Real Cloud Research Articles

Generation of Anisotropic Cloud Cover

This paper introduces an advanced mathematical model for generating and analyzing cloud cover images, specifically designed to enhance photovoltaic (PV) partial shading studies. The model development involved a detailed analysis of real cloud cover images, with a particular emphasis on capturing their anisotropic spectral characteristics. This was achieved through a combination of spectral analysis and advanced image processing techniques. The research methodologically focused on developing a four-parameter model to accurately represent cloud formations' spectral properties. Key parameters were identified and fine-tuned to match the real cloud formations' characteristics. This involved analyzing the magnitude and phase spectra of the cloud covers and fitting them to a model capable of replicating these properties accurately. A significant part of the research was dedicated to formulating a novel phase spectrum generation technique. This technique was specifically designed to control the degree of similarity between the synthesized and original images, thereby ensuring the model's effectiveness in various simulation scenarios. The process involved manipulating the phase information of cloud cover images while maintaining their high-frequency components to enhance the detail and realism of the synthesized images. The model's accuracy in replicating cloud cover features was tested against traditional spectral synthesis methods. This comparative analysis involved generating cloud cover images using the developed model and established methods, then comparing these images to the original cloud covers in terms of visual similarity and approximation error. Additionally, the model was utilized to generate pseudo-random cloud cover images by varying the phase spectrum parameters. This process ensured that the generated images, while being random, adhered to the spectral characteristics of the original cloud covers. The research methodology also involved a detailed examination of the images' key characteristics, such as direction, length, and density, to ensure fidelity to the original samples. In summary, this paper details an approach to cloud cover image synthesis, with a focus on the accuracy of spectral properties and the development of an algorithm of model parameters estimation. The research highlights the use of advanced spectral analysis and image processing techniques in deriving key model parameters, leading to a significant advancement in cloud imaging for solar energy applications.

Point Cloud Estimation During Aerial-Aquatic Transition in Monocular Camera-Based Localization and Mapping

This paper presents a multi-box interpolation method to estimate point clouds during aerial-aquatic transition. Our proposed method is developed based on an investigation of noise characteristics of aerial point clouds and aquatic point clouds. To evaluate the performance of realistic point cloud estimation, we compare the interpolation method with the Gaussian mixture method. We also investigate how single-box and multi-box approaches deal with noise in point cloud estimation. The simulation and the experimental results show that the estimated point cloud is accurate even when the aerial and aquatic point clouds contain noise. Also, the multi-box concept helps the algorithm to avoid taking unwanted noise into consideration when predicting point clouds.

Control Concept for a Molten Salt Receiver in Star Design

A promising approach to reduce the system costs of molten salt solar receivers is to enable the irradiation of the absorber tubes on both sides. The star design is an innovative receiver design, pursuing this approach. The unconventional design leads to new challenges in controlling the system. This paper presents a control concept for a molten salt receiver system in star design. The control parameters are optimized in a defined test cycle by minimizing a cost function. The control concept is tested in realistic cloud passage scenarios based on real weather data. During these tests, the control system showed no sign of unstable behavior, but to perform sufficiently in every scenario further research and development like integrating Model Predictive Controls (MPCs) need to be done. The presented concept is a starting point to do so.

Semantic enhancement based adaptive geometric encoding network for low overlap point cloud registration

The presence of partial or low overlaps in real point cloud pairs poses significant challenges to obtain robust registration. There is an absence of a unified framework that localizes reliable overlapping regions and correspondences. This work proposes an adaptive point cloud geometric encoding network based on semantic enhancement that generates overlap information with geometric-and-semantic consistency in various scenarios. This enables the network to further ensure distinctive geometric representation when the low overlap point cloud pairs have scarce differences in the geometric structures. In addition, our study includes experiments conducted on a self-created semantic-assisted dataset designed to facilitate in-depth research on point cloud registration. The experimental results clearly demonstrate the efficacy and overall improvements of the registration performance, particularly highlighting the potential of our approach in addressing the challenges associated with low overlap scenarios that have repetitive geometry patterns.

