This article introduces a novel approach for the identification of deep fake threats within audio streams, specifically targeting the detection of synthetic speech generated by text-to-speech (TTS) algorithms. At the heart of this system are two critical components: the Vocal Emotion Analysis (VEA) Network, which captures the emotional nuances expressed within speech, and the Supervised Classifier for Deepfake Detection, which utilizes the emotional features extracted by the VEA to distinguish between authentic and fabricated audio tracks. The system capitalizes on the nuanced deficit of deepfake algorithms in replicating the emotional complexity inherent in human speech, thus providing a semantic layer of analysis that enhances the detection process. The robustness of the proposed methodology has been rigorously evaluated across a variety of datasets, ensuring its efficacy is not confined to controlled conditions but extends to realistic and challenging environments. This was achieved through the use of data augmentation techniques, including the introduction of additive white noise, which serves to mimic the variabilities encountered in real-world audio processing. The results have shown that the system's performance is not only consistent across different datasets but also maintains high accuracy in the presence of background noise, particularly when trained with noise-augmented datasets. By leveraging emotional content as a distinctive feature and applying sophisticated machine learning techniques, it presents a robust framework for safeguarding against the manipulation of audio content. This methodological contribution is poised to enhance the integrity of digital communications in an era where synthetic media is proliferating at an unprecedented rate.

One way of handling short-time short-circuits without causing equipment failure is the management of the reclose function integrated into smart switchboard which is described in this article. Due to the cost of the application, the optimal placement of such equipment must be well-planned. During the planning of the placement optimization of a low-voltage electricity network quality, the increase of the consumer supply security must be considered along with the best economic utilization. The research presented in this article aims to introduce a new special decision support methodology calculated with these variables. The management of smart switchboard (SSB) tool installation into low voltage (LV) network was investigated. The selection of the placement nodes is based on the complex investigation of the network characteristics. Investigation is performed by empiric analysis. This method can support the power companies to make the optimal decision. The presented new device can have outstanding importance for the electric industry and for further research as no testing results of such investigations have been published yet.