Miracle-dz/UniFAR-FTUs huggingface dataset:
Instrument Science Experiments on the SNOOPI P-Band Reflectometry Mission

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first in-space validation of P-band (240–380 MHz) SoOp techniques and a prototype science instrument. These techniques have the potential to enable remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE). SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A second priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in August 2022. This presentation will review the instrument science plans aimed at achieving the validation objectives defined for the mission. Mission planning and data processing approaches are described.
Ocean Front Detection from SAR Imagery Using SLIC Superpixel Segmentation Method

Ocean front can influent the distribution of sea material and it has an impact on underwater acoustic communication. Thus, the detection of ocean front is very important. In this paper, the simple linear iterative clustering (SLIC) superpixel segmentation approach is applied to ocean front detection in SAR images, which can suppress the influence of coherent speckle noise and extract the fuzzy boundary from high-resolution SAR images. The results show that this method can extract ocean front with one pixel width and the extraction of ocean front is in good agreement with human visual interpretation. This method provides a new idea to extract ocean fronts from SAR images.
WUP-CD: Towards 2.5D Data for Deep Learning Building Change Detection

We present WUP-CD, a high-resolution aerial dataset for building change detection consisting of both orthorectified aerial imagery and corresponding elevation information ac-quired at two points in time. Detailed analysis of the dataset using several state-of-the-art deep learning methods allows us to show that the best results are obtained using elevation data only, highlighting its importance for future data acquisition and model development. The dataset is available for down-load11https://github.com/tritolol/WUP-CD.
Remote Sensing Observations of Melt Ponds on Top of Antarctic SEA ICE Using Sentinel-3 Data

Contrary to the sea ice surface melt during Arctic summer, melt ponds on top of the Antarctic sea ice during austral summer are thought to be rare. Their observations are limited to few occasional sightings and no regular dataset is published till now. In this work, we employ an existing remote sensing method which was already applied in the Arctic and for the first time detect potential surface melt areas on top of the Antarctic sea ice at a regional to global scale using Sentinel-3 data. We confirm the occurrence of melt ponds in Lützow-Holm Bay in the vicinity of the Shōwa Station observed in situ in January 2017 and detect surface melt in the following years 2018–2020. We also perform global processing of the Antarctic sea ice to detect further regions of potential surface melt.
A Function Design for a Power-Generation Corner Reflector for SAR Analysis

We reported a function design for a novel power-generation corner reflector. Built mainly by solar panels, the solar corner reflector (SCR) is designed firstly to be served as a permanent scatter point on the ground required for time series SAR interferometric processing, and secondly as a power generator and storage for attached sensors for use. Based on the results derived from the on-site experiment, clear backscatter pixels were observed from Sentinel-1 SAR image and data were stored completely in local sensor in day and night time. Since the capability of the prototype SCR was confirmed, it was expected to be of great benefit to support an interpretation of ground displacement together with InSAR analysis.
Selecting Hyperspectral Bands for Leaf Mass Per Area Prediction by Means of Neural Networks

Accurate prediction of Leaf Mass per Area (LMA) from leaf-level hyperspectral reflectance measurements is fundamental for plant health watching. Limiting the number of required bands to a very low number while maintaining a good accuracy of LMA prediction is of very high interest for cost-effective implementations. For such purpose, the best current model consists in a combination of vegetation indices and have been obtained by performing computationally intensive brute force approach. Further accuracy improvements are, at present, limited by current computational capacity limit. We propose to use a neural network-based methodology for selecting a very low number of hyperspectral bands for further improving the LMA prediction accuracy. Testing on the publicly available Angers dataset, we diminished the Root Mean Square Error of prediction by 26% and 19% as compared to the best literature vegetation index and combination of vegetation indices, respectively.
Comparison of Boreal Biomass Estimations Using C- and X-Band Polsar

