Space Domain Research Articles

Modeling the electron cyclotron emission radiation signature from suprathermal electrons in a tokamak.

An Electron Cyclotron Emission (ECE) modeling code has been developed to model ECE radiation with an arbitrary electron momentum distribution, a small oblique angle, both ordinary (O-mode) and extraordinary polarizations (X-mode), and multiple cyclotron frequency harmonics. The emission and absorption coefficients are calculated using the Poynting theorem from the cold plasma dispersion and the electron-microwave interaction from the full anti-Hermitian tensor. The modeling shows several ECE radiation signatures that can be used to diagnose the population of suprathermal electrons in a tokamak. First, in an n = 2 X-mode (X2) optically thick plasma and oblique ECE view, the modeling shows that only suprathermal electrons, which reside in a finite region of the velocity and space domains, can effectively generate cyclotron emissions to the ECE receiver. The code also finds that the O1 mode is sensitive to suprathermal electrons of both a high v⊥ and v‖, while the X2 mode is dominantly sensitive to suprathermal electrons of a high v⊥. The modeling shows that an oblique ECE system with both X/O polarization and a broad frequency coverage can be used to effectively yield information of the suprathermal electron population in a tokamak.

Adaptive quantum accelerated imaging for space domain awareness

The growth in space activity has increased the need for Space Domain Awareness (SDA) to ensure safe space operations. Imaging and detecting space targets is, however, challenging due to their dim appearance, small angular size/separation, dense distribution, and atmospheric turbulence. These challenges render space targets in ground-based imaging observations as point-like objects in the sub-Rayleigh regime, with extreme brightness contrast but a low photon budget. Here, we propose to use the recently developed quantum-accelerated imaging (QAI) for the SDA challenge. We mainly focus on three SDA challenges (1) minimal a priori assumptions (2) many-object problem (3) extreme brightness ratio. We also present results on source estimation and localization in the presence of atmospheric turbulence. QAI shows significantly improved estimation in position, brightness, and number of targets for all SDA challenges. In particular, we demonstrate up to 2.5 times better performance in source detection than highly optimized direct imaging in extreme scenarios like stars with a 1000 times brightness ratio. With over 10 000 simulations, we verify the increased resolution of our approach compared to conventional state-of-the-art direct imaging paving the way towards quantum optics approaches for SDA.

A Study on Dispersion Characteristics for Inverted Microstrip Lines

Inverted microstrip lines are studied for their dispersion characteristics for three different values of permittivity of the substrate using Galerkin Technique in Spectral Domain. This structure is very much useful as the current strip is attached at the lower surface of the dielectric substrate. Therefore it is rarely exposed to the outer medium results no effect due to external hazards and almost zero possibility to expose to electro- magnetic interference. Basis functions for the unknown strip current are chosen according to the ones specified in [1]. Basis functions in [1] were for slot magnetic currents and consequently they are modified for the particular problem by considering the modified boundary conditions for Maxwell’s field equations for this structure. For the theoretical development the Fourier transformation of the basis functions from space domain to spectral domain are considered. Variation of effective permittivity with available dielectric thickness has also been studied and the result shows a saturating tendency with increase in dielectric thickness.

Global well-posedness of the 2D nonlinear Schrödinger equation with multiplicative spatial white noise on the full space

We consider the nonlinear Schrödinger equation with multiplicative spatial white noise and an arbitrary polynomial nonlinearity on the two-dimensional full space domain. We prove global well-posedness by using a gauge-transform introduced by Hairer and Labbé (Electron Commun Probab 20(43):11, 2015) and constructing the solution as a limit of solutions to a family of approximating equations. This paper extends a previous result by Debussche and Martin (Nonlinearity 32(4):1147–1174, 2019) with a sub-quadratic nonlinearity.

Runtime verification on abstract finite state models

Finite-state models are ubiquitous in the study of concurrent systems, especially controllers and servers that operate in a repetitive cycle. In this paper, we show how to extract finite state models from a run of a multi-threaded Java program and carry out runtime verification of correctness properties. These properties include data-oriented and control-oriented properties; the former express correctness conditions over the data fields of objects, while the latter are concerned with the correct flow of control among the modules of larger software. As the extracted models can become very large for long runs, the focus of this paper is on constructing reduced models with user-defined abstraction functions that map a larger domain space to a smaller one. The abstraction functions should be chosen so that the resulting model is property preserving, i.e., proving a property on the abstract model carries over to the concrete model. The main contribution of this paper is in showing how runtime verification can be made efficient through online property checking on property-preserving abstract models. The property specification language resembles a propositional linear temporal logic augmented with simple datatypes and operators. Classic concurrency examples and larger case studies (Multi-rotor Drone Controller, OAuth Protocol) are presented in order to demonstrate the usefulness of our proposed techniques, which are incorporated in an Eclipse plug-in for runtime visualization and verification of Java programs.

