Space Domain Research Articles

Proxy Star Wars: The Growing Threat of Grey-Zone Activities in the Space Domain

he space domain is a contested and congested environment, with an overlap of military and commercial interests leading to an increasing number of “grey-zone” operations. Grey-zone operations are activities which fall below the threshold for a conventional military response, and are performed in ways that make attribution too uncertain to justify kinetic countermeasures. These operations push the boundaries of the law, technology and territorial integrity. A failure to direct an appropriate level of attention to counter space activity, or establish policy and behavioural norms, leaves scope for rapid escalation of force toward the threshold of military intervention. This paper intends to summarise past and current grey-zone activities in the space domain, identify key technologies, as well as forecast development trends. And, finally, it will reflect on the political ramifications of the escalating militarisation of the space-domain. Keywords: Grey-zone, Anti-Satellite, Counter-Space, Hybrid-Operations, Lawfare

Characterization of the friction-induced attractor trajectories during the running-in process in ball-on-disk tribosystem

PurposeThe purpose of this study is to solve the issues of time-consuming and complicated computation of traditional measures, as well as the underutilization of two-dimensional (2D) phase-trajectory projection matrix, so a new set of features were proposed based on the projection of attractors trajectory to characterize the friction-induced attractors and to reveal the tribological behavior during the running-in process.Design/methodology/approachThe frictional running-in experiments were conducted by sliding a ball against a static disk, and the friction coefficient was collected to reconstruct the friction-induced attractors. The projection of the attractors in 2D subspace was then mapped and the distribution of phase points was adapted to conduct the feature extraction.FindingsThe evolution of the proposed moment measures could be described as “initial rapid decrease/increase- midterm gradual decrease/increase- finally stable,” which could effectively reveal the convergence degree of the friction-induced attractors. Moreover, the measures could also describe the relative position of the attractors in phase–space domain, which reveal the amplitude evolution of signals to some extent.Originality/valueThe proposed measures could reveal the evolution of tribological behaviors during the running-in process and meet the more precise real-time running-in status identification.

Truncated pseudo-differential operator √−▽2 and its applications in viscoacoustic reverse-time migration

SUMMARY The pseudo-differential operator with symbol |k|α has been widely used in seismic modelling and imaging when involving attenuation, anisotropy and one-way wave equation, which is usually calculated using the pseudo-spectral method. For large-scale problems, applying high-dimensional Fourier transforms to solve the wave equation that includes pseudo-differential operators is much more expensive than finite-difference approaches, and it is not suitable for parallel computing with domain decomposition. To mitigate this difficulty, we present a truncated space-domain convolution method to efficiently compute the pseudo-differential operator $\sqrt{-\nabla ^2}$, and then apply it to viscoacoustic reverse-time migration. Although $\sqrt{-\nabla ^2}$ is theoretically non-local in the space domain, we take the limited frequency band of seismic data into account, and constrain the approximated convolution stencil to a finite length. The convolution coefficients are computed by solving a least-squares inverse problem in the wavenumber domain. In addition, we exploit the symmetry of the resulting convolution stencil and develop a fast spatial convolution algorithm. The applications of the proposed method in Q-compensated reverse-time migration demonstrate that it is a good alternative to the pseudo-spectral method for computing the pseudo-differential operator $\sqrt{-\nabla ^2}$, with almost the same accuracy but much higher efficiency.

Low-Quality Video Target Detection Based on EEG Signal Using Eye Movement Alignment.

The target detection based on electroencephalogram (EEG) signals is a new target detection method. This method recognizes the target by decoding the specific neural response when an operator observes the target, which has important theoretical and application values. This paper focuses on the EEG detection of low-quality video targets, which breaks through the limitation of previous target detection based on EEG signals only for high-quality video targets. We first design an experimental paradigm for EEG-based low-quality video target detection and propose an epoch extraction method based on eye movement signals to solve the asynchronous problem faced by low-quality video target detection. Then, the neural representation in the process of operator recognition is analyzed based on the time domain, frequency domain, and source space domain, respectively. We design the time-frequency features based on continuous wavelet transform according to the neural representation and obtain an average decoding test accuracy of 84.56%. The research results of this paper lay the foundation for the development of a video target detection system based on EEG signals in the future.

