Despite the remarkable success of semantic token learning in NLP and vision domains, token-level representation mechanisms face fundamental challenges when extended to continuous time series analysis. We identify a core limitation lies in the intrinsic absence of semantically meaningful tokenization boundaries within time-series, which differs substantially from discrete text tokens and presents unique complexities compared to spatially coherent image patches. While existing works mechanically apply fixed-length partitioning, recent evidence from time series foundation models reveals performance ceilings in prediction tasks under such paradigms. This paper introduces a novel tokenization framework known as physics-aware tokenization (PATK), designed to implement adaptive time-frequency tokenization via distribution-sensitive sampling strategies. Key innovations include: 1) A Rate-of-Variation (RoV) distribution is meticulously structured to encompass multi-scale temporal dynamics in the time domain, alongside a Spectral Energy Intensity (SEI) distribution devised to reveal global seasonal patterns within the frequency domain; 2) A physics-aware hidden Markov modeling (PA-HMM) is then established to adaptively breaks down continuous time-series into distinct tokens with elastic lengths, responding to physics-aware probabilities sampled from RoV and SEI distributions. The proposed PATK allows steady integration with both conventional Transformers and advanced large-scale time series models (including LLM-transferred methods and pretrained time series foundation models). Simulations across various datasets demonstrate that PATK excels in classification and forecasting tasks, showing notable adaptability to model long-term dependencies, strengthening resilience against disturbances, and robustness to missing data events.

Inverse Synthetic Aperture Radar (ISAR) imagery of space targets exhibits superior physical fidelity and satisfactory textural representation of components in ISAR images of targets, even under conditions characterized by sparse input optical samples. This paper introduces an innovative optical-to-radar cross-modal framework for the generation of full-attitude, high-fidelity space target ISAR samples, denominated as AORC. Specifically, the attitude encoding module (AEM) assimilates the prior knowledge of analogous targets across different attitudes through a fine-designed NeRF-based encoder, subsequently deriving the encoded features in the latent space. Subsequently, these comprehensive attitude features are input into the modality transformation module (MTM) to undergo a Brownian-Bridge-based diffusion process, facilitating the transformation between optical and ISAR modalities for each feature from each attitude. Extensive simulations on satellite targets validate the effectiveness of the proposed approach.

This paper addresses the challenge of generating high-fidelity Inverse Synthetic Aperture Radar (ISAR) images from optical images, particularly for space targets. We propose a framework for the generation of ISAR images incorporating component refinement, which attains high-fidelity ISAR scattering characteristics through the integration of an advanced generation model predicated on semantic segmentation, designated as Semantic-Aware ISAR Generation (SAIG). SAIG renders ISAR images from optical equivalents by learning mutual semantic segmentation maps. Extensive simulations demonstrate its effectiveness and robustness, outperforming state-of-the-art methods by over 8% across key evaluation metrics.

This paper focuses on generating Inverse Synthetic Aperture Radar (ISAR) images from optical images, in particular, for orbit space targets. ISAR images are widely applied in space target observation and classification tasks, whereas, limited to the expensive cost of ISAR sample collection, training deep learning-based ISAR image classifiers with insufficient samples and generating ISAR samples from emulation optical images via image translation techniques have attracted increasing attention. Image translation has highlighted significant success and popularity in computer vision, remote sensing and data generation societies. However, most of the existing methods are implemented under the discipline of extracting the explicit pixel-level features and do not perform effectively while entailing translation to domains with specific implicit features, such as ISAR image does. We propose a meta-learning based domain prior to implicit feature modelling and apply it to CycleGAN and UNIT models to realize effective translations between the ISAR and optical domains. Two representative implicit features, ISAR scattering distribution feature from the physical domain and the classification identifying feature from the task domain, are elaborately formulated with explicit modelling in statistic form. A meta-learning based training scheme is introduced to leverage the mutual knowledge of domain priors across different samples, and thus allows few-shot learning capacity with dramatically reduced training samples. Extensive simulations validate that the obtained ISAR images have better visible-authenticity and training-effectiveness than the existing image translation approaches on various synthetic datasets. Source codes are available at.