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The NP-hard coordination of heterogeneous robots for time-windowed warehouse tasks remains challenging: metaheuristics are precise but slow, whereas neural methods cannot handle heterogeneous constraints, leading to infeasible allocations. This paper presents the Constraint-Aware Heterogeneous Transformer (CAHT), a lightweight encoder–decoder architecture that performs end-to-end task assignment and sequencing in a single forward pass. The central innovation is a dynamic feasibility masking mechanism that enforces capacity and energy constraints directly within the softmax computation, eliminating infeasible allocations at the architectural level. This is complemented by a spatial-bias Transformer encoder and a two-stage supervised–reinforcement learning training paradigm using ALNS-generated labels. Experiments across four problem scales (5–20 robots, 50–200 tasks) demonstrate that CAHT achieves objective values within 7–13% of the ALNS reference while being 29–91× faster (23–104 ms vs. 2–3 s). Constraint violation rates remain below 6%, with time-window satisfaction above 94%. Ablation analysis identifies dynamic masking as the dominant contribution (+213% degradation upon removal), and cross-scale generalization reveals that the optimality gap decreases from 13.0% to 10.7% as the problem scale grows. With only 0.91 M parameters, CAHT occupies a new trade-off point on the Pareto frontier, offering a practical path toward real-time autonomous warehouse coordination.

Modern fulfillment centers share floor space with human workers, making warehouse multi-robot task allocation a safety-critical problem. We propose SHARP (Safe Heterogeneous Allocation with Risk Prediction), a Transformer-based constrained reinforcement-learning framework with a closed-form deployment-time safety mask. Under a Gaussian pedestrian belief and fixed closest-approach directions, the mask uses Bonferroni-allocated per-pair CVaR scores; a nonnegative mask score implies a conservative trajectory-level chance constraint under the stated assumptions. We also present an idealized primal–dual surrogate analysis, without claiming global convergence for the nonconvex Transformer/PPO implementation. Expanded experiments use ten training seeds per learned method and deterministic final-checkpoint evaluation on twenty independently generated held-out instances. No statistically significant difference between SHARP and Lagrangian-PPO was detected in any of the four scenarios. The held-out analysis further reveals late-training instability and severe over-conservatism in the dense S40_high scenario. These findings position SHARP as an auditable geometric filtering mechanism, while identifying conservatism and training stability as important limitations for deployment.

Maternal pre-pregnancy (pregravid) obesity is associated with adverse outcomes for both mother and offspring. Amongst the complications for the offspring is increased susceptibility and severity of neonatal infections necessitating admission to the intensive care unit, notably bacterial sepsis and enterocolitis. Previous studies have reported aberrant responses to LPS and polyclonal stimulation by umbilical cord blood monocytes that were mediated by alterations in the epigenome. In this study, we show that pregravid obesity dysregulates umbilical cord blood monocyte responses to bacterial and viral pathogens. Specifically, interferon-stimulated gene expression and inflammatory responses to respiratory syncytial virus (RSV) and E. coli were significantly dampened, respectively . Although upstream signaling events were comparable, translocation of the key transcription factor NF-κB and chromatin accessibility at pro-inflammatory gene promoters following TLR stimulation was significantly attenuated. Using a rhesus macaque model of western style diet-induced obesity, we further demonstrate that this defect is detected in fetal peripheral monocytes and tissue-resident macrophages during gestation. Collectively, these data indicate that maternal obesity alters metabolic, signaling, and epigenetic profiles of fetal monocytes leading to a state of immune paralysis during late gestation and at birth.

As mobile networks evolve toward Beyond 5G and 6G architectures, energy efficiency and sustainability have become increasingly critical due to growing traffic volumes, denser base station deployments, and the rising number of connected devices. Supporting Ultra-Reliable Low-Latency Communication (URLLC) services is particularly challenging, as their stringent requirements for both high reliability and minimal latency can lead to a significant increase in energy consumption within the radio access network. This paper examines slot structure mechanisms for concurrently servicing URLLC and enhanced Mobile Broadband (eMBB) traffic within the 5G Advanced framework, with a focus on improving energy efficiency and optimizing radio resource utilization. We propose an adaptive algorithm for managing radio interface time resources, which dynamically allocates sub-slots based on current network load and radio channel conditions. The system model is implemented in Simulink and incorporates URLLC and eMBB traffic generation, signal-to-noise ratio estimation, and a priority-based scheduling mechanism. Simulation results demonstrate that the proposed approach meets URLLC latency and reliability requirements while reducing redundant transmissions and enhancing the energy efficiency of the radio access network. These findings position the proposed method as a promising solution for the design of energy-efficient, next-generation mobile networks.

Increasing the efficiency of transmitters, as the largest consumers of energy, is relevant for any wireless communication devices. For higher efficiency, a number of methods are used, including envelope tracking and envelope elimination and restoration. Increasing the bandwidth of used frequencies requires expanding envelope modulators bandwidth up to 250–500 MHz or more. The possibility of using amplifiers with input signal quantization (AISQ), as an alternative to the most common hybrid envelope tracking modulators, is considered. An approach has been developed for optimizing AISQ characteristics according to the criterion of minimum loss when amplifying modern telecommunication signals with Rayleigh envelope distribution. The optimal quantization levels are determined and the energy characteristics of AISQ are calculated. AISQ loss power is shown to decrease by 1.66 times with two-level quantization, by 2.4 times with three-level quantization, and by a factor of 3.0–3.7 for four–five quantization levels compared to a class B amplifier. With these parameters, AISQ becomes competitive with respect to hybrid envelope tracking modulators but does not have electromagnetic interference from the pulse width modulation (PWM) path.

