The preservation of cultural heritage in coastal marine environments requires continuous, high-fidelity monitoring of microclimatic conditions. This paper presents an advanced Distributed Measurement System (DMS) tailored for the Roman Villa of Casignana (Italy). The primary metrological challenge involves monitoring the thermal gradient between the modern protective roof and the ancient mosaic floor, as a temperature difference exceeding 6°C can cause irreversible mechanical stress and the mosaic tesserae to detach. We propose a non-invasive, low-impact Wireless Sensor Network (WSN) based on passive thermography and high-precision environmental sensors. To overcome the inherent absolute measurement uncertainty of commercial microbolometers, an in-situ dynamic calibration procedure is introduced, referencing a high-accuracy local absolute temperature sensor. Furthermore, the electronic architecture leverages memory-safe bare-metal firmware (Rust) and deterministic SPI bus arbitration to ensure the integrity of the digitized measurement signals. The proposed system achieves Technology Readiness Level (TRL) 8, providing a highly reliable metrological framework for real-time preventive conservation.

Power quality (PQ) monitoring plays a crucial role in the operating conditions of electrical distribution networks and ensuring compliance with power quality standards. The increasing need for pervasive and distributed monitoring motivates the development of low-cost measurement instrumentation capable of operating directly at the network edge. With these aims, this paper proposes the design of a compact and distributed instrumentation device suitable for deployment in low-voltage networks and resource-constrained measurement scenarios implemented on an ESP32 microcontroller platform. The power signal is acquired through a cost-effective sensing front-end and processed using a multisinusoidal decomposition technique, which provides the feature extraction for the detection and classification of PQ events such as harmonics, voltage sags and swells, and transients. For the classification, the extracted features are used as inputs to a machine learning algorithm. In order to select the most suitable one, a further contribution of this paper is to test several supervised machine learning algorithms, which are systematically compared in terms of classification accuracy, robustness to measurement noise, and computational complexity. Particular emphasis is placed on algorithm suitability for real-time execution on embedded measurement hardware with limited memory and processing resources, such as the ESP32. Experimental results are obtained using emulated PQ signals. The results confirm that the integration of multisinusoidal signal analysis with lightweight machine learning techniques represents an effective solution for cost-effective and scalable PQ instrumentation.

Power quality (PQ) disturbances can significantly affect the performance and longevity of electrical equipment, leading to system downtime, hardware degradation, and substantial economic losses. Real-time detection and accurate classification of such events are therefore critical to maintaining power system reliability. In this context, an Automatic Power Quality Event Classifier (APQEC) is crucial for the timely identification, segmentation, and classification of anomalies in electrical signals, enabling prompt grid maintenance and intervention. Many existing APQEC solutions proposed in the literature are based on centralised systems that are high-cost and therefore difficult to distribute, resulting in a low scalability monitoring system. This paper proposes the implementation of a PQ event classification framework based on a hybrid CNN-LSTM model combined with a multisinusoidal decomposition of the input signal. This framework can provide accurate PQ event classification in a distributed edge computing architecture under noisy conditions, combining decentralised detection with centralised classification in an edge computing architecture. A low-cost Local Distributed Node (LDN) is designed to be deployed across the monitored grid to detect PQ events and transmit compact features to a centralised Central Control Unit (CCU). The CCU receives the data from multiple LDNs and, for each of them, performs the classification. To permit the implementation of LDN with low-cost hardware, it is proposed the use of a Multisine Fitting Algorithm, which requires low computational capabilities and is therefore suitable for implementation on low-cost devices. This algorithm, which extracts the harmonic content required for classification, substantially reduces data exchange and system cost. At the CCU, a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model performs the classification and delivers robust performance under noisy conditions. The classifier is trained to recognise both single PQ events and composite event sequences for a total of 20 different classes. Numerical results demonstrate a classification accuracy exceeding 99% for single-event and event-combination classification within a signal-to-noise ratio (SNR) range of 10-30 dB, indicating the effectiveness of the proposed approach for scalable, real-world PQ monitoring.

