Small-scale Space Research Articles

Kernel Principal Component Analysis for Allen–Cahn Equations

Different researchers haveanalyzed effective computational methods that maintain the precision of Allen–Cahn (AC) equations and their constant security. This article presents a method known as the reduced-order model technique by utilizing kernel principle component analysis (KPCA), a nonlinear variation of traditional principal component analysis (PCA). KPCA is utilized on the data matrix created using discrete solution vectors of the AC equation. In order to achieve discrete solutions, small variations are applied for dividing up extraterrestrial elements, while Kahan’s method is used for temporal calculations. Handling the process of backmapping from small-scale space involves utilizing a non-iterative formula rooted in the concept of the multidimensional scaling (MDS) method. Using KPCA, we show that simplified sorting methods preserve the dissipation of the energy structure. The effectiveness of simplified solutions from linear PCA and KPCA, the retention of invariants, and computational speeds are shown through one-, two-, and three-dimensional AC equations.

High-precision spatial measurement method of accelerator pre-alignment based on multiple total stations

Abstract With the development of accelerator technology, the scale of accelerators is becoming larger, ranging from hundreds of meters to several kilometers. For stable operation of accelerators, high-precision alignment, positioning, and installation are crucial. Installing all equipment inside the tunnel poses safety risks as personnel would be in a closed environment with potential radiation exposure for prolonged periods. To address the challenges of long adjustment and maintenance periods inside the tunnel due to the installation of equipment for large-scale accelerators, most accelerator devices under construction or in research have pre-alignment assemblies. Each assembly consists of a certain number of magnets distributed on girders. The magnets in one unit are pre-aligned with high precision in the laboratory and then transported to the tunnel. Aligning the entire magnet girder can significantly improve installation efficiency inside the tunnel. To meet the pre-alignment accuracy requirement of 10 μm in the horizontal and vertical directions for the magnet units in the high energy photon source (HEPS) storage ring, a system for high-precision pre-alignment of accelerator units using four total stations for angle observation has been designed in this paper. By employing different instrument layout configurations and incorporating reliable distance benchmarks, high-precision pre-alignment of the magnet are achieved. By arranging targets and utilizing recognition for automatic targeting, real-time point calculations during pre-alignment enhance efficiency. Subsequently, based on this system, pre-alignment simulation calculations and experimental verification of eight magnet focusing–defocusing units in the HEPS storage ring are conducted and ultimately realizing the 10 μm transverse and vertical pre-alignment measuring error within the units. This method, based on high-precision measurements in a small-scale space, reduces the period required for personnel on-site and improves pre-alignment efficiency. It also provides a reference for pre-alignment of multiple magnet units in large accelerators such as the Southern Advanced Photon Source and Circular Electron Positron Collider.

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.

Design of a locally resonant system to reduce noise inside the payload fairing of a launcher during the lift-off

The high sound pressure exerted on the payload during the launch of space vehicles can jeopardize its structural integrity. Given space and weight restrictions, designing fairing noise protection systems is not easy and the number of alternatives is limited, especially for small launchers. This work proposes the design of an acoustic protection based on an simultaneous increase of insulation and absorption in the system tailored to the payload fairing of a space launcher. To this end, the use of a panel made of Helmholtz resonators is investigated. The panel presents a deep subwavelength thickness, as well as a highly efficient acoustic protection (90% of absorption and 13 dB of Transmission Loss) over the frequency range of interest. The panel is designed by considering a reciprocal and non mirror symmetric transmission problem and the acoustic incidence from both sides of the panel. A high Transmission Loss in the frequency range of interest is then obtained when considering an incidence coming from the outside of the payload fairing, whereas the quasi-perfect absorption of acoustic waves is observed in the case where the incidence comes from the inside of the fairing. The panels are subsequently prototyped and their performance is experimentally evaluated. Measurements are correlated and discussed in view of the theoretical and numerical predictions. This mitigation approach sets a new trajectory for innovative noise reduction in small-scale space launchers.

Influence of habitat on fine-scale space use by brown lemmings (Lemmus trimucronatus) in the High Arctic.

