arXiv subjects

Luca Barbieri

Publications and source records attributed to Luca Barbieri.

At least 19 recordsLinked to original sources

arXiv2026

The Ambipolar electric field in multispecies plasma atmospheres: effects of alpha particles and stochastic heating

We investigate stationary states of a collisionless, gravitationally stratified plasma atmosphere composed of electrons, protons, and alpha particles by extending Pannekoek--Rosseland theory to multispecies and multi-temperature plasmas. Starting from Liouville's theorem, we derive the self-consistent ambipolar electric field from kinetic equilibrium and charge neutrality. For a single-temperature atmosphere, we obtain analytical expressions for the ambipolar field, show its dependence on alpha-particle abundance, and determine the relative stratification of the three species. A first-order analytical approximation to the electrostatic potential accurately reproduces the numerical solution. We then generalize the formalism to multi-temperature plasmas generated by stochastic boundary heating, representing the stationary distribution as a superposition of Maxwellian populations. Gravitational filtering produces non-exponential density profiles and increasing temperatures with altitude, while preserving the relative species stratification, with alpha particles most strongly stratified and protons least. The ambipolar electric field contains a dominant gravitational contribution, corresponding to the generalized Pannekoek--Rosseland field, and a thermoelectric contribution arising from species-dependent temperature gradients, which accounts for its non-monotonic structure. These results provide a framework for studying the combined effects of plasma composition and stochastic heating in gravitationally stratified astrophysical plasmas.

physics.plasm-ph↗

arXiv2026

An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks

Centrally-managed Wi-Fi solutions are increasingly leveraging Distributed Artificial Intelligence (AI) to predict key operational statistics of Access Points (APs) and proactively optimize network performance. In this context, Clustered Federated Learning (CFL) represents a fitting methodology, enabling the generation of multiple AI models that account for diverse statistical properties of the APs data distribution. However, identifying informative clusters for grouping APs models remains a significant challenge. In this paper, we address this problem by proposing a novel CFL tool integrating a two step clustering procedure. Initially, multiple clustering solutions are generated and filtered based on a minimum set of desired clustering criteria. Subsequently, if no solutions meet sufficient quality metrics, a global model is produced by aggregating all AP models. Otherwise, the final clustering solution is selected as the one that maximizes the informativeness (quantified via differential entropy) for the smallest cluster. Our results, focusing on a Wi-Fi traffic prediction problem, demonstrate that the developed CFL tool achieves the best predictive performance among all evaluated distributed strategies and the lowest communication and energy footprint among the clustered ones, exceeding the cost of single-model FL only in the regimes where it markedly improves accuracy.

eess.SP↗

arXiv2026

Collisionless stationary states of a stratified plasma in an expanding magnetic tube with stochastic heating

We investigate the collisionless kinetic structure of the upper solar atmosphere in the presence of an expanding magnetic field. We consider a stationary two-component plasma confined within an expanding magnetic flux tube and subject to gravity, self-electrostatic interactions, the Pannekoek-Rosseland electric field, and magnetic moment conservation. Starting from the Vlasov equation, we derive fully analytical expressions for the particle distribution functions, density profiles, and the parallel, perpendicular, and total temperature profiles. We show that the combined conservation of energy and magnetic moment generates a loss-cone distribution, reducing the density with respect to the corresponding unmagnetized atmosphere and producing a pronounced temperature anisotropy. For a single-temperature boundary condition, the competition between magnetic moment conservation and gravity causes the parallel temperature to develop a maximum. We derive analytical scaling laws for its location and amplitude and validate them against numerical calculations. We further show that the anisotropy persists independently of the temperature distribution at the lower boundary. In the regime of rare but intense heating events, gravitational filtering enhances the contribution of the hottest particle populations at coronal heights, while magnetic moment conservation further amplifies the resulting velocity-space anisotropy. This work provides a fully analytical kinetic description of the combined effects of gravitational filtering and magnetic moment conservation in an expanding coronal magnetic flux tube undergoing stochastic heating at its base. These results establish a theoretical framework for investigating the role of magnetic-field expansion in shaping the density and temperature structure of weakly collisional stellar coronae.

