Objects In Space Research Articles

Transformation Equation from Wavefront Aberrations to Ray Aberrations Based on Coordinate Frames in Object Space

Object and image spaces are widely used in geometrical optics to describe the functions of optical systems. However, the mapping between these two spaces is non-linear, meaning that a function expressed in coordinate frames in the image space cannot be directly applied in the object space, and vice versa. For example, the relationship between the wavefront and ray aberrations given by [1] is valid for coordinate frames in the image space but fails for coordinate frames in the object space. To overcome this limitation, this study converts the wavefront-ray aberration relationship using the chain rule so that it can also be applied to coordinate frames in the object space. The numerical results obtained for the primary ray aberrations using the proposed converted relationship are shown to be in close agreement with the Zemax simulation results.

Development of an Aerial Manipulation System Using Onboard Cameras and a Multi-Fingered Robotic Hand with Proximity Sensors

Recently, aerial manipulations are becoming more and more important for the practical applications of unmanned aerial vehicles (UAV) to choose, transport, and place objects in global space. In this paper, an aerial manipulation system consisting of a UAV, two onboard cameras, and a multi-fingered robotic hand with proximity sensors is developed. To achieve self-contained autonomous navigation to a targeted object, onboard tracking and depth cameras are used to detect the targeted object and to control the UAV to reach the target object, even in a Global Positioning System-denied environment. The robotic hand can perform proximity sensor-based grasping stably for an object that is within a position error tolerance (a circle with a radius of 50 mm) from the center of the hand. Therefore, to successfully grasp the object, a requirement for the position error of the hand (=UAV) during hovering after reaching the targeted object should be less than the tolerance. To meet this requirement, an object detection algorithm to support accurate target localization by combining information from both cameras was developed. In addition, camera mount orientation and UAV attitude sampling rate were determined by experiments, and it is confirmed that these implementations improved the UAV position error to within the grasping tolerance of the robot hand. Finally, the experiments on aerial manipulations using the developed system demonstrated the successful grasping of the targeted object.

Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets

Major space agencies such as NASA and the ESA have long reported the growing dangers caused by resident space objects orbiting our planet. These objects continue to grow in number as satellites are imploded and space debris impacts each other, causing fragmentation. As a result, significant efforts by both the public and private sectors are geared towards enhancing space domain awareness capabilities to protect future satellites and astronauts from impact by these orbiting debris. Current approaches and standards implement very large radar arrays, telescopes, and laser ranging systems to detect and track such objects. These systems are very expensive, may take significant amounts of time to develop, and are still only sparingly able to efficiently track debris targets less than 10 cm in diameter. This work proposes a theoretical passive-radar-based method using illuminators of opportunity for detecting space debris while estimating motion direction and Doppler. We show that by using a signal processing chain based on the self-mixing technique and digital filters, Doppler information can be extracted and continuously tracked by a uniform linear receiver array. This can be achieved by a passive sensor system, which has the advantage of lower cost without the need to emit signals that constrain the spectrum sharing issues.

Real-time deconvolution of light fields through pixel selection in the point-spread-function and direct inversion of the image formation process

This paper explores the optimization of light field deconvolution, a key process in image processing that reconstructs a 3D object space or a 2D refocus plane from a light field. Despite the critical role of deconvolution in light field technology, existing methods are often slow, computationally intensive, and unsuitable for real-time processing. Existing algorithms, such as the Richardson-Lucy approach, while groundbreaking, still suffer performance limitations due to their iterative nature and high computational costs. Central to our approach is the strategic selection of influential pixels within the point-spread-function, reducing redundant computations by focusing only on pixels contributing to a significant portion of the point-spread-function’s total intensity. In addition, we explore the potential to directly invert the image formation model, bypass iterative computations, and further accelerate the deconvolution process. Our findings reveal notable improvements in computational efficiency, with some of our methods achieving real-time performance. The reconstruction quality, measured using metrics such as the mean squared error, remained comparable to existing approaches, indicating a favorable balance between speed and reconstruction quality.