Space use by small mammals should mirror their immediate needs for food and predator shelters but can also be influenced by seasonal changes in biotic and abiotic factors. Lemmings are keystone species of the tundra food web, but information on their spatial distribution in relation to habitat heterogeneity is still scant, especially at a fine scale. In this study, we used spatially explicit capture-recapture methods to determine how topography, hydrology, vegetation, and soil characteristics influence the fine-scale spatial variations in summer density of brown lemmings (Lemmus trimucronatus). Lemmings were monitored throughout the summer in wet and mesic tundra habitats and in a predator exclusion grid, which was also located in mesic tundra. We found that in wet tundra, lemming densities were higher at sites with a rugged topography dominated by hummocks, but only during snow melt. In both mesic tundra sites, lemming densities were higher in sites with poor drainage and low aspect throughout the summer. We found no clear association between lemming densities and any tested vegetation or soil variables. Overall, hydrology and topography appear to play a dominant role in small-scale space use of brown lemmings with a secondary role for predator avoidance and food plant abundance.

A framework for constrained large-scale multi-objective white-box problems based on two-scale optimization through decision transfer

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not so efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To overcome white-box CLSMOPs with definitive objective functions, a two-scale optimization framework based on decision transfer, which integrates dimensionality reduction of large-scale decision variables and constraint handling technology, is proposed. The Lagrange multiplier is first used to construct the two-scale optimization model, which bridges original large-scale decision space of variables and small-scale (2-scale) decision space of objective-constraint parameter. The decision transfer algorithm is then designed to switch between large-scale original decision space and small-scale parametric decision space, while achieving the maximum dimensionality reduction. Finally, the two-scale evolution strategy is proposed for the alternative optimizations in the two decision spaces, which emphasize objectives and constraints, respectively. In summary, the optimization in the large-scale space pushes the population to unconstrained Pareto front (PF), the optimization in the small-scale space helps the population cross the infeasible areas to approach constrained PF, and the offspring generation by Lagrange multiplier is beneficial to both objectives and constraints. Eight representative and state-of-the-art CMOEAs have been embedded into the CLDTEA framework to demonstrate its effectiveness through comparative experiments on CLSMOPs with equalityandinequalityconstraints and 1000 decision variables. Experimental results show that CLDTEA can significantly improve the performance of these basic CMOEAs.

Study on the Relationship between the Spatial Distribution of Shared Bicycle Travel Demand and Urban Built Environment

As a green and sustainable trip mode, shared bicycles play an essential role in completing short-distance trips in cities. This paper proposes a method to analyze the impact of the urban built environment on the distribution of shared bicycles in a small-scale space. First, the Fishnet function of ArcGIS is utilized to divide the study area into grids of 500 m × 500 m. Then, three indicators are proposed to describe the characteristics of the urban built environment, including point of information (POI) comprehensive index, the intensity of public transportation coverage, spatial accessibility, providing them the ways to be assigned to the grids. Finally, the multivariable linear regression model and support vector regression (SVR) models are applied to reveal the impacts of built environment factors on the spatial distribution of shared bicycles. Results show that SVR models based on linear kernel function, Gaussian radial basis kernel function, and Polynomial kernel function can achieve better analysis results. The SVR model based on the Gaussian radial basis function shows higher explanatory power (adjusted R2 = 0.978) than the multivariable linear regression model (adjusted R2 = 0.847). This paper can aid in understanding the demand and supply of shared bicycles and help operators or governments to improve service quality.

Dyna-Validator: A Model-based Reinforcement Learning Method with Validated Simulated Experiences

Dyna is a planning paradigm that naturally weaves learning and planning together through environmental models. Dyna-style reinforcement learning improves the sample efficiency using the simulation experience generated by the environment model to update the value function. However, the existing Dyna-style planning methods are usually based on tabular methods, only suitable for tasks with low-dimensional and small-scale space. In addition, the quality of the simulation experience generated by the existing methods cannot be guaranteed, which significantly limits its application in tasks such as continuous control of high-dimensional robots and autonomous driving. To this end, we propose a model-based approach that controls planning through a validator. The validator filters high-quality experiences for policy learning and decides whether to stop planning. To deal with the exploration and exploitation dilemma in reinforcement learning, a combination of ϵ-greedy strategy and simulated annealing (SA) cooling schedule control is designed as an action selection strategy. The excellent performance of the proposed method is demonstrated in a set of classical Atari games. Experimental results show that learning dynamic models in some games can improve sample efficiency. This benefit is maximized by choosing the proper planning steps. In the optimization planning phase, our method maintains a smaller gap with the current state-of-the-art model-based reinforcement learning (MuZero). In order to achieve a good compromise between model accuracy and optimal programming step size, it is necessary to control the programming reasonably. The practical application of this method in a physical robot system helps reduce the influence of an imprecise depth prediction model on the task. Without human supervision, it is easier to collect training data and learn complex skills (such as grabbing and carrying items) while being more effective at scaling tasks that have never been seen before.

