Estimating subsurface dielectric properties is essential for applications ranging from environmental surveys of soils to nondestructive evaluation of concrete in infrastructure. Conventional wave inversion methods typically assume few discrete homogeneous layers and require dense measurements or strong prior knowledge of material boundaries, limiting scalability and accuracy in realistic settings where properties vary continuously. We present a physics informed machine learning framework that reconstructs subsurface permittivity as a fully neural, continuous function of depth, trained to satisfy both measurement data and Maxwells equations. We validate the framework with both simulations and custom built radar experiments on multilayered natural materials. Results show close agreement with in-situ permittivity measurements (R^2=0.93), with sensitivity to even subtle variations (Delta eps_r=2). Parametric analysis reveals that accurate profiles can be recovered with as few as three strategically placed sensors in two layer systems. This approach reframes subsurface inversion from boundary-driven to continuous property estimation, enabling accurate characterization of smooth permittivity variations and advancing electromagnetic imaging using low cost radar systems.

We present PitchFlower, a flow-based neural audio codec with explicit pitch controllability. Our approach enforces disentanglement through a simple perturbation: during training, F0 contours are flattened and randomly shifted, while the true F0 is provided as conditioning. A vector-quantization bottleneck prevents pitch recovery, and a flow-based decoder generates high quality audio. Experiments show that PitchFlower achieves more accurate pitch control than WORLD at much higher audio quality, and outperforms SiFiGAN in controllability while maintaining comparable quality. Beyond pitch, this framework provides a simple and extensible path toward disentangling other speech attributes.

The rapid deployment of unmanned aerial vehicle (UAV) corridors in sixth-generation (6G) networks requires safe, intelligence-driven integrated sensing and communications (ISAC). Reconfigurable intelligent surfaces (RIS) enhance spectrum efficiency, localisation accuracy, and situational awareness, while introducing new vulnerabilities. The rise of quantum computing increases the risks associated with harvest-now-decrypt-later strategies and quantum-enhanced spoofing. We propose a Quantum-Resilient Threat Modelling (QRTM) framework for RIS-assisted ISAC in UAV corridors to address these challenges. QRTM integrates classical, quantum-ready, and quantum-aided adversaries, countered using post-quantum cryptographic (PQC) primitives: ML-KEM for key establishment and Falcon for authentication, both embedded within RIS control signalling and UAV coordination. To strengthen security sensing, the framework introduces RIS-coded scene watermarking validated through a generalised likelihood ratio test (GLRT), with its detection probability characterised by the Marcum Q function. Furthermore, a Secure ISAC Utility (SIU) jointly optimises secrecy rate, spoofing detection, and throughput under RIS constraints, enabled by a scheduler with computational complexity of O(n^2). Monte Carlo evaluations using 3GPP Release 19 mid-band urban-canyon models (7-15 GHz) demonstrate a spoof-detection probability approaching 0.99 at a false-alarm rate of 1e-3, secrecy-rate retention exceeding 90 percent against quantum-capable adversaries, and signal-interference utilisation improvements of about 25 percent compared with baselines. These results show a standards-compliant path towards reliable, quantum-resilient ISAC for UAV corridors in smart cities and non-terrestrial networks.

This paper investigates narrow-beam channel characterization and performance evaluation for 5G for railway (5G-R) systems based on ray-tracing (RT) simulation. Three representative high-speed railway (HSR) scenarios including viaduct, cutting, and station are established, and RT-based dynamic narrow-beam channel simulations are conducted using a designed beam tracking scheme that ensures continuous alignment with the moving train. The channel characteristics are analyzed in terms of both large-scale and small-scale fading, as well as non-stationarity, providing statistical insights into path loss, shadow fading, fading severity, time-frequency-space dispersion, and stationarity interval. The influence of beamwidth on these channel properties is also examined. Furthermore, the performance of 5G-R systems operating in such narrow-beam channels is evaluated using the Vienna 5G simulator, with a focus on block error rate, throughput, and spectral efficiency. A hardware-in-the-loop simulation platform is developed to further assess synchronization signal reference signal received power, signal-to-interference-plus-noise ratio, and reference signal received quality. The results provide valuable guidance for the design and optimization of 5G-R systems in HSR environments.

