Space Domain Research Articles

Structured community transitions explain the switching capacity of microbial systems

Microbial systems appear to exhibit a relatively high switching capacity of moving back and forth among few dominant communities (taxon memberships). While this switching behavior has been mainly attributed to random environmental factors, it remains unclear the extent to which internal community dynamics affect the switching capacity of microbial systems. Here, we integrate ecological theory and empirical data to demonstrate that structured community transitions increase the dependency of future communities on the current taxon membership, enhancing the switching capacity of microbial systems. Following a structuralist approach, we propose that each community is feasible within a unique domain in environmental parameter space. Then, structured transitions between any two communities can happen with probability proportional to the size of their feasibility domains and inversely proportional to their distance in environmental parameter space-which can be treated as a special case of the gravity model. We detect two broad classes of systems with structured transitions: one class where switching capacity is high across a wide range of community sizes and another class where switching capacity is high only inside a narrow size range. We corroborate our theory using temporal data of gut and oral microbiota (belonging to class 1) as well as vaginal and ocean microbiota (belonging to class 2). These results reveal that the topology of feasibility domains in environmental parameter space is a relevant property to understand the changing behavior of microbial systems. This knowledge can be potentially used to understand the relevant community size at which internal dynamics can be operating in microbial systems.

Symmetry Breaking and Dynamic Transition in the Negative Mass Term Klein–Gordon Equations

Through the discussion of three physical processes, we show that the Klein–Gordon equations with a negative mass term describe special dynamics. In the case of two classical disciplines—mechanics and thermodynamics—the Lagrangian-based mathematical description is the same, even though the nature of the investigated processes seems completely different. The unique feature of this type of equation is that it contains wave propagation and dissipative behavior in one framework. The dissipative behavior appears through a repulsive potential. The transition between the two types of dynamics can be specified precisely, and its physical meaning is clear. The success of the two descriptions inspires extension to the case of electrodynamics. We reverse the suggestion here. We create a Klein–Gordon equation with a negative mass term, but first, we modify Maxwell’s equations. The repulsive interaction that appears here results in a charge spike. However, the Coulomb interaction limits this. The charge separation is also associated with the high-speed movement of the charged particle localized in a small space domain. As a result, we arrive at a picture of a fast vibrating phenomenon with an electromagnetism-related Klein–Gordon equation with a negative mass term. The calculated maximal frequency value ω=1.74×1021 1/s.

Accelerated discovery and mapping of block copolymer phase diagrams

Block copolymers are widely used in many applications due to their spontaneous self-assembly into a variety of nanoscale morphologies. However, a grand challenge in navigating this diverse and ever-growing array of possible structures is the accelerated discovery, design, and implementation of materials. Here, we report a versatile and efficient strategy to accelerate materials discovery by rapidly building expansive, high-quality, and detailed block copolymer libraries through a combination of controlled polymerization and chromatographic separation. To illustrate the potential of this approach, a family of 16 parent diblock copolymers was synthesized and separated, leading to over 300 distinct and well-defined samples at the multigram scale. The resulting materials span a wide range of compositions with exceptional resolution in volume fraction and domain spacing that allows for the impact of monomer design on polymer self-assembly to be elucidated. Phase behavior that can be gleaned from these libraries includes the precise location of order-order boundaries and the identification of morphologies with extremely narrow windows of stability. This user-friendly, scalable, and automated approach to discovery significantly increases the availability of well-defined block copolymers with tailored molecular weights, molar-mass dispersities, compositions, and segregation strengths, accelerating the study of structure-property relationships in advanced soft materials. Published by the American Physical Society 2024

Theoretical exploration of a free-piston Stirling generator with a gas-compressing self-circulating heat exchanger

The free-piston Stirling generator (FPSG) holds great promise as a thermal-to-electrical conversion device for space applications. However, heat transfer remains a critical factor obstructing its scalability to high power levels. In this study, we propose a high-power FPSG with a gas-compressing self-circulating heat exchanger (GSHX) that utilizes high-pressure helium as the heat transfer fluid, enhancing the reliability of the heat exchanger. By incorporating a built-in gas-compressing device and check valves, the performance degradation caused by significant pressure fluctuations in the resonant self-circulating heat exchanger is effectively addressed. Through detailed simulations, we demonstrate the principle feasibility of the GSHX with a 4 m heat transfer loop, achieving an output electric power of 11 kW and a thermal-to-electric efficiency of 20.4%. Furthermore, the GSHX exhibits similar operating characteristics to traditional FPSGs, as we analyze the systems' stability under various operating conditions. Importantly, our findings reveal that the acoustic characteristics of the loop play a pivotal role in system performance, with operating parameters exhibiting periodic variations related to helium wavelength. By carefully optimizing system parameters, GSHX with a long loop length of 29 m can achieve comparable performance to systems with shorter loops. This study provides valuable insights into the operational characteristics and performance optimization of the GSHX, offering a comprehensive heat transfer solution for long-distance, high-efficiency, and highly reliable applications of high-power FPSGs in the space domain.

