Space Research Articles

A Point Cloud Registration Algorithm Based on Weighting Strategy for 3D Indoor Spaces

Point cloud registration is a technology that aligns point cloud data from different viewpoints by computing coordinate transformations to integrate them into a specified coordinate system. Many cutting-edge fields, including autonomous driving, industrial automation, and augmented reality, require the registration of point cloud data generated by millimeter-wave radar for map reconstruction and path planning in 3D environments. This paper proposes a novel point cloud registration algorithm based on a weighting strategy to enhance the accuracy and efficiency of point cloud registration in 3D environments. This method combines a statistical weighting strategy with a point cloud registration algorithm, which can improve registration accuracy while also increasing computational efficiency. First, in 3D indoor spaces, we apply PointNet to the semantic segmentation of the point cloud. We then propose an objective weighting strategy to assign different weights to the segmented parts of the point cloud. The Iterative Closest Point (ICP) algorithm uses these weights as reference values to register the entire 3D indoor space’s point cloud. We also show a new way to perform nonlinear calculations that yield exact closed-form answers for the ICP algorithm in generalized 3D measurements. We test the proposed algorithm’s accuracy and efficiency by registering point clouds on public datasets of 3D indoor spaces. The results show that it works better in both qualitative and quantitative assessments.

A method to rapidly construct 3D canopy scenes for maize and their spectral response evaluation

Three-dimensional (3D) scenes of a maize canopy can be utilized to depict its vertical structure and serve as the foundation for a 3D radiative transfer model. Given the inherent heterogeneity of canopy structures, evaluating their spectral response is crucial for remote-sensing inversion algorithms. Yet existing methods to model 3D canopy scenes of crops lack the high efficiency required for large-scale breeding applications. Hence, this study aims to develop a method to rapidly construct 3D canopy scenes of maize based on key structural parameters—namely, leaf area, base angle, and inclination angle—across multiple cultivars and growth stages. The accuracy of this canopy scenes modeling method was validated by comparison with a fine-scale 3D model and a multi-growth stage reflectance dataset. The root mean square error (RMSE) and normalized root mean square error (NRMSE) between the fine-scale 3D model and the constructed 3D model were less than 0.021 and 6.4%, respectively. Moreover, the RMSE and NRMSE between the simulated reflectance and measured reflectance were less than 0.042 and 11.1%, respectively. Next, we integrated the 3D radiative transfer model with 3D scenes to analyze the spectral response of maize canopy structure. The findings revealed that the near-infrared band was affected more by leaf area than leaf base angle, while the opposite is observed for the red-edge band. Furthermore, the middle layer contributed more to the canopy reflectance than either the upper or lower layers. Notably, two maize scenes where two structural parameters simultaneously change could elicit the same canopy spectra. The proposed method thus offers the dual advantages of rapid modeling and high efficiency, making it highly applicable for maize and other similar crops.

Dynamic behavior of floating ferrofluid droplet through an orifice with a magnetic field

In this study, we utilize the simplified lattice Boltzmann method (SLBM) to investigate numerically the motion of buoyancy-driven deformable ferrofluid droplets through the orifice of varying widths and depths in two-dimensional (2D) space. Positioned directly beneath a plate with a central hole, the magnetic fluid droplets undergo acceleration to meet the plate under the influence of buoyancy and magnetic forces. We investigate the impact of magnetic field strength (Bom), pore ratio (PR), plate thickness ratio (WR), droplet viscosity (Re), and the plate's wettability (contact angle) on the dynamic behavior of ferrofluid droplets ascending through the orifice. Our results reveal significant effects on the efficiency and morphology of ferrofluid droplets passing through the hole. The introduction of a magnetic field facilitates a larger volume of liquid droplets passing through the hole at PR = 0.25. Moreover, increasing magnetic field intensity leads to the generation of secondary droplets during passage through the orifice. In practical applications, to prevent the generation of secondary droplets, we recommend Bom < 3 when the pore ratio falls within 0.35 < PR < 0.45 and plate thickness ratio WR = 1. Additionally, with increasing obstacle thickness, ferrofluid droplets on the hydrophobic wall can pass through the orifice more easily. Furthermore, when the magnetic field strength exceeds a certain threshold (Bom = 6.08), the droplets can pass through the orifice regardless of the wall's hydrophilicity or hydrophobicity. For practical applications with the pore ratio PR = 0.25 and plate thickness ratio WR > 1, we suggest Bom > 3.

