Monitoring situational awareness is critical in highly demanding environments where sustained attention and vigilance are essential for safety and performance. Electroencephalography (EEG) and eye-tracking (ET) provide complementary insights into the perceptual layer of situational awareness, capturing neural and ocular signatures of information processing, attention, and fatigue. However, studies have typically examined perception-related conditions such as workload, fatigue, stress, and drowsiness in isolation, limiting understanding of their shared and distinct physiological patterns. This systematic review synthesizes findings from studies that recorded EEG and ET concurrently to investigate perception-related conditions. Following the PRISMA 2020 statement, five databases were searched, and 47 studies met the inclusion criteria. The most frequently reported EEG features included theta, alpha, and beta activity, while ET metrics commonly involved fixation patterns, pupil diameter, blink dynamics, and percentage of eyes closed (PERCLOS). Across studies, fatigue, mental workload, and stress exhibited overlapping physiological signatures, although multimodal data helped differentiate these closely related states. Drowsiness and vigilance decrement appeared along a shared continuum, with microsleeps showing distinct physiological profiles. Classification models generally achieved higher accuracy when integrating EEG and ET features than when using either modality alone. This review highlights the potential of concurrent EEG and ET monitoring for improving the detection of perception-related conditions and for disambiguating closely related states. These findings also support the need for standardized multimodal protocols and real-time multimodal classification models to strengthen cognitive-state monitoring, operational performance, and error prevention in high-risk domains.
Operational organizations increasingly require Cyber Situational Awareness (CySA) capabilities that go beyond isolated technical alerts, providing mission-relevant artefacts that can be embedded into heterogeneous toolchains and cyber security or cyber defense processes. ECYSAP EYE addresses this need through an adoption-oriented System-of-Systems (SoS) architecture centered on seven groups of mission-focused artefacts: the Recognized Cyberspace Picture (RCyP), Cyber Situational Reports (CySRs), the What-If Analysis Report (WIAR), Option Recommendations (OPRE), an operator Dashboard/HMI (DSH), Action Enforcement (AE), and After-Action Reports (AAR). The ECYSAP EYE architecture structures the transition from perception (full-spectrum RCyP views), to decision-oriented reasoning (WIAR/CySRs/OPRE), and to operational execution and learning (DSH/AE/AAR), with explicit integration surfaces that support incremental deployment and validation. This paper presents this innovative project from a technology transfer perspective, summarizing the updated architecture, the functional role of seven groups of artefacts, and the expected impact of cyber situations on the decision-making process in the context of a mission planning and execution.
Background Maritime activity is expanding globally, increasing the demand for robust port security systems capable of detecting illegal trafficking. Due to the growing sophistication of smuggling methods, law enforcement agencies require advanced surveillance and prevention technologies such as those developed in the SMAUG project. In this context, initiatives such as the SMAUG project aim to deliver integrated surveillance capabilities coordinated by a high-level deep reinforcement learning (DRL) decision-making system that operates on image-based environmental representations. Despite their effectiveness, DRL models are closed-boxes, complicating continuous model monitoring (CMM). Conventional drift detection captures shifts in input or output distributions yet often fails to explain underlying problems. Explainable AI (XAI) techniques can provide a complementary approach with insights into the agent’s inner workings, enabling monitoring of the concept rather than just the data. Methods We propose FADMON, an XAI-driven concept drift detection method for image-based models. FADMON performs statistical drift tests on feature attributions to detect deviations in learned policies. We demonstrate how FADMON can enhance CMM with a three-stage model monitoring architecture that enables semi-supervised explainable model monitoring. We validate our approach with SMAUG’s decision-making DRL model on a simulated maritime port surveillance environment under multiple unforeseen scenarios. Results FADMON consistently flags drift on all drifted scenarios with mean p-values of 0.000 with no variance trough 30 repetitions, with lower mean p-values (0.553±0.215) on non-drifted scenarios with respect to other established drift detection methodologies such as prior probability shift detection (0.65 ± 0.000), though well above the standard 0.05 threshold. Conclusions FADMON can add an explainability layer to the monitoring system while also supporting detection of changes in the underlying interpretation of the input data by the model, monitoring the concept rather than the data, while matching established drift detection methods metrics-wise.
The Madrid quantum network, namely MadQCI and its ecosystem, is testing the scalability and adaptability of the network to multiple application niches. To achieve this, it is essential adapting the quantum communications infrastructure (QCI) to the specific requirements of each niche, as well as having versatile and adaptative key management system. In addition, collaboration from the industrial ecosystem is necessary, so several open calls were articulated as public procurement files, as well as multiple demonstrators, besides the usual collaboration of the industrial partners in the related R&D projects. This work presents these multiple applications; the design decisions made to tailor the quantum network to them in a scalable and adaptable manner; and some preliminary results.
The rapid growth of resident space objects is increasing the complexity of space situational awareness sensor tasking, challenging classical optimization methods as they allocate finite, heterogeneous, and distributed sensing resources across ever-larger catalogues. Existing deep reinforcement learning approaches show promise in reduced settings, but fixed-dimensional state and action representations limit their ability to scale to large, dynamic catalogues and distributed sensing networks. We introduce VISTA (Variable-Entity Intelligent Sensor Tasking Architecture), a scalable deep reinforcement learning architecture for persistent uncertainty-driven catalogue maintenance across variable object populations and sensor configurations. VISTA combines physics- and mission-informed top-K retrieval with entity-centric attention, recurrent memory, and pointer-based action decoding, thereby keeping each agent's observation and action spaces independent of catalogue size. We evaluate VISTA across different scenarios, from fixed-size single-sensor benchmarks to large-scale space-based tasking and heterogeneous cooperative sensing. With 30 orbiting targets, VISTA recovers the catalogue 31.2