Improved particle swarm optimization with reverse learning and neighbor adjustment for space surveillance network task scheduling

Space Surveillance Network (SSN) plays a crucial role in Space Domain Awareness (SDA) as it helps to maintain the catalog of Resident Space Objects (RSOs). Howe`ver, as the population of RSOs continues to grow, the contradiction between the increasing number of RSOs and limited sensor resources will be further apparent. How to effectively utilize existing sensor resources becomes a vital concern. This paper focuses on the Multi-sensor Collaborative Observation Scheduling (MCOS) in SSN. The workflow of SSN is summarized, and a Mathematical Programming Model (MPM) is established to maximize observation profit. Given the complexity of MPM, a modified Particle Swarm Optimization is proposed by combining Reverse learning, Neighbor adjustment, and Particle perturbation (RNP-PSO). In the MPM, multiple constraints such as observation time for each RSO, observation frequency, and sensor capability are considered, enabling a comprehensive description of the MCOS problem for the first time. The RNP-PSO incorporates a reverse learning algorithm to improve the quality of the initial population. Additionally, continuous mapping is added to the neighbor adjustment process to enhance the algorithm's search capabilities. Furthermore, a particle perturbation strategy is proposed to increase the diversity of solutions and avoid local optima. A series of experiments have been conducted to evaluate the performance of the RNP-PSO algorithm in solving the MCOS problem. The results demonstrate that compared to several other effective algorithms, the RNP-PSO algorithm significantly improves the Task Completion Rate (TCR) by approximately 17.01%, 19.1%, 13.53%, and 13.99%, while simultaneously reduces the Resource Consumption Rate (RCR) by about 55.48% and 36.38%. The experiments also include strategy and parameter sensitivity verification, which thoroughly validate the performance of the RNP-PSO algorithm in various aspects.

Video Abnormal Action Recognition Based on Multimodal Heterogeneous Transfer Learning

Human abnormal action recognition is crucial for video understanding and intelligent surveillance. However, the scarcity of labeled data for abnormal human actions often hinders the development of high-performance models. Inspired by the multimodal approach, this paper proposes a novel approach that leverages text descriptions associated with abnormal human action videos. Our method exploits the correlation between the text domain and the video domain in the semantic feature space and introduces a multimodal heterogeneous transfer learning framework from the text domain to the video domain. The text of the videos is used for feature encoding and knowledge extraction, and knowledge transfer and sharing are realized in the feature space, which is used to assist in the training of the abnormal action recognition model. The proposed method reduces the reliance on labeled video data, improves the performance of the abnormal human action recognition algorithm, and outperforms the popular video-based models, particularly in scenarios with sparse data. Moreover, our framework contributes to the advancement of automatic video analysis and abnormal action recognition, providing insights for the application of multimodal methods in a broader context.

A time-domain FEM-BEM iterative coupling algorithm with dual-independent discretizations in both time and space domains

A time-domain (TD) FEM-BEM iterative coupling algorithm with dual-independent discretizations in both time and space domains for dynamics is presented, allowing the independent temporal and spatial discretizations and solutions for each sub-domain in the coupled model. According to the continuity and equilibrium equations between the sub-domains of TD-FEM and TD-BEM, as well as the continuity condition along time, the displacement and force converting matrices and the time inter/extra-polation scheme in time domain are derived to exchange data between TD-FEM and TD-BEM nodes on the interface, which are not required to be positionally matched. The proposed iterative coupling algorithm with dual-independent discretizations in time and space domains is verified by two examples to be of the advantages of the independence for one's own formulation, high computational accuracy, good flexibility and versatility.

