Certificate management is needed for securing certificates which has been widely deployed in satellite networks to support security-related services. However, directly utilizing existing certificate management mechanisms in satellite networks would cause critical issues in terms of security, privacy, and practicality. Typically, the trustworthiness of certificate revocation checking (CRC) cannot be guaranteed in the presence of active adversaries; The certificate to be checked contains the satellite’s identity, which is sensitive in some applications but could be exposed during CRC; CRC cannot be trivially launched when the satellite is under constrained networks (e.g., it enters dead zones where direct communication with base stations fails).Worse still, compromising the secret key of a single certificate authority (CA) leads to certificate forgery. In this paper, we propose a forward-secure and privacy-preserving certificate management scheme, dubbed SNCM, for satellite networks, where a forward-secure signature algorithm is used to issue certificates. We utilize a neighboring-assisted forwarding paradigm in SNCM to support CRC in constrained networks. SNCM is secure against adversaries who invalidate CRC results or violate related sensitive information about the satellite, which is achieved by utilizing authenticated encryption with associated data (AEAD). Furthermore, SNCM utilizes a 2-layer revocation checking protocol to perform lightweight CRC, where the CA and base stations handle CRC tasks from satellites in a cooperative way, which frees the CA from heavy costs and reduces CRC delay significantly. We analyze the security of SNCM, implement an SNCM prototype, and conduct a comprehensive performance evaluation, which demonstrates its security, efficiency, and practicality.

Certificate revocation checking (CRC) is a fundamental component for securing certificates which has been widely deployed in satellite networks to support security-related services. However, directly utilizing existing CRC mechanisms in satellite networks would cause critical issues in terms of security, privacy, and practicality. Typically, the trustworthiness of checking results cannot be guaranteed in the presence of active adversaries; the certificate to be checked contains the satellite’s identity, which is sensitive in some applications but could be exposed during CRC; CRC cannot be trivially launched when the satellite is being under constrained networks (e.g., it enters dead zones where direct communication with base stations fails). In this paper, we propose a privacy-preserving and lightweight CRC scheme, dubbed SNCRC, for satellite networks, where a neighboring-assisted forwarding paradigm is utilized to support CRC in constrained networks. SNCRC is secure against adversaries who invalidate checking results or violate related sensitive information about the satellite, which is achieved by utilizing authenticated encryption with associated data (AEAD). Furthermore, SNCRC utilizes a 2-layer revocation checking protocol to perform lightweight CRC, where the certificate authority (CA) and base stations handle CRC tasks from satellites in a cooperative way, which frees CA from heavy costs and reduces CRC delay significantly. We analyze the security of SNCRC, implement an SNCRC prototype, and conduct a comprehensive performance evaluation, which demonstrates its security, efficiency, and practicality.

Improving motion speed and efficiency remains a critical challenge in snake robots gait control. This paper introduces the Loop gait, a novel locomotion gait designed to enhance both speed and energy efficiency of snake robots without passive wheels. Compared to Crawler gait and S-pedal gait, which are more widely used, the Loop gait has a better motion speed (1.8 times of the Crawler gait in the same parameter) and a better motion efficiency (1.6 times of the Crawler gait in the same parameter) due to its more loop body morphology. A static stability model is developed to guide parameter optimization, addressing potential instability caused by elevated center of mass of snake robots. Experiments confirm the Loop gait’s exceptional energy efficiency and propulsion, validating the static stability model’s utility in selecting parameters.

Knowledge distillation is often used to transfer knowledge from a strong teacher model to a relatively weak student model. Traditional methods include response-based methods and feature-based methods. Response-based methods are widely used but suffer from lower upper limits of performance due to their ignorance of intermediate signals, while feature-based methods have constraints on vocabularies, tokenizers and model architectures. In this paper, we propose a liberal feature-based distillation method (LEAD). LEAD aligns the distribution between the intermediate layers of teacher model and student model, which is effective, extendable, portable and has no requirements on vocabularies, tokenizers, or model architectures. Extensive experiments show the effectiveness of LEAD on widely-used benchmarks, including MS MARCO Passage Ranking, TREC 2019 DL Track, MS MARCO Document Ranking and TREC 2020 DL Track. Our code is available in https://github.com/microsoft/SimXNS/tree/main/LEAD.

Knowledge distillation is an effective way to transfer knowledge from a strong teacher to an efficient student model. Ideally, we expect the better the teacher is, the better the student performs. However, this expectation does not always come true. It is common that a strong teacher model results in a bad student via distillation due to the nonnegligible gap between teacher and student. To bridge the gap, we propose PROD, a PROgressive Distillation method, for dense retrieval. PROD consists of a teacher progressive distillation and a data progressive distillation to gradually improve the student. To alleviate catastrophic forgetting, we introduce a regularization term in each distillation process. We conduct extensive experiments on seven datasets including five widely-used publicly available benchmarks: MS MARCO Passage, TREC Passage 19, TREC Document 19, MS MARCO Document, and Natural Questions, as well as two industry datasets: Bing-Rel and Bing-Ads. PROD achieves the state-of-the-art in the distillation methods for dense retrieval. Our 6-layer student model even surpasses most of the existing 12-layer models on all five public benchmarks. The code and models are released in https://github.com/microsoft/SimXNS.

Hand synergy from neuroscience provides an effective tool for anthropomorphic hands to realize versatile grasping with simple planning and control. This paper aims to extend the synergy-inspired design from anthropomorphic hands to multi-fingered robot hands. The synergy-inspired hands are not necessarily humanoid in morphology but perform primary characteristics and functions similar to the human hand. At first, the biomechanics of hand synergy is investigated. Three biomechanical characteristics of the human hand synergy are explored as a basis for the mechanical simplification of the robot hands. Secondly, according to the synergy characteristics, a three-fingered hand is designed, and its kinematic model is developed for the analysis of some typical grasping and manipulation functions. Finally, a prototype is developed and preliminary grasping experiments validate the effectiveness of the design and analysis.