A recent publication (Cui et al., 2025) presents an inversion method to extract temperature from multi-wavelength radiation measurements that is claimed to be accurate despite the absence of any information on emissivity. This Comment recalls that when no information on emissivity is available, we are faced with an unsolvable problem because it has a continuous infinity of physically acceptable solutions, among which the true solution is indistinguishable from the others. It is therefore impossible to extract reliably the true temperature. No inversion or optimization method can be successful, except by chance. However, in Cui et al. (2025), the validation tests showed systematic success, which can then only be explained by the fact that the "true" solution was known in advance. When this is not the case, the inversion method cannot be better than a random draw. Blind tests of multiwavelength thermometry methods allegedly not requiring emissivity information were recently organized. The results confirmed, if proof were needed, that this approach is a dead end.
A workshop on Ceramic Matrix Composites for applications (CMCs) in the 800–1300 °C temperature range was held at the 4th International Conference on High-Speed Vehicle Science and Technology in Tours, France September 22–26, 2025. The aim of the workshop was to generate recommendations for the technology development of such materials for aerospace applications. The comparison with traditional carbon and silicon carbide-based CMCs was made, establishing that they offer a key option in the search of optimized designs, especially regarding cost. These CMCs exhibit damage behavior. Taking this behavior into account offers an opportunity to reduce design margins but also to complexify the building of material databases and mechanical models. Two strategies have been adopted to demonstrate CMC technologies. The first is establishing a materials database with damage-tolerance properties before manufacturing complex components. The second is to crash-test the flight components to directly assess the component’s viability. Both have advantages and drawbacks regarding the cost and time scale of the development. Getting the design offices aware of the opportunities and specificities of these materials is key to developing viable designs. It requires setting in place dedicated training.
This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain (ODD), addressing issues like unrealistic landing scenarios and expanding coverage to multi-runway airports; (3) Benchmarking ML models for autonomous landing systems, introducing a framework for evaluating object detection subtask in a complex multi-instances setting, and providing associated open-source models as a baseline for AI models' performance.
Simple Temporal Networks with Uncertainty are a powerful and widely used formalism for representing and reasoning over convex temporal constraints, when some of them are subject to uncertainty. Since their introduction, they have been used in planning and scheduling applications to model situations where some agent acting in the real world does not control some activity durations or event timings, which are called contingent constraints. Depending on when uncertainties are revealed, one needs now to check the Weak, Dynamic or Strong controllability of the network, i.e., that there is a valid execution strategy, whatever the values of the contingent constraints. This paper proposes for the first time a semantic characterization of the possible extensions to multi-agent settings and reviews previous approaches that tried to address such topics, in order to thoroughly introduce a new type of multi-agent collaborative model, where, as opposed to previous works, each agent manages its own separate STNU, and the control over activity durations is shared among the agents: what is called here a contract is a mutual constraint controllable for some agent and contingent for others. We introduce the cSTNU, a semantically enriched version of an STNU, a set of cSTNUs forming the global Multiple Interdependent STNUs model. Then, controllability issues are revisited, and a new problem called the Repair problem is introduced, which goal is to find how to regain failed controllability by shrinking some of the shared contract durations. In this paper, we also propose the very first SMT-based centralized algorithms that are able to solve both the Weak and Strong repair problems, supported by detailed experimentation with different SMT solvers. We finally discuss why that first approach remains limited in terms of scalability, and suggest some promising alternatives to design more effective methods, both paving the way towards a Dynamic repair solving method and distributed algorithms.
