Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase operators, phase states and vector phase states for SU3 and SU2, 1

This paper focuses on phase operators, phase states, and vector phase states for the sl3 Lie algebra. We introduce a one-parameter generalized oscillator algebra Aκ(2) which provides a unified scheme for dealing with su3 (for κ 0), and h4⊗h4 (for κ = 0) symmetries. Finite- and infinite-dimensional representations of Aκ(2) are constructed for κ < 0 and κ ⩾ 0, respectively. Phase operators associated with Aκ(2) are defined and temporally stable phase states (as well as vector phase states) are constructed as eigenstates of these operators. Finally, we discuss a relation between quantized phase states and a quadratic discrete Fourier transform and show how to use these states for constructing mutually unbiased bases.
PLANET HUNTERS: NEW KEPLER PLANET CANDIDATES FROM ANALYSIS OF QUARTER 2

We present new planet candidates identified in NASA Kepler Quarter 2 public release data by volunteers engaged in the Planet Hunters citizen science project. The two candidates presented here survive checks for false positives, including examination of the pixel offset to constrain the possibility of a background eclipsing binary. The orbital periods of the planet candidates are 97.46 days (KIC 4552729) and 284.03 (KIC 10005758) days and the modeled planet radii are 5.3 and 3.8 R⊕. The latter star has an additional known planet candidate with a radius of 5.05 R⊕ and a period of 134.49 days, which was detected by the Kepler pipeline. The discovery of these candidates illustrates the value of massively distributed volunteer review of the Kepler database to recover candidates which were otherwise uncataloged.
Domain Adaptation for SAR Target Recognition with Limited Training Data Via Rigid Transformation-Based Feature Conversion

This paper presents a semi-supervised domain adaptation method for SAR target recognition. The proposed method only requires a few real data to be labeled. The challenge is that due to the high angle sensitivity of SAR images, a network can easily overfit the training data at seen angles and fails to classify data at unseen angles. To overcome this, we design a conversion module that can infer what CNN features of images at unseen angles look like. This conversion module is designed as a rigid-body transformation followed by a conditional generative network. This design enables the network to gain high-level 3D understanding from 2D images. Thus, we name our network 3D converter. The network mainly learns from fully-labeled simulated SAR data and then the knowledge is adapted to fit the scarcely-labeled real SAR data. Our method improves the performance over the baseline by 3.48% on MSTAR benchmark when only 10 images/class are labeled. It also achieves comparable results to strongly supervised methods.
Combinatorial Topology and the Global Dimension of Algebras Arising in Combinatorics

In a highly influential paper, Bidigare, Hanlon and Rockmore showed that a number of popular Markov chains are random walks on the faces of a hyperplane arrangement. Their analysis of these Markov chains took advantage of the monoid structure on the set of faces. This theory was later extended by Brown to a larger class of monoids called left regular bands. In both cases, the representation theory of these monoids played a prominent role. In particular, it was used to compute the spectrum of the transition operators of the Markov chains and to prove diagonalizability of the transition operators. In this paper, we establish a close connection between algebraic and combinatorial invariants of a left regular band: we show that certain homological invariants of the algebra of a left regular band coincide with the cohomology of order complexes of posets naturally associated to the left regular band. For instance, we show that the global dimension of these algebras is bounded above by the Leray number of the associated order complex. Conversely, we associate to every flag complex a left regular band whose algebra has global dimension precisely the Leray number of the flag complex.
Microscopic folding model analysis of the radiative ( n , γ ) reactions near the Z = 28 shell closure and the weak s process

Radiative thermal neutron capture cross sections over the range of thermal energies from 1 keV to 1 MeV are studied in the statistical Hauser-Feshbach formalism. The optical model potential is constructed by folding the density-dependent M3Y nucleon-nucleon interaction with radial matter densities of target nuclei obtained from relativistic-mean-field (RMF) theory. The standard nuclear reaction code talys1.8 is used for calculation of cross sections. The nuclei studied in the present work reside near the $Z=28$ proton shell closure and are of astrophysical interest, taking part in $p,\phantom{\rule{0.16em}{0ex}}s$, and $r$ processes of nucleosynthesis. The Maxwellian-averaged cross-section (MACS) values for energies important for astrophysical applications are presented.
Multiple Deep learning Methods for Small and Blurry Volcanic Ash Cloud Detection for Satellite Imagery

