Space Domain Research Articles

Analyzing the European Union’s CSDP and the evolution of European security and defense training at the ESDC: prospects and pathways

ABSTRACT This article presents a thorough examination of the European Union’s Common Security and Defense Policy (CSDP) in comparison with the educational programs offered by the European Security and Defense College (ESDC), with a specific focus on the High-Level CSDP Course. We conducted a comprehensive analysis using a meta-synthesis research approach. Our research commenced with a Systematic Literature Review and was further validated through a qualitative case study, which included interviews with the 2023 high-Level CSDP course participants. Our research findings highlight the need to prioritize emerging topics such as climate security and identify new areas in cyber and space domains to potentially expand the CSDP. We emphasized the importance of developing strategic approaches to facilitate the dissemination of comprehensive CSDP knowledge generated by the ESDC to higher education institutions. This initiative aims to equip future EU leaders with the necessary knowledge and skills to effectively engage in CSDP-related matters.

Analytical modelling and analysis of the meander-line coil EMATs

The meander-line coil electromagnetic acoustic transducer (EMAT) is widely used in the field of ultrasonic nondestructive testing due to its convenience to generate specific mode of guided waves. Some design methods of the meander-coil EMATs are developed in the frequency-wavenumber domain while others in the time–space domain. In this paper, a unified theoretical framework is developed by proposing an analytical model from the system perspective. Signal transfers between different physical fields in EMAT excitation, wave propagation and EMAT reception are represented as linear time–space-invariant systems. Taking the Rayleigh wave EMAT detection as an example, the analytical model for the transfer functions of these systems is established. The analytical model is experimentally verified by different Rayleigh wave detection techniques: the conventional EMAT, the spatial pulse compression (SPC) EMAT, temporal-spatial pulse compression (TSPC) EMAT and detection cases employing the same receiving EMAT. From the system perspective, the received signal of EMAT is interpreted as the response of the filter system to the input signal. It is found that the meander-coil EMAT can be regarded as the frequency domain expression during the detection. And the frequency domain expression plays different roles in different techniques.

An in vivo screen for proteolytic switch domains that can mediate Notch activation by force.

Notch proteins are single pass transmembrane receptors activated by sequential extracellular and intramembrane cleavages that release their cytosolic domains to function as transcription factors in the nucleus. Upon binding, Delta/Serrate/LAG-2 (DSL) ligands activate Notch by exerting a "pulling" force across the intercellular ligand/receptor bridge. This pulling force is generated by Epsin-mediated endocytosis of ligand into the signal-sending cell, and results in the extracellular cleavage of the force-sensing Negative Regulatory Region (NRR) of the receptor by an ADAM10 protease [Kuzbanian (Kuz) in Drosophila ]. Here, we have used chimeric Notch and DSL proteins to screen for other domains that can function as ligand-dependent proteolytic switches in place of the NRR in the developing Drosophila wing. While many of the tested domains are either refractory to cleavage or constitutively cleaved, we identify several that mediate Notch activation in response to ligand. These NRR analogues derive from widely divergent source proteins and have strikingly different predicted structures. Yet, almost all depend on force exerted by Epsin-mediated ligand endocytosis and cleavage catalyzed by Kuz. We posit that the sequence space of protein domains that can serve as force-sensing proteolytic switches in Notch activation is unexpectedly large, a conclusion that has implications for the mechanism of target recognition by Kuz/ADAM10 proteases and is consistent with a more general role for force dependent ADAM10 proteolysis in other cell contact-dependent signaling mechanisms. Our results also validate the screen for increasing the repertoire of proteolytic switches available for synthetic Notch (synNotch) therapies and tissue engineering.

