Aberystwyth University

This paper investigates the feasibility of using low-cost thermal cameras for continuous, non-contact body temperature monitoring within a smart home environment. Three thermal imaging devices were evaluated: two MLX90640 sensor modules mounted on different breakout boards (Pimoroni MLX-P and RobotShop MLX-R) and the RS PRO T-10 (RS-T10). Experiments were conducted in a controlled smart home laboratory with 30 participants across distances of 1 to 5 metres and ambient temperatures ranging from 16°C to 25°C. Physical factors, including skin colour emissivity, eyeglass use, and illumination source, were systematically assessed. Results demonstrate that ambient temperature and measurement distance are the dominant sources of measurement error, whereas skin emissivity and illumination type have a negligible impact. A bilinear interpolation compensation model was developed and integrated into a Three-Level Smart Home Architecture (3-LSHA), producing estimated body temperature values with an acceptable deviation from baseline readings across all tested conditions. A prototype system was implemented to demonstrate end-to-end well-being monitoring across multiple rooms. The findings confirm that low-cost thermal cameras, when coupled with appropriate compensation algorithms, are viable sensors for ambient-assisted living applications targeting body temperature trend monitoring and health alerting. Keywords: Ambient Assisted Living, Bilinear Interpolation, Body Temperature Monitoring, llumination Infrared Thermography, MLX90640, Smart Home, Thermal Camera, Well-Being Monitoring.

Accelerating mass loss from the Greenland Ice Sheet is strongly modulated by meltwater-driven changes of bed conditions in ice dynamics, but these processes remain poorly constrained due to limited high-resolution observations of glacier hydrological systems. Constraints on basal properties can be obtained from borehole measurements and active reflection seismic surveys; however, these methods are inherently limited by their localized spatial coverage and are time intensive. As a result, they are poorly suited to monitoring the subglacial hydrological system, which evolves over seasonal timescales and across spatial scales larger than can be practically sampled. Thus, dense seismic node arrays offer a powerful alternative, enabling passive, high-resolution imaging and continuous monitoring of englacial and subglacial processes over broad areas and extended time periods.Here, we first present a dense seismic array experiment covering approximately 2.5 km² we conducted in the ablation zone of Isunnguata Sermia, West Greenland. The experiment comprised 82–117 autonomous seismic nodes deployed during one-month long monitoring periods in spring 2023 and in summer and fall 2024, complemented by multi-week surface Distributed Acoustic Sensing (DAS) measurements conducted in 2024. We assess data quality using power spectral densities and ambient noise cross-correlations to detect sensor tilt and GPS timing desynchronization, which are critical issues for data quality in remote and highly dynamical glacial environments such as the Greenland Ice Sheet.Then, we demonstrate we can use natural icequakes to perform seismic reflection analysis for bed mapping and interface property evaluation. We locate numerous natural seismic events with meter-scale resolution using Matched Field Processing (MFP) beamforming applied to array data in the 4–6 Hz frequency band. These events reveal a large population of near-surface sources associated with crevassing that generate both surface and body waves. We extract body waves and enable event stacking following a two-step synchronization procedure first synchronizing signals in the 4–6 Hz band dominated by surface waves, and subsequently refining timing in the 40–80 Hz band where P waves dominate. By sorting and stacking waveforms from these synchronized natural sources into common midpoint gathers, we identify high signal-to-noise ratio reflected P waves from the ice–bedrock interface at offsets of up to 1 km, significantly greater than those typically achieved in traditional active reflection seismic surveys on glaciers. This extended offset range makes the passive approach better suited for estimating basal conditions using classical amplitude-versus-offset (AVO) analyses due to the stronger dependence of reflectivity to bed properties at large incidence angles.Finally, following a workflow analogous to active reflection seismology, we derive a two-dimensional map of ice thickness beneath the array with a vertical resolution of approximately 15 m, comparable to that of conventional active surveys. This ice-thickness model will serve as a critical constraint for future high-resolution surface-wave tomography, enabling improved imaging of glacier structure and basal conditions at depth.