This paper proposes a small and blurry foreground object detection method from highly similar textured background images based on Deep Learning (DL). In satellite imagery, volcanic smoke can be observed as ash clouds that present highly similar shape and texture to surrounding normal clouds. Moreover, very small size of such ash clouds can further impede the detectability when applying previous image processing-based methods. Therefore, a special care for discriminating foreground and background images has been desired. For this issue, this paper presents a multiple, i.e., three-step, DL based important object detection method for satellite imagery, i.e., specially mixed infrared channels of the Himawari-8, with volcanic smoke/ash clouds. Moreover, DL, i.e. Fully Convolutional Network, generalization performances with blurry object contours have been enhanced by multiple loss functions and re-selection of pooling layer. Experimental results on world-wide volcanos have demonstrated that the proposed multiple DL models have outperformed a single DL based detection methods, i.e., edge and contour detection.
Indirect Method of Identifying Fluoride (F) Affected Soil

The study indirectly identified fluoride (F) stressed areas using existing multispectral indices. The regions where stress-affected plants were identified from the plant senescence reflectance index (PSRI). The areas affected by salinity were identified from the modified salinity index (MSI). The spatial correlation was carried out between PSRI and SI to determine the influential factor for crop stress. The places with a negative correlation were found to have low salinity and high plant stress. According to the field investigation, the places with a negative correlation prevail with high F content in the soil. The results of remote sensing simulations have shown a high correlation ($\mathrm{R}^{2}=0.9$) and an overall accuracy of 90% with respect to ground F measurement. The analysis quantified around 880 sq.km area as F affected area.
Panoptic Segmentation Using Multispectral Worldview-3 Images in Beach Areas

Panoptic segmentation combines instance and semantic seg-mentation, enabling the classification of objects and back-grounds. It is still a method little explored in the remote sensing field, mostly due to the difficulty of generating the data. Moreover, the beach areas have great interest due to many objects and elements that may guide public policies. In this regard, we propose the first study on beach areas using panop-tic segmentation and the first panoptic segmentation study using multispectral data. We used the Gram-Schmidt pan-sharpening method for the multispectral bands and created a dataset with 850 samples with 128x 128 dimensions in the COCO panoptic annotation format. To evaluate the dataset, the Panoptic-FPN was used with modifications in the input (changing from three to eight channels). Results show 59.43 Panoptic Quality (PQ), 77.96 Segmentation Quality (SQ), and 75.07 Recognition Quality (RQ).
INBOUND WAVES IN THE SOLAR CORONA: A DIRECT INDICATOR OF ALFVÉN SURFACE LOCATION

The tenuous supersonic solar wind that streams from the top of the corona passes through a natural boundary—the Alfvén surface—that marks the causal disconnection of individual packets of plasma and magnetic flux from the Sun itself. The Alfvén surface is the locus where the radial motion of the accelerating solar wind passes the radial Alfvén speed, and therefore any displacement of material cannot carry information back down into the corona. It is thus the natural outer boundary of the solar corona and the inner boundary of interplanetary space. Using a new and unique motion analysis to separate inbound and outbound motions in synoptic visible-light image sequences from the COR2 coronagraph on board the STEREO-A spacecraft, we have identified inbound wave motion in the outer corona beyond 6 solar radii for the first time and used it to determine that the Alfvén surface is at least 12 solar radii from the Sun over the polar coronal holes and 15 solar radii in the streamer belt, well beyond the distance planned for NASA's upcoming Solar Probe Plus mission. To our knowledge, this is the first measurement of inbound waves in the outer solar corona and the first direct measurement of lower bounds for the Alfvén surface.
CV-RotNet: Complex-Valued Convolutional Neural Network for SAR three-dimensional rotating ship target recognition

