Luciano Leonel Mendes

Electromagnetic heating in magnetodielectric photonic resonators is driven by dissipative polarization and magnetization currents; thus, the local time‐averaged loss density is set by material losses weighted by the internal electromagnetic fields. Thermal analyses are often formulated using total currents, obscuring how individual resonant multipoles contribute to heat generation and subsequent heat‐flux dynamics. Here, we introduce a semianalytical, mode‐resolved link between multipolar electrodynamics and thermal dissipation by expressing the temperature distribution as a superposition of multipolar contributions with a controlled correction for interorder field coupling. For isolated resonances, each multipole order acts as an independent heat source with a distinctive spatiotemporal signature that locally governs the resulting spatial and temporal heat‐transfer behavior. For the dual‐symmetric reference case, dipolar resonances preferentially concentrate dissipation near the sphere center, whereas higher‐order resonances produce increasingly peripheral, shell‐like loss localization, yielding distinct thermal pathways. Spherical symmetry provides exact analytical expressions for internal multipolar fields and electromagnetic loss decomposition. The approach can extend to other scatterers when suitable internal‐field modal representations are available, including T‐matrix multipoles or numerically computed eigenmodes. By linking electromagnetic resonances mode by mode to irreversible thermal dissipation, resonances can be selected to favor core heating, shell‐localized heating, or distinct early‐time outward heat‐transport pathways.

Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limited network coverage and insufficient support for both low- and high-throughput applications, which hinder the deployment of conventional and broadband-intensive Internet of Things solutions. A central challenge is the lack of adequate field-level network infrastructure, with connectivity often unavailable or unreliable. This article presents a comprehensive survey of Broadband-based IoT (B-IoT) as a solution for supporting both low- and high-data-rate digital agriculture applications, including UAVs, computer vision, and extended reality, even in settings without continuous internet connectivity. Using a structured narrative-review approach, this survey synthesizes relevant peer-reviewed and technical literature on B-IoT-enabled digital agriculture and organizes the evidence around communication key performance indicators (KPIs), deployment constraints, and four technology domains: sensing, connectivity, intelligence/compute, and control/application. It examines how technologies such as 5G/6G, dynamic spectrum access, non-terrestrial networks, and edge computing can help address connectivity and infrastructure gaps in underserved agricultural areas. Furthermore, we introduce and analyze the concept of Evolved-Variety Technologies, which combines modified state-of-the-art modules with next-generation networks to create flexible, modular, and scalable system designs adaptable to diverse topographical and operational conditions. Beyond technical evaluations, the article examines economic feasibility, environmental sustainability, and policy implications, emphasizing the need for coordinated roles among governments, telecom providers, and agribusiness stakeholders. Our findings advocate for hybrid telecom architectures that integrate terrestrial and non-terrestrial components, leveraging emerging technologies to reduce the rural–urban digital divide and enable scalable, data-driven agriculture in underserved regions.

his paper presents a frequency-selective metasurface that provides strong linear-polarization selectivity through the suppression of the Cartesian electric and toroidal dipole moments, with weak toroidal–electric dipole interference further enhancing the transparency response. The structure is a two-dimensional periodic array of Tai-Chi-shaped metallic unit cells that exhibits a transparency frequency under normal incidence. Using a multipole-based analytical model together with full-wave simulations and microwave measurements, we show that this transparency frequency is characterized by a weakened radiating total electric-dipole contribution, yielding near-unity transmission. Measurements in both transmission and reflection geometries confirm the predicted polarization-dependent spectra, and additional measurements up to 45° incidence demonstrate that the transparent-metasurface response remains largely angle-robust. Bistatic radar cross-section measurements at the corresponding frequency (4.75 GHz) further indicate low backscattering around the backward direction.Waveform integrity was evaluated in 256-QAM experiments which show that the error vector magnitude (EVM) remains below 1% for both polarizations (0.96% for TE and 0.81% for TM) for the transparency frequency, while polarization-dependent EVM degradation was observed at the transmission minima (e.g., 0.81%!3.58% at 3.4 GHz and 1.06%!11.74% at 9.2 GHz). The metasurface produced here therefore represents a compact platform for polarization-selective RF filtering and transparent operation at resonance, with straightforward scalability to millimeter-wave and terahertz bands via geometric scaling and re-optimization.his paper presents a frequency-selective metasurface that provides strong linear-polarization selectivity through the suppression of the Cartesian electric and toroidal dipole moments, with weak toroidal–electric dipole interference further enhancing the transparency response. The structure is a two-dimensional periodic array of Tai-Chi-shaped metallic unit cells that exhibits a transparency frequency under normal incidence. Using a multipole-based analytical model together with full-wave simulations and microwave measurements, we show that this transparency frequency is characterized by a weakened radiating total electric-dipole contribution, yielding near-unity transmission. Measurements in both transmission and reflection geometries confirm the predicted polarization-dependent spectra, and additional measurements up to 45° incidence demonstrate that the transparent-metasurface response remains largely angle-robust. Bistatic radar cross-section measurements at the corresponding frequency (4.75 GHz) further indicate low backscattering around the backward direction.Waveform integrity was evaluated in 256-QAM experiments which show that the error vector magnitude (EVM) remains below 1% for both polarizations (0.96% for TE and 0.81% for TM) for the transparency frequency, while polarization-dependent EVM degradation was observed at the transmission minima (e.g., 0.81%!3.58% at 3.4 GHz and 1.06%!11.74% at 9.2 GHz). The metasurface produced here therefore represents a compact platform for polarization-selective RF filtering and transparent operation at resonance, with straightforward scalability to millimeter-wave and terahertz bands via geometric scaling and re-optimization.T

