The design of metal-organic frameworks (MOFs) that incorporate more than one metal cluster constituent is ac hallenging task. Conventionalo ne-pot reactionp rotocols requirej udiciouss election of ligand and metal ion precursors, yet remainu npredictable. Stable, preformed nanoclusters, with ligand shells that can undergo additional coordination-driven reactions, provide ap latform for assembling multi-cluster solids with precision.H erein, ad iscrete Co 6 S 8 (PTA) 6 (PTA = 1,3,5-triaza-7-phosphaadamantane) superatomic-metalloligand is assembled into a three-dimensional (3D) coordination polymer comprising Cu 4 I 4 secondary building units (SBUs). The resulting heterobimetallic framework (1)c ontains two distinct cluster constituents and bifunctional PTAl inkers. Solid-state diffuse reflectances tudies revealt hat 1 is an optical semiconductor with ab and-gap of 1.59 eV.F ramework-modified electrodes exhibit reversible redox behaviori nt he solid state arising from the Co 6 S 8 superatoms, which remain intact during framework synthesis.
This article describes a software package called ROSgeoregistration intended for use with the Robot Operating System (ROS) and the Gazebo 3D simulation environment. ROSgeoregistration provides tools for the simulation, test and deployment of aerial georegistration algorithms and is made available with a link provided in the paper. A model creation package is provided which downloads multi-spectral images from the Google Earth Engine database and, if necessary, incorporates these images into a single, possibly very large, reference image. Additionally a Gazebo plugin which uses the real-time sensor pose and image formation model to generate simulated imagery using the specified reference image is provided along with related plugins for UAV relevant data. The novelty of this work is threefold: (1) this is the first system to link the massive multi-spectral imaging database of Google's Earth Engine to the Gazebo simulator, (2) this is the first example of a system that can simulate geospatially and radiometrically accurate imagery from multiple sensor views of the same terrain region, and (3) integration with other UAS tools creates a new holistic UAS simulation environment to support UAS system and subsystem development where real-world testing would generally be prohibitive. Sensed imagery and ground truth registration information is published to client applications which can receive imagery synchronously with telemetry from other payload sensors, e.g., IMU, GPS/GNSS, barometer, and windspeed sensor data. To highlight functionality, we demonstrate ROSgeoregistration for simulating Electro-Optical (EO) and Synthetic Aperture Radar (SAR) image sensors and an example use case for developing and evaluating image-based UAS position feedback, i.e., pose for imagebased Guidance Navigation and Control (GNC) applications.
Deep learning-based image retrieval has been emphasized in computer vision. Representation embedding extracted by deep neural networks (DNNs) not only aims at containing semantic information of the image, but also can manage largescale image retrieval tasks. In this work, we propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning to perform large-scale synthetic aperture radar (SAR) image search tasks. Moreover, we propose a training method for the DNNs induced by contrastive learning that does not require any labelling procedure. This may enable tractability of large-scale datasets with relative ease. Finally, we verify the performance of the proposed method by conducting experiments on the polarimetric SAR image datasets.
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