Robotic positioning plays a crucial role in enabling accurate navigation, control, and autonomy across diverse domains, including industrial automation, autonomous vehicles, unmanned aerial vehicles (UAVs), underwater exploration, swarm robotics, and biomedical systems. Despite notable advancements, challenges such as sensor drift, environmental variability, computational complexity, and energy constraints continue to affect positioning accuracy. To address these limitations, diverse measurement systems and sensor fusion techniques have been developed, integrating inertial measurement units (IMUs), Global Positioning System (GPS), Light Detection and Ranging (LIDAR), optical motion capture, ultrasonic sensors, radio-frequency identification (RFID), and infrared (IR) tracking. This review introduces a structured classification of positioning systems into on-board, out-board, and hybrid types, highlighting trade-offs and application-specific strengths. Measurement methods are categorized into relative and absolute approaches. Furthermore, artificial intelligence (AI)-driven fusion strategies such as Kalman filtering, particle filtering, visual–inertial odometry (VIO), and simultaneous localization and mapping (SLAM) are analyzed for their roles in enhancing robustness and mitigating drift. Evaluation is based on accuracy, latency, efficiency and adaptability. Cross-domain comparisons illustrate how sensor-algorithm integration impacts outcomes across aerial, mobile, underwater, industrial and biomedical platforms. Finally, emerging directions such as 6G-enabled ultra-precise localization, neuromorphic computing for low-power SLAM, and blockchain-based decentralized frameworks are proposed to improve trust, adaptability, and reliability in next-generation robotic autonomy.

Tomatoes are highly vulnerable to a wide range of leaf diseases, which significantly reduce agricultural yield and quality. Timely and precise detection of these diseases is essential for sustainable crop management and food security. This study analyzes configuration-level bidirectional multi-scale feature propagation within the native YOLOv12-s architecture, with emphasis on architectural behavior under controlled experimental conditions. The computational topology and parameterization of YOLOv12 are preserved, while bidirectional feature aggregation is activated at configuration level to examine its influence on cross-scale semantic consistency and localization reliability. The framework was trained and evaluated on a curated dataset of 4030 annotated RGB images spanning ten tomato leaf disease categories. All models were trained under an augmentation-free protocol and unified evaluation settings to isolate architectural effects from data-driven performance inflation. Under these controlled conditions, configuration-level bidirectional activation yields measurable improvements in detection consistency and spatial agreement while maintaining identical model complexity. Performance is evaluated using mAP, precision, recall, F1-score, and error-type decomposition within a measurement-consistency framework. The proposed configuration achieves 95.9% mAP@50 and 87.1% mAP@50–95 under identical experimental conditions, providing empirical evidence that topology-preserving feature routing influences multi-scale semantic stability in lesion detection.

This paper explores the role of metrology in the assessment of image quality in the field of radiomics. Image Quality Assessment (IQA) is central to ensuring the reliability and reproducibility of radiomic analyses, as it directly affects the accuracy of feature extraction and segmentation, ultimately impacting diagnostic outcomes. From the analysis of approximately 20,000 papers sourced from three databases (PubMed, Scopus, IEEE Xplore), last searched in December 2025, the need for standardized imaging protocols and quality control measures emerges as a critical theme. Studies were included if they involved radiomic feature extraction and evaluated the impact of image quality variations on feature robustness and no formal risk-of-bias assessment was performed. A total of 105 studies were included, covering different medical imaging modalities. Across the included studies, noise, motion, acquisition and reconstruction parameters, and other artifacts consistently emerged as major sources of radiomic feature instability. Indeed, in most papers, IQA is neglected, while the effect of poor-quality images is reported. This research identifies and discusses the relevant issues reported in clinical practice, as well as the main metrics adopted for image quality evaluation. Through a comprehensive review of current literature and an analysis of emerging trends, this paper highlights the urgent need for innovative solutions in image quality metrics tailored to radiomics applications.

This paper presents the development of a remote laboratory system for distance learning applications, designed to replicate hands-on experiences in a digital environment. The system, based on the “miniverse” concept, allows students to interact with real instruments, such as the Tektronix TDS210 oscilloscope, through a client-server platform. It communicates using the MQTT protocol for reliability and scalability, and uses a GPIB interface supported by an ESP32 card for wireless connectivity. On the software side, the lab uses modern technologies such as Node.js and TypeScript to ensure stability and ease of use. This innovative approach bridges the gap between theory and practice in distance learning, providing an immersive and interactive experience. The system is a scalable solution for improving technical education, expanding its accessibility and quality, and opening new perspectives for science and engineering education.

