Search Results (129)

High-pitched noise generated by dental air turbine handpieces (ATHs) causes discomfort and anxiety, discouraging dental visits. Understanding the time-dependent noise generation mechanism associated with compressed airflow in ATHs is crucial for effective noise reduction. However, the direct investigation of airflow dynamics within ATHs is challenging. The transient-state modeling of computational fluid dynamics (CFD) simulations remains unexplored owing to the complexities of high rotational speeds and air compressibility. This study develops a novel CFD framework for transient (time-dependent) modeling under high-speed rotational conditions. Simulations were performed using a three-dimensional model reconstructed from a commercial ATH. Simulations were conducted at 320,000 rpm using a novel framework that combines the immersed boundary and building cube methods. A fine 0.025 mm mesh spacing near the ATH, combined with supercomputing resources, enabled the simulation of hundreds of millions of cells. The simulation results were validated using experimental noise measurements. The CFD simulation revealed transient airflow and aeroacoustic behavior inside and around the ATH that closely matched the prominent frequency peaks from the experimental data. This study is the first to simulate the transient airflow of ATHs. The proposed CFD model can accurately predict aeroacoustics, contributing to the future development of quieter and more efficient dental devices. Full article

The present study concentrates on passive damping technology, in which the damping of vibrations is accomplished by the integration of lattice structures into the boring bar. To complete this process, several steps must be followed. First, the largest possible hollow space within the boring bar was determined, and the two main influencing factors—stiffness and natural frequency—were harmonized. A rigorous analysis of vibration reduction was conducted on the basis of a validated simulation model. This analysis involved six distinct lattice structures designed using ANSYS SpaceClaim 19.0. In light of the findings, a specialized, application-specific CAD simulation tool was developed, employing appropriate methodologies to circumvent the limitations of conventional CAD software. For the hollow integrated into the boring bar, ellipsoidal shapes were shown to be preferable to cylindrical ones due to their superior dynamic performance. The initial lattice structure, namely a cube lattice with side cross supports, exhibited an enhancement in damping of 55.58% in comparison with the reference model. Following this result, five additional modelling steps were performed, leading to an optimal outcome with a 67.79% reduction in vibrations. Moreover, the modifications made to the beam diameter of the lattice units yielded enhanced dynamic performance, as evidenced by a vibration suppression of 69.81%. The implementation of complex modelling steps, such as the integration of a hollow and the integration of lattice structures, could be successfully achieved through the development of a suitable and user-friendly simulation tool. The effectiveness of the simulation tool in enabling parameterized modelling for scalable lattice structures was demonstrated. This approach was found to be expeditious in terms of the time required for implementation. The potential exists for the extension of this simulation tool, with the objective of facilitating research projects with a view to optimization, i.e., a large number of research projects. Full article

In the age of big data, multimedia communication is becoming one of the most important communication methods. Therefore, it is important to overcome the challenge of processing numerous images safely and efficiently. In this paper, a 3D cross-image encryption scheme is proposed based on the Rubik’s Cube–poker model. The proposed scheme has the following properties: Firstly, it has flexible input image requirements to improve its applicability. Secondly, it has a strong ability to recover from data loss and retains important information in the reconstructed image. Finally, it achieves a lower time cost. Under the same input conditions in the grayscale color space, our proposed scheme achieves an execution time of 0.62 s, which is significantly lower than that of other schemes. The simulation results confirm the correctness, security, and robustness of the scheme. Full article

Drones are widely used in urban air pollution monitoring. Although studies have focused on single-drone applications, collaborative applications for air pollution detection are relatively underexplored. This paper presents a 3D cube-based adaptive cooperative search algorithm that allows two drones to collaborate to explore air pollution. The search space is divided into cubic regions, and each drone explores the upper or lower halves of the cubes and collects data from their vertices. The vertex with the highest measurement is selected by comparing the collected data, and an adjacent cube-shaped search area is generated for exploration. The search continues iteratively until any vertex measurement reaches a predefined threshold. An improved algorithm is also proposed to address the divergence and oscillation that occur during the search. In simulations, the proposed method consumed 21 times less CPU time and required 23 times less search distance compared to linear search. Additionally, the cooperative search method using multiple drones was more efficient than single-drone exploration in terms of the same parameters. Specifically, compared to single-drone exploration, the collaborative drone search reduced CPU time by a factor of 2.6 and search distance by approximately a factor of 2. In experiments in real-world scenarios, multiple drones equipped with the proposed algorithm successfully detected cubes containing air pollution above the threshold level. The findings serve as an important reference for research on drone-assisted target exploration, including air pollution detection. Full article

