Resource
2026 EN
Luigi Di Michele · Anna Verlanti · Angelo Gifuni
+4 more
In this study, the sensitivity of microwaves to plastic waste in the aquatic environment is analyzed through a laboratory experiment. A bistatic radar configuration is developed to observe plastic material deployed in a small tank filled with fresh water, under different submersion and moisture conditions, in a broad range of frequencies and in both co- and cross-polarization. Experimental results indicate that at higher frequencies (> 4 GHz) the signal resulting from plastic-covered water surface can be distinguished from the reference calm water one. Both co- and cross-polarized radar configurations allow discriminating plastics from water surface; however, the cross-polar configuration calls for a lower amplitude signal. The degree of wetness of the plastic items plays a key role in radar observation performance, with wet plastics resulting in the best detectability.
Resource
2026 EN
Malik Qamar Abbas · Lassi Aarniovuori · Pasi Peltoniemi
+2 more
The rapid adoption of electric vehicles (EVs) is increasing the demand for charging solutions that are compact, cost-effective, and grid-friendly. Conventional off-board charging stations face challenges such as high infrastructure cost, distribution-grid stress from high-power charging, and limited flexibility for RE integration. Integrated on-board chargers (IOBCs) offer a promising alternative by reusing the traction inverter and motor for charging, thereby reducing system size, weight, and cost, while improving hardware utilization. Despite growing research interest, a consolidated and critical assessment of IOBC architectures and control strategies remains limited. This paper presents a comprehensive review of IOBC technologies, covering key topologies and control approaches for charging, traction, and vehicle-to-grid (V2G) operation. It critically examines major implementation challenges, including unintended charging torque and torque ripple, harmonic current distortion and grid-code compliance, thermal stress due to high power density, and safety and isolation constraints. Finally, future research directions are discussed, with an emphasis on advanced converter topologies, wide-bandgap devices, enhanced thermal management, cybersecurity, and data-driven resilience for secure and reliable EV charging systems.
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2026 EN
Devis Bianchini · Valeria De Antonellis · Massimiliano Garda
+1 more
Agri-food supply chains are complex and distributed ecosystems involving heterogeneous actors, from primary producers to retailers and consumers. Ensuring information consistency and coordination among these actors is essential to improve traceability, efficiency, and transparency. In this context, Blockchain Technology (BCT) has emerged as a promising enabler for trustworthy and decentralized data management. Its ability to provide immutable and transparent records makes it particularly suitable for supporting traceability and accountability in agri-food processes. However, challenges emerge in complex, intertwined supply chains, where different supply chains may adopt heterogeneous blockchain platforms, each characterized by its own technological infrastructure, smart contract language, and approach to on-chain/ off-chain data management. These differences hinder interoperability, scalability, and cost-effective data sharing. This issue is particularly relevant in agri-food ecosystems, where it is common for the same actor to participate in multiple intertwined supply chains. Despite its importance, the considered problem is still underexplored in the literature. We propose MAIA, a Model-based methodological Approach for blockchain Integration in intertwined Agri-food supply chains, covering the full lifecycle from requirements elicitation, through technology-agnostic resource and service modeling, to blockchain-specific implementation. Calibrated on a batch-oriented model, the approach ensures a resource-oriented perspective and scalable integration across heterogeneous platforms. A proof-of-concept case study, validated on both Ethereum and Hyperledger Fabric, demonstrates its effectiveness in addressing integration challenges and, at the instantiation level, optimizing resource management in terms of costs, execution time, and scalability.
