Setup Parameters Research Articles

Advancing airborne Doppler lidar wind profiling in turbulent boundary layer flow – an LES-based optimization of traditional scanning-beam versus novel fixed-beam measurement systems

Abstract. There is a need for improved wind measurements inside the planetary boundary layer (PBL), including the capability to sample turbulent flow. Airborne Doppler lidar (ADL) provides unique capabilities for spatially resolved and targeted wind measurements in the PBL. However, ADL wind profiling in the PBL is challenging, as turbulence violates the flow homogeneity assumption used in wind profile retrieval and thereby introduces error in the retrieved wind profiles. As turbulence is a dominant source of error it is necessary to investigate and optimize ADL wind profiling capabilities in turbulent PBL flow. This study investigates the potential of a novel multiple-fixed-beam ADL system design to provide improved wind information in turbulent PBL flow compared to traditional single-scanning-beam ADL systems. To achieve this, an LES-based (LES: large eddy simulation) airborne Doppler lidar simulator presented in Gasch et al. (2020) is employed and extended in this study. Results show that a multiple-fixed-beam system with settings comparable to those of commonly used single-scanning-beam systems offers distinct advantages. Advantages include overall reduced wind profile retrieval error due to turbulence and improved spatial representation alongside higher wind profile availability. The study also offers insight into the dependence of the retrieval error on system setup parameters and retrieval parameters for both fixed-beam and scanning-beam systems. When using a fixed-beam system, an order of magnitude higher wind profile resolution appears possible compared to traditional scanning systems at comparable retrieval accuracy. Thus, using multiple-fixed-beam systems opens the door to better sampling of turbulent PBL flow. Overall, the simulator provides a cost-effective tool to investigate and optimize wind profile error characteristics due to turbulence and to optimize system setup and retrieval strategies for ADL wind profiling in turbulent flow.

MOCVD deposited double-sided CeO2 buffer layer for YBCO superconducting film grow on R-plane Al2O3 substrate

Double-sided CeO2/YBCO films were grown on the double-sided polishing R-plane Al2O3 substrate using MOCVD technology. The effects of deposition parameters of the MOCVD setup on the structural orientation and surface morphology of CeO2 buffer layers grown on sapphire substrates were investigated. A series of CeO2 buffer layers were prepared by varying the substrate temperature and oxygen flow rate. The results show that when the substrate temperature is lower than 750 °C and the oxygen flow rate is lower than 400 sccm, CeO2 films is grown in a mixed (00l) and (111) orientations, and when the substrate temperature is 750 °C-840 °C and the oxygen flow rate is higher than 400 sccm, the CeO2 films are grown with a single (001) orientation. When the deposition temperature was 800 °C and the oxygen flow rate was 800 sccm, the CeO2 buffer layer structure had the best orientation with double-sided ω-scan FWHM of 0.019° and 0.02°; the FWHM of double-sided Ф-scan were 1.8° and 1.7°; the RMS roughness on both sides measured by AFM were 1.28 nm. YBCO films grown on this buffer layer have good c-axis epitaxial orientation, and the test results of the pole figure show an in-plane rotation of 45° between YBCO and CeO2 lattices. Thus, the obtained YBCO film has a critical current density of 2 MA/cm2, indicating that the films can be prepared to meet the needs of microwave devices using R-plane Al2O3 substrates.

Simulation of truncated and unimodal gamma distributions

An efficient random variable generator for a truncated gamma distribution with shape parameter greater than 1 is designed using an acceptance-rejection algorithm. Based on an approximation to a transformed gamma density function by the standard normal density, numerical information for the standard normal density is prepared in advance, and the calculation is performed with reference to that information. An improvement via a squeezing method is proposed to reduce the computational burden and time. The algorithm's acceptance rate for generating truncated gamma variables is very high and almost 1 when the truncated distribution is unimodal. Numerical experiments for one- and two-sided truncated domain cases are conducted to measure the execution time, including the parameter setup time. Compared with existing truncated gamma variate generators, the proposed method performs better when the distribution is unimodal and the shape parameter is equal to or greater than 3.3.

