Text-to-Visualization (Text2VIS) enables users to create visualizations from natural language queries, making data insights more accessible. However, Text2VIS faces challenges in interpreting ambiguous queries, as users often express their visualization needs in imprecise language. To address this challenge, we introduce nBench 2.0, a new benchmark designed to evaluate Text2VIS systems in scenarios involving ambiguous queries. nvBench 2.0 includes 7,878 natural language queries and 24,076 corresponding visualizations, derived from 780 tables across 153 domains. It is built using a controlled ambiguity-injection pipeline that generates ambiguous queries through a reverse-generation workflow. By starting with unambiguous seed visualizations and selectively injecting ambiguities, the pipeline yields multiple valid interpretations for each query, with each ambiguous query traceable to its corresponding visualization through step-wise reasoning paths.We evaluate various Large Language Models (LLMs) on their ability to perform ambiguous Text2VIS tasks using nBench 2.0. We also propose Step-Text2Vis, an LLM-based model trained on nvBench 2.0, which enhances performance in ambiguous scenarios through step-wise preference optimization. Our results show that Step-Text2Vis outperforms all baselines, setting a new state-of-the-art for ambiguous Text2VIS tasks. Our source code and data are available at https://nvbench2.github.io/

The critical applications of global navigation satellite system in the field of national defense and security are seriously limited by the susceptibility of its system to spoofing interference. At present, The spoof interference detection algorithms commonly utilize temporal consistency and other time-frequency information, as well as angle resolution and other spatial information, which the types of referenced information are limited and singular. In this article, a spacetime spoofing interference detection algorithm is proposed, which simultaneously combines both temporal and spatial information. A joint generalized likelihood ratio test framework involving relative Direction of Arrival (DoA) and Doppler shift difference is established, and the characteristics of inconsistent incoming angular angle and similar Doppler shift of spoofing signals are fully utilized. Experimental findings indicate that the proposed algorithm can effectively detect spoofing in scenarios involving a limited number of spoofed satellites. Moreover, the detection probability has been reached 99% compared with the traditional DoA estimation technique and metric combination technique, which verifies the efficacy and dependability of the proposed approach.