Emerging technologies such as Reconfigurable Intelligent Surfaces (RIS) make it possible to optimize some parameters of wireless channels. Conventional approaches require relating the channel and its programmable parameters via a simple model that supports rapid optimization, e.g., re-tuning the parameters each time the users move. However, in practice such models are often crude approximations of the channel, and a more faithful description can be obtained via complex simulators, or only by measurements. In this work, we introduce a novel approach for rapid optimization of programmable channels based on AI-aided Annealed Langevin Dynamics (ALD), which bypasses the need for explicit channel modeling. By framing the ALD algorithm using the MAP estimate, we design a deep unfolded ALD algorithm that leverages a Deep Neural Network (DNN) to estimate score gradients for optimizing channel parameters. We introduce a training method that overcomes the need for channel modeling using zero-order gradients, combined with active learning to enhance generalization, enabling optimization in complex and dynamically changing environments. We evaluate the proposed method in RIS-aided scenarios subject to rich-scattering effects. Our results demonstrate that our AI-aided ALD method enables rapid and reliable channel parameter tuning with limited latency.

This paper presents a comprehensive analysis of motion vectors extracted from AV1-encoded video streams and their application in accelerating optical flow estimation. We demonstrate that motion vectors from AV1 video codec can serve as a high-quality and computationally efficient substitute for traditional optical flow, a critical but often resource-intensive component in many computer vision pipelines. Our primary contributions are twofold. First, we provide a detailed comparison of motion vectors from both AV1 and HEVC against ground-truth optical flow, establishing their fidelity. In particular we show the impact of encoder settings on motion estimation fidelity and make recommendations about the optimal settings. Second, we show that using these extracted AV1 motion vectors as a "warm-start" for a state-of-the-art deep learning-based optical flow method, RAFT, significantly reduces the time to convergence while achieving comparable accuracy. Specifically, we observe a four-fold speedup in computation time with only a minor trade- off in end-point error. These findings underscore the potential of reusing motion vectors from compressed video as a practical and efficient method for a wide range of motion-aware computer vision applications.

Permanent Magnet Synchronous Motors (PMSMs) are widely employed in high-performance drive systems due to their high efficiency, power density, and precise dynamic behavior. However, nonlinearities, load disturbances, and parameter uncertainties present persistent challenges to control. Sliding-Mode Control (SMC) remains one of the most reliable strategies for high-performance PMSM drives. Yet, the rapid proliferation of adaptive, fractional-order, and intelligent variants has fragmented recent literature. This paper presents a comprehensive review and taxonomy of SMC-based PMSM speed-control methods published between 2020 and 2025. More than 200 studies are systematically analyzed and classified according to control order, surface design, disturbance-observer integration, optimization approach, and intelligent augmentation. Trends in publication activity, dominant hybrid structures, and application domains are quantitatively summarized. The review reveals a clear evolution from conventional discontinuous SMC toward adaptive, higher-order, and data-driven frameworks that mitigate chattering while preserving robustness. Persistent research gaps are identified in hardware validation, energy-efficiency assessment, and real-time tuning strategies. The taxonomy and critical synthesis provided herein establish a coherent reference for researchers and form the conceptual foundation for the companion paper (Part II), which delivers a unified benchmark and comparative simulation study of representative SMC designs.

Reconfigurable antennas (RAs), capable of dynamically adapting their radiation patterns, polarization states, and operating frequencies, have emerged as a promising technology to meet the stringent performance requirements of sixth-generation (6G) wireless networks. This article systematically introduces essential hardware implementations of RAs and investigates advanced array architectures, such as fully-digital and tri-hybrid designs, emphasizing their capability to synergistically integrate electromagnetic (EM) reconfigurability with analog and digital signal processing. By facilitating coordinated beamforming across the EM and signal processing domains, RA arrays offer unprecedented flexibility and adaptability compared to conventional static antenna systems. Representative applications empowered by RA arrays, including integrated sensing and communication (ISAC), physical layer security (PLS), and near-field communications, are highlighted. A case study illustrates the effectiveness of RA arrays in optimizing beam steering, improving link robustness, and alleviating system power consumption. Finally, several open challenges and future research directions are outlined, emphasizing the need for advancements in theoretical modeling, hardware reliability, channel estimation techniques, intelligent optimization methods, and innovative network architectures, to fully realize the transformative impact of RAs in future 6G wireless networks.

