Mega satellite constellations (MSCs) based on low Earth orbit (LEO) satellites and inter-satellite links (ISLs) have become increasingly important due to the seamless coverage and high throughput. Unfortunately, the communication components of satellites are susceptible to radiation-induced single event upsets (SEUs), which lead to the failure of ISLs and the decline in network throughput. In this paper, we study the impact of SEUs on network throughput and propose MSC design algorithms to enhance the throughput. To mitigate the impact of SEUs, each satellite is equipped with low-cost mitigation techniques, under which ISLs experience different levels of impairment. Furthermore, we derive the expressions of network throughput and observe the mismatch between the traffic pattern and the network topology. Based on the expressions, we develop the MSC design algorithm to address the gap for throughput enhancement. Simulation results validate the accuracy of the theoretical results, and demonstrate that the proposed algorithm can effectively enhance the network throughput by 8.42% compared to the classical topology under the impact of SEUs.

Mega-constellation networks (MCNs) based on low Earth orbit (LEO) satellites have become increasingly important due to the high throughput and seamless coverage. However, due to single-event upsets (SEUs) caused by cosmic radiation, satellites will suffer failure, which deteriorates the network throughput. This paper aims to elucidate the relationship between network throughput and the impacts of SEUs. Taking into account the long-term impacts caused by SEUs, we present the availability of satellites based on the reliability theory. Furthermore, we model the effective data rate of inter-satellite links (ISLs) and find that the upper bound of network throughput $C \propto {\left( {\frac{{1 - {e^{ - \kappa {T_p}}}}}{{\kappa \left( {{T_p} + \gamma } \right)}}} \right)^2}\sqrt {{R_o}{R_h}} $, where κ denotes the SEU rate, and Tpis the scrubbing period for SEU mitigation. Roand Rhdenote the data rates of intra-plane ISLs and inter-plane ISLs, respectively. Consequently, the throughput decline caused by SEUs can be mitigated by adjusting the structure of MCNs. Guided by the throughput upper bound, we propose an optimal throughput constellation design algorithm (OTCDA) to enhance the network throughput considering the impact of SEUs. Experimental results illustrate that the proposed OTCDA can achieve the throughput that is only 6.49% lower than the upper bound.

The network topology is a typical element that affects the network capacity of low Earth orbit (LEO) mega-constellations. Considering the relative relationship between satellites, most mega-constellations with inclined orbits adopt the classic torus-like topology in which each satellite establishes four inter-satellite links (ISLs) with neighboring satellites. However, do we need so many ISLs to achieve the required capacity? In this paper, we provide an in-depth capacity analysis for quasi-torus topologies in which each satellite is connected by fewer than four ISLs. Based on the topological regularity and constellation scale, we first introduce the models of typical quasi-torus topologies. Considering that all the satellites are not symmetrical, we divide a quasi-torus topology into the same regions exhibiting axial symmetry. Combining the uniform traffic distribution and the symmetry of each region, we derive the closed-form formulae of network capacity by determining the maximum traffic load among ISLs. The formulae provide insights into the impact of ISL distribution and the constellation scale. Simulation results validate the theoretical analysis and show that the quasi-torus topology with fewer ISLs can achieve high capacity comparable to the torus-like topology. The theoretical analysis obtained is instrumental to the topology design of mega-constellations.

Hyperspectral image change detection (HSI-CD) possesses strong capabilities in exploring subtle changes in land cover. Due to sensor noise and imaging conditions, different semantic land covers in the same spatial location may exhibit similar spectral characteristics, leading to pseudoinvariant phenomena (identification of changed areas as unchanged areas) and causing a higher rate of false negatives in the model. Existing methods primarily focus on obtaining auxiliary discriminative information from spatial correlations or temporal dependencies. However, the frequency domain, which possesses rich global gradient distribution information, is often overlooked. The fractional Fourier transform (FrFT) is an extension of the Fourier transform (FT), representing a temporal-frequency local transformation suitable for processing nonstationary signals. Furthermore, multiorder fractional Fourier domains provide more observable domains for change discrimination. In this work, the application of FrFT is extended to the field of HSI-CD, and an adaptive weighted metric learning network based on fractional domain decoupling (FrFTML) is proposed. Specifically, the fractional domain decoupling (FrDD) module transforms the original HSI into multiorder FrFT domains and extracts their rich spatial-frequency mixed information, effectively suppressing noise while enhancing the representation of subtle differences. In addition, an adaptive weighted metric learning (AWML) framework is designed to merge multiorder fractional Fourier domain information in an adaptively weighted fusion manner. It introduces deep metric learning to explore the distances between samples of different categories that have relatively high similarity, so as to guide the direction of adaptive weighted fusion. Finally, the differential mask attention (DMA) module is designed to explore global contextual differences between bitemporal HSIs, obtaining change features with well-represented differences. Some experiments conducted on three public datasets indicate that FrFTML outperforms other state-of-the-art methods. Furthermore, the proposed method exhibits superiority in dealing with land cover that may lead to pseudoinvariant phenomena (identification of changed areas as unchanged areas).

In a low earth orbit (LEO) mega-constellation, each satellite establishes four inter-satellite links (ISLs) with its neighboring satellites to provide high capacity. However, is it necessary to establish so many ISLs in every mega-constellation? This paper investigates redundant ISLs caused by the structure asymmetry. We illustrate the existence of redundant ISLs in a mega-constellation with$P$orbital planes and$S$satellites per plane when$P\neq S$. According to the relative relationship between$P$and$S$, we determine redundant ISLs in mega-constellations with different scales. Then we prove a proposition ensuring that the capacity experiences only a minor decrease after removing redundant ISLs. Guided by the proposition, we propose an asymmetric redundant ISL removal (ARIR) algorithm to remove the maximum number of redundant ISLs while achieving the expected capacity of the complete four- Islconnecting mode. Simulation results validate that our proposed approach can remove at most 25% ISLs and achieve the expected capacity in the classical Starlink constellation.