Space Scales Research Articles

Pit slope unmanned aerial vehicle image matching based on hybrid scale space and boosted efficient binary local image descriptor

Pit slope unmanned aerial vehicle image matching based on hybrid scale space and boosted efficient binary local image descriptor

Impacts of inflow turbulence on the flow past a permeable disk

A permeable disk serves as a simplified model for the conversion of wind energy by a horizontal axis wind turbine. In this study, we investigate how inflow turbulence intensity (TI), $I_\infty$ , and inflow turbulence integral length scale, $L_\infty$ , influence the flow recovery in the wake, the capability of a permeable disk in extracting turbulence kinetic energy (TKE) of the incoming flow, and the statistics of wake-added turbulence using large-eddy simulation. The simulated inflows include various TIs (i.e. $I_\infty =2.5\,\%$ – $25\,\%$ ) and integral length scales (i.e. $L_\infty / D =0.5$ – $2.0$ ) for two thrust coefficients. Simulation results show that both inflow TI and integral length scale influence flow recovery via enhanced ejections and sweeps across the wake boundary, with the former strongly affecting the position where the wake starts to recover and the latter mainly on the recovery rate. Moreover, it is shown that increasing $I_\infty$ and $L_\infty$ increases the TKE extraction by the disk, occurring mainly at scales ( $s$ ) greater than $0.5D$ and frequencies depending on the inflow integral length scale. As for the wake-added TKE, the inflow TI mainly affects its intensity, while the inflow integral length scale affects both its intensity and the sensitive frequencies, with the spectral distributions in scale space ( $s$ ) being similar and the peak located around $s/D=1.0$ for the considered inflows.

Leveraging protohalos and scale-dependent bias to calibrate the BAO scale in real space

Leveraging protohalos and scale-dependent bias to calibrate the BAO scale in real space

Exploring the spatiotemporal effects of meteorological factors on hand, foot and mouth disease: a multiscale geographically and temporally weighted regression study.

The influence of meteorological factors on hand, foot, and mouth disease (HFMD) is not on the same scale, it's rare for previous studies to measure and recognize the independent regression relationship between each variable in space and time scale. This study used a multiscale geographically and temporally weighted regression (MGTWR) model to explore the relationship between the incidence of HFMD and related meteorological factors in Shandong Province, China, during 2015-2019 and attempted to quantify the influence of meteorological factors on HFMD under different spatiotemporal effects. Meanwhile, we used the Global Moran's I statistic and Local Moran's I statistic to test the spatial autocorrelation of the incidence of HFMD. HFMD had spatial autocorrelation at the county level in Shandong Province. The MGTWR model outperformed the OLS and GTWR models in determining the relationship between meteorological factors and HFMD. The study highlights significant spatiotemporal non-stationarity in the relationship between meteorological factors and HFMD. Temperature was predominantly positively correlated with HFMD, especially in the peninsula region during spring and summer. Humidity exhibited a predominantly positive correlation, especially in the Shandong Peninsula. Precipitation also showed a positive correlation with HFMD, particularly in western regions and during the winter months. Wind speed had a predominantly negative correlation with HFMD in the central and southwestern regions. The results might help public health authorities set priorities for targeted prevention and control measures in different regions and weather conditions, and provide guidance for the government to rationally allocate public health resources.