Algorithm unrolling methods have proven powerful for solving the regularized least squares problem in computational magnetic resonance imaging (MRI). These approaches unfold an iterative algorithm with a fixed number of iterations, typically alternating between a neural network-based proximal operator for regularization, a data fidelity operation and auxiliary updates with learnable parameters. While the connection to optimization methods dictate that the proximal operator network should be shared across unrolls, this can introduce artifacts or blurring. Heuristically, practitioners have shown that using distinct networks may be beneficial, but this significantly increases the number of learnable parameters, making it challenging to prevent overfitting. To address these shortcomings, by taking inspirations from proximal operators with varying thresholds in approximate message passing (AMP) and the success of time-embedding in diffusion models, we propose a time-embedded algorithm unrolling scheme for inverse problems. Specifically, we introduce a novel perspective on the iteration-dependent proximal operation in vector AMP (VAMP) and the subsequent Onsager correction in the context of algorithm unrolling, framing them as a time-embedded neural network. Similarly, the scalar weights in the data fidelity operation and its associated Onsager correction are cast as time-dependent learnable parameters. Our extensive experiments on the fastMRI dataset, spanning various acceleration rates and datasets, demonstrate that our method effectively reduces aliasing artifacts and mitigates noise amplification, achieving state-of-the-art performance. Furthermore, we show that our time-embedding strategy extends to existing algorithm unrolling approaches, enhancing reconstruction quality without increasing the computational complexity significantly.

Unmanned Aerial Vehicles (UAVs) or drones, are increasingly used in search and rescue missions to detect human presence. Existing systems primarily leverage vision-based methods which are prone to fail under low-visibility or occlusion. Drone-based audio perception offers promise but suffers from extreme ego-noise that masks sounds indicating human presence. Existing datasets are either limited in diversity or synthetic, lacking real acoustic interactions, and there are no standardized setups for drone audition. To this end, we present DroneAudioset (The dataset is publicly available at this https URL under the MIT license), a comprehensive drone audition dataset featuring 23.5 hours of annotated recordings, covering a wide range of signal-to-noise ratios (SNRs) from -57.2 dB to -2.5 dB, across various drone types, throttles, microphone configurations as well as environments. The dataset enables development and systematic evaluation of noise suppression and classification methods for human-presence detection under challenging conditions, while also informing practical design considerations for drone audition systems, such as microphone placement trade-offs, and development of drone noise-aware audio processing. This dataset is an important step towards enabling design and deployment of drone-audition systems.

Massive number of simultaneous Internet of Things (IoT) uplinks strain gateways with interference and energy limits, yet devices often lack neighbors' Channel State Information (CSI) and cannot sustain centralized Mobile Edge Computing (MEC) or heavy Machine Learning (ML) coordination. Classical Bayesian solvers help with uncertainty but become intractable as users and strategies grow, making lightweight, distributed control essential. In this paper, we introduce the first-ever, novel epistemic Bayesian game for uplink power control under incomplete CSI that operates while suppressing interference among multiple uplink channels from distributed IoT devices firing at the same time. Nodes run inter-/intra-epistemic belief updates over opponents' strategies, replacing exhaustive expected-utility tables with conditional belief hierarchies. Using an exponential-Gamma SINR model and higher-order utility moments (variance, skewness, kurtosis), the scheme remains computationally lean with a single-round upper bound of $O\!\left(N^{2} S^{2N}\right)$. Precise power control and stronger coverage amid realistic interference: with channel magnitude equal to $1$ and a signal-to-interference-plus-noise ratio (SINR) threshold of $-18$ dB, coverage reaches approximately $60\%$ at approximately $55\%$ of the maximum transmit power; mid-rate devices with a threshold of $-27$ dB achieve full coverage with less than $0.1\%$ of the maximum transmit this http URL $80\%$ interference, a fourth-moment policy cuts average power from approximately $52\%$ to approximately $20\%$ of the maximum transmit power with comparable outage, outperforming expectation-only baselines. These results highlight a principled, computationally lean path to optimal power allocation and higher network coverage under real-world uncertainty within dense, distributed IoT networks.