Working Papers: Astronomy and Astrophysics Panel Reports

The papers of the panels appointed by the Astronomy and Astrophysics survey Committee are compiled. These papers were advisory to the survey committee and represent the opinions of the members of each panel in the context of their individual charges. The following subject areas are covered: radio astronomy, infrared astronomy, optical/IR from ground, UV-optical from space, interferometry, high energy from space, particle astrophysics, theory and laboratory astrophysics, solar astronomy, planetary astronomy, computing and data processing, policy opportunities, benefits to the nation from astronomy and astrophysics, status of the profession, and science opportunities

Pathway to the Square Kilometre Array - The German White Paper -

The Square Kilometre Array (SKA) is the most ambitious radio telescope ever planned. With a collecting area of about a square kilometre, the SKA will be far superior in sensitivity and observing speed to all current radio facilities. The scientific capability promised by the SKA and its technological challenges provide an ideal base for interdisciplinary research, technology transfer, and collaboration between universities, research centres and industry. The SKA in the radio regime and the European Extreme Large Telescope (E-ELT) in the optical band are on the roadmap of the European Strategy Forum for Research Infrastructures (ESFRI) and have been recognised as the essential facilities for European research in astronomy. This "White Paper" outlines the German science and R&D interests in the SKA project and will provide the basis for future funding applications to secure German involvement in the Square Kilometre Array.Comment: Editors: H. R. Kl\"ockner, M. Kramer, H. Falcke, D.J. Schwarz, A. Eckart, G. Kauffmann, A. Zensus; 150 pages (low resolution- and colour-scale images), published in July 2012, language English (including a foreword and an executive summary in German), the original file is available via the MPIfR homepag

Effect of curing conditions and harvesting stage of maturity on Ethiopian onion bulb drying properties

The study was conducted to investigate the impact of curing conditions and harvesting stageson the drying quality of onion bulbs. The onion bulbs (Bombay Red cultivar) were harvested at three harvesting stages (early, optimum, and late maturity) and cured at three different temperatures (30, 40 and 50 oC) and relative humidity (30, 50 and 70%). The results revealed that curing temperature, RH, and maturity stage had significant effects on all measuredattributesexcept total soluble solids

Cloud Segmentation and Classification from All-Sky Images Using Deep Learning

For transforming the energy sector towards renewable energies, solar power is regarded as one of the major resources. However, it is not uniformly available all the time, leading to fluctuations in power generation. Clouds have the highest impact on short-term temporal and spatial variability. Thus, forecasting solar irradiance strongly depends on current cloudiness conditions. As the share of solar energy in the electrical grid is increasing, so-called nowcasts (intra-minute to intra-hour forecasts) are beneficial for grid control and for reducing required storage capacities. Furthermore, the operation of concentrating solar power (CSP) plants can be optimized with high resolution spatial solar irradiance data. A common nowcast approach is to analyze ground-based sky images from All-Sky Imagers. Clouds within these images are detected and tracked to estimate current and immediate future irradiance, whereas the accuracy of these forecasts depends primarily on the quality of pixel-level cloud recognition. State-of-the-art methods are commonly restricted to binary segmentation, distinguishing between cloudy and cloudless pixels. Thereby the optical properties of different cloud types are ignored. Also, most techniques rely on threshold-based detection showing difficulties under certain atmospheric conditions. In this thesis, two deep learning approaches are presented to automatically determine cloud conditions. To identify cloudiness characteristics like a free sun disk, a multi-label classifier was implemented assigning respective labels to images. In addition, a segmentation model was developed, classifying images pixel-wise into three cloud types and cloud-free sky. For supervised training, a new dataset of 770 images was created containing ground truth labels and segmentation masks. Moreover, to take advantage of large amounts of raw data, self-supervised pretraining was applied. By defining suitable pretext tasks, representations of image data can be learned facilitating the distinction of cloud types. Two successful techniques were chosen for self-supervised learning: Inpainting- uperresolution and DeepCluster. Afterwards, the pretrained models were fine-tuned on the annotated dataset. To assess the effectiveness of self-supervision, a comparison with random initialization and pretrained ImageNet weights was conducted. Evaluation shows that segmentation in particular benefits from self-supervised learning, improving accuracy and IoU about 3% points compared to ImageNet pretraining. The best segmentation model was also evaluated on binary segmentation. Achieving an overall accuracy of 95.15%, a state-of-the art Clear-Sky-Library (CSL) is outperformed significantly by over 7% points

Machine Learning of Scientific Events: Classification, Detection, and Verification

Classification and segmentation of objects using machine learning algorithms have been widely used in a large variety of scientific domains in the past few decades. With the exponential growth in the number of ground-based, air-borne, and space-borne observatories, Heliophysics has been taking full advantage of such algorithms in many automated tasks, and obtained valuable knowledge by detecting solar events and analyzing the big-picture patterns. Despite the fact that in many cases, the strengths of the general-purpose algorithms seem to be transferable to problems of scientific domains where scientific events are of interest, in practice there are some critical issues which I address in this dissertation. First, I discuss the four main categories of such issues and then in the proceeding chapters I present real-world examples and the different approaches I take for tackling them. In Chapter II, I take a classical path for classification of three solar events; Active Regions, Coronal Holes, and Quiet Suns. I optimize a set of ten image parameters and improve the classification performance by up to 36%. In Chapter III, in contrast, I utilize an automated feature extraction algorithm, i.e., a deep neural network, for detection and segmentation of another solar event, namely solar Filaments. Using an off-the-shelf algorithm, I overcome several of the issues of the existing detection module, while facing an important challenge; lack of an appropriate evaluation metric for verification of the segmentations. In Chapter IV, I introduce a novel metric to provide a more accurate verification especially for salient objects with fine structures. This metric, called Multi-Scale Intersection over Union (MIoU), is a fusion of two concepts; fractal dimension from Geometry, and Intersection over Union (IoU) which is a popular metric for segmentation verification. Through several experiments I examine the advantages of using MIoU over IoU, and I conclude this chapter by a follow-through on the segmentation results of the previously implemented filament detection module

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 162, January 1977

This bibliography lists 189 reports, articles, and other documents introduced into the NASA scientific and technical information system in December 1976

Aeronautics and space report of the President, 1980 activities

The year's achievements in the areas of communication, Earth resources, environment, space sciences, transportation, and space energy are summarized and current and planned activities in these areas at the various departments and agencies of the Federal Government are summarized. Tables show U.S. and world spacecraft records, spacecraft launchings for 1980, and scientific payload anf probes launched 1975-1980. Budget data are included