In Synthetic Aperture Radar (SAR) images, ship targets suffer from blurring due to pitch, yaw, and sway, resulting in poor recognition accuracy. This paper proposes a complex-valued convolutional neural network (CV-CNN) architecture called CV-RotNet. This neural network could realize the recognition of SAR defocused ship targets without three-dimensional rotation refocusing. CV-RotNet makes full use of the amplitude and phase information of SAR images. Based on the classic deep learning architecture, RotNet and CV-RotNet were designed from the real domain and the complex domain. CV-RotNet and RotNet are tested on the five types of SAR three-dimensional rotating target simulation samples and the three types of GF-3 real ship target samples. Experimental results show that the average accuracy of CV-RotNet is higher than RotNet with the same degree of freedom, which reflects the advantages of CV-RotNet over RotNet.
Exploring Similarity in Polarization: Contrastive Learning with Siamese Networks for Ship Classification in Sentinel-1 SAR Images

In this paper, we focus on synthetic aperture radar automatic target recognition for ships, and modify the Simple Siamese (SimSiam) framework, a contrastive self-supervised representation learning method, to improve ship classification accuracy. We design a novel sampling method that takes polarization information into account, in addition to the image augmentation based positive pair sampling method that is commonly used in contrastive learning approaches. The results of the experiments show that positive pairs of VH and VV polarized images can provide complementary information about ship targets to strengthen the classifiers. Besides, different similarity measurement functions are analyzed in the experiments.
Machines à noyaux pour le filtrage d'alarmes : application à la discrimination multiclasse en environnement maritime

Infrared systems are keys to provide automatic control of threats to military forces. Such operational systems are constrained to real-time processing and high efficiency (low false-alarm rate) implying the recognition of threats among numerous irrelevant objects.In this document, we combine OneClass Support Vector Machines (SVM) to discriminate in the multiclass framework and to reject unknown objects (preserving the false-alarm rate). While learning, we perform variable selection to control the sparsity of the decision functions. We also introduce a new classifier, the Discriminative OneClass-SVM. It combines properties of both the biclass-SVM and the OneClass-SVM in a multiclass framework. This classifier detects novelty and has no dependency to the amount of categories, allowing to tackle large scale problems. Numerical experiments, on real world infrared datasets, demonstrate the relevance of our proposals for highly constrained systems, when compared to standard methods.
Refocusing of SAR Ground Moving Target Based on Generative Adversarial Networks

Due to the range and azimuth velocity of moving targets, severe defocusing occurs in synthetic aperture radar (SAR) images. The traditional ground moving target imaging algorithm generally needs to estimate the parameters of the moving target, and then conduct the refocusing of the moving target according to the estimated parameters. In this paper, a SAR moving target refocusing algorithm based on generative adversarial network (GAN) is proposed without estimating the motion parameters of the targets. To get a sufficiently trained network, we propose to use simulated moving target data to train the model and evaluate its performance using real data. The results of numerical experiments show that the trained network using simulated data can be well transferred to real test data and effectively achieve to refocus multiple moving targets with distinct velocities at the circumstance of heavy noise.
COLLABORATIVE-COMPETITIVE REPRESENTATION WITH SPATIAL REGULARIZATION FOR HYPERSPECTRAL ANOMALY DETECTION

Recently, Collaborative representation (CR) has drawn much attention towards anomaly detection for hyperspectral imagery. Pixels in the background can be represented with spatial neighbors. In CR, an l2norm is implemented for estimating the weight vector in a closed-form solution. In this paper, locality constraints are imposed on the representation framework called collaborative-competitive representation for hyperspectral anomaly detection (CCRD). By incorporating competition among neighboring atoms lying in various subspaces, local information can be incorporated into the global framework of CR. In addition, distance weighted Tikhonov regularization is used to enhance the performance of CCRD, named CCRDT. Moreover, spatial information is used in the objective function of CCRDT, resulting in SCCRDT, to further enhance the performance of anomaly detection. Experimental results on several hyperspectral datasets demonstrate the superiority of proposed methods in comparison to traditional detectors, such as Reed-Xiaoli (RX) algorithm, kernel RX (KRX) algorithm, and existing collaborative representation-based anomaly detectors.
THE BOLOCAM GALACTIC PLANE SURVEY. XIV. PHYSICAL PROPERTIES OF MASSIVE STARLESS AND STAR-FORMING CLUMPS

