Nokia Bell Labs (formerly named Bell Labs Innovations (1996–2007), AT&T Bell Laboratories (1984–1996) and Bell Telephone Laboratories (1925–1984)) is an American industrial research and scientific development company owned by Finnish company Nokia. With headquarters located in Murray Hill, New Jersey, the company operates several laboratories in the United States and around the world. Bell Labs has its origins in the complex past of the Bell System. In the late 19th century, the laboratory began as the Western Electric Engineering Department and was located at 463 West Street in New York City. In 1925, after years of conducting research and development under Western Electric, the Engineering Department was reformed into Bell Telephone Laboratories and under the shared ownership of American Telephone & Telegraph Company and Western Electric. Researchers working at Bell Labs are credited with the development of radio astronomy, the transistor, the laser, the photovoltaic cell, the charge-coupled device (CCD), information theory, the Unix operating system, and the programming languages B, C, C++, and S. Nine Nobel Prizes have been awarded for work completed at Bell Laboratories.
The long-term stability of Bi-strengthened Sn–Ag–Cu solders is an important but insufficiently understood factor for the reliability of stored electronic assemblies. In this study, (Bi) precipitates in Sn-2.25Ag-0.5Cu-6Bi (wt. {100}_βSn∥{011̅2}_(Bi) for OR1; {011}_βSn∥{011̅2}_(Bi) for OR2), resulting in multiple precipitate alignments within the matrix. Notably, OR2 is preferentially observed for bulk precipitates. Two additional ORs are identified between (Bi) and Cu₆Sn₅. After prolonged room-temperature storage, however, the bulk (Bi) precipitates become coarsened and widely spaced (> 1 μm in size and > 9 μm spacing), and (Bi) is observed to accumulate adjacent to Cu₆Sn₅ and Ag₃Sn particles. These observations indicate over-ageing and significant microstructural evolution even at room temperature, suggesting a potential long-term reliability concern for Bi-strengthened solder joints in stored electronics.
The integration of Artificial Intelligence (AI) systems into Earth Observation (EO) research and innovation has catalyzed significant advancements in environmental monitoring, humanitarian response, and urban planning. However, these developments also raise novel regulatory and ethical challenges, particularly in light of the European Union’s Artificial Intelligence Act (EU AI Act), which introduces a tiered risk-based framework for the governance of AI systems. This paper provides the first comprehensive examination of how the EU AI Act, and its provisions concerning high-risk AI systems as delineated in Annex III, apply to EO-based applications. Through a structured analysis of EO use cases across key domains, such as access to public and private services, law enforcement, critical infrastructure, migration, and biometric surveillance, we illustrate how the same EO AI system may be variably classified depending on its intended purpose, autonomy level, and deployment context. We demonstrate that while many current EO AI systems are not yet autonomous enough to trigger high-risk classification, the rapid technological trajectory suggests an increasing prevalence of high-risk EO applications in the near future. Furthermore, we argue that EO researchers and developers must proactively engage with the regulatory demands of the EU AI Act, not merely to ensure compliance, but to contribute to the development of methodological tools, such as explainability, risk assessment, and auditability, that are essential for ensuring responsible AI innovation. By linking legal interpretation with technical and ethical considerations, this paper contributes to an emerging interdisciplinary framework for governing AI in the EO domain under conditions of legal uncertainty and accelerating innovation.
We report the first experimental characterization of stimulated Raman scattering (SRS) in coupled-core multi-core fibers (MCFs). The experiment was performed using field-deployed fibers, with two complementary methodologies. First, the effective Raman gain coefficient was directly measured over a bandwidth of 120 nm – covering the upper S-, C- and lower L-bands – with the Raman pump being injected into different combinations of fiber cores and using either co- or counter-propagating signal. Second, we analyzed the spectral power tilt induced by SRS when transmitting a wavelength-division multiplexed signal spanning the entire C-band from 1529.4 nm to 1564.2 nm, which was found to be in agreement with the Raman gain coefficient measurements. Measurements performed on a co-deployed standard single-mode fiber (SMF) produced similar results. This observation, together with the notion that the Raman gain coefficient should be similar in MCFs and SMFs, validates our earlier derived model of SRS in fibers with strong mode mixing.
Internet of things (IoT) ecosystems introduce significant cybersecurity challenges due to device heterogeneity, firmware opacity, constrained resources, distributed deployment, and the integration of devices within wider socio-technical systems where they are used. Existing approaches to address IoT cybersecurity typically address isolated aspects of this problem, such as vulnerability enumeration, anomaly detection, or risk assessment; but without integrating them across the full lifecycle of devices and systems. This paper presents an extensible architecture that unifies cybersecurity testing, runtime monitoring, contextual risk modelling, secure update mechanisms, and auditable evidence management for IoT ecosystems that aims to address these challenges. The framework supports both device under test and system under test perspectives and integrates component-level techniques (such as SBOM generation, network fuzzing, machine learning-based anomaly detection, and access control risk evaluation) with system-level, knowledge-based, risk modelling to capture threat propagation across interconnected assets. A distributed ledger-backed auditable data infrastructure ensures integrity and traceability of indicators, results, and decisions. Automated workflow orchestration enables flexible tool chaining and lifecycle-aware execution aligned with established security development lifecycles. The approach is validated through three industrial use cases in aviation cargo monitoring, smart manufacturing, and telecommunication residential gateways. Results demonstrate the feasibility of combining static analysis, runtime indicators, and dynamic risk assessment to prioritise vulnerabilities contextually, detect anomalous behaviour, and support secure patch deployment in resource-constrained environments. The work advances lifecycle-integrated, system-aware cybersecurity assurance for IoT ecosystems and highlights the need for contextualised, interoperable tooling to address systemic vulnerability and risk propagation in complex systems where IoT, ICT and people interact.
Real-time urban traffic surveillance is vital for Intelligent Transportation Systems (ITS) to ensure road safety, optimize traffic flow, track vehicle trajectories, and prevent collisions in smart cities. Deploying edge cameras across urban environments is a standard practice for monitoring road conditions. However, integrating these with intelligent models requires a robust understanding of dynamic traffic scenarios and a responsive interface for user interaction. Although multimodal Large Language Models (LLMs) can interpret traffic images and generate informative responses, their deployment on edge devices is infeasible due to high computational demands. Therefore, LLM inference must occur on the cloud, necessitating visual data transmission from edge to cloud, a process hindered by limited bandwidth, leading to potential delays that compromise real-time performance. To address this challenge, we propose a semantic communication framework that significantly reduces transmission overhead. Our method involves detecting Regions of Interest (RoIs) using YOLOv11, cropping relevant image segments, and converting them into compact embedding vectors using a Vision Transformer (ViT). These embeddings are then transmitted to the cloud, where an image decoder reconstructs the cropped images. The reconstructed images are processed by a multimodal LLM to generate traffic condition descriptions. This approach achieves a 99.9% reduction in data transmission size while maintaining an LLM response accuracy of 89% for reconstructed cropped images, compared to 93% accuracy with original cropped images. Our results demonstrate the efficiency and practicality of ViT and LLM-assisted edge-cloud semantic communication for real-time traffic surveillance.