Advancements in Autonomous Driving Systems: Enhancing Environmental Perception with Lidar Integration and Innovative Algorithms

This paper discusses the intricacies of autonomous driving systems, focusing on the four core modules: perception, sensing, decision-making, and control. The sensing module integrates various sensors like LiDAR, cameras, millimeter-wave radars, IMU, and GPS to gather essential data. The paper emphasizes the role of LiDAR in 3D object detection and point cloud segmentation, which are crucial for environmental perception. It also addresses the challenge of fusing data from multiple sensors into a unified coordinate system for effective decision-making. This research presents several significant advances in environmental perception. First there is a single-stage 3D object detection algorithm using sparse convolutional neural networks for improved speed and accuracy. Moreover there is a two-stage 3D object detection method focuses on detecting difficult targets through target center prediction. This method employs a novel neural network architecture, AOAP-SCNN, for feature extraction. Furthermore there is a point cloud instance segmentation algorithm (a method that separates and identifies individual objects in 3D space) utilizes a front view approach and a DLA network with Tiny-PointNet for feature extraction. Finally there is also an automatic calibration algorithm for LiDAR and camera systems based on segmentation, designed for real-time operation on embedded platforms, which corrects the extrinsic matrix and improves the self-calibration system's practical value. Building upon these technical innovations, the paper provides a comprehensive analysis of intelligent vehicle technology including the history of development, the importance of environmental perception, and the application and limitations of LiDAR in intelligent vehicles.

Meta-Device for Field-of-View Tunability via Adaptive Optical Spatial Differentiation.

Optical edge detection is a crucial optical analog computing method in fundamental artificial intelligence, machine vision, and image recognition, owing to its advantages of parallel processing, high computing speed, and low energy consumption. Field-of-view-tunable edge detection is particularly significant for detecting a broader range of objects, enhancing both practicality and flexibility. In this work, a novel approach-adaptive optical spatial differentiation is proposed for field-of-view-tunable edge detection. This method improves the ability to acquire spatial information and facilitates edge detection over a wider angular range. The adaptive optical spatial differentiation meta-device relies on two core components: the spatial differentiation dielectric metasurface and the adaptive liquid prism. The meta-device is shown to function as a highly efficient (≈85%) isotropic spatial differentiator, operating across the entire visible spectrum (400 to 700nm) within a wide-angle object space, expanding up to 4.5 times the original field of view. The proposed scheme presents new opportunities for efficient, flexible, high-capacity integrated data processing and imaging devices. And simultaneously provides a novel optical analog computing architecture for the next generation of wide field-of-view phase contrast microscopy.

Stable sheaves on bielliptic surfaces: from the classical to the modern

Bielliptic surfaces are the last family of Kodaira dimension zero algebraic surfaces without a classification result for the Chern characters of stable sheaves. We rectify this and prove such a classification using a combination of classical techniques, on the one hand, and derived category and Bridgeland stability techniques, on the other. Along the way, we prove the existence of projective coarse moduli spaces of objects in the derived category of a bielliptic surface that Bridgeland semistable with respect to a generic stability condition. By systematically studying the connection between Bridgeland wall-crossing and birational geometry, we show that for any two generic stability conditions τ,σ, the two moduli spaces Mτ(v) and Mσ(v) of objects of Chern character v that are semistable with respect to τ (resp. σ) are birational. As a consequence, we show that for primitive v, the moduli space of stable sheaves of class v is birational to a moduli space of stable sheaves whose Chern character has one of finitely many easily understood “shapes".

Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty

We present a multi-objective Bayesian active learning strategy, which greatly accelerates the discovery of super high-strength and high-ductility lead-free solder alloys. The active learning strategy demonstrates that a machine learning model will have high generalizability if experimental data uncertainty is included, which greatly improves the model prediction or the material design accuracy. The feature-point-start forward method in multi-objective optimization adopts two Gaussian process regression (GPR) models, one for strength and one for elongation, and their outputs build up the acquisition-function-modified objective space of strength and elongation. Then, Bayesian sampling is applied to design the next experiments by balancing exploitation and exploration. Seven multi-objective active learning iterations discovered two novel super high-strength and high-ductility lead-free solder alloys. After that, various material characterizations were conducted on the two novel solder alloys, and the results exhibited their high performances in melting properties, wettability, electrical conductivity, and shear strength of the solder joint and explored the mechanism of high strength and high ductility of the alloys. The present work systematically analyzes the important role of experimental uncertainty in machine learning, especially in the global optimization for material design, which demands high generalizability of predictions.

Uncovering the Euclidean Geometry of Data

Suppose you have a collection of objects in some exotic metric space and you’d like to see what they would look like if they were instead in Euclidean space. Or suppose these objects aren’t even points in a metric space, they are just entities for which you have some rough, intuitive notion of distance between each pair of them—one that need not satisfy any official mathematical properties like the triangle inequality. There is a linear algebraic optimization procedure called multidimensional scaling that embeds these objects in Euclidean space in a manner that approximates their original distances. This uncovers the Euclidean geometry hidden in data. This article explores how it works and what it’s useful for, particularly in a data science context.