Energy spectrum computed tomography (CT) technology based on photon-counting detectors has been widely used in many applications such as lesion detection, material decomposition, and so on. But severe noise in the reconstructed images affects the accuracy of these applications. The method based on tensor decomposition can effectively remove noise by exploring the correlation of energy channels, but it is difficult for traditional tensor decomposition methods to describe the problem of tensor sparsity and low-rank properties of all expansion modules simultaneously. To address this issue, an algorithm for spectral CT reconstruction based on photon-counting detectors is proposed, which combines Kronecker-Basis-Representation (KBR) tensor decomposition and total variational (TV) regularization (namely KBR-TV). The proposed algorithm uses KBR tensor decomposition to unify the sparse measurements of traditional tensor spaces, and constructs a third-order tensor cube through non-local image similarity matching. At the same time, the TV regularization term is introduced into the independent energy spectrum image domain to enhance the sparsity constraint of single-channel images, effectively reduce artifacts, and improve the accuracy of image reconstruction. The proposed objective minimization model has been tackled using the split-Bregman algorithm. To evaluate the algorithm’s performance, both numerical simulations and realistic preclinical mouse studies were conducted. The ultimate findings indicate that the KBR-TV method offers superior enhancement in the quality of spectral CT images in comparison to several existing methods. Full article

This study presents a system framework by which cloud resources are utilized to analyze crop germination status in a 2U CubeSat. This research aims to address the onboard computing constraints in nanosatellite missions to boost space agricultural practices. Through the Espressif Simple Protocol for Network-on-Wireless (ESP-NOW) technology, communication between ESP-32 modules were established. The corresponding sensor readings and image data were securely streamed through Amazon Web Service Internet of Things (AWS IoT) to an ESP-NOW receiver and Roboflow. Real-time plant growth predictor monitoring was implemented through the web application provisioned at the receiver end. On the other hand, sprouts on germination bed were determined through the custom-trained Roboflow computer vision model. The feasibility of remote data computational analysis and monitoring for a 2U CubeSat, given its minute form factor, was successfully demonstrated through the proposed cloud framework. The germination detection model resulted in a mean average precision (mAP), precision, and recall of 99.5%, 99.9%, and 100.0%, respectively. The temperature, humidity, heat index, LED and Fogger states, and bed sprouts data were shown in real time through a web dashboard. With this use case, immediate actions can be performed accordingly when abnormalities occur. The scalability nature of the framework allows adaptation to various crops to support sustainable agricultural activities in extreme environments such as space farming. Full article

Rapid urbanization in Kathmandu Valley has strained its aquifer system, causing significant land subsidence. This study employs LiCSBAS for InSAR processing of Sentinel-1 data (2017–2024) to map subsidence-prone areas. The significant subsidence was found in northwest (Baluwatar, Samakhusi, and Manmaiju), southern (Gwarko, Patan, and Koteshwor), and northeast (Madhapur Thimi and Gathhaghar) regions with a maximum subsidence rate ~21 cm/yr. Subsidence has also expanded towards the outskirts and open areas in the eastern and southern parts of Lalitpur and Bhaktapur districts. Emerging hot spot analysis reveals a slowing subsidence trend in high-risk zones, possibly linked to the MWSP project reducing groundwater extraction from 58 MLD (2021) to 26 MLD (2024). Many subsidence-affected areas are located over the Kalimati and Gokarna Formations in highly urbanized areas. The key contributing factors to subsidence are soil compaction, excessive groundwater use, and urban sprawl encroaching open areas and recharge zones. These findings underscore the urgent need for sustainable groundwater management and land-use planning to promote urban resilience. Full article

