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The MitraClip is the most widely percutaneous treatment for mitral regurgitation, typically performed under the real-time guidance of 3D transesophagel echocardiography (TEE). However, artifacts and low image contrast in echocardiography hinder accurate clip visualization. This study presents an automated pipeline for clip detection from 3D TEE images. An Attention UNet was employed to segment the device, while a DenseNet classifier predicted its configuration among ten possible states, ranging from fully closed to fully open. Based on the predicted configuration, a template model derived from computer-aided design (CAD) was automatically registered to refine the segmentation and enable quantitative characterization of the device. The pipeline was trained and validated on 196 3D TEE images acquired using a heart simulator, with ground-truth annotations refined through CAD-based templates. The Attention UNet achieved an average surface distance of 0.76 mm and 95% Hausdorff distance of 2.44 mm for segmentation, while the DenseNet achieved an average weighted F1-score of 0.75 for classification. Post-refinement, segmentation accuracy improved, with average surface distance and 95% Hausdorff distance reduced to 0.75 mm and 2.05 mm, respectively. This pipeline enhanced clip visualization, providing fast and accurate detection with quantitative feedback, potentially improving procedural efficiency and reducing adverse outcomes., Comment: 29 pages, 7 figures

The Real-time Transport Protocol (RTP)-based real-time communications (RTC) applications, exemplified by video conferencing, have experienced an unparalleled surge in popularity and development in recent years. In pursuit of optimizing their performance, the prediction of Quality of Service (QoS) metrics emerges as a pivotal endeavor, bolstering network monitoring and proactive solutions. However, contemporary approaches are confined to individual RTP flows and metrics, falling short in relationship capture and computational efficiency. To this end, we propose Packet-to-Prediction (P2P), a novel deep learning (DL) framework that hinges on raw packets to simultaneously process concurrent RTP flows and perform end-to-end prediction of multiple QoS metrics. Specifically, we implement a streamlined architecture, namely length-free Transformer with cross and neighbourhood attention, capable of handling an unlimited number of RTP flows, and employ a multi-task learning paradigm to forecast four key metrics in a single shot. Our work is based on extensive traffic collected during real video calls, and conclusively, P2P excels comparative models in both prediction performance and temporal efficiency.

Radiative transfer interactions with material ablation are critical contributors to vehicle heating during high-altitude, high-velocity atmospheric entry. However, the inherent complexity of fully coupled multi-physics models often necessitates simplifying assumptions, which may overlook key phenomena that significantly affect heat loads, particularly radiative heating. Common approximations include neglecting the contribution of ablation products, applying simplified frozen wall boundary conditions, or treating radiative transfer in a loosely coupled manner. This study introduces a high-fidelity, tightly coupled multi-solver framework designed to accurately capture the multi-physics challenges of hypersonic flow around an ablative body. The proposed approach consistently accounts for the interactions between shock-heated gases, surface material response, and radiative transfer. Our results demonstrate that including radiative heating in the surface energy balance substantially influences the ablation rate. Ablation products are shown to absorb radiative heat flux in the vacuum-ultraviolet spectrum along the stagnation line, while strongly emitting in off-stagnation regions. These findings emphasize the necessity of a tightly coupled multiphysics framework to faithfully capture the complex, multidimensional interactions in hypersonic flow environments, which conventional, loosely coupled models fail to represent accurately.

Among the numerous heat conduction models, the Guyer--Krumhansl equation has a special role. Besides its various application possibilities in nanotechnology, cryotechnology, and even in case of modeling heterogeneous materials, it poses additional mathematical challenges compared to the Fourier or Cattaneo {(a.k.a. Maxwell--Cattaneo--Vernotte)} equations. Furthermore, the Guyer--Krumhansl equation is the first heat conduction model, which includes the curl of the heat flux density in the evolution equation. In the present paper, we place our focus on the consequences of the existence of such whirling heat current density by solving the two-dimensional Guyer--Krumhansl equation with a space and time-dependent heat pulse boundary condition. The discretization poses further challenges in regard to the boundary condition for which we propose a particular extrapolation method. Furthermore, with the help of the Helmholtz decomposition, we show the analogy with the linearized acoustics of Newtonian fluids, which reveals how the heat flux density plays the role of the velocity field. Our solutions also reveal an unexpected temperature evolution caused by the whirling heat flux density, namely, the temperature can locally be decreased for a short time in a case when the curl of the heat flux density dominates the heat conduction process.