Initial study for the response of Ti–6Al–4V plate used in tank material

We have been conducting hypervelocity impact tests and numerical simulations to develop a Japanese spacecraft design guideline for the protection of satellites from a certain degree of M/OD impacts. A working group is formed by members of JAXA, spacecraft manufacturers, experts, and researchers in the field of hypervelocity impacts to investigate the effects of M/OD impacts on satellite critical parts and bumpers by hypervelocity impact (HVI) tests and analysis. The knowledge acquired is now being reflected in the spacecraft design guidelines.We investigated the effect of hypervelocity impacts of micrometeoroids and small-scale space debris on space structures by comparing numerical simulation results obtained using the AUTODYN-2D hydrocode with the results of experiments using a two-stage light gas gun since 2005. We will consider the case of a satellite hydrazine fuel tank and the consequences of a hypervelocity impact from space debris. The final purpose of this study is to better understand the mechanisms of detonation of hydrazine vapor during a hypervelocity impact on a low-pressure reservoir.As part of this effort and an initial study, the response of a Ti–6Al–4V plate which is used tank material to 6 km/s high-velocity impacts is shown and discussed. It was found that the ballistic limit of the titanium tank material for both curved and flat plates was generally predictable using the existing ballistic limit equation. The effect of curvature on the impact scar size of flat (t = 1.2 mm) and curved plates was examined, and no significant difference was found.

Multi-point discharge model: study on corona discharge of double-ended needle in large space

Corona discharge, as a common means to obtain non-equilibrium plasma, can generally obtain high-concentration plasma by increasing discharge points to meet production needs. However, the existing numerical simulation models used to study multi-point corona discharge are all calculations of small-scale space models, which cannot obtain the distribution characteristics of plasma in large space. Based on our previous research, this paper proposes a hybrid model for studying the distribution of multi-point discharge plasma in large-scale spaces, which divides the computational domain and computes separately with the hydrodynamic model and the ion mobility model. The simulation results are verified by a needle–ball electrode device. Firstly, the electric field distribution and plasma distribution of the needle electrodes with single tip and double tips are compared and discussed. Secondly, the plasma distribution of the needle electrode with the double tip at different voltages is investigated. Both computational and experimental results indicate that the charged particle concentration and current of the needle electrode with double tips are both twice as high as those of the needle electrode with a single tip. This model can extend the computational area of the multi-point corona discharge finite element model to the sub-meter (25 cm) or meter level, which provides an effective means to study the plasma distribution generated by multiple discharge points in large-scale space.

Spatial–Temporal Tensor Graph Convolutional Network for Traffic Speed Prediction

Accurate traffic speed prediction is crucial for the guidance and management of urban traffic, which at the same time requires a model with a satisfactory computational burden and memory space in applications. In this paper, we propose a factorized Spatial-Temporal Tensor Graph Convolutional Network for traffic speed prediction. Traffic networks are modeled and unified into a graph tensor that integrates spatial and temporal information simultaneously. We extend graph convolution into tensor space and propose a tensor graph convolution network to extract more discriminating features from spatial-temporal graph data. We further introduce Tucker decomposition and derive a factorized tensor convolution to reduce the computational burden, which performs separate filtering in small-scale space, time, and feature modes. Besides, we can benefit from noise suppression of traffic data when discarding those trivial components in the process of tensor decomposition. Extensive experiments on the three real-world datasets demonstrate that our method is more effective than traditional prediction methods, and achieves state-of-the-art performance.

Does path integration contribute to human navigation in large-scale space?

Path integration is a process in which navigators estimate their position and orientation relative to a known location by using body-based internal sensory cues that arise from navigation-related bodily motion (e.g., vestibular and proprioceptive signals). Although humans are capable of navigating via path integration in small-scale space, a question has been raised as to whether path integration plays any role in human navigation in large-scale space because it is inherently prone to accumulating error. In this review, we examined whether there is evidence that path integration contributes to large-scale human navigation. It was found that navigation with path integration (e.g., walking in a large-scale environment) can enhance learning of the layout of the environment as compared with mere exposure to the environment without path integration (e.g., viewing a walk-through video while sitting), suggesting that the body-based cues are reliably processed and encoded through path integration when they are present during navigation. This facilitatory effect is clearer when proprioceptive cues are available than when the navigators receive vestibular cues only (e.g., driving or being pushed in a wheelchair). More specifically, path integration with proprioceptive cues may help build survey knowledge of the environment in which metric distance and direction between landmarks are represented. Overall, the existing data are indicative of path integration's contributions to large-scale navigation. This suggests that instead of dismissing it as too error-prone, path integration should be characterised as a fundamental mechanism of human navigation irrespective of the scale of a space in which it is carried out.