Spiralling Inverse Method: A New Inverse Method to Estimate Ocean Mixing

Abstract Here, we introduce the Spiralling Inverse Method (SIM) that provides estimates of the small-scale and mesoscale mixing strength. The SIM uses a vertical integral over a balance between the water mass transformation equation and the thermal wind equation. The result is an equation where all terms, except for the mixing strengths, can be obtained from hydrographic data of temperature and salinity. As an advantage, the SIM estimates the mixing strengths without the need for further knowledge of a reference velocity or streamfunction. Here, we apply the SIM to a small region in the North Atlantic. We find that the estimates obtained by the SIM compare well to observations and other (inverse) estimates of the mixing strength. The SIM therefore has the potential to improve and constrain parameterizations used for climate and ecosystem modeling using readily available hydrographic data. Significance Statement Ocean mixing is a combination of many different physical processes over a large range of scales in time (from seconds to years) and space (from millimeters to 100 km). Most of these processes are too small to compute in climate models and need to be simplified (parameterized). These parameterizations have a strong influence on climate projections, and their shape and magnitude need to be constrained using observations and indirect estimates of ocean mixing strength. The Spiralling Inverse Method (SIM) is a new method to obtain such constraints of mixing strengths using readily available observations of temperature and salinity. We here test the SIM and confirm its potential to improve mixing estimates and therewith ultimately climate simulations.

TransWild: Enhancing 3D interacting hands recovery in the wild with IoU-guided Transformer

The recovery of 3D interacting hands meshes in the wild (ITW) is crucial for 3D full-body mesh reconstruction, especially when limited 3D annotations are available. The recent ITW interacting hands recovery method brings two hands to a shared 2D scale space and achieves effective learning of ITW datasets. However, they lack the deep exploitation of the intrinsic interaction dynamics of hands. In this work, we propose TransWild, a novel framework for 3D interactive hand mesh recovery that leverages a weight-shared Intersection-of-Union (IoU) guided Transformer for feature interaction. Based on harmonizing ITW and MoCap datasets within a unified 2D scale space, our hand feature interaction mechanism powered by an IoU-guided Transformer enables a more accurate estimation of interacting hands. This innovation stems from the observation that hand detection yields valuable IoU of two hands bounding box, therefore, an IOU-guided Transformer can significantly enrich the Transformer’s ability to decode and integrate these insights into the interactive hand recovery process. To ensure consistent training outcomes, we have developed a strategy for training with augmented ground truth bounding boxes to address inherent variability. Quantitative evaluations across two prominent benchmarks for 3D interacting hands underscore our method’s superior performance. The code will be released after acceptance.

The Geometric Scale of an Urban Space—Neuro-perceptual Limitations for Face-to-Face Driver Behavior

For the last century, the roadway design paradigm has been grounded in the physics of point masses, adjusted for human limitations. However, this is inadequate to assure pedestrian safety in complete streets where vehicle kinematics no longer control. Instead, social and psychological factors manage behavior. Unfortunately, the appropriate design critical psychological principles have yet to be elucidated. We posit that interpersonal perception governs drivers’ behavior, attentiveness and speed in streets. This is bounded by previously delineated human neurological and perceptual propensities. Using these perceptual limitations as postulates in a geometric style proof, we derive the person perception panorama (PPP): a window of interactivity around the moving driver that is continuously monitored for human presence, roughly 60 to 90 feet wide. In this area interpersonal interaction is implicit, functional, and has an impact on driver behavior. Validating evidence, additional governing principles, and an initial speed prediction formula are also included.

Analysis of group evaporation characteristics of droplet clusters in an evaporating spray

Droplet clustering in sprays refers to the dynamic evolution of highly concentrated regions due to the preferential accumulation of the polydisperse droplets in the turbulent airflow entrained by the spray. In the current study, we aim to experimentally investigate the collective vaporization of the droplets in droplet clusters in an air-assisted acetone spray characterized by the Group number, $G$ . The magnitude of $G$ depends on the cluster length scale and interdroplet spacing, and it indicates the vaporization mode that may vary from the isolated mode ( $G \ll 1$ ) to external group mode ( $G \gg 1$ ). The droplet measurements were obtained under atmospheric conditions at different axial and radial locations within the spray. Application of the Voronoi analysis to particle image velocimetry images of the spray droplets facilitated the identification and characterization of the droplet clusters, which allowed the measurement of $G$ for each cluster. The results highlighted that multiscale clustering of the evaporating droplets leads to multimode group evaporation of the clusters (characterized by a wide range of $G$ : 0.001–10). The trend of interdroplet spacing versus cluster area allowed the classification of the droplet clusters into small-scale clusters (which are of the order of the Kolmogorov length scale) and large-scale clusters (that scale with the large-scale turbulent eddies), that are found to exhibit distinct group evaporation behaviour. A theoretical model is invoked to correlate $G$ with the droplet evaporation rate for individual clusters, and some interesting observations are identified, which are explained in the paper.

