Miracle-dz/UniFAR-FTUs huggingface dataset:
BERT-Enhanced with Context-Aware Embedding for Instance Segmentation in 3D Point Clouds

Inspired by the successful implementation of transformer network in the Natural Language Processing (NLP), we propose a novel Bidirectional Encoder Representations from Transformers (BERT)-based point cloud segmentation method. Specifically, the whole point cloud is scanned by multiple overlapping windows. We made the first attempt ever to input each window-point-cloud into the BERT model which outputs points' semantic labels and high-dimensional context-aware point embeddings. In the process of training, the Kullback-Leibler (KL)-Divergence-based clustering loss is utilized to optimize the network's parameters by calculating similarity matrices between the point embeddings and the predicted semantic labels. The final instance labels can be obtained by softmax function on these optimized point embeddings. By evaluating on the Stanford 3D Indoor Scene (S3DIS) dataset, our proposed method has reached a micro-mean accuracy (mAcc) of 87.3% on the semantic segmentation task and an Average Precision (mAP) on the instance segmentation task. The results on both tasks have surpassed the traditional point cloud segmentation models.
DISK MASS-TO-LIGHT RATIO DISTRIBUTION FROM STELLAR POPULATION SYNTHESIS: APPLICATION TO ROTATION CURVE DECOMPOSITION OF NGC 5278 (KPG 390 A)

In this work we extend the study on the mass distribution of the spiral galaxy NGC 5278, performing 1D and 2D (GALFIT) bulge–disk decomposition to determine which components constitute the baryonic mass in this galaxy. Our analysis does not detect any bulge; instead we find a bright source probably related to the central active galactic nucleus and an exponential disk. We fix the stellar disk contribution to the rotation curve (RC) with broadband photometric observations and population synthesis models, to obtain the 2D mass distribution of the stellar disk. In the particular case of NGC 5278, we find that the typical assumption of considering the mass-to-luminosity ratio (M/L) of the disk as constant along the galactocentric radius is not valid. We also extract a baryonic RC from the mass profile to determine the inability of this baryonic RC and also the baryonic RC with more than and less than 30% disk mass (in order to consider the disk mass errors) to fit the entire RC. We perform the RC decomposition of NGC 5278 by considering the baryonic RC and four types of dark matter (DM) halo: Hernquist, Burkert, Navarro, Frenk, & White, and Einasto. Our results determine that the Hernquist DM halo better models our observed RC in the case of disk mass Md = 5.6 × 1010 M☉ and also with less than 30% disk mass. In the case of more than 30% disk mass, the cored Einasto (n < 4) DM halo is the best-fitting model.
Estimation of Atmospheric PM2.5 based on Photos and Deep Learning

Particulate matter with a diameter of less than 2.5 microns (PM2.5) in the air is one of the most critical pollutants related to air quality. Exposure to high levels of PM2.5, which can be inhaled and carry harmful chemicals deep into the lungs and bloodstream, can have acute or chronic adverse health effects. A reliable, convenient and low-cost method to obtain PM2.5 concentration can help people improve their awareness of it. At the same time, it can also provide a certain reference for the prevention and control of haze, for air purification and for some other work to reduce the harm of air pollution to human health. In this paper, from the perspective of deep learning, with the help of photos that can be taken anywhere, we combined convolution neural network and support vector regression machine to estimate PM2.5. The method used in this paper is compared with other methods using the same dataset to prove its effectiveness.
Nonlinear control techniques for an atomic force microscope system

Two nonlinear control techniques are proposed for an atomic force microscope system.Initially,a learning-based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories.Specifically,the control approach utilizes a learning-based feedforward term to compensate for periodic dynamics and high-gain terms to account for non-periodic dynamics.An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters.Simulation results are provided to illustrate the efficacy and performance of the control strategies.
Real-Time Implementation of Segmental Aperture Imaging Algorithm for Multirotor-Borne Minisar

