An architectural approach has been explored as a means to describe a system for managing a metrological process known as a hybrid comparison. This top-down approach, based on the international standard for architectural description—ISO/IEC/IEEE 42010—focuses on stakeholder concerns and contrasts with the more common bottom-up development of tools, services, and file formats typically seen in the metrology community. By clearly separating stakeholder concerns, the approach also reveals structural relationships with other external metrological systems. This exploratory study suggests that the perspective provided by architectural descriptions supports the coordinated development of digital metrological systems.

Virtual experiments and digital twins are virtual representations of simulation processes and can aid in crucial tasks, such as interpreting measurement data or evaluating uncertainties. Therefore, virtual experiments and digital twins are essential components in the digital transformation in metrology. However, it is important that the digital model is properly validated, to make sure its output is reliable. This research shows how to validate a virtual coordinate measurement machine (CMM) by means of reference measurements. The output of the digital model is compared to reference measurements of a ring gauge and its consistency is evaluated. The results show that the output of the digital model for the diameter of the ring gauge is consistent with the reference data. The roundness measurements seem to contain an inherent bias, introduced by the CMM. After correcting for this bias, the output of the digital model for the roundness of the ring gauge is also consistent with the reference data.

This paper addresses critical metrological challenges in marine power systems—including measurement deficiencies for 23 key parameters, digital fragmentation hindering cross-system data integration, and inconsistent standardization across lifecycle phases. This paper proposes a Cloud-Edge-Device Integrated Architecture with five functional strata: the Data Layer consolidating laboratory/vessel datasets, the Perception Layer enabling edge-computing acquisition via adaptive tools, the Platform Layer standardizing metadata through API services, and the Application Layer deploying predictive maintenance and lifecycle simulation. This framework establishes closed-loop data management for 53 critical parameters, resolving "unmeasurable/inaccurate/incomplete" data issues while implementing robust security via national encryption algorithms, RBAC/ABAC access controls, and blockchain-anchored trusted timestamps. Implementation demonstrates reliability improvement and accelerated design iteration, providing end-to-end metrological support compliant with international maritime standards.

The transition to digital metrology requires structured and machine-readable data for traceable measurement systems. This article presents a methodology for developing machine-readable data formats IES/LDT for total luminous flux measurements from Type C goniophotometers. A proposed data model aligns with digital calibration certificate (DCC) projects, enabling improved traceability, automation, and interoperability. IES (Illuminating Engineering Society) files are ASCII text files that contain photometric data for lighting fixtures. They are machine-readable and contain detailed information about how light is distributed from a fixture, including luminous intensity, distribution angles, and other relevant information. Lighting design tools and rendering software can use this information to accurately simulate how a fixture would behave in a virtual environment. An actual case study from an SASO-NMCC (National Metrology Institute of Saudi Arabia) illustrates how these methods enhance calibration and function with smart lighting systems.

In this paper, we propose a method of data collection from different sensors through a software developed by Sensichips srl, SLM-Studio, which can then be compared with MLP machine learning models trained through training data available on the Sensichips website. The measurements performed by the SCW water sensors, the SCA air sensors and the SCP multispectral sensor can be applied in different fields of precision agriculture ranging from irrigation water monitoring to the health status of plants and soils up to the monitoring of the chemical-physical conditions of the air.