Research on the Registration of Aerial Images of Cyclobalanopsis Natural Forest Based on Optimized Fast Sample Consensus Point Matching with SIFT Features

The effective management and conservation of forest resources hinge on accurate monitoring. Nonetheless, individual remote-sensing images captured by low-altitude unmanned aerial vehicles (UAVs) fail to encapsulate the entirety of a forest’s characteristics. The application of image-stitching technology to high-resolution drone imagery facilitates a prompt evaluation of forest resources, encompassing quantity, quality, and spatial distribution. This study introduces an improved SIFT algorithm designed to tackle the challenges of low matching rates and prolonged registration times encountered with forest images characterized by dense textures. By implementing the SIFT-OCT (SIFT omitting the initial scale space) approach, the algorithm bypasses the initial scale space, thereby reducing the number of ineffective feature points and augmenting processing efficiency. To bolster the SIFT algorithm’s resilience against rotation and illumination variations, and to furnish supplementary information for registration even when fewer valid feature points are available, a gradient location and orientation histogram (GLOH) descriptor is integrated. For feature matching, the more computationally efficient Manhattan distance is utilized to filter feature points, which further optimizes efficiency. The fast sample consensus (FSC) algorithm is then applied to remove mismatched point pairs, thus refining registration accuracy. This research also investigates the influence of vegetation coverage and image overlap rates on the algorithm’s efficacy, using five sets of Cyclobalanopsis natural forest images. Experimental outcomes reveal that the proposed method significantly reduces registration time by an average of 3.66 times compared to that of SIFT, 1.71 times compared to that of SIFT-OCT, 5.67 times compared to that of PSO-SIFT, and 3.42 times compared to that of KAZE, demonstrating its superior performance.

PDeT: A Progressive Deformable Transformer for Photovoltaic Panel Defect Segmentation.

Defects in photovoltaic (PV) panels can significantly reduce the power generation efficiency of the system and may cause localized overheating due to uneven current distribution. Therefore, adopting precise pixel-level defect detection, i.e., defect segmentation, technology is essential to ensuring stable operation. However, for effective defect segmentation, the feature extractor must adaptively determine the appropriate scale or receptive field for accurate defect localization, while the decoder must seamlessly fuse coarse-level semantics with fine-grained features to enhance high-level representations. In this paper, we propose a Progressive Deformable Transformer (PDeT) for defect segmentation in PV cells. This approach effectively learns spatial sampling offsets and refines features progressively through coarse-level semantic attention. Specifically, the network adaptively captures spatial offset positions and computes self-attention, expanding the model's receptive field and enabling feature extraction across objects of various shapes. Furthermore, we introduce a semantic aggregation module to refine semantic information, converting the fused feature map into a scale space and balancing contextual information. Extensive experiments demonstrate the effectiveness of our method, achieving an mIoU of 88.41% on our solar cell dataset, outperforming other methods. Additionally, to validate the PDeT's applicability across different domains, we trained and tested it on the MVTec-AD dataset. The experimental results demonstrate that the PDeT exhibits excellent recognition performance in various other scenarios as well.

