The paper explores the accuracy of WiFi-Round Trip Timing (RTT) positioning in indoor environments. Filtering techniques are applied to WiFi-RTT positioning in indoor environments, enhanced by Residual Signal Strength Indicator (RSSI)-based outlier detection. A Genetic and Grid filter are compared with a Particle filter and single-epoch least-squares across a range of test scenarios. In static scenarios, 67% of trials had sub-metre accuracy and 90.5% had a root mean square error (RMSE) below 2 m. In Non-Line-of-Sight (NLOS) conditions, 38% of trials had sub-metre accuracy, whereas for environments with full Line-of-Sight (LOS) conditions, 95.2% of trials had sub-metre accuracy. In scenarios with motion, 22.2% of trials had sub-metre accuracy. RSSI-based outlier detection in NLOS conditions, provided an average improvement of 41.3% over no outlier detection across all algorithms in the static and 14% in the dynamic tests. The Genetic filter achieved a mean improvement of 49.2% in the static and 47% in the dynamic tests compared with least squares.

Given the pace of port digitalisation, this study provides a mapping of the smart port cybersecurity literature to clarify its intellectual structure and emerging research directions. Bibliographic records period 2010–2025 were retrieved from the Scopus and analysed Bibliometrix/Biblioshiny. The dataset comprises 460 publications from 344 sources, with an annual growth rate of 11.02% and an average of 9.97 citations per article, indicating a expanding research domain. The analysis examines publication trends, co-authorship and citation networks, and conceptual structures through bibliometric and text-mining techniques. Results show a sharp increase in publications after 2017, driven by the integration of the Internet of Things (IoT), artificial intelligence and automation in port systems. International collaboration is prominent, with research leadership concentrated in the USA, China, India and the UK. Conceptual analysis highlights network defence, intrusion detection and AI-based security, while revealing gaps at the intersection of governance, cyber–physical resilience and operational security in smart ports.

Adapting Barker’s ((2019). The Journal of Navigation, 72(3), 539–554) taxonomy of wayfinding behaviours – originally developed for man-made environments, paper and screen – we examined which behaviours are also found in the outdoors. In the analysis of the collected data from a questionnaire (n=401), we find that participants employ every category in Barker’s framework of social, semantic and spatial behaviours. Our respondents report the use of digital maps on a mobile phone as the most common behaviour, with following directional signs as the second most used. Furthermore, social wayfinding behaviours figure prominently and the participants express preferences for various information sources. We demonstrate similarities of behaviours across the different types of environments and we confirm the applicability of Barker’s taxonomy of wayfinding behaviours also in nature. Our study generates knowledge that potentially can make navigation simpler and more efficient through wayfinding design, and lead to heightened feeling of safety in the outdoors. Wayfinding behaviour studies, like this one, can serve as a bridge between human psychology and practical design.

Safe navigation of maritime autonomous surface ships (MASS) relies on two capabilities: path planning and collision avoidance. This review surveys classical algorithms and modern AI techniques for embedding the International Regulations for Preventing Collisions at Sea (COLREGs) into autonomous navigation. We organise prior work into three families—classical search/optimisation, real-time reactive methods, and learning-based approaches—and discuss their strengths and limitations with respect to rules compliance, computational cost, and onboard constraints. Building on these insights, we outline a large-language-model framework, Navigation-GPT, which couples reasoning-and-acting (ReAct) prompting with low-rank adaptation (LoRA). We further propose a three-phase deployment roadmap for MASS: core model integration, domain fine-tuning, and integrated operations. The paper concludes with open challenges and research directions toward reliable, explainable, and fully compliant MASS navigation.

Ship path planning represents a fundamental challenge in intelligent navigation, requiring careful balance between route optimality, safety in complex marine environments. To address the limitations of conventional A* algorithms, this paper proposes an improved multi-factor and multi-scale A* algorithm. The methodology begins with processing ENC data, where canny edge detection combined with adaptive thresholding constructs obstacle maps. A novel dual-layer multi-scale grid framework is established: They are used to rapid global path searching, and precise collision avoidance. The algorithm innovatively integrates a multi-factor function that simultaneously considers obstacle distribution, environment effects, navigation rules, and ship dynamic constraints, with adaptive weight adjustment optimizing the search process. Path refinement employs smoothing algorithms to significantly reduce waypoint numbers. Simulation experiments conducted in Dalian port demonstrate the algorithm’s superior performance: maintaining safe clearance even in obstacle-dense areas and using the shorter length. Experimental results confirm that generated paths better satisfy practical navigation requirements.