A repulsive-distance-based maximum diversity selection algorithm for multimodal multiobjective optimization

Multimodal multiobjective optimization problems (MMOPs) are a challenging class of problems. Several advanced evolutionary algorithms have been developed to solve MMOPs, but these algorithms still have some limitations, such as having many parameters, slow convergence speed, and unsatisfactory performance. To solve these problems, we propose a repulsive-distance-based maximum diversity selection algorithm (RMDS) which aims, during environmental selection, to select individuals with the best comprehensive diversity through the repulsive distance. The repulsive distance is the comprehensive Euclidean distance between an individual and selected individuals with the consideration of distribution of individuals in both the decision and objective spaces. RMDS has the following advantages: first, the repulsive distance allows rational selection of well-distributed solutions in the non-parameterized case. Second, the repulsive distance acts both in the decision and objective spaces, so it provides a good balance between the distribution of the solution set in these two spaces. Third, RDMS has a straightforward principle. Experimental results show that RMDS has superior performance incomparison with other well-known multimodal multiobjective evolutionary algorithms on 31 test functions.

Monte–Carlo Techniques Applied to CGH Generation Processes and Their Impact on the Image Quality Obtained

ABSTRACTComputer graphics aim to create visual representations for screens, where depth is simulated. In contrast, Computed Generated Holograms (CGH) focus on encoding and recreating light patterns to generate a true 3D holographic image that appears as a physical object in space. Therefore, although both use digital models, the computation of CGHs necessitates additional phase‐related calculations, which in turn escalate computational demands. These calculations often result in excessively long development times or, at worst, render the process unfeasible. In order to reduce computational time, Partial Monte–Carlo Sampling (PMCS) techniques for CGH generation are presented, integrating them into the whole process of generating a CGH for a synthetic 3D scene, from design to rendering. PMCS is based on the random choice of a subset of rays used to compute the CGH and relates the computation time spent to the quality of the reconstructed scene. Quantitative analysis shows that PMCS does not significantly compromise image quality. Both simulated and in‐laboratory image reconstruction from holograms demonstrates consistent trends, showcasing improved quality with higher numbers of rays and increased resolution. Furthermore, we establish a direct relationship between image quality and computational time, which effectively addresses specific requirements.

Activation Function-Assisted Objective Space Mapping to Enhance Evolutionary Algorithms for Large-Scale Many-Objective Optimization

Activation Function-Assisted Objective Space Mapping to Enhance Evolutionary Algorithms for Large-Scale Many-Objective Optimization

International Legal Regime of Lunar Orbits: Will There Be Enough Space for Everybody?

INTRODUCTION. The exploration and use of the Moon, including its natural resources, is a priority in the space research activities of many participants. This is evidenced by the adoption of lunar space programmes at the national level, the preparation and launch of spacecrafts to the Moon and its surface, and discussions of future international legal regulation in international intergovernmental and nongovernmental organisations. It is important to ensure communication with the spacecraft through the use of lunar orbits in carrying out such lunar activities. In order to understand this process, it is necessary to examine the scientific and technical, as well as regulatory aspects that are interrelated, including the international legal regime for lunar orbits.MATERIALS AND METHODS. The materials used in the research and for writing cover fundamental works of Soviet/Russian scientists in the field of international space law, as well as research works and analyses devoted to CIS-lunar space and orbits around the Moon. The assessment of the existing international legal regime on lunar orbits requires the application of not only formal-legal and comparative-legal research methods inherent in legal sciences, but also general scientific methods, such as analysis and synthesis, to understand the specific scientific and technical nature of the issue under consideration. An important place in the article was taken by the study of the conceptual definitions, namely such terms as “a Moon orbit”, “lunar orbit”, “CIS-lunar space”, “frozen orbit’”, “haloorbit”, “shielded zone of the Moon”, etc.RESEARCH RESULTS. The lunar orbit refers to various trajectories of natural or artificial objects around the Moon. Orbits around the Moon are distinguished by the altitudes of migration, aposecretion and inclination. Separately distinguish ‘frozen’ orbits and haloorbits, which are characterized by special properties in terms of space objects placed on them. Spacecrafts use different radio-frequency spectrum in all types of lunar orbits. Most of the orbits around the Moon are unstable. The international legal status and regime of lunar orbits are regulated by documents of the International Telecommunication Union, among which there are a number of documents dealing with the issue of radio astronomy in the shielded zone of the Moon. The fundamental provisions of international space law also apply to the activity on use and exploration of CIS-lunar space and its orbits.DISCUSSION AND CONCLUSIONS. The following conclusions were obtained from the study. By 2027, the international community within the ITU will have to solve a number of issues related to the use of CISlunar space and orbits around the Moon, including radio astronomy, the use of repeater satellites, and the radio-frequency spectrum. Additionally, a system for coordinated lunar time is discussed. Disorderly use of lunar orbits may lead to the problem of space debris. An increase in the number of objects sent to the Moon may cause difficulties in astronomical research and limit the number of stable orbits for all interested participants. International cooperation of States and other interested participants in lunar activities within the framework of international organizations will contribute to solving the outlined problems.