Wave–Tide–Surge Interaction Modulates Storm Waves in the Bohai Sea

Typhoons, extratropical cyclones, and cold fronts cause strong winds leading to storm surges and waves in the Bohai Sea. A wave–flow coupled numerical model is established for storm events observed in 2022 caused by three weather systems, to investigate how storm waves are modulated by wave–tide–surge interaction (WTSI). Wave response is basically controlled by water level change in coastal areas, where bottom friction or breaking dominates the energy dissipation, and determined by the current field in deep water by altering whitecapping. Wave height increases/decreases are induced by positive/negative water level or obtuse/acute wave–current interaction angle, leading to six types of field patterns for significant wave height (Hs) responses. For the three storm events, Hs basically changed within ±5% in central deep water, while the maximum increase/decrease reached 160%/−60% in the coastal area of Laizhou Bay/Liaodong Bay. Based on maximum Hs and its occurrence time, WTSI modulation is manifested as the superposition effect of wave–tide and wave–surge interactions in both space and time scales, and occurrence time depends more on tide than surge for all three storms. The enhancement/abatement of WTSI modulation happens for consistent/opposite changing trends of wave–tide and wave–surge interaction, with the ultimate result showing the side with a higher effect.

Children’s participation in green space planning: a pathway to environmental justice

ABSTRACT Green spaces provide multiple benefits to physical and mental health, especially for children. Meanwhile, children are not typically included in most participatory processes regarding green spaces and their needs, if at all, tend to be represented by adults. This study attempts to answer the following research questions: (1) Does the degree of children’s participation differ depending on the spatial scale of green space planning processes? (2) How can children’s participation be interpreted in the context of environmental justice? We used mixed methods to assess the legal condition, process flow and products of three participatory processes regarding green space planning in Lodz (Poland). Our findings demonstrate that even the seemingly well-intentioned processes may fail to adequately incorporate children because of procedural inconsistencies and other deficiencies. Even if a process allows children to express their opinions, it fails to translate these voices into reality. We highlight the need to increase awareness of the importance of children’s inclusion in participatory processes to guarantee that their needs are considered, endow children with pro-environmental behaviours, and promote the idea of civil society. We contribute to the environmental justice debate by demonstrating the necessity of differentiating between procedural and participatory environmental justice dimensions.

Multi-channel machine learning based nonlocal kinetic energy density functional for semiconductors

Abstract The recently proposed machine learning-based physically-constrained nonlocal (MPN) kinetic energy density functional (KEDF) can be used for simple metals and their alloys [Phys. Rev. B 109, 115135 (2024)]. However, the MPN KEDF does not perform well for semiconductors. Here we propose a multi-channel MPN (CPN) KEDF, which extends the MPN KEDF to semiconductors by integrating information collected from multiple channels, with each channel featuring a specific length scale in real space. The CPN KEDF is systematically tested on silicon and binary semiconductors. We find that the multi-channel design for KEDF is beneficial for machine-learning-based models in capturing the characteristics of semiconductors, particularly in handling covalent bonds. In particular, the CPN5 KEDF, which utilizes five channels, demonstrates excellent accuracy across all tested systems. These results offer a new path for generating KEDFs for semiconductors.

Examining Environmental Influences on Pain Perception Using Virtual Reality

Virtual reality (VR) and biophilic design are two emerging areas of research in interior design and medical settings. VR is a rapidly developing technology which has revolutionized the field of interior design. This technology allows designers to create immersive environments that simulate the scale and depth of spaces in reality. Biophilic design focuses on incorporating natural elements into interior spaces to promote a human centered approach to design. Studies have found an association between the use of biophilic design and improvement in patient outcomes within the medical setting. However, little research has been conducted on how changes in the virtual environment may impact pain perception. This study aims to explore the impact of VR and biophilic design on human pain perception. Pain perception was measured via pain-pressure threshold (PPT) using a pressure algometer in kilogram-force (kgf) and Numeric Pain Rating Scales (NPRS). The study involved a series of measurements comparing participants' pain perceptions through PPTs and NPRS scores. Measurements were taken on five separate occasions. The first set of measurements were recorded outside of VR to establish baselines, and the remaining four measurements were taken within VR. Participants viewed a series of VR rooms modeled after a hospital setting with each room exhibiting increasing levels of environmental stimulation. This study examined the relationship between environmental stimuli and pain perception. The level of environmental stimuli that each participant was exposed to was increased by presenting a higher degree of biophilic elements. Biophilic elements incorporate plants, colors, natural materials, and the manipulation of architectural surfaces to provide a naturally calming effect to spaces. Results from this study support the idea that increasing the level of environmental stimuli in a virtual environment decreases the pain perception of those viewing the environment.