Unsupervised domain adaptation for remote sensing semantic segmentation with the 2D discrete wavelet transform

There would be the differences in spectra, scale and resolution between the Remote Sensing datasets of the source and target domains, which would lead to the degradation of the cross-domain segmentation performance of the model. Image transfer faced two problems in the process of domain-adaptive learning: overly focusing on style features while ignoring semantic information, leading to biased transformation results, and easily overlooking the true transfer characteristics of remote sensing images, resulting in unstable model training. To address these issues, we proposes a novel dual-space generative adversarial domain adaptation segmentation framework, DS-DWTGAN, to minimize the differences between the source domain and the target domain. DS-DWTGAN aims to mitigate the distinctions between the source and target domains, thereby rectifying the imbalances in style and semantic representation.The framework introduces a network branch leveraging wavelet transform to capture comprehensive frequency domain and semantic information. It aims to preserve semantic details within the frequency domain space, mitigating image conversion deviations. Furthermore, our proposed method integrates output adaptation and data enhancement training strategies to reinforce the acquisition of domain-invariant features. This approach effectively diminishes noise interference during the migration process, bolsters model stability, and elevates the model’s adaptability to remote sensing images within different domains. Experimental validation was conducted on the publicly available Potsdam and Vaihingen datasets. The findings reveal that in the PotsdamIRRG to Vaihingen task, the proposed method attains outstanding performance with mIoU and mF1 values reaching 56.04% and 67.28%, respectively. Notably, these metrics surpass the corresponding values achieved by state-of-the-art (SOTA) methods, registering an increase of 2.81% and 2.08%. In comparison to alternative approaches, our proposed framework exhibits superior efficacy in the domain of unsupervised semantic segmentation for UAV remote sensing images.

A GAN-based stepwise full-field mechanical prediction model for architected metamaterials

The exploration of mechanical metamaterials, characterized by unique unit cells with significant macroscopic mechanical properties, is of crucial importance. Traditional methods relying on bioinspired structures lack broad design space and controllability to some extent, especially when practical requirements have no biological counterparts. Hence, there is a pressing need to advance methodologies for the expansive design of unit cells, catering to diverse practical demands. Inspired by architected material, a latent design paradigm gains prominence for its extensive possibilities and unique distribution. However, wide design space faces challenges in accurately assessing the deformation history and stress states of diverse units, requiring validation through experiments or simulations, and imposing efficiency constraints on design. Using patterns generated by a mimetic corrosion algorithm and computational simulations, a GAN-based machine learning surrogate model is constructed to predict the history of deformation and stress fields of architected unit cells with accuracy and efficiency, in which a stepwise mapping strategy is proposed to augment comprehension of mechanical information in image processing. In distinct cases covering both explored domains and several unexplored domains, the model achieves effective predictions, demonstrating the robustness and adaptability of the model in extrapolating predictions of both time domain and design space. The proposed method demonstrates efficacy, stability, and generalizability, pioneering a solution for the mechanical history prediction of architected metamaterials in a sparse data setting, thereby overcoming the limitations of the training set.

Earthquake Predictability and Forecast Evaluation Using Likelihood-Based Marginal and Conditional Scores

Abstract Earthquake probability forecasts are typically based on simulations of seismicity generated by statistical (point process) models or direct calculation when feasible. To systematically assess various aspects of such forecasts, the Collaborative Studies on Earthquake Predictability testing center has utilized N- (number), M- (magnitude), S- (space), conditional likelihood-, and T- (Student’s t) tests to evaluate earthquake forecasts in a gridded space–time range. This article demonstrates the correct use of point process likelihood to evaluate forecast performance covering marginal and conditional scores, such as numbers, occurrence times, locations, magnitudes, and correlations among space–time–magnitude cells. The results suggest that for models that only rely on the internal history but not on external observation to do simulation, such as the epidemic-type aftershock sequence model, test and scoring can be rigorously implemented via the likelihood function. Specifically, gridding the space domain unnecessarily complicates testing, and evaluating spatial forecasting directly via marginal likelihood might be more promising.

On boundedness of Hausdorff-type operators on Sobolev spaces

A new notion of a Hausdorff-type operator on function spaces over domains in Euclidean spaces is introduced, and a sufficient condition for the boundedness of this operator on Sobolev spaces is proved. It is shown that this condition cannot be weakened in general.