Effective Compliance to Technical Regulations - Why is Compliance to the Orbital Debris Mitigation Rules So Poor?

Adherence to debris mitigation guidelines is critical for future robust space traffic management (STM). Debris mitigation guidelines are reviewed and their effectiveness at managing the space object population are contrasted against the means to comply with the guidelines. Specific concerns related to the deployment of large constellations are detailed to provide potential trade-offs for consideration in enhanced tracking systems to enable safe and efficient STM practices. The balance between reducing future debris deposition and minimizing the operational burden on satellites to maintain reliable operations are examined.

Mixed track-to-track association of uncatalogued space objects in radar and optical space surveillance

Mixed track-to-track association of uncatalogued space objects in radar and optical space surveillance

Use of Practices to Reduce of Sudden Unexpected Infant Death among Caregivers of Opioid Exposed Newborns.

Sudden unexpected infant death (SUID) occurs disproportionately among opioid exposed newborns (OENs) compared to those unexposed. The extent that primary caregivers of OENs adhere to SUID-reducing infant care practices is unknown. We examined rates of SUID-reducing practices (smoking cessation, breastfeeding, and safe sleep [supine sleep, room-sharing not bed-sharing, nonuse of soft bedding or objects]) in a pilot sample of caregivers of OENs. We surveyed 50 English-speaking mothers and other primary caregivers of OENs 1-6 months old between 02/2022 and 10/2023. We examined prenatal and postnatal cigarette smoking exposure, breastfeeding initiation and duration, and sleep position (side, back, or stomach), sleep location (bed-sharing, room-sharing not bed-sharing, or separate room), and soft bedding or objects in the infant sleep space. Thirty-six (72%) of 50 respondents were birth mothers and 14/50 (28%) were other caregivers. Sixty-one percent of infants were exposed to cigarette smoking prenatally (mothers only) and 34% postnatally (41% mothers vs 14% other caregivers). Sixty percent of infants received breast milk ≤4 weeks. Among respondents, 32% placed infants in nonsupine sleep position (36% mothers vs 21% other caregivers), 38% shared a bed with their infant for sleep (47% mothers vs 14% other caregivers), and 30% used soft bedding in the infant sleep space (33% among mothers vs 21% among other caregivers). Infant care practices associated with SUID were highly prevalent in our pilot sample of OENs and differed among mothers versus other caregivers. Further investigation and tailored approaches to this population are needed.

APPLICATION OF PHOTOGRAMMETRY IN REVERSE ENGINEERING OF MACHINE-BUILDING FACILITIES

To recreate an object in the form of a virtual model based on an existing physical detail, the paper proposes to use the Autodesk ReCap Photo cloud service. Using this service, it is possible to create virtual three-dimensional models by laser scanning a real object or using a digital camera and subsequent digital reproduction of this object in digital space. However, the widespread use of laser scanners and software is very limited due to their high cost. The paper proposes a cheaper, more accessible and no less accurate method that is easy to apply in practice - the method of digital reverse engineering photogrammetry. The method is based on creating a three-dimensional model from two-dimensional photographs using the proposed software. With its help, the user uploads a set of photos to Autodesk's servers and receives a triangulated model of the object with all real dimensions and high quality textures. The data obtained was subsequently imported for further processing into another program of this family, the Autodesk Inventor package. The Autodesk Inventor package has a user-friendly interface and a large set of tools for modeling engineering products. To demonstrate the scale and level of sophistication of ReCap Photo, we demonstrate the options included in the application: first of all, the application supports almost all laser scan file formats. In addition, point clouds can be edited arbitrarily - deleted, copied, highlighted, or even added survey points. As a result, it is possible not only to create technical documentation, but also to reconstruct objects with millimeter accuracy. Visualization and design make it possible to eliminate possible errors. It has been demonstrated that such models can be used to design and obtain working documentation. Due to the identity of the spatial virtual model and the real physical object, the accuracy and quality of design is improved. Thus, it is clearly shown that the presented methodology significantly reduces labor intensity and increases efficiency.