Flow field characteristics and vibration responses of saddle-shaped membrane structures

Elastically mounted flexible membrane roofs exposed to flows are prone to vortex-induced vibrations and even aero-instability due to the strong fluid–structure interaction (FSI). This study is to investigate the FSI mechanism in the saddle-shaped membrane structure over a range of Reynolds numbers and wind directions in laminar flows, by bridging structural vibration responses and flow dynamics. The aeroelastic characteristics of membrane structures, including statistics of displacement responses, oscillation frequency, and oscillation damping ratios, were identified from the perspective of time and frequency domains. Simultaneously, the particle image velocimetry system was employed to visualize the flow features, including velocity vector, turbulence intensity, and vortex evolution in both space and time. The flow modes were further decomposed by proper orthogonal decomposition (POD) to capture the salient aspects of the flow. Three patterns of POD modes are identified, and the first mode plays the dominant role in POD modes. It showed that as the wind Reynolds number increases, the space between the shear layer and membrane surface would be narrowed, and resultantly the vortices turn out smaller in scale and closer in space. This trend leads to an increase in the frequency of vortex shedding and a stronger FSI effect. When the frequency of vortex shedding approaches the fundamental frequency of structures, the vibration of the membrane would be shifted from turbulent buffeting to vortex-induced resonance, featured with lock-in frequency, significant amplified displacement, and negative aerodynamic damping ratio.

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

Research on Typical Fault Diagnosis of Gear based on GHM Multiwavelet Transform and MCKD Algorithm

Background: Gear is the key part of the transmission part of mechanical equipment, which has a high failure rate under complex working conditions or long-term operation, directly affecting the reliability of mechanical equipment. To realize online monitoring of gears and predict gear faults, whether the diagnosis of typical gear faults is accurate and timely is a key link, so it is necessary to study the diagnosis of typical gear faults. Objective: This paper aims to propose a method that can effectively diagnose typical faults of gears and is easy to program, which is beneficial to realize online monitoring of rotating machinery equipment faults. Method: A typical gear fault diagnosis method based on GHM multiwavelet transform and maximum correlation kurtosis deconvolution algorithm (MCKD) is proposed in this paper. The measured vibration signal is decomposed into components with multiple scale spaces and frequency bands through the GHM multiwavelet transform. Then, the MCKD algorithm is run to suppress the noise of GHM multiwavelet transform coefficients, and the periodic components containing fault information are retained and reconstructed. The typical faults such as gear pitting and broken teeth are tested. Results: GHM multiwavelet transform combined with MCKD algorithm can accurately locate the frequency band containing fault information from the vibration signal with multiple modulations, double sideband, and multi-frequency interference and effectively extract the fault frequency and its frequency multiplication. The root means a square error of the extracted fault frequency is 0.08. This method has a good noise suppression effect and high accuracy in extracting fault frequency. Conclusion: The method proposed in this paper is helpful in realizing automatic programming of gear fault diagnosis and is of great significance in realizing real-time monitoring and online diagnosis of rotating machinery equipment faults. More related patents will appear in the future.

