Abstract
In recent years, simultaneous participation in energy and ancillary services (AS) markets has been very profitable for microgrids (MG). High penetration of renewable energy sources (RES) in energy supply, due to the uncertainties of these products, increases the need for AS. Also, active and reactive powers are completely related, so in this paper the microgrid simultaneous participation in the active and reactive power and ancillary services (regulation up and regulation down, spinning reserve and non-spinning reserve) markets is modeled considering uncertainty of wind and solar generations. The relation between active and reactive power generation of each generator is calculated based on capability diagrams and mathematical equations. Conditional value at risk (CVaR) is used for risk management, and probability of calling ancillary services is calculated. Uncertainties of wind and solar generations are modeled using their probability distribution functions (PDFs). The ERCOT market simulation is discussed to calculate the participation of each unit in all the mentioned markets based on realworld data.

Abstract
This paper proposes a two-objective linearized resilient architecture (LRA) model for distribution networks to achieve a strictly resilient network during natural disasters like earthquakes and floods. To obtain this goal, the proposed LRA framework is based on the planning of the energy storage system (ESS), hardening and tie lines, and backup distributed generation (DG). Therefore, the proposed model minimizes the sum of planning and expected operation costs in the first objective function, and the total load shedding and repair costs originates from earthquakes and floods in the second objective function. Also, it constraints to the network planning model, linearized equations of the system operation, and system reconfiguration formulation. Moreover, stochastic programming models the uncertain availability of the network equipment during the natural disaster condition, the load and electricity price. In the next step, the e-constraint-based Pareto optimization is used to achieve an equivalent single-objective LRA model and obtain the best compromise solution. Finally, the proposed strategy is applied to a standard test distribution network. Numerical simulation confirms the capability of the proposed method in obtaining a resilient distribution network during natural disasters.

Abstract
Small and medium-sized enterprises (SMEs) in Europe are conscious that their competitive position depends on their success to embrace digitalisation challenges. However, some decision-makers in companies discard digital transformation because they do not understand how it can be incorporated into their businesses. Therefore, academia, research centres, and technological clusters are responsible for building the infrastructures and providing the support and the training that will progressively change this mindset. This paper aims to report an experience on designing a training program to train the trainers under the digital transformation topic. To define strategies to understand better the companies (and professionals) needs and motivations and the requisites to deliver the training course, the focus group methodology was applied. In this paper, we present a training program methodology and structure that intend to respond to industrial requests and, in this way to accelerate the digital transformation of companies, especially SMEs.

Abstract
Hyperspectral sensors mounted in unmanned aerial vehicles offer new opportunities to explore high-resolution multitemporal spectral analysis in remote sensing applications. Nevertheless, the use of hyperspectral data still poses challenges mainly in postprocessing to correct from high geometric deformation of images. In general, the acquisition of high-quality hyperspectral imagery is achieved through a time-consuming and complex processing workflow. However, this effort is mandatory when using hyperspectral imagery in a multisensor data fusion perspective, such as with thermal infrared imagery or photogrammetric point clouds. Push-broom hyperspectral sensors provide high spectral resolution data, but its scanning acquisition architecture imposes more challenges to create geometrically accurate mosaics from multiple hyperspectral swaths. In this article, an efficient method is presented to correct geometrical distortions on hyperspectral swaths from push-broom sensors by aligning them with an RGB photogrammetric orthophoto mosaic. The proposed method is based on an iterative approach to align hyperspectral swaths with an RGB photogrammetric orthophoto mosaic. Using as input preprocessed hyperspectral swaths, apart from the need of introducing some control points, the workflow is fully automatic and consists of: adaptive swath subdivision into multiple fragments; detection of significant image features; estimation of valid matches between individual swaths and the RGB orthophoto mosaic; and calculation of the best geometric transformation model to the retrieved matches. As a result, geometrical distortions of hyperspectral swaths are corrected and an orthomosaic is generated. This methodology provides an expedite solution able to produce a hyperspectral mosaic with an accuracy ranging from two to five times the ground sampling distance of the high-resolution RGB orthophoto mosaic, enabling the hyperspectral data integration with data from other sensors for multiple applications.

Abstract
Purpose Portugal has been experiencing a continuous growth in tourism activity, with hospitality industry as one of the main tourism sectors. Therefore, the assessment of hotel companies' performance is very important to assist decision processes. The purpose of this paper is to assess the financial performance (FP) of 570 hotel companies operating hotel units in Portugal in 2017. To explore the question of brand affiliation, a comparison was made between hotel companies with similar stars rating and market orientation. In addition, this paper intends to fill a gap in literature studying the Portuguese reality on the subject of brand affiliation. Design/methodology/approach The present study uses a methodology based on data envelopment analysis (DEA) to assess the overall performance for each company, which further decomposed into the within-group performance and the technological gap. The performance of the hotel company is assessed through the aggregation of multiple financial indicators using the composite indicator (CI) derived from the DEA model. A bivariate analysis based on the Tobit regression to test the robustness of brand effect on FP of hotel companies (HC) was also included. Findings The empirical results show that branded companies, on average, have significantly better overall FP than non-branded companies. On the one hand, the brand effect tends to improve the within-group FP of HCs and the brand presents a statistically significant positive effect on the FP. On the other hand, the best practices are observed in both branded and non-branded companies. Practical implications The results of this study illustrate that, globally, the better FP of the branded companies is because of their individual relative companies' performance and a better model of operation given by the brand effect. Brand affiliation will generally allow for a better FP and essentially a better profitability for invested equity, a higher return on sales and a higher value added per employee. Originality/value The study provides important theoretical and practical contributions that can assist the strategic decision of the HCs in choosing to operate independently or to adopt brand affiliation. Also, it is innovative because the FP of branded and non-branded HCs is measured not using a set of individual financial ratios but through a single CI that aggregates those financial ratios, using a DEA model.

Abstract