Realising Ukraine’s military potential in the Space Domain

Whilst conventional assistance (such as artillery, tanks, fighter jets etc) has been widely discussed, the most indispensable asset has gone largely unrecognised even whilst it shapes all of Ukraine’s operations. From the essential communications role of Starlink to the ‘space-powered targeting information and processes underpinning Ukraine’s strike capabilities, space is the decisive supporting element. During the current war, the leverage provided by the west’s decisively superiorspace capabilities has proved to be vital in allowing Ukraine to resist an ostensibly more powerful force. However, both the need for and potential of Ukraine’s own sovereign space power is evident. Ukraine has the history and resources to be a leading space power in Europe and worldwide.In order to further this aim, once there is the conceptual space to do so, Ukraine must begin the process of renewing and developing a National Space Strategy. This should be followed by, or be concurrent to the development of formal space doctrine. There are many excellent examples of national space strategies which provide much food for thought. These ae resources from which to draw. Ukraine like all countries has a unique set of resources and experiences which must be reflected in such strategy and doctrine. The process of drafting this guidance itself promotes thinking and planning and the product can and must be a guide to all military officers as to what the space domain can offer. It can also promote and drive essential co-operation.

A new surrogate method for the neutron kinetics calculation of nuclear reactor core transients

Reactor core transient calculation is very important for the reactor safety analysis, in which the kernel is neutron kinetics calculation by simulating the variation of neutron density or thermal power over time. Compared with the point kinetics method, the time-space neutron kinetics calculation can provide accurate variation of neutron density in both space and time domain. But it consumes a lot of resources. It is necessary to develop a surrogate model that can quickly obtain the temporal and spatial variation information of neutron density or power with acceptable calculation accuracy. This paper uses the time-varying characteristics of power to construct a time function, parameterizes the time-varying characteristics which contains the information about the spatial change of power. Thereby, the amount of targets to predict in the space domain is compressed. A surrogate method using the machine learning is proposed in this paper. In the construction of a neural network, the input is processed by a convolutional layer, followed by a fully connected layer or a deconvolution layer. For the problem of time sequence disturbance, a structure combining convolutional neural network and recurrent neural network is used. It is verified in the tests of a series of 1D, 2D and 3D reactor models. The predicted values obtained using the constructed neural network models in these tests are in good agreement with the reference values, showing the powerful potential of the surrogate models.

ADAMTS13-Mediated Proteolytic Cleavage of Unusually Large von Willebrand Factor Polymers on Endothelial Cells in the Absence of Fluid Shear Stress