EGCG mildly reduced single atom Co-MXene catalytic membrane: Catalytic and antioxidant properties

As a 2D nanomaterial with high hydrophilic and co-catalytic properties, MXene has been widely studied in the field of membrane separation and advanced oxidation. However, its susceptibility to oxidation in catalytic environments poses a significant challenge to its long-term stability and performance. Therefore, constructing an oxidation-resistant MXene catalytic membrane technology and ensuring its excellent flux and catalytic performance are important for the practical application of MXene membranes. Herein, we prepared a cobalt monoatomic intercalated M/E0.1-Co catalytic separation membrane by mild reduction of epigallocatechin-3-gallate (EGCG). EGCG not only connects the MXene lamellae during filtration through strong hydrogen bonding, but also protects the chemical stability of the MXene substrate through its antioxidant ability. Furthermore, the trace amount of EGCG in the confined domain space also promotes the rate-limiting step of the transition from the high-valent state to the low-valent state in Co single-atom catalysis. The stability of the catalytic separation layer was ensured along with a 100% degradation performance of sulfamethoxazole. This study establishes a bridge between the catalytic oxidation and chemical stability of a wide range of MXene-based materials nowadays and provides a viable proposal for the practical application of long-term utilized MXene materials in catalysis.

Application of a finite element method variant in nonconvex domains to parabolic problems

In this paper we address one of the major difficulties which is the nonconvex behavior of the domains while finding the solution of the problems. The part of the domain where the nonsmoothness appears is where the challenge arises and the way that area is handled using different numerical methods reveals the effectiveness of these techniques. Here in this article, we study the semilinear parabolic problem in nonconvex polygonal domain. For the approximation of the solution we use the Composite Finite Element (CFE) method, which is a classification of the Finite Element Method. CFE discusses the two-scale discretization — the larger mesh also known as the coarse mesh with the size H and the smaller mesh, also known as the fine mesh with the size h. It helps in reducing the dimension of the domain space of consideration. The fine scale grid is used to resolve the nonconvexity of the boundary whereas the coarse scale grid is comprised of larger grids at an appropriate distance from the boundary. The degrees of freedom depends on the coarse grid. This is the precedence of CFE over other methods, i.e., it eases the task of reducing the domain complexity. In this article, we consider two approaches — the semi discrete analysis where only space discretization is carried out, and the fully discrete analysis where both the time and space discretization is done using both backward Euler and Crank–Nicolson method. We study the error analysis in the L∞(L2)-norm and in the L∞(H1)-norm for the semidiscrete case whereas for the fully discrete case, we study the error analysis in the L∞(L2)-norm. Also, we check for the optimal results. For the CFE technique in the L∞(L2)-norm, we derive the convergence having optimal order in time and almost optimal order in space even if the domain is nonconvex. We consider a T-shaped domain and another star shaped domain to carry out the theoretical findings. Thereafter, numerical computations are implemented to validate the theoretical results.

Resolution of the bidimensional shallow water equations with horizontal temperature gradients by a well Balanced Roe scheme

This work presents a numerical resolution of the two-dimensional Ripa system using Roe scheme. A discretization of the source term satisfying the conservation property is presented. The mathematical model is based on the ordinary shallow water equations in merging the horizontal temperature gradient. The numerical approach employs unstructured grids and integrates Runge–Kutta method and the minmod limiter to achieve second order accuracy in both time and space domains. A strategy of mesh adaptation based on temperature gradient is implemented to refine the study domain and gain in computational cost. Various numerical tests are conducted to verify the effeciency of the numerical method.

Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for in-plane accelerated reconstruction increases fMRI detection power

In fMRI, capturing brain activity during a task is dependent on how quickly the k-space arrays for each volume image are obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or “folded,” images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of k-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can be merged to reconstruct the subsampled k-space arrays more accurately. Here, we propose a Bayesian approach to this merged model where prior distributions for the unknown parameters are assessed from a priori k-space arrays. The prior information is utilized to estimate the missing spatial frequency values, unalias the voxel values from the posterior distribution, and reconstruct into full field-of-view images. Our Bayesian technique successfully reconstructed simulated and experimental fMRI time series with no aliasing artifacts while decreasing temporal variation and increasing task detection power.