The ANN Architecture Analysis: A Case Study on Daylight, Visual, and Outdoor Thermal Metrics of Residential Buildings in China

Selecting an appropriate ANN model is crucial for speeding up the process of building performance simulation during the design phase of residential building layouts, particularly when evaluating three or more green performance metrics simultaneously. In this study, daylight, visual, and outdoor thermal metrics were selected as main green performance. To find the suitable ANN model, sensitivity analysis was used to obtain a set of proper parameters applied to the ANN structure. To train the ANN model with a higher predicting accuracy, this paper tested four different scenarios of ANN parameter setups to find some general guidelines about how to set up an ANN model to predict DF, sunlight hours, QuVue and UTCI. The results showed that an ANN model with a combined output variable demonstrated better average prediction accuracy than ANN models with a separated output variable. Having two times the number of training samplings compared to the number of input variables can lead to a high accuracy of prediction. The ideal number of neurons in the hidden layer was approximately 1.5 times the number of input variables. These findings of how to improve the ANN model may provide guidance for modeling an ANN for building performance.

Designing of Edge Infrastructure for Establishing Signal Transmission in Palm Oil Field

Edge computing is real-time processing that gathers, analyses, and transports data at the edge of the network using sensor technologies. It has become one of the IoT era's computing solutions because it improves signal transmission while preventing network problems like delay, congestion, or latency. It is advantageous for farm management to be able to monitor farm activities like cropping and fertilization in real-time, which helps increase palm oil production. However, the farm's remote location limits the amount of signal transmission available from its current Internet service providers, which prevents real-time processing. Hence, suitable edge infrastructure must be designed for establishing signal accessibility for data communication. It also required determining the optimal number of edge nodes to be allocated in the palm field due to different terrain and various tree sizes. The number of edge nodes will have an impact on cost utilization because it relates to setup costs and parameters. In response to data communication issues in the palm oil field, our work focuses on developing the edge computing infrastructure to maintain stable signal transmission for real-time processing. A network topology is designed to identify suitable parameters and settings that aligned with topography of the sample palm oil field. Furthermore, by having the right topology and signal frequency settings, the number of edge nodes required in the field can be determined. In our simulated design, the appropriate setting is to add a router with less than 4K million instructions per second (MIPS) compared to connecting through three mobile hot spots. Note that different palm oil fields will have different settings for their edge infrastructure. Optimistically, our study can provide guidance for promoting signal transmission in areas with limited network accessibility.

Droplet characterization of a pressure-swirl atomizer by means of high-fidelity modelling based on DNS simulations

Liquid injection optimization is key to enhance the efficiency of many processes in a wide variety of fields. Combustion processes are one of the most challenging ones, due to the direct emissions and greenhouse gases implications. This work aims at studying the external two-phase flow produced when injecting fuel with a pressure-swirl atomizer in an operating condition typical of an academic burner. The main objective is to have a better comprehension of the atomization process in this type of nozzles, getting information on the droplets’ characteristic size and how they are produced and arranged. For that regard, a high-fidelity Direct Numerical Simulation (DNS) is used to analyse the very near field of the spray produced, where primary atomization takes place. Pre-processing tasks are explained in terms of geometry comprehension, computational domain and mesh selection, boundary conditions and main simulation setup parameters. Then, through post-processing tasks, both quantitative and qualitative results are extracted, which will serve to validate the modelling against previous works and to provide novel data about the atomization process in pressure-swirl atomizers. Results show that smaller droplets predominate over bigger ones as expected, since just the first millimetres of the spray are modelled. However, there is a clear trend of droplet’s size growing when increasing both axial and radial distances, indicating coalescence in regions relatively far from the nozzle. The achieved results, together with results from simulating the injection event with other fuels or at other operating conditions, can be used to develop a phenomenological model able to predict how atomization is going to be as a function of the non-dimensional Reynolds and Weber numbers that could be implemented in lower-resolution RANS and LES codes for modelling atomization. This investigation proves that it is possible to faithfully predict the near field of these sprays through DNS simulation, getting similar trends to those of the experimental data, and that the study through numerical models is necessary in the investigation process. The information they can bring, together with the experimental knowledge, can make a good synergy that will eventually lead to a better understanding of this type of atomizers.