Simulating arbitrary interactions between small-scale space debris and a space-based pulsed laser system

This research investigates the performance of a space-based laser system to remove debris objects with size smaller than 10 cm. The laser system is placed in a 800 km Sun Synchronous Orbit and consists of a 20 kW laser that shoots 300 J energy pulses with a repetition frequency of 66.66 Hz. The system is able to detect and track debris objects in situ using a 2.0 m mirror from 800 km distance. From a distance of about 500 km, the laser fluence on the targets is sufficiently high to trigger ablation, which decelerates the debris objects and reduces their lifetime. The feasibility of the concept is tested in scenarios where the laser system targets the debris objects from a different orbiting altitude and from varying azimuth angles. For many geometries, the laser is capable of significantly reducing the lifetime of the debris object. Extrapolating to longer periods of operation, the laser can be expected to provide a significant reduction of the population of small debris objects in LEO.

The influence of language, culture, and environment on the use of spatial referencing in a multilingual context: Taiwan as a test case

AbstractThis study examines the Sociotopographic Model (Palmer, Bill, Jonathon Lum, Jonathan Schlossberg & Alice Gaby. 2017. How does the environment shape spatial language? Evidence for sociotopography.Linguistic Typology21(3). 457–491. DOI:10.1515/lingty-2017-0011) by exploring the use of spatial frames of reference in two genealogically related languages with rather distinct spatial features in a multilingual context. Taiwanese Min Nan (TMN) and Taiwanese Mandarin Chinese (MC), exhibit distinct features in spatial reference in small-scale space, including a geocentric preference among TMN speakers for using cardinal directions or external landmarks and a relative preference among MC speakers for projecting the viewer’s perspective onto the object. The study extended the research design of Bohnemeyer and his colleagues (Bohnemeyer, Jürgen, Katharine T. Donelson, Randi Tucker, Elena Benedicto, Alejandra Capistrán Garza, Alyson Eggleston, Néstor Hernández Green, María Hernández Gómez, Samuel Herrera Castro, Carolyn K. O’Meara, Enrique Palancar, Gabriela Pérez Báez, Gilles Polian & Rodrigo Romero Méndez. 2014. The cultural transmission of spatial cognition: Evidence from a large-scale study. In Paul Bello, Marcello Guarini, Marjorie McShane & Brian Scassellati (eds.),Proceedings of the 36th Annual Conference of the Cognitive Science Society, 212–217. Austin, TX: Cognitive Science Society) to the scope of bilingualism in order to explore how language, culture, and environment affect the use of spatial reference in bilinguals as compared to monolinguals. The research method comprised a discourse and a recall memory study. Contrary to the alignment between discourse and recall memory found in monolinguals, bilinguals displayed mixed patterns. Regression analyses indicated that, in addition to language, effects of environment emerged: topography was positively correlated with geocentric use, supporting the Topographic Correspondence Hypothesis (Palmer, Bill, Alice Gaby, Jonathon Lum & Jonathan Schlossberg. 2016. Topography and frame of reference in the threatened ecological niche of the atoll.Paper presented at “Geographic grounding: Place, direction and landscape in the grammars of the world”, University of Copenhagen, May 2016). A strong education effect had an impact on geocentric use in both studies, suggesting the possibility of the cultural mediation between language and environment.

Reference frames in language and cognition: cross-population mismatches

AbstractNumerous studies have found evidence of a speech community’s referential practices in discourse being predictive of its members’ behavior in nonverbal tasks. In this article, we discuss a series of exceptions to this alignment pattern, drawing on data from eleven populations of Asia, Europe, the Middle East, North and Central America, and Oceania. These exceptions have not been discussed in conjunction with one another and the striking commonalities across the findings of these studies have gone unnoticed: (a) in discourses referring to small-scale space, either intrinsic frame use is dominant or both relative and geocentric frames are used frequently in addition to intrinsic frames; and (b) in recall/recognition memory, geocentric coding is more common than egocentric coding (in tasks that involve stationary stimulus configurations) in nine of the populations, while in the remaining two, there is evidence of extensive intrinsic coding even in nonverbal cognition. We discuss these findings in light of Haun’s innate geocentrism hypothesis (Haun, D. B. M., C. Rapold, J. Call, G. Janzen & S. C. Levinson. 2006. Cognitive cladistics and cultural override in hominid spatial cognition.Proceedings of the National Academy of Sciences103(46). 17568–17573). Our data offers partial support for this hypothesis, but simultaneously calls into question whether any extrinsic reference frames are available innately.

