Abstract
We introduce a novel perspective on set-class analysis combining the DFT magnitudes with the music visualisation technique of wavescapes. With such a combination, we create a visual representation of a piece's multidimensional qualia, where different colours indicate saliency in chromaticity, diadicity, triadicity, octatonicity, diatonicity, and whole-tone quality. At the centre of our methods are: 1) the formal definition of the Fourier Qualia Space (FQS), 2) its particular ordering of DFT coefficients that delineate regions linked to different musical aesthetics, and 3) the mapping of such regions into a coloured wavescape. Furthermore, we demonstrate the intrinsic capability of the FQS to express qualia ambiguity and map it into a synopsis wavescape. Finally, we showcase the application of our methods by presenting a few analytical remarks on Bach's Three-part Invention BWV 795, Debussy's Reflets dans l'eau, andWebern's Four Pieces for Violin and Piano, Op. 7, No. 1, unveiling increasingly ambiguous wavescapes.

Abstract
Expanding upon the potential of generative machine learning to create atemporal latent space representations of musical-theoretical and cognitive interest, we delve into their explainability by formulating and testing hypotheses on their alignment with DFT phase spaces from {0, 1}(12) pitch classes and {0, 1}(128) pitch distributions - capturing common-tone tonal functional harmony and parsimonious voice-leading principles, respectively. We use 371 J.S. Bach chorales as a benchmark to train a Variational Autoencoder on a representative piano roll encoding. The Spearman rank correlation between the latent space and the two before-mentioned DFT phase spaces exhibits a robust rank association of approximately .65 +/- .05 for pitch classes and .61 +/- .05 for pitch distributions, denoting an effective preservation of harmonic functional clusters per region and parsimonious voice-leading. Furthermore, our analysis prompts essential inquiries about the stylistic characteristics inferred from the rank deviations to the DFT phase space and the balance between the two DFT phase spaces.

Abstract
We introduce a computational model that quantifies melodic pitch attraction in diatonic modal folk music, extending Lerdahl's Tonal Pitch Space. The model incorporates four melodic pitch indicators: vertical embedding distance, horizontal step distance, semitone interval distance, and relative stability. Its scalability is exclusively achieved through prior mode and tonic information, eliminating the need in existing models for additional chordal context. Noteworthy contributions encompass the incorporation of empirically-driven folk music knowledge and the calculation of indicator weights. Empirical evaluation, spanning Dutch, Irish, and Spanish folk traditions across Ionian, Dorian, Mixolydian, and Aeolian modes, uncovers a robust linear relationship between melodic pitch transitions and the pitch attraction model infused with empirically-derived knowledge. Indicator weights demonstrate cross-tradition generalizability, highlighting the significance of vertical embedding distance and relative stability. In contrast, semitone and horizontal step distances assume residual and null functions, respectively.

Abstract
This paper presents an optically transparent 2-bit unit-cell for reflective intelligent surface applications in Wi-Fi 6E. The unit-cell is based on a metalmesh and can be reconfigured electronically by adjusting the voltage applied to a varactor diode. The performance of the RIS is demonstrated through simulation, which shows that the results are in good agreement with the theoretical predictions.

Abstract
This paper presents a 1-bit graphene-based reflective reconfigurable intelligent surface (RIS), namely a reflectarray antenna, that operates in the Ka-band (27 - 31 GHz). The reflectarray unit-cell features a simple structure with one metal layer, a Rogers RT5880 substrate and a Graphene Sandwich Structure (GSS) on top. The GSS comprises two layers of graphene separated by a diaphragm paper and a thin PVC layer to enhance its durability. The reflectarray can ensure a 1-bit phase shift resolution, by alternating the bias voltage applied to the graphene. The unit-cell simulation shows that the losses are around 3 dB over the studied band for both unit-cell states. An equivalent circuit model is presented to facilitate the analysis and design of GSS-based unit-cells. The full-wave simulation results of a 32x32 reflectarray indicate a gain of 25 dBi for a steering angle of 10 deg., displaying a 1 dB gain bandwidth of 15%, confirming the promise of the graphene-based radiating elements.

Abstract
Unmanned Aerial Vehicles (UAVs) are versatile platforms for carrying communications nodes such as Wi-Fi Access Points and cellular Base Stations. Flying Networks (FNs) offer on-demand wireless connectivity where terrestrial networks are impractical or unsustainable. However, managing communications resources in FNs presents challenges, particularly in optimizing UAV placement to maximize Quality of Service (QoS) for Ground Users (GUs) while minimizing energy consumption, given the UAVs' limited battery life. Existing multi-UAV placement solutions primarily focus on maximizing coverage areas, assuming static UAV positions and uniform GU distribution, overlooking energy efficiency and heterogeneous QoS requirements. We propose the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which defines and optimizes UAV trajectories to reduce energy consumption while ensuring QoS based on Signal-to-Noise Ratio (SNR) in the links with GUs. Additionally, we introduce the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate energy consumption. Using both MUAVE and ns-3 simulators, we evaluate SUPPLY in typical and random networking scenarios, focusing on energy consumption and network performance. Results show that SUPPLY reduces energy consumption by up to 25% with minimal impact on throughput and delay.