Abstract
Determining the PSF remains a key challenge for post adaptive-optics (AO) observations regarding the spatial, temporal and spectral variabilities of the AO PSF, as well as itx complex structure. This paper aims to provide a non-exhaustive but classified list of techniques and references that address this issue of PSF determination, with a particular scope on PSF reconstruction, or more generally pupil-plane-based approaches. We have compiled a large amount of references to synthesize the main messages and kept them at a top level. We also present applications of PSF reconstruction/models to post-processing, more especially PSF-fitting and deconvolution for which there is a fast progress in the community. © 2020 SPIE.

Abstract
Like incubators, regulatory sandboxes constitute a prominent mechanism to enable entrepreneurial activities that guide financial technology (FinTech) firms through regulatory frameworks in the financial industry. Because they are new, there is a lack of research on regulatory sandboxes; most studies have investigated legal aspects while overlooking the management perspective. To address this gap, this paper builds on incubation research studies to explore how social interactions within regulatory sandboxes influence the practices of regulators and regulatees, using social capital theory. An exploratory-abductive approach is adopted, using data collected from 16 semi-structured interviews. The findings indicate that regulator-regulatee social interactions increase the legitimacy, risk management capabilities, and knowledge of regulatory frameworks among regulatees and, as to regulators, increase their understanding of regulatory constraints and potential risks in enabling technologies, better inform them of regulatees’ support needs, and offer them early access to regulatory innovations. The findings also reveal that the practices of regulators and regulatees may be negatively affected due to lowered trust and discrepancies in expectations and underlying goals. This research contributes to the incubation literature by focusing on the micro and meso levels of knowledge exchange and the entrepreneurial finance literature by promoting the role of incubation models.

Abstract
The objective of this work is study, implementation and evaluation of compression techniques used in bioelectrical signals, applied to electroencephalography. For that, the fundamental concepts of Fast Walsh Hadamard Transform (FWHT), the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT), in essence, the mathematical models were studied. In these systems, the applicability and principles of operation were considered the Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Mean Absolute Error (MAE) and mean squared error. Later, it is proposed the implementation of the compression algorithms. For the implementation of the techniques, computational tools of tests were developed, and for the purposes of validation and comparison of the results were used, with the appropriate adaptations, and described in the work, being these among the most recognised in terms of evaluation of signal quality. Finally, we present the results and the conclusions, where we sought a compromise of the implementations between the estimated percentage of DCT and the level of degradation of the signal provided by the compression application. In this sense, it was verified that they presented satisfactory results.

Abstract
Conventional electrical networks are slowly changing. A strong sense of policy urges as well as commitments have recently been surfacing in many countries to integrate more environmentally friendly energy sources into electrical systems. In particular, stern efforts have been made to integrate more and more solar and wind energy sources. One of the major setbacks of such resources arises as a result of their intermittent nature, creating several problems in the electrical systems from a technical, market, operation, and planning perspectives. This work focuses on the operation of an electrical system with large-scale integration of solar and wind power. In order to cope with the intermittency inherent to such power sources, it is necessary to introduce more flexibility into the system. In this context, demand response, energy storage systems, and dynamic reconfiguration of the system are introduced, and the operational performance of the resulting system is thoroughly analyzed. To carry out the required analysis, a stochastic mixed-integer linear programming operational model is developed, whose efficacy is tested on an IEEE 119-bus standard network system. Numerical results indicate that the joint deployment and management of various flexibility mechanisms into the system can support a seamless integration of large-scale intermittent renewable energies.

Abstract
Unpredictable system component contingencies have imposed vulnerability on power systems, which are under high renewables penetration nowadays. Intermittent nature of renewable energy sources has made this unpredictability even worse than before and calls for excellent adaptability. This study proposes a flexible security-constrained structure to meet the superior flexibility by coordination of generation and demand sides. In the suggested model, demand-side flexibility is enabled via an optimum real-time (RT) pricing program, while the commitment of conventional units through providing up and down operational reserves improves the flexibility of supply-side. The behaviour of two types of customers is characterised to define an accurate model of demand response and the effect of customers' preferences on the optimal operation of power networks. Conclusively, the proposed model optimises RT prices in the face of contingency events as well as wind power penetration. System operators together with customers could benefit from the proposed method to schedule generation and consumption units reliably.

Abstract
The large-scale penetration of electric vehicles (EVs) into the power system will provoke new challenges needed to be handled by distribution system operators (DSOs). Demand response (DR) strategies play a key role in facilitating the integration of each new asset into the power system. With the aid of the smart grid paradigm, a day-ahead charging operation of large-scale penetration of EVs in different regions that include different aggregators and various EV parking lots (EVPLs) is propounded in this study. Moreover, the uncertainty of the related EV owners, such as the initial state-of-energy and the arrival time to the related EVPL, is taken into account. The stochasticity of PV generation is also investigated by using a scenario-based approach related to daily solar irradiation data. Last but not least, the operational flexibility is also taken into consideration by implementing peak load limitation (PLL) based DR strategies from the DSO point of view. To reveal the effectiveness of the devised scheduling model, it is performed under various case studies that have different levels of PLL, and for the cases with and without PV generation.