Immigration of Indian indentured labourers to work on various overseas destinations began over the debris of slavery and this led to a never ending debate over the nature of indenture labour regime in terms of the freedom and unfreedom of the labour. This article attempts to argue that the problematic of this incessant debate lies in referring to 'classic' slavery as the closed model of reference for all forms of labour servitude. By studying the historical experiences of Indian indentured labourers in Mauritius, this article attempts to shift the core of this debate from institutional definitions to the decisive role of circumstantial necessities and perspectives culminating in multiple forms of labour servitude. This article examines the question of freedom and unfreedom in the indentured labour regime at two levels of ascendency-the physical and the moral-being exemplified through the regulation of vagrancy among the Indian indentured labourers, the language of command and the colonial lexicon while referring to the Indian labourers; and it concludes that the indenture labour regime was a form of servitude, though not essentially a new system of slavery, as the labour was denied of its economic freedom and occupational and territorial mobility in the indenture system. This article attempts to revisit the incessant debate around the typological dichotomy of free and unfree labour by situating it within the historical context of the indentured immigration of Indian labourers to Mauritius under the British colonial rule. The deliberations over the nature of labour regime under the indenture system are not very typical and recent and it occupies an indispensable space among almost all the references of the indenture system. However, most of these studies intervene only in generally predictable terms like the system of recruitment, the levels of exploitation and protection in the indenture system, etc. which makes the discussion rather perfunctory. The argument this paper endeavours to put forth is that the root of the problem in assessing the question of freedom and unfreedom in the context of indentured immigrant labour is not only the use of slavery as a derivative discourse of analysis but also in using it in the analogous sense with servitude. Therefore, the essential rationale is to underline the need to reassess this enduring debate beyond these institutional terms and to trace the multiple

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Lightning strokes create powerful electromagnetic pulses that routinely cause very low frequency (VLF) waves to propagate across hemispheres along geomagnetic field lines. VLF antenna receivers can be used to detect these whistler waves generated by these lightning strokes. The particular time/frequency dependence of the received whistler wave enables the estimation of electron density in the plasmasphere region of the magnetosphere. Therefore the identification and characterisation of whistlers are important tasks to monitor the plasmasphere in real time and to build large databases of events to be used for statistical studies. The current state of the art in detecting whistler is the Automatic Whistler Detection (AWD) method developed by Lichtenberger ( 2009) [1]. This method is based on image correlation in 2 dimensions and requires significant computing hardware situated at the VLF receiver antennas (e.g. in Antarctica). The aim of this work is to develop a machine learning based model capable of automatically detecting whistlers in the data provided by the VLF receivers. The approach is to use a combination of image classification and localisation on the spectrogram data generated by the VLF receivers to identify and localise each whistler. The data at hand has around 2300 events identified by AWD at SANAE and Marion and will be used as training, validation, and testing data. Three detector designs have been proposed. The first one using a similar method to AWD, the second using image classification on regions of interest extracted from a spectrogram, and the last one using YOLO, the current state of the art in object detection. It has been shown that these detectors can achieve a misdetection and false alarm of less than 15% on Marion's dataset.