This paper will review food packaging gas sensors, emphasising their pivotal role in preventing food spoilage by detecting carbon dioxide, ammonia and other gases. When present above the levels established by national regulations, these gases are clear indicators of food spoilage, making sensors that detect them a fundamental tool for intelligent food packaging. As the demand for healthy, affordable, fast, and fresh foods continues to rise, the role of sensors in food packaging becomes increasingly crucial. Thus, sensors are crucial in early spoilage detection, preventing potential health risks and economic losses. Among the various types of sensors available, colourimetric sensors have been the most investigated. These sensors offer a straightforward yet effective method to monitor food quality, triggering visible colour changes in response to the presence of specific gases, providing a clear indication of food spoilage and instilling confidence in the safety of the food supply.

Radiomics is a crucial discipline for personalized medicine, specifically by enhancing diagnostic accuracy and treatment decisions. However, the reliability of radiomics features heavily relies on the quality of medical imaging as well as on the standardization methods for the acquisition and the processing protocols. Significant challenges include the variability introduced by different equipment, operator handling, and patient-related factors. All above factors may affect the consistency and the clinical applicability of radiomics outcomes. This work highlights the critical aspects of image quality and standardized image processing techniques as pivotal to improve the reproducibility of radiomics data. Furthermore, the need for rigorous metrology approaches in image evaluation is emphasized to ensure that radiomics may fulfil its potential in clinical settings. To boost the research in this field, an overview of the recent literature is presented, and a critical analysis is performed, by considering all stages of the radiomics workflow. The final goal is to highlight the influence quantities potentially affecting the reliability of the radiomics features extraction.

Precise positioning is essential for anthropomorphic robots in industrial automation, medical robotics, and human-robot interaction. However, sensor drift, mechanical tolerances, and environmental disturbances introduce errors, necessitating robust compensation strategies. This review categorises measurement techniques into sensor-based, vision-based, kinematic, dynamic, and hybrid approaches. Sensor-based measurement methods, including Light Detection and Ranging (LiDAR), Inertial Measurement Units (IMUs), and ultrasonic sensors, provide real-time spatial data but require drift correction. Vision-based measurement methods, such as monocular and stereo cameras and Simultaneous Localization and Mapping (SLAM), enhance environmental perception but demand effective calibration. Kinematic and dynamic models support motion estimation but require frequent recalibration. Hybrid sensor fusion, integrating multiple modalities with artificial intelligence (AI)driven metrology, significantly improves accuracy but increases computational complexity. This review compares measurement trade-offs in accuracy, efficiency, and real-time applicability, demonstrating that AI-enhanced sensor fusion outperforms standalone methods. Emerging technologies, including quantum sensors, next-generation LiDAR, and deep learning-based uncertainty correction, offer promising advancements.

Practical exercises are vital in STEM education, reinforcing theoretical knowledge through hands-on activities. Access to labs is crucial from primary schools to universities, even in crowded classrooms or during movement restrictions like pandemics. To meet these challenges, a research team from the Universities of Naples Federico II, Sannio, and Calabria proposes a network of labs enabling remote experiments via extended reality. Students or workers can perform real-device experiments from home at any time, ensuring access to critical training despite physical constraints. Each experiment is unique to an individual or group, preserving authenticity and quality. This paper presents the development of an automated measurement system designed to simultaneously measure and correlate the thermal, electrical, and mechanical properties of Nichel Titanium Naval Ordinance Laboratory (NiTiNol), a Shape Memory Alloy (SMA) widely used in biomedical applications. The importance of this didactical experience is highlighted by the increasing implementation of NiTiNol-based actuators in biomedical applications. In the immersive experience guaranteed by the propsal, the students will be able to acquire a comprehensive understanding of the NiTiNol complex thermo-electro-mechanical behavior.

