Abstract
Demand response (DR) services have the potential to enable large penetration of renewable energy by adjusting load consumption, thus providing balancing support to the grid. The success of such load flexibility provided by industry, communities, or prosumers and its integration in electricity markets, will depend on a redesign and adaptation of the current interactions between participants. New challenges are, however, bound to appear with the large scale contribution of smaller assets to flexibility, including, among others, the dispatch coordination, the validation of delivery of the DR provision, and the corresponding settlement of contracts, while assuring secured data access among interested parties. In this study we applied distributed ledger (DLT)/blockchain technology to securely track DR provision, focusing on the validation aspect, assuring data integrity, origin, fast registry, and sharing within a permissioned system, between all relevant parties (including transmission system operators (TSOs), aggregators, distribution system operators (DSOs), balance responsible parties (BRP), and prosumers). We propose a framework for DR registry and implemented it as a proof of concept on Hyperledger Fabric, using real assets in a laboratory environment, in order to study its feasibility and performance. The lab set up includes a 450 kW energy storage system, scheduled to provide DR services, upon a system operator request and the corresponding validations and verifications are done, followed by the publication on a blockchain. Results show the end to end execution time remained below 1 s, when below 32 requests/sec. The smart contract memory utilization did not surpass 1% for both active and passive nodes and the peer CPU utilization, remained below 5% in all cases simulated (3, 10, and 28 nodes). Smart Contract CPU utilization remained stable, below 1% in all cases. The performance of the implementation showed scalable results, which enables real world adoption of DLT in supporting the development of flexibility markets, with the advantages of blockchain technology.

Abstract
Context. GCIRS 7, the brightest star in the Galactic central parsec, formed 6 2 Myr ago together with dozens of massive stars in a disk orbiting the central black-hole. It has been argued that GCIRS 7 is a pulsating body, on the basis of photometric variability.Aims. Our goal is to confirm photospheric pulsations based on interferometric size measurements to better understand how the mass loss from these massive stars enriches the local interstellar medium.Methods. We present the first medium-resolution (R = 500), K-band spectro-interferometric observations of GCIRS 7, using the GRAVITY instrument with the four auxiliary telescopes of the ESO VLTI. We looked for variations using two epochs, namely 2017 and 2019.Results. We find GCIRS 7 to be moderately resolved with a uniform-disk photospheric diameter of theta (*)(UD)=1.55 +/- 0.03 theta UD*=1.55 +/- 0.03 mas ( R-UD(*)=1368 +/- 26 RUD*=1368 +/- 26 R-circle dot) in the K-band continuum. The narrow-band uniform-disk diameter increases above 2.3 mu m, with a clear correlation with the CO band heads in the spectrum. This correlation is aptly modeled by a hot (T-L = 2368 +/- 37 K), geometrically thin molecular shell with a diameter of theta (L) = 1.74 +/- 0.03 mas, as measured in 2017. The shell diameter increased (theta (L) = 1.89 +/- 0.03 mas), while its temperature decreased (T-L = 2140 +/- 42 K) in 2019. In contrast, the photospheric diameter theta (*)(UD)theta UD* and the extinction up to the photosphere of GCIRS 7 ( AKS=3.18 +/- 0.16KS=3.18 +/- 0.16 ) have the same value within uncertainties at the two epochs.Conclusions. In the context of previous interferometric and photo-spectrometric measurements, the GRAVITY data allow for an interpretation in terms of photospheric pulsations. The photospheric diameter measured in 2017 and 2019 is significantly larger than previously reported using the PIONIER instrument (theta * = 1.076 +/- 0.093 mas in 2013 in the H band). The parameters of the photosphere and molecular shell of GCIRS 7 are comparable to those of other red supergiants that have previously been studied using interferometry. The extinction we measured here is lower than previous estimates in the direction of GCIRS 7 but typical for the central parsec region.

Abstract
Having an online presence is essential for any company regardless of its size and type of business. Users are currently striving to interact with companies through the web, regardless of their access device. In this sense, responsive web design emerged as a very useful technique that allows the dynamic adaptation of the design regardless of the size and resolution of the access device. Despite the unequivocal advantages associated with this technique, there are also limitations which turn this approach not feasible or advisable for all projects. This study, through the realization of five case studies, seeks to identify the main limitations of responsive design and responsive design frameworks. Additionally, this study suggests further development models that may be more effective in the dynamic adaptation of the design and contents according to the features of the access device, such as the adoption of adaptive design, use of native apps, and hybrid models.

Abstract
Unpredictable faults always reduce the stability and reliability of the electrical system. The increasing use of renewable energy sources (RES) in recent decades has exacerbated power system problems. Micro grids (MG) participation in Ancillary Services (AS) market is a suitable solution for the optimal performance of power systems in these conditions. MGs can also maximize their profits by participating in the AS market. In this paper, the optimal stochastic bidding strategy in joint energy and AS (regulation up and regulation down, spinning reserve and non-spinning reserve) market is modeled. Uncertainties of wind speed and solar radiation are modeled using Weibull and Beta probability distribution functions (PDFs) and probability of call AS is computed for all available AS. Therefore, the risk of the bidding strategy is controlled using conditional value at risk (CVaR). ERCOT market simulation has been carried out in order to determine the participation of each generator in all of the mentioned markets for different prices of energy and also to present the bidding curve, based on real-world data.

Abstract
The importance of agriculture and the production of fruits and vegetables has stood out mainly over the past few years, especially for the benefits for our health. In 2021, in the international year of fruit and vegetables, it is important to encourage innovation and evolution in this area, with the needs surrounding the different processes of the different cultures. This paper compares the performance between two datasets for robotics fruit harvesting using four deep learning object detection models: YOLOv4, SSD ResNet 50, SSD Inception v2, SSD MobileNet v2. This work aims to benchmark the Open Images Dataset v6 (OIDv6) against an acquired dataset inside a tomatoes greenhouse for tomato detection in agricultural environments, using a test dataset with acquired non augmented images. The results highlight the benefit of using self-acquired datasets for the detection of tomatoes because the state-of-the-art datasets, as OIDv6, lack some relevant characteristics of the fruits in the agricultural environment, as the shape and the color. Detections in greenhouses environments differ greatly from the data inside the OIDv6, which has fewer annotations per image and the tomato is generally riped (reddish). Standing out in the use of our tomato dataset, YOLOv4 stood out with a precision of 91%. The tomato dataset was augmented and is publicly available (See https://rdm.inesctec.pt/ and https://rdm.inesctec.pt/dataset/ii-2021-001).

Abstract
Cardiac auscultation is the key screening procedure to detect and identify cardiovascular diseases (CVDs). One of many steps to automatically detect CVDs using auscultation, concerns the detection and delimitation of the heart sound boundaries, a process known as segmentation. Whether to include or not a segmentation step in the signal classification pipeline is nowadays a topic of discussion. Up to our knowledge, the outcome of a segmentation algorithm has been used almost exclusively to align the different signal segments according to the heartbeat. In this paper, the need for a heartbeat alignment step is tested and evaluated over different machine learning algorithms, including deep learning solutions. From the different classifiers tested, Gate Recurrent Unit (GRU) Network and Convolutional Neural Network (CNN) algorithms are shown to be the most robust. Namely, these algorithms can detect the presence of heart murmurs even without a heartbeat alignment step. Furthermore, Support Vector Machine (SVM) and Random Forest (RF) algorithms require an explicit segmentation step to effectively detect heart sounds and murmurs, the overall performance is expected drop approximately 5% on both cases.