We present a proof-of-concept methodology for generating curvilinear polygonal meshes suitable for high-order discretisations by the Virtual Element Method (VEM). A VEM discretisation requires the definition of a set of boundary and internal points used to define basis functions and compute integrals of polynomials. The procedure to locate these points on the boundary borrows ideas from previous work on a posteriori high-order mesh generation in which the geometrical inquiries to a B-rep model of the computational domain are performed via an interface to CAD libraries. Here we describe the steps of the procedure that transforms a straight-sided polygonal mesh, generated using third-party software, into a curvilinear boundary-conforming mesh. We discuss criteria for ensuring and verifying the validity of the mesh. Using an elliptic partial differential equation with Dirichlet boundary conditions as a model problem, we show that VEM discretisations on such meshes achieve the expected rates of convergence as the mesh resolution is increased. This is followed by an illustrative application of the method to the generation of a curvilinear polygonal mesh for an aerofoil geometry. We discuss polygonal curvilinear mesh quality and its enhancement, and use the motion of a cell vertex to appraise three elemental quality metrics, namely convexity, regularity and isotropy, and highlight some of the difficulties associated in their use for mesh quality optimisation. A derivative-free optimisation method is utilised to enhance curvilinear polygonal meshes by maximising a suitable measure of mesh quality. We propose such measure as a combination of the three quality metrics and apply it to optimise a distorted initial mesh for a ring geometry. We show that a suitable version of the convexity metric is effective in untangling invalid meshes. The VEM solution of a model elliptic equation is obtained for a ring geometry where a distorted and an optimised mesh show low errors, indicating that the VEM is robust and relatively insensitive to mesh distortion, and a reduction of the error in the optimised mesh. Finally, we use a more complex geometry, a computational domain for an aerofoil, as a benchmark to further illustrate the ability of the convexity metric to untangle meshes, and also to assess the suitability of two quality measures as optimisation targets to improve the overall quality of curvilinear polygonal meshes.

Global navigation satellite system-based bistatic synthetic aperture radar interferometry (GNSS-based InBSAR) can improve the monitoring interval to one day due to the using of navigation satellites. Meanwhile, the low signal-to-noise ratio (SNR), poor image resolution, and the random focus position offsets cause large interferometric phase noise. In this letter, an interferometric phase noise reduction algorithm is proposed for GNSS-based InBSAR based on adaptive edge detection and temporal area filtering. An improved edge detection algorithm is adopted to solve the overlapping of resolution cells and phase interference caused by poor resolution. Then, to compensate the random focus position, an area filtering algorithm is proposed to find the temporal supporting area of persistent scatterers (PSs). Finally, the principal phase is extracted to reduce the interferometric phase error. The raw data are used to indicate the effectiveness of the proposed algorithm, and the best monitoring accuracy can reach millimeter level.

The most prominent advantage of the global navigation satellite system-based bistatic synthetic aperture radar interferometry (GNSS-InBSAR) system is its capability to achieve high-frequency 3-D deformation monitoring by combining measurements from different satellites. However, multisatellite, multiband, and short-interval imaging also introduces challenges such as large data amount and high computational requirements. In this article, an adaptive-segmentation-oriented multiband synthesis fast imaging algorithm is proposed for GNSS-InBSAR system. First, multiband synthetic signal model is established for the Beidou navigation signals. Then, optimization methods for segment parameters are proposed along range and azimuth directions, respectively. The limitation of the optimization methods are next discussed based on the GNSS-InBSAR system characteristics. The raw data of multiband navigation signals are used to indicate the effectiveness of the proposed algorithm. The computation time for single-band signals has been reduced by 77%, while for multiband signals, the computation time has been reduced by 71%.

This paper describes the Altay 2024 airborne field campaign in support of snow observation retrieved from spaceborne InSAR measurements from the Chinese LuTan-1 (a spaceborne L-band SAR constellation launched in 2022). The airborne and field measurements that are synchronized with LuTan-1 InSAR acquisitions will be conducted in January-February 2024 (snow on) and May-July 2024 (snow off). The remote sensing and in-situ measurements include various in-situ observations and drone-based lidar measurements. We first provide the overview of the Altay 2024 campaign including the choice of the in-situ measurement locations and flight tracks of the drone-based lidar. Then, historical InSAR dataset from all the available L/C-band SAR’s (e.g. JAXA’s ALOS, ESA’s Sentinel-1, China’s LuTan-1) over the study area are used to generate SWE change products, which are further compared against the in-situ measurements when available. This synergetic spaceborne airborne field campaign will directly validate the LuTan-1 derived snow products using the acquired airborne and field dataset, which can also support the design of future spaceborne mission concepts for snow retrieval.

The uncertainty of the elevation of target area in bistatic synthetic aperture radar (BiSAR) introduces image defocus. This uncertainty becomes much worse in global navigation satellite system-based BiSAR interferometry (GNSS-based InBSAR) applications, where the primary problem is a decrease in the coherence of the image pairs. In this paper, a high-coherence oriented imaging algorithm based on adaptive elevation ramp fitting is proposed for GNSS-based InBSAR systems. First, GNSS-based InBSAR signal model is established considering elevation error. From this model, the expressions for position offset and interferometric phase error caused by the elevation error are derived. Then, to improve the elevation fitting accuracy, full-scene fitting is replaced by subarea fitting, and adaptive subarea segmentation is achieved based on the points with complete resolution cells and image valleys. Finally, elevation fitting is performed in the subareas. The algorithm can obtain high-coherence image pairs with low image resolution introduced by GNSS-based InBSAR systems. Simulation and raw data are used to prove the effectiveness of the proposed algorithm in GNSS-based InBSAR.

Global navigation satellite system-based synthetic aperture radar interferometry (GNSS-based InSAR) systems can achieve 3-D deformation retrieval by associating multiangle images of different satellites. However, the difference in the scene radar cross section (RCS) and resolution cells makes multiangle images vary considerably. In addition, low resolution will further aggravate the difference in multiangle images. In this letter, a multiangle images association algorithm is proposed for GNSS-based InSAR systems. First, the supervised area is introduced to describe the areas of the same deformation based on the persistent scatter (PS) point. Then, the initial multiangle images association results are obtained by overlapping all PS points supervised areas of all satellites. Finally, the associated areas are normalized to obtain valid associated results. The raw data from eight Beidou satellites are used to prove the effectiveness of the proposed algorithm.

Computational recovery of gene regulatory network (GRN) has recently undergone a great shift from bulk-cell towards designing algorithms targeting single-cell data. In this work, we investigate whether the widely available bulk-cell data could be leveraged to assist the GRN predictions for single cells. We infer cell-type-specific GRNs from both the single-cell RNA sequencing data and the generic GRN derived from the bulk cells by constructing a weakly supervised learning framework based on the axial transformer. We verify our assumption that the bulk-cell transcriptomic data are a valuable resource, which could improve the prediction of single-cell GRN by conducting extensive experiments. Our GRN-transformer achieves the state-of-the-art prediction accuracy in comparison to existing supervised and unsupervised approaches. In addition, we show that our method can identify important transcription factors and potential regulations for Alzheimer's disease risk genes by using the predicted GRN. Availability: The implementation of GRN-transformer is available at https://github.com/HantaoShu/GRN-Transformer.