Optimal transport-based domain adaptation for semantic segmentation of remote sensing images

ABSTRACT Thanks to its great power in feature representation, deep learning (DL) is widely used in semantic segmentation tasks. However, the requirements for high distribution consistency of different domains are too tight to be met by large-scale remote sensing tasks due to the domain shift in imaging modes and geographic environments. In this case, trained models in a source domain can hardly achieve sufficient accuracy in a target domain with domain shift. To address this issue, a novel unsupervised domain adaptation (UDA) method driven by optimal transport (OT) with two-stage training is proposed to alleviate domain shift in remote sensing images (RSIs). In the first stage, a colour distribution alignment (CDA) module and a feature joint alignment (FJA) module based on OT were designed to mitigate the discrepancy between different domains. CDA transports source-domain distribution according to the target-domain colour style, and FJA aligns source and target domains in both feature and output spaces by minimizing OT-based losses. In the second stage, self-training with pseudo-label denoising (STPD) was proposed, which alleviated the interference of noises in pseudo-labels based on a joint OT distance. For the experiments, the Potsdam, Vaihingen and UAVid datasets were employed. Based on the characteristics of these datasets, five UDA tasks were introduced. The results of these UDA experiments indicate the superiority of our method. Our code will be available at https://github.com/Hcshenziyang/OT-Domain-Adaptation-Semantic-Segmentation.

Optimization of Electro-Optic Sampling by Photodiode's Choice for Terahertz Detection

Despite being widely used in a lot of measurements involving the generation of electromagnetic radiation, most of the optimization of this EOS technique only focused on the detection crystal and nonlinear coefficient of the said crystal. After the detection crystal, the combined pump-probe beam should be separated again based on its polarization and measured simultaneously with photodiodes. The authors found out that there are not many studies focusing on this part of the EOS technique and decided to present in this paper to contribute to the better optimization of detection with the EOS technique. We found out that the change of photodiode could have a relationship with the detected signal’s shape in the time and space domain, alongside the change of delay-time detection.

Interfacial Charge Transfer for Enhancing Nonlinear Saturable Absorption in WS2/graphene Heterostructure.

Interlayer charge-transfer (CT) in 2D atomically thin vertical stacks heterostructures offers an unparalleled new approach to regulation of device performance in optoelectronic and photonics applications. Despite the fact that the saturable absorption (SA) in 2D heterostructures involves highly efficient optical modulation in the space and time domain, the lack of explicit SA regulation mechanism at the nanoscale prevents this feature from realizing nanophotonic modulation. Here, the enhancement of SA response via CT in WS2/graphene vertical heterostructure is proposed and the related mechanism is demonstrated through simulations and experiments. Leveraging this mechanism, CT-induced SA enhancement can be expanded to a wide range of nonlinear optical modulation applications for 2D materials. The results suggest that CT between 2D heterostructures enables efficient nonlinear optical response regulation.

A new MIB-based time integration method for transient heat conduction analysis of discrete and continuous systems

By incorporating the matched interface and boundary (MIB) method into time integration computation, a new MIB-based time integration (MIBTI) approach is proposed for solving transient heat conduction problems in this work. Indeed, this is a truly effective time-domain MIB method that can directly discretize space and time derivatives, and the transient heat conduction problem (an initial-value problem) is transformed into a pseudo-space boundary-value problem. The initial and final time points are regarded as boundary points in the transformed space domain. Two algorithms are developed to deal with the final time point. Unlike conventional recursive schemes for time integration, all unknown variables can be determined by solving the transformed boundary-value problem in the present approach. Various discrete and continuous systems without and with internal heat sources under different boundary conditions are considered to examine the working performance of the proposed method. The applicability of the time stability criterion in the conventional recursive schemes to the proposed method is also discussed. It is found that excellent numerical stability and convergence can be achieved by using the present MIBTI method for solving transient heat conduction problems.

Post-disaster building damage detection using multi-source variational domain adaptation

Timely detection of building damage after a disaster can provide support and help in saving lives and reducing losses. The emergence of transfer learning can solve the problem of difficulty in obtaining several labeled samples to train deep models. However, some degree of differences exists among different scenarios, which may affect the transfer performance. Furthermore, in reality, data can be collected from multiple historical scenarios but cannot be directly combined using single-source domain adaptation methods. Therefore, this study proposes a multi-source variational domain adaptation (MVDA) method to complete the task of post-disaster building assessment. The MVDA method consists of two stages: first, the distributions of each pair of source and target domains in specific feature spaces are aligned separately; second, the outputs of the pre-trained classifiers are aligned using domain-specific decision boundaries. This method maximizes the relevant information in the historical scene, solves the problem of inconsistent image classification in the current scene, and improves the migration efficiency from the history to the current disaster scene. The proposed approach is validated by two challenging multi-source transfer tasks using the post-disaster hurricane datasets. The average accuracy rate of 83.3% for the two tasks is achieved, obtaining an improvement of 0.9% compared with the state-of-the-art methods.

