Distant Cloud Research Articles

Insights into 3D cloud radiative transfer effects for the Orbiting Carbon Observatory

Abstract. Clouds impose radiance perturbations upon Orbiting Carbon Observatory (OCO-2)-measured spectra. The Spherical Harmonic Discrete Ordinate radiative transfer Method (SHDOM) code is applied in both idealized bar cloud and scene-specific calculations of 1D and 3D radiances in order to understand 3D cloud effects for a wide range of gas vertical optical depths and solar- and sensor-viewing geometries for ocean and land scenes. SHDOM calculations for 36 scenes over the Amazon and the Pacific are co-analyzed with Moderate Resolution Imaging Spectroradiometer (MODIS) radiance-based cloud distance data and the OCO-2 Lite file rawXCO2 for both quality flag =0 (QF0; best quality) and quality flag =1 (QF1; poor quality) data. SHDOM calculations of the ocean and land scenes indicate that the 1D / 3D radiance intensity ratios and rawXCO2 decrease concurrently as the nearest-cloud distance decreases towards zero, especially for the ocean glint QF1 data, which provide the clearest evidence of 3D cloud effects in OCO-2 retrievals. Yearly analysis of OCO-2 O2 A-band continuum radiances indicate that 3D cloud-brightening events are predominant over cloud-shadowing events; therefore, 1D / 3D intensity ratios are predominantly less than unity. Bias corrected (bcXCO2) at cloud distances between 0 and 20 km are calculated for 20∘ latitude bands for 2015–2018. These zonal averages are used to calculate 3D-cloud-effect biases for bcXCO2 data (with a positive bias indicating that OCO-2 underestimates bcXCO2). Averages of 3D-cloud-effect biases, weighted by the number of Lite file data points in each of the nearest-cloud distance bins, in the Northern and Southern hemispheres, are 0.16 (1.31) and 0.26 (1.41) ppm (parts per million), respectively, over the ocean, and −0.13 (0.51) and −0.08 (0.47) ppm over land for QF0 (QF1) data.

Distances to Nearby Molecular Clouds Traced by Young Stars

I present a catalog of distances to 63 molecular clouds located within ∼2.5 kpc of the Sun. The cloud distances are derived based on utilizing the Gaia DR3 parallaxes of the young stellar objects (YSOs). By identifying AllWISE YSO candidates (YSOCs) with infrared excesses and combining them with published YSOC catalogs, I compile an all-sky YSOC sample that is devoid of a significant proportion of contaminants. Using Gaia DR3 astrometric measurements, I associate over 3000 YSOCs with 63 local clouds and obtain the average distance to each cloud by fitting the YSOC parallax distribution within the cloud. I find good agreements with typical scatter of ≲10% between my new cloud distances and previous distance estimates. Unlike cloud distances obtained using stellar extinction, my catalog provides distances to the relatively dense areas of local clouds, which makes them more appropriate references for investigating the physical properties of nearby dense regions.

CFD simulations of instantaneously released liquefied gas in urban areas: A case study of LPG tank truck accident in Wenling, China

The accidental release and ignition of flammable liquefied gas can cause catastrophic events, such as flash fires, vapor cloud explosions, and boiling liquid expanding vapor explosion, which severely endanger urban residents and the environment. In the current study, a method for simulating liquefied gas instantaneous release accidents was established to explore the vapor cloud diffusion behavior in urban areas. The rare liquefied petroleum gas instantaneous release accident in Wenling, China is considered as a test case. To this end, an instantaneous release source model that considers the gas phase, liquid pool, and droplets was developed. The CFD model was validated using the damage distribution at the accident site. The simulation results revealed that droplets in the release source, terrains, and large obstacles significantly affected the dispersion behavior and extension distance of the vapor cloud. The evaporation of droplets resulted in a mushroom-shaped cloud. In addition, when the cloud was ignited, the area covered by the LFL was 1.94 and 2.07 times larger in the cases with droplets than those without. The method developed in this study is applicable to developing hazardous chemical transportation and emergency response measures in urban environments to maintain the sustainable cities development.