Control of precision grip in children with heavy prenatal alcohol exposure.

Fine motor skill deficits have been reported for children with histories of prenatal alcohol exposure, but little is known whether impaired motor skill extends to the regulation of precision grip control. Children with (n = 15) and without (n = 17) histories of heavy prenatal alcohol exposure used their dominant hand to grasp, lift, and hold in space a small-instrumented object with a mass of 19 g. Object mass was also experimentally increased by separately adding two aluminum cubes with mass of 200 and 400 g. Participants completed a block of eight trials for each object mass with the last six trials in each trial block being statistically analyzed. Selected temporal and kinetic parameters of grip force (GF) and load force (LF) were examined to quantitatively index precision grip performance of the two groups. Compared to typically developing peers, children with prenatal alcohol exposure used excessive and more variable LF and greater GF to lift each object mass, with more finger GF than thumb GF being applied to the apparatus. The GF/LF ratio for the clinical group was greater when lifting the smallest mass load. When holding the object in space, children with prenatal alcohol exposure produced greater GF for the smallest mass load, again with more GF being applied via the finger compared to the thumb. Children with heavy prenatal alcohol exposure demonstrate force deficits when using precision grip to manipulate an object with three different masses. Chronic irregular precision grip could manifest as a fine motor skill developmental delay that may negatively impact completion of functional activities of daily living requiring grasping an object with the index finger and thumb.

Global Climate Effect from Space Debris Reentry: Engineering and Policy Implications

Space objects are routinely reentered into Earth’s atmosphere for disposal. During reentry, space objects demise and produce ablation by-products, including alumina ([Formula: see text]) particles that can interact with Earth’s radiative balance, and possibly influence climate change. In this work, we model a future reentry scenario with [Formula: see text] of alumina entering into the South Pacific from controlled reentries to estimate the implications of a hypothetical policy that requires all reentries to demise over this region. We find that reentry alumina particles remain aloft in the atmosphere for 714 days, generating a direct radiative cooling effect of [Formula: see text] at the top of the atmosphere and [Formula: see text] at the tropopause. These South Pacific reentries create a concentration of particles in the southern stratosphere, leading to asymmetrical radiative forcing. When compared to aviation, the direct radiative forcing from a single reentry is greater in magnitude than a single commercial airliner flight, but reentries as a whole produce less radiative forcing per dollar of industry revenue than commercial aviation. These results support policies that encourage satellite design for demise and reentry as a form of decommissioning but warn against limiting reentry to the South Pacific only.

Solving Multi-Objective Satellite Data Transmission Scheduling Problems via a Minimum Angle Particle Swarm Optimization

With the increasing number of satellites and rising user demands, the volume of satellite data transmissions is growing significantly. Existing scheduling systems suffer from unequal resource allocation and low transmission efficiency. Therefore, effectively addressing the large-scale multi-objective satellite data transmission scheduling problem (SDTSP) within a limited timeframe is crucial. Typically, swarm intelligence algorithms are used to address the SDTSP. While these methods perform well in simple task scenarios, they tend to become stuck in local optima when dealing with complex situations, failing to meet mission requirements. In this context, we propose an improved method based on the minimum angle particle swarm optimization (MAPSO) algorithm. The MAPSO algorithm is encoded as a discrete optimizer to solve discrete scheduling problems. The calculation equation of the sine function is improved according to the problem’s characteristics to deal with complex multi-objective problems. This algorithm employs a minimum angle strategy to select local and global optimal particles, enhancing solution efficiency and avoiding local optima. Additionally, the objective space and solution space exhibit symmetry, where the search within the solution space continuously improves the distribution of fitness values in the objective space. The evaluation of the objective space can guide the search within the solution space. This method can solve multi-objective SDTSPs, meeting the demands of complex scenarios, which our method significantly improves compared to the seven algorithms. Experimental results demonstrate that this algorithm effectively improves the allocation efficiency of satellite and ground station resources and shortens the transmission time of satellite data transmission tasks.