This paper addresses the unique motion errors that must be compensated for miniaturized synthetic aperture radar (MiniSAR) onboard multi-rotor unmanned aerial vehicle (UAV). Segmental aperture imaging (SAI) algorithm is proposed which simply divides the platform trajectory into segments. Each segment is first independently focused with motion compensation and then stitched together to form a complete SAR image. A real-time SAI algorithm implementation is proposed. It performs overlapping subaperture for each segment and performs polynomial fitting of its phase. It is implemented on graphics processing unit (GPU). Finally, the data obtained by the FuSaR-Ku system is used to verify the effectiveness of the real-time SAI algorithm.
Deep Learning Based Overcomplete Representations for Paddy Rice Crop and Weed Segmentation

Weed detection and removal is an essential task for a better crop yield as they compete for resources along with crops. Manual weed control methods are time-consuming and labour-intensive tasks with room for errors. The critical challenge is to reliably and accurately detect weed from the field. To achieve this, UAV and ground-based sensing along with deep learning, especially CNN models are used to automate crop management. CNNs based on encoder-decoder models are preferred for semantic segmentation. However, they perform poorly for low-level features such as noisy boundaries. This paper presents our work on paddy rice data collection for crop-weed segmentation and a network model based on an overcomplete representation (Kite-Net) augmented on a transfer learning-based encoder-decoder model (TernausNet) for segmenting the RGB image into three classes: crop, weed and background. The network results show convincing seg-mentation mask output on overlapping crop-weed images.
IDENTIFICATION OF TWO BRIGHT z > 3 SUBMILLIMETER GALAXY CANDIDATES IN THE COSMOS FIELD

We present high-resolution interferometric Submillimeter Array imaging at 890 μm (∼2″ resolution) of two millimeter selected galaxies—MMJ100015+021549 and MMJ100047+021021—discovered with the Max-Planck Millimeter Bolometer (MAMBO) on the IRAM 30 m telescope and also detected with Bolocam on the CSO, in the COSMOS field. The first source is significantly detected at the ∼11σ level, while the second source is tentatively detected at the ∼4σ level, leading to a positional accuracy of ∼0. ″2–0. ″3. MM100015+021549 is identified with a faint radio and K-band source. MMJ100047+021021 shows no radio emission and is tentatively identified with a very faint K-band peak which lies at ∼1. ″2 from a clumpy optical source. The submillimeter-to-radio flux ratio for MM100015+021549 yields a redshift of ∼4.8, consistent with the redshift implied by the UV-to-submillimeter photometry, z ∼ 3.0–5.0. We find evidence for warm dust in this source with an infrared luminosity in the range ∼(0.9–2.5) × 1013 L☉, supporting the increasing evidence for a population of luminous submillimeter galaxies at z > 3. Finally, the lack of photometric data for MMJ100047+021021 does not allow us to investigate its properties in detail; however, its submillimeter-to-radio flux ratio implies z > 3.5.
Multi-Structure Extraction Kernel Dictionary Learning for SAR Target Recognition

This paper presents a multi-structure extraction kernel dictionary learning (MSEK-DL) method for synthetic aperture radar (SAR) automatic target recognition (ATR). In order to extract the multi-structure features of SAR images for data enhancement and noise suppression, a matrix approximation method is used. Instead of using traditional linear dictionary learning method, non-linear kernel function is used to map the targets into a high-dimensional space, in order to obtain a better classification performance. The training method and optimization steps of MSEK-DL are presented in this paper. We carried out the experiment based on MSTAR dataset to demonstrate the effectiveness of the proposed classification algorithm. The experimental results show that the classifi-cation algorithm has better classification performance than some representative dictionary learning algorithms, espe-cially for small training datasets.
A Spatial-Channel Attention-Based Convolutional Neural Network for Remote Sensing Image Classification