Cepheid metallicity in the Leavitt law (C-MetaLL) survey – III. Simultaneous derivation of theGaiaparallax offset and period–luminosity–metallicity coefficients

ABSTRACTClassical Cepheids (DCEPs) are the most important standard candles in the extra-galactic distance scale thanks to the period–luminosity ($\rm PL$), period–luminosity–colour ($\rm PLC$), and period–Wesenheit ($\rm PW$) relations that hold for these objects. The advent of the Gaia mission, and in particular the Early Data Release 3 (EDR3), provided accurate parallaxes to calibrate these relations. In order to fully exploit Gaia measurements, the zero point (ZP) of Gaia parallaxes should be determined with an accuracy of a few $\rm \mu as$. The individual ZP corrections provided by the Gaia team depend on the magnitude and the position on the sky of the target. In this paper, we use an implicit method that relies on the Cepheid $\rm PL$ and $\rm PW$ relations to evaluate the ensemble Gaia parallax zero point. The best inferred estimation of the offset value needed to additionally correct (after the Gaia team correction) the Gaia parallaxes of the present DCEP sample amounts to $\rm -22\pm 4\, \mu as$. This value is in agreement with the most recent literature values and confirms that the correction proposed by the Gaia team overcorrected the parallaxes. As a further application of our results, we derive an estimate of the Large Magellanic Cloud distance ($\rm \mu _0=18.49\pm 0.06\, mag$) in very good agreement with the currently accepted value obtained through geometric methods.

Study on deformation measurement method based on structured light point clouds

For the requirement of structural deformation measurement, a deformation measurement method based on structured light point clouds is proposed. A structured light measurement system is built, and the calibration process based on the assumption of the projector's reverse camera and with the help of the checkerboard calibration board is completed. The pattern projected by the projector is designed by using a four-step phase-shift technique assisted by Gray code. By combining with system parameters which have been calibrated, the three-dimensional reconstruction of the object surface is completed. A structural deformation measurement method based on the statistical analysis of the distance between point clouds is proposed, and the principles of three points cloud distance calculation methods including point-to-point, point-to-surface and surface-to-surface are analyzed. The feasibility and accuracy of the deformation measurement method based on the structured light point cloud proposed in this paper are verified via experiments. Experimental results show that this method can achieve out-plane high-precision displacement measurement of structures.

Human-vision-inspired cluster identification for single-molecule localization microscopy.

Single-molecule localization microscopy has enabled scientists to visualize cellular structures at the nanometer scale. However, researchers are facing great challenges in analyzing images presented by point clouds. Existing algorithms for cluster identification are coordinate-based analyses requiring users to input cutoff thresholds based on the distance or density of the point cloud. These thresholds are often one's best guess with repeated visual inspections, making the cluster assignment user-dependent. Here, we present a cluster identification algorithm mimicking the modulation transfer function of human vision. This approach does not require any input parameters and produces visually satisfactory cluster assignments. We tested this algorithm by identifying the clusters of the fusion proteins of the Nipah virus on its host cells. This algorithm was further extended to analyze three-dimensional point clouds using virus-like particles as an example.

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.

Study on the LAI Single Tree Model Based on Terrestrial Laser Scanning

Leaf area index (LAI) is a key parameter for studying global terrestrial ecology and environment and has great ecological significance. How to accurately measure and calculate structural parameters of trees has become an urgent matter. This paper reports the use of terrestrial laser scanning (TLS) as a measurement tool to achieve accurate LAI estimation through point cloud preprocessing measures, the LeWos algorithm, and voxel methods. The accuracy and feasibility of this indirect measurement method were explored. It is found that the single wood structure parameters extracted from TLS have a good linear relationship with manual measurement, and the extraction errors meet the requirements of real-scene conversion. The study also found when the voxel size is consistent with the minimum distance of the point cloud set by TLS instrument, it has a strong correlation with the measured value of canopy analyser. These results lay the foundation for conveniently and quickly obtaining structural parameters of trees, tree growth state detection, and canopy ecological benefit assessment.

Observation Model for Robust Localization of Mobile Robots based on Particle Filter in Changing Environments

For autonomous mobile robot navigation, localization is an essential capability. Given a mobile robot equipped with a 3D LiDAR sensor, an environment map composed of point cloud is built beforehand. The robot is thus allowed to localize the position in the map using the sensor scan data. However, the environment sometimes changes due to obstacles. Under the changing environment, the localization capability of the robot might be decreased. For this challenge, we propose a sensor observation model in a framework of particle filter based localization. In the observation model, we focus on the distance and distribution of point clouds of the map and sensor scan data. In the experiments, a mobile robot is moved by an operator in a virtual environment with obstacles. The robot based on the proposed observation model is able to localize the position in both the original and changing environments with the same accuracy. From the results, we finally show the robustness of the localization capability for changing environments.