astro-ph.SR↗

arXiv2026

Collisionless and collisional kinetics of a plasma atmosphere with spatially and temporally intermittent heating at its base

The solar corona exhibits a pronounced temperature inversion, with plasma temperatures increasing by nearly two orders of magnitude from the chromosphere to the corona. We investigate how spatially sparse and temporally intermittent stochastic heating at the base of the transition region shapes the temperature and density structure of coronal loops within a kinetic framework. Stochastic thermal boundary conditions and surface coarse graining are introduced. Analytical solutions are derived in the collisionless limit for heating-event time scales shorter or longer than the particle crossing time, and Coulomb collisions are incorporated through a reduced kinetic model describing the thermalization of suprathermal particles. In the short-time-scale regime, spatial filling factor and temporal intermittency combine into a single effective parameter controlling the suprathermal population, producing a transition region and a hot corona both within individual loops and after coarse graining. Collisions preserve this thermal structure while reducing the coronal density through progressive thermalization. In the long-time-scale regime, individual loops are nearly isothermal and the temperature inversion emerges only after coarse graining, depending solely on the spatial filling factor. Here, Coulomb collisions and optically thin radiative losses have only minor effects, while density and temperature profiles remain broadly consistent with coronal observations. These results show that sparse, intermittent heating naturally generates suprathermal particle distributions and reproduces the observed thermal structure of the solar corona within a kinetic framework, highlighting the different sensitivity of the two regimes to collisional effects.

astro-ph.SR↗

arXiv2026

Generalized flux-weighted boundary walls in kinetic models

We present a technique to investigate the stationary states of a system of a collisionless system confined by an external potential and coupled to boundary reservoirs through prescribed reinjection rules. We consider a family of boundary conditions parametrized by an integer $n$, corresponding to different velocity distributions imposed at the boundaries, generalizing the standard flux-weighted Maxwellian scheme. By combining Liouville's theorem with the boundary injection rule, we derive an explicit analytical expression for the stationary distribution function. This framework provides a direct link between microscopic boundary dynamics and macroscopic stationary profiles. We show that thermal equilibrium is recovered only for the standard flux-weighted injection method, while for all other cases the system relaxes to manifestly non-thermal stationary states. The resulting density and temperature profiles exhibit non-trivial spatial structures, including non-monotonic behaviour and temperature gradients induced by the boundary conditions alone. Analytical predictions for stationary moments are obtained in closed form for representative cases and are nicely reproduced by particle-based numerical simulations.

cond-mat.stat-mech↗

arXiv2026

Cluster-Specific Predictive Modeling: A Scalable Solution for Resource-Constrained Wi-Fi Controllers

This manuscript presents a comprehensive analysis of predictive modeling optimization in managed Wi-Fi networks through the integration of clustering algorithms and model evaluation techniques. The study addresses the challenges of deploying forecasting algorithms in large-scale environments managed by a central controller constrained by memory and computational resources. Feature-based clustering, supported by Principal Component Analysis (PCA) and advanced feature engineering, is employed to group time series data based on shared characteristics, enabling the development of cluster-specific predictive models. Comparative evaluations between global models (GMs) and cluster-specific models demonstrate that cluster-specific models consistently achieve superior accuracy in terms of Mean Absolute Error (MAE) values in high-activity clusters. The trade-offs between model complexity (and accuracy) and resource utilization are analyzed, highlighting the scalability of tailored modeling approaches. The findings advocate for adaptive network management strategies that optimize resource allocation through selective model deployment, enhance predictive accuracy, and ensure scalable operations in large-scale, centrally managed Wi-Fi environments.

eess.SP↗

arXiv2025

Target Classification for Integrated Sensing and Communication in Industrial Deployments

