Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Approach to Automatic Construction of 3D Watertight Geometry of Buildings Using Point Clouds

Recent years have witnessed an increasing use of 3D models in general and 3D geometric models specifically of built environment for various applications, owing to the advancement of mapping techniques for accurate 3D information. Depending on the application scenarios, there exist various types of approaches to automate the construction of 3D building geometry. However, in those studies, less attention has been paid to watertight geometries derived from point cloud data, which are of use to the management and the simulations of building energy. To this end, an efficient reconstruction approach was introduced in this study and involves the following key steps. The point cloud data are first voxelised for the ray-casting analysis to obtain the 3D indoor space. By projecting it onto a horizontal plane, an image representing the indoor area is obtained and is used for the room segmentation. The 2D boundary of each room candidate is extracted using new grammar rules and is extruded using the room height to generate 3D models of individual room candidates. The room connection analyses are applied to the individual models obtained to determine the locations of doors and the topological relations between adjacent room candidates for forming an integrated and watertight geometric model. The approach proposed was tested using the point cloud data representing six building sites of distinct spatial confirmations of rooms, corridors and openings. The experimental results showed that accurate watertight building geometries were successfully created. The average differences between the point cloud data and the geometric models obtained were found to range from 12 to 21 mm. The maximum computation time taken was less than 5 min for the point cloud of approximately 469 million data points, more efficient than the typical reconstruction methods in the literature.
Theoretical Investigation of 3-D Shock Wave Turbulent Boundary Layer Interactions.

Abstract : This report summarizes the research project Theoretical Investigation of 3-D Shock Wave - Turbulent Boundary Layer Interactions during the period 1 October 1992 to 31 July 1996. The principal objectives of the research are the investigation of 3-D shock wave-turbulent boundary layer interactions (3-D turbulent interactions) for complex configurations (e.g., asymmetric shock), and the investigation of the accuracy of Chien's k - epsilon turbulence model, a newly developed Reynolds Stress Equation turbulence model and a k - epsilon model with a new low Reynolds number correction for 3-D turbulent interactions, with specific focus on the ability to predict surface heat transfer and skin friction. Accomplishments during the project include the development of a Reynolds Stress Equation (RSE) model (including determination of all constants), and computation of 3-D asymmetric and symmetric shock interactions at Mach 4 using Chien's k - epsilon turbulence model, the RSE model and the k - epsilon model with the new low Reynolds number correction. The report also provides a list of publications, scientific interactions and personnel.
Channel Modeling and Analysis of Reconfigurable Intelligent Surfaces Assisted Vehicular Networks

The new concept named reconfigurable intelligent surfaces (RIS) is becoming an appealing enabler due to its uniqueness with having low hardware complexity and low power consumption advantages simultaneously. In this paper, an RIS-aided vehicular Adhoc network (VANET) is considered, where the beacon vehicle is enabled with a passive RIS, the communication links between the beacon vehicle and client vehicle caused due to the multipath fading effects, are modeled with Fox’s H-function distribution. This paper first models the inter-vehicle links for the given system setup and then investigates the outage probability and effective rate as performance metrics. More specifically, the unsupervised expectation-maximization (EM) algorithm is consequently used to characterize the distribution of the received signal-to-noise ratio (SNR) at the client vehicle, which is modeled as the mixture of Gaussian (MoG) distribution. The accuracy of our approach is further validated with the Kolmogorov-Smirnov (KS) goodness of fit test. The MoG-based approach successfully tackles the RIS-enabled inter-vehicle communication with an easy, accurate, and tractable solution compared to the widely used central limit theorem (CLT) method. It leads to the closed-form outage probability and effective rate expressions.
A Hybrid Approach for Scene Matching used for Autonomous vehicles

We demonstrate the use of a hybrid approach for scene matching. We exploit a multi-step deep feature based image retrieval using the generated hybrid landmark map which represents both trajectory as well as scene features. The success of an accurate pose estimation depends on the ability to extract the most closest gallery image given any query scene. This work explores scene recognition problem using state of the art deep learning models with an integrated set based classifier. We utilize compressed networks which can generate acceptable results in minimal inference time. We then trained capsule network for landmark classification, post landmark detection using the compressed network. Further to increase the reliability of the system we utilized GAN based learning for generating dustbin classes such that outlier classes can easily be detected. Experimental results demonstrate that the network confidence about a dustbin class can be considerably lowered, when trained with known class as well as dustbin classes rather than when trained with known classes only. This can be further used for estimating relative image poses for scene localization. We present the overall system using a X80SV Wifi Mobile Robot operated inside the University Campus.
3D Mesh Pre-Processing Method Based on Feature Point Classification and Anisotropic Vertex Denoising Considering Scene Structure Characteristics

