Coatings or films are applied to a substrate for several applications, such as waterproofing, corrosion resistance, adhesion performance, cosmetic effects, and optical coatings. When applying a coating to a substrate, it is vital to monitor the coating thickness during the coating process to achieve a product to the desired specification via real time production control. There are several different coating thickness measurement methods that can be used, either in-line or off-line, which can determine the coating thickness relative to the material of the coating and the substrate. In-line coating thickness measurement methods are often very difficult to design and implement due to the nature of the harsh environmental conditions of typical production processes and the speed at which the process is run. This paper addresses the current and novel coating thickness methodologies for application to chromium coatings on a ferro-magnetic steel substrate with their advantages and limitations regarding in-line measurement. The most common in-line coating thickness measurement method utilized within the steel packaging industry is the X-ray Fluorescence (XRF) method, but these systems can become costly when implemented for a wide packaging product and pose health and safety concerns due to its ionizing radiation. As technology advances, nanometer-scale coatings are becoming more common, and here three methods are highlighted, which have been used extensively in other industries (with several variants in their design) which can potentially measure coatings of nanometer thickness in a production line, precisely, safely, and do so in a non-contact and non-destructive manner. These methods are optical reflectometry, ellipsometry and interferometry.

Classical antennas in small satellites such as reflectors and phased arrays have encountered significant challenges, including restricted beam steering capabilities and complex deployment mechanisms. Reflectarray antennas have emerged as a promising alternative, offering a compact and lightweight solution. However, performance constraints related to high-gain, phase control, and multi-beam operation require continued innovation to meet the demands of long-distance communication. This article explores recent advancements in reflectarray antenna design, fabrication, and implementation techniques specifically tailored for small satellite platforms. The study traces the technological evolution of reflectarrays from the 1960s to the present, with a particular emphasis on deployment strategies including foldable and stowable configurations, as well as the integration of solar energy harvesting. A comparative analysis of various reflectarray architectures is presented, highlighting their electromagnetic performance characteristics and suitability for diverse mission profiles. The article also explores novel applications of reflectarrays in emerging domains such as low Earth orbit (LEO) satellite constellations, 6G communication networks, Earth observation, and defense systems. By addressing current challenges and identifying future research directions, this study aims to support the development of next-generation reflectarray systems for advanced small satellite applications.

This paper reports on a preliminary study using EutelSat Hotbird 13A beacon data at 19.7 GHz and 29.5 GHz (scaled data) to evaluate the benefit of using Time Diversity (TD) and Maximal Ratio Combining (MRC) on an experimental next generation Ka‐band (26.5–40 GHz) satellite link in the UK. The authors have used the 2nd generation of video broadcasting via satellite (DVB‐S2) as the broadcasting standard to investigate the novel integration of TD and MRC. The benefit of the TD and MRC scheme was quantified in terms of percentage enhancement of the link availability. Long‐term statistics of rain and atmospheric attenuation were derived from a period of three year’s measurements made in Pontypridd, South Wales and in Chilbolton, England, at 19.7 GHz. A hypothetical Ka‐band satellite broadcast link between Pontypridd and Chilbolton has been designed to use 29.5 GHz as the uplink frequency while 19.7 GHz is used as the downlink frequency. The paper discusses the performance enhancement provided by TD and MRC during different types of fading events. The integration of TD and MRC into the DVB‐S2 standard provides the capability to continue delivering services at lower carrier‐to‐noise (C/N) levels by lowering the bit error rate (BER).

In this paper, we present LightBAL, an ultra-lightweight deep learning framework for real-time active cell balancing and onboard balancing control in electric vehicle (EV) battery management systems (BMSs). Although active cell balancing can improve battery utilisation and performance, applying deep learning-based balancing control strategies remains prohibitive in typical embeddable BMS platforms because of the computational complexity and inference latency of deep models. In response to this issue, we propose an AI-physics-informed controller that forecasts the voltage difference of a single cell, the SoC variation, and the optimal balancing current based on proportional feedback closed-loop (FCLL) control. The introduced framework exploits wavelet-based adaptive denoising, multi-scale hierarchical feature learning using a cooperative Principal Component Analysis (PCA) and autoencoder feature extraction technique, and a lightweight One-Dimensional Convolutional Neural Network (Conv1D) coupled with Bidirectional Long Short-Term Memory (BiLSTM) (Conv1D-BiLSTM). The implemented lightweight network is further trained by model compression methodologies such as knowledge distillation and 8-bit quantisation-aware training, aiming for efficient deployment on edge devices. Experimental validation on the multivariate battery time-series dataset demonstrates that LightBAL achieves an F1-score of 96.64%, a balancing efficiency of 94.30%, and a Mean Absolute Error (MAE) of 0.0379, outperforming methods based on conventional ANN, LSTM, and CNN. LightBAL without compression takes only 1.26 s to conclude on a PC workstation; the inference latency of the embedded light model is as low as 28.7 ms. In addition, hardware-in-the-loop (HIL) validation on the Raspberry Pi 4 platform indicates that the framework can fulfil real-time inference requirements under normal operating conditions, taking 28.7 ms per balancing process. Simulation shows that the proposed approach significantly decreases cumulative balancing energy loss by 12.4% across several driving cycle conditions.