Application of Airborne Radar Variable Altitude Aerial Survey in High Precision Mapping of Mountainous Areas

This paper studies how to use UAV aerial survey equipment to carry laser radar and high-definition real-scene lenses to achieve high-precision mountain mapping. The study aims to solve the problems of complex terrain, difficulties in surveying, and controlling the accuracy of surveying and mapping in mountainous, canyon, and hilly areas. Firstly, the LK tea production area development project is selected as an example for example analysis. The aerial survey equipment and data acquisition equipment are selected according to the characteristics of the project. Secondly, aiming at the problem that the mountainous terrain undulates greatly and that the accuracy of ground object mapping is difficult to control, this paper puts forward the “variable height aerial survey” method. It collects aerial survey data according to the terrain characteristics. Finally, this paper proposes a “point cloud overlay” modeling method. It combines laser point cloud data with real 3D data to form a high-precision real 3D model point cloud reorganization and establish a high-precision real 3D model.

A Cloud Coverage Image Reconstruction Approach for Remote Sensing of Temperature and Vegetation in Amazon Rainforest

Remote sensing involves actions to obtain information about an area located on Earth. In the Amazon region, the presence of clouds is a common occurrence, and the visualization of important terrestrial information in the image, like vegetation and temperature, can be difficult. In order to estimate land surface temperature (LST) and the normalized difference vegetation index (NDVI) from satellite images with cloud coverage, the inpainting approach will be applied to remove clouds and restore the image of the removed region. This paper proposes the use of the neural network LaMa (large mask inpainting) and the scalable model named Big LaMa for the automatic reconstruction process in satellite images. Experiments are conducted on Landsat-8 satellite images of the Amazon rainforest in the state of Acre, Brazil. To evaluate the architecture’s accuracy, the RMSE (root mean squared error), SSIM (structural similarity index) and PSNR (peak signal-to-noise ratio) metrics were used. The LST and NDVI of the reconstructed image were calculated and compared qualitatively and quantitatively, using scatter plots and the chosen metrics, respectively. The experimental results show that the Big LaMa architecture performs more effectively and robustly in restoring images in terms of visual quality. And the LaMa network shows minimal superiority for the measured metrics when addressing medium marked areas. When comparing the results achieved in NDVI and LST of the reconstructed images with real cloud coverage, great visual results were obtained with Big LaMa.

A chameleon and remora search optimization algorithm for handling task scheduling uncertainty problem in cloud computing

Task scheduling in cloud computing is responsible for serving the user requirements. The scheduling strategy must handle the problems of high load over virtual machines (VMs), high-cost consumption and lengthier scheduling time effectively. The greatest challenge in the cloud computing environment is achieving the intended outcome of task scheduling under the uncertain user request demands as it is responsible for assigning specific resources to requests for achieving effective task completion. However, most of the task scheduling approaches contributed to the literature mainly focused on the design and development of scheduling algorithms but ignored to explore the impact of uncertain factors such as millions of instructions per second (MIPS) and network bandwidth during the scheduling process. In this paper, A Chameleon and Remora Search Optimization Algorithm (CRSOA) is proposed for achieving efficient scheduling process by exploring the impact of MIPS and network bandwidth which directly affects the virtual machine (VM) performance. Further the work includes the uncertainty factors of task completion rate, load balance, scheduling cost and makespan in a simultaneous manner during the process of scheduling. It is formulated a multi-objective cloud task scheduling optimization model by integrating the merits of Chameleon Search Algorithm (CSA) and Remora Search Optimization Algorithm (RSOA) using a greedy methodology for simulating the real cloud computing task scheduling process. The simulation results evidently confirmed that the proposed CRSOA approach is minimizing the completion time and effective in handling the load balancing between the available VMs against other competitive metaheuristic task scheduling algorithms. The experimental investigation of this CRSOA confirmed its predominance in minimizing the makespan by 18.96%, cost by 22.18%, and degree of imbalance by 20.54%, compared to the baseline approaches with different number of tasks and VMs.