Magnetic AB stars are known to produce periodic radio pulses by the electron cyclotron maser emission (ECME) mechanism. Only 19 such stars, known as ‘Main-sequence Radio Pulse emitters’ (MRPs), are currently known. The majority of MRPs have been discovered through targeted observation campaigns that involve carefully selecting a sample of stars that are likely to produce ECME and which can be detected by a given telescope within reasonable amount of time. These selection criteria inadvertently introduce bias in the resulting sample of MRPs, which affects subsequent investigation of the relation between ECME properties and stellar magnetospheric parameters. The alternative is to use all-sky surveys. Until now, MRP candidates obtained from surveys were identified based on their high circular polarisation ($\gtrsim 30\%$). In this paper, we introduce a complementary strategy, which does not require polarisation information. Using multi-epoch data from the Australian SKA Pathfinder (ASKAP) telescope, we identify four MRP candidates based on the variability in the total intensity light curves. Follow-up observations with the Australia Telescope Compact Array (ATCA) confirm three of them to be MRPs, thereby demonstrating the effectiveness of our strategy. With the expanded sample, we find that ECME is affected by temperature and the magnetic field strength, consistent with past results. There is, however, a degeneracy regarding how the two parameters govern the ECME luminosity for magnetic A and late-B stars (effective temperature $\lesssim 16$ kK). The current sample is also inadequate to investigate the role of stellar rotation, which has been shown to play a key role in driving incoherent radio emission.

Maritime safety faces growing challenges due to an expanding global fleet, tighter schedules, and increasingly complex stakeholder interactions. This study integrates multiple data sources to determine a more accurate representation of major marine accident causative factors in the United Kingdom. Logistic regression and data modelling are applied to Automatic Identification System data (2011–2017) and reported accidents from the Marine Accident Investigation Branch (2013–2019). Results show that larger vessels, daytime transits, service ships, winter conditions, and confined high-density areas such as ports impact accident likelihood. Interviews validate the data and emphasize the influence of port geometry and channel complexity. Among major UK ports, London, Plymouth and Milford Haven exhibit the highest accident-to-traffic densities. While maritime regulations and safety management systems in ports and vessels are seen as adequate by industry professionals, human factors require the greatest attention to improve maritime safety.

Maritime transport plays a vital role in global logistics and trade; however, its environmental impact, particularly CO₂ emissions, has become a growing concern. Current estimation methodologies are divided into top-down and bottom-up approaches. Top-down methods rely on macro-statistical data but often lack specificity regarding individual ship characteristics, leading to high uncertainty. Bottom-up methods, increasingly prevalent due to advancements in ship equipment and big data technology, estimate CO₂ emissions based on detailed ship activity trajectories, offering greater precision. This study integrates data from multiple vessel-position transmitting devices — AIS, V-Pass, and LTE-Maritime — to estimate CO₂ emissions from maritime activities in the coastal regions of South Korea. By combining these data sources, the study develops a comprehensive and accurate emissions assessment, improving reliability and supporting more informed decision-making in maritime environmental management and policy development.

This work introduces GalProTE, a proof-of-concept Machine Learning model, leveraging Transformer Encoder architecture to efficiently determine the stellar age, metallicity, and dust attenuation of galaxies from optical spectra. Designed to address the challenges posed by the vast datasets produced by modern astronomical surveys, GalProTE offers a significant improvement in processing speed while maintaining accuracy. Using the E-MILES spectral library, we generate a dataset of 111936 diverse templates by expanding the original 636 simple stellar population models with varying extinction levels, combinations of multiple spectra, and noise modifications. This ensures robust training over the spectral range of 4750–7100 Å at a resolution of 2.5 Å. GalProTE architecture employs four parallel attention-based encoders with varying kernel sizes to capture diverse spectral features. The model demonstrates a mean squared error (MSE) of 0.27% with a standard deviation of 0.10% between the input spectra and the GalProTE-generated spectra for the synthetic test dataset. Performance evaluation against real data from two galaxies in the PHANGS-MUSE survey (NGC4254 and NGC5068) demonstrates its ability to extract physical parameters efficiently, with spectral fit residuals showing a mean of -0.02% and 0.28%, and standard deviations of 4.3% and 5.3%, respectively. To contextualize these results, we compare age, metallicity and dust attenuation maps generated by GalProTE with those of pPXF, a state-of-the-art spectral fitting tool. While pPXF achieves robust results, it requires approximately 11 sec per spectrum. In contrast, GalProTE processes a spectrum in less than 4 ms – a speedup factor exceeding 2750, while also consuming 68 times less power per spectrum. The comparison with pPXF maps from PHANGS-MUSE underscores GalProTE’s capacity to enhance traditional methods through machine learning, paving the way for faster, more energy-efficient, and more comprehensive analyses of galactic properties. This study demonstrates the potential of GalProTE as an efficient, scalable, and sustainable solution for processing large astronomical surveys.

