Raul Alejandro Vargas-Acosta

arXiv2026

SensorWF: A FAIR Generalizable Workflow Framework for Scientific Time-Series Analysis

Scientific sensor data is foundational across disciplines including spacecraft engineering, clinical medicine, and atmospheric science. In each context, pipelines are constructed to ingest raw archives, assess data quality, perform feature engineering and semantic annotation, and record provenance. However, these pipelines are often implemented as monolithic, domain-specific scripts with implicit assumptions and limited reusability across fields. This work introduces SensorWF, a FAIR-annotated workflow framework for generalizable scientific time-series analysis. The framework features a five-module reusable core (M1-M5) with typed input/output contracts. A domain adapter pattern isolates all domain-specific logic within M1, enabling modules M2-M5 to operate identically across disciplines. Domain assumptions are encoded in a machine-readable component registry, enabling reuse across sensor domains without modifying the analytical core. SensorWF also generates runtime PROV-O/ProvONE provenance traces with SHA-256 checksums for all file-path entities and emits SSN/SOSA-aligned OWL ontologies as primary outputs. To assess generalizability, SensorWF is instantiated in three distinct scientific domains: spacecraft telemetry, ambulatory ECG, and atmospheric climate, with synthetic fault injection and multi-detector anomaly detection demonstrated as use-case extensions. Results show that a single codebase, parameterized solely through M1 adapters of approximately 170-500 lines each, supports analytical pipelines across domains with varying sampling rates, channel counts, and fault taxonomies. All code, the component registry, ontology artifacts, and datasets are made available as an open scientific object. Our codebase is publicly available at https://purl.archive.org/sensor-wf.

cs.DC↗

arXiv2026

Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance

Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO). However, these events have been growing in frequency as orbital congestion worsens with the launch of megaconstellations. Consequently, conjunction alerts and collision risks are becoming increasingly common. Current practices, which are commonly manual or rule-based, have difficulty scaling to these worsening dynamic environments. To address this intensifying situation, we propose a reinforcement-learning policy for autonomous collision avoidance, trained via Proximal Policy Optimization (PPO) along with an open-source, high-fidelity astrodynamics simulator for training and evaluation. In 1,000 deterministic GEO episodes, our agent achieves a 97.5% collision avoidance success rate, outperforming traditional controllers such as a rule-based baseline (20.7% success) and an impulsive delta-v planner baseline (27.5% success). To achieve these results, we designed a simulator to train and evaluate our agent, using real-world and simulated debris. We simulate Newtonian two-body dynamics using Sun/Moon third-body perturbations, fuel-dependent thrust, and configurable debris fields. The agent is trained with curriculum learning and shaped rewards oriented toward encouraging survival, adequate projected miss distance, and delta-v conservation. Finally, our evaluation consisted of a fully deterministic pipeline, including shared seeds, per-episode logs, and telemetry exports. Our work is a publicly available framework at https://purl.org/sat-trajectory-avoidance

cs.LG↗