A Pre-Procession Module for Point-Based Deep Learning in Dense Point Clouds in the Ship Engineering Field

Recently, point cloud technology has been applied in the ship engineering field. However, the dense point cloud acquired by terrestrial laser scanning (TLS) technology in ship engineering applications brings an obstacle to some powerful and advanced point-based deep learning point cloud processing methods. This paper presents a deep learning pre-procession module to ensure the feasibility of processing dense point clouds on commonly available computer devices. The pre-procession module is designed according to the traditional point cloud processing methods and the PointNet++ paradigm, and is evaluated on two ship structure datasets and two popular point cloud datasets. Experimental results illustrate that (i) the proposed module improves the performance of point-based deep learning semantic segmentation networks, and (ii) the proposed module empowers the existing point-based deep learning networks with the capability to process dense input point clouds. The proposed module may provide a useful semantic segmentation tool for realistic dense point clouds in various industrial applications.

CloudSim express: A novel framework for rapid low code simulation of cloud computing environments

AbstractCloud computing environment simulators enable cost‐effective experimentation of novel infrastructure designs and management approaches by avoiding significant costs incurred from repetitive deployments in real Cloud platforms. However, widely used Cloud environment simulators compromise on usability due to complexities in design and configuration, along with the added overhead of programming language expertise. Existing approaches attempting to reduce this overhead, such as script‐based simulators and graphical user interface (GUI) based simulators, often compromise on the extensibility of the simulator. Simulator extensibility allows for customization at a fine‐grained level, thus reducing it significantly affects flexibility in creating simulations. To address these challenges, we propose an architectural framework to enable human‐readable script‐based simulations in existing Cloud environment simulators while minimizing the impact on simulator extensibility. We implement the proposed framework for the widely used Cloud environment simulator, the CloudSim toolkit, and compare it against state‐of‐the‐art baselines using a practical use case. The resulting framework, called CloudSim Express, achieves extensible simulations while surpassing baselines with over a % reduction in code complexity and an 89.42% reduction in lines of code.

DFMCloudsim: an extension of cloudsim for modeling and simulation of data fragments migration over distributed data centers

Due to the increasing volume of data for applications running on geographically distributed Cloud systems, the need for efficient data management has emerged as a crucial performance factor. Alongside basic task scheduling, the management of input data on distributed Cloud systems has become a genuine challenge, particularly with data-intensive applications. Ideally, each dataset should be stored in the same data center as its consumer tasks so as to lead to local data accesses only. However, when a given task does not need all items within one of its input datasets, sending that dataset entirely might lead to a severe time overhead. To address this concern, a data fragmentation strategy can be considered in order to partition the datasets and process them in that form. Such a strategy should be flexible enough to support any user-defined partitioning, and suitable enough to minimize the overhead of transferring the data in their fragmented form. To simulate and estimate the basic statistics of both fragmentation and migration mechanisms prior to an implementation in a real Cloud, we chose Cloudsim, with the goal of enhancing it with the corresponding extensions. Cloudsim is a popular simulator for Cloud Computing investigations. Our proposed extension is named DFMCloudsim, its goal is to provide an efficient module for implementing fragmentation and data migration strategies. We validate our extension using various simulated scenarios. The results indicate that our extension effectively achieves its main objectives and can reduce data transfer overhead by 74.75% compared to our previous work.

Multi-Client Boolean File Retrieval With Adaptable Authorization Switching for Secure Cloud Search Services

Secure cloud search services provide a cost-effective way for resource-constrained clients to search encrypted files in the cloud, where data owners can customize search authorization. Despite providing fine-grained authorization, traditional attribute-based keyword search (ABKS) solutions generally support single keyword search. Towards expressive queries over encrypted data, multi-client searchable symmetric encryption (MC-SSE) was introduced. However, current search authorizations of existing MC-SSEs:(i) cannot support dynamic updating; (ii) are (semi-)black-box implementations of attribute-based encryption; (iii) incur significant cost during system initialization and file encryption. To address these limitations, we present AasBirch, an MC-SSE system with fast fine-grained authorization that supports adaptable authorization switching from one policy to any other one. AasBirch achieves constant-size storage and lightweight time cost for system initialization, file encryption and file searching. We conduct extensive experiments based on Enron dataset in real cloud environment. Compared to state-of-the-art MC-SSE with fine-grained authorization, AasBirch achieves 30∼200× smaller public parameter and secret key size, with the assumed least frequent keyword in a query ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> -term) as 21. Moreover, it runs 10∼20× faster for file encryption and >20× faster for file searching. In addition, AasBirch outperforms 80,000× (resp. 7,850×) faster with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</i> -term = 1 (resp. = 21), as compared to classic dynamic ABKS system.

