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We've known for years what works to stop the spread of HIV. So why are there still so many new infections? [ABSTRACT FROM AUTHOR]

As the energy system transitions to an intelligent smart grid with a mostly renewable energy supply, synthetic energy time series are required to facilitate the development and improvement of methods for smart grid applications. These synthetic energy time series must exhibit characteristics similar to the real energy time series and applicable to specific use cases. Furthermore, evaluation methods must be applied to verify that synthetic energy time series have the desired characteristics. Whilst many methods exist in the literature to generate synthetic energy time series, up until now, no work has focused on analysing and comparing these methods. Therefore, this study provides a structured literature review of generating synthetic energy time series. The review focuses on five aspects: (1) Identifying methods used to generate synthetic energy time series, (2) categorising these methods according to the generation approach taken, (3) analysing the characteristics of these generated synthetic energy time series, (4) identifying the uses cases for which the time series are generated, and (5) considering how the generated synthetic energy time series are evaluated. In total, this study reviews 169 articles focusing on generating synthetic energy time series and identifies several key research gaps leading to multiple open research fields. The most important open research fields include the need for a standardised evaluation, more generation methods for synthetic time series from generation and battery storage systems, and a stronger focus on further use cases. Word count: 11395 [Display omitted] • A large range of methods are used to generate synthetic energy time series. • Most generation methods are applied with bottom-up or direct generation approaches. • Most generated energy time series are low aggregated residential consumption. • Typical use cases are customer profiling, power load, & energy output forecasts. • Most identified articles evaluate the fidelity of generated synthetic time series. [ABSTRACT FROM AUTHOR]

FIREBIRD-II is a National Science Foundation funded CubeSat mission designed to study the scale size and energy spectrum of relativistic electron microbursts. The mission consists of two identical 1.5 U CubeSats in a low earth polar orbit, each with two solid state detectors that differ only in the size of their geometric factors and fields of view. Having two spacecraft in close orbit allows the scale size of microbursts to be investigated through the intra-spacecraft separation when microbursts are observed simultaneously on each unit. Each detector returns high cadence (10 s of ms) measurements of the electron population from 200 keV to >1 MeV across six energy channels. The energy channels were selected to fill a gap in the observations of the Heavy Ion Large Telescope instrument on the Solar, Anomalous, and Magnetospheric Particle Explorer. FIREBIRD-II has been in orbit for 5 years and continues to return high quality data. After the first month in orbit, the spacecraft had separated beyond the expected scale size of microbursts, so the focus has shifted toward conjunctions with other magnetospheric missions. FIREBIRD-II has addressed all of its primary science objectives, and its long lifetime and focus on conjunctions has enabled additional science beyond the scope of the original mission. This paper presents a brief history of the FIREBIRD mission's science goals, followed by a description of the instrument and spacecraft. The data products are then discussed along with some caveats necessary for proper use of the data. [ABSTRACT FROM AUTHOR]