Capture and removal of space debris is challenging in robotic on-orbit servicing (OOS) activities. A large portion of space debris does not possess any graspable features, which makes conventional grippers inapplicable. To handle such non-graspable objects, a space robotic capture system is presented. A dual-arm space robot simulator that has the advantages of miniaturization and scalability is designed for ground tests. Inspired by robotic caging, we propose a novel capture method that uses a series of hollow-shaped end-effector pairs to cage the antipodal pairs of non-graspable objects. To apply the caging-pair method steadily, space robots need exerting a squeezing action on objects, which can be characterized by the motion and force manipulation of two robotic arms in the assigned directions. Based on the velocity and force manipulability transmission ratios, a caging compatibility index is proposed to describe the capturing ability in this manner. Via the optimization of the desired caging compatibility index, an effective algorithm is proposed to plan near-optimal joint configurations for pre-grasping cages. Finally, both simulation studies and experimental tests are conducted to evaluate the performance of the proposed capture method. Index Terms-Non-graspable objects, space robot simulator, caging-pair method, caging compatibility index. R ! ! q q w J v J J Mv k J T Mv E k k k = t k J

Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.

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Normal alkane is an unbranched alkane whose structural formula is H-CH 2 -CH 2 -… -CH 2 -… -CH 2 -H, which can be regarded as a reconfigurable chain-type structure composed of -CH 2 -modules. Inspired by normal alkane, a normal-alkane-like reconfigurable modular robot (NAR) is proposed. The module consists of two differential gear trains mounted orthogonally. Each differential gear train contains two input degrees of freedom and two output degrees of freedom. Due to the genderless interface design, multiple modules can be assembled into chain-type configuration. With the genderless interfaces and flexible degrees of freedom, NAR can be reconfigured into different dimensions of spatial configuration. The bond matrix is used to describe the configuration, which represents the bond attitude of the adjacent connected modules. In addition, full interconnected geometric feature (FIGF) algorithm is proposed for non-isomorphic configuration enumeration and judgment. The configurations with three modules are simulated and the results verify the feasibility of the algorithm. Finally, a prototype with three modules is fabricated and the configuration motion sequence is demonstrated.