Air pollution is a global problem and it affects the health of millions of people every day, not to mention the damage it also causes to the environment. It is still an issue that does not reach all world territories. In many countries, monitoring air quality is regulated, standardized, and mandatory. However, it remains underdeveloped, leaving countries with insufficient monitoring of pollution levels. Our work aims to develop a low-cost mobile air pollution monitoring system. The system was designed to monitor environmental factors such as: temperature and relative humidity; as well as common pollutants CO, NO2, SO2 and O-3. The first phase of our project led to the development of a prototype deployed at a fixed site to collect air pollutant data, store, and analyze it. The results qualitatively demonstrated the good performance of the sensors and of the overall system, guiding us in identifying additional features and improvements needed to enhance its functionality. These results contributed to a modular, compact, and geo-referenced monitoring system.

Robotic positioning is a cornerstone of high-precision automation, yet conventional techniques often struggle with environmental variability, sensor drift, and dynamic real-time demands. This review critically analyses the evolving integration of Artificial Intelligence (AI) and metrology in robotic positioning measurement systems. It identifies the limitations of traditional sensor modalities, including optical encoders, inertial units, LiDAR, and GPS, while emphasising the importance of metrology in achieving traceable accuracy and compliance with standards. This paper focuses on systems that integrate physics-based metrology with AI-driven algorithms to support dynamic calibration, traceability, and autonomous error correction. Key AI advancements such as deep learning for vision localisation, reinforcement learning for dynamic control, and sensor fusion for adaptive error mitigation are highlighted. These hybrid systems synergise deterministic precision with learning-based adaptability, providing a promising future for robotic accuracy. Key performance benchmarks, error metrics (e.g., RMSE, MAE), and international standards (ISO 9283, ISO 10360) are analysed to assess real-world applicability. Finally, the study identifies emerging trends, such as blockchain-enabled traceability, Explainable AI (XAI), and quantumenhanced inference. The convergence of AI and metrology is shown to redefine robotic positioning, advancing toward self-calibrating, regulation-compliant systems with high accuracy and resilience.

The integration of extended reality (XR) technologies in remote practical training offers immersive learning experiences through virtual simulations. In this paper, the first step to offer a new virtual living environment implementing a measurement laboratory is proposed. Differently from a pure simulative environment, the proposal goes further allowing the design of a real didactic experience by using real measurement instruments. The proposed XR-enabled measurement laboratory provides a blend of virtual and real-world environments, fostering practical skills and critical thinking abilities that can be acquired only with “first hand” experiences. Despite technical obstacles, and open didactical questions, XR presents opportunities for innovation and collaborative learning experiences. Enhancing interactivity through multiplayer and social engagement features, the proposed laboratory will foster a sense of community among learners and researchers.

The new power generation systems, the increasing number of equipment connected to the power grid, and the introduction of technologies such as the smart grid, underline the importance and complexity of the Power Quality (PQ) evaluation. In this scenario, an Automatic PQ Events Classifier (APQEC) that detects, segments, and classifies the anomaly in the power signal is needed for the timely intervention and maintenance of the grid. Due to the extension and complexity of the network, the number of points to be monitored is large, making the cost of the infrastructure unreasonable. To reduce the cost, a new architecture for an APQEC is proposed. This architecture is composed of several Locally Distributed Nodes (LDNs) and a Central Classification Unit (CCU). The LDNs are in charge of the acquisition, the detection of PQ events, and the segmentation of the power signal. Instead, the CCU receives the information from the nodes to classify the PQ events. A low-computational capability characterizes low-cost LDNs. For this reason, a suitable PQ event detection and segmentation method with low resource requirements is proposed. It is based on the use of a sliding observation window that establishes a reasonable time interval, which is also useful for signal classification and the multi-sine fitting algorithm to decompose the input signal in harmonic components. These components can be compared with established threshold values to detect if a PQ event occurs. Only in this case, the signal is sent to the CCU for the classification; otherwise, it is discarded. Numerical tests are performed to set the sliding window size and observe the behavior of the proposed method with the main PQ events presented in the literature, even when the SNR varies. Experimental results confirm the effectiveness of the proposal, highlighting the correspondence with numerical results and the reduced execution time when compared to FFT-based methods.