Selecting the Modulation and Coding Scheme (MCS) from the measured Signal-to-Noise Ratio (SNR) is commonly implemented via precomputed Look-Up Tables (LUTs) derived from analytical models, link-level simulations under idealized assumptions, or empirical Bit Error Rate (BER)/Block Error Rate (BLER) thresholds. In operational Software-Defined Radio (SDR) platforms, however, non-ideal channel estimation, pilot-grid interpolation, equalizer noise enhancement, decoder non-idealities, and Radio Frequency (RF) front-end impairments distort this mapping, causing conventional tables to miss the expected goodput. This paper proposes a measurement-driven Adaptive Modulation and Coding (AMC) strategy based on Contextual Bandit (CB), which learns a stationary SNR→MCS policy directly from link measurements by maximizing downlink Medium Access Control (MAC)-layer goodput. The target application is a TV White Space (TVWS) backhaul in the Ultra High Frequency (UHF) band, designed to provide Internet connectivity to remote areas: both the Base Station (BS) and the remote terminal are fixed, typically under Line-of-Sight (LoS), yielding quasi-stationary channel conditions over many transmission intervals. We implement and evaluate the proposed approach on an SDR-based radio access system in two representative deployments: (i) an RF wireless link that exercises the full Physical Layer (PHY) stack under regulatory transmit-power constraints, and (ii) a Radio over Fiber (RoF) extension enabling tens-of-kilometers reach. The context is the measured SNR (discretized into bins), the action is the MCS index, and the reward is the delivered goodput; transmission errors and retransmissions are naturally captured through reduced goodput. For controlled experimentation and reproducible operating regimes, we constrain the effective SNR within a desired interval by programmatically adjusting the transmit-chain gain. As baselines, we consider as references the link adaptation based on an Inner Loop Link Adaptation (ILLA) obtained from laboratory calibration, an Outer Loop Link Adaptation (OLLA) that periodically adjusts the selected MCS using a BER target, and a supervised Histogram-Based Gradient Boosting Regressor (HGBR) baseline, trained offline from measured samples to approximate the goodput-maximizing SNR→MCS mapping. Experimental results show that the learned CB policy improves the area under the goodput–SNR curve and the mean Spectral Efficiency (SE) over both the baselines within the operational SNR range, while satisfying the BER target. In addition, by directly learning the optimal SNR→MCS mapping from interaction with the real environment, the proposed approach avoids the approximation stage required by the offline HGBR model and eliminates the transient convergence period required by the BER-driven OLLA to adapt from the initial ILLA table toward its steady-state operating point, thereby enabling immediate near-optimal performance in quasi-stationary wireless backhaul links.

Rural smart agriculture faces significant challenges, including limited connectivity, high deployment costs, and the need for real-time decision-making. To address these challenges, this paper proposes an integrated framework that combines non-terrestrial networks, specifically low-earth orbit satellites and high-altitude platforms, together with unmanned aerial vehicles, integrated with fifth-generation private edge networks. The framework leverages advanced artificial intelligence algorithms, including reinforcement learning for adaptive control, federated learning for decentralized training, graph neural networks for efficient network representation and routing, and particle swarm optimization for coordinated operations of unmanned aerial vehicles. The simulation results demonstrate that the proposed artificial intelligence-driven system reduces energy consumption by 35%, improves latency by 45%, and reduces operational cost by up to 40% compared to traditional centralized systems. The paper also explores security enhancements using artificial intelligence-enabled anomaly detection and privacy-preserving data exchange. These results suggest a promising direction for scalable, secure, and cost-effective deployment of smart agricultural systems in underserved rural areas.

Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limited network coverage and insufficient support for both low- and high-throughput applications, which hinder the deployment of conventional and broadband-intensive Internet of Things solutions. A central challenge is the lack of adequate field-level network infrastructure, with connectivity often unavailable or unreliable. This article presents a comprehensive survey of Broadband-based IoT as a solution for supporting both low- and high-data-rate digital agriculture applications, including UAVs, computer vision, and extended reality, even in settings without continuous internet connectivity. It examines how technologies such as 5G/6G, dynamic spectrum access, non-terrestrial networks, and edge computing can help address connectivity and infrastructure gaps in underserved agricultural areas. Furthermore, we introduce and analyze the concept of Evolved-Variety Technologies, which combines modified state-of-the-art modules with next-generation networks to create flexible, modular, and scalable system designs adaptable to diverse topographical and operational conditions. Beyond technical evaluations, the article examines economic feasibility, environmental sustainability, and policy implications, emphasizing the need for coordinated roles among governments, telecom providers, and agribusiness stakeholders. Our findings advocate for hybrid telecom architectures that integrate terrestrial and non-terrestrial components, leveraging emerging technologies to reduce the rural–urban digital divide and enable scalable, data-driven agriculture in underserved regions.

While Radio-over-Free-Space-Optics (RoFSO) is a key xG enabler, most research focuses on high-complexity systems unsuitable for cost-sensitive rural deployments. This work presents and experimentallyvalidates a low-complexity, sub-6 GHz RoFSO architecture designed for heterogeneous networks. We transport a 70 MHz intermediate frequency signal, carrying a spectrally-efficient TV Whitespace (TVWS) waveform, over an 800 m turbulent optical link using direct subcarrier intensity modulation. At the remote end, a mixer up-converts the signal back to the TVWS band for RF transmission, validating a cost-effective mix-amplify-and-forward remote antenna unit. We provide a detailed performance characterisation of the optical backhaul under real-world atmospheric conditions and demonstrate the end-to-end viability of the hybrid architecture. Our results establish a practical pathway for physically separating complex baseband processing from low-power and low-cost remote radio heads in challenging environments.

Sixth-generation (6 G) mobile networks is expected to revolutionize wireless connectivity, paving the way for the Internet of Everything (IoE) and enabling seamless communication between devices, people, and organizations. Advanced techniques, such as non-orthogonal multiple access (NOMA), massive multiple input multiple output (MIMO), and millimeter wave (mmWave) are proposed to deliver high data rates and increase the capacity and spectral efficiency of ultra-dense device networks. However, in scenarios like enhanced remote area communications (eRAC), the use of sub-gigahertz frequencies and TV white spaces (TVWS) is essential to provide Internet services in rural and remote areas, making these advanced techniques impractical. In this context, this paper proposes a new multiple access approach based on the faster-than-Nyquist generalized frequency division multiplexing (FTN-GFDM) waveform to improve user throughput and network capacity in remote areas. Resources in the FTN-GFDM time-frequency grid are allocated with the goal of avoiding inter-user interference (IUI) caused by signal overlap in the time or frequency domains, enabling equalization and detection of user signals without loss of bit error rate (BER) performance. This paper exploits resource allocation strategies and uplink and downlink scenarios for faster-than-Nyquist generalized frequency division multiple access (FTN-GFDMA) schemes, evaluating the effects of interference on BER performance and detection complexity.

In [1], we introduced a NN designed to reduce the PAPR in OFDM systems. However, the original study did not include explicit generalization tests to assess how well the NN would perform on previously unseen data, which prevented a comprehensive evaluation of the model's robustness and applicability in diverse scenarios. To address this gap, we conducted additional generalization assessments, the results of which are presented in this case study. These results serve both to complement and to refine the original analysis reported in [1]. Most importantly, the overall conclusions of the initial study remain valid: the NN is still able to reduce the PAPR level to a desired reference value, also with a lower computational cost, confirming the effectiveness and practical applicability of the proposed method across a more generalized setting.