Remote Sensing refers to the utilization of a distributed acoustic sensing (DAS) system over a significant distance, specifically exceeding 1,000 kilometers. DAS is a technology that allows for the monitoring and detection of acoustic signals along an optical fiber. This involves a hybrid link composed of a mixture of field and laboratory fibers, and it employs bi-directional inline Raman amplification after each span to enhance signal quality.
Satellite imagery is vast, high-resolution, and rich with information, but finding specific objects within it using natural language has remained a stubborn challenge. Open-SAT, developed by researchers at NEC Laboratories America and North South University, tackles this problem without retraining any models.
In satellite applications, user queries often take the form of open-ended natural language, extending beyond a fixed set of predefined categories. This open-vocabulary nature poses significant challenges for retrieving relevant image tiles, as the retrieval system must generalize to a wide range of unseen objects and concepts. While vision-language models (VLMs) such as CLIP are widely used for text-image retrieval, even fine-tuned variants often struggle to accurately align such queries with satellite imagery. To address this, we propose Open-SAT, a training-free query embedding refinement algorithm that operates at inference time to improve alignment between user queries and satellite image content. Open-SAT uses VLMs to compute embeddings for image tiles, which are stored in a vector database for efficient retrieval. At query time, it leverages Large Language Models (LLMs) to refine the text embeddings by incorporating contextual information about objects of interest and their surroundings. A threshold-free retrieval mechanism further enhances accuracy and efficiency. Experimental results in three public benchmarks demonstrate that Open-SAT improves the F1 score by up to 16.04%, while retrieving a comparable number of image tiles. These results demonstrate the effectiveness of Open-SAT in open-vocabulary satellite image retrieval, leveraging LLM guidance without the need for additional training or supervision.
Transformer-based methods have demonstrated strong potential in hyperspectral pansharpening by modeling long-range dependencies. However, their effectiveness is often limited by redundant token representations and a lack of multiscale feature modeling. Hyperspectral images exhibit intrinsic spectral priors (e.g., abundance sparsity) and spatial priors(e.g., non-local similarity), which are critical for accurate reconstruction. From a spectralspatial perspective, Vision Transformers (ViTs) face two major limitations: they struggle to preserve high-frequency componentssuch as material edges and texture transitions, and suffer from attention dispersion across redundant tokens. These issues stem from the global self-attention mechanism, which tends to dilute high-frequency signals and overlook localized details. To address these challenges, we propose the Token-wise High-frequency AugmentationTransformer (THAT), a novel framework designed to enhance hyperspectral pansharpening through improved high-frequency feature representation and token selection. Specifically, THAT introduces: (1) Pivotal Token Selective Attention (PTSA) to prioritize informative tokens and suppress redundancy; (2) a Multi-level Variance-aware Feed-forward Network (MVFN) to enhance high-frequency detail learning. Experiments on standard benchmarks show that THAT achieves state-of-the-art performance with improved reconstruction quality and efficiency.
We report the first distributed acoustic sensing (DAS) experiment with over >1,000 km reach on a hybrid link comprising of a mixture of field and lab fibers with bi-directional inline Raman amplification after each span. We used 20× frequency-diversity chirped-pulses for the probe signal,and recovered the Rayleigh backscatter using a coherent receiver with correlation detection and diversity combining. A measurand resolution of ∼100 pϵ/√ Hz at a gauge length of 20 meters achieved in the offline experiment. We also demonstrate the first real-time FPGA implementation of chirped-pulse DAS without frequency diversity over a range of 210 km.
We experimentally investigate the scattering effect on an 80 Gbit/s orbital angular momentum (OAM) multiplexed optical wireless communication link. The power loss, mode purity, cross talk, and bit error rate performance are measured and analyzed for different OAM modes under scattering levels from ballistic to diffusive regions. Results show that (i) power loss is the main impairment in the ballistic scattering, while the mode purities of different OAM modes are not significantly affected; (ii) in the diffusive scattering, however, the performance of an OAM-multiplexed link further suffers from the increased cross talk between the different OAM modes.
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NEC Laboratories America, Inc. (NEC Labs) is the US-based center for NEC Corporation’s global network of corporate research laboratories. Our diverse research groups collaborate with industry, academia and governments to provide disruptive solutions to complex problems. A leader in the integration of IT and network technologies with more than 100 years of expertise, NEC provides a combination of products and solutions that cross-utilize the company’s experience and global resources to meet the complex and ever-changing needs of its customers.