A mesoscopic approach for nanoscale evaporation heat transfer characteristics

We propose a mesoscopic approach for investigating steady/transient nanoscale evaporation heat transfer characteristics, whose kinetic nature makes it an ideal tool to model the evaporation kinetics at the liquid-vapor interface and the microscopic wall-fluid interactions. Simulation of the evaporation of a flat nanoscale thin liquid film demonstrates its capability of resolving kinetically limited evaporation and liquid film adsorption. For the simulation of an evaporating meniscus in a nanochannel, our proposed mesoscopic approach considers the conservation of mass, momentum and energy of both liquid and vapor phases, thereby naturally incorporating both the multi-dimensional heat transfer effect and vapor transport resistance in nano-confined space. We demonstrate that solid-fluid interaction plays dominant roles on the interfacial transport during nanoscale evaporation, and vapor transport resistance has nonnegligible influences on the evaporation heat/mass transport in nano-confined spaces. By handling statistical behavior of molecule ensembles, this mesoscopic approach not only preserves microscopic physics but also has high computational efficiencies, enabling the simulation of space and time domains on the practical application level. This work paves the way for quantitative investigations of steady/transient nanoscale evaporation heat transfer characteristics using mesoscopic approach.

Research on Vehicle AEB Control Strategy Based on Safety Time–Safety Distance Fusion Algorithm

With the increasing consumer focus on automotive safety, Autonomous Emergency Braking (AEB) systems, recognized as effective active safety technologies for collision avoidance and the mitigation of collision-related injuries, are gaining wider application in the automotive industry. To address the issues of the insufficient working reliability of AEB systems and their unsatisfactory level of accordance with the psychological expectations of drivers, this study proposes an optimized second-order Time to Collision (TTC) safety time algorithm based on the motion state of the preceding vehicle. Additionally, the study introduces a safety distance algorithm derived from an analysis of the braking process of the main vehicle. The safety time algorithm focusing on comfort and the safety distance algorithm focusing on safety are effectively integrated in the time domain and the space domain to obtain the safety time–safety distance fusion algorithm. A MATLAB/Simulink–Carsim joint simulation platform has been established to validate the AEB control strategy in terms of safety, comfort, and system responsiveness. The simulation results show that the proposed safety time–safety distance fusion algorithm consistently achieves complete collision avoidance, indicating a higher safety level for the AEB system. Furthermore, the application of active hierarchical braking minimizes the distance error, at under 0.37 m, which meets psychological expectations of drivers and improves the comfort of the AEB system.

Self-assembly of curdlan molecules for the formation of thermally induced gels

The differences in the self-assembly process between cold-set (CCS) and heat-set (CHS) curdlan gels were investigated using micro-differential scanning calorimetry, atomic force microscopy, and small-angle X-ray diffraction. The results indicate that the triple-helix structure of curdlan dissociated into single helices in its suspension state (C-SUS) at 65 °C. Upon cooling to around 38 °C, these single helices assembled into long, thick fibers with a height of up to 11.50 nm, contributing to the gel structure of CCS. Heating C-SUS from 65 °C to 90 °C caused the single helices to transform into triple helices due to hydrophobic interactions. Subsequent cooling to 25 °C, led to the formation of short, thick bundles with a height of up to 8.78 nm, forming the network structure of CHS gel. As a consequence, CHS gels exhibited higher hardness and elasticity but lower fatigue resistance. This is attributed to an increased degree of physical cross-linking, as evidenced by a reduction in the size (Ξ) and spacing (ξ) of high-density domains within the gel structure, and a simpler mechanism of energy dissipation through alignment of the molecular chain segment. These findings are valuable for the design and development of curdlan-based gels with tailored mechanical properties.

Increasing Multienzyme Cascade Efficiency and Stability of MOF via Partitioning Immobilization.

Enhancing the stability of multienzyme cascade reactions in metal-organic frameworks (MOFs) is a challenging task in the fields of biotechnology and chemistry. However, addressing this challenge could yield far-reaching benefits across the application range in the biomedical, food, and environmental sectors. In this study, multienzyme partitioning immobilization that sequentially immobilizes cascade enzymes with hierarchical MOFs is proposed to reduce substrate diffusion resistance. Conversion results of ginsenosides indicate that this strategy improves the cascade efficiency up to 1.26 times. The substrate diffusion model is used to investigate the dual-interenzyme mass transfer behavior of substrates in the restricted domain space and evaluate the substrate channeling effect under partitioning immobilization. Molecular docking and kinetic simulations reveal that the MOFs effectively limit the conformational changes of cascade enzymes at high temperatures and in organic solvents while maintaining a large pocket of active centers. This phenomenon increased efficient substrate docking to the enzyme molecules, further optimizing cascade efficiency. The results of the immobilization of GOX and horseradish peroxidase as model enzymes indicate that the partitioned MOF immobilization strategy could be used for universal adaptation of cascade enzymes.

FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization

Conventional deep learning methods for fault detection often assume that the training and the testing sets share the same fault domain spaces. However, some fault patterns are rare, and many real-world faults have not appeared in the training set. As a result, it is hard for the trained model to achieve desirable performance on the testing set. In this paper, we introduce a novel domain generalization, Load-Domain (LD) domain generalization, which is based on the analysis of the Case Western Reserve University (CWRU) bearing dataset and takes advantage of the physical information of this dataset. For this scenario, we propose a feature shift model called Feature Shift Network (FSN). FSN is trained for feature shift on adjacent source domains and finally shifts target domain features into adjacent source domain feature space to achieve the purpose of domain generalization. Furthermore, through the hybrid classification method, the generalization performance of the model on unseen target domains is effectively improved. The results on the CWRU bearing dataset demonstrate that FSN is better than the existing models in the LD domain generalization. Furthermore, we have another test on the rotated MNIST, which also shows FSN can achieve the best performance.

Riemann problem for multiply connected domain in Besov spaces

In this paper, we obtain conditions of the solvability of the Riemann boundary value problem for sectionally analytic functions in multiply connected domains in Besov spaces embedded into the class of continuous functions. We indicate a new class of Cauchy-type integrals, which are continuous on a closed domain with continuous (not Hölder) density in terms of Besov spaces, and for which the Sokhotski–Plemelj formulas are valid.

Consciência situacional como ferramenta estratégica da defesa cibernética

The technological growth of the last decades has changed the way many sectors of society and the state carry out their respective activities. The defense sector is part of this framework due to its essentially technological nature. With these changes, discussions began about phenomena known as ‘cyberspace’, ‘cyber power’, and ‘cyber domain’. Because cyberspace permeates and can even influence the other operational domains (sea, land, air, and space), having the right knowledge of cyberspace – both one’s own and that of one’s adversaries – has become a competitive advantage in interstate conflicts. This knowledge is called situational awareness, a concept conceived in the 1980's by Mica Endsley, then Chief Scientist of the United States Air Force, and which can be defined as the ability to perceive and understand activities in a given time and space and to predict the future evolution of the situation. This concept can be unfolded in the cyber domain, giving rise to the socalled cyber situational awareness, which generally means the full knowledge of the actions occurring in the cyber space of interest. Thus, this article aims to elucidate the strategic role of cyber situational awareness, identifying the theories and models of its emerging concept and, subsequently, its developments in military strategy.

Space and cybersecurity: Challenges and opportunities emerging from national strategy narratives

Modern societies are increasingly dependent on space technology. The number of activities that rely on space infrastructure includes global positioning and communications systems, financial transactions and global trade, public and private scientific research, environmental monitoring and fore-casting, and audio-visual entertainment. Within the security and defence domain, this reliance becomes even more pronounced as satellites enhance command, control, communications and intelligence, surveillance, and recon-naissance (C4ISR), missile defence, or advanced autonomous systems. Furthermore, ongoing advancements in science and technology are opening new frontiers in outer space, promising significant economic potential through ventures like space travel and space mining. Considering the geopolitical implications of the dependence on space technology, the objective of this study is to examine how Western countries and organizations understand space within their strategic thinking. By conducting a comparative analysis of the most recent national security strategies and security and defence space strategies released by a sample of Western countries and organizations, including the United States, the United Kingdom, France, the European Union and NATO, this study aims to discern the narratives employed to depict the space domain and to identify the key trends within it, with a specific focus on the interplay between space and cybersecurity.This exercise will facilitate the identification of areas where enhanced collaboration among the selected actors is feasible or where competition may define their relationships. Consequently, it will help determine the potential for a coordinated response to collective challenges.

Universal Access to Water-Compatible and Nanostructured Materials via the Self-Assembly of Cationic Alternating Copolymers.

Herein, we report the water-assisted self-assembly of alternating copolymers bearing imidazolium cations and hydrophobic groups to create water-compatible and nanostructured materials. The copolymers efficiently absorbed water into the cationic segments from the outer environments, depending on the relative humidity. The absorbed water serves as hydrophilic molecules to modulate the weight fraction of hydrophilic/hydrophobic units in the samples. Thus, the morphologies and domain spacing of the nanostructures can be controlled by not only the side chains, but also the amount of absorbed water. The self-assembly of the cationic copolymers, developed herein, afforded universal access to various morphologies, including lamella, gyroid, and cylinder, in addition to the precision control of the domain spacing at the 0.01 nm level.