Who's Afraid of Electrical Stimulation? Let's Revisit the Application of NMES at the Knee.

BACKGROUND: Restoring quadriceps strength is essential for successful rehabilitation of knee injuries, but many athletes return to their previous activity with persisting muscle weakness. Strong evidence supports using neuromuscular electrical stimulation (NMES) to improve quadriceps strength; however, there is a lack of widespread clinical implementation. We believe there is a critical need to provide clinical approaches that promote using NMES to improve patients' quadriceps strength and ensuring clinicians provide high-value rehabilitation care. CLINICAL QUESTION: What is best practice when using NMES to facilitate strength after injury, what are barriers to its use, and how can they be addressed? KEY RESULTS: We discuss the low clinical implementation of NMES, perceived barriers to using NMES, and provide recommendations for setup and dosage parameters for effective use of NMES. CLINICAL APPLICATION: We aim for this commentary, with accompanying videos, to serve as a resource for clinicians who are using commercially available NMES units in clinical practice. J Orthop Sports Phys Ther 2024;54(2):1-6. Epub 31 October 2023. doi:10.2519/jospt.2023.12028.

Combined effect of surface treatment and heat treatment on the fatigue properties of AISI 316L, manufactured by powder bed fusion of metals using a laser (PBF-LB/M)

This work deals with the combined influence of surface treatment and heat treatment of 316L manufactured by powder bed fusion of metals using a laser (PBF-LB/M/316L) on the fatigue behavior. Two surface treatment methods (manual polishing, vibratory grinding) were investigated. Fatigue testing of six different surface and heat treatment parameter setups was conducted. Metallurgical investigations such as etching patterns, density measurements, surface roughness measurements, fracture surface analysis with a scanning electron microscope (SEM), X-ray diffraction (XRD) and electron backscatter diffraction (EBSD) were performed. The mechanical properties were examined by tensile tests and fatigue tests. The influence of grain size on fatigue life was found to be the primary factor, followed by the surface roughness and subsequently the residual stresses on the surface ultimately contributing to the overall effect.

Comparison of Activation Functions on Convolutional Neural Networks (CNN) to Identify Mung Bean Quality

Mung bean production levels by farmers in Indonesia are not stable. When there is a surplus, the stock of mung beans in the warehouse will accumulate, the storage factor affects the quality of mung beans. Indicators of quality mung beans can be seen from the color and size through direct observation. However, the aspect of view and assessment and the level of health of each observer is a human error in the classification of mung bean quality so that the results are less than optimal. One alternative way to identify object quality is to use deep learning algorithms. One of the popular deep learning algorithms is convolution neural network (CNN). This study aims to build a model to classify the feasibility of mung beans. The process of building the model also goes through the image preprocessing stage. In the process of building the model, there are ten setup parameters and four setup data used to produce the best model. As a result, the best CNN algorithm model performance is obtained from data setup I, with accuracy, precision, recall and F1 score above 75%. In addition, this study also analyzes Rel U and Adam activation functions on CNN algorithm on model performance in identifying mung bean quality. CNN algorithm with Adam activation function has 92% accuracy, 92.53% precision, 91.9% recall, and 92.19% F1 score. In addition, the performance of CNN algorithm with Adam activation function is superior compared to CNN algorithm with Adam activation function and previous study

Experimental study on positioning uncertainty of patients treated with stereotactic encephalic radiotherapy using surgical mask in Covid-19 era: analysis of dosimetric and set-up parameters