To investigate the molecular mechanism of proteolytic cleavage of unusually large von Willebrand Factor(ULVWF) on endothelial cells by ADAMTS13 (a disintegrin and metalloprotease with thrombospondin type 1 repeats-13) in the absence of fluid shear stress, so as to provide a theoretical basis for the pathogenesis of thrombotic thrombocytopenic purpura (TTP) and other thrombotic disorders. The ADAMTS13-mediated proteolysis of ULVWF on the surface of endothelial cells in the absence of fluid shear stress was observed through immunofluorescence microscopy. The variation in VWF antigen levels in the conditioned media were determined by ELISA assay. The levels of VWF and the proteolytic fragments released into the conditioned media were determined by ELISA assay and Western blot in the absence and presence of fluid shear stress or FVIII. The effect of ADAMTS13-mediated ULVWF cleavage on the normal distribution of plasma VWF multimers was evaluated by multimer analysis. Histamine stimulated human umbilical vein endothelial cells (HUVECs) were incubated with ADAMTS13 and various N- and C-terminally truncated mutants. Then the ULVWF that maintained binding to the cells were observed through immunofluorescence microscopy and the soluble ULVWF released from endothelial cells was determined by ELISA, so as to demonstrate the domains of ADAMTS13 required for proteolysis of ULVWF on endothelial cells. The ULVWF strings on the endothelial cell surface were rapidly proteolyzed by recombinant and plasma ADAMTS13 in the absence of fluid shear stress. This proteolytic processing of ULVWF depended on incubation time and ADAMTS13 concentration, but not shear stress and FVIII. The distribution of VWF releaseded by ADAMTS13-mediated proteolysis was quite similar to that secreted by endothelial cells under histamine stimulation, suggesting the ULVWF cleavage occured at the cell surface. The proteolysis of the ULVWF on endothelial cells required the Cys-rich(CysR) and spacer domains, but not the TSP1 2-8 and CUB domains of ADAMTS13. The ULVWF polymers on endothelial cells are sensitive to ADAMTS13-mediated cleavage even in the absence of fluid shear stress. The findings provide novel insight into the molecular mechanism of ADAMTS13-mediated ULVWF cleavage at the cellular level and may contribute to understanding of the pathogenesis of TTP and other thrombotic disorders.

WELL-POSEDNESS OF A NONLINEAR INTERFACE PROBLEM DESCRIBED BY HEMIVARIATIONAL INEQUALITIES VIA BOUNDARY INTEGRAL OPERATORS

This paper is devoted to the well-posedness of a novel nonlinear interface problem on an unbounded domain with nonmonotone set-valued transmission conditions. This interface problem involves a nonlinear monotone partial differential equation in the interior domain and the Laplacian in the exterior domain. Such a scalar interface problem models nonmonotone frictional contact of elastic infinite media. The variational formulation of the interface problem leads to a hemivariational inequality (HVI), which however lives on the unbounded domain, and thus cannot be analyzed in a reflexive Banach space setting. Boundary integral methods lead to another HVI that is amenable to functional analytic methods using standard Sobolev spaces on the interior domain and Sobolev spaces of fractional order on the coupling boundary. Broadening the scope of the paper, we consider extended real-valued HVIs augmented by convex extended real-valued functions. Under a smallness hypothesis, we provide existence and uniqueness results and, moreover, establish a stability result for extended real-valued HVIs with respect to the extended real-valued function as a parameter. Based on the latter general stability result, we provide various stability results for the interface problem, as well as the stability of a related bilateral obstacle interface problem with respect to the obstacles.

Space Domain Awareness Observations Using the Buckland Park VHF Radar

There is increasing interest in space domain awareness worldwide, motivating investigation of the use of non-traditional sensors for space surveillance. One such class of sensor is VHF wind profiling radars, which have a low cost relative to other radars typically applied to this task. These radars are ubiquitous throughout the world and may potentially offer complementary space surveillance capabilities to the Space Surveillance Network. This paper updates an initial investigation on the use of Buckland Park VHF wind profiling radars for observing resident space objects in low Earth orbit to further investigate the space surveillance capabilities of the sensor class. The radar was operated during the Australian Defence “SpaceFest” 2019 activity, incorporating new beam scheduling and signal processing functionality that extend upon the capabilities described in the initial investigation. The beam scheduling capability used two-line element propagations to determine the appropriate beam direction to use to observe transiting satellites. The signal processing capabilities used a technique based on the Keystone transform to correct for range migration, allowing the development of new signal processing modes that allow the coherent integration time to be increased to improve the SNR of the observed targets, thereby increasing the detection rate. The results reveal that 5874 objects were detected over 10 days, with 2202 unique objects detected, representing a three-fold increase in detection rate over previous single-beam direction observations. The maximum detection height was 2975.4 km, indicating a capability to detect objects in medium Earth orbit. A minimum detectable RCS at 1000 km of −10.97 dBm2 (0.09 m2) was observed. The effects of Faraday rotation resulting from the use of linearly polarised antennae are demonstrated. The radar’s utility for providing total electron content (TEC) measurements is investigated using a high-range resolution mode and high-precision ephemeris data. The short-term Fourier transform is applied to demonstrate the radar’s ability to investigate satellite rotation characteristics and monitor ionospheric plasma waves and instabilities.

