Abstract
Wireless Sensor Networks (WSNs) based on the Long-Range radio modulation (LoRa) can use the LoRaWAN protocol as the medium access layer. However, this protocol only supports a single-hop star topology. As a consequence, devices can not use retransmissions along the network to extend their coverage area or to circumvent signal attenuation with distance, obstacles or interference from other radio sources. This paper proposes a multi-hop LoRaWAN wide-scale WSN based on a scheduled cluster-tree topology. This methodology can expand the spatial coverage of the network, decrease collisions, and improve overall network performance. A multi-hop cluster-tree topology eliminates the need for adjustments of LoRa radio parameters as an attempt to expand the single-hop coverage limitation. Simulation results show that the scheduled cluster-tree topology can scale the network coverage and significantly improve communication and energy consumption performances.

Abstract
Over the years, methods and algorithms have been extensively studied to solve variations of the rectangular twodimensional strip packing problem (2D-SPP), in which small rectangles must be packed inside a larger object denominated as a strip, while minimizing the space necessary to pack all rectangles. In the rectangular 2D-SPP, constraints are used to restrict the packing process, satisfying physical and real-life practical conditions that can impact the material cutting. The objective of this paper is to present an extensive literature review covering scientific publications about the rectangular 2D-SPP constraints in order to provide a useful foundation to support new research works. A systematic literature review was conducted, and 223 articles were selected and analyzed. Real-life practical constraints concerning the rectangular 2D-SPP were classified into seven different groups. In addition, a bibliometric analysis of the rectangular 2D-SPP academic literature was developed. The most relevant authors, articles, and journals were discussed, and an analysis made concerning the basic constraints (orientation and guillotine cutting) and the main solving methods for the rectangular 2D-SPP. Overall, the present paper indicates opportunities to address real-life practical constraints.

Abstract
This study contributes to learning improvement in practical classes in Computer Network technology courses, using the Physical Technological Laboratory (PTL) as a tool. Multimodal narration content analysis was used, which aggregates and organises the data collected in the PTL environment. Based on the results, we infer that both the student and the teacher use the physical laboratory as a tool since the detected physical interactions prove its use and reuse. Evidence of causality between teacher epistemic movements and learning in terms of physical interactions, epistemic practices, and student autonomy was also noted. Contributions were: (1) In the context of work in networks PTL, the variety and quality of epistemic practices of students are enhanced if there is autonomous work concomitant with the physical interaction of students with the respective artifacts. (2) Teacher action can better promote epistemic practices, stretching beyond direct action if there is an 'orchestration' of teacher mediation patterns.

Abstract
This paper presents a multi-level optimization framework for power system operators' joint electricity markets capacity-withholding assessment. The main contribution of this research is that three capacity-withholding indices are introduced for day-ahead, intra-day, and real-time scheduling of the system that detect the capacity withholding and arbitrage opportunities of Virtual Power Plants (VPPs) and non-utility fossil-fueled GENeration COmpanies (GENCOs) in an ex-ante procedure. A three-level optimization process is used so that the system operator can estimate the coordinated bidding of VPPs/GENCOs in different energy and ancillary services markets to prevent the formation of withholding groups. The first level problem consists of two stages. The first stage estimates the optimal capacity withholding and arbitrage bidding strategy of VPPs/GENCOs, and the second stage determines the optimal system scheduling for the day-ahead horizon. A full competition algorithm is also carried out to evaluate the competition states of VPPs/GENCOs. The second and third level problems consist of two optimization stages for the intra-day and real-time optimization horizons. At the first stage of each level, the process estimates the coordinated bidding of VPPs/GENCOs, and at the second stage of each level, the system resources are optimally dispatched. The proposed method is applied to 30-bus and 118-bus IEEE test systems. The proposed algorithm reduced the maximum locational marginal prices of 30-bus and 118-bus test systems by about 57.04% and 44.73% compared to the normal and the worst-case contingency operating conditions, respectively. Furthermore, the proposed method reduced the average values of day-ahead, intra-day and real-time dynamic capacity withholding indices of the 118-bus test system by about 32.92%, 40.1%, and 46.85%, respectively.

Abstract
In order to enhance the restoration capability of the distribution system during emergency conditions, a resiliency-driven critical load restoration strategy is propounded in this paper. Electric vehicles (EVs) are considered for the grid-support services to deal with challenges on such occasions, in order to maintain the power supply continuity of critical loads by reducing the number of outage periods. The collaboration between fleet operator and distribution system operator is considered in the proposed scheme, making it possible to direct available EVs to the damaged areas. The random characteristic of the seismic event is captured by generating numerous hazard scenarios using a probabilistic approach with the Monte Carlo Simulation (MCS) technique. Afterwards, the unavailability of overhead distribution branches is determined within the fragility curve concept. Besides, the uncertainties caused by EV mobility are considered by performing learning-based analyses for forecasting the location and amount of EVs in the related zone. The obtained data is processed as input parameters in a mixed-integer linear programming (MILP) framework-based stochastic model. Besides, the conceptually developed interfaces for all stakeholders in the proposed scheme are described in detail for bridging the gap between the theoretical background of the concept and practical real-world implementation.

Abstract
This paper aims to analyze some different solutions that were adopted in control education activities during the pandemic. The authors of this paper are educators in the control education field from different countries on all the continents, who have developed a questionnaire with the idea of collecting data about the COVID-19 pandemic impact on the control education activities. The main objective is to study the diverse alternatives that were used worldwide to perform the online educational activities during that period, such as methodologies, tools, learning management systems (LMS), theoretical exercises, laboratory experiments, types of exams, simulators, software for online lecturing, etc. As a result, comparisons between preand during-pandemic educational resources and methods are performed, where useful ideas and discussions are given for the control education community. Copyright (C) 2022 The Authors.