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    Implementation of a "Design of experiments" methodology for the prediction of phototransistor degradation in a space environment

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    A Design Of Experiments (DOE) methodology was suggested to define an optimized irradiation test plan. In this paper, the proposed test plan was used to model the degradation of the main performances (photo and dark current) of silicon based phototransistors arrays with respect to the Total Ionizing Dose (TID) and to the Displacement Damage Dose (DDD), over a wide range of space-kind environments. The expected performance degradation after an 18-year Low Earth Orbit (LEO) mission was calculated using this model. End-Of-Life (EOL) prediction results were compared to experimental ones obtained on devices irradiated with a proton beam degrader that simulates the 18 year LEO environment. The excellent agreement found between theoretical and experimental data makes this methodology particularly valuable for the space qualification of such devices

    An original DoE-based tool for silicon photodetectors EoL estimation in space environments

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    In our previous works we have demonstrated that Design of Experiments (DoE) is an innovative methodology defining optimized irradiation test plan and particularly valuable for the space qualification of silicon photodetectors. In particular, it provided us with the degradation model of photocurrent, darkness current, and spectral responsivity of silicon based phototransistors arrays with respect to the Total Ionizing Dose (TID) and to the Displacement Damage Dose (DDD), over a wide range of space-mission profiles. In this paper, we will summarize at first main results obtained thanks to the DoE methodology. Then we present how we can easily obtain, by exploiting DoE collected data, End-of-Life predictions of such devices with a reduced number of experiments, with a small batch of devices, and in relatively short time

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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