Four-Leg Inverter Based on Three-Dimensional Space Vector Modulation Method for Desired Output Frequency

Four-Leg Inverter Based on Three-Dimensional Space Vector Modulation Method for Desired Output Frequency

EllipsoHyperbola A Common Approach that Joins the Conic Sections in 2D and 3D Space

This work aspires to interactively reveal through the use of the GeoGebra Software the relationship between the Conic Sections in 3D and the 2D symmetric forms of Conic Sections around O in a coordinate system Oxy, that is Circle, Ellipse, axis x'x, and Hyperbola, showing that these Conic Sections arise from the same algebraic formula and therefore have common characteristics. This manuscript includes short research on the four kinds of Conic Sections through a common approach that joins them in the two-but also in three-dimensional space, revealing the role of the slope of the Generator line of the conic surface and the role of the slope of the cutting plane in their equations.

Muslim Parents’ Communication Approach to Adolescents according to Al-Tarbiyyah Al-Rashīdah by ‘Abd al-Karīm Bakkār

The current study attempted to define and examine the concept of family in the Islamic context. Specifically, it addressed the issues and challenges related to parental communication in the modern day based on the strategies and suggestions proposed by ‘Abd al-Karīm Bakkār in his book Al-Tarbiyyah Al-Rashīdah (Successful Family Upbringing). This book is a guide to cultivating and nurturing a Muslim family in order to achieve the best outcomes through effective communication. The ultimate aim is to fortify the familial bonds. The current study used content analysis to examine and interpret the data extracted from the fourth volume of series entitled Al-Murāhiq (The Adolescents). This volume was the study’s main reference as it is set against the background of contemporary Muslim family issues along with suggested solutions by the author who identified certain strategies to enhance family ties and bonds. These strategies include kindness to relatives, effective interaction, listening, respecting, non-domination, and not pressurizing 5the children. Moreover, avoiding provocation of anger, paying attention to their problems as well as being polite to them are also among the strategies to enhance familial ties. The findings highlight the effectiveness of these strategies in bringing about positive parenting. Muslim parents therefore, can use ‘Abd al-Karīm Bakkār’s communication strategies to enhance their children’s moral, physical, mental, and spiritual development.

Four-Hour-Delayed Gadolinium 3D REAL IR and SPACE FLAIR MRI Correlated to Meniere Disease Histology.

Objectives: This study aims to showcase the complementary nature of utilizing both histopathology and magnetic resonance imaging (MRI) in understanding the otologic pathophysiology of Meniere disease. In addition, it seeks to raise awareness of the value of preserving and curating historical temporal bone collections which continue to inform our understanding of otologic diseases. Methods: The essential anatomical feature of Meniere disease-the distended membranous labyrinth-is explored through a comparison of early temporal bone studies with contemporary MRI techniques. The histopathologic photomicrographs are of inner ear specimens from deceased patients with symptoms consistent with Meniere disease. The MRI sequences from living patients exhibiting classic Meniere disease symptoms during life are captured 4 hours post-administration of gadolinium. Results: Both histopathologic examination and MRI imaging reveal consistent distention of the saccule, utricle, and scala media in patients with Meniere disease. The study shows the histologic photomicrographs of actual Meniere patients compared to the MRIs and successfully demonstrates the correlation between postmortem histological findings and MRI evidence of distension in living patients. Conclusions: A corresponding distension of the membranous labyrinth is seen in both the histologic specimens and the Meniere MRIs. This correlation suggests the potential utility of utilizing MRI to aid in diagnosing atypical Meniere disease and distinguishing it from other disease processes, such as migraine equivalent vertigo. The integration of historical temporal bone studies with modern MRI techniques offers valuable insights into the pathophysiology of otologic diseases. In addition, it emphasizes the importance of preserving and curating historical temporal bone collections for continued research and medical education purposes. Previous studies of delayed MRIs did not use Meniere disease temporal bone histopathology images.