We present Evolutionary Map of the Universe Search Engine (EMUSE), a tool designed for searching specific radio sources within the extensive datasets of the Evolutionary Map of the Universe (EMU) survey, with potential applications to other Big Data challenges in astronomy. Built on a multimodal approach to radio source classification and retrieval, EMUSE fine-tunes the OpenCLIP model on curated radio galaxy datasets. Leveraging the power of foundation models, our work integrates visual and textual embeddings to enable efficient and flexible searches within large radio astronomical datasets. We fine-tune OpenCLIP using a dataset of 2 900 radio galaxies, encompassing various morphological classes, including FR-I, FR-II, FR-x, R-type, and other rare and peculiar sources. The model is optimised using adapter-based fine-tuning, ensuring computational efficiency while capturing the unique characteristics of radio sources. The fine-tuned model is then deployed in the EMUSE, allowing for seamless image and text-based queries over the EMU survey dataset. Our results demonstrate the model’s effectiveness in retrieving and classifying radio sources, particularly in recognising distinct morphological features. However, challenges remain in identifying rare or previously unseen radio sources, highlighting the need for expanded datasets and continuous refinement. This study showcases the potential of multimodal machine learning in radio astronomy, paving the way for more scalable and accurate search tools in the field. The search engine is accessible at https://askap-emuse.streamlit.app/ and can be used locally by cloning the repository at https://github.com/Nikhel1/EMUSE.

Accurate vessel traffic prediction is significant for efficient waterway management and lock scheduling. This paper presents a deep-learning framework that integrates a multi-graph convolutional network with a gated recurrent unit network, considering spatio-temporal patterns appropriately, for vessel traffic flow prediction. Three unstructured graphs are constructed to represent spatio-temporal relationships among traffic flows at different locations. Subsequently, multi-graph convolution is employed to quantitatively extract such patterns among adjacent nodes in the graphs. Those extracted patterns are then passed to a gated recurrent unit layer for further temporal features extraction in sequential data. The model is believed to improve prediction accuracy and reliability. To prove this, extensive experiments on regional and station-based predictions are conducted using two real-world datasets to evaluate the model’s capability. The jointly trained model demonstrates superior performance and outperforms conventional methods. The strong forecasting ability enables managers to adjust schedules promptly, enhancing efficiency and intelligence of waterway operations.

Coherent beam combining (CBC) of laser arrays is increasingly attracting attention for generating free-space structured light, unlocking greater potential in aspects such as power scaling, editing flexibility and high-quality light field creation. However, achieving stable phase locking in a CBC system with massive laser channels still remains a great challenge, especially in the presence of heavy phase noise. Here, we propose an efficient phase-locking method for a laser array with more than 1000 channels by leveraging a deep convolutional neural network for the first time. The key insight is that, by elegantly designing the generation strategy of training samples, the learning burden can be dramatically relieved from the structured data, which enables accurate prediction of the phase distribution. We demonstrate our method in a simulated tiled aperture CBC system with dynamic phase noise and extend it to simultaneously generate orbital angular momentum (OAM) beams with a substantial number of OAM modes.

Many mission-critical systems today have stringent timing requirements. Especially for cyber-physical systems (CPS) that directly interact with real-world entities, violating correct timing may cause accidents, damage or endanger life, property or the environment. To ensure the timely execution of time-sensitive software, a suitable system architecture is essential. This paper proposes a novel conceptual system architecture based on well-established technologies, including transition systems, process algebras, Petri Nets and time-triggered communications (TTC). This architecture for time-sensitive software execution is described as a conceptual model backed by an extensive list of references and opens up several additional research topics. This paper focuses on the conceptual level and defers implementation issues to further research and subsequent publications.