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低空新型基础设施是低空经济高质量发展的核心支撑，其价值闭环构建对促进产业可持续发展具有重要意义。本文分析了EPC+O、投建营一体化、政企合作、特许经营和LATOD等商业模式创新的价值闭环途径，探讨空域管理改革、数据要素市场化配置和产业基金等政策支持体系如何降低运营成本、提升价值创造效率，研究通信导航监视反制、“一塔一城”和设施/人员复用等技术路径如何支撑价值闭环实现。研究表明，低空新型基础设施的价值闭环构建需要商业模式创新、政策支持机制和技术应用路径的协同演进，通过“公益底座+市场服务”的组合模式，构建“建设-运营-价值创造-再投资”的良性循环。

We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. Although PaddleOCR-VL-1.5 establishes a strong 0.9B baseline, its remaining errors concentrate in under-optimized regions where model behavior is unstable, data coverage is sparse, or supervision is unreliable. Rather than expanding the training corpus indiscriminately, PaddleOCR-VL-1.6 introduces a region-aware data optimization framework that identifies weak regions from the previous model, applies targeted enhancement to these regions, and improves the reliability of supervision signals. It further adopts a progressive post-training recipe based on curated data selection and reinforcement learning, pushing model performance to a higher level through staged optimization. PaddleOCR-VL-1.6 achieves a new state-of-the-art score of 96.33% on OmniDocBench v1.6, demonstrates strong competitiveness against top-tier VLMs, and provides a practical post-training recipe for the PaddleOCR-VL series.

In this paper, the shadow of the four-dimensional charged AdS black hole surrounded by string cloud and quintessence is derived. The shadow radius shows a strictly monotonic and invertible correlation with the event horizon radius. The phase structures of the black hole for different parameters are reproduced through traditional thermodynamic geometry, which are similar to a van der Waals system. By analyzing the phase structure of the black hole in the context of shadows, thermodynamical phase structures with the shadow radius as the variable replicate the phase transition with the event horizon radius as the variable. We present the energy emission rates for massless and massive particles and discover that the maximum emission frequency can also serve as a useful tool for thermodynamic analysis. We firstly study and systematically establish shadow thermodynamics under the background of string cloud and quintessence, and our results reveal the independent regulatory mechanism of dark components on phase transitions as well as the universal topological invariance of the phase transition structure.

Deep learning surrogates for 3D Partial Differential Equations (PDEs) often fail to generalize across geometric transformations because they depend heavily on specific coordinate systems. While equivariant networks offer a solution, they typically rely on local operations in the spatial domain, making the global receptive field, which is essential for PDE dynamics, computationally expensive. Conversely, Fourier Neural Operators (FNOs) efficiently capture global interactions, yet establishing 3D equivariance within them remains impractical due to the prohibitive cost of spectral group convolutions. To bridge this gap, we introduce EqGINO, a geometrically robust framework that enforces isotropy in the spectral domain. By design, EqGINO guarantees exact equivariance to the discrete symmetries inherent to the discretized computational domain. Beyond this discrete guarantee, our structural prior enables effective generalization to arbitrary continuous orientations even with a limited number of SE(3)-transformed training samples. Consequently, our method robustly models coordinate-invariant physical laws on complex irregular 3D geometries. Our code is available at https://github.com/sung-won-kim/EqGINO

Translation quality depends on purpose: the same source text demands different translations depending on audience, tone, and communicative intent. Yet MT models and metrics treat translation as a fixed mapping from source to target. LLMs enable users to explicitly specify purpose alongside source text, yet this capability has not been evaluated at scale. We introduce a systematic evaluation of purpose-driven MT across 50 languages, 5 model sizes and 8 text domains. We find that (1) explicit instructions substantially improve translation adaptedness, with larger gains on informal domains (conversation, social media), for larger model sizes and for higher-resource languages; (2) instructions outperform semantically-matched few-shot examples and paragraph-level context; (3) traditional MT metrics fail to capture adaptation quality, often penalizing adapted translations; (4) when curated instructions are unavailable, models can self-generate them from surrounding document context, closing up to 80% of the adaptedness gap to curated instructions. Our results establish that purpose-adapted MT is a viable and measurable capability of LLMs, while highlighting the need for purpose-aware metrics.