We sort 4683 molecular clouds between 10° < ℓ < 65° from the Bolocam Galactic Plane Survey based on observational diagnostics of star formation activity: compact 70 μm sources, mid-IR color-selected YSOs, H2O and CH3OH masers, and UCH ii regions. We also present a combined NH3-derived gas kinetic temperature and H2O maser catalog for 1788 clumps from our own GBT 100 m observations and from the literature. We identify a subsample of 2223 (47.5%) starless clump candidates (SCCs), the largest and most robust sample identified from a blind survey to date. Distributions of flux density, flux concentration, solid angle, kinetic temperature, column density, radius, and mass show strong (>1 dex) progressions when sorted by star formation indicator. The median SCC is marginally subvirial (α ∼ 0.7) with >75% of clumps with known distance being gravitationally bound (α < 2). These samples show a statistically significant increase in the median clump mass of ΔM ∼ 170–370 M⊙ from the starless candidates to clumps associated with protostars. This trend could be due to (i) mass growth of the clumps at M ˙ ∼ 200 – 440 M⊙ Myr−1 for an average freefall 0.8 Myr timescale, (ii) a systematic factor of two increase in dust opacity from starless to protostellar phases, and/or (iii) a variation in the ratio of starless to protostellar clump lifetime that scales as ∼M−0.4. By comparing to the observed number of CH3OH maser containing clumps, we estimate the phase lifetime of massive (M > 103 M⊙) starless clumps to be 0.37 ± 0.08 Myr (M/103 M⊙)−1; the majority (M < 450 M⊙) have phase lifetimes longer than their average freefall time.
Polarimetric Signatures of SAR Image of Complex Targets Over Sea Surface

This paper analyze polarimetric signatures SAR image of an electrically large cargo ship sitting over a sea surface. For the systematic and illustrative purpose, we applied a fully coherent SAR image simulation in streamlining the data flow from backscattered field to complex single look images recently developed to generate high-resolution full polarization SAR images. Three scenes of the ship heading parallel, orthogonal, and 45° to the azimuth direction were simulated. An attempt is made to exploit the polarimetric analysis via the Y4R decompositions. Results reveal that the polarimetric features of sea surface and its interactions with the cargo ship at different headings are quite distinct. For headings parallel to the SAR azimuth direction, richer and diverse polarimetric signatures are presented, but they lose or diminish when the heading is 45° to the azimuth, where surface scattering becomes predominant. A large look angle may attribute this change.
Effect of Fe-doping on nonlinear optical responses and carrier trapping dynamics in GaN single crystals

We presented a quantitative study on the Fe-doping concentration dependence of optical nonlinearities and ultrafast carrier dynamics in Fe-doped GaN (GaN:Fe) single crystals using picosecond Z-scan and femtosecond pump-probe with phase object techniques under two-photon excitation. In contrast to the two-photon absorption that was found to be independent on the Fe-doping, the nonlinear refraction decreased with the Fe concentration due to the fast carrier trapping effect of Fe3+/Fe2+ deep acceptors, which simultaneously acted as an efficient non-radiative recombination channels for excess carriers. Remarkably, compared to that of Si-doped GaN bulk crystal, the free-carrier refraction effect in GaN:Fe crystals was found to be enhanced considerably since Fe-doping and the effective carrier lifetime (∼10 ps) could be tuned over three orders of magnitude at high Fe-doping level of 1 × 1019 cm−3.
Field Demonstrations of Spctor: Sensing Policy Controller and Optimizer

A ground-based distributed sensing network is described in this work that leverages elements of wireless sensor networks (WSN) and uncrewed areal vehicles (UAVs) with software-defined radar payloads. Hardware and software advancements are made towards combining the operations of WSNs and UAVs for dynamic spatiotemporal monitoring of surface to subsurface soil moisture at kilometer scales. The multi-agent and distributed sensing approach demonstrates coordination, collaboration, and parallel operation of discrete assets for optimal soil moisture monitoring. Results from the first field experiment showing this coordinated operation are reported.
From Raster Predictions to Vector Layers of Buildings: A Computational Geometry Approach

