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The effect of fire exposure on the thermal behavior of carbon fiber reinforced (CFR) composite plates has been studied. Samples have been exposed to fire produced by a standard burner. The temperature evolution of both faces of the sample has been recorded by a two-camera system providing co-referenced and synchronized images. The camera imaging the "hot face" has been spectrally tuned in order to minimize the flame effect on the measurement. Several fire experiments have been performed with different exposure times. An adaptation for insulating materials of the classical flash method has been developed to obtain the values of thermal parameters for the burned sample. Different states of samples damage produced by fire application have been determined and characterized providing quantitative information about the thermal behavior of these materials exposed to fire.

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An innovative solution named Annotation as a Service (AaaS) has been specifically designed to integrate heterogeneous video annotation workflows into containers and take advantage of a cloud native highly scalable and reliable design based on Kubernetes workloads. Using the AaaS as a foundation, the execution of automatic video annotation workflows is addressed in the broader context of a semi-automatic video annotation business logic for ground truth generation for Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADAS). The document presents design decisions, innovative developments, and tests conducted to provide scalability to this cloud-native ecosystem for semi-automatic annotation. The solution has proven to be efficient and resilient on an AD/ADAS scale, specifically in an experiment with 25 TB of input data to annotate, 4000 concurrent annotation jobs, and 32 worker nodes forming a high performance computing cluster with a total of 512 cores, and 2048 GB of RAM. Automatic pre-annotations with the proposed strategy reduce the time of human participation in the annotation up to 80% maximum and 60% on average.