AimTo evaluate the positioning uncertainty in patients treated with encephalic stereotactic technique using the combination of a surgical mask with thermoplastic mask for immobilization of radiotherapy treatment.Materials and methodsRetrospective “Matched Pair” review and evaluation of a sample of 19 patients, divided into two groups, treated between November 2018 and March 2022 with a number of active lesions less than or equal to 2 on magnetic resonance imaging (MRI): the first group includes patients treated in the pre-Covid-19 era and immobilized without a surgical mask, while the second treated in the Covid-19 era with a surgical mask. ResultsThe study showed that there were no statistically significant differences between the two study groups by obtaining as positioning error in Verticality (Y-axis) values of 0.5 mm without surgical mask compared with -0.4 mm with surgical mask;in Longitudinal (Z-axis) values of -0.3 mm compared with 0.2 mm with surgical mask and finally in Laterality (X-axis) values of -0.6 mm without surgical mask compared with 0.1 mm with surgical mask. Subsequently, dose fluence analysis also confirmed the set-up reliability datum with the online in vivo dosimetry reproducibility datum.DiscussionThe association of the surgical mask with the thermoplastic mask, which was necessary in the Covid-19 era, proved to be essential in order to ensure the quality of treatments delivered without compromising the safety of operators and patients within the Operating Unit.

Development of a piston-actuated 3D bio-printer with performance prediction system for the reliable printing of tubular structures

3D bio-printing is a promising approach for creating tubular structures within the human body by precisely controlling the distribution of cells. While several 3D bio-printers have been developed for printing tubular structures, achieving reliable and repeatable construction of effective human tubular structures remains a challenge. This paper presents a piston-actuated 3D bio-tubular structures printer that uses a rotary rod-support printing method and a printing performance prediction system. The printing performance prediction system is based on a two-phase flow computational fluid dynamics model that simulates the tubular structure forming process and provides optimal printing setup parameters, such as extrusion nozzle movement speed, nozzle height, and rod rotating speed. Experimental testing has validated the performance prediction system, which achieved a fair prediction accuracy with an average error of around 10%. The proposed bio-printer and prediction system have the potential to improve the efficiency and effectiveness of tubular structure printing for various biomedical applications.

Pulsed wave Doppler ultrasound: Accuracy, variability, and impact of acquisition parameters on flow measurements.

Pulsed wave Doppler ultrasound is a useful modality for assessing vascular health as it quantifies blood flow characteristics. To facilitate accurate diagnosis, accuracy and consistency of this modality should be assessed through Doppler quality assurance (QA). The purpose of this study was to characterize the accuracy, reproducibility, and inter-scanner variability of ultrasound flow velocity measurements via a flow phantom, with a focus on the effect of systematic acquisition parameters on measured flow velocity accuracy. Using a manufacturer-calibrated flow phantom, pulsed wave measurements were acquired on five clinical systems (iU22, Philips) with three models of transducers, including both linear and curvilinear models. The peak and mean flow velocities were estimated by vendor-supplied spectral analysis tools. To investigate intra- and inter-scanner variability, measurements were repeated using each scanner-transducer pair under a standardized set of conditions. Inter-scanner variability was assessed using ANOVA. Flow velocity accuracy was investigated by mean absolute percentage error. The impacts of receive gain, measurement depth, and beam steering on measured flow velocity accuracy were examined by varying each parameter over its available range and comparing to the ground truth flow velocity. Inter-scanner variability was statistically significant for peak flow measurements made using both linear and curvilinear transducers, though absolute differences in measured velocity were small. Inter-scanner variability was not statistically significant for mean flow velocity. Receive gain, measurement depth, and beam steering were all found to impact the accuracy of measured flow characteristics for linear transducers. Accuracy of the flow measurements made with the curvilinear transducer demonstrated high consistency to changes in receive gain at a constant depth, though were impacted by increasing the measurement depth. Carefully and consistently selected acquisition and set-up parameters are essential in order to establish a reliable and meaningful QA program.

