Engineering

Abstract: Cycling is one of the most popular sports and recreational activities. Millions of new people start to integrate bicycling into their daily routines every year. Fitness and activity trackers are the most powerful motivation tools for cycling novices and serious cycling enthusiasts. For this purpose, we present LaserFit, a laser-based direct force power meter for fitness and activity tracking during cycling. We developed embedded hardware to collect the torque and the wheel rotation data, which is produced by a laser-based position sensing system mounted on the rear wheel to precisely record the power output produced by the rider during cycling. The sensor data transmits to a smartphone via Bluetooth/ANT+ for data acquisition and analysis. Our device can be produced at low costs and deliver a level of accuracy similar to that obtained with the most expensive systems available on the market. To evaluate the accuracy of our system extensive experiments were conducted. The results of the present study suggest that the LaserFit power meter provides a strong relationship (r = 0.97) across a range of trials in laboratory and field conditions when compared with the SRM power meter. The LaserFit is therefore considered a valid alternative for training and performance measurement during continuous cycling.

Abstract: The growing demand for high-efficiency, high-power-density converters in data centers, electric vehicle chargers, and renewable energy systems has accelerated the adoption of wide bandgap (WBG) devices. Gallium nitride (GaN) transistors offer superior switching speed, lower losses, and higher power density compared with Silicon (Si) devices. Accurate characterization of GaN switching dynamics is essential due to parasitic effects and transient phenomena affecting performance and reliability. The Double Pulse Test (DPT) is widely used to quantify critical parameters, including switching energy losses, dynamic RDS(on) and transient voltage and current waveforms. This paper reviews DPT techniques for GaN devices, focusing on measurement methodologies, parasitic mitigation, and reliability considerations, providing practical guidance for optimizing high-frequency GaN-based power converters.

Abstract: Expanding renewable energy capacity in sub-Saharan transmission systems is a cornerstone of sustainable development, yet weak grid infrastructure and the absence of flexible storage remain principal barriers to reliable and low-carbon energy access. This paper addresses the economic and environmental dimensions of that challenge by proposing a hierarchical multi-objective framework for the optimal siting and sizing of Battery Energy Storage Systems (BESS), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs the NSGA-II evolutionary algorithm to simultaneously maximize daily price-arbitrage revenue—the economic sustainability indicator—and minimize active power losses—the environmental efficiency indicator. For each candidate design, the lower level solves a multi-period DC Optimal Power Flow (DC-OPF) via CasADi/IPOPT, with thermal branch constraints embedded as hard linear inequalities through the Power Transfer Distribution Factor (PTDF) matrix, and voltage-corrected loss estimates recovered via a vectorized Extended DC Power Flow (EDCPF) model. Over 500 NSGA-II generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving maximum daily revenue of approximately € 10,033 at a marginal loss increment of 6.7×10⁻³ MWh. The Pareto front provides Mali system planners with a quantitative tool for balancing private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is both technically tractable and economically viable in the resource-constrained context of sub-Saharan sustainable energy transitions.

Abstract: This study evaluates the feasibility of using asphalt concrete as an impermeable core material for rockfill dams under tropical conditions. Laboratory testing and numerical modeling were conducted to assess the hydraulic and mechanical performance of asphalt concrete mixtures produced with locally available aggregates in Thailand. Asphalt mixtures were designed using the Marshall method with asphalt binder contents of 6% and 7% and target air void contents between 1-4%. Laboratory testing included permeability testing, Marshall stability testing, and triaxial compression tests to determine hydraulic conductivity, shear strength parameters, and deformation characteristics. Results show that asphalt concrete mixtures with air void contents below 1% exhibit extremely low permeability, with hydraulic conductivity on the order of 10⁻¹¹–10⁻¹² m/s, satisfying requirements for impervious dam cores. Triaxial compression tests yielded cohesion values between 97-572 kPa and friction angles ranging from 31° to 52°, indicating adequate shear resistance. Numerical simulations performed using GeoStudio compared rockfill dams with asphalt concrete cores and conventional clay cores. The results demonstrate that a 0.5‑m‑thick asphalt concrete core provides comparable seepage control and slope stability while requiring significantly smaller material volume. The findings suggest that asphalt concrete cores represent a technically feasible and economically advantageous alternative to clay cores, particularly in regions where suitable clay materials are limited.

