Abstract
[No abstract available]

Abstract
Whispers represent a common and secondary mechanism of communication. Nonetheless, individuals with aphonia, including those with laryngectomy, rely on whispers as their primary means of communication. Due to the substantial acoustic differences between whispered and normally phonated speech, the task of effective whispered-to-normal speech conversion (W2NSC) remains a significant challenge and has been widely discussed in the speech processing community. This study explores several enhancements to the k-nearest neighbors voice conversion (kNN-VC) model from multiple perspectives, including experiments with alternative feature extraction models, exploring fine-tuning of pre-trained models using Low-Rank Adaptation (LoRA), and the development of mapping strategies for parallel whispered and normal speech data using both KNN (pkNN-W2NSC) and multilayer perceptron (pMLP-W2NSC) approaches. Based on these experiments, a subjective evaluation using the MUSHRA test showed that the pMLP-W2NSC system achieved the highest overall performance among all evaluated configurations.

Abstract
The authors regret that a minor inconsistency was identified in Algorithm 1 of our published paper during subsequent experiments conducted to further improve the G16 constructive heuristic. Specifically, the original implementation of G16 did not distinguish between regular and merged earliest time windows when computing [Formula presented], which could, in some cases, affect the consistency of [Formula presented], [Formula presented], [Formula presented], [Formula presented], and [Formula presented] for requests simultaneously served at the same location, leading to infeasible routes under specific configurations. The correction is as follows (Algorithm 1, Line 15): Original: [Formula presented] Corrected: [Formula presented] where [Formula presented] denotes the merged earliest time window when a merged time service is applied; otherwise, it equals [Formula presented]. As a result, the total costs obtained with the corrected version of G16 slightly deviate, either positively or negatively, from those originally published. The average percentage gaps between the published and corrected G16 results are 2.55, 0.61, -0.64, 0.42, and -2.27% for instances with 50, 100, 200, 400, and 700 requests, respectively. Complementarily, a Spearman correlation (p = 0.98) and a Wilcoxon signed-rank test (p = 0.106) revealed no statistically significant difference between both sets of results. Therefore, the overall performance patterns and comparative findings discussed in the original paper remain valid. Updated computational results are available in the same Mendeley Data repository (DOI: https:/doi.org/10.17632/7fb9jn2wcs.1). The authors would like to apologise for any inconvenience caused.

Abstract
[No abstract available]

Abstract
Future Sixth-Generation (6G) networks are expected to provide global, secure, and resilient connectivity by tightly integrating terrestrial and Non-Terrestrial Networks (NTNs). Beyond high-data-rate classical communications, this new generation of mobile communication systems opens the door to the deployment of quantum services at a global scale, leveraging space and aerial network elements as native components of the 6G ecosystem. This article explores the joint provision of classical and quantum communications over Free Space Optical (FSO) links in 6G NTNs, with a focus on Low Earth Orbit (LEO) satellite constellations. It also discusses how quantum communication services, as illustrated through Quantum Key Distribution (QKD), can be integrated with classical feeder and inter-satellite links when FSO payloads on LEO satellites become available. Key physical-layer challenges arising from the coexistence of classical and quantum optical channels are analyzed, including atmospheric effects, crosstalk, filtering, Doppler shifts, and pointing constraints. At the network level, the architectural trade-offs related to satellite orbits, inter-satellite links, and optical ground station deployment are discussed, highlighting open challenges and opportunities toward scalable quantum-enabled 6G NTNs.

Abstract
Unmanned Aerial Vehicles (UAVs) offer a promising solution for enhancing wireless connectivity and Quality of Service (QoS) in urban environments, acting as aerial Wi-Fi access points or cellular base stations to support vehicular users and Vehicle-to-Everything (V2X) applications. Their flexibility and rapid deployment capabilities make them suitable for addressing infrastructure gaps and traffic surges. However, optimizing UAV positions to maintain Line of Sight (LoS) links with ground User Equipment (UEs) remains challenging in obstacle-dense urban scenarios. Existing approaches rely on probabilistic blockage models or require dedicated infrastructure such as Reconfigurable Intelligent Surfaces. This paper proposes VTOPA, a Vision-Aided Traffic- and Obstacle-Aware Positioning Algorithm that complements these approaches by autonomously extracting environmental information-such as obstacle geometries and UE locations-via computer vision, enabling infrastructure-free deployment. The algorithm employs Particle Swarm Optimization to determine UAV positions that maximize aggregate throughput while prioritizing LoS connectivity and accounting for heterogeneous traffic demands. VTOPA is particularly suited for rapid deployment scenarios such as emergency response and temporary events. Evaluated through simulations in ns-3, VTOPA achieves up to 50% increase in aggregate throughput and 50% reduction in delay, outperforming state of the art benchmarks in obstacle-rich environments.