The Hydro-ABC model (Version 2.0): a simplified convective-scale model with moist dynamics

Abstract. The prediction of convection (in terms of position, timing, and strength) is important to achieve for high-resolution weather forecasting. This problem requires not only good convective-scale models, but also data assimilation systems that give initial conditions which neither improperly hinder nor improperly hasten convection in the ensuing forecasts. Solving this problem is difficult and expensive using operational-scale numerical weather prediction systems, and so a simplified model of convective-scale flow is under development (called the “ABC model”). This paper extends the existing ABC model of dry convective-scale flow to include mixing ratios of vapour and condensate phases of water. The revised model is called “Hydro-ABC”. Hydro-ABC includes transport of the vapour and condensate mixing ratios within a dynamical core, and it transitions between these two phases via a micro-physics scheme. A saturated mixing ratio is derived from model quantities, which helps determine whether evaporation or condensation happens. Latent heat is exchanged with the buoyancy variable (ABC's potential-temperature-like variable) in such a way to conserve total energy, where total energy is the sum of dry energy and latent heat. The model equations are designed to conserve the domain-total mass, water, and energy. An example numerical model integration is performed and analysed, which shows the development of a realistic looking anvil cloud and excitation of inertio-gravity and acoustic modes over a wide range of frequencies. This behaviour means that Hydro-ABC is a sufficiently challenging model which will allow experimentation with innovative data assimilation strategies in future work. An ensemble of Hydro-ABC integrations is performed in order to study the possible forecast error covariance statistics (knowledge of which is necessary for data assimilation). These show patterns that are dependent on the presence of convective activity (at any model's vertical column), thus giving a taste of flow-dependent error statistics. Candidate indicators/harbingers of convection are also evaluated (namely relative humidity, hydrostatic imbalance, horizontal divergence, convective available potential energy, convective inhibition, vertical wind, and the condensate mixing ratio), some of which appear to be reliable diagnostics concerning the presence of convection. These diagnostics will be useful in the selection of the relevant forecast error covariance statistics when data assimilation for Hydro-ABC is developed.

A case study of quality control application with BIM-laser scanning collaboration in building construction process

Housing accounts for the largest share of the volume of the construction sector. Housing is the living space that people use on a permanent or temporary basis. Living space is also expected to have the desired quality. Quality requires construction in accordance with specified conditions and rules. In order to minimize possible errors in the design and construction process and to achieve the desired level of quality, continuous, scientific and instrumental controls are required. In recent years, with the rapid development of technology, methods have been developed that have achieved good results using integrated technology, which has begun to replace the quality controls performed by traditional methods. This new technology partnership called Building Information Modelling and Laser Scanning is described in this study. The aim of the study is to show that human error in building inspection can be minimized with the help of technology. In the study, a building was selected from a sample public housing project. Two dimensional projects of the building were converted into three dimensions using Building Information Modelling. In the current state of construction, point clouds were captured using laser scanning. The point clouds were converted to three dimensions. The construction defects were calculated by overlaying the BIM model with the real point cloud data. After checking, it was found that 2.2 % of the productions were defective productions. It was found that 97.8 % of the productions were within acceptable tolerance limits. The results obtained on a sample residential building demonstrate the importance of these new solutions for quality control and error-free production

Integrating OpenAI Gym and CloudSim Plus: A simulation environment for DRL Agent training in energy-driven cloud scaling

