Abstract
5G has become increasingly popular nowadays, mainly due to its characteristics which enable high data rates and low latency. At the same time, mobile robotic platforms, such as drones and robots, appeared as suitable platforms to carry radio stations, enabling the on-demand placement of 5G communications cells. The main contribution of this paper is an obstacle-aware on-demand 5G network. The proposed solution consists of a 5G radio station (gNB) carried by a mobile robotic platform capable of providing obstacle-aware wireless connectivity to 5G User Equipments (UEs), leveraged by a novel virtual network function - On-Demand Mobility Management Function (ODMMF). ODMMF is designed to integrate the 5G Core network and it allows to monitor the radio conditions provided to the served UEs, while enabling the positioning of the mobile robotic platform remotely by taking advantage of the visual information provided by on-board video cameras. The proposed solution was validated using an experimental prototype, under a representative networking scenario.

Abstract
To enhance the economic viability and address the labour shortage in the forestry industry, alternative solutions using robotization and automation are emerging. However, due to technological barriers and lack of solid business models, successful commercialization in the forestry sector is yet to be challenging. As an initial market analysis for developing a business model for new forestry machineries, this study was conducted to reveal clusters of EU countries to seek the potential market opportunities outside of Portugal. To identify similar market conditions and restrictions, EU countries were clustered using a hierarchical clustering algorithm and selection of variables while considering the geographic, economic, and social conditions of each country. Preliminary results showed that Austria and Poland had similar social capital and geographic conditions.

Abstract
This article presents a new robust incentive-based integrated demand response (IDR) model for energy hub systems (EHSs). The considered incentive-based demand response (IBDR) schemes are interruptible/curtailable service and capacity market program. The proposed IDR model integrates the arbitrage ability of EHS storages as well as energy conversion into the IDR model. The objective of the IDR optimization problem is to maximize/minimize the allocated incentives/penalties in targeted time periods by IBDR schemes while supplying must-run processes with no interruption. Uncertainties of load and energy prices are considered through user-defined polyhedral uncertainty sets. A trilevel robust optimization (RO) is developed, which includes a trilevel min-max-min problem. To solve the trilevel adaptive robust model, the column-and-constraint generation technique is employed by means of a decomposition methodology recasting the trilevel model into a single-level min problem and a bilevel max-min problem. Unlike previous RO models that solve the inner max-min problem by duality theory, a block-coordinate-descent (BCD) methodology is used to solve the max-min problem by means of the first-order Taylor series in this study. The use of the BCD technique instead of duality theory enables a recourse-based characterization of integer variables, such as EHS storage status, which was not applicable in previous models (due to the use of duality theory). Moreover, Lagrange multipliers are eliminated as no duality is conducted. A postevent analysis is conducted to justify the long-term performance of the robust solutions and determine the optimal settings of the BCD robust approach. Results indicate that the IDR model significantly reduces the EHS input electricity in targeted time periods (four hours per day) by IBDR schemes and covers the required electricity with must-run processes by combined heat and power unit, using natural gas. This implies a 2.13% reduction in the operation cost as incentives are obtained through IBDR schemes.

Abstract
The Internet of Things (IoT) is based on objects or things that have the ability to communicate and transfer data. Due to the large number of connected objects and devices, there has been a rapid growth in the amount of data that are transferred over the Internet. To support this increase, the heterogeneity of devices and their geographical distributions, there is a need for IoT gateways that can cope with this demand. The SOFTWAY4IoT project, which was funded by the National Education and Research Network (RNP), has developed a software-defined and virtualized IoT gateway that supports multiple wireless communication technologies and fog/cloud environment integration. In this work, we propose a planning method that uses optimization models for the deployment of IoT gateways in smart campuses. The presented models aimed to quantify the minimum number of IoT gateways that is necessary to cover the desired area and their positions and to distribute IoT devices to the respective gateways. For this purpose, the communication technology range and the data link consumption were defined as the parameters for the optimization models. Three models are presented, which use LoRa, Wi-Fi, and BLE communication technologies. The gateway deployment problem was solved in two steps: first, the gateways were quantified using a linear programming model; second, the gateway positions and the distribution of IoT devices were calculated using the classical K-means clustering algorithm and the metaheuristic particle swarm optimization. Case studies and experiments were conducted at the Samambaia Campus of the Federal University of Goias as an example. Finally, an analysis of the three models was performed, using metrics such as the silhouette coefficient. Non-parametric hypothesis tests were also applied to the performed experiments to verify that the proposed models did not produce results using the same population.

Abstract
A critical factor in immersive educational narratives is identification by students with the characters. In this work-in-progress analyzes the process of rendering characters from textual narratives into visual form by non-artists (i.e., instructors). We tried to match archetypes with their visual representation through the platforms: Pixton, Powtoon (both 2D) and The Sims4 (3D). The limitations of characterization can impact students' narrative immersion. As future work we intend to test with the target group and observe the improvements needed to increase identification and sense of immersion in the narrative.

Abstract
Digital presence, especially using websites, is currently one of the pillars for effective communication in the field of marketing. However, it appears that many organizations, despite making high investments in development, even if they present a visually attractive website, face difficulties in attracting visitors. In the case of the tourism sector, in which competition takes place on a global level, it is even more pressing for technologies to reach potential stakeholders, first attracting visitors, then seducing them and converting them into customers. In this context, digital marketing techniques, and more specifically, website optimization techniques to improve their visibility by search engines (Search Engine Optimization) are the key to leveraging the potential for visits to any website. In this article, digital marketing concepts and the technological aspects that affect the indexing made by search engines will be explored. The case study presented intends to demonstrate, in a simplified way, the importance and applicability of these concepts and techniques, proposing a set of improvements that should be implemented to improve the positioning of the “Termas de Chaves” website in the search engine page results. © 2022 IEEE Computer Society. All rights reserved.