This paper presents a method for detecting soft anomalies in Ethernet cables using time domain signal analysis and machine learning. A dataset consisting of oscilloscope captured signals from a CAT5e cable using passive measurement technique under four controlled scenarios was used. To reduce data dimensionality while preserving statistical characteristics, a histogram-based feature extraction process was applied prior to classification. Classification was performed using Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms under various downsampling rates. Models performances were good at generalizing and predicting the classes using those features. The results demonstrate that histogram features are effective in distinguishing between different anomaly types, even at significantly reduced sampling rates showing potential for real time implementation on low powered microcontroller platforms.

The use of digital tools such as digital twins is spreading throughout the O&G sector. For integrity management, digital models of relevant asset degradation phenomena have been used to estimate and predict its health, aiming to improve maintenance planning and to provide information about the risk of failure and its evolution over time. The input data of models for O&G integrity monitoring are commonly found in different databases and present very different characteristics, such as sampling rate, temporal stability and variability, influencing the data quality in different ways. This work addresses the use of FAIR principles and data quality assessment for O&G asset integrity management. A brief review of established concepts is discussed, and a practical case study is presented, which illustrates the very important role that data quality assessment and the use of FAIR principles play in digital models’ reliability.

CSIR-National Physical Laboratory, India an apex level body which provides calibration services to various secondary, tertiary calibration and testing labs and industries across the country. In general, calibration reports are commonly created in paper or word format to produce PDFs, which are time-consuming, tedious, and susceptible to human errors. Due to this several issues, BIPM- France is taking suitable goals to overcome this issue through the development of DCC. As globally leading NMIs like PTB, Germany, etc were working towards DCC developments, CSIR-NPL (NMI of India) takes part in DCC working group which are in-line with the leading NMIs. The main aim of this work is to create and implement a web application based on react.js to create digital calibration certificates. By deploying this solution, there will be substantial gains in data traceability, security, reliability, and accessibility. The project encompasses the creation of a Digital Calibration Certificate (DCC) platform that substitutes the conventional manual procedures with an automated, scalable system with the ability to produce structured and verifiable digital certificates. To further verify the authenticity and ease of validation, a model based on QR codes has been implemented that produces a unique, scannable code corresponding to a cloud-hosted copy of the certificate, permitting secure and instantaneous access. Also, advanced cryptographic algorithms like cryptographic hashing and PDF encryption are applied to secure the digital certificates and to ensure that the certificates remain tamper-proof and intact over their lifecycle. This end-to-end digital solution not only updates the laboratory's calibration report process but also establishes a new standard for secure, transparent, and effective calibration services.

How do we best transition from an outdated QI with many manual processes and analogue quality documents to an efficient and interconnected QI that fully leverages digital and automatized processes and granular quality data? This paper aims to identify digital transition strategies that can account for the realistic boundary conditions that exist in today’s diverse QI landscape, particularly the different digital maturity levels across institutions and nations as well as elaborate regulatory frameworks. Informed by related initiatives, the paper presents promising approaches to map out transition roadmaps for individual starting conditions and transition velocities.

In response to the accelerating digitalization of society, the Swiss Quality Infrastructure (QI) is undergoing a strategic transformation. To support this, METAS organized a two-part workshop series in 2024 and 2025. The first workshop mapped the complex stakeholder landscape and explored key QI concepts like competence and trust, highlighting both formal and informal relationships. The second workshop moved from analysis to action, identifying pain points and generating digital solution ideas, including AI, and machine-readable standards. Participants identified possible initiatives such as a QI Data Space, Digital Calibration Certificate, and quality-IoT systems, while also addressing internal readiness challenges. Key outcomes included the need for shared infrastructure, international alignment, and seed funding to support implementation. The workshops provided a starting point for shaping a resilient, digitally enabled QI system tailored to Switzerland’s strengths and future needs.