A Deep Reinforcement Learning-Based Offloading Scheme for Multi-Access Edge Computing-Supported eXtended Reality Systems

In recent years, eXtended Reality (XR) applications have been widely employed in various scenarios, e.g., health care, education, manufacturing, etc. Such application are now easily accessible via mobile phones, tablets, or wearable devices. However, such devices normally suffer from constraints in terms of battery capacity and processing power, limiting the range of applications supported or lowering Quality of Experience. One effective way to address these issues is to offload the computation tasks to the edge servers that are deployed at the network edges, e.g., base stations or WiFi access point, etc. This communication fashion, also named as Multi-access Edge Computing (MEC), is proposed to overcome the limitations in terms of long latency due to long propagation distance of traditional cloud computing approach. XR devices, that are limited in computation resources and energy, can then benefit from offloading the computation intensive tasks to MEC servers. However, as XR applications are comprised of multiple tasks with variety of requirements in terms of latency and energy consumption, it is important to make decision whether one task should be offloaded to MEC server or not. This paper proposes a Deep Reinforcement Learning-based offloading scheme for XR devices (DRLXR). The proposed scheme is used to train and derive the close-to optimal offloading decision whereas optimizing a utility function optimization equation that considers both energy consumption and execution delay at XR devices. The simulation results show how our proposed scheme outperforms the other counterparts in terms of total execution latency and energy consumption.

Latency-Aware Strategies for Deploying Data Stream Processing Applications on Large Cloud-Edge Infrastructure

Internet of Things (IoT) applications often require the processing of data streams generated by devices dispersed over a large geographical area. Traditionally, these data streams are forwarded to a distant cloud for processing, thus resulting in high application end-to-end latency. Recent work explores the combination of resources located in clouds and at the edges of the Internet, called cloud-edge infrastructure, for deploying Data Stream Processing (DSP) applications. Most previous work, however, fails to scale to very large IoT settings. This paper introduces deployment strategies for the placement of DSP applications onto cloud-edge infrastructure. The strategies split an application graph into regions and consider regions with stringent time requirements for edge placement. The proposed Aggregate End-to-End Latency Strategy with Region Patterns and Latency Awareness (AELS+RP+LA) decreases the number of evaluated resources when computing an operator's placement by considering the communication overhead across computing resources. Simulation results show that, unlike the state-of-the-art, AELS+RP+LA scales to environments with more than 100k resources with negligible impact on the application end-to-end latency.

Automatic body condition scoring for dairy cows based on efficient net and convex hull features of point clouds

Body condition score (BCS) is an important indicator to evaluate the nutritional and health status of perinatal dairy cows. The insufficient accuracy of the automatic body condition score is the core bottleneck restricting the popularization and application of this technology. To further improve the accuracy of automatic scoring of cow body condition, a 3D structure feature map based on convex hull distance of point clouds was constructed and used as the input of a two-level model based on EfficientNet network, which realized the improvement of recognition accuracy within the error of 0.25 in the automatic scoring of cow body condition. First, the depth image of the cow’s back was preprocessed to obtain the point cloud from the hook bone to the needle bone region containing the main body condition information. Second, the point cloud was voxelized and convex hulled, and the distance between each peripheral voxel and the nearest convex hull surface was calculated and projected to the X-Y plane to obtain the structural feature map. Then, a two-level classification model based on the EfficientNet network was constructed, and the whale optimization algorithm (WOA) was used to optimize the network scaling coefficient in the model. Finally, 5119 depth images of 77 cows were used to train and test the model, and the dataset ratio was 1:1. The results showed that when the BCS value was in the range of 2.25 to 3.75 and the two-class classification limit was 2.75, the proportions of images with recognition errors less than 0.25 and 0.50 were 91.21% and 97.60%, respectively, and the average recognition speed was 3.446 frames/s. The recognition effect was better than that of the MobileNet-V2, XceptionNet and LeNet-5 network models. This method could realize the noncontact assessment of individual body conditions of dairy cows in large-scale farms and had the characteristics of high accuracy, strong applicability and low cost.

