西班牙电信

A promising use of quantum networking is quantum key distribution (QKD), which can provide information-theoretic security unattainable by classical means. While optical fiber-based QKD networks suffer from exponential loss, satellite-assisted quantum communication offers a scalable solution for long-distance secure key exchange. In this work, we propose and evaluate a satellite-based QKD setup covering the Iberian Peninsula, linking Madrid with Barcelona, Bilbao, and Lisbon. Our proposed setup uses a Low-Earth-Orbit (LEO) state-of-the-art satellite equipped with a spontaneous parametric down-conversion (SPDC) source to distribute entangled photon pairs to ground stations. Considering vibrations in the satellite, we optimize the beam waist to enhance the transmission probability and improve the secret key rate (SKR). Our results show that key rates sufficient for real-world applications, such as secure communication between hospitals, using hybrid classical-quantum protocols are feasible with existing protocols. Our results highlight the viability of near-term satellite-based QKD networks for national-scale secure communications.

Recently, large pretrained multimodal embedding models such as Qwen3-VL Embedding have shown strong promise for sequential recommendation, as they provide reusable semantic item representations across modalities and domains. However, directly using these embeddings often leads to suboptimal performance because of domain misalignment. Efficient side adaptation is therefore an attractive solution. Although adapting all backbone layers should help, existing side adapters often degrade with depth, prompting layer dropping despite the loss of useful hidden states. This is due to two major challenges: (1) the lack of modeling in selecting fused representations during residual addition, and (2) the insufficient preservation of earlier representations during progressive sigmoid fusion. This paper therefore asks a practical question: How can we design a side adaptation approach that effectively unlocks the potential of large pre-trained multimodal embedding models? To address this question, we propose Stresa, a stream-aware side-adaptation framework for frozen large pre-trained multimodal embedding models in sequential recommendation. Stresa introduces Stream-aware Hidden-Adapter Fusion (SHAF) to preserve historical side memory during fusion and Residual Stream Adapter (ReSA) to produce selective residual updates across layers. Empirically, Stresa consistently outperforms standard side adapters and state-of-the-art baselines on public datasets across multiple backbone embedding models. These results highlight the promise of adapting large embedding models for sequential recommendation. Our code is publicly available at https://github.com/GAIR-Lab/Stresa.

As 6G networks emerge, the boundaries between communication infrastructure and applications are rapidly dissolving, giving rise to an integrated, cloud-native ecosystem where services and network functions coexist and evolve together. Central to this transformation is the mobile core, which acts as the "brain" of the network, crucial for enabling intelligent and dynamic service delivery through the use of application programming interfaces (APIs), which facilitate seamless interaction between network functions and external applications. In this article, we explore two key dimensions of next-generation mobile core evolution: 1) network-aware service orchestration, where real-time core network insights inform and optimize service lifecycle management (LCM); and 2) context-aware core network orchestration, where information from services and network conditions drive adaptive decisions in the cloud-native 6G core, such as network function scaling and slice reconfiguration. Our contribution is threefold: 1) we assess the current state and standardization of service-network interplay, 2) we demonstrate proof-of-concept (PoC) implementations highlighting the practical value of "API-driven" integration, and iii) we outline the challenges and opportunities shaping the future of the mobile core in this converged network-service paradigm.

This paper focuses on the design of a Service-Based Management Architecture (SBMA) that enables intelligent, crossdomain, and sustainable orchestration. SBMA adopts a servicebased paradigm to abstract domain heterogeneity, integrates distributed AI-driven control, and provides a foundation for achieving unified management. The proposed architecture is illustrated by mapping specific domains (such as the radio access (RAN), transport, and core networks) to the architecture, demonstrating how domain-specific orchestration aligns with SBMA principles and interfaces. A representative experiment validates the architecture's capabilities, and the results show rapid recovery times for redeploying RAN, core, and transport components, highlighting the resilience and agility of the proposed approach.

Internet of Things (IoT) application providers rely on Mobile Network Operators (MNOs) and roaming infrastructures to deliver their services globally. In this complex ecosystem, where the end-to-end communication path traverses multiple entities, it became increasingly challenging to guarantee communication availability and reliability. Further, most platform operators use a reactive approach to communication issues, responding to user complaints only after incidents have become severe, compromising service quality. This paper presents our experience in the design and deployment of ANCHOR-an unsupervised anomaly detection solution for the IoT connectivity service of a large global roaming platform. ANCHOR assists engineers by filtering vast amounts of data to identify potential problematic clients (i.e., those with connectivity issues affecting several of their IoT devices), enabling proactive issue resolution before the service is critically impacted. We first describe the IoT service, infrastructure, and network visibility of the IoT connectivity provider we operate. Second, we describe the main challenges and operational requirements for designing an unsupervised anomaly detection solution on this platform. Following these guidelines, we propose different statistical rules, and machine- and deep-learning models for IoT verticals anomaly detection based on passive signaling traffic. We describe the steps we followed working with the operational teams on the design and evaluation of our solution on the operational platform, and report an evaluation on operational IoT customers.