The performance of Automated Recognition (ATR) algorithms on side-scan sonar imagery has shown to degrade rapidly when deployed on non benign environments. Complex seafloors and acoustic artefacts constitute distractors in the form of strong textural patterns, creating false detections or preventing detections of true objects. This paper presents two online seafloor characterisation techniques to improve explainability during Autonomous Underwater Vehicles (AUVs) missions. Importantly and as opposed to previous work in the domain, these techniques are not based on a model and require limited input from human operators, making it suitable for real-time onboard processing. Both techniques rely on an unsupervised machine learning approach to extract terrain features which relate to the human understanding of terrain complexity. The first technnique provides a quantitative, application-driven terrain characterisation metric based on the performance of an ATR algorithm. The second method provides a way to incorporate subject matter expertise and enables contextualisation and explainability in support for scenario-dependent subjective terrain characterisation. The terrain complexity matches the expectation of seasoned users making this tool desirable and trustworthy in comparison to traditional unsupervised approaches. We finally detail an application of these techniques to repair a Mine Countermeasures (MCM) mission carried with SeeByte autonomy framework Neptune., Comment: Presented at UACE (Underwater Acoustics Conference & Exhibition) 2023, Kalamata, Greece

This paper presents a comprehensive review of the current state of the art in quantum navigation systems, with a specific focus on their application in maritime navigation. Quantum technologies have the potential to revolutionise navigation and positioning systems due to their ability to provide highly accurate and secure information. The review covers the principles of quantum navigation and highlights the latest developments in quantum-enhanced sensors, atomic clocks, and quantum communication protocols. The paper also discusses the challenges and opportunities of using quantum technologies in maritime navigation, including the effects that the maritime environment and the specificity of marine applications can have on the performance of quantum sensors. Finally, the paper concludes with a discussion on the future of quantum navigation systems and their potential impact on the maritime industry. This review aims at providing a valuable resource for researchers and engineers interested in the development and deployment of quantum navigation systems.

This paper provides a comparison between the vibrational-specific state-to-state (StS) model for nitrogen plasma elaborated in Part I of this work and conventional two-temperature (2-T) models for simulating inductively coupled plasma (ICP) discharges under non-Local Thermodynamic Equilibrium (NLTE) conditions. Simulations are performed within the multi-physics computational framework established for ICP in Part I. Based on the findings of Part I, the quasi-steady-state (QSS) assumption is validated in the plasma core, thereby enabling the calculation of global rate coefficients under this assumption. This facilitates the reduction of the StS model to a "consistent" macroscopic 2-T model. Results from the StS model for nitrogen ICP torch exhibit considerable discrepancies when compared against predictions from the widely utilized Park 2-T model. On the contrary, the comparison between the newly proposed 2-T model, consistently derived from the original vibronic StS model, and the full StS results demonstrate excellent agreement in terms of plasma core location, morphology, and peak temperature distributions. This demonstrates the ability of the proposed 2-T model to capture the energy transfer and reactive processes predicted by the comprehensive StS model. Additionally, the study identifies the vibrational-translational (VT) energy transfer term in the 2-T model as the predominant factor in dictating plasma core morphology. This suggests a strong sensitivity of the ICP flow field to heavy-impact vibrational excitations and dissociative events., Comment: 27 pages, 16 figures

Adoption and deployment of robotic and autonomous systems in industry are currently hindered by the lack of transparency, required for safety and accountability. Methods for providing explanations are needed that are agnostic to the underlying autonomous system and easily updated. Furthermore, different stakeholders with varying levels of expertise, will require different levels of information. In this work, we use surrogate models to provide transparency as to the underlying policies for behaviour activation. We show that these surrogate models can effectively break down autonomous agents' behaviour into explainable components for use in natural language explanations.

The purpose of this work is the development of a self-consistent multi-physics modeling framework for ICP discharges. Unlike a monolithic approach, the hydrodynamics and electromagnetic field are handled by separate solvers, all developed within the Center for Hypersonics and Entry Systems Studies (CHESS) at the University of Illinois. Hydrodynamics is modeled using HEGEL , a finite volume solver for non-equilibrium plasmas. This solver is interfaced with the PLATO library, which is responsible for evaluating all plasma-related quantities (e.g., thermodynamic and transport properties). The electric field is handled by FLUX , a finite element solver. Coupling is realized using the PRE CICE open-source library. Applications are here presented and discussed to demonstrate the effectiveness of the proposed modeling strategy.

Three-dimensional transesophageal echocardiography (3DTEE) is the recommended imaging technique for the assessment of mitral valve (MV) morphology and lesions in case of mitral regurgitation (MR) requiring surgical or transcatheter repair. Such assessment is key to thorough intervention planning and to intraprocedural guidance. However, it requires segmentation from 3DTEE images, which is timeconsuming, operator-dependent, and often merely qualitative. In the present work, a novel workflow to quantify the patient-specific MV geometry from 3DTEE is proposed. The developed approach relies on a 3D multi-decoder residual convolutional neural network (CNN) with a U-Net architecture for multi-class segmentation of MV annulus and leaflets. The CNN was trained and tested on a dataset comprising 55 3DTEE examinations of MR-affected patients. After training, the CNN is embedded into a fully automatic, and hence fully repeatable, pipeline that refines the predicted segmentation, detects MV anatomical landmarks and quantifies MV morphology. The trained 3D CNN achieves an average Dice score of $0.82 \pm 0.06$, mean surface distance of $0.43 \pm 0.14$ mm and 95% Hausdorff Distance (HD) of $3.57 \pm 1.56$ mm before segmentation refinement, outperforming a state-of-the-art baseline residual U-Net architecture, and provides an unprecedented multi-class segmentation of the annulus, anterior and posterior leaflet. The automatic 3D linear morphological measurements of the annulus and leaflets, specifically diameters and lengths, exhibit differences of less than 1.45 mm when compared to ground truth values. These measurements also demonstrate strong overall agreement with analyses conducted by semi-automated commercial software. The whole process requires minimal user interaction and requires approximately 15 seconds

