Zero-shot remote sensing is attractive for scene classification because new regions, sensors, and label taxonomies often appear before sufficient annotated data are available for supervised adaptation. We present OATS-RS, an inference-centric framework that keeps a remote sensing vision–language model (VLM) backbone frozen and improves zero-shot decisions through ontology-aware prompt construction, hierarchical and contrastive scoring, adaptive multi-view aggregation, unlabeled transductive refinement, ambiguity-aware local re-ranking, and selective prediction. The method targets the common remote sensing regime in which neighboring classes such as annual crop, permanent crop, forest, pasture, herbaceous vegetation, river, and sea or lake overlap strongly in red–green–blue (RGB) appearance, meaning that they require more than a single class-name prompt. On the supplied final EuroSAT RGB evaluation with a GeoRSCLIP Contrastive Language–Image Pre-training (CLIP)-family Vision Transformer Base with 32 × 32-pixel patches (ViT-B-32) backbone, the complete pipeline obtains top-1 accuracy of 0.522, balanced accuracy of 0.522, macro-averaged F1 score (macro-F1) of 0.535, and top-3 accuracy of 0.887. The strongest classes are industrial area, residential area, river, highway, and pasture, whereas the weakest classes remain herbaceous vegetation and several fine-grained vegetation categories. Selective prediction increases accepted-example accuracy to 0.538 at 0.934 coverage, but the expected calibration error (ECE) remains high at 0.384. These results support a qualified conclusion: ontology-guided zero-shot inference can already recover useful semantic shortlists for structured remote-sensing scenes, but fine-grained natural-class disambiguation, calibrated confidence, multi-dataset transfer, component-level ablations, and measured runtime remain essential before dependable deployment claims can be made.

A multi-disciplinary team – the ROBOMINERS consortium – is creating a robot-miner for the future exploitation of difficult to access deposits. The approach builds on using robotics-related capacities for the mining sector. In particular, the ROBOMINERS vision foresees the use of a modular and reconfigurable robot in a mining setting where activities are nearly invisible. Mining will be more socio-environmentally viable, thus contributing to a more safe and sustainable supply of mineral raw materials fostered by the EU Raw Materials policies. When compared to current mining methods, the ROBOMINERS approach aims at: no presence of people in the mine, less mining waste produced and mining infrastructure needed, less investment, the possibility to explore currently uneconomic resources and development of new underground small-sized mines.In the past two years, work focused on studying and designing enabling technologies, robot components and capabilities. The next steps will include integration of different software and hardware components leading to the development of the first robotic prototype (December 2022). Critical aspects of previous studies included 1) biological inspiration, 2) perception and localisation tools, 3) robot's behaviour, navigation and control, 4) actuation methods, 5) modularity, 6) autonomy and resilience, and 7) the selective mining ability, including development of ore perception and specialized production tools. Knowledge and technology transfer from these sub-fields to the robot-miner concept were possible thanks to collaborative work developed by the different mining and robotics teams in the laboratory and online, even during the COVID-19 times.At the same time, the vision of a new mining robotic "ecosystem" is being developed: 1) computer models and simulations, 2) data management and visualisation systems, 3) rock mechanical and geotechnical characterisation, 4) analysing ground/rock support methods, bulk transportation methods, backfilling types and mining methods, and 5) sketching  upstream and downstream mining industry analogues for the ROBOMINERS concept.Merging of robotics and geoscientific know-how for the purpose of creating test environments (simulated and real), construction of scale models (actual and virtual), iterative development and testing key robotic functions, together with the creation of a pool of deposits that could become viable targets for future extraction, and economical studies, back up the implementation capacity of the technology.Thanks to the integration of the previous mentioned aspects, the mining machine will be able to perform autonomous selective ore extraction. The prototype will be tested at targeted areas representatives, including abandoned and/or operating mines, small but high-grade mineral deposits, unexplored/explored non-economic occurrences and ultra-depth, not easily accessible environments. Possible current candidates for testing purposes include mines in Estonia, Slovenia or Belgium. The trials are scheduled for 2023 and will provide a first case for the operability of this new mining machine and concept.ROBOMINERS aims at delivering a proof of concept for the feasibility of this technology line at Technology Readiness Level 4, being validated in the lab and in the test mine locations. With future-proof improvements to the technology (deriving from roadmapping) it could enable the EU to access mineral raw materials from domestic sources that are otherwise inaccessible or uneconomic.

A cikk a földközeli objektumok (near earth objects, NEO) becsapódási kockázatának értékelési módszereit mutatja be, különös tekintettel az Apophis aszteroidára, amely a bolygóvédelem egyik aktuális kihívása. Először ismertetjük a hagyományos Torino- és Palermo-skálákat. Majd bevezetni javaslunk egy kozmikus becsapódás kockázatbecslő skálát: a „Cosmic Impact Risk Assessment Scale” (CIRAS-) skálát, amely hét kulcsfontosságú tényező – energia, becsapódási valószínűség, hátralévő idő, becsapódási helyszín, atmoszferikus hatás, másodlagos veszélyek és elhárítási nehézség – integrált értékelésével finomítja a kockázat meghatározását. Az Apophis aszteroida 2029-es földközelsége kivételes példaként szolgál, hiszen a kezdeti, bizonytalan pályaadatok magas kockázatot sugalltak, míg a legfrissebb mérések a szinte elhanyagolható ütközési esélyt mutatják. A skálák kombinált alkalmazása nem csupán a kockázatok átláthatóbb kommunikációját teszi lehetővé, hanem támogatja a célzott bolygóvédelmi stratégiák kialakítását is. Az új megközelítés révén megbízhatóbb előrejelzések készíthetők, amelyek hozzájárulhatnak a társadalmi és technológiai felkészültség javításához, valamint a potenciális veszélyek időben történő felismeréséhez. A részletes, többdimenziós kockázatértékelés hozzájárulhat a gyors reagáláshoz és a megelőző intézkedések megtételéhez, így biztosítva a Föld (lakóinak) biztonságát a kozmoszból érkező potenciális veszélyekkel szemben. Ez a megközelítés új távlatokat nyithat a planetáris védelem területén.

Gamma-ray bursts (GRBs) are bright transient sources that can be observed at high redshift. They can therefore be used as tracers of the distant large-scale structure, although the GRB redshift sample is sparse and affected by strong selection effects. We analyze the three-dimensional distribution of 542 GRBs with spectroscopic redshifts. The method extends our earlier spherical-window search by replacing spherical counting volumes with axisymmetric spheroids. The angular positions of the observed GRBs are kept fixed in the Monte-Carlo null samples, while the redshifts are shuffled within each Galactic hemisphere. In the Northern Galactic hemisphere, the overdensity associated with the Hercules–Corona Borealis Great Wall remains significant for a range of spheroidal shapes. This supports the stability of the previously reported signal and shows that it is not only a consequence of using spherical counting volumes. In the Southern Galactic hemisphere, the same method finds no structure with comparable significance. A small candidate grouping is present, but its significance is only marginal, and it is sensitive to the small number of events. We conclude that spheroidal resampling is a useful check of GRB overdensity searches, but the physical nature of any candidate structure still requires confirmation with independent tracers such as galaxy or quasar samples.