Abstract: Moisture content is a critical parameter influencing the durability, structural performance, and maintenance of timber structures. However, current building inspection practices often rely on subjective interpretation, resulting in inconsistent assessment outcomes and ineffective maintenance decision-making. Despite the availability of various moisture measurement techniques, a standardized framework for interpreting moisture levels in relation to timber condition is still lacking. This study presents a structured review and synthesis of moisture content thresholds reported in the literature and proposes a standardized classification framework for timber defect assessment. The findings indicate that moisture content levels can be systematically categorized into four condition states dry, moderate, poor, and critical for each associated with specific maintenance actions. The proposed framework provides a practical linkage between moisture measurements and condition-based maintenance strategies, enabling more consistent and reliable inspection practices. The study contributes by transforming dispersed moisture-related data into a unified and actionable classification system, serving as a decision-support tool for building inspectors and maintenance practitioners. The proposed framework enhances the implementation of condition-based maintenance by reducing subjectivity and improving the accuracy of timber condition assessment.

Abstract: Rising complexity in cyber-physical systems development exposes challenges in the consistent and reusable specification of graphical domain-specific languages (DSLs). Despite the benefits of model-based systems engineering (MBSE), the absence of a standardized, lifecycle-wide specification process results in semantic inconsistencies, tool dependence, and limited interoperability. While our previous work has addressed individual stages of DSL definition, a comprehensive, standards-based process integrating these stages remains missing. Building on these foundations, this paper introduces a unified language specification process for graphical DSLs grounded in established standards---the Meta-Object Facility (MOF), Unified Modeling Language (UML), Web Ontology Language (OWL), and Resource Description Framework (RDF). The process integrates three core artifacts: a tool-independent ontology capturing domain semantics, a MOF-conformant metamodel unifying abstract syntax, semantics, and concrete syntax, and a UML-profile-based implementation. To support and exemplify this process, a prototypical toolchain is introduced that enables automated transformations between these artifacts, thereby facilitating the consistent propagation of semantics from ontology to implementation. The applicability of the proposed process is demonstrated through both a top-down automotive case and a bottom-up cybersecurity DSL, illustrating its cross-domain generalizability. By explicitly structuring and connecting ontology, metamodel, and implementation, this work contributes a semantically consistent, machine-interpretable, and tool-independent specification process for graphical DSLs in MBSE.

Abstract: The flotation of oxidized lead–zinc ores presents a significant challenge due to the low floatability of oxidized minerals and their weak interaction with conventional reagents. This study investigates the influence of the electrochemical parameters of the pulp (redox potential, Eh, and pH) on the flotation kinetics of oxidized lead–zinc ore from the Koskuduk deposit. It was established that the use of sodium sulfide Na₂S leads to the selective activation of lead-bearing minerals (Pb recovery up to 40.74%) with low zinc recovery (~12%). The use of a polysulfide-lime system S:CaO:H₂O is proposed, providing more uniform and stable sulfidization of the mineral surface. It is shown that the application of this reagent increases recovery to 65.10% for lead and 56.89% for zinc. It was established that the maximum recoveries are achieved within an Eh range of -120 to -180 mV at pH 11-12. Kinetic studies demonstrated that the main contribution to metal recovery occurs within the first 2-6 minutes of flotation. The obtained results indicate that flotation efficiency is determined both by the type of reagent and by the electrochemical state of the pulp, and that the use of polysulfide systems represents a promising approach for the processing of oxidized lead-zinc ores.

Abstract: Highway–rail grade crossing (HRGC) safety analysis is often based on raw incident counts or site-level models that do not control for exposure and ignore spatial dependence. This limits the ability to identify where risk is structurally concentrated across the rail network. The problem is important because misidentifying high-risk environments leads to inefficient allocation of limited safety resources and weakens corridor-level intervention strategies. This study introduces accumulated incidents per crossing (AIPX), an exposure-normalized metric that measured cumulative incident burden at the county level over a 51-year period (1975–2025). The study developed an algorithmic framework that integrates data reconciliation with spatial autocorrelation analysis, distributional modeling, and nonparametric machine learning to identify and interpret high-intensity risk environments. Global Moran’s I indicates statistically significant positive spatial autocorrelation (I = 0.359, p = 0.001), confirming that incident intensity is spatially clustered rather than random. Local indicators identify coherent high and low intensity county clusters. Distributional analysis shows that AIPX in high intensity clusters follows heavy-tailed behavior best represented by lognormal and Johnson SU distributions, indicating concentrated risk in a small subset of counties. Machine learning models achieve strong classification performance (AUC ≈ 0.85), with explainability methods consistently identifying temperature, train direction, crossing warning configuration, train composition, and track class as dominant associated features. These variables function as proxies for exposure intensity and network structure rather than causal drivers. The findings demonstrate that HRGC risk is a regional, network-driven phenomenon concentrated along freight-intensive corridors. The study provides a transparent and transferable framework that supports corridor-level prioritization of safety interventions and more effective allocation of infrastructure investments.

Abstract: We present a systematic experimental investigation of the primary breakup of a planar liquid film subjected to high-speed co-flowing gas streams. A water film of thickness D_ℓ ≈ 150 μm is produced from a symmetric airfoil lip and sheared on both sides by compressed air. Interfacial dynamics were recorded with a high-speed camera and analyzed to extract transverse wavelengths, rupture modes, and their dependence on operating conditions. We find that the transverse wavelength λ_tra decreases strongly with increasing gas speed and that, for a given dynamic pressure ratio M = (ρ_gV²_g )/(ρ_ℓV²_ℓ ), different absolute combinations of V_g and V_l produce markedly different λ_tra. These observations indicate that gas-shear intensity and the gas flow instability modes (vortex shedding) control the breakup of the liquid film; the liquid inflow plays a secondary role under our conditions. The results provide experimental benchmarks for model validation and suggest routes to tune atomizer performance via gas-side control.

Abstract: Moisture damage in buildings has conventionally been discussed mainly in relation to winter condensation in cold climates. In hot-humid buildings, however, deterioration develops under different boundary conditions, including persistently warm and humid outdoor air, frequent rainfall, air-conditioning operation, air leakage, and limited drying after wetting. Climate change is increasing atmospheric moisture loading and weakening nighttime recovery. These changes make hot-humid moisture risks more consequential not only in established hot-humid regions, but also in regions shifting toward more persistently humid climates. This review examines moisture damage in hot-humid buildings as a coupled problem linking climate change, building-envelope moisture response, risk assessment, microbial implications, and building adaptation. Representative scenarios include biological contamination on exterior surfaces, summer condensation and moisture accumulation within envelope assemblies, localized dampness at indoor surfaces and behind furniture, moisture stagnation in semi-enclosed spaces, and material deterioration or performance loss. These phenomena are interpreted not as isolated defects, but as manifestations of drying deficit. The review discusses climatic drivers, building-physics mechanisms, and major moisture and mold risk indices, including the Fungal Index (FI), the VTT Mold Index, isopleth-based approaches, Mold Resistance Design (MRD), and the Dose-Response Simple Isopleth for Mold (DR-SIM). It also highlights implications for envelope design, retrofit, ventilation, dehumidification, and operation. Overall, moisture damage in hot-humid buildings is best understood as the outcome of climate-driven drying deficit.

Abstract: Critical ventilation velocity is crucial for smoke control in tunnel fires, yet its behavior in tunnels with unconventional cross-sections remains inadequately quantified. This study numerically investigates the critical velocity in a full-scale, 1000-m-long semi-circular tunnel using Fire Dynamics Simulator (FDS). A systematic parametric analysis was conducted to evaluate the effects of fire heat release rate (HRR, 4-10 MW), cross-sectional geometry (semi-circular vs. three arched sections of equal area), and longitudinal slope (-1% to +2%). The critical velocity was determined using a successive approximation method, validated against multi-criteria safety thresholds including smoke back-layering length, upstream temperature, and visibility height. Results demonstrate that HRR is the dominant factor, with critical velocity increasing from 2.2 to 2.7 m/s. More importantly, cross-sectional shape exhibits a significant, non-monotonic influence; the streamlined semi-circular arch requires a lower critical velocity (2.2 m/s) compared to arched sections (2.4-2.6 m/s) of the same area, attributed to reduced flow resistance and a more coherent ceiling jet. Within the studied range, the effect of slope is minor compared to HRR and geometry, showing only a slight decrease in critical velocity for uphill gradients. These findings provide quantitative insights into optimizing ventilation design for semi-circular tunnels, highlighting that an aerodynamically favorable shape can reduce the required longitudinal airflow, thus balancing safety and energy efficiency.

Abstract: The global energy crisis drives the search for sustainable biomass resources. Microalgae, particularly Chlorella vulgaris, represent a promising third-generation feedstock for bi-ochar and biofuels. However, detailed kinetic schemes for its slow devolatilization are still scarce. This work compares the thermogravimetric behavior of commercially Chlorella vulgaris with data reported in the literature under identical experimental conditions and develops a multi-stage kinetic scheme using model-free methods and simultaneous global optimization. A complete set of kinetic parameter is provided in conjunction with a mass wights in order to close the reaction scheme. Biological composition of microalgae was experimentally determined resulting in 21.20, 59.30 and 19.50% for carbohydrates, protein and lipids. Thermogravimetric (TG/DTG) analyses were conducted with 5, 10 and 20 °C/min heating rates. Activation energy distribution was obtained through isoconversional model-free methods (Fried-man, FWO, KAS and Starink). A parallel multi-stage kinetic model was subsequently optimized globally against the experimental data to determine the complete kinetic tri-plet (E, A, n). TG/DTG profiles exhibited in general a good agreement with the literature refer-ence in the number and the temperature of features, peaks and shoulders, however different in intensity probably due to the different amount of biological components, carbohydrates, proteins and lipids. The multi-stage model achieved excellent fitting quality accounting for 5 reactions. Activation energies for the principal devolatilization stages ranged from 140 to 220 kJ/mol, while ln(A) values lay between 20 and 35 s⁻¹. The findings an results provided by this study is considered useful for the community con-tributing with discussion and a robust kinetic scheme suitable for example for slow pyrolysis process simulation.

Abstract: The social dimension of sustainability in building construction has long occupied an uncomfortable position in the research literature; acknowledged in theory, yet sidelined in practice. Environmental performance dominates assessment frameworks globally, while economic viability commands institutional attention. What is left, all too often, is a thin layer of social indicators that lack contextual grounding, statistical validation, or practical operationalizability, particularly in rapidly urbanizing settings across the developing world. This study confronts that gap directly. Drawing on an original mixed-methods investigation conducted across the twin cities of Rawalpindi and Islamabad, Pakistan, we develop and validate a comprehensive social sustainability assessment framework specifically tailored to building construction projects in South Asian urban environments. Employing a three-round Delphi technique with industry experts, a structured Likert-scale survey administered to 50 experienced construction professionals, and confirmatory factor analysis (CFA) using AMOS, the study identifies and statistically validates eighteen social sustainability indicators organized under five latent constructs: (i) Social Responsibility and Human Well-being, (ii) Institutional Governance and Knowledge, (iii) Stakeholder Engagement and Community Trust, (iv) Workforce Development and Labor Equity, and (v) Inclusive Design and Service Accessibility. Reliability analysis returned a Cronbach's alpha of 0.984, while the Relative Importance Index (RII) ranked project experience (SCL8, RII = 0.856), health, safety and environment at the site (SCL7, RII = 0.848), and project manager awareness (SCL12, RII = 0.828) as the most influential indicators. Kruskal-Wallis tests confirmed cross-group consensus. Crucially, the study finds that social sustainability is not merely a welfare afterthought, it is deeply interwoven with economic performance and environmental stewardship through measurable cross-pillar correlations. The resulting framework, the first of its kind validated through expert consensus and inferential statistics within the Pakistan context, offers a practical decision-support tool for project managers, urban planners, and regulatory bodies including the Pakistan Engineering Council (PEC) and the Capital Development Authority (CDA). Broader implications for South Asian and developing-country construction governance are discussed.

Abstract: Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of the Real-time Cognitive Grid, the analytical companion to the previously reported hardware architecture, which equips a fixed-wiring biosensor assembly with real-time physiological-state classification through an asymmetric edge-cloud workflow. The proposed framework assigns analytical responsibility across tiers: a locked 17-feature schema comprising 5 EMG features, 6 EEG spectral features, 2 cross-modal features, 2 HRV features, 1 EOG feature, and 1 EEG quality indicator governs window-bounded inference on the Arduino Nano RP2040 Connect with an LDA edge artefact requiring approximately 716 B RAM, whereas the cloud tier supports public-dataset pretraining, hardware-aligned refinement, multimodal fusion, deployment comparison, and feature-importance analysis under the same schema contract. To evaluate analytical consistency across physiological diversity, five public repositories covering stress physiology (WESAD), affective EEG (DEAP), inertial activity recognition (PAMAP2), sEMG gesture decoding (EMG Gestures), and motor-imagery EEG (EEGMMIDB) were evaluated under subject-disjoint GroupKFold (k=5) protocols. To test whether the same contract survives translation to the physical rig, the hardware branch was evaluated under session-disjoint GroupKFold across five bench-acquired sessions. Unimodal performance was strongest in sEMG- and IMU-dominant tasks, whereas multimodal fusion improved macro-F1 by up to 0.141 over the strongest unimodal baseline in WESAD and by 0.109 in PAMAP2. In the hardware branch, the deployed edge LDA artefact reached 0.9435 macro-F1 with 0.9470 accuracy, while the retained cloud Random Forest reached 0.8792 macro-F1 with 0.8799 accuracy; feature-importance analysis further showed that the final 17-feature branch was dominated by EMG descriptors, with EEG spectral terms contributing secondary support and hardware-exclusive variables remaining weak under the present bench regime. These results show that a compact multimodal sensing assembly can be elevated beyond passive signal capture into an intelligent portable biosensor that performs context-aware interpretation with minimal user intervention, supported by a reproducible analytical workflow that remains coherent across heterogeneous benchmark repositories, hardware-specific refinement, and microcontroller-class deployment, thereby establishing cross-session bench feasibility as a structured basis for future multi-subject wearable validation.

Abstract: Institutional shuttle fleets with fixed routes and predictable terminal parking are well suited to dedicated photovoltaic–battery (PV–BESS) charging infrastructure, yet siting and sizing are usually solved numerically without clear interpretation of the governing constraints. This study develops a closed-form active-constraint sizing rule, derived via Karush–Kuhn–Tucker (KKT) analysis under verified monotonicity of the net-present-value (NPV) objective over the feasible design region, for a 10-van electric academic shuttle fleet operating between the Huay Kaew and Doi Saket campuses of Rajamangala University of Technology Lanna, Chiang Mai, Thailand. One centralized station is compared with two distributed stations under reliability, cost, solar-fraction, autonomy, charger, budget, and rooftop-area constraints. The two-station configuration eliminates 47,600 km/year of dead-run travel and increases system NPV from USD 36,980 to USD 86,293 after the year-10 BESS replacement cost. The KKT analysis identifies two binding constraints—BESS one-day autonomy and PV rooftop area—giving 30 kWp PV and 94.85 kWh BESS per station, rounded to 100 kWh. The full transition achieves IRR = 12.9%, simple payback = 6.1 years, and 95.9% annual CO₂ reduction. Monte Carlo simulation with 5,000 scenarios yields P(NPV > 0) = 100% within the simulated scenario set, VaR_5% = USD 28,959, and CVaR_5% = USD 21,248, confirming financial robustness under the adopted uncertainty ranges.

Abstract: Coastal zones are facing rising exposure to climate-related hazards alongside intensifying human pressures, which highlights the need for robust tools to assess vulnerability. This study uses a GIS-based Coastal Vulnerability Index (CVI) to quantify and map relative vulnerability along ~13 km of shoreline in Al Hoceima Bay (northern Morocco). The proposed CVI integrates eight geological and physical indicators, including geomorphology, shoreline erosion and accretion rates, coastal slope, elevation, natural habitats, relative sea-level rise, significant wave height, and tidal range. Spatial analyses were performed using remote sensing data, historical records, field measurements, and Geographic Information Systems (GIS). The analysis reveals that 37% of the shoreline is categorized as high vulnerability, 44% is moderate, and 19% is low. Highly vulnerable sectors are primarily associated with low elevations, gentle coastal slopes, sandy beach systems, limited natural habitat protection, and proximity to river mouths. These findings demonstrate that the applied CVI provides a rapid and cost-effective framework for identifying priority areas for coastal management and climate adaptation. The proposed approach offers valuable decision-support insights for sustainable coastal planning in Al Hoceima Bay and other Mediterranean coastal environments characterized by limited data availability.