This paper proposes a novel model for remote sensing image classification based on CBAM-CNN (Convolutional Block Attention Module-Convolutional Neural Network). CBAM-CNN is a well-known and effective model. However, it is limited when it comes to high-level features due to its shared spatial attention mechanism and narrow sampling range in squeeze-and-excitation module (SEM). We disentangle the shared attention to channel-independent spatial attention and expand the sampling range of SEM. The results show that the proposed approach outperforms other CNN-based models on two large-scale benchmark datasets.
Analysis of SAR Backscatter Intensity Characteristics for Inverse Estimation of Earthquake-Damaged Buildings

SAR image simulation is a radar imaging simulation technology developed in recent years, which helps the analysis design, and verification of SAR system, assists SAR image interpretation, SAR image processing algorithm verification, and SAR image geometry correction, and thus has important theoretical significance and practical value. A 3-dimensional canonical target known as SLICY (Sandia Laboratory Implementation of Cylinders) and a collapse model are used for synthetic aperture radar imaging simulations. Reflectivity maps corresponding to signals with different types of radar backscatter are plotted. Combined with the geometrical physical information of the target, the correspondence between the point and line features, different signals of scattering, and object-specific structures in the simulated SAR images are analyzed. The results show that reflection levels of the radar signal are closely related to the object structure details. The analysis of the reflective intensity characteristics of this paper provides a good basis for later inverse estimation of damage to earthquake-damaged buildings.
A Study on Light-to-Moderate Earthquakes Based on Moment Tensor Solutions and Remote Sensing Insar Data

We develop a method of investigation to analyze the variability of the moment tensor solutions in case of light-to-moderate earthquakes. To this purpose, we compare the seismic-induced displacement field predicted from the available moment tensor solutions and estimated from Sentinel-1 InSAR data. The differences between simulated and real deformation scenarios are then evaluated both in terms of epicenter location and kinematics of the fault responsible for the event. Our test dataset consists of two events with a magnitude ranging from light to moderate occurred on 2019 in France and Turkey, respectively.
On Adaptive Unscented Kalman Filtering for Floating Doppler Wind-Lidar Motion Correction: Effect of the Number of Lidar Measurement Heights

This work studies the influence of the number of lidar measurement heights on the performance of the floating Doppler wind lidar motion-correction algorithm, recently published by the authors. The work is in the context of offshore wind energy and continuous-wave focusable ZephirTM 300 lidar. A downsampling technique applied over the lidar-measured wind speed time-series is used to simulate different height-sounding configurations. The operation of the filter under one, three, and five measurement heights of the lidar is studied by using data from El Pont del Petroli measurement campaign. The filter is proved to remove apparent turbulence addition in all three cases, showing a deterioration of statistical indicators as the number of sounding heights increase.
Theory Emergence in IS Research: The Grounded Theory Method Applied

Where IS research aims at theory building and testing, the vast bulk of theory is borrowed from reference disciplines. While this provides some momentum for research output, it also tends to shift the focus of research away from direct observation of central, core IS issues. The purpose of this paper is to contribute to a larger goal of propelling forward indigenous IS theory. To do so we begin with grounded theory (GT) studies published in the Basket of Eight journals. For each identified paper, we analyze the topics, constructs, results, and implicit or stated theory considering the degree to which these papers individually and collectively contribute to an indigenous body of IS theory. Based on our analysis, we consider GT studies suggestive of positive directions for indigenous IS theory development. We encourage continued research and publication of inductive studies as a promising means toward this end.
Tomography imaging of Terrestrial snow for SWE retrieval using frequency-angular correlation functions and asymmetrical distorted Born's approximation

Stratification in terrestrial snow is a key factor in the retrieval of snow water equivalence (SWE) due to the different snow volume fractions and particle sizes. In studying the layered structure of snow, radar tomography has been used and multiple ground based experiments are performed. The conventional back projection method has been used to construct the image based on radar measurements at different incident angles and frequencies. However, the conventional back projection method based on Born's approximation would show deformation in the final snow image. In this paper, we use the asymmetrical distorted Born's approximation to correct the deformation in the image.
Post Fire Reforestation Detection of Siberian Boreal Forest with Radar Polarimetric and Multispectral Data Analysis

