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The detection of seismic activity precursors as part of an alarm system will provide opportunities for minimization of the social and economic impact caused by earthquakes. It has long been envisaged, and a growing body of empirical evidence suggests that the Earth's electromagnetic field could contain precursors to seismic events. The ability to capture and monitor electromagnetic field activity has increased in the past years as more sensors and methodologies emerge. Missions such as Swarm have enabled researchers to access near-continuous observations of electromagnetic activity at second intervals, allowing for more detailed studies on weather and earthquakes. In this paper, we present an approach designed to detect anomalies in electromagnetic field data from Swarm satellites. This works towards developing a continuous and effective monitoring system of seismic activities based on SWARM measurements. We develop an enhanced form of a probabilistic model based on the Martingale theories that allow for testing the null hypothesis to indicate abnormal changes in electromagnetic field activity. We evaluate this enhanced approach in two experiments. Firstly, we perform a quantitative comparison on well-understood and popular benchmark datasets alongside the conventional approach. We find that the enhanced version produces more accurate anomaly detection overall. Secondly, we use three case studies of seismic activity (namely, earthquakes in Mexico, Greece, and Croatia) to assess our approach and the results show that our method can detect anomalous phenomena in the electromagnetic data. [ABSTRACT FROM AUTHOR]

The paper uses fifty-three social tables, ranging from Greece in 330 BCE to Mexico in 1940 to estimate the share and level of income of the top 1 % in pre-industrial societies. The share of the top 1 % covers a vast range from around 10 % to more than 40 % of society's income and does not always move together with the estimated Gini coefficient and the Inequality Extraction Ratio. I provide a taxonomy of pre-industrial societies based on the social class and type of assets (land, control of government, merchant capital, citizenship) that are associated with the top classes as well as lack of assets associated with poverty. [ABSTRACT FROM AUTHOR]