Accurate monitoring and assessment of forest disturbance and recovery dynamics are essential for sustainable forest management, particularly in ecological transition zones. This study analyzed forest disturbance and recovery patterns in China’s Funiu Mountains from 1991 to 2020 by integrating the LandTrendr algorithm with space-time cube analysis. Using Landsat time series data and the Geodetector method, we examined both the spatiotemporal characteristics and driving factors of forest change across three periods. The results showed that (1) between 1991 and 2020, the study area experienced 131.19 km² of forest disturbance and 495.88 km² of recovery, with both processes most active during the 1990s; (2) spatiotemporal analysis revealed that both disturbance and recovery patterns were predominantly characterized by cold spots, suggesting relatively stable forest conditions despite localized changes; (3) human activities were the primary drivers of forest disturbance in the early period, while forest recovery was consistently influenced by the combined effects of topographic conditions and precipitation. Additionally, forest fires emerged as an important factor affecting both disturbance and recovery patterns after 2010. These findings enhance our understanding of forest dynamics in transition zones and provide empirical support for regional forest management strategies. The results also highlight the importance of considering both spatial and temporal dimensions when monitoring long-term forest changes. Full article

As a result of progress in space technology, more scientific missions are benefiting from using CubeSats equipped with radiometers. CubeSat constellations are especially effective in overcoming obstacles in cost, weight, and power. However, these benefits have certain significant downsides, including the difficulty in calibration due to the increased sensitivity of instruments to ambient conditions. Such limitations prevent conventional calibration methods from being reliably applied to CubeSat radiometers. A novel, constellation-level calibration framework called “Adaptive Calibration of CubeSat Radiometer Constellations (ACCURACy)” is being developed to address this issue. ACCURACy, in its current version, uses telemetry data obtained from thermistors in each CubeSat to cluster constellation members into time-adaptive groups of radiometers in similar states. Each radiometer is assigned membership to a cluster and this status is updated as in-orbit measurements shift in the clustering model. This paper introduces the ACCURACy framework, discusses its theoretical background, and presents a MATLAB prototype with performance and uncertainty analyses using synthetic radiometer data in comparison with traditional radiometer calibration methods. Full article

As public concern for maritime safety grows, there is a pressing need to delve deeper into the root causes of maritime accidents and develop effective preventive strategies. Spatial-temporal analysis stands out as a powerful approach to pinpointing accident hot spots. While previous research has shed light on the spatial aspects of these incidents, a comprehensive understanding of their temporal dimensions remains elusive. This paper bridges this gap by leveraging the Space-Time Cube tool in conjunction with traditional Kernel Density analysis to chart the spatial-temporal dynamics of maritime accident hot spots. Focusing on the East China Sea, a region notorious for its high incidence of maritime accidents and home to numerous world-class ports, we present a case study that offers fresh insights. Data spanning from 1994 to 2020, sourced from the Lloyd’s List Intelligence (LLI) database, reveal the evolving landscape of maritime accidents in the area. Notably, since 2005, the Yangtze River Delta Region in China has emerged as a persistent hot spot for accidents, underscoring its significance in maritime safety discourse. Furthermore, our analysis from the 2010s detects a new hot spot expanding towards the southwest of Kaohsiung Port, China, signaling a burgeoning area of concern for maritime safety. While the Fujian coast of China has seen its share of accidents, it is not qualified as a hot spot zone. The Space-Time Cube proves to be an indispensable tool in unraveling the progression of maritime accidents, and our findings indicate that maritime accidents in certain areas may not be merely random occurrences but exhibit intricate patterns. Full article

Vegetation cover in urban and peri-urban areas is threatened by urban sprawl, through habitat fragmentation, loss of green space, biodiversity reduction, and the urban heat island effect intensifying. The intrusion of cities into natural landscapes reduces vital ecosystem services provided by vegetation. Hence, sustainable and integrated urban planning practices are required. Our study aims to investigate the dynamics of the urban and peri-urban fabric by exploring the relationship between the green fabric distribution and recent trends in urban expansion, focusing specifically on the peri-urban areas of Iași Municipality, Romania. We designed a mixed-method approach combining a multivariate analysis of four critical indicators (vegetation cover, built-up space, land surface temperature, and population density), emerging hot-spots, and space-time cubes in a GIS environment to achieve our research aims. Our results demonstrate that uncontrolled urban expansion has manifested in diverse patterns, impacting territories next to road transport networks and with construction-suitable topography. Concurrently, the development of green spaces prevails in forests and unexpected locations such as brownfields, railway corridors, and old industrial zones, through the growth of urban greenery. This approach provides a comprehensive understanding of how urban sprawl impacts the environment and how different land types are prone to this transformation, creating a path towards sustainability, resilience, and equitable development. Full article