The method of forest restoration monitoring after wildfires is proposed. It is based on the joint use of ALOS-2 PALSAR-2 polarimetric radar data to assess the temporal dynamics of physical scattering mechanisms and the analysis of long time series of normalized difference vegetation index (NDVI) calculated with help of Google Earth Engine (GEE) cloud platform. This made it possible to quantitatively analyze the process of post fire reforestation in terms of changes in the projective coverage and geometric dimensions of forest undergrowth. Such a new approach will make it possible further to take into account more accurately the role of boreal forests as one of the largest carbon stocks.
A CNN-Based Multichannel Interferometric Phase Denoising Method Applied to Tomosar Imaging

Tomographic synthetic aperture radar (TomoSAR) is an advanced SAR interferometric technique to retrieve 3-D spatial information. However, decorrelation effects degrade the quality of interferometric phases, resulting in errors in the reconstruction. In this paper, we propose a denoising method based on the unsupervised convolution neural network (CNN) with a loss function combining the deterministic descriptive regularization and total variation (TV) term. It can improve both the accuracy and completeness of the reconstructed 3-D point clouds, which is verified by experiments on simulated and real SAR images.
Classification of an 8-Band Multi-Spectral Dataset Using DCNNs with Weight Initializations Derived from Pre-Trained RGB Networks

Modern satellite sensors can capture reflected optical energy from wavelengths well outside of the range of human perception. Indeed, it is well known that electromagnetic energy from the “non-visible” spectrum can be used to identify materials in geological, oceanographic, and agricultural contexts. What is less understood, however, is if and how this additional spectral information can be leveraged to aid in difficult computer vision tasks, e.g., classification or detection of man-made objects. Thus, we present the results from a series of experiments that evaluate the benefits that Deep Convolutional Neural Networks (DCNN) can garner through the inclusion of more spectral information. For training and testing these networks in image classification, we extract image tiles from the xView multi-spectral dataset. These images contain eight channels of spectral data including five wavelengths bands beyond the three bands traditionally used in computer vision researcher (red, green, and blue). In this work, we report the results of experiments that use an 80–20 train-test split with four DCNN architectures on full 8-band Multispectral Imagery (MSI) and various subsets of this eight banded imagery. The results show that networks trained on MSI have an average testing F1-score around 1.0 point higher than RGB networks trained with the same methods.
Triggering Freeze/Thaw Surface State Monitoring from High Inclination Orbit GNSS-R Missions: A CYGNSS-Based Study

NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission has been in orbit for more than 5 years. Following the Earth Science Division 2020 Senior Review, NASA announced last year it is extending the CYGNSS mission through at least 30th September 2023. The extended CYGNSS mission phase will focus on both ocean and land surface scientific investigations. One new recent application of CYGNSS is FreezelThaw (FIT) surface state detection and imaging [1]. The main objective of this presentation is the application of the retrieval algorithm developed in [1] over different high-interest target areas within the CYGNSS coverage to show the capability to provide long-term trending of FIT behaviour.
Monitoring Variation of Reservoir Water Volume Using SAR Sentinel-1 Observations and Jason-3 Radar Altimetry Data

This study estimates monthly variation of surface water volume of Thac Mo water reservoir, located in Binh Phuoc province, in South Vietnam, for the 2016–2021 period, using SAR Sentinel-1 observations and Jason-3 altimetry data. SAR Sentinel-1 observations have been pre-processed mainly using the Google Earth Engine cloud computing platform to estimate variation of surface water extent, while Jason-3 radar altimetry data has been processed using the AlTiS software to estimate variation of surface water level of the reservoir. Results show a very good agreement between variations of the reservoir's surface water volume derived from satellite data and that derived from in situ measurement, with a correlation of 95.4%.
Microwave Thermal Features of Korolev Basin Revealed by CE-2 MRM Data

