The science and politics of an Indian ASAT capability

India has recently stated an intention to develop an anti-satellite (ASAT) capability. The reasons for this may include the country’s growing economic and political clout, alongside the increasing importance of space to this status, the significance of space assets to the military, a perceived threat from China, and fear of being disadvantaged in future treaty negotiations if not ‘in the club’. Nevertheless, development and use of an ASAT would have potentially catastrophic debris-related consequences that would also create major political problems for the user. The state of India’s ASAT activities is described and recommendations on avoiding hasty development, such as researching methods of protecting satellites, strengthening bilateral relations with China and pushing for a legally binding norm on ASATs, are made.     

Formation of nucleobases from formamide in the presence of iron oxides: implication in chemical evolution and origin of life

Simple compounds like HCN, which have one C and one N, are proposed as the probable precursors for biomonomers. Formamide, a hydrolysis product of HCN, is known as the precursor of various biologically important compounds, for example, nucleobases (purines and pyrimidines). In this paper, we report our results on the synthesis of nucleobases, adenine, cytosine, purine, 9-(hydroxyacetyl) purine, and 4(3H)-pyrimidinone from formamide, using iron oxide (hematite) and oxide hydroxides (goethite and akaganeite) as a catalyst. Goethite and hematite produced purine in higher yield. The products formed were characterized by high-performance liquid chromatography and electrospray ionization mass spectrometry techniques. Results of our study reveal that iron oxides might have worked as efficient prebiotic catalysts.     

Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system

Identification of the appropriate combination of classifier and dimensionality reduction method has been a recurring task for various hyperspectral image classification scenarios. Image classification by multiple classifier system has been evolving as a promising method for enhancing accuracy and reliability of image classification. Because of the diversity in generalization capabilities of various dimensionality reduction methods, the classifier optimal to the problem and hence the accuracy of image classification varies considerably. The impact of including multiple dimensionality reduction methods in the MCS architecture for the supervised classification of a hyperspectral image for land cover classification has been assessed in this study. Multi-source airborne hyperspectral images acquired over five different sites covering a range of land cover categories have been classified by a multiple classifier system and compared against the classification results obtained from support vector machines (SVM). The MCS offers acceptable classification results across the images or sites when there are multiple dimensionality reduction methods in addition to different classifiers. Apart from offering acceptable classification results, the MCS indicates about 5% increase in the overall accuracy when compared to the SVM classifier across the hyperspectral images and sites. Results indicate the presence of dimensionality reduction method specific empirical preferences by land cover categories for certain classifiers thereby demanding the design of MCS to support adaptive selection of classifiers and dimensionality reduction methods for hyperspectral image classification.