{'fraction_non_alphanumeric': 0.00984009840098401, 'fraction_numerical': 0.0, 'mean_word_length': 5.564516129032258, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Linear Array For Covariance Differencing Via Hyperbolic SVD

We consider a problem pertaining to bearing estimation in unknown noise using the covariance differencing approach, and propose a linear array of processors which exhibits a linear speed-up with respect to a uniprocessor system. Our solution hinges on a new canonic matrix factorization which we term the hyperbolic singular value decomposition. The parallel algorithm for hyperbolic SVD based bearing estimation is an adaptation of a well known biorthogonalization technique developed by Hestenes. Parallel implementations of the algorithm are based on earlier works on one-sided Jacobi methods. It turns out that strategies for parallelization of Jacobi methods are equally well applicable for computing the hyperbolic singular value decomposition.