This study investigated the potential of using polarimetric synthetic aperture radar (PolSAR) data acquired at C- and X-band to estimate forest aboveground biomass (biomass) at a test site in southern Sweden. The SAR data were acquired with RADARSAT-2 and TerraSAR-X in September 2015, and the biomass estimations were cross-validated with 48 field inventoried plots of 40 m radius (0.5 ha), covering dominantly coniferous hemi-boreal forest. The quad-pol SAR data (HH, HV, VH, VV) were decomposed using the Yamaguchi model and the mean modelled scatter components for the plots were extracted and used as predictors in linear regression models to estimate the biomass. The model performances were quantified using the root mean square error (RMSE) and adjusted coefficient of determination, R2adj. The leave-one-out cross-validated RMSEs were 58.1 (37.1%) and 60.6 (38.6%) tons/ha for C-and X-band, respectively, and the R2adj were 0.52 and 0.47, respectively. The regression model for C-band data used the volume and helix scattering components in the Yamaguchi decomposition as predictors, while the corresponding X-band model used the double bounce and surface components. The differences were likely due to the different penetrations at C-and X-band. We noticed a tendency to saturation at about 300 tons/ha in the X-band predictions, while no such tendency was noticed for the C-band model. We conclude that the Yamaguchi polarimetric decomposition technique is a useful approach when estimating biomass in a hemi-boreal forest. The strengths are not comparable to approaches where height information are available (e.g., single-pass interferometry from TanDEM-X), but this polarimetric approach based on single satellite images showed sensitivity to the entire biomass range (0 - 400 tons/ha) in our dataset. The potential of using quad-pol SAR data is, therefore, important to investigate further.
Hyperspectral Unmixing Powered by Deep Image Priors and Denoising Regularization

Properly exploiting image properties is crucial for boosting the hyperspectral unmixing performance. Recent advanced image processing methods use deep architectures to learn image priors. However, these deep priors take effect in an implicit manner and it is nontrivial to characterize their properties. Introducing extra regularization terms is an explicit way of encoding image priors, and the plug-and-play technique enables to construct priors from data by denoisers. In this work, we propose a new unmixing framework to combine both the deep image priors (DIP) and plug-and-play (PnP) priors to further enhance the unmixing performance. The alter-nating direction method of multipliers (ADMM) framework is used to separate the optimization problem into two subproblems. The first one is solved using a U-net training step to obtain DIP, and a proximal denoising step is then used to solve the second subproblem to add denoiser priors. Experiment results demonstrate the effectiveness of our proposed method.
Classification and Verification of Surface Anisotropic Reflectance Characteristics

The surface reflectance anisotropy is usually described by bidirectional reflectance distribution function (BRDF), and the BRDF archetypes extracted based on the Anisotropic Flat Index (AFX) have been used as the prior reflectance anisotropy knowledge of quantitative inversion. In this study, we introduce a new index, i.e., the Perpendicular Anisotropic Flat Index (PAFX), based on the AFX in a prior study, to refine the BRDF classification of the land surface. Based on the MODIS BRDF parameter products sampled at global intervals over multiple land cover types in 2015, we used AFX and PAFX as classification indicators to divide MODIS BRDF parameter space into some orthogonal clusters through use of the ISODATA (Iterative Self organizing data Analysis technique) Clustering Algorithm. As a case study, the MODIS BRDF parameters are divided into three classes by using the AFX and PAFX, respectively, in an orthogonal way, and therefore a total of nine cluster classes of BRDF parameters are generated, correspondingly generating nine BRDF archetypes. We use the average BRDF fitting error, algorithm complexity and classification accuracy to evaluate this classification method for classifying BRDF parameters, showing that the difference within the BRDF archetypes class is significantly reduced after the introduction of PAFX (RMSE = 0.0083) compared with only using AFX (RMSE = 0.0139), and the error for fitting all BRDF shapes with the average BRDF shape, i.e., the BRDF archetype presents a V-shaped trend as functions of AFX and PAFX, indicating an optimized minimum. In general, jointing the AFX and PAFX has improved the classification accuracy of the MODIS BRDF parameter space and is expected a further application in near future.
REVERSE-SHOCK IN TYCHO’S SUPERNOVA REMNANT