3D mesh denoising plays an important role in 3D model pre-processing and repair. A fundamental challenge in the mesh denoising process is to accurately extract features from the noise and to preserve and restore the scene structure features of the model. In this paper, we propose a novel feature-preserving mesh denoising method, which was based on robust guidance normal estimation, accurate feature point extraction and an anisotropic vertex denoising strategy. The methodology of the proposed approach is as follows: (1) The dual weight function that takes into account the angle characteristics is used to estimate the guidance normals of the surface, which improved the reliability of the joint bilateral filtering algorithm and avoids losing the corner structures; (2) The filtered facet normal is used to classify the feature points based on the normal voting tensor (NVT) method, which raised the accuracy and integrity of feature classification for the noisy model; (3) The anisotropic vertex update strategy is used in triangular mesh denoising: updating the non-feature points with isotropic neighborhood normals, which effectively suppressed the sharp edges from being smoothed; updating the feature points based on local geometric constraints, which preserved and restored the features while avoided sharp pseudo features. The detailed quantitative and qualitative analyses conducted on synthetic and real data show that our method can remove the noise of various mesh models and retain or restore the edge and corner features of the model without generating pseudo features.
Long-Term Variation in the Tropospheric Nitrogen Dioxide Vertical Column Density over Korea and Japan from the MAX-DOAS Network, 2007-2017

We investigated long-term observations of the tropospheric nitrogen dioxide vertical column density (NO2 TropVCD) from the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) network in Russia and ASia (MADRAS) from 2007 to 2017 at urban (Yokosuka and Gwangju) and remote (Fukue and Cape Hedo) sites in East Asia. The monthly mean in the NO2 TropVCD from MAX-DOAS measured at ~13:30 local time, which is the Ozone Monitoring Instrument (OMI) overpass time, shows good agreement with OMI data during summer, but differences between the two datasets increase in winter. The Theil-Sen slope of the long-term trend indicate a relatively rapid and gradual reduction in NO2 at Yokosuka and two remote sites (Fukue and Cape Hedo), respectively, regardless of the season except for fall at Fukue, but significant changes in NO2 are not observed at Gwangju, Korea. In contrast, OMI satellite data reveal an increase in the NO2 TropVCD at all sites except for Yokosuka, where a decreasing trend common to MAX-DOAS is found, suggesting that the results from satellites need to be cautiously used for investigating long-term trends in less polluted or remote areas. Using backward trajectories, potential source regions are identified for the two urban sites. The spatial distribution from OMI data shows good agreement with the potential source regions at Yokosuka. The potential source regions in Gwangju are identified as the National Industrial Complex in Yeosu and Gwangyang, while the transport route is not clearly visible with OMI data because of their low sensitivity in less polluted areas. The proposed approach is suitable for identifying potential source areas that might not be recognized by satellite observations.
Microphysical Characteristics of Rainfall Observed by a 2DVD Disdrometer during Different Seasons in Beijing, China

The seasonal variations of raindrop size distribution (DSD) and rainfall are investigated using three-year (2016–2018) observations from a two-dimensional video disdrometer (2DVD) located at a suburban station (40.13°N, 116.62°E, ~30 m AMSL) in Beijing, China. The annual distribution of rainfall presents a unimodal distribution with a peak in summer with total rainfall of 966.6 mm, followed by fall. Rain rate (R), mass-weighted mean diameter (Dm), and raindrop concentration (Nt) are stratified into six regimes to study their seasonal variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/light rain (R2: 0.2~2.5 mm h−1) has the maximum occurrence frequency throughout the year, while the total rainfall in summer is primarily from heavy rain (R4: 10~50 mm h−1). The rainfall for all seasons is contributed primarily from small raindrops (Dm2: 1.0~2.0 mm). The distribution of occurrence frequency of Nt and the relative rainfall contribution exhibit similar behavior during four seasons with Nt of 10~1000 m−3 registering the maximum occurrence and rainfall contributions. Rainfall in Beijing is dominated by stratiform rain (SR) throughout the year. There is no convective rainfall (CR) in winter, i.e., it occurs most often during summer. DSD of SR has minor seasonal differences, but varies significantly in CR. The mean values of log10Nw (Nw: mm−1m−3, the generalized intercept parameter) and Dm of CR indicate that the CR during spring and fall in Beijing is neither continental nor maritime, at the same time, the CR in summer is close to the maritime-like cluster. The radar reflectivity (Z) and rain rate (? ) relationship (Z = ? R? ) showed seasonal differences, but were close to the standard NEXRAD Z-R relationship in summer. The shape of raindrops observed from 2DVD was more spherical than the shape obtained from previous experiments, and the effect of different axis ratio relations on polarimetric radar measurements was investigated through T-matrix-based scattering simulations.
An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system.
Mechanical properties of anion exchange membranes by combination of tensile stress–strain tests and dynamic mechanical analysis