Integrated Sensing and Communication (ISAC) systems enable cellular networks to jointly operate as communication technology and sense the environment. While opportunities and potential performance have been largely investigated in simulations, few experimental works have showcased Automatic Target Recognition (ATR) effectiveness in a real-world deployment based on cellular radio units. To bridge this gap, this paper presents an initial study investigating the feasibility of ATR for ISAC. Our ATR solution uses a Deep Learning (DL)-based detector to infer the target class directly from the radar images generated by the ISAC system. The DL detector is evaluated with experimental data from a ISAC testbed based on commercially available mmWave radio units in the ARENA 2036 industrial research campus located in Stuttgart, Germany. Experimental results demonstrate accurate classification performance, demonstrating the feasibility of ATR ISAC with cellular hardware in our setup. We finally provide insights about the open generalization challenges, that will fuel future work on the topic.

eess.SP↗

arXiv2025

Kinetic collisionless model of the solar transition region and corona with spatially intermittent heating

We develop a three-dimensional kinetic model of the solar transition region and corona in which the plasma above the chromosphere is collisionless and embedded in a uniform magnetic field. Heating occurs intermittently at discrete locations on the chromospheric surface, modeled through a surface coarse-graining procedure that produces non-thermal boundary conditions for the Vlasov equation. The resulting stationary distribution functions generate suprathermal particle populations and naturally lead to a temperature inversion via gravitational filtering, without any local coronal heating. The model reproduces realistic temperature and density profiles with a thin transition region and a hot corona, consistent with solar observations. These results demonstrate that the spatial intermittency of heating at the chromospheric interface is sufficient to account for the formation of the transition region and the high-temperature corona.

astro-ph.SR↗

arXiv2025

Extended temporal coarse-graining in a stratified and confined plasma under thermal fluctuations

We present an extended investigation of a recently introduced model of gravitationally confined, collisionless plasma (Barbieri et al. 2024a), which showed that rapid temperature fluctuations at the base of the plasma, occurring on timescales much shorter than the electron crossing time, can drive the system into a non-thermal state characterized by anti-correlated temperature and density profiles, commonly referred to as temperature inversion. To describe this phenomenon, a temporal coarse-graining formalism was developed (Barbieri et al. 2024b). In this work, we generalize that approach to cover regimes where the timescales of temperature fluctuations are comparable to or exceed the electron crossing time. We derive a set of kinetic equations that incorporate an additional term arising from the coarse-graining procedure, which was not present in the earlier formulation. Through numerical simulations, we analyze the plasma dynamics under these broader conditions, showing that the electric field influences the system when fluctuation timescales approach the electron crossing time. However, for timescales much larger than the proton crossing time, the electric field becomes negligible. The observed behaviours are interpreted within the framework of the extended temporal coarse-graining theory, and we identify the regimes and conditions in which temperature inversion persists.

physics.plasm-ph↗

arXiv2025

Self-consistent generation of the ambipolar electric field in collisionless plasmas via multi-mode electrostatics

In this work, we investigate the generation of the ambipolar electric field in a gravitationally stratified, collisionless plasma atmosphere. In such environments, gravity tends to separate charged species. To prevent separation an electric field, classically described by the Pannekoek-Rosseland expression, is usually imposed externally. Here, we propose a self-consistent method to recover this field based on a multi-mode Fourier expansion of the electrostatic interaction. We show that, under suitable conditions, this approach naturally leads to the ambipolar electric field and restores charge neutrality. The method is tested in both isothermal and multi-temperature plasma configurations. This framework provides a foundation for future developments that may include collisions, ionization, and asymmetric boundary conditions to model more realistic stellar atmospheres.

physics.plasm-ph↗

arXiv2025

Bayesian Federated Learning for Continual Training

Bayesian Federated Learning (BFL) enables uncertainty quantification and robust adaptation in distributed learning. In contrast to the frequentist approach, it estimates the posterior distribution of a global model, offering insights into model reliability. However, current BFL methods neglect continual learning challenges in dynamic environments where data distributions shift over time. We propose a continual BFL framework applied to human sensing with radar data collected over several days. Using Stochastic Gradient Langevin Dynamics (SGLD), our approach sequentially updates the model, leveraging past posteriors to construct the prior for the new tasks. We assess the accuracy, the expected calibration error (ECE) and the convergence speed of our approach against several baselines. Results highlight the effectiveness of continual Bayesian updates in preserving knowledge and adapting to evolving data.