Studying Korolev basin may provide meaningful information about the Megabasin on the lunar surface. In this study, the brightness temperature (TB) maps derived with Chang'e-2 microwave radiometer data are used to study the thermophysical features of the surface materials within and outside Korolev basin. Here, for the first time, a high TB anomaly in the highland crater is revealed with the TB data. Moreover, four surface units are indicated by the TB performances at day and night, which likely reflects the thermophysical features of the ejecta mainly from Orientale, Hertzsprung, and Apollo events. This study is essential to improve understanding the surface and thermal evolution of the highland craters.
IEEE Standard for Information Technology--Telecommunications and Information Exchange between Systems - Local and Metropolitan Area Networks--Specific Requirements - Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications - Redline

Technical corrections and clarifications to IEEE Std 802.11 for wireless local area networks (WLANs) as well as enhancements to the existing medium access control (MAC) and physical layer (PHY) functions are specified in this revision. Amendments 1 to 5 published in 2016 and 2018 have also been incorporated into this revision.
Visual Odometry in challenging environments: an urban underground railway scenario case

Localization is one of the most critical tasks for an autonomous vehicle, as position information is required to understand its surroundings and move accordingly. Visual Odometry (VO) has shown promising results in the last years. However, VO algorithms are usually evaluated in outdoor street scenarios and do not consider underground railway scenarios, with low lighting conditions in tunnels and significant lighting changes between tunnels and railway platforms. Besides, there is a lack of GPS, and it is not easy to access such infrastructures. This research proposes a method to create a ground truth of images and poses in underground railway scenarios. Second, the EnlightenGAN algorithm is proposed to face challenging lighting conditions, which can be coupled with any state-of-the-art VO techniques. Finally, the obtained ground truth and the EnlightenGAN have been tested in a real scenario. Two different VO approaches have been used: ORB-SLAM2 and DF-VO. The results show that the EnlightenGAN enhancement improves the performance of both approaches.
Convolutional Deep Kernel Method for Land Cover Mapping from Hyperspectral Imagery

In recent years, kernel-based methods and Deep Learning (DL) models have become the two most successful Remote Sensing (RS) analysis techniques for various Earth observations, particularly hyperspectral images. However, kernel-based methods are generally considered shallow models and intrinsically inconsistent with end-to-end learning. On the other hand, end-to-end learning is one of DL models' essential features as it seems to be responsible for their proven higher performances. Nevertheless, kernel methods are based on rigid mathematical theory and can efficiently cope with high-dimensional data. This paper proposed a hybrid deep kernel model to benefit from both kernel-based methods and DL models. This novel deep kernel model, namely Convolutional Kernel Network (CKN), was applied to two benchmark hyperspectral image datasets. Moreover, the proposed hybrid method was compared to Support Vector Machine (SVM) classifiers with various kernel functions. The experimental results indicated that the CKN's outperforms SVM.
Deep Blind Unmixing using Minimum Simplex Convolutional Network

This paper proposes a deep blind hyperspectral unmixing network for datasets without pure pixels called minimum simplex convolutional network (MiSiCNet). MiSiCNet is the first deep learning-based blind unmixing method proposed in the literature which incorporates both spatial and geometrical information of the hyperspectral data, in addition to the spectral information. The proposed convolutional encoder-decoder architecture incorporates the spatial information using convolutional filters and implicitly applying a prior on the abundances. We added a minimum simplex volume penalty term to the loss function to exploit the geometrical information. We evaluate the performance of MiSiCNet on simulated and real datasets. The experimental results confirm the robustness of the proposed method to both noise and absence of pure pixels. Additionally, MiSiCNet considerably outperforms the state-of-the-art unmixing approaches. The results are given in terms of spectral angle distance in degree for the endmember estimation, and root mean square error in percentage for the abundance estimation. MiS-iCNet was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/MiSiCNet.