Recent rapid advancements in communication technology have brought forth the era of Web 3.0, representing a substantial transformation in the Internet landscape. This shift has led to the emergence of various decentralized metaverse applications that leverage blockchain as their underlying technology to enable users to exchange value directly from point to point. However, blockchains are blind to the real world, and smart contracts cannot directly access data from the external world. To address this limitation, the technology of oracles has been introduced to provide real-world data for smart contracts and other blockchain applications. In this paper, we focus on mitigating the risks associated with oracles providing corrupt or incorrect data. We propose a novel Web 3.0 architecture for the Metaverse based on the multi-identifier network (MIN), and its decentralized blockchain oracle model called MetaOracle. The experimental results show that the proposed scheme can achieve minor time investment in return for significantly more reliable data and increased throughput.

Nonlinearity impairments and distortions have been bothering the bandwidth constrained passive optical network (PON) system for a long time and limiting the development of capacity in the PON system. Unlike other works concentrating on the exploration of the complex equalization algorithm, we investigate the potential of constellation shaping joint support vector machine (SVM) classification scheme. At the transmitter side, the 8 quadrature amplitude modulation (8QAM) constellation is shaped into three designs to mitigate the influence of noise and distortions in the PON channel. On the receiver side, simple multi-class linear SVM classifiers are utilized to replace complex equalization methods. Simulation results show that with the bandwidth of 25 GHz and overall bitrate of 50 Gbit/s, at 10 dBm input optical power of a 20 km standard single mode fiber (SSMF), and under a hard-decision forward error correction (FEC) threshold, transmission can be realized by employing Circular (4, 4) shaped 8QAM joint SVM classifier at the maximal power budget of 37.5 dB.