ARCHITECTURAL AND CIRCUIT MODEL OF NAVIGATION DEVICE FOR ULTRA-LIGHT LAUNCH VEHICLE

The paper considered the architectural and circuitry model of a navigation device for an ultra-lightweight launch vehicle designed to improve the accuracy and reliability of positioning and orientation in flight conditions. The study utilized modern approaches to the integration of global navigation satellite system (GNSS) and inertial navigation system (INS) to achieve high accuracy and stability of the system. The key components of the device including UM980 module, LSM6DSV32X inertial measurement module, STM32 F7 series microcontroller, E220-400T30S radio module, LMR50410XDBVR converter based power supply and RS422 interface were described in detail. The UM980 module, including SAW and LNA filter blocks, demonstrated the ability to efficiently process incoming RF signals. The NebulasIV main function block combined GNSS BB, GNSS RF, interfaces and power management unit (PMU) modules, supporting multiple communication interfaces such as UART, SPI and I²C, as well as RTK and PPS signals. The study analyzed the integration and accuracy aspects of the module in navigation systems. The signal processing scheme of the inertial navigation system module was also presented, including digital filters, free-fall and activity/inactivity detection functions, and interfaces for data communication. User bias functions and filter modes are considered to ensure the accuracy and stability of the navigation device. The work lays the groundwork for further improvements in navigation technology, especially in the context of increasing availability and accuracy for small scale space launches.

Assessment of Machine Learning Techniques for Simulating Reacting Flow: From Plasma-Assisted Ignition to Turbulent Flame Propagation

Combustion involves the study of multiphysics phenomena that includes fluid and chemical kinetics, chemical reactions and complex nonlinear processes across various time and space scales. Accurate simulation of combustion is essential for designing energy conversion systems. Nonetheless, due to its multiscale, multiphysics nature, simulating these systems at full resolution is typically difficult. The massive and complex data generated from experiments and simulations, particularly in turbulent combustion, presents both a challenge and a research opportunity for advancing combustion studies. Machine learning facilitates data-driven techniques to manage the substantial amount of combustion data that is either obtained through experiments or simulations, and thereby can find the hidden patterns underlying these data. Alternatively, machine learning models can be useful to make predictions with comparable accuracy to existing models, while reducing computational costs significantly. In this era of big data, machine learning is rapidly evolving, offering promising opportunities to explore its integration with combustion research. This work provides an in-depth overview of machine learning applications in turbulent combustion modeling and presents the application of machine learning models: Decision Trees (DT) and Random Forests (RF), for the spatio-temporal prediction of plasma-assisted ignition kernels, based on the initial degree of ionization, with model validations against DNS data. The results demonstrate that properly trained machine learning models can accurately predict the spatio-temporal ignition kernel profile based on the initial energy deposition and distribution.

Significance analysis and evaluation of the impact of a new sustainable ecosystem on the microenvironment of multi-level thermal humidity

Active living walls represent sustainable green technology that improves indoor thermal environment. However, the improvement effect of active living walls in these environments still needs to be further explored, especially with regard to humidity. This study aims to effectively improve the indoor thermal humidity microenvironment of residential buildings, a new sustainable ecosystem (NSES) was developed. NSES integrates plants, spray cooling, and a fan, offering four operation modes, all controlled through intelligent monitoring based on the indoor conditions. Under the working scale of limited space in the microenvironment, evaluating system performance has solved the problem of thermal comfort. In addition, subjective questionnaires are used to evaluate human thermal comfort indicators. The results indicate that (1) Mode 1 exhibits the best performance on the microenvironment throughout the season at a distance of 0.5 m horizontally and 1.1 m vertically from NSES; as the vertical height and horizontal distance increase, performance gradually weakens. (2) Under the conditions of 0.5 m horizontal distance and 1.1 m vertical height, NSES has the best improvement effect on the microenvironment on sunny summer days; an average temperature decrease of 5.6 °C and an average humidity increase of 27.5 % were achieved. (3) NSES can improve human comfort, with thermal comfort zones ranging from 22.4 to 24.3 °C in autumn, and 22.4–23.8 °C in spring. These results highlight the potential value of NSES in improving the microenvironment and enhancing human thermal comfort. This study emphasizes the importance of NSES in appropriately selecting distance, height, season, and climate.