The challenge of limited access to laboratory equipment relative to the students is a pervasive issue across many academic institutions. This limitation can deny practical learning experiences and hinder the understanding of theoretical concepts. Consequently, the development of innovative solutions to address this challenge is crucial for enhancing the quality of university education and Vocational Education and Training (VET).The approach presented by the authors in this paper provides an effective solution using an IoT protocol for remote control of laboratory instrumentation and the Device Under Test (DUT). This solution not only addresses the issue of insufficient laboratory equipment availability but also opens new opportunities to enrich students' educational experiences. By enabling direct interaction with instrumentation and the DUT through remote platforms, students can acquire practical skills in a more flexible and accessible manner, without physical space constraints or equipment availability limitations.

The widespread adoption of IoT devices has led to the development of Distributed Measurement Systems (DMS). However, cyber attacks aimed at destroying critical infrastructure and retrieving sensitive data are rising. We proposed a security-oriented VLAN testbed deployed with opensource firmware and constrained hardware to address this issue. The approach uses local MQTT brokers, TLS tunnels for local sensor data, and an SSL tunnel to transmit encrypted data to a cloud-based central broker. The proposal evaluates critical metrics such as Total Ratio, Total Runtime, Average Runtime, Message time, Average Bandwidth, and Total Bandwidth to predict the minimum network throughput for the selected QoS and security. From a measurement science perspective, lower productivity may result in phenomena being observed less frequently, potentially leading to misinterpretations. This paper does not introduce a new method for safeguarding measurement data, but rather evaluates the network performance of commonly used methods applicable to captive and commercial hardware. This scenario is typical for IoT-based DMS. The proposal considers a security-focused VLAN approach, along with targeted solutions for hardware constraints. Given the nature of the worst case, this allows for broad application of the results obtained. Our study marks the initial phase of a larger and more extensive research effort. Specifically, we used commercial hardware such as the Raspberry Pi 4. Our goal is to extend the scope of the research and delve into more complex and detailed questions in our next work. The primary objective is to identify algorithms that ensure optimal data transmission and encryption ratios and explore algorithms that ensure maximum compatibility with existing infrastructures supporting MQTT technology and will facilitate secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments.

Background Comprehensive datasets quantifying the coupled thermo-mechanical and electrical properties of shape memory alloys (SMAs) are lacking, as are standardized techniques for robust characterization. This hampers accurate modeling and design of SMA-based components. Objective: This work develops an automated experimental system to enable simultaneous measurement of stress-strain-temperature behavior and electrical resistivity evolution in NiTi SMA wires under controlled stress conditions. Methods: Customized test frames apply precise mechanical stresses while allowing for in situ electrical measurements and infrared imaging during complete thermal cycling protocols. Specialized instrumentation including a Keithley 2002 multimeter, Agilent E3631A programmable power supply, and FLIR A615 thermal camera are integrated with LabVIEW-based software routines for complete automation of the characterization process. Rigorous metrology principles are implemented throughout the measurement procedure to improve accuracy, repeatability, and consistency compared to prior manual techniques. Results: Extensive datasets are generated which reveal pronounced stress-dependencies in key SMA material parameters including transformation temperatures, recoverable strain, and electrical resistivity. A 3D regression model describes the comprehensive relationship between resistivity, temperature, and applied stress across the entire characterization domain. Conclusions: The automated measurement framework and methodology establishes a foundation for high-fidelity, reliable acquisition of coupled SMA property data. This will enable more accurate modeling and design of components and systems incorporating SMA actuation or sensing functions.

Air quality influences the life of the living beings that inhabit planet Earth. High levels of air pollutants in ambient air are in fact able to affect human health and also ecosystem integrity. Their monitoring allows for regulating the most appropriate air quality indices and verifying whether they are below the established harmful levels. However, this is a task that requires many physical and economic resources. For this reason, the research activity herein presented proposes a low-cost system, which is able to qualitatively evaluate the meteorological conditions and the main pollutants that affect a certain area. Through this proposal, the behavior of Alphasense's electrochemical gas low-cost sensors is studied. Their response to the concentrations of the main air pollutants regulated by the relevant European Directive (2008/50/CE), such as Carbon Monoxide, Nitrogen Dioxide, Ozone, and Sulfur Dioxide, is characterized and analyzed under various environmental conditions of Temperature and Relative Humidity and as a function of the electrical current signal obtained by the chemistry of the sensor.

The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal’s effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS.