Reactive jamming remains a critical threat to low-latency telemetry of Unmanned Aerial Vehicles (UAVs) using Orthogonal Frequency Division Multiplexing (OFDM). In this paper, a Bi-scale Mahalanobis approach is proposed to detect and classify reactive jamming attacks on UAVs; it jointly exploits window-level energy and the Sevcik fractal dimension and employs self-adapting thresholds to detect any drift in additive white Gaussian noise (AWGN), fading effects, or Radio Frequency (RF) gain. The simulations were conducted on 5000 frames of OFDM signals, which were distorted by Rayleigh fading, a ±10 kHz frequency drift, and log-normal power shadowing. The simulation results achieved a precision of 99.4%, a recall of 100%, an F1 score of 99.7%, an area under the receiver operating characteristic curve (AUC) of 0.9997, and a mean alarm latency of 80 μs. The method used reinforces jam resilience in low-power commercial UAVs, yet it needs no extra RF hardware and avoids heavy deep learning computation.

As microgrids are being deployed globally for off-grid electrification in islanded hybrid configurations,there is a growing need for energy management systems (EMS) that optimize energy generationcost, degradation, and performance, subject to reliability constraints, to achieve optimal performance.Although multi-objective EMS optimization of hybrid microgrids has recently attracted interest inliterature, there is still a need to address the challenging problem of simultaneously enforcing hardreliability constraints and quantifying equipment wear as an explicit optimization objective within aconstrained Pareto framework. This paper formulates a 24-hour dispatch problem for a fully islandedhybrid microgrid as a Type II constrained bi-objective optimization problem (CMOP) with 𝐷 = 72continuous decision variables and three simultaneously enforced hard constraints: loss of powersupply probability (LPSP ≤ 2%), battery state-of-charge cyclicity, and hydrogen tank level cyclicity.Three dedicated constrained multi-objective evolutionary algorithms (CMOEA), BiCo, C-TSEA,and CMOSMA, were benchmarked over 20 independent runs across three meteorological scenarios,ranging from surplus-renewable toweather-stressed conditions, using the hypervolume (HV) indicatorand Friedman–Nemenyi post-hoc analysis. BiCo achieved the highest median hypervolume in allthree scenarios (0.8723, 0.1245, and 0.8421 for SC1–SC3, respectively) and the lowest averageFriedman rank of 1.08, with statistically confirmed superiority over both competitors. At the BiCobest-compromise operating point, the cost of energy reached as low as 0.0598 e/kWh under bestconditions, with a CO2 intensity of 0.063𝑘𝑔𝐶𝑂2∕𝑘𝑊 ℎ. These findings establish explicit CMOPformulation with bidirectional coevolution as a rigorous and practically deployable framework forday-ahead energy management of islanded hybrid microgrids.