Competition Between Radiative and Seeding Effects of Overlying Clouds on Underlying Marine Stratocumulus

AbstractOverlying cloud can affect the radiative and precipitation features of underlying cloud via its radiative and seeding effects. Using 4 years of CALIPSO and CloudSat observations, this study demonstrates that overlying clouds usually suppress the vertical development of underlying stratocumulus (Sc) by their radiative effect, thus reducing the precipitation, cloud liquid water path and thickness of Sc on a global scale. The radiative effect intensity increases with increasing optical depth (COD) of overlying cloud and decreasing cloud distance between cloud layers. By dividing the overlying clouds into seeder and non‐seeder groups, we find that seeding by overlying clouds can partly or even totally offset the precipitation suppression caused by their radiative effect. The net effect on precipitation of Sc switches from suppression to enhancement when COD of overlying cloud exceeds 0.16. This finding indicates that the competition between radiative and seeding effects of overlying cloud should be considered in cloud‐precipitation interaction.

A Novel Real-Time LiDAR Data Streaming Framework

Data transmission of familiar sensor data sources like video and audio is well explored. However, data acquired from the sensor emitting voluminous data cannot easily be transmitted due to a lack of bandwidth, which causes bottlenecks in the transmission. One valuable sensor in autonomous vehicles is light detection and ranging (LiDAR). In this article, a novel framework has been proposed, which is capable of efficiently streaming LiDAR data over the Internet. The proposed framework consists of various subsystems that handle data in multiple stages. On the sender side, the stages include data acquisition from the sensor, compression, encryption, and encoding of data, followed by transmission of encoded data over the Internet. On the receiver side, decoding, decryption, decompression, and data visualization are performed. The end-to-end framework runs over the robot operating system (ROS) platform. The data is acquired via ROS topics on the sender side and again published on ROS topics from the browser on the receiver side. This allows the users to apply any processing algorithm online with tolerable latency. The challenge of Clock Synchronization was addressed during the testing process. A detailed analysis to validate the quality of decompressed data has also been performed for which cloud distance was used as a metric. The implemented model was able to perform data transmission with an average latency of 160.62 ms for low-resolution compression mode.

Double-mode RR Lyrae star — robust distance and metallicity indicators

AbstractRR Lyrae (RR Lyr) stars are a well-known and useful distance indicator for old stellar populations such as globular clusters and dwarf galaxies. Fundamental-mode RR Lyr (RRab) stars are commonly used to measure distances, and the accuracy of the determined distance is strongly constrained by metallicity. Here, we investigate the metallicity dependence in the period–luminosity (PL) relation of double-mode RR Lyr (RRd) stars. We find and establish a linear relation between metallicity and period or period ratio for RRd stars. This relation can predict the metallicity as accurately as the low-resolution spectra. Based on this relation, we establish a metallicity-independent PL relation for RRd stars. Combining the distance of the Large Magellanic Cloud and Gaia parallaxes, we calibrate the zero point of the derived PL relation to an error of 0.022 mag. Using RRd stars, we measure the distances of globular clusters and dwarf galaxies with an accuracy of 2-3% and 1-2%, respectively. In the future, RRd stars could anchor galaxy distances to an accuracy of 1.0% and become an independent distance ladder in the Local Group.

Review on the Research and Applications of TLS in Ground Surface and Constructions Deformation Monitoring.

With the gradual maturity of the terrestrial laser scanners (TLS) technology, it is widely used in the field of deformation monitoring due to its fast, automated, and non-contact data acquisition capabilities. The TLS technology has changed the traditional deformation monitoring mode which relies on single-point monitoring. This paper analyzes the application of TLS in deformation monitoring, especially in the field of ground surface, dam, tunnel, and tall constructions. We divide the methods for obtaining ground surface deformation into two categories: the method based on point cloud distance and the method based on displacement field. The advantages and disadvantages of the four methods (M2M, C2C, C2M, M3C2) based on point cloud distance are analyzed and summarized. The deformation monitoring methods and precisions based on TLS for dams, tunnels, and tall constructions are summarized, as well as the various focuses of different monitoring objects. Additionally, their limitations and development directions in the corresponding fields are analyzed. The error sources of TLS point cloud data and error correction models are discussed. Finally, the limitations and future research directions of TLS in the field of deformation monitoring are presented in detail.

X-ray from outflow-cloud interaction and its application in tidal disruption events

ABSTRACT Tidal disruption events (TDEs) may occur in supermassive black holes (SMBHs) surrounded by clouds. TDEs can generate ultrafast and large opening-angle outflow with a velocity of ∼0.01–0.2 c, which will collide with clouds with time lags depending on outflow velocity and cloud distances. Since the fraction of the outflow energy transferred into cloud’s radiation is anticorrelated with the cloud density, high-density clouds was thought to be inefficient in generating radiation. In this work, we studied the radiation from the outflow-cloud interactions for high-density clouds, and found that thermal conduction plays crucial roles in increasing the cloud’s radiation. Up to 10 per cent of the bow shock energy can be transferred into clouds and gives rise to X-ray emission with equivalent temperature of 105–6 K due to the cooling catastrophe. The inverse Compton scattering of TDE UV/optical photons by relativistic electrons at bow shock generates power-law X-ray spectra with photon indices Γ ∼ 2–3. This mechanism may account for some TDE candidates with delayed X-ray emission, and can be examined by delayed radio and gamma-ray emissions.