Topological analysis of three-dimensional magnetic reconnection in SPERF-AREX for simulated magnetopause events

A scientific research device for geospace physics, the Space Plasma Environment Research Facility (SPERF), is going to be in operation this year at the Harbin Institute of Technology, China. One of its laboratory simulation platforms, the Asymmetric Reconnection EXperiment (AREX), is designed to investigate the asymmetric magnetic reconnection relevant to the process in the dayside magnetopause of the Earth. As a ground-based experimental platform, AREX emphasizes three-dimensional (3D) asymmetric driving features with topological similarity relevant to dayside reconnection. Thus, in this paper, we focus on topological analysis based on numerical simulation of AREX reconnection processes. 3D topological features in various scenarios relevant to magnetopause reconnection in different interplanetary magnetic field (IMF) conditions are investigated by adjusting setup parameters of plasma sources and magnetic coils. The simulation results reveal that the plasma distribution of asymmetric reconnection in SPERF-AREX is analogous to that at the magnetopause. Various types of the 3D magnetic topology relevant to typical IMF conditions, including the X-line and the A–B null chain structures, are, respectively, identified and analyzed in detail.

A novel sensitive DDoS attacks against statistical test in network traffic fusion

AbstractDistributed denial of service (DDoS) attacks are common attacks that limit casual users from cracking critical network resources. A method to find the DDoS attack comes to light as an essential and hot research issue. Therefore, the increment in network usage scale demands more challenges to see the DDoS attack. Non‐real‐time collection of network traffic will make sincere complications inaccuracy; DDoS attack detection, and efficiency. Lately, sketch data structures play an excellent role in high‐speed networks to suppress and combine network traffic issues. However, the sketch data structure has issues rebuilding the reverse keys that behave abnormally due to the reversible hash function. To rectify the above difficulties, we include the Chinese Remainder Theorem‐based Reverse Sketching (CRTRS). However, Reverse Sketching is not good at suppressing and combining heavy network traffic, but it can easily detect abnormal keys. According to the network traffic records setup by CRTRS, we add the Modified Cumulative Sum Quality Control Scheme (MCSQCS) that encourages DDoS attack detection. The enforcement of the proposed work with DDoS attack detection has two kind of different data sets. The result of the experiments shows that it gives more accuracy and efficiency in detecting DDoS attacks. Therefore, comparing other DDoS detecting methods with the sketch data structure has fewer computational complications. Improving the anomalous keys is the most critical challenge in this proposed work. To illustrate the accuracy rate, we experiment with a new technique called CIC‐Friday Heavy network traffic fragment. We also explain the experimental results of the MAWI heavy network fragment with the various parameter setups. To compare our proposed method with other similar existing methods, we use a sketch data structure to manage the network traffic data. It also uses the MSPCA method to detect the DDoS attack. The information‐based analysis helps to see the DDoS attack. The result analysis of True Positive Value (TPV) and False Positive Value (FPV) with MSPCA techniques has 79% and .1% accuracy. Similarly, information‐based detection has an accuracy of about 90% and 5%. Hence, the proposed method gives more accuracy for the detection of DDoS attacks.

Data Driven Approach to Exactrac Setup Parameters after CBCT for Cranial Radiotherapy