Deep learning model for automatic limb detection and gripping in a novel meat factory cell

The goal of this work has been the development of a deep-learning model which may be used in conjunction with a novel so-called “Meat Factory Cell” platform, namely to enable an industrial robot within the system to identify and successfully grip the limbs of an entire pig carcass. The model consists of three main components: (1) a U-Net-based deep learning model that predicts heatmaps with a probability distribution of gripping and key point locations on the limbs, within RGB-D images; (2) a post-processing element for the extraction of keypoints from heatmaps, and transferral of these points into 3D space using a pinhole camera model; and (3) gripper orientation estimation, which uses the predicted limb key points to define gripper orientation in 3D space. The proposed system demonstrates high precision and robustness in estimating gripping points on pig limbs based on a data test set, which includes two gripping definitions: Norwegian and Danish. These gripping definitions account for variation in the slaughter process in two different European countries. The Norwegian definition gives mAP(0.5…0.95) = 0.971, mAR(0.5…0.95) = 0.982, and distance error 13 mm, while the Danish definition gives mAP(0.5…0.95) = 0.985, mAR(0.5…0.95) = 0.995, and distance error 14 mm. The model was validated in practice during experimental trials at the Meat Factory Cell test facility at the Norwegian University of Life Science (Ås, Norway), with whole pig carcasses (n=25).

A Revisit of Electromagnetic Wave Scattering by a Metal Isotropic Body in a Lossless Environment with Magnetic Sensor Excitation.

This paper investigates the electromagnetic fields being scattered by a metal spherical object in a vacuum environment, providing a numerical implementation of the obtained analytical results. A time-harmonic magnetic dipole source, far enough, emits the incident field at low frequencies, oriented arbitrarily in the three-dimensional space. The aim is to find a detailed solution to the scattering problem at spherical coordinates, which is useful for data inversion. Based on the theory of low frequencies, the Maxwell-type problem is transformed into Laplace's or Poisson's interconnected equations, accompanied by the proper boundary conditions on the perfectly conducting sphere and the radiation conditions at infinity, which are solved gradually. Broadly, the static and the first three dynamic terms are sufficient, while the terms of a higher order are negligible, which is confirmed by the field graphical representation.

Eficacia de la técnica autoasistida de Boss-Holzach- Matter para la reducción de luxaciones glenohumerales anteriores: revisión sistemática

Background: Anterior shoulder dislocation represents a common reason for emergency room visits. Despite the wide range of motion provided by this joint in three-dimensional space, it is prone to dislocation, with a significant incidence rate. The choice of reduction technique lacks consensus, with over 25 possible maneuvers, largely dependent on the physician's preference and experience. Additionally, potential neurovascular injuries resulting from the maneuver or its poor execution add complexity to this issue, challenging emergency physicians and orthopedic surgeons. The self-assisted technique Boss-Holzach-Matter (BHM) shows promising benefits in reducing patient anxiety and medical costs. Materials and Methods: A manual bibliographic search was conducted in PubMed, Scielo, and Central databases for studies from the last 10 years analyzing reduction techniques using the keywords "techniques for shoulder dislocation". Titles, abstracts, and full articles were reviewed, extracting pertinent data. Out of the 17 articles found, including systematic reviews, clinical trials, and other publications, 10 were selected without language restrictions. Results: Reduction methods varied significantly among studies. Comparison between self-assisted techniques like BHM and Spaso revealed differences in success rate and pain tolerance. The Davos technique demonstrated high effectiveness, although it was associated with potential complications. Comparative studies supported the efficacy of BHM and Milch in unassisted reduction. Conclusions: The need for further research to validate these findings, identify limitations, and evaluate clinical applicability is acknowledged. Additional studies are required to confirm the effectiveness of self-assisted techniques such as BHM, Milch, and Davos, as well as to address the potential limitations and considerations associated with each technique

