The enhanced long-range navigation (eLoran) system serves as a reliable backup to the global navigation satellite system (GNSS) and holds strategic significance in critical applications. To address the limitations of the traditional eLoran system under cold start and incomplete chain conditions, this paper proposes a two-stage pseudorange positioning framework. First, this framework projects the Earth to a standard sphere, enabling the derivation of closed-form solutions based on measured pseudorange values. Second, a Newton-Raphson method is employed for iterative refinement, utilizing closed-form solutions as an initial position to obtain the accurate position. This framework requires less prior position information and supports flexible station combinations. Also, the framework is capable of positioning under cold start conditions and in areas beyond the coverage of the entire chain. Third, the framework is comprehensively validated through theoretical simulations and practical applications. The results demonstrate significant improvements in positioning availability and accuracy, compared to the traditional eLoran system that utilizes time difference of arrival (TDOA) and pseudorange-based local optimization methods. The proposed framework exhibits superior robustness and broader applicability, establishing a solid foundation for eLoran-based resilient navigation.

Aeromagnetic interference is one of the important factors limiting the application of aeromagnetic data on aircraft platforms. Therefore, magnetic compensation is necessary for aeromagnetic data processing, which is of great significance to improve the accuracy of geomagnetic navigation. In recent years, aeromagnetic compensation methods can be mainly divided into two categories: linear regression methods based on the Tolles–Lawson (T–L) model and data-driven methods based on machine learning. However, the accuracy of linear regression methods is subject to the complexity of the model and the problem of multicollinearity, while data-driven methods require the quantity and quality of aeromagnetic measurement data. To solve this problem, we proposed an aeromagnetic compensation method taking advantage of both the T–L model and neural network. The T–L model parameters are trained through our network, while the attention mechanism is applied in the hidden layer to enhance the feature extraction ability of the model for time series. We validate our method by applying it to an open-access dataset. The Experimental results demonstrate that our method has higher compensation accuracy and generalization performance than the classical algorithms.

Large-scale services are generally hosted on multiple application servers to scale out in today's data centers. Load balancers distribute users' requests across these servers. Software load balancer and switch-based load balancer are two typical classes of load balancers. However, most of the existing mechanisms either exhibit high processing latency at load balancers or likely lead to unbalanced requests distribution without considering the disparity of the application servers. In this paper, we study how the disparity of application servers significantly impacts the response time of requests. A fast switch-based Load Balancer considering Application Server states (LBAS) then is proposed to minimize the processing latency at both load balancers and application servers. The data plane of LBAS is well designed to store millions of connections in limited storage capacity without violating per-connection consistency. Besides, a partial dynamic weighting algorithm based on the Ridge Regression theory is designed and implemented to decrease the processing latency at application servers. We implement LBAS using the P4 programming language and conduct a series of extensive experiments to evaluate the performance. The results demonstrate that the proposed LBAS mechanism significantly reduces the response time of requests compared with Uniform random, Static weight, and Spotlight in various scenarios.

Oblivious RAM (ORAM) is a security-provable approach for protecting clients' access patterns to remote cloud storage. Recently, numerous ORAM constructions have been proposed to improve the communication efficiency of the ORAM model, but little attention has been paid to the storage efficiency. The state-of-the-art ORAM constructions have the storage overhead of O(N) or O(N log N) blocks at the server, when N data blocks are hosted. To fill the blank, this paper proposes a storage-efficient ORAM (SE-ORAM) construction with configurable security parameter λ and zero storage overhead at the server. Extensive analysis has also been conducted and the results show that, SE-ORAM achieves the configured level of security, introduces zero storage overhead to the storage server (i.e., the storage server only storages N data blocks), and incurs O(log N) blocks storage overhead at the client, as long as λ ≥ 2 and each node on the storage tree stores 4 log N or more data blocks.

As outsourcing data to remote storage servers gets popular, protecting user's pattern in accessing these data has become a big concern. ORAM constructions are promising solutions to this issue, but their application in practice has been impeded by the high communication and storage overheads incurred. Towards addressing this challenge, this paper proposes a segmentation-based ORAM (S-ORAM). It adopts two segment-based techniques, namely, piece-wise shuffling and segment-based query, to improve the performance of shuffling and query by factoring block size into design. Extensive security analysis proves that S-ORAM is a highly secure solution with a negligible failure probability of O(N-log N). In terms of communication and storage overheads, S-ORAM outperforms the Balanced ORAM (B-ORAM) and the Path ORAM (P-ORAM), which are the state-of-the-art hash and index based ORAMs respectively, in both practical and theoretical evaluations. Particularly under practical settings, the communication overhead of S-ORAM is 12 to 23 times less than B-ORAM when they have the same constant-size user-side storage, and S-ORAM consumes 80% less server-side storage and around 60% to 72% less bandwidth than P-ORAM when they have the similar logarithmic-size user-side storage.

We address the problem of 6DOF spacecraft formation control in a leader-follower architecture using Robust Integral of the Sign Error (RISE) based Neural Network (NN) technique. Based on 6DOF relative dynamic model of the spacecraft formation, RISE based NN is introduced to approximate the dynamics of the follower as well as various practical disturbances. It is shown that the errors of the entire formation closed-loop are guaranteed to be asymptotical stable (AS), which takes significant advantage over the typical Uniformly Upper Bounded (UUB) property of most NN controllers in high-precision formation tasks. Numerical simulation is provided to illustrate the effectiveness of the proposed algorithm.