Multi-modality frequency-aware cross attention network for fake news detection

An increasing number of fake news combining text, images and other forms of multimedia are spreading rapidly across social platforms, leading to misinformation and negative impacts. Therefore, the automatic identification of multimodal fake news has become an important research hotspot in academia and industry. The key to multimedia fake news detection is to accurately extract features of both text and visual information, as well as to mine the correlation between them. However, most of the existing methods merely fuse the features of different modal information without fully extracting intra- and inter-modal connections and complementary information. In this work, we learn physical tampered cues for images in the frequency domain to supplement information in the image space domain, and propose a novel multimodal frequency-aware cross-attention network (MFCAN) that fuses the representations of text and image by jointly modelling intra- and inter-modal relationships between text and visual information whin a unified deep framework. In addition, we devise a new cross-modal fusion block based on the cross-attention mechanism that can leverage inter-modal relationships as well as intra-modal relationships to complement and enhance the features matching of text and image for fake news detection. We evaluated our approach on two publicly available datasets and the experimental results show that our proposed model outperforms existing baseline methods.

Multi-Array Visible-Light Optical Generalized Spatial Multiplexing–Multiple Input Multiple-Output System with Pearson Coefficient-Based Antenna Selection

To address the limitations of poor environmental adaptability, unsatisfactory bit-error performance, and high complexity of conventional antenna selection algorithms applied to a multi-array visible-light optical generalized spatial multiplexing–multiple-input multiple-output (OGSMP-MIMO) system, an OGSMP-MIMO system based on Pearson coefficient antenna selection is proposed. The algorithm adopts the correlation of Pearson coefficients among photoelectric detector (PD) terminals at different positions and active transmit antennas to select the optimal antenna combination without relying on the accuracy of channel estimation, for realizing the multiplexing of the time and space domains, and to improve the bit-error performance. Finally, experiments were conducted to verify the feasibility of the antenna selection algorithm, based on the Pearson coefficients. The results indicated that when the bit-error rate reached 10−6, using the antenna selection algorithm based on the Pearson coefficient, the signal-to-noise ratio was improved by 2.7 dB and 3.7 dB when compared with the norm-based antenna and random selection algorithms, respectively. In addition, increasing the number of active transmitting antennas can improve the transmission rate; however, the bit-error performance will be compromised. In the same modulation mode, increasing the number of transmitting antennas will reduce the bit-error performance.

Efficient simulation of two-dimensional time-fractional Navier–Stokes equations using RBF-FD approach

This study is focused on the Navier–Stokes equations. The numerical method employed in this study for solving the Navier–Stokes equations is a meshless approach that combines the radial basis function (RBF) and finite difference (FD) schemes on two-dimensional arbitrary domains. This method, known as radial basis function-generated finite differences (RBF-FD), utilizes the finite difference technique to discretize the time domain and the RBF-FD approximation to discretize the space domain. The RBF-FD method offers several advantages, including high-order convergence rates and the ability to effectively handle sparse node layouts within arbitrary domains. The efficacy and accuracy of the suggested approach are evaluated through the analysis of four unique instances: comprising Taylor–Green vortices, lid-driven cavity flow, backward-facing step flow, and flow around a square cylinder. In the last example, we examined a simulation of air pollution around buildings.

TC-GCN: Triple cross-attention and graph convolutional network for traffic forecasting

With the rapid development of urbanization, increasingly more data are being acquired by intelligent transportation systems (ITSs), which is of great significance for traffic flow forecasting. Efficient intelligent traffic management systems (TMSs) depend on accurately forecasting traffic flows as well as reasonably researching and judging traffic states. However, the current prediction models used in transportation forecasting tasks are traditional temporal- and spatial-dimensional prediction approaches. Especially when faced with road channelization, the numbers of lanes and lane types increase significantly, causing significant increases in the dimensionality and complexity of traffic data, and research models utilizing the cross-interaction relationships among multiple dimensions have not been considered. This has led to unsatisfactory prediction results in complex traffic environments. Therefore, it is very important to explore the exchange–correlation of complex dimensional data and the mining of hidden attributes. This paper presents a method for performing multidimensional cross-attention and spatiotemporal graph convolution. This method fully considers cross-information and constructs an attention cross-view between every pair of dimensions among the channel, time and space domains to model the cross-dimensional dependencies of traffic data. We innovatively propose a triple cross-attention and graph convolutional network (TC-GCN), which can achieve further improved traffic forecasting performance. The TC-GCN is verified on two real-world traffic datasets, namely, METR-LA and PEMS-BAY, and the experimental results are compared with those of multiple advanced baselines, showing that the proposed approach outperforms most of these baselines, which proves the effectiveness of the method proposed in this paper.