Achieving local differential location privacy protection in 3D space via Hilbert encoding and optimized random response

The widespread use of spatial location-based services not only provides considerable convenience, but also exposes the downsides of location privacy leakage. Most of the existing user-side location privacy protection techniques are limited to planar locations. However, the extensive use of aircraft, sensor equipment and acquisition devices with positioning functions promotes the urgency of protecting the privacy of 3D spatial locations. Therefore, this study suggests a local differential privacy protection approach for 3D spatial locations. A 3D spatial decomposition and Hilbert encoding method are designed to reduce the 3D location data into one-dimensional encoding. The optimized random response mechanism was utilized to perturb the dimensional-reduced location encoding, which not only achieves user-side location privacy protection but also improves the accuracy of aggregated data on the server-side. Experiments on the real spatial location datasets show that the suggested method can reduce spatial location service quality loss, maintain the availability of perturbed spatial location and improve the operation efficiency of the spatial location perturbation algorithm.

Axial de-scanning using remote focusing in the detection arm of light-sheet microscopy

Rapid, high-resolution volumetric imaging without moving heavy objectives or disturbing delicate samples remains challenging. Pupil-matched remote focusing offers a promising solution for high NA systems, but the fluorescence signal’s incoherent and unpolarized nature complicates its application. Thus, remote focusing is mainly used in the illumination arm with polarized laser light to improve optical coupling. Here, we introduce a novel optical design that can de-scan the axial focus movement in the detection arm of a microscope. Our method splits the fluorescence signal into S and P-polarized light, lets them pass through the remote focusing module separately, and combines them with the camera. This allows us to use only one focusing element to perform aberration-free, multi-color, volumetric imaging without (a) compromising the fluorescent signal and (b) needing to perform sample/detection-objective translation. We demonstrate the capabilities of this scheme by acquiring fast dual-color 4D (3D space + time) image stacks with an axial range of 70 μm and camera-limited acquisition speed. Owing to its general nature, we believe this technique will find its application in many other microscopy techniques that currently use an adjustable Z-stage to carry out volumetric imaging, such as confocal, 2-photon, and light sheet variants.

Macroscopic finite-difference scheme and modified equationsof the general propagation multiple-relaxation-time lattice Boltzmann model.

In this paper we first present the general propagation multiple-relaxation-time lattice Boltzmann (GPMRT-LB) model and obtain the corresponding macroscopic finite-difference (GPMFD) scheme on conservative moments. Then based on the Maxwell iteration method, we conduct the analysis on the truncation errors and modified equations(MEs) of the GPMRT-LB model and GPMFD scheme at both diffusive and acoustic scalings. For the nonlinear anisotropic convection-diffusion equation(NACDE) and Navier-Stokes equations(NSEs), we also derive the first- and second-order MEs of the GPMRT-LB model and GPMFD scheme. In particular, for the one-dimensional convection-diffusion equation(CDE) with the constant velocity and diffusion coefficient, we can develop a fourth-order GPMRT-LB (F-GPMRT-LB) model and the corresponding fourth-order GPMFD (F-GPMFD) scheme at the diffusive scaling. Finally, three benchmark problems, the Gauss hill problem, the CDE with nonlinear convection and diffusion terms, and the Taylor-Green vortex flow in two-dimensional space, are used to test the GPMRT-LB model and GPMFD scheme, and it is found that the numerical results not only are in good agreement with corresponding analytical solutions, but also have a second-order convergence rate in space. Additionally, a numerical study on one-dimensional CDE also demonstrates that the F-GPMRT-LB model and F-GPMFD scheme can achieve a fourth-order accuracy in space, which is consistent with our theoretical analysis.

