Laser satellite communications are increasingly being adopted throughout the space sector, with large-scale constellations relying on optical links to provide high-data-rate and secure communications. A key challenge in optical communications is the acquisition phase, during which two optical communication terminals (OCTs) must establish a bidirectional link despite the presence of pointing uncertainties. Reducing these uncertainties can significantly improve acquisition performance and reduce the associated search burden.

This thesis investigates an in-orbit calibration strategy for predicting OCT pointing errors using sensors and telemetry already available within the terminal. A review of existing calibration approaches identified a research gap in the use of OCT-internal measurements for the continuous estimation and prediction of pointing errors throughout the mission lifetime. To address this gap, a mission-lifetime calibration strategy was developed and evaluated using a dedicated simulation and development framework.

The dominant pointing-error contributors considered in this work are mechanical alignment errors, thermal distortions, satellite attitude determination and control system (ADCS) errors, and counter-satellite propagation errors. It is shown that a significant portion of the resulting pointing error can be predicted using operational parameters such as time, temperature, and gimbal orientation. An ordinary least squares regression framework was developed using time, temperature, azimuth, and elevation as inputs, and pointing errors in the terminal frame as outputs. The calibration model is continually updated using data collected during operational tracking phases, enabling the prediction of pointing errors under a wide range of mission conditions.

Simulation results demonstrate that the proposed approach can substantially reduce residual pointing errors and generalise across multiple mission scenarios and link geometries. Furthermore, calibration performance improves as calibration data are accumulated throughout the mission lifetime. The results also show that acquisition performance can be enhanced through informed acquisition scheduling, whereby links are established during periods of favourable operating conditions and reduced predicted pointing errors.

Overall, this work demonstrates that mission-lifetime in-orbit calibration can reduce pointing uncertainty, improve acquisition performance, and increase the operational robustness of optical communication terminals without requiring additional dedicated calibration hardware. ... Read more

The Eco-design for Sustainable Products Regulation (ESPR) mandates Digital Product Passports (DPPs) to enhance product traceability and enable circular value chains. While DPPs are expected to improve material transparency and compliance, their implementation also impacts value exchanges across industrial ecosystems. This research-in-progress paper examines how DPPs reconfigure information flows and value exchanges within the green steel transition, reshaping business ecosystems and generating tensions. Using value modelling of the Groeien met Groen Staal (GGS) ecosystem, the study identifies inter-actor tensions and develops a preliminary taxonomy across three dimensions: transparency versus control, coordination versus autonomy, and value creation versus value capture. The taxonomy provides a structured lens for analysing how digital infrastructures redistribute information, governance, and value in circular ecosystems. Future empirical iterations will validate and refine the taxonomy through stakeholder engagement within the GGS consortium. ... Read more

Electroencephalography Source Imaging (ESI) reconstructs cortical activity from scalp measurements. Current methods face three distinct bottlenecks: they require externally computed leadfields, struggle to generalize across varying electrode montages, and often produce deterministic point estimates that ignore spatial uncertainty. We introduce a geometry-conditioned architecture that overcomes these limitations by processing continuous 3D electrode coordinates. The imager predicts an internal leadfield to bypass external forward models at inference, while an autoregressive decoder samples alternative sets of active parcels to model spatial ambiguity. On matched 256-channel synthetic data, our deterministic estimate reduces median localization error by 13.4% to 9.7 mm and increases AUPRC from 0.749 to 0.787. This mapping successfully transfers to real clinical EEG featuring known intracranial stimulation sites. The autoregressive architecture reduces median peak-to-site distance by 15.5% to 22.3 mm relative to the best-performing baseline. Furthermore, training the imager jointly across four standard layouts enables genuine zero-shot transfer to unseen 21-, 76-, and 256-channel montages. On these unseen configurations, the median localization error ranges from 10.4 to 16.5 mm, remaining comparable to the 10.6 mm error achieved on the training layouts. ... Read more

The integration of massive offshore wind into hybrid AC-HVDC grids demands robust DC voltage regulation, yet conventional fixed-gain droop controllers struggle under severe stochastic volatility. This paper bridges the gap between system-level dispatch and converter-level control by proposing a Stochastic Optimal Power Flow (SOPF)-based adaptive droop framework. Rather than relying on heuristic or reactive tuning, wind forecast uncertainty is modeled using a zone-wise Beta distribution that accurately captures the heteroscedastic nature of wind errors across low, mid, and high-power regimes. By leveraging Polynomial Chaos Expansion (PCE) within a chance-constrained SOPF, the system’s stochastic states are formulated analytically. Crucially, the adaptive droop gain is extracted directly from the first-order PCE coefficients through a sensitivity analysis, transferring the probabilistic voltage-security information of the stochastic dispatch to the local converter control. Validation on a 4-terminal AC-HVDC system demonstrates that scenario-adaptive gains significantly outperform standard fixed-coefficient approaches, effectively minimizing active-power tracking errors during extreme wind disturbances. ... Read more

The transition from point-to-point HVDC links to multiterminal HVDC (MT-HVDC) grids increases the need to understand how the interruption of DC current shapes transient dielectric stress on connected cable systems. This paper develops a compact parameterization of the DC circuit breaker–cable interface in a reduced 525 kV MT-HVDC grid. The interruption process is characterized by transient interruption voltage (TIV), fault neutralization time, fault current limiting reactor (FCLR) value, and source-side condition. Kirchhoff-based circuit relations interpret the dominant mechanisms, while EMT simulations with a frequency-dependent cable model quantify the healthy-cable voltage response. A 54-case screening study shows that the highest peak stress occurs for high TIV, long tf, low FCLR, and source-supported operation. Several cases exceed the DC long transient overvoltage (DC-LTrOV) reference level, showing that TIV reduction or source removal alone is insufficient to mitigate the associated cable stresses. The results provide traceable input for insulation coordination and assessment of interruption-driven cable stress exposure. ... Read more