Modeling analysis of pulsatile non-Newtonian blood flow in a renal bifurcated artery with stenosis

A transient transport flow model is adopted in a renal bifurcated artery with different stenosis cases to examine the flow phenomena when the non-Newtonian blood flow regime within the three-dimensional space domain is chosen as a pulsatile nature. The nonlinear governing equations are solved using the Finite Element Method with suitable initial-boundary conditions. During numerical computation, distinct hemodynamic parameters including Wall shear stress, Reynolds number, power law index are evaluated based on different stenosis severities (0%, 25%, and 50%) and stenosis lengths (0mm, 0.5mm, and 1mm). For 0%, 25%, and 50% stenosis, the approximate values of velocity and pressure profiles are estimated at t = 5s at various positions, and achieved the highest velocities that were around 0.07m/s, 0.08m/s, and 0.13m/s respectively. The unsteady response of the stress due to stenosis-induced shear on the outside wall is described as a result of stenosis at t = 0.1s, 0.2s, 5s. The flow is severely restricted by constriction, which increases wall movement and produces stress, depending on the amount of stenosis. As pulsatile flow time increases due to the presence of stenosis, the wall shear stress decreases gradually. The impact of Reynolds numbers on pressure profiles and velocity profiles are displayed for Re = 100, 300, 500, and 700, and found that both velocity and pressure outlines increase significantly. In order to identify the flow phenomena, the influences of the power law index for n = 0.1, 0.3568, 0.5 are presented on the velocity and pressure profiles. Shear-thinning flow occurs for 0<n<1, pseudoplastic flow occurs for n = 0.1 and Bingham plastic flow occurs for n = 0.5. The modeling investigations of blood flow via arteries could assist in the diagnosis and treatment of arterial illnesses; as well as expand the knowledge about cardiovascular physiology and lead the development of revolutionary medical interventions to enhance patient treatment outcomes.

Lamellar Microphase Separation and Phase Transition of Hydrogen-Bonding/Crystalline Statistical Copolymers: Amide Functionalization at the Interface.

Microphase separation of random copolymers, as well as that of high χ-low N block copolymers, is promising to construct sub-10-nm structures into materials. Herein, we designed statistical copolymers consisting of 2-hydroxyethyl acrylate (HEA) and N-octadecylacrylamide (ODAAm) to produce crystallization and hydrogen bond-assisted lamellar structure materials. The copolymers not only formed a crystalline lamellar structure with 3-4 nm domain spacing but also maintained an amorphous lamellar structure via phase transition above the melting temperature up to approximately 100 °C. The key is to introduce hydrogen-bonding amide junctions between the octadecyl groups and the polymer backbones, by which the polymer chains are physically fixed at the interface of lamellar structures even above the melting temperature. The stabilization of the lamellar structure by the amide units is also supported by the fact that the lamellar structure of all-acrylate random copolymers bearing hydroxyethyl and crystalline octadecyl groups is disordered above the melting temperature. By spin-coating on a silicon substrate, the HEA/ODAAm copolymer formed a multilayered lamellar thin film consisting of a hydrophilic hydroxyethyl/main chain phase and a hydrophobic octadecyl phase. The structure and order-disorder transition were analyzed by neutron reflectivity.

