Resistance of natural glass

Natural glass is a well-known phenomenon found in different geological formations on both the earth and the lunar surface. This demonstrates that the vitreous state has been resistant to change on these surfaces for more than a hundred million years. Glass formation is determined by magmatic, metamorphic and sedimentary processes. The quantities of naturally produced glass vary between 10^-6 and 10^9 g, and the occurrence is given in microscopie glassy inclusions as well as in 'glassy mountains'. The chemical composition of natural glass is mainly rhyolitic and peraluminous. Industrially produced glass usually contains less AI2O3 (< 10 wt%) than glass found in its natural state. The transformation of the glassy state occurs in different processes (in many cases at constant volume): crystallization ('devitrification'), solution (corrosion), or hydratization and pallagonitization. By these processes, glass is transformed into sheet silicates, zeolithes, feldspars etc. and amorphous gels of silicon, iron, manganese etc. Natural glass has been under consideration for use as a suitable material for isolating complex and dangerous wastes. To help solve problems of waste management particular to industrial production, and to achieve a greater productive use of natural glass resources, glass scientists and engineers as well as geoscientists will have to cooperate in the future

Experimental investigations of the internal friction of optical and technical glasses with a flexure pendulum equipment

Α special flexure pendulum equipment for investigation of the mechanical relaxation behaviour (internal friction) of glasses, glassceramics and other materials is presented. With the help of this apparatus it is possible to investigate the internal friction in the temperature ränge from room temperature until beyond Tg with a frequency of about 1 Hz. For analyzing the experimental results the shear behaviour of the investigated material is described in terms of a Maxwell body characterized by the rheological parameters Young's modulus and relaxation time. From the theoretical treatment of the bending problem for the experimental equipment calculation, formulae are obtained which connect the rheological parameters with the measured quantities period of oscillation and logarithmic decrement. Investigations of the internal friction of optical and technical glasses show weak effects in the ränge from room temperature until Tg and very strong damping peaks with different peak widths and maxima temperatures in the ränge beyond Tg. The syntheses and glass transformation ranges of the investigated glasses are very different. The activation energies of the hightemperature relaxation processes are obtained from the measured temperature dependences of relaxation times. These activation energies of the high-temperature relaxation process beyond Tg are smaller than the activation energies of viscosity determined by means of a bar elongation method

Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios

This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to a 50 megapixel camera with per-pixel depth available. Existing datasets, such as the KITTI benchmark, provide only sparse reference measurements with an order of magnitude lower angular resolution - these sparse measurements are treated as ground truth by existing depth estimation methods. We propose an evaluation methodology in four characteristic automotive scenarios recorded in varying weather conditions (day, night, fog, rain). As a result, our benchmark allows us to evaluate the robustness of depth sensing methods in adverse weather and different driving conditions. Using the proposed evaluation data, we demonstrate that current stereo approaches provide significantly more stable depth estimates than monocular methods and lidar completion in adverse weather. Data and code are available at https://github.com/gruberto/PixelAccurateDepthBenchmark.git.Comment: 3DV 201

Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather

The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant information in good environmental conditions, they fail in adverse weather where the sensory streams can be asymmetrically distorted. These rare "edge-case" scenarios are not represented in available datasets, and existing fusion architectures are not designed to handle them. To address this challenge we present a novel multimodal dataset acquired in over 10,000km of driving in northern Europe. Although this dataset is the first large multimodal dataset in adverse weather, with 100k labels for lidar, camera, radar, and gated NIR sensors, it does not facilitate training as extreme weather is rare. To this end, we present a deep fusion network for robust fusion without a large corpus of labeled training data covering all asymmetric distortions. Departing from proposal-level fusion, we propose a single-shot model that adaptively fuses features, driven by measurement entropy. We validate the proposed method, trained on clean data, on our extensive validation dataset. Code and data are available here https://github.com/princeton-computational-imaging/SeeingThroughFog

5. Wochenbericht MSM105

FS MARIA S. MERIAN Fahrt MSM105 11.01.2022 – 23.02.2022 Walvis Bay – Mindelo BUSUC II Das Benguela-System im Klimawandel - Auswirkungen der Variabilität des physikalischen Antriebs auf den Kohlenstoff- und Sauerstoffhaushalt 5. Wochenbericht 07. - 13.02.202