Controllable multi-domain semantic artwork synthesis

We present a novel framework for the multi-domain synthesis of artworks from semantic layouts. One of the main limitations of this challenging task is the lack of publicly available segmentation datasets for art synthesis. To address this problem, we propose a dataset called ArtSem that contains 40,000 images of artwork from four different domains, with their corresponding semantic label maps. We first extracted semantic maps from landscape photography and used a conditional generative adversarial network (GAN)-based approach for generating high-quality artwork from semantic maps without requiring paired training data. Furthermore, we propose an artwork-synthesis model using domain-dependent variational encoders for high-quality multi-domain synthesis. Subsequently, the model was improved and complemented with a simple but effective normalization method based on jointly normalizing semantics and style, which we call spatially style-adaptive normalization (SSTAN). Compared to the previous methods, which only take semantic layout as the input, our model jointly learns style and semantic information representation, improving the generation quality of artistic images. These results indicate that our model learned to separate the domains in the latent space. Thus, we can perform fine-grained control of the synthesized artwork by identifying hyperplanes that separate the different domains. Moreover, by combining the proposed dataset and approach, we generated user-controllable artworks of higher quality than that of existing approaches, as corroborated by quantitative metrics and a user study.

A space-time second-order method based on modified two-grid algorithm with second-order backward difference formula for the extended Fisher–Kolmogorov equation

In this paper, a modified two-grid algorithm based on block-centred finite difference method is developed for the fourth-order nonlinear extended Fisher–Kolmogorov equation. To further improve the computational efficiency, an effective second-order accurate backward difference formula is considered. The modified two-grid method based on Newton iteration is constructed to linearize the nonlinear system. The method solves a miniature nonlinear system on a coarse grid accompanying a larger time step to get the numerical solution, then computes a linear system constructed by the previous result with the Taylor expansion on a fine grid accompanying a smaller time step to get the correct numerical solution. Theoretical analysis shows that the modified two-grid algorithm can achieve second-order convergence accuracy both in time and space domain. Several numerical experiments are provided to verify the theoretical result and the high efficiency of this approach. The practical problem illustrates the actual applicable value of the algorithm.

Exploring traffic breakdown with vehicle-level data

Traffic breakdown involves complicated vehicle behavior, and is regarded as a probabilistic event with macroscopic traffic data from fixed detectors. However, with the advent of connected vehicle technologies, traffic data will develop to the vehicle-level, such as trajectory data, and provide unprecedented opportunities to better understand various traffic phenomena. Using novel vehicle-level data from drone videos, this research explores the traffic breakdown by interactions between vehicles. Specifically, this paper categorizes the typical behavior of individual vehicles that causes or resolves traffic congestion. Based on the behavior that occurred in the extensive time–space domain, this research develops a novel measurement method to quantify the behavior as temporal delay or spatial residual. With real-world data, this research verifies the vehicle-level congestion can be estimated from specific vehicle behavior, and their aggregation could describe the change in flow speed or traffic breakdown. The proposed framework can address the traffic phenomenon better when more extensive data is available.

Modern Security Dilemma: A Space Security Perspective for the Future World

In an increasingly interconnected and technologically advanced world, the domain of space has emerged as a critical frontier for both national security and international cooperation (Johnson, 2019). However, as nations expand their presence in space, the risks and challenges associated with ensuring security in this domain have become more pronounced (Fountain, 2020). This research article examines the modern security dilemma through the lens of space security, exploring the complexities, tensions, and opportunities that arise as states navigate the dual imperatives of safeguarding their interests while promoting stability and cooperation in outer space (Weeden & Sampson, 2021). By analyzing current trends, emerging threats, and potential pathways for collaboration, this article seeks to provide insights and recommendations for addressing the space security dilemma in the future world.