Study on the Estimation of Forest Volume Based on Multi-Source Data.

We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus sylvestris plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources.

Understanding the City-transport System of Urban Agglomeration through Improved Space Syntax Analysis

From the perspective of city-transport system, this article applies space syntax to analyze the physical integration of cities. Traditionally, space syntax is mainly applied to urban areas, buildings, and other scales. However, when space syntax is applied to the configuration analysis of urban agglomeration, the change of scale causes changes in spatial perception and human behavioral patterns. Thus, we present a new method of space syntax. This method defines the lane and track between entrance and exit, and city as node, which represents small-scale space. Infrastructure, such as stations, entrances, and exits, are defined as links. The urban agglomeration is thus transformed into a topological network, and then displayed as a bipartite graph of cities and routes. We take the urban agglomerations in the Yangtze River Middle Reaches (YRMR) as the case study area and analyze its spatial configuration from the perspectives of local and integral, interfaces at different scales, gaps, evolution of the dual foreground and background networks, and evolution of the transport networks. The results reveal the way cities integrate with each other and further reveal the multi-scale spatial structure of urban agglomeration.

Simulation method of urban evacuation based on mesoscopic cellular automata

In the traditional cellular automata evacuation model, the space is divided into fine grids at a micro level, which is mainly used in a two-dimensional plane case. The evacuation space is mostly a small-scale architectural space or local area. Therefore, it is difficult to simulate a wide range of evacuation scenario, and there are less researches of the cellular automata model for a wide range of evacuation. Therefore, this article combines the movement characteristics and status of the pedestrian flow to establish a mesoscopic cellular automata model of evacuation applied to larger evacuation scenarios. This model uses road cell division instead of planar grid cell division, which augments the area of a single cell physically, increases the number of people occupied by a single cell, and expresses the number of people in each cell in the form of state variables. By changing personnel density and personnel speed, and by introducing “source loading” cell loading to simulate the evacuation of people in the scene, the behavior of pedestrians evacuating from the building to the road in the actual evacuation process can be simulated. The state transition equation simulates the movement of people in the evacuation process. When the number of people in the cell is larger, the density of people in the cell is higher, and their walking speed also decreases, which reflects the distribution and movement characteristics of pedestrian flow. This paper uses this model to divide the evacuation area of the college campus, and divides the entire campus into four evacuation areas. The evacuees in each area are evacuated corresponding to the corresponding exit, by planning the evacuation path, pedestrians walking from the “source loading” cell to the exit for evacuation. Through simulation, it is possible to analyze the macro-evacuation situation in the scene and observe the status change of a single cell. There are observed a high density of people in local road sections during campus evacuation, and the problem about the distribution of people on campus problems such as unevenness of pedestrian distribution and long evacuation schedules in certain places. Through the simulation of this model, possible problems in the actual evacuation process are found, and the improvement guidance and opinions are presented correspondingly.

Space in Hand and Mind: Gesture and Spatial Frames of Reference in Bilingual Mexico.

Speakers of many languages prefer allocentric frames of reference (FoRs) when talking about small-scale space, using words like "east" or "downhill." Ethnographic work has suggested that this preference is also reflected in how such speakers gesture. Here, we investigate this possibility with a field experiment in Juchitán, Mexico. In Juchitán, a preferentially allocentric language (Isthmus Zapotec) coexists with a preferentially egocentric one (Spanish). Using a novel task, we elicited spontaneous co-speech gestures about small-scale motion events (e.g., toppling blocks) in Zapotec-dominant speakers and in balanced Zapotec-Spanish bilinguals. Consistent with prior claims, speakers' spontaneous gestures reliably reflected either an egocentric or allocentric FoR. The use of the egocentric FoR was predicted-not by speakers' dominant language or the language they used in the task-but by mastery of words for "right" and "left," as well as by properties of the event they were describing. Additionally, use of the egocentric FoR in gesture predicted its use in a separate nonlinguistic memory task, suggesting a cohesive cognitive style. Our results show that the use of spatial FoRs in gesture is pervasive, systematic, and shaped by several factors. Spatial gestures, like other forms of spatial conceptualization, are thus best understood within broader ecologies of communication and cognition.

WITHDRAWN: Experimental and numerical study on flame propagation characteristics of coal dust explosion in small-scale space

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