Theoretical analysis for dynamic model of U-shaped electrothermal microactuator based on Chebyshev spectral method

Abstract This paper presents a largely and accurate analysis for dynamic model of U-shaped actuator that considers the material parameters dependent on temperature. The electro-thermal analysis for temperature distribution and thermo-mechanical analysis for deflection is carried out. The electro-thermal dynamic behaviour is described by piecewise one-dimensional (1D) heat transfer partial differential equations (PDE) for each beam of U-shaped actuator. A combination of Chebyshev spectral method for space domain and Euler forward difference method for time domain is used to solve these equations. The convergence test is first done to ensure the result convinced.

Attitude motion classification of resident space objects using light curve spectral analysis

The characterization of Earth-orbiting objects in terms of attitude motion, material composition, and shape is crucial for Space Domain Awareness. In this respect, light curves, i.e., graphs showing the brightness variation of space objects over a specific time interval, can be used to infer both their dynamical and physical properties. However, such light curve inversion problem is challenging due to measurement noise, data gaps, and the intrinsic difficulty in separating the multiple effects impacting on the object’s brightness. Existing light curve inversion methods rely on estimation filters exploiting data fusion, Machine Learning approaches, or are based on the analysis of the light-curve frequency spectra. Many of these approaches make assumptions about a-priori knowledge of target object’s characteristics, such as shape, size, surface reflective properties, and/or observation parameters, e.g., target’s orbit and observation geometry. In this framework, this paper presents an innovative architecture for the classification of the attitude motion of space objects using only photometric light curve data, not requiring any a-priori assumption or knowledge. The proposed architecture exploits the Lomb-Scargle Periodogram algorithm to retrieve a frequency spectrum from light curve measurements, and then performs the classification by analyzing the spectrum characteristics testing different candidate rotation periods through the Phase Dispersion Minimization method. The proposed classification algorithm is first tested using a light curve simulator, in which any complex geometric model and different surface properties can be generated. Finally, the method is applied to real light curves retrieved from a publicly available database to assess the classification performance.

Leveraging Synthetic Data for Star and Satellite Photometry

In the realm of Space Domain Awareness (SDA), precise photometric measurements are essential for applications such as stability analysis, shape recovery, and material studies of satellites. However, current methods that rely on manual data collection and analysis are not scalable to autonomous frameworks, which are increasingly necessary due to the growing congestion in space. This research presents an approach to automate photometric measurements within a network of telescopes operating in non-ideal conditions. Our work focuses on achieving reliable photometry in degraded weather conditions, where traditional methods might fail, leading to false detections and unnecessary follow-up efforts. We utilize the SatSim space scene simulator to generate synthetic data for training and testing photometry algorithms. These algorithms include both traditional aperture photometry and machine learning-based approaches. Our methodology employs dynamic segmentation techniques to optimize the detection of satellites and stars under various adverse conditions. The segmentation methods were evaluated for their robustness in different scenarios, with the Depth-First Search + Interquartile Range (DFS + IQR) approach showing the most promise. Through extensive experimentation, we demonstrate that our approach can achieve a photometric precision of approximately 10<sup>−1</sup>, even in adverse conditions. This represents a significant advancement in the field, as it enables more reliable satellite detection and tracking in real-world, non-photometric environments. Additionally, our ablation studies highlight the importance of balanced datasets in reducing error metrics, particularly for underrepresented satellite classes. This work contributes to the development of more effective autonomous SDA systems, capable of operating efficiently in a wide range of environmental conditions.

Geometry of Selberg's bisectors in the symmetric space SL(n,R)/SO(n,R)$SL(n,\mathbb {R})/SO(n,\mathbb {R})$

AbstractI study several problems about the symmetric space associated with the Lie group . These problems are connected to an algorithm based on Poincaré's Fundamental Polyhedron Theorem, designed to determine generalized geometric finiteness properties for subgroups of . The algorithm is analogous to the original one in hyperbolic spaces, while the Riemannian distance is replaced by an ‐invariant premetric. The main results of this paper are twofold. In the first part, I focus on questions that occurred in generalizing Poincaré's Algorithm to my symmetric space. I describe and implement an algorithm that computes the face‐poset structure of finitely sided polyhedra, and construct an angle‐like function between hyperplanes. In the second part, I study further questions related to hyperplanes and Dirichlet–Selberg domains in my symmetric space. I establish several criteria for the disjointness of hyperplanes and classify particular Abelian subgroups of based on whether their Dirichlet–Selberg domains are finitely sided or not.