The purpose of this study is to 1) Determine the congruence of shifts derived from Cone-Beam CT (CBCT) and from Exactrac dynamic KV X-rays 2) To setup action levels for Exactrac imaging to determine if patient potentially moved between CBCT and Exactrac acquisition and 3) To analyze if Exactrac alone can be reliably used for cranial setup forgoing the CBCT step MATERIALS/METHODS: Exactrac dynamic, uses in-room KV X-ray imaging that is mounted on the floor to monitor patient positioning during treatment. The congruence of imaging and radiation isocenter for the LINAC and Exactrac system was verified using the Winston-Lutz (WL) test over a period of one year. The WL pointer was set up and its coincidence with imaging and radiation isocenter was confirmed. Exactrac images were then acquired at various couch angles and the pointer deviation from Exactrac imaging isocenter is noted. Secondly, translation and rotation shifts for 175 consecutive cranial cases were examined to determine the deviation between shifts as denoted by CBCT and Exactrac. These patients were initially set up using CBCT and then once in treatment position, Exactrac KV images were acquired to check for any deviation. The deviation between the two systems was collected and a Kolomogorov-Smirnov (KS) test was performed on the data to check for the normality of the distribution. Statistical Process Control (SPC) was performed to derive action levels for Exactrac based shifts after CBCT. Based on repeated Winston-Lutz tests over a period of one year, the maximum deviation between radiation and imaging isocenters and Exactrac isocenter was 0.3mm and 0.3 deg over all couch angles. This number remained stable over the entire time period without necessitating any recalibration of Exactrac isocenter. Based on patient data for cranial cases, the mean and standard deviations for the largest shifts were (0.45 mm, 0.40 mm) for translations (range 0 - 2.03mm) and (0.44 deg and 0.32 deg) for rotations (range 0 - 2.21 degree). Using SPC, it was decided that the action level for translations and rotations could be set to 0.8mm and 0.5 deg respectively. Any deviation beyond this action level necessitates re-imaging to verify that the patient did not move in between the CBCT and Exactrac imaging acquisitions CONCLUSION: Exactrac system derived shifts show excellent agreement with CBCT. The isocentricity of the systems is maintained over a long period of time and shows no drift. Either system can be reliably used to set up cranial patients. With the added advantage of speed of acquisition, matching and shifts, the Exactrac system could replace CBCT for daily setup of patients undergoing cranial radiotherapy.

Stereolithography for manufacturing of advanced porous solids

The aim of this paper is focused on benefits of stereolithography (SLA) technology for the fabrication of the lightweight lattice structures with potential for load-bearing function and high absorption of impact energy. SLA is an additive manufacturing technology employing the principle of liquid resins curing moderated by radiation of a wavelength from ultra-violet band where resulting material parameters are tunable by setting of the curing process. The batches of samples manufactured using three different resins were subjected to quasi-static uni-axial tensile and compression tests. Acquired data were processed to derive deformation behaviour expressed as stress-strain diagrams and fundamental material properties. Based on the knowledge obtained from the mechanical tests, the setup of the fabrication parameters, the most suitable resin for manufacturing of the lattice structures and the overall suitability of SLA technology for the fabrication of advanced porous materials, were determined.

Sedimentation of a starch microsphere: What is usually missed and why?

Gravimetric sedimentation is known as a relatively simple method of determining density of spherical particles. When the method is applied to water-swollen starch microparticles of about submillimeter sizes, it becomes evident that a careful selection of the experimental setup parameters is needed for producing accurate testing results. The main reason for this is that the mean particle density is very close to the density of water, and therefore, a dynamic model accounting for the so-called Bassett history force should be employed for describing the unsteady accelerating particle settling. A main novelty of this study consists in deriving a priori estimates for the settling time and distance.

Multi-parameter optimization of gamma emission tomography instruments for irradiated nuclear fuel examination