Thermal X-ray emission from young supernova remnants (SNRs) is usually dominated by the emission lines of the supernova ejecta, which are widely believed to be crossed and thus heated by the inward-propagating reverse shock (RS). Previous works using X-ray imaging data have shown that the ejecta are heated by the RS by locating the peak emission region of the most recently ionized matter, which is found to be well separated toward the inside from the outermost boundary. Here we report the discovery of a systematic increase of the Sulfur (S) to Silicon (Si) Kα line flux ratio with radius in Tycho’s SNR. This allows us, for the first time, to present continuous radial profiles of the ionization age and, furthermore, the elapsed ionization time since the onset of the ionization, which gives the history of the propagation of the ionization front into the SNR ejecta.
The Foam Python Package and Applications to Ocean Salinity Mission Architecture Studies

We present the Forward Ocean Atmosphere Microwave (FOAM) radiative transfer model, a Python package designed to simulate passive microwave observations of ocean state, and discuss its basic architecture. FOAM is particularly useful for exploring concept architectures for future missions measuring ocean salinity from space. We discuss how FOAM can be used to design missions meeting a range of accuracy and revisit requirements, and we consider an example of ocean salinity retrieval with a wideband radiometer system.
Randomized benchmarking of single- and multi-qubit control in liquid-state NMR quantum information processing

Being able to quantify the level of coherent control in a proposed device implementing a quantum information processor (QIP) is an important task for both comparing different devices and assessing a device's prospects with regards to achieving fault-tolerant quantum control. We implement in a liquid-state nuclear magnetic resonance QIP the randomized benchmarking protocol presented by Knill et al (2008 Phys. Rev. A 77 012307). We report an error per randomized π/2 pulse of 1.3±0.1×10−4 with a single-qubit QIP and show an experimentally relevant error model where the randomized benchmarking gives a signature fidelity decay which is not possible to interpret as a single error per gate. We explore and experimentally investigate multi-qubit extensions of this protocol and report an average error rate for one- and two-qubit gates of 4.7±0.3×10−3 for a three-qubit QIP. We estimate that these error rates are still not decoherence limited and thus can be improved with modifications to the control hardware and software.
Shared Weighted Continuous Wavelet Capsule Network for Electrocardiogram Biometric Identification

In recent times, researchers are showing more interest in the subject of biometric identification, which uses biological traits to confirm a user's identity. Traditional authentication techniques are prone to damage, fraud, and negligence. We investigate a unique biometric based on electrocardiogram (ECG) signals generated from the heart as a biometric security attribute for access control verification. In this research, we propose a shared weighted continuous wavelet capsule network for ECG biometric identification, in which a continuous wavelet transform (CWT) is utilized to convert one-dimensional time-domain ECG signals into scalograms of two-dimensional images to obtain good quality training data. Then, a siamese capsule network framework is utilized to predict the right match or mismatch of ECG query samples using the extracted specific attributes from the scalograms. The dataset utilized in this work is collected from the Physionet MIT-BIH Normal Sinus Rhythm database. Experimental result shows that the proposed approach properly predicted ECG query samples with 99.2% accuracy, which makes our model more robust.
Multifractality of the multiplicative autoregressive point processes

Multiplicative processes and multifractals have earned increased popularity in applications ranging from hydrodynamic turbulence to computer network traffic, from image processing to economics. We analyse the multifractality of the recently proposed point process models generating the signals exhibiting 1/f^b noise. The models may be used for modeling and analysis of stochastic processes in different systems. We show that the multiplicative point process models generate multifractal signals, in contrast to the formally constructed signals with 1/f^b noise and signals consisting of sum of the uncorrelated components with a wide-range distribution of the relaxation times.
PS-InSAR Target Classification Using Deep Learning

