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We propose a rapid restoration strategy against PNE-node failure during postdisaster cooperation among DC providers and optical-network carriers. Our strategy reduces disruption and improves DC-service restoration by 35% in 20% less time compared to baseline. 

Cooperation among telecom carriers and datacenter (DC) providers (DCPs) is essential to ensure resiliency of network-cloud ecosystems. To enable efficient cooperative recovery in case of resource crunch, e.g., due to traffic congestion or network failures, we previously studied several frameworks for cooperative recovery among different stakeholders (e.g., telecom carriers and DCPs). Now, we introduce a novel Multi-entity Cooperation Platform (MCP) for implementing cooperative recovery planning, to achieve efficient use of carriers’ valuable optical-network resources during recovery. We adopt a Distributed Ledger Technology (DLT) that ensures decentralized and tamper-proof information exchange among stakeholders to achieve open and fair cooperation. To support diverse types of cooperation, we develop a state machine representing the MCP operation and define state transitions associated to stakeholders’ cooperation within the state machine. Moreover, we propose a signaling system in MCP to ensure simple and reliable state transitions for stakeholders during the cooperative recovery planning in large ecosystems. We experimentally demonstrate a proof-of-concept DLT-based MCP on a testbed. We showcase a DCP-carrier cooperative planning process, showing the flexibility of the proposed MCP to support diverse types of cooperation. 

Large-scale network-cloud ecosystems are fundamental infrastructures to support future 5G/6G services, and their resilience is a primary societal concern for the years to come. Differently from a single-entity ecosystem (in which one entity owns the whole infrastructure), in multi-entity ecosystems (in which the networks and datacenters are owned by different entities) cooperation among such different entities is crucial to achieve resilience against large-scale failures. Such cooperation is challenging since diffident entities may not disclose confidential information, e.g., detailed resource availability. To enhance the resilience of multi-entity ecosystems, carriers are important as all the entities rely on carriers’ communication services. Thus, in this study we investigate how to perform carrier cooperative recovery in case of large-scale failures/disasters. We propose a two-stage cooperative recovery planning by incorporating a coordinated scheduling for swift recovery. Through preliminary numerical evaluation, we confirm the potential benefit of carrier cooperation in terms of both recovery time and recovery cost/burden reduction. 

We investigate the problem of future disaster-resilient optical network-cloud ecosystems. We introduce our solutions considering openness/disaggregation and cooperation for single- and multi-entity network-cloud ecosystems, respectively. 

Abstract In this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time‐varying vector fields. Our approach is motivated by the large amount of data typically generated in numerical simulations, and in turn the types of data that domain scientists can generatein situthat are compact, yet useful, for post hoc analysis. One type of data commonly acquired during simulation are samples of the flow map, where a single sample is the result of integrating the underlying vector field for a specified time duration. In our work, we treat a collection of flow map samples for a single dataset as a meaningful, compact, and yet incomplete, representation of unsteady flow, and our central objective is to find a representation that enables us to best recover arbitrary flow map samples. To this end, we introduce a technique for learning implicit neural representations of time‐varying vector fields that are specifically optimized to reproduce flow map samples sparsely covering the spatiotemporal domain of the data. We show that, despite aggressive data reduction, our optimization problem — learning a function‐space neural network to reproduce flow map samples under a fixed integration scheme — leads to representations that demonstrate strong generalization, both in the field itself, and using the field to approximate the flow map. Through quantitative and qualitative analysis across different datasets we show that our approach is an improvement across a variety of data reduction methods, and across a variety of measures ranging from improved vector fields, flow maps, and features derived from the flow map. 

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on ${}^{40}\mathrm{Ar}$and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called “brems flipping,” as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE’s burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage. Published by the American Physical Society2025 

ABSTRACT We report on the detection of pulsations of three pulsating subdwarf B stars observed by the Transiting Exoplanet Survey Satellite (TESS) satellite and our results of mode identification in these stars based on an asymptotic period relation. SB 459 (TIC 067584818), SB 815 (TIC 169285097), and PG 0342 + 026 (TIC 457168745) have been monitored during single sectors resulting in 27 d coverage. These data sets allowed for detecting, in each star, a few tens of frequencies that we interpreted as stellar oscillations. We found no multiplets, though we partially constrained mode geometry by means of period spacing, which recently became a key tool in analyses of pulsating subdwarf B stars. Standard routine that we have used allowed us to select candidates for trapped modes that surely bear signatures of non-uniform chemical profile inside the stars. We have also done statistical analysis using collected spectroscopic and asteroseismic data of previously known subdwarf B stars along with our three stars. Making use of high precision trigonometric parallaxes from the Gaia mission and spectral energy distributions we converted atmospheric parameters to stellar ones. Radii, masses, and luminosities are close to their canonical values for extreme horizontal branch stars. In particular, the stellar masses are close to the canonical one of 0.47 M⊙ for all three stars but uncertainties on the mass are large. The results of the analyses presented here will provide important constrains for asteroseismic modelling.