The development of new AUV technology increased the range of tasks that AUVs can tackle and the length of their operations. As a result, AUVs are capable of handling highly complex operations. However, these missions do not fit easily into the traditional method of defining a mission as a series of pre-planned waypoints because it is not possible to know, in advance, everything that might occur during the mission. This results in a gap between the operator's expectations and actual operational performance. Consequently, this can create a diminished level of trust between the operators and AUVs, resulting in unnecessary mission interruptions. To bridge this gap between in-mission robotic behaviours and operators' expectations, this work aims to provide a framework to explain decisions and actions taken by an autonomous vehicle during the mission, in an easy-to-understand manner. Additionally, the objective is to have an autonomy-agnostic system that can be added as an additional layer on top of any autonomy architecture. To make the approach applicable across different autonomous systems equipped with different autonomies, this work decouples the inner workings of the autonomy from the decision points and the resulting executed actions applying Knowledge Distillation. Finally, to present the explanations to the operators in a more natural way, the output of the distilled decision tree is combined with natural language explanations and reported to the operators as sentences. For this reason, an additional step known as Concept2Text Generation is added at the end of the explanation pipeline.

This work presents a detailed investigation of the energy transfer and dissociation mechanisms in $\text{N}_2(\text{X}^1\Sigma_g^+)$+$\text{O}({}^3\text{P})$ and $\text{NO}(\text{X}^2\Pi)$+$\text{N}({}^4\text{S})$ systems using rovibrational-specific quasi-classical trajectory (QCT) and master equation analyses. The complete set of state-to-state kinetic data, obtained via QCT, allows for an in-depth investigation of the Zel'dovich mechanism leading to the formation of $\text{NO}$ molecules at microscopic and macroscopic scales. The master equation analysis demonstrates that the low-lying vibrational states of $\text{N}_2$ and $\text{NO}$ have dominant contributions to the $\text{NO}$ formation and the corresponding extinction of $\text{N}_2$ through the exchange process. For the considered temperature range, it is found that while nearly 50% of the dissociation processes for $\text{N}_2$ and $\text{NO}$ occurs in the molecular quasi-steady-state (QSS) regime, the amount of the Zel'dovich reaction is zero. Using the QSS approximation to model the Zel'dovich mechanism leads to an overestimation of $\text{NO}$ production by more than a factor of 4 in the high-temperature range. The breakdown of this well-known approximation has profound consequences for the approaches that heavily rely on the validity of QSS assumption in hypersonic applications. The investigation of the rovibrational state population dynamics reveals substantial similarity among different chemical systems for the energy transfer and the dissociation processes, providing promising physical foundations for the use of reduced-order strategies to other chemical systems without significant loss of accuracy., Comment: Please see SupplementaryMaterial.pdf together with this article

A non-equilibrium model for laser-induced plasmas is used to describe how nano-second temporal mode-beating affects plasma kernel formation and growth in quiescent air. The chemically reactive Navier-Stokes equations describe the hydrodynamics, and non-equilibrium effects are modeled based on a two-temperature model. Inverse Bremsstrahlung and multiphoton ionization are self-consistently taken into account via a coupled solution of the equations governing plasma dynamics and beam propagation and attenuation (i.e., Radiative Transfer Equation). This strategy, despite the additional challenges it may bring, allows to minimize empiricism and enables for more accurate simulations since it does not require an artificial plasma seed to trigger breakdown. The benefits of this methodology are demonstrated by the good agreement between the predicted and the experimental plasma boundary evolution and absorbed energy. The same goes for the periodic plasma kernel structures which, as suggested by experiments and confirmed by the simulations discussed here, are linked to the modulating frequency.

Multi-Party Computation (MPC) is a technique enabling data from several sources to be used in a secure computation revealing only the result while protecting the original data, facilitating shared utilization of data sets gathered by different entities. The presence of Field Programmable Gate Array (FPGA) hardware in datacenters can provide accelerated computing as well as low latency, high bandwidth communication that bolsters the performance of MPC and lowers the barrier to using MPC for many applications. In this work, we propose a Secret Sharing FPGA design based on the protocol described by Araki et al. We compare our hardware design to the original authors' software implementations of Secret Sharing and to work accelerating MPC protocols based on Garbled Circuits with FPGAs. Our conclusion is that Secret Sharing in the datacenter is competitive and when implemented on FPGA hardware was able to use at least 10$\times$ fewer computer resources than the original work using CPUs., Comment: 7 pages, 6 figures