The Evaluation of Relationships between Milk Composition Traits and Breeds with Categorical Principal Component Analysis in Akkaraman and Awasi Sheep

Background: This study aims to determine the relationship between milk composition traits and breed in the Akkaraman and Awasi sheep as well as to provide ease of interpretation by showing the relationships structure between variables and between categories of variables in two-dimensional space with Categorical principal component analysis. Methods: Categorical principal component analysis determines relationships between continuous and categorical variables as well as ordinal variables. It aims to reduce system dimensionality through optimal scaling while maintaining variable measurement levels (nominal, multiple nominal, ordinal and interval). In this research, data obtained from Akkaraman and Awasi Breed Sheep Raised by Public Hands in Tuşba District of Van Province were used. In order to determine relationship with breed, the traits were divided into two categories, “low” and “high” and all variables (9 variables) were considered together and a Categorical principal components analysis was performed. Result: As a results, Dimension 1 accounted for 35.58% of the total variation while dimension 2 accounted for 15.21%. Two dimensions together accounted for 50.79% of the variation. Thus it can be noted that Categorical principal component analysis can be used in the analysis of data sets containing a large number of different types of variables with linear or non-linear relationships between them.

Color image encryption scheme for distributed architecture with SCFP chaotic map

Image protection mechanism in distributed cloud network is an essential component of information security field. In this paper, a novel one-dimensional sine-cosine fractional power chaotic map (SCFP) is proposed. Results of various dynamical system tests illustrate that SCFP exhibits superior chaotic behavior over its infinite positive real parameter range, whose complexity and unpredictability can guarantee the strength of image cryptosystem. Furthermore, a color image encryption scheme tailored for distributed architecture is devised. Firstly, a hybrid cryptographic mechanism is designed to perform diffusion and confusion encryption for image data and ECC public key encryption for intermediate keys. Secondly, the diffusion structure elevates processing units to row-column level, and the diffusion order is dictated by a pseudo-random sequence generated by SCFP. Thirdly, the confusion structure extends the unbiased and efficient Fisher-Yates algorithm into a 2D space, and adopts a design of dual plaintext-related key. Lastly, three techniques namely QOI lossless compression, DE information embedding and threshold secret sharing are integrated to resolve issues of data volume inflation, key synchronization difficulty and poor fault tolerance. Simulation experiments conducted on multiple color images demonstrate that the proposed scheme offers significant ciphertext randomness, sufficiently large key space and strong key sensitivity, which can ensure the integrity of image data and resist various typical cryptographic attacks, and outperforms existing schemes oriented to centralized architecture in terms of security and efficiency.

Dynamical traceback age of the Octans young stellar association

Octans is one of the most distant ($d pc) young stellar associations of the solar neighbourhood, and it has not yet been sufficiently explored. Its age is still poorly constrained in the literature and requires further investigation. We take advantage of the state-of-the-art astrometry delivered by the third data release of the Gaia space mission combined with radial velocity measurements obtained from high-resolution spectroscopy to compute the 3D positions and 3D spatial velocities of the stars and derive the dynamical traceback age of the association. We created a clean sample of cluster members by removing potential outliers from our initial list of candidate members. We then performed an extensive traceback analysis using different subsamples of stars, different metrics to define the size of the association, and different models for the Galactic potential to integrate the stellar orbits in the past. We derive a dynamical age of $34^ $ Myr that is independent from stellar models and represents the most precise age estimate currently available for the Octans association. After correcting the radial velocity of the stars for the effect of gravitational redshift, we obtain a dynamical age of $33^ $ Myr, which is in very good agreement with our first solution. This shows that the effect of gravitational redshift is small for such a distant young stellar association. Our result is also consistent with the less accurate age estimates obtained in previous studies from lithium depletion (30-40 Myr) and isochrones (20-30 Myr). By integrating the stellar orbits in time, we show that the members of Octans and Octans-Near had different locations in the past, which indicates that the two associations are unrelated despite the close proximity in the sky. This is the first reliable and precise dynamical age result for the Octans young stellar association. Our results confirm that it is possible to derive precise dynamical ages via the traceback method for $ Myr old stellar clusters at about $ pc with the same precision level that has been achieved in other studies for young stellar groups within 50 pc of the Sun. This represents one more step towards constructing a self-consistent age scale based on the 3D space motion of the stars in the young stellar clusters of the solar neighbourhood.

