instrumentation and calibration

In long-term IoT sensor deployments, aging-induced drift severely degrades data quality, while limited access to ground-truth measurements exacerbates calibration challenges. To address this, we propose a unified probabilistic drift correction and uncertainty-driven calibration scheduling framework. First, we model sensor dynamic response using Gaussian process regression, enabling explicit quantification of measurement uncertainty. Second, we formulate an adaptive scheduling optimization framework that uses real-time uncertainty as feedback, jointly optimizing calibration accuracy and resource constraints. Unlike conventional methods reliant on abundant ground-truth labels, our approach operates effectively under sparse supervision. Evaluated on dissolved oxygen sensors in field deployments, the drift correction alone reduces mean squared error by over 20% on average; when integrated with optimal calibration scheduling, the reduction reaches up to 90%. This significantly enhances the reliability and sustainability of long-term environmental monitoring.

Earth system science has long suffered from heterogeneous and decentralized sensor metadata standards, hindering trustworthy environmental data analysis and cross-domain reuse. To address this, we propose the first FAIR-compliant, modular sensor metadata modeling framework and develop an open-source Sensor Management System (SMS) that comprehensively covers the full sensor lifecycle—including devices, platforms, configurations, sites, and dynamic operational history. SMS integrates semantic modeling with established open standards (ISO 19115, Schema.org), persistent identifier (PID) registration, and controlled vocabularies, and is implemented as a microservice-based architecture exposing RESTful APIs. Its key innovation lies in enabling structured, traceable, and interoperable metadata across institutions. Deployed across multiple national Earth observation networks, SMS has significantly improved metadata consistency, long-term sustainability, and reuse efficiency.

High-precision online estimation algorithms for robotics are highly sensitive to sensor timestamp accuracy; however, existing synchronization solutions struggle to simultaneously achieve real-time operation, low cost, and high temporal precision. To address this, we propose a real-time, trigger-based time synchronization system built on commodity hardware. Our approach employs a hardware-triggered mechanism to jointly schedule heterogeneous sensors operating at different frequencies, and integrates an enhanced clock synchronization protocol with nanosecond-resolution timestamping to ensure precise coordination between sensors and the onboard computer. Crucially, the system eliminates reliance on expensive dedicated timing hardware, thereby substantially mitigating the impact of timing errors on online estimation. Experimental evaluation on a physical robot platform demonstrates sub-microsecond synchronization accuracy, along with significant improvements in both estimation robustness and real-time performance.

Low-cost climate sensors suffer from poor accuracy, frequent calibration requirements, and limited adaptability. Method: This study proposes an end-to-end machine learning calibration framework tailored for agile hardware—featuring a rapidly reconfigurable embedded sensing system supporting modular multi-pollutant integration. A field co-calibration architecture was deployed at the Cape Point Global Atmospheric Watch station in South Africa, integrating reference sensor data and applying random forest regression for in-situ CO₂ calibration. Results: The proposed method significantly outperforms conventional calibration strategies, reducing error by over 40%, enabling low-cost sensors to achieve near-reference-grade performance, and extending manual calibration intervals by more than threefold. This work represents the first validation in the Southern Hemisphere of a hardware–algorithm co-driven calibration paradigm for low-cost environmental sensors, establishing a reusable technical pathway for large-scale, long-term, and highly robust environmental monitoring networks.

Current satellite telemetry anomaly detection suffers from a lack of interpretable and reproducible multivariate time-series benchmarks, hindering practical machine learning deployment. To address this, we introduce the first publicly available, spacecraft-oriented multivariate time-series anomaly detection benchmark, incorporating real-world, expert-annotated telemetry data from two ESA missions. We propose a dedicated benchmarking framework for spacecraft telemetry and a hierarchical evaluation methodology grounded in operational semantics—better aligned with mission control requirements. We systematically evaluate state-of-the-art supervised and unsupervised algorithms, exposing critical performance limitations under realistic conditions. All benchmark datasets, source code, and evaluation tools are fully open-sourced. This work advances methodological standardization and reproducibility in anomaly detection research and establishes foundational infrastructure for intelligent satellite operations and maintenance.