Multi-temporal InSAR (MT-InSAR) observations, which enable deformation monitoring at an unprecedented scale, are usually affected by decorrelation and other noise inducing factors. Such observations (PS - Persistent scatterers), are usually in the order of several thousand, making their respective evaluation frequently computationally expensive. In the present study, we propose an approach for the detection of MT-InSAR outlying observations through the implementation of Convolutional Neural Networks (CNN) classification models. For each PS, the corresponding MT-InSAR parameters and the respective parameters of the neighboring scatterers and its relative position are considered. Tests in two independent datasets, covering the regions of Bratislava city and the suburbs of Prievidza, Slovakia, were performed. The results showed that such models offer a robust and reduced computation time method for the evaluation of MT-InSAR outlying observations. However, the applicability of these models is limited by the deformation pattern in which such models were trained.
Perturbation Theory of Coulomb Gauge Yang-Mills Theory Within the First Order Formalism

Perturbative Coulomb gauge Yang-Mills theory within the first order formalism is considered. Using a differential equation technique and dimensional regularization, analytic results for both the ultraviolet divergent and finite parts of the two-point functions at one-loop order are derived. It is shown how the nonultraviolet divergent parts of the results are finite at spacelike momenta with kinematical singularities on the light-cone and subsequent branch cuts extending into the timelike region.
Perturbative Reheating After Multiple-Field Inflation: The Impact on Primordial Observables

We study the impact of perturbative reheating on primordial observables in models of multiple{ eld ination. By performing a sudden decay calculation, we derive analytic expressions for the local{type non{linearity parameterf local NL , the scalar spectral indexn , and the tensor{to{scalar ratio rT as functions of the decay rates of the inationary elds. We compare our analytic results to a fully numerical classical eld theory simulation, nding excellent agreement. We nd that the sensitivity of fNL, n , and rT to the reheating phase depends heavily on the underlying inationary model. We quantify this sensitivity, and discuss conditions that must be satised if observable predictions are to be insensitive to the dynamics of reheating. We demonstrate that upon completion of reheating, all observable quantities take values within nite ranges, the limits of which are determined completely by the conditions during ination. Furthermore, uctuations in both elds play an important role in determining the full dependence of the observables on the dynamics of reheating. By applying our formalism to two concrete examples, we demonstrate that variations in fNL, n , and rT caused by changes in reheating dynamics are well within the sensitivity of Planck, and as such the impact of reheating must be accounted for when making predictions for models of multiple{eld ination. Our nal expressions are very general, encompassing a wide range of two{eld inationary models, including the standard curvaton scenario. We show that the curvaton scenario is a limiting case of two{eld ination, and recover the standard curvaton results in the appropriate limit. Our results allow a much more unied approach to studying two{eld ination including the eects of perturbative reheating. As such, entire classes of models can be studied together, allowing a more systematic approach to gaining insight into the physics of the early universe through observation.
Instance Segmentation of Piled Rock Particles Based on Mask R-CNN

Instance segmentation of piled rock particles is a fundamental problem in many industry applications, yet traditional methods struggle to provide satisfactory results. To overcome the limitations that inherently lie in the traditional segmentation methods, we developed a neural network model based on the Mask R-CNN architecture. The annotation mask used in training is costly to produce, which can be the main bottle-neck when developing such a model. To cope with this problem, we take a pragmatic approach to reduce as much human effort as possible. We demonstrate the results with digital photos containing even 1000+ rock particles obtained from oilfield wells. Our segmentation results show significant improvements compared with previous attempts. We believe the same approach can be taken to efficiently build a model to run instance segmentation on rock particle images from different types of imaging systems by training the model with the relevant datasets.
Moving Target Shadow Detection using Transformer in Video Sar