An insight on technical regulations for new activities in space

Space Traffic Management (STM) is becoming increasingly important as new space activities emerge, such as mega-constellations, on-orbit servicing etc. These developments, although promising great advances, nevertheless present complicated issues that demand stringent technical constraints. Effective STM necessitates extensive rules to ensure the safety, sustainability, and orderly execution of space operations. This research emphasizes the critical need for updated technical regulations that address the operational complexities involved with these new activities. It seeks to build a secure and collaborative space environment by establishing detailed legislative frameworks and emphasizing international cooperation, so ensuring the long-term profitability and success of space exploration and usage. Through meticulous analysis, the paper identifies regulatory gaps and proposes a robust framework capable of accommodating the evolving landscape of regulations. Drawing upon humanity's collective experience across different domains, the proposed framework aims to foster responsible growth in outer space while safeguarding its integrity for future generations. This paper examines the existing regulatory framework applicable to four distinct categories of New Technologies and Activities in space; identifies gaps where they exist; and suggests a capable and robust regulatory framework. This framework leverages humankind's experience in other domains to nurture and assist responsible growth in the outer space domain while ensuring proper stewardship of that domain, now and in the future. This paper is based on the report prepared by members of working group 4.2 of the Space Traffic Management Committee (TC 26).

A uniformly convergent analysis for multiple scale parabolic singularly perturbed convection-diffusion coupled systems: Optimal accuracy with less computational time

This study addresses time-dependent multiple-scale reaction-convection-diffusion initial boundary value systems characterized by strong coupling in the reaction matrix and weak coupling in the convection terms for a locally optimal accurate solution. The discrete problem, which typically loses its tridiagonal structure, expands its bandwidth to four in such coupled systems, resulting in a substantial computational load. Our objective is to mitigate this computational burden through a splitting approach that transforms the non-tridiagonal matrix into a tridiagonal form while maintaining the consistency, local optimal accuracy in space, and stability of the numerical scheme. We employ equidistributed non-uniform grids, guided by a carefully chosen monitor function, to approximate the continuous space domain. The discretization strategy targets local optimal linear accuracy across space and time on the domain's interior points. In addition, we have also provided the global convergence analysis of the present splitting approach, mathematically. The mathematical evidence is also obtained from the numerical experiments by comparing the splitting approach (either diagonal or triangular forms) of the reaction matrix to its coupled form. The results strongly confirm the effectiveness of this approach in delivering uniform linear accuracy, based on the present problem discretizations while significantly reducing the computational costs.

A joint time–space extrapolation approach within the Wiener path integral technique for efficient stochastic response determination of nonlinear systems

A joint time–space extrapolation approach within the Wiener path integral (WPI) technique is developed for determining, efficiently and accurately, the non-stationary stochastic response of diverse nonlinear dynamical systems. The approach can be construed as an extension of a recently developed space-domain extrapolation scheme to account also for the temporal dimension. Specifically, based on a variational principle, the WPI technique yields a boundary value problem (BVP) to be solved for determining a most probable path corresponding to specific final boundary conditions. Further, the most probable path is used for evaluating, approximately, a point of the system response joint probability density function (PDF) corresponding to a specific time instant. Remarkably, the BVP exhibits two unique features that are exploited in this paper for developing an efficient joint time–space extrapolation approach. First, the BVPs corresponding to two neighboring grid points in the spatial domain of the response PDF not only share the same equations, but also the boundary conditions differ only slightly. Second, information inherent in the time-history of an already determined most probable path can be used for evaluating points of the response PDF corresponding to arbitrary time instants, without the need for solving additional BVPs. In a nutshell, relying on the aforementioned unique and advantageous features of the WPI-based BVP, the complete non-stationary response joint PDF is determined, first, by calculating numerically a relatively small number of PDF points, and second, by extrapolating in the joint time–space domain at practically zero additional computational cost. Compared to a standard brute-force implementation of the WPI technique, the developed extrapolation approach reduces the associated computational cost by several orders of magnitude. Two numerical examples relating to an oscillator with asymmetric nonlinearities and fractional derivative elements, and to a nonlinear structure under combined stochastic and deterministic periodic loading are considered for demonstrating the reliability of the extrapolation approach. Juxtapositions with pertinent Monte Carlo simulation data are included as well.

