According to a very popular belief -very widespread among non-scientific communitiesthe exploitation of narrow beams, a.k.a. ''pencil beamforming'', results in a prompt increase of exposure levels radiated by 5G Base Stations (BSs). To face such concern with a scientific approach, in this work we propose a novel localization-enhanced pencil beamforming technique, in which the traffic beams are tuned in accordance with the uncertainty localization levels of User Equipment (UE). Compared to currently deployed beamforming techniques, which generally employ beams of fixed width, we exploit the localization functionality made available by the 5G architecture to synthesize the direction and the width of each pencil beam towards each served UE. We then evaluate the effectiveness of pencil beamforming in terms of ElectroMagnetic Field (EMF) exposure and UE throughput levels over different realistic casestudies. Results, obtained from a publicly released open-source simulator, dispel the myth: the adoption of localization-enhanced pencil beamforming triggers a prompt reduction of exposure w.r.t. other alternative techniques, which include e.g., beams of fixed width and cellular coverage not exploiting beamforming. The EMF reduction is achieved not only for the UE that are served by the pencil beams, but also over the whole territory (including the locations in proximity to the 5G BS). In addition, large throughput levels -adequate for most of 5G services -can be guaranteed when each UE is individually served by one dedicated beam.INDEX TERMS 5G cellular networks, 5G localization service, pencil beam management, EMF analysis, throughput analysis.

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Weakly Supervised Semantic Segmentation (WSSS) research has explored many directions to improve the typical pipeline CNN plus class activation maps (CAM) plus refinements, given the image-class label as the only supervision. Though the gap with the fully supervised methods is reduced, further abating the spread seems unlikely within this framework. On the other hand, WSSS methods based on Vision Transformers (ViT) have not yet explored valid alternatives to CAM. ViT features have been shown to retain a scene layout, and object boundaries in self-supervised learning. To confirm these findings, we prove that the advantages of transformers in self-supervised methods are further strengthened by Global Max Pooling (GMP), which can leverage patch features to negotiate pixel-label probability with class probability. This work proposes a new WSSS method dubbed ViT-PCM (ViT Patch-Class Mapping), not based on CAM. The end-to-end presented network learns with a single optimization process, refined shape and proper localization for segmentation masks. Our model outperforms the state-of-the-art on baseline pseudo-masks (BPM), where we achieve 69.3% mIoU on Pas-calVOC 2012 val set. We show that our approach has the least set of parameters, though obtaining higher accuracy than all other approaches. In a sentence, quantitative and qualitative results of our method reveal that ViT-PCM is an excellent alternative to CNN-CAM based architectures.