The thermomechanical properties of anion exchange polymers based on polysulfone (PSU) quaternized with trimethylamine (TMA) or 1,4-diazabicyclo[2.2. 2]octane (DABCO) and containing hydroxide or chloride anions by tensile stress–strain tests and dynamic mechanical analysis (DMA) have been determined. The reported mechanical properties included the Young's modulus, tensile strength, and elongation at break from tensile tests and the storage and loss modulus and glass transition temperature from DMA. The anion exchange membranes behaved as stiff polymers with Young's modulus in the order of 1 GPa, relatively with high strength (about 30 MPa) and low elongation at break (around 10%) was observed. Tensile tests were also made with membranes exchanged with hydrogen-carbonate and carbonate anions to control the absence of important carbonation of the OH form. The glass transition temperatures were of the order of 150 °C (PSU-TMA) or 200 °C (PSU-DABCO) for the hydroxide form, confirmed by differential scanning calorimetry; they increase further by about 50 K, when hydroxide ions are replaced by chloride. This result and the increase of the storage modulus could be interpreted by the higher hydration of hydroxide ions and the plasticizing effect of water, which reduced the Van der Waals interactions between the macromolecular chains. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016, 54, 1180–1187
UAV Remote Sensing Estimation of Rice Yield Based on Adaptive Spectral Endmembers and Bilinear Mixing Model

The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs and background especially at heading stage. The spectral mixture analysis (SMA) can quantitatively obtain the abundance information and mitigate the impacts. Furthermore, according to the spectral variability and information complexity caused by the rice cropping system and canopy characteristics of reflection and scattering, in this study, the multi-endmember extraction by the pure pixel index (PPI) and the nonlinear unmixing method based on the bandwise generalized bilinear mixing model (NU-BGBM) were applied for SMA, and the VIE (VIs recalculated from endmember spectra) was integrated with abundance data to establish the yield estimation model at heading stage. In two paddy fields of different cultivation settings, multispectral images were collected by an unmanned aerial vehicle (UAV) at booting and heading stage. The correlation of several widely-used VIs and rice yield was tested and weaker at heading stage. In order to improve the yield estimation accuracy of rice at heading stage, the VIE and foreground abundances from SMA were combined to develop a linear yield estimation model. The results showed that VIE incorporated with abundances exhibited a better estimation ability than VI alone or the product of VI and abundances. In addition, when the structural difference of plants was obvious, the addition of the product of VIF (VIs recalculated from bilinear endmember spectra) and the corresponding bilinear abundances to the original product of VIE and abundances, enhanced model reliability. VIs using the near-infrared bands improved more significantly with the estimation error below 8.1%. This study verified the validation of the targeted SMA strategy while estimating crop yield by remotely sensed VI, especially for objects with obvious different spectra and complex structures.
Automatic Point Cloud Semantic Segmentation of Complex Railway Environments

The growing development of data digitalisation methods has increased their demand and applications in the transportation infrastructure field. Currently, mobile mapping systems (MMSs) are one of the most popular technologies for the acquisition of infrastructure data, with three-dimensional (3D) point clouds as their main product. In this work, a heuristic-based workflow for semantic segmentation of complex railway environments is presented, in which their most relevant elements are classified, namely, rails, masts, wiring, droppers, traffic lights, and signals. This method takes advantage of existing methodologies in the field for point cloud processing and segmentation, taking into account the geometry and spatial context of each classified element in the railway environment. This method is applied to a 90-kilometre-long railway lane and validated against a manual reference on random sections of the case study data. The results are presented and discussed at the object level, differentiating the type of the element. The indicators F1 scores obtained for each element are superior to 85%, being higher than 99% in rails, the most significant element of the infrastructure. These metrics showcase the quality of the algorithm, which proves that this method is efficient for the classification of long and variable railway sections, and for the assisted labelling of point cloud data for future applications based on training supervised learning models.
Efficient Generic Arithmetic for KKW Practical Linear: MPC-in-the-Head NIZK on Commodity Hardware without Trusted Setup