Contrails, or condensation trails, left by aircraft, significantly contribute to global warming by trapping heat in the Earth’s atmosphere. Despite their critical role in climate dynamics, the environmental impact of contrails remains underexplored. This research addresses this gap by focusing on the use of CubeSats for real-time contrail monitoring, specifically over major air routes such as the Europe–North Atlantic Corridor. The study proposes a 3 × 3 CubeSat constellation in highly eccentric orbits, designed to maximize coverage and data acquisition efficiency. Simulation results indicate that this configuration can provide nearly continuous monitoring with optimized satellite handovers, reducing blackout periods and ensuring robust multi-satellite visibility. A machine learning-based system integrating space-based humidity and temperature data to predict contrail formation and inform flight path adjustments is proposed, thereby mitigating environmental impact. The findings emphasize the potential of CubeSat constellations to revolutionize atmospheric monitoring practices, offering a cost-effective solution that aligns with global sustainability efforts, particularly the United Nations Sustainable Development Goal 13 (Climate Action). This research represents a significant step forward in understanding aviation’s non-CO₂ climate impact and demonstrates the feasibility of real-time contrail mitigation through satellite technology. Full article

CubeSats have emerged as a promising alternative to satellite missions for studying remote areas where satellite data are scarce and insufficient, such as coastal and marine environments. However, their standard size and weight limitations make integrating remote sensing optical instruments challenging. This work presents the development of Bentayga-I, a CubeSat designed to validate PANDORA, a self-made, lightweight, cost-effective multispectral camera with interchangeable spectral optical filters, in near-space conditions. Its four selected spectral bands are relevant for ocean studies. Alongside the camera, Bentayga-I integrates a power system for short-time operation capacity; a thermal subsystem to maintain battery function; environmental sensors to monitor the CubeSat’s internal and external conditions; and a communication subsystem to transmit acquired data to a ground station. The first helium balloon launch with B2Space proved that Bentayga-I electronics worked correctly in near-space environments. During this launch, the spectral capabilities of PANDORA alongside the spectrum were validated using a hyperspectral camera. Its scientific applicability was also tested by capturing images of coastal areas. A second launch is planned to further validate the multispectral camera in a real-world scenario. The integration of Bentayga-I and PANDORA presents promising results for future low-cost CubeSats missions. Full article

This article presents an alternative approach to detecting and mapping space debris in low Earth orbit by utilizing commercially available automotive LiDAR sensors mounted on CubeSats. The main objective is to leverage the compact size, low weight, and minimal power consumption of these sensors to create a “Large Cosmic LiDAR” (LCL) system. This LCL system would operate similarly to a giant radar circling the Earth, with strategically positioned LiDAR sensors along the target orbit. The article examines the feasibility of this concept by analyzing the relative orbital velocity between the sensor and debris objects, and calculating the time required to scan a complete orbit. Full article

Neural networks (NNs) have proven their ability to deal with many computer vision tasks, including image-based remote sensing such as the identification and segmentation of hyperspectral images captured by satellites. Often, NNs run on a ground system upon receiving the data from the satellite. On the one hand, this approach introduces a considerable latency due to the time needed to transmit the satellite-borne images to the ground station. On the other hand, it allows the employment of computationally intensive NNs to analyze the received data. Low-budget missions, e.g., CubeSat missions, have computation capability and power consumption requirements that may prevent the deployment of complex NNs onboard satellites. These factors represent a limitation for applications that may benefit from a low-latency response, e.g., wildfire detection, oil spill identification, etc. To address this problem, in the last few years, some missions have started adopting NN accelerators to reduce the power consumption and the inference time of NNs deployed onboard satellites. Additionally, the harsh space environment, including radiation, poses significant challenges to the reliability and longevity of onboard hardware. In this review, we will show which hardware accelerators, both from industry and academia, have been found suitable for onboard NN acceleration and the main software techniques aimed at reducing the computational requirements of NNs when addressing low-power scenarios. Full article