This paper investigates the possibility of realizing ice sensors based on the electrical response of thin strips made from pressed graphene nano-platelets. The novelty of this work resides in the use of the same graphene strips that can act as heating elements via the Joule effect, thus opening the route for a combined device able to both detect and remove ice. A planar capacitive sensor is designed and fabricated, in which the graphene strip acts as one of the armatures. The sensing principle is based on the high sensitivity of the planar capacitor to the change in electrical permittivity in the presence of ice, as shown in the experimental case study discussed here, can also be interpreted by means of a simple circuit and electromagnetic model. The properties of the sensor are analyzed, and the frequency range for its use as an ice detector has been established.

Smart Energy Applications are particularly impacting, especially due to energy resource scarcity and its high associated costs. A smart management of the energy consumption derives both from the user lifestyle, in terms of efficient and responsible behaviors, and from automatic algorithms that control and counteract energy waste and inefficient management. Focusing the attention on the latter, the development of methodologies and well-working techniques to monitor and optimize consumptions often requires an important effort in long measurement campaigns to get raw data to work with. Whenever this should be too much expensive or proper instrumentation unavailable, public datasets could solve the problem. Current literature review on the dataset availability showed a large presence of information, especially related to electrical energy consumptions. Nevertheless, several limitations affect them, from the low number of calculated electrical parameters (i.e. 4-5 in the most of cases), to short analysis periods, passing by the lack of detailed frequency domain information or poor consumption habit transitions analysis. Accordingly, this work aims to overcome current dataset limitations, by proposing a real-measurement based simulated dataset, extracting more than 400 discriminative electrical parameters on 36 different home appliances, discussing preliminary acquisition set-ups, simulation process, extracted electrical parameters and examples of applicability to smart energy applications. To provide a data quality index, a validation procedure has also been carried out, showing how simulated data match real acquisition with a reference measurement instrument. The produced dataset is available for downloading and analysis in public free access and its repository link is provided in the reference section.

In the context of energy digitisation, Load Profiling can be a useful tool for making decisions about the use and the health of an electrical load and could be adopted for strategic services related to the energy efficiency, characterisation, prediction, optimisation, and diagnosis of monitored systems. To accomplish for this task it is important a spread of a new generation of smart meters. In this scenario, useful information for the developers of services based on Load Profiling could be the minimum metrological characteristics of smart meters, i.e. the monitoring quality, and the electrical parameters to extract from the electrical signature in order to have a reliable Load Profiling process, i.e. the electrical signature quality. This paper tries to answer to these questions investigating the impact of the monitoring quality and the electrical signature quality on the performance of Load Profiling. The main results of the paper are: i ) generally the Load Profiling process requires metrological characteristics lower than those required for energy billing reducing sub-metering infrastructure costs; ii ) the increasing of the number of energy features adopted in the Load Profiling not always improves reliability and accuracy; this happens when many of the features considered do not have great sensitivity with respect to changes in the energy states for the considered case study; iii ) the use of functions that measure the sensitivity of a feature to the Load Profiling process, such as the considered “KDE” ( Kernel Density Estimation ), and a suitable threshold process can delete the parameters with poor sensitivity and can greatly improve the Load Profiling reliability; iv ) the method considered in the paper could help to analyze Load Profiling problems related to other physical quantities (i.e., thermal energy profiling or even multi-physical systems), allowing to the definition of a target “MQ” ( Monitoring Quality ) and selecting a minimum number of useful features to be adopted.

Communication technologies are of primary importance for today's activity, since they contribute to achieving a lot of services for humans, from simple phone calls to advanced multimedia services, banking activities, and healthcare, to cite a few [1]. They are also the key factor of many measurement applications in the contexts of industry 4.0, transportation, environmental monitoring, telemetering, building automation, and the emerging applications of the Internet of Things (IoT) and Industrial Internet of Things (IIoT) [2]–[4]. In other words, communication technologies enable the concept of “Networking for measurements” which explains the crucial role of networks for measurement applications.

In recent years, advances related to Artificial Intelligence (AI) have had a major impact on the scientific world. These methods are even more permeating various technical fields such as technology, medicine, science and sociology. Currently, applied AI involves the use of AI-based methods as tools to refine or optimize systems within specific domains. As an example, this includes the use of AI techniques to refine weather forecasting in meteorology, improve the efficiency of warehouse logistics in the warehousing and shipping industry, enable early diagnosis of diseases in medicine, and in the field of instrumentation and measurement (I&M), optimize measurement methods by improving the accuracy of measuring instruments and techniques [1].

This study presents a comprehensive analysis aimed at validating the use of an innovative nanosensor based on graphitic nanomembranes for the smart monitoring of industrial wastewater. The validation of the potential of the nanosensor was carried out through the development of advanced analytical methodologies, a direct experimental comparison with commercially available electrode sensors commonly used for the detection of chemical species, and the evaluation of performance under conditions very similar to real-world field applications. The investigation involved a series of controlled experiments using an organic pollutant—benzoquinone—at varying concentrations. Initially, data analysis was performed using classical linear regression models, representing a conventional approach in chemical analysis. Subsequently, a more advanced methodology was implemented, incorporating machine-learning techniques to train a classifier capable of detecting the presence of pollutants in water samples. The study builds upon an experimental protocol previously developed by the authors for the nanomembranes, based on electrochemical impedance spectroscopy. The results clearly demonstrate that integrating the nanosensor with machine-learning algorithms yields significant performance. The intrinsic properties of the nanosensor make it well-suited for potential integration into field-deployable platforms, offering a real-time, cost-effective, and high-performance solution for the detection and quantification of contaminants in wastewater. These features position the nanomembrane-based sensor as a promising alternative to overcome current technological limitations in this domain.