Seth Roffe

h-index2
2papers

2 Papers

42.0OSMay 20
Where Linux Breaks Under Radiation: A Cross-Architecture Kernel-Level Characterization of Proton-Induced Failures in COTS SoCs

Saad Memon, Rafal Graczyk, Tomasz Rajkowski et al.

Linux is increasingly deployed in Low Earth Orbit on commercial off the shelf systems on chip that were not designed for space radiation. Ionizing particles can trigger single event functional interrupts that crash the kernel without warning. Prior work mainly measured board level cross sections, leaving unclear which Linux subsystems fail and how a single upset propagates into an operating system wide failure across architectures, stress conditions, and irradiation conditions. We address this gap by subjecting three Linux platforms to proton irradiation in the 20 to 58 MeV range: a Raspberry Pi Zero 2W with a 40 nm planar ARM Cortex A53, an NXP i MX 8M Plus with a 14 nm FinFET ARM Cortex A53, and an OrangeCrab ECP5 FPGA hosting a VexRiscV RV32I soft core at 40 nm. Through kernel log forensics, we trace all 133 observed Linux failures, most of which have not been previously reported, to their originating kernel handlers. Failure profiles differ sharply across nodes. On the two 40 nm platforms, memory management and driver handlers account for 67 to 78% of events, while on the 14 nm SoC approximately 90% of failures funnel through a single eMMC storage path, comprising 56% filesystem failures and 34% driver failures. This shows that a SEFI susceptible peripheral can strongly dictate system reliability. The 14 nm SoC also shows roughly an order of magnitude lower Linux SEFI cross section, although irradiation geometry and DRAM exposure differences preclude isolating the contribution of process scaling. Reconstructed propagation chains show that faults can cascade through up to six kernel subsystems before terminal failure in severe events. Rather than motivating blanket redundancy, these results identify the kernel subsystem boundaries where radiation induced faults originate, enabling targeted mitigations for hardening COTS Linux systems for orbit.

CVDec 2, 2025
PyroFocus: A Deep Learning Approach to Real-Time Wildfire Detection in Multispectral Remote Sensing Imagery

Mark Moussa, Andre Williams, Seth Roffe et al.

Rapid and accurate wildfire detection is crucial for emergency response and environmental management. In airborne and spaceborne missions, real-time algorithms must distinguish between no fire, active fire, and post-fire conditions, and estimate fire intensity. Multispectral and hyperspectral thermal imagers provide rich spectral information, but high data dimensionality and limited onboard resources make real-time processing challenging. As wildfires increase in frequency and severity, the need for low-latency and computationally efficient onboard detection methods is critical. We present a systematic evaluation of multiple deep learning architectures, including custom Convolutional Neural Networks (CNNs) and Transformer-based models, for multi-class fire classification. We also introduce PyroFocus, a two-stage pipeline that performs fire classification followed by fire radiative power (FRP) regression or segmentation to reduce inference time and computational cost for onboard deployment. Using data from NASA's MODIS/ASTER Airborne Simulator (MASTER), which is similar to a next-generation fire detection sensor, we compare accuracy, inference latency, and resource efficiency. Experimental results show that the proposed two-stage pipeline achieves strong trade-offs between speed and accuracy, demonstrating significant potential for real-time edge deployment in future wildfire monitoring missions.