Video synthetic aperture radar (SAR) has been found to be very valuable for detecting and tracking moving targets and observing areas of interest. Shadows produced by target motion in sequential radar images can be used to detect targets themselves. Since existing deep learning shadow detection methods often require many hand-designed components, in this paper, we propose a shadow detection method for video SAR moving target based on transformer, which is named Deformable Shadow-DETR. Deformable Shadow-DETR can better extract shadow features, and use the transformer encoder-decoder network to treat shadow detection as a direct set prediction problem, eliminating the need for cumbersome hand-designed components. Experiments on the real video SAR data published by the Sandia National Laboratories show that our proposed moving target shadow detection method can achieve excellent performance.
Dual-Branch Fusion Network for Residential Area Extraction from a Ziyuan-3 Multi-Spectral and Multi-View Data Set

The accurate extraction of residential area is of great significance to disaster assessment, urban management, and climate change research. Deep learning-based methods are limited by annotations acquisition and single data source. In this paper, a dual-branch encoder is proposed to extract the features of different inputs, and a multi-attention fusion module is proposed to effectively fuse dual-branch features. Furthermore, we propose a novel encoder-decoder architecture, called the dual-branch fusion network (DBNet). In addition, we propose a large-scale residential area extraction data set (ZRA) containing 43 Ziyuan-3 (ZY3) multi-spectral (MS) and multi-view (MV) images. Experiments on ZRA, our DBNet achieve state-of-the-art performance with a F1 score of 84.6% and an IoU of 73.3%.
Chaotic Cuckoos Optimization with Graph Convolution Network for Hyperspectral Data Classification

This work proposes a new hyperspectral image classification method based on chaotic cuckoos (CC) search with graph convolution network (CC-GCN). Although GCNs can extract inherent features by node embeddings, most models neglect fragmented relations in the spectral domain. The proposed CC-GCN can refine the graph structure and reduce the redundancy information of spectral dimension, further improving the classification accuracy of hyperspectral images. The experiment results demonstrate the effectiveness of the proposed CC-GCN.
A Low-profile Wide-angle Reconfigurable Transmitarray Antenna Using Phase Transforming Lens with Virtual Focal Source

A low-profile wide-angle reconfigurable transmitarray antenna (RTA) is proposed in this communication. This RTA consists of a feeding antenna, a phase transforming lens and a 1-bit phase-reconfigurable transmitting antenna. The feeding antenna is composed of a 12×12 slot array fed by 36-way waveguide power divider, which possesses the characteristics of equal amplitude and in-phase distribution to obtain high efficiency. The desired 1-bit phase-reconfigurable transmitting antenna uses 16×16 array of C-shaped probe-fed patch elements to achieve flexible wide-angle beam scanning. To eliminate the mirror lobe effects of 1-bit phase quantization, the phase transforming lens with a 16×16 array of perforated dielectric element is designed to generate a spherical phase front from virtual focal source. By spatially cascading these three components, a low-profile RTA architecture is constituted, where the two spatial separation between adjacent components are 0. 20λ0 and 0. 08λ0 at 12.0 GHz, respectively. Finally, the proposed antenna prototype with an effective aperture of 7. 68λ0×7. 68λ0 (192 mm×192 mm) is fabricated and measured. The measured peak gain is 23.5 dBi at 12.0 GHz with aperture efficiency of 30.2% and a 3-dB gain bandwidth of 1.1 GHz. The measured results demonstrate that the proposed design has the two-dimensional wide-angle beam scanning capability up to 60°.
Humans, Automation, and Workflow: Seeking Balance in Service of Art

Since the advent of “workflow” during the Industrial Revolution, companies have been continually seeking to increase efficiency while reducing extraneous tasks or human interaction, including through automation. The media and entertainment (M&E) industry has followed a similar trajectory, accelerated most recently by the transition to digital. While automation is inherent to modern workflows, M&E relies on creativity and the subjectivity of art and therefore requires human intervention. Mitigating issues of algorithm bias, over-reliance on technology, and limited or flawed data inputs creating flawed outputs is vital in promoting healthy collaboration that allows elevated creativity.