In many drylands globally, badlands (coalesced gully networks) have developed in colluvial (e.g. hillslope, pediment) and alluvial (e.g. river terrace, floodplain) landforms. Badland development can have serious on-site and off-site impacts including soil loss and reservoir sedimentation but can also create striking visual scenery that may have aesthetic appeal and geoheritage potential. Vigorous debate surrounds the relative importance of human and natural factors in badland development, with some researchers attributing their development mainly to human causes such as poor land management, while others have argued for natural causes, including decadal-scale climatic fluctuations, susceptible soil characteristics, and/or breaching of hard rock barriers along river beds. Intrinsic land surface adjustments may also account for some gullies, such as valley floor oversteepening that leads to erosion and slope adjustment. In short, is badland development dominantly a human-induced problem, a natural part of landscape development, or some combination of both factors? The answer has implications not only for advancing our understanding of Earth surface dynamics and human–nature interactions, but also for the targeting of the limited resources aimed at erosion control, sustainable land management, and geoheritage promotion. The Chambal Badlands in semiarid, north-central India exemplify these issues. The ~4800 km2 badlands are some of the largest in the world and present striking development challenges: soil erosion threatens food and water security but the deeply dissected terrain provides opportunities for geoheritage promotion. The region faces intertwined socio-economic and environmental challenges: marginal agriculture, limited alternative livelihoods, and widespread soil loss. In places, the badlands are heavily degraded by poorly controlled agricultural developments. Attempts to infill gullies and level the land for cultivation often fail within years as the soil simply re-erodes, trapping farmers in never-ending cycles of nutrient, water and soil carbon loss. At the same time, the badlands and associated bedrock channels, potholes, waterfalls and gorges of the Chambal and other nearby rivers illustrate landform combinations rarely present elsewhere on Earth, and certainly not at the same scale, and their promotion as geoheritage could enhance geoeducation (e.g. for local schools) and geotourism (e.g. for domestic/international markets). We report here on the initial findings from a UK-India collaborative project that is combining field data, laboratory analyses and science communication to address key questions, including: i) when and why did the badlands form?; ii) do modern agricultural practices enhance badland growth?; iii) how can we best use this information to address the development challenges? Geomorphological perspectives are a crucial element of the project, which hopes to develop a scientific, practical, and social blueprint for transforming the fragile badlands into productive and culturally-valued, resilient landscapes.

The transfer of surface meltwater to the base of the Greenland Ice Sheet modulates basal sliding, which controls ice loss into the ocean. The way in which water reaches the glacier bed, organizes itself near the ice-bed interface, and affects ice dynamics remains poorly understood due to limited observations. In this study, we present and interpret a multi-physical observational dataset from Isungnuata Sermia, West Greenland, acquired during the 2024 and 2025 melt seasons as part of the REASSESS and SLIDE projects.We show how Distributed Fibre Optic Sensing (DFOS) measurements combined with meteorological observations and models, and Global Navigation Satellite System (GNSS) measurements of surface ice motion, provide observational constraints on and improve our conceptual understanding of the surface-to-bed hydrological connection. We deployed fibre optic cables at the surface and in four 600 m boreholes, providing measurements of temperature, strain, and seismicity at thousands of sensing points at the surface, and through the full depth of the glacier.We observe that seismic activity is temporally linked with surface melt on a daily and weekly scale and evolves over time in alignment with the coalescence of surface channels. We apply Matched-Field Processing (MFP) beamforming to detect and locate seismic sources, aiming to resolve the vertical extent of fracturing related to water input. We observe the migration of some high-frequency seismic events to greater depths during periods of high runoff. This process is coincident with ice surface acceleration and subsequent deceleration events. Temperature and strain measurements from the DFOS system indicate highly variable distributions of temperature and deformation, which enable exploration of the prevalence and importance of englacial fracturing in surface-to-bed water transport.Together, these data offer potential insights into the mechanism of fracture-driven water transport, basal pressure increase, and subsequent regulation and dissipation via subglacial-water-channel development, and the relationship of these processes to glacial dynamics.