Forecasting water resources from satellite image time series using a graph-based learning strategy

Abstract. In the context of climate change, it is important to monitor the dynamics of the Earth’s surface in order to prevent extreme weather phenomena such as floods and droughts. To this end, global meteorological forecasting is constantly being improved, with a recent breakthrough in deep learning methods. In this paper, we propose to adapt a recent weather forecasting architecture, called GraphCast, to a water resources forecasting task using high-resolution satellite image time series (SITS). Based on an intermediate mesh, the data geometry used within the network is adapted to match high spatial resolution data acquired in two-dimensional space. In particular, we introduce a predefined irregular mesh based on a segmentation map to guide the network’s predictions and bring more detail to specific areas. We conduct experiments to forecast water resources index two months ahead on lakes and rivers in Italy and Spain. We demonstrate that our adaptation of GraphCast outperforms the existing frameworks designed for SITS analysis. It also showed stable results for the main hyperparameter, i.e., the number of superpixels. We conclude that adapting global meteorological forecasting methods to SITS settings can be beneficial for high spatial resolution predictions.

Analysis of local strain fields around individual threading dislocations in GaN substrates by nanobeam x-ray diffraction

Position-dependent three-dimensional reciprocal space mapping (RSM) by nanobeam x-ray diffraction (nanoXRD) was performed to reveal the strain fields produced around individual threading dislocations (TDs) in GaN substrates. The distribution and Burgers vector of TDs for the nanoXRD measurements were confirmed by prerequisite analysis of multi-photon excited photoluminescence and etch pit methods. The present results demonstrated that the nanoXRD can identify change in the lattice plane structure for all types of TDs, i.e., edge-, screw-, and mixed TDs with the Burgers vector of b = 1a, 1c and 1m + 1c. Strain tensor components related to edge and/or screw components of the TDs analyzed from the three-dimensional RSM data showed a nearly symmetrical strained region centered on the TD positions, which were in good agreements with simulation results based on the isotropic elastic theory using a particular Burgers vector. The present method is beneficial in that it allows non-destructive analysis of screw components of TDs that tend to contribute to leakage characteristics and are not routinely accessible by conventional structural analysis. These results indicate that nanoXRD could be a powerful way to reveal three-dimensional strain fields associated with arbitrary types of TDs in semiconductor materials, such as GaN and SiC.

Comparative Evaluation of NeRF Algorithms on Single Image Dataset for 3D Reconstruction

Abstract. The reconstruction of three-dimensional scenes from a single image represents a significant challenge in computer vision, particularly in the context of cultural heritage digitisation, where datasets may be limited or of poor quality. This paper addresses this challenge by conducting a study of the latest and most advanced algorithms for single-image 3D reconstruction, with a focus on applications in cultural heritage conservation. Exploiting different single-image datasets, the research evaluates the strengths and limitations of various artificial intelligence-based algorithms, in particular Neural Radiance Fields (NeRF), in reconstructing detailed 3D models from limited visual data. The study includes experiments on scenarios such as inaccessible or non-existent heritage sites, where traditional photogrammetric methods fail. The results demonstrate the effectiveness of NeRF-based approaches in producing accurate, high-resolution reconstructions suitable for visualisation and metric analysis. The results contribute to advancing the understanding of NeRF-based approaches in handling single-image inputs and offer insights for real-world applications such as object location and immersive content generation.

Laser powder bed fusion of 316L stainless steel and K220 copper multi-material

ABSTRACT Multi-material additive manufacturing holds immense potential for performance and functionality enhancement. Past research efforts primarily focused on the horizontal interface (perpendicular to the sample build direction) during the laser-powder bed fusion (LPBF) of multi-materials, whereas a few studies demonstrated that the vertical interface (parallel to the sample build direction) is in fact the main obstacle towards high-integrity multi-material fabrication in a three-dimensional space. In this work, facilitated by our own patented powder spreading device, we explored the mechanisms behind defect formation along both the horizontal and vertical interfaces during the LPBF production of 316L stainless steel and K220 copper multi-materials. High-fidelity fluid dynamics simulations were also conducted to rationalise the experimental observations. A practical process parameter optimisation approach is also proposed at the end, with the aim of mitigating those large defects currently occurring near the vertical interface.