Understanding the wheel–terrain interaction (WTI) is of great importance to improve the traversability of the planetary rovers. In this article, the design and implementation of an instrumented wheel (IW) for characterizing the WTIs are presented. Such an IW is the key technique of the wheel-on-limb detection (WOLD) system that aims for the forward terrain sensing and traversability prediction before the traversal of the rover. In situ measurements of the WTI forces, wheel velocity, and wheel contact angle, as well as the wheel sinkage, can be achieved via such an IW. Experimental calibration is first implemented to guarantee the IW performance. Then, field tests in the representative environments are conducted to verify the measured parameters of WTIs. It has indicated that such a system concept would be potentially applicable in the future long-safe planetary traverses concerning the rover payloads development and functional validation.

The nonreceptor tyrosine kinase c-Abl plays important roles in T cell development and immune responses; however, the mechanism is poorly understood. IFN regulatory factor 3 (IRF3) is a key transcriptional regulator of type I IFN–dependent immune responses against DNA and RNA viruses. The data in this study show that IRF3 is physically associated with c-Abl in vivo and directly binds to c-Abl in vitro. IRF3 is phosphorylated by c-Abl and c-Abl–related kinase, Arg, mainly at Y292. The inhibitor AMN107 inhibits IFN-β production induced by poly(dA:dT), poly(I:C), and Sendai virus in THP-1 and mouse bone marrow–derived macrophage cells. IRF3-induced transcription of IFN-β is significantly reduced by the mutation of Y292 to F. Moreover, AMN107 suppresses gene expression of absent in melanoma 2 (AIM2) and subsequently reduces inflammasome activation induced by cytosolic bacteria, dsDNA, and DNA viruses. Consistent with this finding, Francisella tularensis subsp. holarctica live vaccine strain (Ft LVS), which is known as an activator of AIM2 inflammasome, induces death in significantly more C57BL/6 mice treated with the Abl inhibitor AMN107 or c-Abl/Arg small interfering RNA than in untreated mice. This study provides new insight into the function of c-Abl and Arg in regulating immune responses and AIM2 inflammasome activation, especially against Ft LVS infection. Key Points IRF3 is phosphorylated by Abl kinase family. c-Abl and Arg regulate innate immunity by promoting type I IFN production. AIM2 inflammasome activation during Ft LVS infection is prompted by Abl kinase.

It requires terrain classification for unmanned Mars Rover to identify the safe areas. The current deep learning-based semantic segmentation and object recognition suffer from a large number of parameters and long training time. In this paper, a lightweight segmentation framework called Mobile-DeepRFB is proposed for the Martian terrain classification. It improves from the DeepLabV3 $+$ by taking the MobileNetV3 as the backbone module to decrease the parameters and the Receptive Field Block (RFB) module to strengthen the feature extraction capability as well as to enlarge the receptive field. Experimental results on the NASA Mars terrain dataset AI4MARS show that the presented method reduces 94% about the parameter number and improves the mean pixel accuracy by 2% compared to the existing ResNet101 and Xception backbone networks. The deployment of this framework on a low-computing power embedded platform (NVIDIA Jetson Xavier) demonstrates its great potential to apply to Mars rovers. Note to Practitioners —This paper was motivated by the problem of terrain classification of planetary rovers. Existing methods are typically based on semantic segmentation technology to recognize various terrains while suffering from the drawback of a large number of parameters. We propose a lightweight segmentation framework to address this issue. In particular, the lightweight backbone network is applied to significantly reduce the number of parameters. The receptive field module is substantially improved to enhance the feature extraction capability. Eventually, we deploy the framework on a low-computing platform. Experimental tests show that the framework can significantly reduce the number of network parameters and it can be used for planetary rovers with limited computational resources.

Stealthy attacks in sensor and actuator loops are the research priorities in the security of cyber-physical systems. Existing attacks define the stealthiness conditions against the Chi-square or Kullback-Leibler divergence detectors and parameterize the attack model based on additive signals. Such conditions ignore the potential anomalies of the vulnerable outputs in the control layer, and the attack sequences need to be generated online, increasing the hardware and software costs. This paper investigates a type of multiplicative attack with essential stealthiness where the employed model is a novel form. The advantage is that the parameters can be designed in a constant form without having to be generated online. An essential stealthiness condition is proposed for the first time and complements the existing ones. Two sufficient conditions for the existence of constant attack matrices are given in the form of theorems, where two methods for decoupling the unknown variables are particularly considered. A quadruple-tank process, an experimental platform for attack and defense, is developed to verify the theoretical results. The experiments indicate that the proposed attack strategy can fulfill both the attack performance and stealthiness conditions.

It needs to predict the soft terrain trafficability of the Mars rover to avoid hazards in emergencies. The current prediction models of Mars terrain trafficability simply transfer information between humans and machines, with lower levels of human-machine interaction fusion, which hinders the creation of effective emergency handling measures. In this paper, a human-in-the-loop multi-indices fusion decision framework called HITL-FD is proposed for Mars rover trafficability prediction. The framework deals with the fuzziness of the trafficability boundaries by fuzzy theory, achieving the fusion processing of multiple evaluation indices and adopting a fuzzy decision tree algorithm to predict trafficability. Additionally, human-assisted decision is incorporated into the decision loop, and a reward function based on decision error is proposed to generate a closed-loop decision framework. The experimental results show that the framework has excellent trafficability prediction performance and reduces 5.1% of MAYBE terrain uncertainty compared to machine-based decision. Note to Practitioners —The trafficability prediction is crucial for Mars rovers traversing soft terrain to avoid emergencies including sinkage. Conventional trafficability prediction models have a low degree of human-machine fusion, which is not beneficial for applications of planetary rovers in practical engineering. In this paper, a multi-indices human-machine fusion decision framework is proposed, specifically designed for predicting the trafficability of Mars rover. In this framework, fuzzy theory is used to address the ambiguity associated with trafficability boundaries. It provides an overall evaluation of trafficability by fusing a single index and the use of a fuzzy decision tree algorithm to make predictions regarding trafficability. Furthermore, the framework incorporates human-assisted decision-making within the closed decision loop, and proposes a reward function based on decision error. Experimental results show that the framework can increase the correctness of trafficability prediction.

In current evaluations of intelligent system capabilities, there is a problem with the difficulty of unifying quantitative scoring results with qualitative grading standards, which severely limits cross-platform and cross-task intelligent comparisons and grading certifications. To address this issue, this paper proposes a hierarchical parameter partitioning equivalent mapping method (HPP-EM) to construct a mapping mechanism from quantitative scores to qualitative grades. The method comprises three modules: Nonlinear Interval Partitioning (NIP) constructs intelligent capability intervals based on the principle of diminishing capability margins; Quantification-to-Grade Mapping (QGM) introduces a set of criteria to determine mapping rules; and Dynamic Threshold Generation (DTA) calculates the lower score thresholds and total score thresholds for each grade based on indicator weights. The model is interpretable and supports mapping across multiple metrics and multi-level systems. Experimental validation was conducted using evaluation data from a deep-space unmanned system, demonstrating that HPP-EM accurately reflects system intelligence levels and exhibits good adaptability and scalability. This paper provides a traceable, structured mapping scheme for unified intelligence level assessment.