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2026 EN
Egle Maria Orlando · Federica Nenna · Federico Maria Lorusso
+3 more
Recent industrial paradigms have increasingly adopted human-centric approaches that prioritize worker well-being in collaborative robotics, where humans and robots share workspaces and coordinate their actions in real time. Understanding these interactions requires tools capable of capturing the embodied, dynamic nature of human-robot collaboration while remaining non-intrusive and ecologically valid. Markerless pose estimation offers a promising solution, yet existing approaches track humans and robots separately, missing the collaborative dynamics that emerge from their synchronized movements.We present an integrated pipeline for concurrent 3D pose estimation of humans and the UR10e cobot using a single RGB-D camera. The system combines 2D keypoint detection with depth-based reconstruction to generate synchronized 3D trajectories for both agents within a unified reference frame. Quantitative validation was conducted on a custom-trained UR10e model using encoder-based ground truth, demonstrating the system’s reliability across both collaborative assembly tasks and controlled trajectories. By capturing human and robot as a unified dyadic system rather than independent entities, the pipeline enables quantitative analysis of interaction fluency, embodied coordination, and shared action dynamics, fundamental aspects for advancing safe, efficient, and human-centered collaborative robotics. Link Github: https://github.com/egleorl/Markerless-3D-Human-Cobot-Pose-Estimation.
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2026 EN
Lucia Pepa · Marianna Capecci · Antonia Antoniello
+7 more
In-home monitoring and rehabilitation are promising approaches for efficient and effective care of people with Parkinson’s Disease (PwPD). However, their adoption is still minimal, few evidences come from real in-home studies, and the integration of telemonitoring and telerehabilitation functions is absent. This study presents a novel system with remote smartwatch-based monitoring and rehabilitation integrated functions. Furthermore, an original machine learning approach is proposed to predict the clinical improvement, stability, or worsening of motor symptoms severity after a 3-month at-home rehabilitation period. Ninety-five PwPD were enrolled. An exploratory data analysis based on linear regression was applied to the collected smartwatch data to drive the extraction of meaningful features about the trend of physiological variables. Then, a time-series forest classification approach was applied to predict motor outcome from extracted features. The performance was evaluated against the clinical evaluation of the Unified Parkinson’s Disease Rating Scale before and after the 3-month period. Performance metrics reached 63% for accuracy, F1-score and recall, and 62% precision in nested cross-validation. Shapley analysis revealed an interesting agreement between model classification strategy and known clinical markers of overall health conditions and physical activity. Features related to heart rate and stress emerged as the most discriminative, showing on average decreased values of stress and heart rate in improved participants and increased values of the same features in worsened ones. Overall, these findings support the potential of combining remote monitoring and machine learning approaches to better understand the complex interaction between physiological signals, physical activity, and clinical status in PwPD, but the identified limitations suggest it should be considered a starting point for further research and improvements.
Resource
2026 EN
Wen-Xuan Long · Shengyu Ye · Marco Moretti
+4 more
Sixth-generation (6G) wireless communication systems are expected to embrace extremely large aperture arrays (ELAAs), novel antenna architectures, and operation in high-frequency bands to meet the rapidly growing demand for data transmission. By increasing the number of antenna elements, ELAAs enable finer spatial resolution and enhanced beamforming capabilities. At these high operating frequencies, an ELAA aperture may span tens or even hundreds of wavelengths, causing the propagation conditions to gradually depart from the conventional far-field assumption, and making spherical-wave effects increasingly prominent. More generally, near-field behavior is jointly determined by the array size relative to the wavelength and the link distance, and it may also arise in short-range deployments. Consequently, near-field propagation is expected to play a key role in 6G systems. In the near-field region, the electromagnetic field impinges on the array with a non-negligible wavefront curvature. Consequently, the channel is parameterized by both angular information and the propagation distance (range) between transmitter and receiver. This additional distance-dependent degree of freedom increases the dimensionality of the channel parameters and alters the structural properties exploited by far-field channel estimators. As a result, straightforward extensions of conventional far-field channel estimation techniques, typically designed to exploit only angular information, to near-field scenarios may lead to significant high computational complexity. These challenges motivate the development of estimation methods tailored to the distinctive characteristics of near-field propagation. This paper provides a comprehensive overview of recent advances in near-field channel estimation. From an electromagnetic-wave perspective, we first delineate the boundary between near- and far-field regions and highlight the fundamental differences in their propagation mechanisms. We then summarize representative ELAA architectures, spherical-wavefront control techniques, emerging near-field applications, and ongoing standardization efforts related to near-field communications. Next, we introduce widely used near-field channel models and contrast them with their far-field counterparts. Finally, we systematically review major estimation techniques under various system configurations, including single- and multiuser, as well as single- and multi-carrier settings, covering both direct channel estimation between the base station and user equipments, and cascaded channel estimation assisted by a reconfigurable intelligent surface. The surveyed techniques reflect different trade-offs among estimation accuracy, complexity, and robustness. Overall, this survey aims to provide technical insights and theoretical foundations for efficient and scalable near-field channel estimation in 6G systems, while also highlighting key challenges and promising future research directions.
