive Interference Cancellation for Underdetermined Platforms (WAIC-UP) algorithm using realistic noise sources including reaction wheels, pulse-width modulation (PWM) converters, and digital clocks. Findings reveal performance degradation (correlation < 0.6-0.8) when multiple high-frequency sources spectrally overlap due to constant-Q wavelet characteristics, establishing operational boundaries and motivating iterative and complementary mitigation strategies including adaptive high-resolution time-frequency decompositions and telemetry-based subtraction. Additional system elements—switching circuitry, coil-core design, and attitude control algorithms—are discussed at higher TRL maturity levels.

Abstract Glaucoma affects mobility measured in clinic. This work investigated associations of structural measures of glaucoma with at-home mobility, an indicator of real-world function. We recruited 38 participants with glaucoma and captured spatiotemporal gait characteristics in clinic and at home using inertial measurement units. Recent optical coherence tomography retinal nerve fiber layer (RNFL OCT) measurements – a structural severity indicator – were obtained from participants’ medical records. We performed two sets of regression analyses: In the first, each gait metric was a dependent variable and RNFL OCT an independent variable; in the second, within-subject differences between in-clinic and at-home gait were dependent variables. Thinner superior RNFL (more advanced glaucoma) in the better eye was associated with reduced step regularity (p = 0.04) and increased step duration variability (p = 0.03) in clinic. Thinner inferior RNFL in the worse eye was associated with slower gait (p = 0.05) and shorter steps (p = 0.03) at home. Additionally, thinner inferior RNFL in the worse eye was associated with slower gait (p = 0.01), shorter step length (p = 0.02), and longer step duration (p &amp;lt; 0.01) at home vs in-clinic. Our findings expand the common perceptions that a person’s better eye drives function in glaucoma and that mobility is most impacted by superior RNFL thinning. We find new associations of inferior RNFL thinning in the worse eye with slower gait and shorter steps at home and with greater differences between at-home and in-clinic gait. The finding that more glaucomatous worse eye may make someone susceptible to gait change in real-world environments has not been reported previously.

BACKGROUND: Effective balance rehabilitation requires training at an appropriate level of exercise intensity given an individual's needs and abilities. Typically balance intensity is assessed through in-clinic visual observation by physical therapists (PTs), which limits the ability to monitor and progress intensity during home-based components of training programs. The goal of this study was to train and evaluate machine learning models for estimating physical therapists' perceived balance exercise intensity using data from full-body wearable sensors to support the development of home-based training exercise dosage monitoring. METHODS: Balance exercise participants (n = 47) participated in a single-day balance training session where they were filmed performing static standing exercises at various levels of intensity. Kinematic data from 13 full-body wearable inertial measurement units (IMUs) and self-ratings of balance intensity were also collected. An additional cohort of PT participants (n = 42) was recruited to watch the videos of the balance exercise participants and provide ratings of balance intensity. The mean PT rating for each video was used as a ground truth (GT) label of balance intensity. We trained and evaluated Convolutional Neural Networks (CNN)-based models to predict balance intensity based on performance as captured through the IMUs. Model performance was evaluated by calculating the root-mean-square error (RMSE) of predications. A sensitivity analysis was also performed to assess the effect of the number of IMUs used on model performance. RESULTS: Models trained on orientation derived from all 13 IMUs achieved good predictive performance as indicated by a RMSE of 0.66 [0.62, 0.69], which was within the threshold defined by typical inter-rater variabilities between PTs (RMSE of 0.74 [0.72, 0.76]). Sensitivity analysis indicated that model performance stabilized at four sensors with the best performance corresponding to sensors placed on both thighs and the lower and upper back. CONCLUSIONS: Findings from this study indicated that balance intensity assessment can be achieved through wearable sensors and a CNN model, which could support the supervision and effectiveness of home-based balance rehabilitation.

Landmines have been extensively used in conflict zones as an indiscriminate weapon to control military movements, often remaining active long after hostilities have ended. Their presence poses a persistent danger to civilians, hindering post-war recovery efforts, causing injuries or death, and restricting access to essential land for agriculture and infrastructure. Unmanned aerial vehicles (UAV) equipped with magnetometers are commonly used to detect remnant hidden landmines but come with significant technical challenges due to magnetic field interference from UAV electronics such as motors. We propose the use of a frame-mounted UAV-borne two-magnetometer payload to perform a two-step automated interference removal and landmine detection analysis. The first step removes interference via the Wavelet-Adaptive Interference Cancellation for Underdetermined Platform (WAIC-UP) method designed for spaceflight magnetometers. The second method uses the Rapid Unsupervised Detection of Events (RUDE) algorithm to detect landmine signatures. This two-step WAIC-UP/RUDE approach with multiple magnetometers achieves high-fidelity ordinance detection at a low computational cost and simplifies the design of magnetic survey payloads. We validate the method through a Monte Carlo simulation of randomized landmine placements in a 10 x 10 m square grid and drone motor interference. Additionally, we assess the efficacy of the algorithm by varying the drone's altitude, examining its performance at different heights above the ground.

Abstract In situ magnetic field measurements are often difficult to obtain due to the presence of stray magnetic fields generated by spacecraft electrical subsystems. The conventional solution is to implement strict magnetic cleanliness requirements and place magnetometers on a deployable boom. However, this method is not always feasible on low‐cost platforms due to factors such as increased design complexity, increased cost, and volume limitations. To overcome these problems, we propose using the Quad‐Mag CubeSat magnetometer with an improved Underdetermined Blind Source Separation (UBSS) noise removal algorithm. The Quad‐Mag consists of four magnetometer sensors in a single CubeSat form‐factor card that allows distributed measurements of stray magnetic fields. The UBSS algorithm can remove stray magnetic fields without prior knowledge of the magnitude, orientation, or number of noise sources. UBSS is a two‐stage algorithm that identifies signals through cluster analysis and separates them through compressive sensing. We use UBSS with single‐source point detection to improve the identification of noise signals and iteratively‐weighted compressed sensing to separate noise signals from the ambient magnetic field. Using a mock CubeSat, we demonstrate in the lab that UBSS reduces four noise signals producing more than 100 nT of noise at each magnetometer to below the expected instrument resolution (5 nT at 65 Hz). Additionally, we show that the integrated Quad‐Mag and improved UBSS system works well for 1U, 2U, 3U, and 6U CubeSats in simulation. Our results show that the Quad‐Mag and UBSS noise cancellation package enables high‐fidelity magnetic field measurements from a CubeSat without a boom.