Experimentation in real cloud environments for training Deep Reinforcement Learning (DRL) agents can be costly, time-consuming, and non-repeatable. To overcome these limitations, simulation-based approaches are promising alternatives. This paper introduces a specialized simulation environment that integrates OpenAI Gym, a popular platform for reinforcement learning, with CloudSim Plus, a versatile cloud simulation framework. The proposed simulator specifically focuses on the case study of energy-driven cloud scaling. By leveraging the strengths of both Python-based OpenAI Gym and Java-based CloudSim Plus, the simulation environment offers a flexible and extensible platform for DRL-Agent training. The integration is facilitated through a gateway that enables seamless interaction between the two frameworks. The simulation environment is designed to support the training process of DRL agents, enabling them to tackle the complexities of cloud scaling in an energy-aware context. It provides configurable settings that represent various cloud scaling scenarios, allowing researchers to explore different parameter configurations and evaluate the performance of DRL agents effectively. Through extensive experimentation, the proposed simulation environment demonstrates its functionality and applicability in measuring the performance of DRL agents with respect to energy-driven cloud scaling. The results obtained from the case study validate the effectiveness and potential of the simulation environment for training DRL agents in cloud scaling scenarios. Overall, this work presents a novel simulation environment that bridges the gap between DRL-Agent training and cloud scaling challenges, offering researchers a valuable tool for advancing the field of energy-driven cloud scaling through reinforcement learning.

Evaluating Large‐Domain, Hecto‐Meter, Large‐Eddy Simulations of Trade‐Wind Clouds Using EUREC4A Data

AbstractThe meso‐scale variability in cloudiness of the marine trade‐wind layer is explored with large‐eddy simulations of regional extent and validated against observations of the EUREC4A field campaign. 41 days of realistically forced simulations present a representative, statistical view on shallow convection in the winter North Atlantic trades that includes a wide range of meso‐scale variability including the four recently identified patterns of spatial organization: Sugar, Gravel, Flowers and Fish. The results show that cloud cover is on average captured well but with discrepancies in its vertical and spatial distribution. Cloudiness at the lifting condensation level depends on the model resolution with the finer one producing on average a more realistic cloud profile. Independent of the resolution, the variability in cloudiness below the trade inversion is not captured, leading to a lack of stratiform cloudiness with implications on the detectability of meso‐scale patterns whose cloud patches are characterized by stratiform clouds. The simulations tend to precipitate more frequently than observed, with a narrower distribution of echo intensities. The observed co‐variability between cloudiness and environmental conditions is well captured.

Wave propagation through a stationary field of clouds: A homogenisation approach

AbstractThe effect of a subgrid‐scale cloud field on the propagation of long atmospheric waves is investigated using a new scale‐consistent formulation based on the asymptotic theory of homogenisation. A key aim is to quantify potential model errors in wave propagation speeds, introduced by using averaged fields in place of the fully resolved circulation, in the setting of a simple stratified Boussinesq midlatitude ‐channel model. The effect of the cloud field, represented here by a random array of strongly nonlinear axisymmetric circulations, is found to appear in the large‐scale governing equations through new terms which redistribute the large‐scale buoyancy and horizontal momentum fields in the vertical. These new terms, which have the form of nonlocal integral operators, are linear in the cloud number density and are fully determined by the solution of a linear elliptic equation known as a cell problem. The cell problem in turn depends on the details of the nonlinear cloud circulations. The integral operators are calculated explicitly for example cloud fields and then dispersion relations are compared for different waves in the presence of clouds at realistic densities. The main finding is that baroclinic Rossby waves are significantly slowed and damped by the clouds, whilst inertia–gravity waves are affected almost exclusively by damping, most strongly at the lowest frequencies. In contrast, all waves with a barotropic structure are found to be almost unaffected by the presence of clouds, even at the highest realistic cloud densities. An important consequence of this study is a new approach to the closure of subgrid‐scale cloud fields in the parameterisation of convection in large‐scale atmospheric models.

The MAMA Algorithm for Fast Computations of Upwelling Far- and Mid-Infrared Radiances in the Presence of Clouds