cs.LG↗

arXiv2025

The Security of Quantum Computing in 6G: from Technical Perspectives to Ethical Implications

Quantum technologies hold promise as essential components for the upcoming deployment of the future 6G network. In this future network, the security and trustworthiness requirements are not considered fulfilled with the current state of the quantum computers, as the malicious behaviour on the part of the service provider towards the user may still be present. Therefore, this article provides an initial interdisciplinary work of regulations and solutions in the scope of trustworthy quantum computing for future 6G that can be viewed as complimentary regulations to the existing strategies shared by different actors of states and organizations. More precisely, we describe the importance of a reliable quantum service provider and its implication on the ethical aspects concerning digital sovereignty. By exploring the critical relationship between trustworthiness and digital sovereignty in the context of future 6G networks, we analyse a trade-off between accessibility to this new technology and preservation of digital sovereignty engaging in parallel the United Nation's (UN's) sustainable development goals. Furthermore, we propose a partnership model based on cooperation, coordination, and collaboration giving rise to a trusted, ethical, and inclusive quantum ecosystem, whose implications can spill over to the entire global scenario.

quant-ph↗

arXiv2024

Temperature and density profiles in the corona of main-sequence stars induced by stochastic heating in the chromosphere

All but the most massive main-sequence stars are expected to have a rarefied and hot (million-Kelvin) corona like the Sun. How such a hot corona is formed and supported has not been completely understood yet, even in the case of the Sun. Recently, Barbieri et al. (A&A 2024, J. Plasma Phys. 2024) introduced a new model of a confined plasma atmosphere and applied it to the solar case, showing that rapid, intense, intermittent and short-lived heating events in the high chromosphere can drive the coronal plasma into a stationary state with temperature and density profiles similar to those observed in the solar atmosphere. In this paper we apply the model to main-sequence stars, showing that it predicts the presence of a solar-like hot and rarefied corona for all such stars, regardless of their mass. However, the model is not applicable as such to the most massive main-sequence stars, because the latter lack the convective layer generating the magnetic field loop structures supporting a stationary corona, whose existence is assumed by the model. We also discuss the role of stellar mass in determining the shape of the temperature and density profiles.

astro-ph.SR↗

arXiv2024

A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers

The rapid evolution of artificial intelligence (AI) technologies holds transformative potential for the healthcare sector. In critical situations requiring immediate decision-making, healthcare professionals can leverage machine learning (ML) algorithms to prioritize and optimize treatment options, thereby reducing costs and improving patient outcomes. However, the sensitive nature of healthcare data presents significant challenges in terms of privacy and data ownership, hindering data availability and the development of robust algorithms. Federated Learning (FL) addresses these challenges by enabling collaborative training of ML models without the exchange of local data. This paper introduces a novel FL platform designed to support the configuration, monitoring, and management of FL processes. This platform operates on Platform-as-a-Service (PaaS) principles and utilizes the Message Queuing Telemetry Transport (MQTT) publish-subscribe protocol. Considering the production readiness and data sensitivity inherent in clinical environments, we emphasize the security of the proposed FL architecture, addressing potential threats and proposing mitigation strategies to enhance the platform's trustworthiness. The platform has been successfully tested in various operational environments using a publicly available dataset, highlighting its benefits and confirming its efficacy.