The fault tolerance of a swarm of small aerial vehicles (SAVs) is directly dependent on the reliability of data transmitted over communication channels. One of the key threats is the intentional distortion of signal sequences by an attacker, such as Barker codes or M-sequences, which are used for synchronization and control of the swarm. Such an attack can disable the entire swarm. The aim of this study is to develop a method for detecting such intrusions. The proposed algorithm analyzes mathematical expressions that describe the sidelobes’ levels of the autocorrelation function of the code. This approach not only detects unauthorized changes but also accurately identifies the location and magnitude of the distorted element. The conducted experiments confirm the high accuracy of the algorithm. The practical significance of the work lies in the possibility of integrating this method into the security subsystem of group interaction for small aerial vehicles. This creates a mechanism for active anomaly detection in communication channels: when a threat is detected, the swarm can respond promptly by switching to a backup channel, requesting data retransmission, or isolating the compromised channel, which in turn enhances the survivability and fault tolerance of the system’s functioning within the group.

Compact multi-channel airborne radar stations increasingly rely on an LED-based visible light communication (VLC) service link under radio-frequency spectrum restrictions and strict end-to-end delay constraints. Despite the directional nature of optical links, the VLC channel remains vulnerable to active optical interference and signal injection; furthermore, when an AI-enabled integrity monitor is embedded into the receiver, the AI decision layer becomes a direct target of evasion and online poisoning. This paper proposes a lightweight, interpretable AI-based attack detection architecture in which a Poisson photon-counting observation model is used to form physically consistent features over the preamble and control-sequence interval, while the final decision is produced by an AI ensemble combining a monotonic logistic detector and a one-class detector. The considered threat profile includes sustained illumination and synchronized flashes (jamming/blinding), spoofing via false preambles, replay of recorded fragments, and online data poisoning during self-calibration. The adequacy of solutions is assessed using the detection probability PD (ensemble: PD ≥ 0.90 for DC-jamming mean-count increment ΔλDC ≈ 7.56, pulsed-interference mean-count increment Δλpulse ≈ 12.89, and spoofing signal-scaling factor α ≈ 1.02), the false-alarm probability PFA = 0.045, and the per-packet end-to-end latency (bounded by the observation-window duration LΔT = 20 μs, where window length L = 20 and interval duration ΔT = 1 μs), which confirms real-time CPU operation without GPU acceleration.

The main characteristics of high-efficiency switching-mode solid-state power amplifiers with envelope elimination and restoration (EER) methods depend on all their elements. In this article, we study the influence of the types and parameters of the envelope path low-pass filters (LPFs) on the EER transmitter out-of-band emissions. This article presents for the first time an analysis of EER transmitter operation where the output impedance of the PWM modulator is not equal to zero, as usual (with a one-sided loaded LPF), but is matched with the low-pass filter and the load (with a double-sided loaded LPF). Theoretical comparisons of EER transmitters’ out-of-band emissions were carried out with four envelope path LPF configurations (one-sided and double-sided loaded LPFs with a smooth and sharp transition, respectively), for both the nominal load (broadband antenna) and resonant antennas with a limited bandwidth. The analysis showed that for the case of transmitter operation on a resonant antenna with a limited bandwidth, the preferable option was the use of a sixth-order double-sided loaded LPF with a smooth transition. The use of the proposed modulator configuration allowed the transmitter to operate on an antenna with VSWR = 1.07 at the edges of the transmitted signal band with a minimum LPF bandwidth equal to 5.8 bands of the amplified signal. This could significantly expand its application capabilities and allow one to reduce the PWM clock frequency and increase efficiency.

This article presents a comprehensive study of the potential utilization of the L-band and S-band frequency ranges for satellite non-terrestrial network (NTN) technologies. This study encompasses an interference analysis in the S-band, investigating the coexistence of NTN satellite systems with mobile satellite networks such as Omnispace and Lyra, and an interference analysis in the L-band between NTN satellites and the mobile satellite network Inmarsat. This study simulates an NTN satellite network with typical characteristics defined by 3GPP and ITU-R for the n255 and n256 bands. Furthermore, it provides calculations illustrating the signal-to-noise ratio degradation of low-Earth-orbit (LEO), medium-Earth-orbit (MEO), and geostationary-Earth-orbit (GEO) satellite networks operating in the L-band and S-band when exposed to interference from NTN satellites.

RedCap technology is set to become a critical component in the growth of the Internet of Things (IoT), enabling sensors and wearable devices for medical, industrial, and commercial applications. However, because RedCap primarily operates in non-public networks and does not synchronize its time-division duplexing (TDD) mode with 5G NR networks, interference risks arise. This is particularly concerning as traditional 5G NR networks prioritize downlink communication, whereas RedCap is designed for uplink. This study investigates the potential interference between RedCap non-public networks and 5G NR in TDD FR1 and FR2 frequency bands using Monte Carlo simulation techniques. The results illustrate how RedCap deployments may impact 5G NR performance in urban and suburban environments. Key insights are provided to inform strategies for minimizing interference and ensuring coexistence between these technologies.