Effect of internal fluid and external shear flow on vortex-induced vibration response of drilling riser

This study introduces a new numerical tool for exploring the impact of various dimensionless parameters on vortex-induced vibration in risers. The tool is based on an innovative coupled model of a riser and a modified wake oscillator, which integrates the effects of internal axial flow and external cross flow. The dynamic equations are discretized using a second-order finite difference method in both time and space domains. Simulations reveal multi-modal behavior in the riser's vortex-induced vibration response, including lock-in and synchronous vibration. It is demonstrated that increasing internal flow velocity reduces response mode number and periodicity, and the riser becomes unstable when centrifugal force equals axial tension. The study also shows that higher maximum external flow velocities increase the frequency components of the riser's response and vortex shedding frequency range. A quarter-period phase difference between structural displacement and lift coefficient is observed, while lift coefficient and structural velocity remain synchronous. The lift frequency, as a function of position, jumps between neighboring sections of the riser. This tool provides a foundation for future research, with the cases explored serving as examples of its potential applications in investigating VIV phenomena.

Microcracking evolution and clustering fractal characteristics in coal failure under multi step and cyclic loading

During the deep mining process, coal mass encounter intricate geo-environmental stress, such as periodic weighting loading and repeatedly excavation unloading–reloading cycles, which weakens coal’s mechanical integrity and predisposing it to severe coalburst accidents. To investigate the microcracking damage mechanisms and predictive indicators in coal failure under in-situ stress analogs, the multistage step and cyclic loading experiments are conducted on cubic coal specimens. Acoustic emission (AE) technology is employed to track the spatiotemporal-energy evolution of stress-induced damages and discern the microcracking nature through AF/RA assessments, and the power-law scaling relation of AE activity near the catastrophic failure of coal is investigated. Then the clustering fractal structures of microcracking events in the stressed coal are quantified across temporal, spatial and energetic domains, utilizing correlation integral methodologies and b-value derivations from magnitude-frequency relation. Findings indicate that irrespective of the loading mode (step or cyclic), escalating stress triggers an intensification of irreversible fatigue deformations. AE characteristic parameters manifest a gradual rise, culminating in a precipitous peak coinciding with the critical failure point. This escalation adheres to a power-law correlation between AE occurrence frequency and time to failure, observable in the immediate pre-failure seconds, reflecting a universal attribute of coal fracture. Prior to ultimate failure, a marked increase in shear microcracks is discernible, despite tensile-dominated cracks (constituting about 80 % of total microcracks) prevailing as inferred from the variation of AF/RA values, aligning with an inferred “X” conjugate wedge splitting pattern from AE event density and energy mapping. The microcracking events in the loaded coal exhibit a clustering fractal structure that spans across temporal, spatial, and energetic (or magnitude) domains. Notably, the temporal fractal dimension, spatial fractal dimension, and b-value (i.e., a parameter characterized the energetic fractal dimension) all follow a parallel decrease pattern as the loading stress escalates, with a pronounced diminution becoming especially evident as the specimen approaches its catastrophic failure threshold. This insight offers fresh perspectives for predicting rock/coal dynamic disasters, emphasizing the necessity of concurrently monitoring the shift from diffuse microcracking to localized failure across time, space and energy domains. These research findings contribute to a deeper understanding of microcracking damage evolution and failure mechanism of loaded coal, and provide a foundational basis for early warning of rock failure such as the coalburst disasters.