A unifying model for membrane protein biogenesis.

α-Helical integral membrane proteins comprise approximately 25% of the proteome in all organisms. The membrane proteome is highly diverse, varying in the number, topology, spacing and properties of transmembrane domains. This diversity imposes different constraints on the insertion of different regions of a membrane protein into the lipid bilayer. Here, we present a cohesive framework to explain membrane protein biogenesis, in which different parts of a nascent substrate are triaged between Oxa1 and SecY family members for insertion. In this model, Oxa1 family proteins insert transmembrane domains flanked by short translocated segments, whereas the SecY channel is required for insertion of transmembrane domains flanked by long translocated segments. Our unifying model rationalizes evolutionary, genetic, biochemical and structural data across organisms and provides a foundation for future mechanistic studies of membrane protein biogenesis.

Cybersecurity in the Era of Space Domain Awareness

Cybersecurity in the Era of Space Domain Awareness

Distance-guided domain adaptation for bearing fault diagnosis under variable operating conditions

Transfer learning (TL) has demonstrated effective application in diagnosing bearing faults under varying operating conditions. The current TL methods achieve domain alignment by minimizing the variation in the marginal distribution of data between the source and target domains in the feature space. However, this approach yields incomplete similarity, leading to domain shift and a decrease in diagnostic performance. To overcome this problem, this paper proposes a new distance-guided domain adaptation method that consists of two modules: deep domain adaptive correlation alignment (Deep CORAL) combined with joint maximum mean discrepancy (JMMD) for guided domain adaptation. Deep CORAL employs nonlinear transformations to synchronize second-order statistical correlations across source and target domains, thus ensuring feature-level alignment between these domains. JMMD is utilized to align the joint distribution of input features and output labels within the activation layer in the deep network, thereby bolstering domain alignment. Building on this, we propose a network structure that merges ResNet and bidirectional long short-term memory, powered by wavelet kernels, serving as a feature extractor. This structure is designed to learn domain-invariant features and incorporates attention mechanisms to amplify important information while diminishing the impact of redundant data. An analysis of bearing experiments is used to demonstrate the effectiveness of this method, and the proposed method significantly outperforms several popular methods in diagnostic performance.

Sobolev spaces on arbitrary domains and semigroups generated by the fractional Laplacian

We describe a procedure to introduce on an arbitrary domain of Rd Sobolev spaces of arbitrary order s∈R and with an intrinsic characterization via fractional powers of the Dirichlet Laplacian. In particular, the well-definedness of the spaces of both non-homogeneous and homogeneous type together with their duality properties, embeddings, and Gagliardo-Nirenberg inequalities will be discussed. We also show the continuity and the smoothing property of the semigroup generated by the fractional Dirichlet Laplacian.

An operational matrix strategy for time fractional Fokker-Planck equation in an unbounded space domain

In this study, the Caputo fractional derivative is used to define time fractional Fokker-Planck equation in an unbounded domain. To solve this equation, the Jacobi polynomials together with the tanh-Jacobi functions are employed. The operational matrices of the classical and fractional derivatives of these basis functions are obtained to use them in constructing a numerical method for the expressed equation. In the proposed method, the introduced basis functions are used simultaneously to approximate the equation’s unknown solution. More precisely, the shifted Jacobi polynomials are applied to approximate the solution in the temporal direction and the tanh-Jacobi functions are utilized to approximate the solution in the spatial direction. By substitute the expressed approximation into the equation and employing the introduced operational matrix, solving the problem under consideration transforms into solving an algebraic system of equations, which can be solved easily. The accuracy and efficiency of the presented method are investigated numerically by solving some numerical examples. The reported results confirms the high accuracy of the established method.

Heuristic algorithms for design of integrated monitoring of geologic carbon storage sites

Designs for Risk Evaluation and Management (DREAM) is a tool developed under the National Risk Assessment Partnership (NRAP) to enhance geologic carbon storage safety and efficiency. Using potential leakage scenarios generated externally by the users preferred history-matching approach, DREAM constructs ideal combinations of sensor locations in the right place at the right time to detect as many leaks as possible, detect them as early as possible, and minimize cost. This user-friendly tool, developed in Java, features a window-based GUI for input and a 3D visualization tool for viewing the domain space and optimized monitoring plans. DREAM's latest version accommodates real-world usage by allowing for joint optimization of wellbore point sensor placements and surface geophysics survey geometries, and by using more efficient multi-objective optimization algorithms. In an example shown here, these two improvements combined allow us to support containment assurance and go from detecting 80–90 % of the potential CO2 leakage to +99.7 %, a step-change improvement that can make the deciding difference in whether a site is suitable for geologic carbon storage. Though developed for geologic carbon storage, this tool would be equally applicable in many surface or offshore environmental monitoring projects.