Matthew C. Fox

Matthew C. Fox, Gavin M. Baker, David O. Williams Rogers et al.

The escalating accumulation of orbital debris poses a critical threat to future space operations. Space-based lasers leveraging laser ablation have emerged as a promising approach for mitigating debris proliferation and preserving the orbital environment. Current literature, however, treats space-based laser debris remediation as a deterministic problem, assuming that momentum transfer and the resulting debris perturbations are precisely known. In reality, laser-to-debris engagement outcomes are inherently stochastic due to partially known debris characteristics. Compounding this challenge, estimating critical laser-matter parameters in situ, such as the momentum coupling coefficient, requires ablation that consequently perturbs the debris trajectory. This establishes a coupled ablation-and-estimation problem in which the laser platform and target debris encounter geometry influences remediation effectiveness and estimation accuracy. To address this problem, we present a joint ablation-and-estimation methodology that provides insights into the driving factors that make different encounter geometries improve or degrade overall remediation and estimation performance. Results across multiple coplanar and out-of-plane encounter geometries demonstrate how periapsis-lowering capacity, linear system observability, and nonlinear estimation performance evolve as laser parameters and relative orbit geometry vary. By identifying the key drivers behind these metrics, this study highlights critical considerations for the safe and effective operation of space-based lasers under uncertainty.

David O. Williams Rogers, Matthew C. Fox, Paul R. Stysley et al.

The significant expansion of the orbital debris population poses a serious threat to the safety and sustainability of space operations. This paper investigates orbital debris remediation through a constellation of collaborative space-based lasers, leveraging the principle of momentum transfer onto debris via laser ablation. A novel delta-v vector analysis framework quantifies the cumulative effects of multiple concurrent laser-to-debris (L2D) engagements by utilizing the vector composition of the imparted delta-v vectors. The paper formulates the Concurrent Location-Scheduling Optimization Problem (CLSP) to optimize the placement of laser platforms and the scheduling of L2D engagements, aiming to maximize debris remediation capacity. Given the computational intractability of the CLSP, a decomposition strategy is employed, yielding two sequential subproblems: (1) determining optimal laser platform locations via the Maximal Covering Location Problem, and (2) scheduling L2D engagements using a novel integer linear programming approach to maximize debris remediation capacity. Computational experiments evaluate the efficacy of the proposed framework across diverse mission scenarios, demonstrating critical constellation functions such as collaborative and controlled nudging, deorbiting, and just-in-time collision avoidance. A sensitivity analysis further explores the impact of varying the number and distribution of laser platforms on debris remediation capacity, offering insights into optimizing the performance of space-based laser constellations.

Mengkun Chen, Yen-Tung Liu, Fadeel Sher Khan et al.

Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for virtual staining tasks and advancing the field of histological image analysis.