A methodology for the computation of spectrally resolved upwelling radiances in the presence of atmospheric diffusive layers is presented. The algorithm, called MAMA (Martinazzo–Maestri), provides fast simulations over the whole longwave spectrum, with high accuracy, particularly for optically thin scattering layers like cirrus clouds. The solution is obtained through a simplification of the multiple-scattering term in the general equation of the radiative transfer in a plane-parallel assumption. The scattering contribution is interpreted as a linear combination of the mean ambient radiances involved in the forward and back-scatter processes, which are multiplied by factors derived from the diffusive features of the layer. For this purpose, a fundamental property of the layer is introduced, named the angular back-scattering coefficient, which describes the fraction of radiation coming from a hemisphere and back-scattered into a specific direction (the observer in our case). This property, easily derived from the phase function of the particle size distribution, can be calculated from any generic single-scattering properties database, which allows for simple upgrades of the reference optical properties within the code. The paper discusses the solutions for mean upward and downward ambient radiances and their use in the simplification of the general radiative transfer equation for thermal infrared. To assess the algorithm performance, the results obtained with the MAMA code are compared with those derived with a discrete ordinate-based radiative transfer model for a large range of physical and optical properties of ice and liquid water clouds and for multiple atmospheric conditions. It is demonstrated that, for liquid water clouds, the MAMA code accuracy is mostly within 0.4 mW/(m2cm−1sr) with respect to the reference code both at far- and mid-infrared wavelengths. Ice cloud spectra are also accurately simulated at mid-infrared for all realistic cloud cases, which makes the MAMA code suitable for the analysis of any spectral measurements of current satellite infrared sounders. At far infrared, the MAMA accuracy is excellent when ice clouds with an optical depth of less than 2 are considered, which is particularly valuable since cirrus clouds are one of the main targets of the future mission FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) of the European Space Agency. In summary, the MAMA method allows computations of cloudy sky high-resolution radiances over the full longwave spectrum (4–100 μm) in less than a second (for pre-computed gas optical depths and on a standard personal computer). The algorithm exploits the fundamental properties of the scattering layers, and the code can be easily updated in relation to new scattering properties.

Tube inner wall defect detection method based on rotary scanning micro laser distance sensor

In this paper, an inspection device to detect the inner wall defects of oil tubes is proposed, mainly consisting of a micro laser distance sensor and a stepper motor. When the inspection device moves along the axial direction inside the tube, the stepper motor drives the micro laser distance sensor to rotate and scan the inner wall of the tube so that the coordinate data of the 3D point cloud can be obtained. In order to obtain the pose transformation matrix for the laser distance sensor coordinate system with respect to the device flange coordinate system, a particle swarm optimization algorithm is introduced. Finally, experiments are carried out on two real oil tubes and point cloud data are obtained which provide 3D information about the inner walls of the tubes. The measuring error of tube eccentric wear is within 0.2 mm, which verifies the effectiveness of this novel method.

Blowfish based encryption model in real cloud environment

&lt;p&gt;The introduction of the internet has made security a top anxiety. And the preceding of security permits for improved knowledge in the creation of security tools. Several concerns about safekeeping might have arisen just from the way the internet was set up. Many businesses use firewalls and encryption techniques to protect themselves online. Businesses can design an “intranet” that is protected from potential risks while being linked to the internet. For increased security, better encryption techniques are needed to retain data integrity. For better encryption, it is essential to consider into explanation a few of issues, including key size, chunk size, and encoding ratio. Documents transferred to subordinate storage strategies (such as Hard disk or SD card) must be encrypted to provide security and prevent unwanted access. If a key is kept on secondary storage with the document, it is quite simple to decrypt it. It is desirable to create encryption keys from operator passwords when encoding or decoding the folder rather than keeping the key together with the document.&lt;/p&gt;

Quality assurance for building components through point cloud segmentation leveraging synthetic data

Quality Assurance and Quality Control (QA/QC) play a crucial role in the building project life cycle, especially during construction, as discrepancies between as-built structures and as-designed models can lead to cost overruns and schedule delays. Ensuring building quality is of utmost importance, but traditional manual inspections suffer from errors, consume time, and incur significant expenses. This paper describes a deviation detection method for building components using synthetic point clouds and semantic segmentation models. The method entails training a three-dimensional semantic segmentation model using synthetic point clouds generated from BIM to label each point with an object class, resulting in a mean IoU of 41.1% in semantic segmentation. Subsequently, real point clouds collected onsite are segmented using the same model and then compared with the synthetic point cloud to assess the disparities between the building components of the as-designed and as-built structures. This approach can improve the efficiency of QA/QC by reducing the manual workload of field inspectors.