This study addresses the limitations of traditional fixed-interval calibration, which neglects operational condition–induced variations in sensor drift rates and consequently risks either excessive resource consumption or non-compliance. The work reframes calibration scheduling as a predictive maintenance problem, formally casting it as a joint optimization task integrating time-series forecasting and risk-aware decision-making. A compact Transformer architecture is proposed, coupled with quantile regression to predict Time-to-Drift (TTD) and enable an uncertainty-aware calibration policy that enhances robustness. Evaluated on a modified NASA C-MAPSS FD001 dataset, the method achieves state-of-the-art point prediction accuracy and significantly reduces violation rates under high-noise conditions, outperforming both fixed-interval and reactive strategies in terms of calibration cost efficiency.

Conventional geometric calibration of robots relies on expensive, bulky equipment—such as laser trackers—limiting practical deployment in industrial settings. Method: This paper proposes a low-cost, high-robustness calibration method based on a custom-designed calibration plate. By embedding measurement points with known geometric relationships on the plate, a system error model is constructed; key kinematic parameters are then identified via least-squares estimation integrated with constrained optimization. Contribution/Results: The core innovation lies in a miniaturized, mechanically stable calibration plate design that significantly reduces hardware cost and on-site installation complexity. Experimental validation on gantry-type robotic machine tools demonstrates calibration accuracy comparable to that of laser trackers—achieving positional errors below 0.1 mm—while exhibiting strong cross-platform adaptability. This work establishes a practical, rapid, and precise geometric calibration paradigm suitable for industrial environments.

To address the inefficiency of conventional high-resolution SAR ADC linearity testing—reliant on dense sampling and offline post-processing—this paper proposes a real-time closed-loop adaptive testing methodology. Central to the approach is uncertainty-guided measurement sequence planning, integrated with extended Kalman filtering (EKF) for online modeling and parameter estimation; measurement points are dynamically selected to maximize information gain, thereby drastically reducing redundant sampling. The method enables rapid, accurate identification of non-ideal parameters—such as capacitor mismatch—without extensive data acquisition or offline computation. Experimental results demonstrate that, compared to traditional histogram-based and sine-fitting methods, the proposed technique reduces test time by over an order of magnitude and significantly lowers computational overhead, while exhibiting strong robustness and compatibility with high-volume production integration.

This work addresses the critical challenge of jointly designing sensor query rates and noise covariance under resource and cost constraints to meet prescribed trajectory estimation accuracy requirements. It presents the first formalization of this problem as a unified optimization model, leveraging semidefinite programming (SDP) within the Kalman filter error covariance framework to simultaneously optimize measurement scheduling and noise parameters. The proposed approach efficiently determines whether a given accuracy target is achievable and, when feasible, synthesizes a corresponding implementation strategy. Experimental validation demonstrates that the computed sensor configurations consistently attain the desired accuracy in both simulated and real-world scenarios, while also reliably identifying infeasible accuracy demands.

Stewart–Gough platforms suffer from insufficient pose accuracy in micro- and nanoscale precision applications, primarily due to kinematic modeling errors, structural deformations, and environmental disturbances; existing calibration methods—largely conducted under no-load conditions—fail to meet sub-micrometer 3D motion control requirements. This paper presents a systematic review of micro/nano-precision calibration techniques, centered on an inverse-kinematics-based closed-loop self-calibration framework. The proposed approach innovatively integrates: (i) multi-source error joint modeling (encompassing geometric, elastic, and thermally induced errors), (ii) external high-accuracy metrology instruments (e.g., laser interferometers, optical trackers) for auxiliary calibration, and (iii) embedded-sensor-enhanced online compensation. Experimental results demonstrate that this integrated strategy improves positioning repeatability to the hundreds-of-nanometers level, substantially enhancing the engineering viability of parallel robots in photolithography and ultra-precision assembly, while establishing a theoretical foundation and technical pathway for autonomous calibration paradigms.