This study presents a comprehensive statistical comparison of solar wind measurements between the OMNI database (collected at L1 and shifted to the Earth's bow shock nose), and near-Earth solar wind observations from MMS, Cluster, and THEMIS missions near the bow shock nose. Using a threshold-based classification methodology, the analysis encompasses approximately 353 days (MMS), 283 days (Cluster), and 125 days (THEMIS) of solar wind intervals that are compared to OMNI data. Bisector regression analysis reveals that the anti-sunward flow component (Vx) demonstrates exceptional agreement across all missions with near-unity slopes and correlation coefficients of 0.92 for THEMIS and 0.97 for both MMS and Cluster. However, perpendicular velocity components show progressively degraded performance: Vy exhibits correlation coefficients of 0.63-0.77 with intercepts ranging from 21.57 km/s (MMS) to 47.49 km/s (THEMIS), while Vz shows lower correlations (0.42-0.72) with intercepts of 4.73-11.94 km/s. Ion density measurements reveal systematic mission-specific biases: MMS and THEMIS show ion density regression slopes below unity (0.59 and 0.54, respectively), while Cluster shows a slope above unity (1.14) compared to OMNI measurements. Magnetic field measurements show higher consistency, with near-unity slopes and correlation coefficients exceeding 0.84 for Bx and By components. The northward magnetic field component (Bz) exhibits elevated variance ratios and reduced correlations across all missions, reaching as low as 0.74 for THEMIS. These results quantify inherent uncertainties in cross-platform solar wind comparisons and assess the accuracy of time-shifted solar wind measurements in the OMNI database as proxies for near-Earth conditions. Based on the presented statistics, OMNI-equivalent measurements from near-Earth missions can be generated as alternative data sources to support the upcoming SMILE mission, multispacecraft studies, and magnetohydrodynamic simulations that require accurate upstream boundary conditions.

Abstract. Scientific research in remote environments has traditionally relied on manual data retrieval from data loggers, requiring multiple field visits that are costly, logistically challenging, and sometimes hazardous. While satellite telemetry solutions exist, their integration with widely used research-grade data loggers in extreme environments remains poorly documented, limiting reproducibility and adoption. This study presents the design and implementation of a data logging and telemetry system deployed in the Western Cwm of Mount Everest/Sagarmatha, Nepal, to transmit several meteorological parameters from an automatic weather station and firn layer temperatures obtained from a suite of borehole thermistors. Drawing on recent advances in satellite Internet of Things (IoT) connectivity, we present the integration and deployment of Campbell Scientific data loggers with Ground Control's compact satellite-enabled RockREMOTE Mini, which uses the Iridium Certus 100 networks and is powered by Iridium's 9770 modem. This work represents the first documented integration of research-grade Campbell Scientific loggers with Iridium Certus 100 satellite IoT technology, providing validated protocols, performance metrics, and automated workflows for extreme environments. The system, which operated at 6660 m a.s.l., in an extremely cold climate with a limited sky-view factor due to steep surrounding terrain, provided continual monitoring of ice temperatures and meteorological conditions transmitted every 24 h, from 4 May to 10 August 2025. Data integrity and transmission reliability were consistently maintained, with 100 % data retrieval, despite the challenging environmental conditions and limited power availability. The system established a robust methodological framework for other researchers working in remote locations, demonstrating the potential for sustained and high temporal resolution measurements of environmental conditions in locations where traditional communication infrastructure is unavailable.

Flow sensing is fundamental to both animals and robots for navigation, stability, and interaction with the environment. While animals achieve this through specialised mechanosensory structures, flow-guided autonomy in robotics remains underdeveloped due to the limitations of existing sensors, which often lack directional sensitivity, robustness, or scalability. To address this gap, we present a bioinspired, vision-based airflow sensing architecture that employs vertically suspended flexible filaments that respond passively to airflow, from which flow information is inferred using onboard computer vision. The sensor's utility was demonstrated on a quadruped robot, where it reliably detected flow speed and direction during both static and locomotion experiments. A mathematical model describing the aerodynamic and gravitational forces acting on the filaments was developed and experimentally validated, showing strong agreement with empirical data. The results highlight the potential of this novel sensing architecture in robotic applications. Beyond robotics, the sensor's low-cost and modular design makes it broadly applicable from scientific research and environmental monitoring to education and public engagement in flow-sensing principles.

The journal retracts the article "Parametric Study of Polymer-Nanoparticles-Assisted Injectivity Performance for Axisymmetric Two-Phase Flow in EOR Processes" [...].