cs.CY↗

arXiv2024

Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT

Bayesian Federated Learning (FL) has been recently introduced to provide well-calibrated Machine Learning (ML) models quantifying the uncertainty of their predictions. Despite their advantages compared to frequentist FL setups, Bayesian FL tools implemented over decentralized networks are subject to high communication costs due to the iterated exchange of local posterior distributions among cooperating devices. Therefore, this paper proposes a communication-efficient decentralized Bayesian FL policy to reduce the communication overhead without sacrificing final learning accuracy and calibration. The proposed method integrates compression policies and allows devices to perform multiple optimization steps before sending the local posterior distributions. We integrate the developed tool in an Industrial Internet of Things (IIoT) use case where collaborating nodes equipped with autonomous radar sensors are tasked to reliably localize human operators in a workplace shared with robots. Numerical results show that the developed approach obtains highly accurate yet well-calibrated ML models compatible with the ones provided by conventional (uncompressed) Bayesian FL tools while substantially decreasing the communication overhead (i.e., up to 99%). Furthermore, the proposed approach is advantageous when compared with state-of-the-art compressed frequentist FL setups in terms of calibration, especially when the statistical distribution of the testing dataset changes.

cs.LG↗

arXiv2024

On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks

Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of those domains is healthcare, where groups of silos collaborate in order to generate a global predictor with improved accuracy and generalization. However, the inherent challenge lies in the high heterogeneity of medical data, necessitating sophisticated techniques for assessment and compensation. This paper presents a comprehensive exploration of the mathematical formalization and taxonomy of heterogeneity within FL environments, focusing on the intricacies of medical data. In particular, we address the evaluation and comparison of the most popular FL algorithms with respect to their ability to cope with quantity-based, feature and label distribution-based heterogeneity. The goal is to provide a quantitative evaluation of the impact of data heterogeneity in FL systems for healthcare networks as well as a guideline on FL algorithm selection. Our research extends beyond existing studies by benchmarking seven of the most common FL algorithms against the unique challenges posed by medical data use cases. The paper targets the prediction of the risk of stroke recurrence through a set of tabular clinical reports collected by different federated hospital silos: data heterogeneity frequently encountered in this scenario and its impact on FL performance are discussed.

cs.LG↗

arXiv2024

A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications

Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic stroke prediction. This paper provides an overview of the TRUSTroke FL network infrastructure. The proposed architecture adopts a client-server model with a central Parameter Server (PS). We introduce a Docker-based design for the client nodes, offering a flexible solution for implementing FL processes in clinical settings. The impact of different communication protocols (HTTP or MQTT) on FL network operation is analyzed, with MQTT selected for its suitability in FL scenarios. A control plane to support the main operations required by FL processes is also proposed. The paper concludes with an analysis of security aspects of the FL architecture, addressing potential threats and proposing mitigation strategies to increase the trustworthiness level.

cs.AI↗

arXiv2024

Deep Learning-based Cooperative LiDAR Sensing for Improved Vehicle Positioning

Accurate positioning is known to be a fundamental requirement for the deployment of Connected Automated Vehicles (CAVs). To meet this need, a new emerging trend is represented by cooperative methods where vehicles fuse information coming from navigation and imaging sensors via Vehicle-to-Everything (V2X) communications for joint positioning and environmental perception. In line with this trend, this paper proposes a novel data-driven cooperative sensing framework, termed Cooperative LiDAR Sensing with Message Passing Neural Network (CLS-MPNN), where spatially-distributed vehicles collaborate in perceiving the environment via LiDAR sensors. Vehicles process their LiDAR point clouds using a Deep Neural Network (DNN), namely a 3D object detector, to identify and localize possible static objects present in the driving environment. Data are then aggregated by a centralized infrastructure that performs Data Association (DA) using a Message Passing Neural Network (MPNN) and runs the Implicit Cooperative Positioning (ICP) algorithm. The proposed approach is evaluated using two realistic driving scenarios generated by a high-fidelity automated driving simulator. The results show that CLS-MPNN outperforms a conventional non-cooperative localization algorithm based on Global Navigation Satellite System (GNSS) and a state-of-the-art cooperative Simultaneous Localization and Mapping (SLAM) method while approaching the performances of an oracle system with ideal sensing and perfect association.

eess.SP↗