In the context of today's high-pressure, aging society, the demand for large-scale emotional models capable of providing empathetic support is more critical than ever.However, existing benchmarks fail to simultaneously achieve ecological validity, signal clarity, and reliable fine-grained labeling.We introduce EmoS, a high-fidelity bilingual benchmark designed to resolve the limitations of ecological validity and noise in existing datasets by combining strictly filtered static slices with a dynamic Streaming Monologue subset.Supported by a rigorous dual-layer human annotation pipeline, EmoS provides trusted ground truth that captures continuous emotional evolution.Empirical results show that fine-tuning MLLMs (multimodal large language models) on EmoS yields significant gains over zero-shot baselines, laying the foundation for the training and evaluation of future emotion recognition models and empathy models.The dataset and code are publicly available at https://github.com/ NLP2CT/EmoS.

One of the features of the Software Defined Network (SDN) is to integrate network service into the network layer to better support the application and alleviate the burden of client-server end. Multicast as an efficient communication method, it can be exploited to provide more meaningful network services to applications which involve one-to-many and many-to-many communication. Traditional multicast implementation mainly emphasizes on low network overhead and its function is kept as simple as possible. This makes multicast vulnerable to misuse. However, when network service is integrated into multicast based on the architecture of SDN, the robustness and security of multicast can be enhanced. This paper proposed a detailed implementation of secure network services to enhance the whole process of the multicast communication. Furthermore, the pricing policy is also discussed for different kinds of multicast application scenarios.