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Cultural heritage (CH) sites are frequently exposed to natural elements, and their exposure becomes particularly precarious with the onset of climate change. This increased vulnerability places these sites at risk of deterioration or complete destruction. Risks such as land deformation, floods, acid rain, and erosion significantly threaten historic monuments, while water-related hazards, significantly influenced by both climate change and human activities, present a particularly grave risk to these invaluable sites. Considerable research efforts have focused on safeguarding CH sites. However, there remains a deficiency in systemic approaches towards identifying and mitigating risks for CH sites. The TRIQUETRA project proposes a technological toolbox and a methodological framework for tackling climate change risks and natural hazards threatening CH in the most efficient way possible. It aims at creating an evidence-based assessment platform allowing precise risk stratification as well as a database of available mitigation measures and strategies, acting as a Decision Support System (DSS) towards efficient risk mitigation and site remediation. TRIQUETRA is a European project that brings together a diverse group of researchers with varied expertise, encompassing university research groups, research institutes, public entities, as well as small and medium-sized enterprises. In this article, TRIQUETRAs overall methodology is presented, and preliminary results concerning risk identification, TRIQUETRAs knowledge base, as well as novel sensors and coatings, are discussed.

On 19th September 2021, a protracted eruption of the Cumbre Vieja Volcano on the Canary Island of La Palma commenced and continued for a duration of 12 weeks. Lava flows starting from the rift zone at the mid-western flank of Cumbre Vieja advanced toward the western coast of the island. The eruption was monitored by different remote sensing satellites, including the Copernicus Sentinel missions and DESIS. The Sentinel-2 Copernicus satellites acquired multispectral data from 15th September onward. On September 30th, and with a difference of ∼2 h with respect to Sentinel-2 A, the DESIS hyperspectral sensor also acquired data from the volcano and then again on 15th October 2021. Typically, mid-infrared (around 3.8 μm) data are used for the thermal analysis of active lava flows. However, neither Sentinel-2 nor DESIS possesses mid-infrared bands and the Sentinel-2 high-wavelengths bands (∼2 μm) have some limitations. Nevertheless, the hyperspectral character of DESIS enables the analysis of active erupting volcanoes in near-infrared wavelengths. The results of this analysis find fluid lava temperatures of about 1100–1200 K but there are problems associated with the high-temperature lava spectral emissivity.

A vast population of low-cost low-power transmitters sporadically sending small amounts of data over a common wireless medium is one of the main scenarios for Internet of things (IoT) data communications. At the medium access, the use of grant-free solutions may be preferred to reduce overhead even at the cost of multiple-access interference. Unsourced multiple access (UMA) has been recently established as relevant framework for energy efficient grant-free protocols. The use of a compressed sensing (CS) transmission phase is key in one of the two main classes of UMA protocols, yet little attention has been posed to sparse greedy algorithms as orthogonal matching pursuit (OMP) and its variants. We analyze their performance and provide relevant guidance on how to optimally setup the CS phase. Minimum average transmission power and minimum number of channel uses are investigated together with the performance in terms of receiver operating characteristic (ROC). Interestingly, we show how the basic OMP and generalized OMP (gOMP) are the most competitive algorithms in their class. Comment: 6 pages, 4 figures, Accepted for publication at IEEE WCNC 2024 WS

Grasping objects with limited or no prior knowledge about them is a highly relevant skill in assistive robotics. Still, in this general setting, it has remained an open problem, especially when it comes to only partial observability and versatile grasping with multi-fingered hands. We present a novel, fast, and high fidelity deep learning pipeline consisting of a shape completion module that is based on a single depth image, and followed by a grasp predictor that is based on the predicted object shape. The shape completion network is based on VQDIF and predicts spatial occupancy values at arbitrary query points. As grasp predictor, we use our two-stage architecture that first generates hand poses using an autoregressive model and then regresses finger joint configurations per pose. Critical factors turn out to be sufficient data realism and augmentation, as well as special attention to difficult cases during training. Experiments on a physical robot platform demonstrate successful grasping of a wide range of household objects based on a depth image from a single viewpoint. The whole pipeline is fast, taking only about 1 s for completing the object's shape (0.7 s) and generating 1000 grasps (0.3 s). Comment: 8 pages, 10 figures, 3 tables, 1 algorithm, 2023 IEEE-RAS International Conference on Humanoid Robots (Humanoids), Project page: https://dlr-alr.github.io/2023-humanoids-completion