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The purpose of this study was to investigate the tribological properties of bulk Fe₂B with pre-oxidation treatment.

Bulk Fe₂B was oxidized in an electric box furnace with a soaking time of 9 min under 750°C in air. Then, the tribological experiments were carried out on an UMT-Tribolab tester.

The oxide layer was composed of Fe, Fe₂O₃, Fe₃O₄, B₂O₃ and H₃BO₃. The oxidative direction of bulk Fe₂B was perpendicular to the sample surface. But, the oxidative direction of Fe₂B crystals was irregular. At 0.1 m/s, the friction coefficient was the lowest. The effects of shortening the running-in period of friction and reducing the friction coefficient by pre-oxidation treatment at 0.1 m/s were remarkable. Nevertheless, the effect of pre-oxidation treatment was futile at 0.2 m/s. Wear mechanisms of oxidized Fe₂B mainly were adhesive and abrasive wear.

The effects of shortening the running-in period of friction and reducing the friction coefficient by pre-oxidation treatment were remarkable.

Previous knowledge base question answering (KBQA) models only consider the monolingual scenario and cannot be directly extended to the cross-lingual scenario, in which the language of questions and that of knowledge base (KB) are different. Although a machine translation (MT) model can bridge the gap through translating questions to the language of KB, the noises of translated questions could accumulate and further sharply impair the final performance. Therefore, the authors propose a method to improve the robustness of KBQA models in the cross-lingual scenario.

The authors propose a knowledge distillation-based robustness enhancement (KDRE) method. Specifically, first a monolingual model (teacher) is trained by ground truth (GT) data. Then to imitate the practical noises, a noise-generating model is designed to inject two types of noise into questions: general noise and translation-aware noise. Finally, the noisy questions are input into the student model. Meanwhile, the student model is jointly trained by GT data and distilled data, which are derived from the teacher when feeding GT questions.

The experimental results demonstrate that KDRE can improve the performance of models in the cross-lingual scenario. The performance of each module in KBQA model is improved by KDRE. The knowledge distillation (KD) and noise-generating model in the method can complementarily boost the robustness of models.

The authors first extend KBQA models from monolingual to cross-lingual scenario. Also, the authors first implement KD for KBQA to develop robust cross-lingual models.

The purpose of this paper is to plan the penetration trajectory for unmanned aerial vehicle (UAV) in the presence of radar‐guided surface to air missiles (SAMs).

The penetration trajectory planning problem is modelled based on four aspects of radar tracking features. As penetration just utilizes the low observability of radar cross section (RCS) to satisfy temporal constraints of tracking, the problem is formulated as multi‐phase trajectory planning with detected probability (MTP‐DP). While utilizing both the low observability of RCS and the radial velocity blind area of radar, the problem is formulated as multi‐phase trajectory planning with detected probability and radial velocity (MTP‐DP&RV). The pseudospectral multi‐phase optimal control based trajectory planning algorithm is proposed.

The results of the examples illustrate that the multi‐phase trajectory planning method can finely utilize the radar tracking features to optimize the comprehensive efficiency of penetration. The pseudospectral multi‐phase optimal control based trajectory planning algorithm could effectively solve the trajectory planning problem.

This paper provides new structured method to plan UAV penetration trajectory for military application and academic study.