Learning - based approaches are now typically used to extract building rooftop from overhead imagery. However, converting boundaries of segmented objects from raster format to vector coordinates remains a challenging problem. Using re-cent advances in multi-task learning, we propose a fast and scalable approach, based on a polygonal partitioning of the space and discrete optimization, to deliver accurate and sim-ple vectorized building rooftops, that are compared to those produced by state-of-the-art techniques.
A Near-Field 3-D SAR Imaging Method with Non-Uniform Sparse Linear Array based on Matrix Completion

Sparse linear array synthetic aperture radar (SAR) is widely used in near-field 3-D imaging, which can reduce the volume of data and the cost of acquisition. However, the sparsity of array elements means the missing rows or columns of data, leading to the high-level sidelobes and the aliasing of targets. Therefore, in this paper, we develop a near-field 3-D imaging method for non-uniform sparse linear array SAR based on matrix completion (MC), where a rotated expansion algorithm is proposed to make the non-uniform sparse data af-ter range compression satisfies the requirements of the MC. The MC technique is applied to the signal slice of each range cell to reconstruct the echo signal, and the back-projection algorithm (BP) is used to achieve 3-D high-resolution imaging of targets. Moreover, simulations and near-field experi-ments validate the feasibility and superiority of the proposed method.
Approximate and Quick Estimation of Carbon Emissions from a City Using Remotely Sensed Data

Calculating CO2 emissions from a city using Global Protocol for Communities (GPC) guidelines is a data intensive exercise. Moreover, the outcome is difficult to verify because of the data quality issues. We have developed a quick and approximate method for calculating scope 1 and scope 2 CO2 emitted by a city, using Landsat 8 data. We used Vegetation-Impervious surfaces-Soil (VIS) classes extracted from the satellite image, and city population as independent variables and related it with CO2 emissions. The model results match well with the reported total annual carbon emissions by the world carbon budget report.
Diffraction Suppression for Ground Penetrating Radar Data Using F-X Domain Variational Mode Decomposition

Diffracted waves on ground penetrating radar (GPR) radargrams will severely affect the positioning of subsurface anomalies and stratigraphic division of subsurface. I this study, we proposed an f-x domain VMD dip filter to suppress the diffracted waves on GPR radargrams. A simple model and complex model tests are performed. The results indicate that the f-x domain VMD dip filter can suppress the interference of diffraction well. The radargrams after processing can highlight the reflected signals generated by the surface of anomalies and subsurface strata, which is beneficial to further object positioning and stratigraphic division.
NQ-Protonet: Noisy Query Prototypical Network for Few-Shot Remote Sensing Scene Classification

Few-shot remote sensing scene classification, which aims to recognize unseen classes given only a few labeled samples, is a challenge task due to the complex content contained in remote sensing scenes. In this end, we propose a noisy query prototypical network (NQ-ProtoNet), which uses query-mix module (QM) to produce extra query samples with inter-ference information for classification and thus implicitly enhance the feature learning ability of model. Our method alleviates the problem of large intraclass variances and inter-class similarity of remote sensing scenes to some extent, and the positive experimental results on UC Merced and NWPU data sets show that it outperforms several few-shot learning methods.
Behavior of grid forming converters in different grid scenarios - result of a test campaign on a megawatt scale

For the integration of grid forming converters (GFC) into the power system, not only the behavior of individual converters is of interest. In order to maintain a stable power system even with shares of renewable energy sources (RES) of up to 100 %, the interaction of the GFCs with each other, as well as with other feed-in and loads, must also be investigated. Simulations can contribute to the understanding of these interactions. In the next step, however, only laboratory tests and associated measurements will provide the decisive contribution in their analysis for the further development of the grid forming control concepts. Therefore, a laboratory measurement campaign in the megawatt scale was successfully carried out as part of the publicly founded joint research project VerbundnetzStabil. It demonstrates the positive contribution to interconnected power system stability of the grid forming control concept developed in the project during normal operation and during massive disturbances, based on system split scenarios. The measurement campaign was carried out in the multi-megawatt laboratory of Fraunhofer ISE.