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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
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Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Review of technology trends in new space missions using a patent analytics approach

Progress in Aerospace Sciences. 2021. Vol. 125. Article 100727.
Garzaniti N., Tekic Z., Kukolj D., Golkar A.

This review paper analyzes technology trends observed in New Space missions, using a patent analytics approach. The analysis is complemented with a literature review of the subjects identified. The main objective of this review is to draw a comprehensive picture of technology trends in New Space and to discuss potential scenarios for further development of this novel type of missions. Building on the existing scientific literature, we survey alternative definitions and then propose our own definition of New Space missions as synthesis of the ongoing debate in the field.

We identified more than two hundred organizations active in the development of both upstream and downstream products and services for New Space missions. Using a commercially available patent analytics tool, we collected 933 patents protecting technologies disclosed by those organizations. We used the Latent Dirichlet Allocation (LDA), a topic modeling algorithm, to analyze patents' data and to identify the underlying structures in New Space's technology trends. Ten major topics have been identified respectively named “Remote sensing & image acquisition”, “Flying/launch systems”, “Telecommunication systems”, “Constellation management”, “Digital Processing Architectures”, “Image analysis”, “Manufacturing process & materials”, “Feature recognition and extraction”, “Antenna systems”, and “Space platforms”.

A deeper analysis of patents' claims revealed a core cluster defined as “data cluster” including data analysis topics (Remote sensing & image acquisition, Image analysis, and Feature recognition and extraction), data transmission topics (Telecommunication systems and Antenna systems from both hardware and software perspective) and related enabling technologies such as constellation management. Based on this analysis, the evolution of data cluster related technologies is surveyed in order to review the current technology state of the art as well as identifying potential areas that will benefit the most from further technology developments to improve New Space missions’ performance.

Lastly, we identify technology trends for future New Space development based on the insights obtained by the synthesis of our literature and patent survey. We discuss the key directions for future development emerging from our review, such as the use of commercial off the shelf components, the increasing use of miniaturized technology, and software defined technology.

Research target: Engineering and Technology
Priority areas: management engineering science
Language: English
DOI
Text on another site
Keywords: topic modelingtechnology trendspatent analyticsnew spaceSpace systems
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