Spatiotemporal extended homogeneous field correction method for reducing complex interference in OPM-MEG

Novel magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs) have undergone rapid development in recent years. However, environmental interference significantly degrades data quality. When the number of sensors in the OPM-MEG system is small, the traditional subspace projection denoising algorithms reliant on sensor space oversampling will be difficult to apply. Although the recently proposed homogeneous field correction (HFC) method resolves this problem by constructing a low-rank spatial model, it lacks the ability to suppress complex environmental interference such as nonhomogeneous fields. Therefore, this paper proposes a novel OPM-MEG environmental interference suppression method based on HFC. We first use a projection operator constructed from a sensor orientation matrix to project original data and empty-room noise data onto the null space of the homogeneous field; this enables dimensionality reduction to eliminate homogeneous field interference. The remaining interference is then suppressed through subspace projection in the space and time domains. We compare our method to four benchmark algorithms based on simulations and somatosensory-evoked experiments. The experimental results demonstrate that the proposed method has better interference suppression performance than the benchmark algorithms. Therefore, our method can provide high signal-to-noise ratio data for subsequent clinical applications and brain scientific research.

On optimal control in a nonlinear interface problem described by hemivariational inequalities

Abstract This article is devoted to the existence of optimal controls in various control problems associated with a novel nonlinear interface problem on an unbounded domain with non-monotone set-valued transmission conditions. This interface problem involves a nonlinear monotone partial differential equation in the interior domain and the Laplacian in the exterior domain. Such a scalar interface problem models non-monotone frictional contact of elastic infinite media. The variational formulation of the interface problem leads to a hemivariational inequality (HVI), which, however, lives on the unbounded domain, and thus cannot be analyzed in a reflexive Banach space setting. Boundary integral methods lead to another HVI that is amenable to functional analytic methods using standard Sobolev spaces on the interior domain and Sobolev spaces of fractional order on the coupling boundary. Broadening the scope of this article, we consider extended real-valued HVIs augmented by convex extended real-valued functions. Under a smallness hypothesis, we provide existence and uniqueness results; moreover, we establish a stability result with respect to the extended real-valued function as a parameter. Based on the latter stability result, we prove the existence of optimal controls for four kinds of optimal control problems: distributed control on the bounded domain, boundary control, simultaneous boundary-distributed control governed by the interface problem, and control of the obstacle driven by a related bilateral obstacle interface problem.

The upper bound of the harmonic mean of the Neumann eigenvalues in curved spaces

Considering an n-dimensional Riemannian manifold M whose sectional curvature is bounded above by κ and Ricci curvature is bounded below by (n−1)K, we obtain an upper bound for the harmonic mean of the first (n−1) non-zero Neumann eigenvalues of the Laplacian for domains contained in M. This can be viewed as certain isoperimetric inequality and generalizes the results for domains in space forms [5,15].

FMRNet: Image Deraining via Frequency Mutual Revision

The wavelet transform has emerged as a powerful tool in deciphering structural information within images. And now, the latest research suggests that combining the prowess of wavelet transform with neural networks can lead to unparalleled image deraining results. By harnessing the strengths of both the spatial domain and frequency space, this innovative approach is poised to revolutionize the field of image processing. The fascinating challenge of developing a comprehensive framework that takes into account the intrinsic frequency property and the correlation between rain residue and background is yet to be fully explored. In this work, we propose to investigate the potential relationships among rain-free and residue components at the frequency domain, forming a frequency mutual revision network (FMRNet) for image deraining. Specifically, we explore the mutual representation of rain residue and background components at frequency domain, so as to better separate the rain layer from clean background while preserving structural textures of the degraded images. Meanwhile, the rain distribution prediction from the low-frequency coefficient, which can be seen as the degradation prior is used to refine the separation of rain residue and background components. Inversely, the updated rain residue is used to benefit the low-frequency rain distribution prediction, forming the multi-layer mutual learning. Extensive experiments demonstrate that our proposed FMRNet delivers significant performance gains for seven datasets on image deraining task, surpassing the state-of-the-art method ELFormer by 1.14 dB in PSNR on the Rain100L dataset, while with similar computation cost. Code and retrained models are available at https://github.com/kuijiang94/FMRNet.

Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning

This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. However, the rapid advancements in synthesis technology have led to specific artifacts for each generation model. Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources. To address this issue, we introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors. Our method forces the detector to continuously focus on high-frequency information, exploiting high-frequency representation of features across spatial and channel dimensions. Additionally, we incorporate a straightforward frequency domain learning module to learn source-agnostic features. It involves convolutional layers applied to both the phase spectrum and amplitude spectrum between the Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (iFFT). Extensive experimentation involving 17 GANs demonstrates the effectiveness of our proposed method, showcasing state-of-the-art performance (+9.8\%) while requiring fewer parameters. The code is available at https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection.

Travelling modulating pulse solutions with small tails for a nonlinear wave equation in periodic media

Travelling modulating pulse solutions consist of a small amplitude pulse-like envelope moving with a constant speed and modulating a harmonic carrier wave. Such solutions can be approximated by solitons of an effective nonlinear Schrödinger equation arising as the envelope equation. We are interested in a rigorous existence proof of such solutions for a nonlinear wave equation with spatially periodic coefficients. Such solutions are quasi-periodic in a reference frame co-moving with the envelope. We use spatial dynamics, invariant manifolds, and near-identity transformations to construct such solutions on large domains in time and space. Although the spectrum of the linearised equations in the spatial dynamics formulation contains infinitely many eigenvalues on the imaginary axis or in the worst case the complete imaginary axis, a small denominator problem is avoided when the solutions are localised on a finite spatial domain with small tails in far fields.

Approximation by BV-extension Sets via Perimeter Minimization in Metric Spaces

Abstract We show that every bounded domain in a metric measure space can be approximated in measure from inside by closed $BV$-extension sets. The extension sets are obtained by minimizing the sum of the perimeter and the measure of the difference between the domain and the set. By earlier results, in PI spaces the minimizers have open representatives with locally quasiminimal surface. We give an example in a PI space showing that the open representative of the minimizer need not be a $BV$-extension domain nor locally John.

Robustness improvement of optimal control in terms of RBFNN with empirical model reduction and transfer learning

This paper proposes a method to compute solutions of optimal controls for dynamic systems in terms of radial basis function neural networks (RBFNN) with Gaussian neurons. The RBFNN is used to compute the value function from Hamilton–Jacobi–Bellman equation with the policy iteration. The concept of dominant system is introduced to create initial coefficients of the neural networks to stabilise unstable systems and guarantee the convergence of policy iteration. Model reduction and transfer learning techniques are introduced to improve robustness of RBFNN optimal control, and reduce computational time. Numerical and experimental results show that the resulting optimal control has excellent control performance in stabilisation and trajectory tracking, and much-improved robustness to disturbances and model uncertainties even when the system responses move to a domain of the state space that is larger than the domain where the neural networks are trained.

Leveraging national security: private equity and bankruptcy in the United States defense industry

Abstract Defense contractors play an essential role in US security operations. Among other responsibilities, contractors manufacture arms, manage logistics, analyze intelligence, and carry out cybersecurity operations. Historically, defense contractors were either publicly traded corporations or privately owned companies. The past two decades, however, have seen a major shift in the ownership structure of the US defense industry. Private equity firms—once niche actors in the US national security marketplace—have carried out over 1,500 deals involving defense contractors since 2000. This study employs a mixed-methods research design to assess the effects of private equity investment on the financial stability of the US defense industry. Using data on over 8,000 defense contractors, the inquiry finds that contractors with private equity backing experience bankruptcy at higher rates than contractors with no prior private equity investment. In addition, the study evaluates private equity activity in the military satellite services market—a field of growing importance in the strategically important domain of outer space. The inquiry’s findings have notable international security implications. Given the US’ role as a lynchpin in numerous alliances around the world, higher rates of bankruptcy among defense contractors have the potential to affect the security of the United States and its allies.