Katz et al., CCS 2018 (KKW) is a popular and e(cid:14)cient MPC-in-the-head non-interactive ZKP (NIZK) scheme, which is the technical core of the post-quantum signature scheme Picnic, currently considered for standardization by NIST. The KKW approach simultaneously is concretely e(cid:14)cient, even on commodity hardware, and does not rely on trusted setup. Importantly, the approach scales linearly in the circuit size with low constants with respect to proof generation time, proof ver-i(cid:12)cation time, proof size, and RAM consumption. However, KKW works with Boolean circuits only and hence incurs signi(cid:12)cant cost for circuits that include arithmetic operations. In this work, we extend KKW with a suite of e(cid:14)cient arithmetic operations over arbitrary rings and Boolean conversions. Rings Z 2 k are important for NIZK as they naturally match the basic operations of modern programs and CPUs. In particular, we:
Subtask Attention Based Object Detection in Remote Sensing Images

Object detection in remote sensing images (RSIs) is one of the basic tasks in the field of remote sensing image automatic interpretation. In recent years, the deep object detection frameworks of natural scene images (NSIs) have been introduced into object detection on RSIs, and the detection performance has improved significantly because of the powerful feature representation. However, there are still many challenges concerning the particularities of remote sensing objects. One of the main challenges is the missed detection of small objects which have less than five percent of the pixels of the big objects. Generally, the existing algorithms choose to deal with this problem by multi-scale feature fusion based on a feature pyramid. However, the benefits of this strategy are limited, considering that the location of small objects in the feature map will disappear when the detection task is processed at the end of the network. In this study, we propose a subtask attention network (StAN), which handles the detection task directly on the shallow layer of the network. First, StAN contains one shared feature branch and two subtask attention branches of a semantic auxiliary subtask and a detection subtask based on the multi-task attention network (MTAN). Second, the detection branch uses only low-level features considering small objects. Third, the attention map guidance mechanism is put forward to optimize the network for keeping the identification ability. Fourth, the multi-dimensional sampling module (MdS), global multi-view channel weights (GMulW) and target-guided pixel attention (TPA) are designed for further improvement of the detection accuracy in complex scenes. The experimental results on the NWPU VHR-10 dataset and DOTA dataset demonstrated that the proposed algorithm achieved the SOTA performance, and the missed detection of small objects decreased. On the other hand, ablation experiments also proved the effects of MdS, GMulW and TPA.
Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Stereo Cameras at the DOE ARM Southern Great Plains Site

Summertime continental shallow cumulus clouds (ShCu) are detected using Geostationary Operational Environmental Satellite (GOES)-16 reflectance data, with cross-validation by observations from ground-based stereo cameras at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. A ShCu cloudy pixel is identified when the GOES reflectance exceeds the clear-sky surface reflectance by a reflectance detection threshold of ShCu, ΔR. We firstly construct diurnally varying clear-sky surface reflectance maps and then estimate the ∆R. A GOES simulator is designed, projecting the clouds reconstructed by stereo cameras towards the surface along the satellite’s slanted viewing direction. The dynamic ShCu detection threshold ΔR is determined by making the GOES cloud fraction (CF) equal to the CF from the GOES simulator. Although there are temporal variabilities in ΔR, cloud fractions and cloud size distributions can be well reproduced using a constant ΔR value of 0.045. The method presented in this study enables daytime ShCu detection, which is usually falsely reported as clear sky in the GOES-16 cloud mask data product. Using this method, a new ShCu dataset can be generated to bridge the observational gap in detecting ShCu, which may transition into deep precipitating clouds, and to facilitate further studies on ShCu development over heterogenous land surface.
CO2 Binding Organic Liquids Gas Capture with Polarity Swing Assisted Regeneration

This report outlines the comprehensive bench-scale testing of the CO2-binding organic liquids (CO2BOLs) solvent platform and its unique Polarity Swing Assisted Regeneration (PSAR). This study outlines all efforts on a candidate CO2BOL solvent molecule, including solvent synthesis, material characterization, preliminary toxicology studies, and measurement of all physical, thermodynamic and kinetic data, including bench-scale testing. Equilibrium and kinetic models and analysis were made using Aspen Plus™. Preliminary process configurations, a technoeconomic assessment and solvent performance projections for separating CO2 from a subcritical coal-fired power plant are compared to the U.S. Department of Energy's Case 10 monoethanolamine baseline.