Synthetic Data for Semantic Image Segmentation of Imagery of Unmanned Spacecraft

Images of spacecraft photographed from other spacecraft operating in outer space are difficult to come by, especially at a scale typically required for deep learning tasks. Semantic image segmentation, object detection and localization, and pose estimation are well researched areas with powerful results for many applications, and would be very useful in autonomous spacecraft operation and rendezvous. However, recent studies show that these strong results in broad and common domains may generalize poorly even to specific industrial applications on earth. To address this, we propose a method for generating synthetic image data that are labelled for semantic segmentation, generalizable to other tasks, and provide a prototype synthetic image dataset consisting of 2D monocular images of unmanned spacecraft, in order to enable further research in the area of autonomous spacecraft rendezvous. We also present a strong benchmark result (S{\o}rensen-Dice coefficient 0.8723) on these synthetic data, suggesting that it is feasible to train well-performing image segmentation models for this task, especially if the target spacecraft and its configuration are known.Comment: 7 pages, 4 figures, conditionally accepted to 2023 IEEE Aerospace Conferenc

ZDNS: A Fast DNS Toolkit for Internet Measurement

Active DNS measurement is fundamental to understanding and improving the DNS ecosystem. However, the absence of an extensible, high-performance, and easy-to-use DNS toolkit has limited both the reproducibility and coverage of DNS research. In this paper, we introduce ZDNS, a modular and open-source active DNS measurement framework optimized for large-scale research studies of DNS on the public Internet. We describe ZDNS' architecture, evaluate its performance, and present two case studies that highlight how the tool can be used to shed light on the operational complexities of DNS. We hope that ZDNS will enable researchers to better -- and in a more reproducible manner -- understand Internet behavior.Comment: Proceedings of the 22nd ACM Internet Measurement Conference. 202

Towards energy-proportional anomaly detection in the smart grid

Phasor Measurement Unit (PMU) deployment is increasing throughout national power grids in an effort to improve operator situational awareness of rapid oscillations and other fluctuations that could indicate a future disruption of service. However, the quantity of data produced by PMU deployment makes real-time analysis extremely challenging, causing grid designers to invest in large centralized analysis systems that consume significant amounts of energy. In this paper, we argue for a more energy-proportional approach to anomaly detection, and advocate for a decentralized, heterogeneous architecture to keep computational load at acceptable levels for lower-energy chipsets. Our results demonstrate how anomalies can be detected at real-time speeds using single board computers for on-line analysis, and in minutes when running off-line historical analysis using a multicore server running Apache Spark

Integrating historical and real-time anomaly detection to create a more resilient smart grid architecture: poster

Ensuring the security of the power grid is critical for national interests and necessitates new ways to detect power anomalies and respond to potential failures. In this poster, we describe our efforts to develop and optimize analysis methodologies for a 1000 : 1 scale emulated smart grid at the United States Military Academy [2]. In contrast to previous work [3, 4], we explore historical analysis using Apache Spark [5] and integrate a Raspberry Pi into our testbed for real-time anomaly detection. We also implement a software controlled physical event and fault generator to induce and measure faults. Figure 1 gives an overview of our system