Abstract: This paper addresses the robust trajectory tracking problem of an Unmanned Aerial Vehicle (UAV) equipped with a 2-DOF manipulator, designed for fast aerial manipulation of varying payloads. To overcome the high computational cost and adaptability limitations of traditional model-based controllers, this work introduces a novel hybrid gain-scheduling framework that shifts the computational complexity to the pre-flight phase. The approach utilizes an approximate inverse dynamics linearization, based on fixed nominal models, which transforms the complex nonlinear system into a simple linear plant with bounded, structured uncertainties. The entire configuration space, including manipulator states and a range of payload properties, is partitioned into dynamically similar regions using K-Means clustering. For each local region, a dedicated robust PD controller is designed using a multi-objective Genetic Algorithm (GA). This framework also successfully implements a gain interpolation technique to mitigate the potential for abrupt control actions. Simulation results validate the controller’s ability to maintain high-precision tracking during fast maneuvers and payload switching, confirming the robustness and adaptability of the offline-tuned design.

Abstract: This paper makes a review for the studies of Space Energy Internet. Based on introducing the background of related networks, this paper discusses several key components of the Space Energy Internet (mainly including Space Solar Power Station, Energy Internet, and Artificial Intelligence Data Center), focusing on their corresponding system architectures, main research directions, and related technical challenges. Subsequently, supporting technologies such as discrete signal compression and coding, communication technology, energy transmission, power electronic devices, and artificial intelligence are discussed and analyzed. Furthermore, a highly integrated “data-computing-energy-networks” framework is established based on star computing networks and multi-orbital star link systems, and adopting the technologies like plug-and-play and modular design, which can support many innovative applications further.

Abstract: Both Noise Radar (NR) and Quantum Radar (QR), with alleged common features, aim to use the randomness of the transmitted signal to enhance radar covertness and to reduce mutual interference. While NR has been prototypically developed and successfully tested in many environments by different organizations, research and development investments on QR did not bring to practically operating prototypes. Starting from the well-known fact that radar detection depends on the energy transmitted on the target, the detailed evaluations in this work show that the detection performance of all the QR types proposed in the literature are well below the ones of a much simpler and cheaper equivalent “classical” radar set, for example of the NR type. Moreover, the absence of a “Quantum radar cross section” different from the well-known radar cross section is explained. From these facts it results that, in spite of alleged advantages in some literature, Quantum Radar proposals cannot lead to useful results, including, of course, the detection of stealth targets.

Abstract: Over the past decade, acoustically-actuated magnetoelectric (ME) antennas have been proposed as chip scale radiofrequency (RF) antennas compatible with post Complementary Metal Oxide Semiconductor (CMOS) fabrication processes. These devices have been reported to exhibit antenna gains far exceeding those of conventional electromagnetic (EM) antennas with comparable footprint. However, recent studies have challenged whether this enhanced gain originates from magnetoelastic coupling or from stray radiation sources, like the electric dipole moment in the piezoelectric film or currents in the probing pads. We resolve this controversy through a combined analytical, numerical, and experimental investigation. We model and quantify the radiated power and corresponding gain contributions from (I) magnetoelastic coupling; (II) the strain driven, time-varying electric dipole moment in the piezoelectric layer; and (III) the currents in the probing pads. Our results confirm that the radiation from magnetoelastic coupling exceeds that of the other sources by several orders of magnitude. In addition, we explain how to optimize the return loss and the radiated power of ME antennas when connected to a 50 Ω source, showing that the optimal operating point is the anti-resonance frequency. Based on this finding, we investigate the impact of the electromechanical coupling (k_t²) on gain and-10 dB fractional bandwidth. To corroborate our simulation results, we design, fabricate, and characterize the first two Aluminum Scandium Nitride (AlScN) magnetoelectric Bulk Acoustic Wave (BAW) antennas operating beyond 1.1 GHz. The two prototypes integrate different magnetostrictive materials (FeGaB and FeCoSiB) and exhibit measured realized gains of-31.8 dB and-29.7 dB, with-10 dB fractional bandwidths of 1.28% and 1.27% at 2.62 and 3.08 GHz, respectively. The achieved bandwidths are the highest reported for radiofrequency (RF) ME antennas, owing primarily to the enhanced piezoelectric coefficients of the AlScN. Benchmarking against control structures (unreleased FeGaB and FeCoSiB devices) confirms substantially degraded radiation performance in the absence of a strong magnetoelastic coupling. These results elucidate the working principle of ME antennas and provide RF designers with a rigorous framework for the design and modeling of acoustically actuated ME antennas for wireless communication and sensing.