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2026 EN
Claudio Marche · Vlad Popescu · Luigi Atzori
+1 more
The Internet of Things (IoT) is increasingly supporting the tourism sector by enabling adaptive services that enhance the visitor experience. Among emerging applications, itinerary planning has gained significant attention, leveraging IoT data to deliver personalized and adaptive routes. However, current systems show key limitations. Personalization often depends on static forms rather than adaptive learning; validation is usually restricted to simulations, and the few real implementations mostly rely on mobile apps, tools that tourists are reluctant to download and often abandon after limited use. To overcome these challenges, this paper proposes an itinerary planning system that integrates reinforcement learning for tourist profiling with a genetic algorithm for multi-objective optimization, implemented in a real-world scenario through a cloud infrastructure and delivered via an interactive totem that serves as the access point for tourists. Results confirm both the efficiency and scalability of the approach, showing that the system can be seamlessly extended to diverse urban contexts.
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2026 EN
Ye Chen · Konstantinos Akritidis · Kai-Wen Chen
+48 more
This paper highlights micro-transfer printing (MTP) as a promising scalable approach to heterogeneous integration for silicon photonics. MTP uniquely achieves high integration density, high throughput, and high material efficiency through a low-temperature, back-end integration process. Current demonstrations, including integrated III-V lasers and thin-film electro-optic modulators, confirm MTP's potential. Industrial adoption requires resolving challenges related to final integration yield and throughput, device reliability, and supply chain maturity.
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2026 EN
Klaus Scipal · Clement Albinet · Michele Caccia
+24 more
The European Space Agency’s (ESA) BIOMASS mission is a pioneering Earth observation satellite mission launched on April 29, 2025. Utilizing a P-band synthetic aperture radar (SAR), the objective of BIOMASS is to deliver estimates of above-ground forest biomass, forest height (FH), and forest disturbance (FD), with unprecedented accuracy. The mission’s primary scientific goal is to quantify the distribution and changes in forest biomass, thereby reducing uncertainties in carbon flux estimates and informing climate models. The satellite’s advanced instrumentation and innovative approach allow it to penetrate dense forest canopies, capturing data even in challenging environments. The mission will operate in two distinct phases: the tomographic phase and the interferometric phase, which will support polarimetric interferometric SAR (Pol-InSAR) and tomographic SAR (TomoSAR) processing. Additionally, BIOMASS will provide valuable observational data for ice sheets, deserts, the ionosphere, below canopy topography, and other domains.
Resource
2026 EN
Abdelaziz Bounhar · Mireille Sarkiss · Michele Wigger
This paper characterizes the covert capacity-key tradeoffs for discrete memoryless channels (DMC) and discrete memoryless multiple-access channels (DMMAC). The focus is on channels where communication rates are measured as the number of transmitted bits divided by the square-root of the blocklength and by the square-root of the covertness constraint. Previous results had determined the largest achievable covert rates for these channels, as well as the minimum key rates required to achieve these largest covert rates. In this work, we additionally determine the minimum key rates required at reduced covert rates. Stated differently, we determine the set of all covert rates that are achievable for a given set of key rates. This is termed the covert capacity-key tradeoff . Our results on the covert capacity-key tradeoffs over DMCs and DMMACs show that for small key rates and when the adversary observes the inputs through a better channel than the legitimate receiver, binary signalling at all transmitters is optimal even for larger input alphabets and the capacity-key tradeoff grows linearly. Moreover, in this regime the covert capacity-key tradeoff of DMMACs is square implying that each of the transmitters can simultaneously achieve its own largest covert rate depending only on its own available key rate, irrespective of the other transmitter. For larger key rates or when the adversary is not uniformly stronger than the legitimate receiver over all inputs, the covert capacity-key tradeoff region of DMMACs is non-square and a tradeoff arises between the rates of the various transmitters.