A Multi-Channel Descriptor for LiDAR-Based Loop Closure Detection and Its Application

Simultaneous localization and mapping (SLAM) algorithm is a prerequisite for unmanned ground vehicle (UGV) localization, path planning, and navigation, which includes two essential components: frontend odometry and backend optimization. Frontend odometry tends to amplify the cumulative error continuously, leading to ghosting and drifting on the mapping results. However, loop closure detection (LCD) can be used to address this technical issue by significantly eliminating the cumulative error. The existing LCD methods decide whether a loop exists by constructing local or global descriptors and calculating the similarity between descriptors, which attaches great importance to the design of discriminative descriptors and effective similarity measurement mechanisms. In this paper, we first propose novel multi-channel descriptors (CMCD) to alleviate the lack of point cloud single information in the discriminative power of scene description. The distance, height, and intensity information of the point cloud is encoded into three independent channels of the shadow-casting region (bin) and then compressed it into a two-dimensional global descriptor. Next, an ORB-based dynamic threshold feature extraction algorithm (DTORB) is designed using objective 2D descriptors to describe the distributions of global and local point clouds. Then, a DTORB-based similarity measurement method is designed using the rotation-invariance and visualization characteristic of descriptor features to overcome the subjective tendency of the constant threshold ORB algorithm in descriptor feature extraction. Finally, verification is performed over KITTI odometry sequences and the campus datasets of Jilin University collected by us. The experimental results demonstrate the superior performance of our method to the state-of-the-art approaches.

Experiment Study of the Evolution of Coral Sand Particle Clouds in Water

The motion of particle clouds (i.e., sediment clouds) usually can be found in engineering applications such as wastewater discharge, land reclamation, and marine bed capping. In this paper, a series of laboratory tests are conducted on coral sand to investigate the shape feature of the single particle and the mixing processes of the coral sand particle clouds. The shape of coral sand particle is measured and quantified. The experimental results demonstrate that the shape of coral sand particles tends to be spherical as the particle size decreases, and empirical equations were established to explain the variation of D50 and fS,50 of coral sand. Compared with the silica sand, the evolution of the coral sand particle cloud still experiences three stages, but the threshold for the Reynolds number of particle clouds entering the next stage changes. Further, the normalized axial distance of the coral sand particle clouds is 58% smaller. The frontal velocity exhibits similar varying tendency for the coral sand particle cloud. Considering the difference in shape between coral sand particles and silica sand particles, a semi-empirical formula was proposed based on the original silica sand prediction formula by adding the shape factor and the experimental data of 122 µm⩽D50⩽842 µm. It can predict the frontal velocity of the coral sand particle clouds.

CO(J = 1 – 0) Observations toward the Filamentary Cloud in the Galactic Region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50

We present observations of the J = 1 – 0 transition lines of 12CO, 13CO, and C18O toward the Galactic region of 153.°60 ≤ l ≤ 156.°50 and 1.°85 ≤ b ≤ 3.°50, using the Purple Mountain Observatory 13.7 m millimeter telescope. Based on the 13CO data, one main filament and five subfilaments are found together as a network structure in the velocity interval of [−42.5, −30.0] km s−1. The kinematic distance of this molecular cloud is estimated to be ∼4.5 kpc. The median length, width, excitation temperature, and line mass of these filaments are ∼49 pc, ∼2.9 pc, ∼8.9 K, and ∼39 M ⊙ pc−1, respectively. The velocity structures along these filaments exhibit oscillatory patterns, which are likely caused by the fragmentation or accretion process along these filaments. The maximum accretion rate is estimated to be ∼700 M ⊙ pc−1. A total of ∼162 13CO clumps and ∼103 young stellar objects are identified in this region. Most of the clumps are in gravitationally bound states. Three H ii regions (G154.359+2.606, SH2-211, and SH2-212) are found to be located in the apexes of the filaments. Intense star-forming activity is found along the entire filamentary cloud. The observed results may help us to better understand the link between filaments and massive star formation.