Material test reactors have an extended use in irradiation testing of novel nuclear fuel materials and the fuel behavior in off-normal conditions. The performance of the nuclear fuel is examined in in-pile and out-of-pile post-irradiation examinations (PIEs), e.g., using Gamma Emission Tomography (GET). GET is a nondestructive assay that images the internal spatial distribution of gamma-emitting nuclides built up in the fuel due to irradiation. Since GET can be performed close to the reactor and without intrusion in the fuel object, it can potentially speed up the data generation from PIE in irradiation testing.The performance metrics of GET devices can be identified regarding time requirements, noise in the reconstructed image, signal-to-background ratio, and spatial resolution. However, these are complicated to determine, partly due to inherent trade-offs between the metrics themselves, partly because they depend on the fuel activity and its spectrum (i.e., object dependent), and, finally, on the GET setup and its configuration.This work proposes a structured methodology for optimizing the collimator design for a new generation of GET tomography setups, intending to improve spatial resolution by one order of magnitude: from the millimeter scale to the hundred-micron scale. The conflicting performance metrics are determined based on the controllable parameters of the GET setup and the uncontrollable parameters of an anticipated fuel object, able to provide a signal-to-background ratio above a customized limit, chosen based on the specific application. The trade-off between the performance remaining metrics is then visualized by a Pareto approach, where dominated solutions are rejected. Finally, constraints on noise level and measurement time are used to find the optimal spatial resolution, without applying noise suppression filters.Two GET setups are presented using the outlined method. Firstly, to upgrade the tomography test bench BETTAN at Uppsala University, a new segmented HPGe detector is planned to be used with low-activity fuel rod mock-ups. Secondly, a GET system for investigating high-activity nuclear fuel rods of representative burnup. For a nuclear fuel inspection, the results showed that a spatial resolution of about 300 μm is possible with reasonable noise and measurement time constraints.

EFFECTS OF SOAKING PROCESS AND ROTATING METHOD ON EDIBLE BIRD NEST CLEANING TIME AND CLEANLINESS

The paper examines the effects of rotation and soaking processes on the cleaning time and cleanliness of edible bird nests (EBN). During breeding season, swift’s species known as Aerodramus secrete salivary glands to build a nest that humans harvest. To be sold, the harvested EBN must be cleaned. The raw EBN cleaning process consists of four operations: soaking, cleaning, reshaping, and drying. Generally, harvested EBN is cleaned manually using tweezers. However, this procedure is time-consuming, as cleaning one EBN take about an hour. Therefore, several mechanical cleaning methods were required to clean raw EBN to improve cleaning time and cleanliness. This research aims to improve the two mechanical cleaning methods, rotating and soaking. The Taguchi Method is used to design and optimize the overall parameter setup. The selected optimized parameter set will be tested to determine the best parameter sets. For the current study, EBN wetting process has time settings of 12, 18, and 24 hours. The rotating method's time settings were 30, 45, and 60 seconds. The soaking time can be set to 2, 4, or 6 minutes. Each of these parameters is thoroughly tested to determine the best-optimized one. The experiment is then carried out by combining the two parameters chosen in a sequence to determine the cleanliness of the raw EBN. According to the experiments' results, the cleaning sequence contributes 63.75 percent cleanliness at a cleaning time of 5 minutes, which includes a soaking time of 4 minutes and a rotating method of 60 seconds.

Analysis of built-up classes in urbanised zones using radar images

This paper presents the results of a study to determine the potential of radar imaging to detect classes of built-up areas defined in the Urban Atlas (UA) spatial database. The classes are distinguished by function and building density. In addition to the reflectance value itself, characteristics such as building density or spatial layout can improve the identification of these classes. In order to increase the classification possibilities and better exploit the potential of radar imagery, a grey-level co-occurrence matrix (GLCM) was generated to analyse the texture of built-up classes. Two types of synthetic-aperture radar (SAR) images from different sensors were used as test data: Sentinel-1 and ICEYE, which were selected for their different setup configurations and parameters. Classification was carried out using the Random Forests (RF) and Minimum Distance (MD) methods. The use of the MD classifier resulted in an overall accuracy of 64% and 51% for Sentinel-1 and ICEYE, respectively. In ICEYE, individual objects (e.g. buildings) are better recognised than classes defined by their function or density, as in UA classes. Sentinel-1 performed better than ICEYE, with its texture images better complementing the features of urban area classes. This remains a significant challenge due to the complexity of urban areas in defining and characterising urban area classes. Automatic acquisition of training fields directly from UA is problematic and it is therefore advisable to independently obtain reference data for built-up area categories.