Abstract
As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Abstract
The performance of time-predictable systems can be improved in multi-core processors using parallel programming models (e.g., OpenMP). However, schedulability analysis of parallel applications is a big challenge due to their sophisticated structure. The common drawbacks of current task-to-thread mapping approaches in OpenMP are that they (i) utilize a global queue in the mapping process, which may increase contention, (ii) do not apply heuristic techniques, which may reduce the predictability and performance of the system, and (iii) use basic analytical techniques, which may cause notable pessimism in the temporal conditions. Accordingly, this paper proposes a task-to-thread mapping method in multi-core processors based on the OpenMP framework. The mapping process is carried out through two phases: allocation and dispatching. Each thread has an allocation queue in order to minimize contention, and the allocation and dispatching processes are performed using several heuristic algorithms to enhance predictability. In the allocation phase, each task-part from the OpenMP DAG is allocated to one of the allocation queues, which includes both sibling and child task-parts. A suitable thread (i.e., allocation queue) is selected using one of the suggested heuristic allocation algorithms. In the dispatching phase, when a thread is idle, a task-part is selected from its allocation queue using one of the suggested heuristic dispatching algorithms and then dispatched to and executed by the thread. The performance of the proposed method is evaluated under different conditions (e.g., varying the number of tasks and the number of threads) in terms of application response time and overhead of the mapping process. The simulation results show that the proposed method surpasses the other methods, especially in the scenario that includes overhead of the mapping. In addition, a prototype implementation of the main heuristics is evaluated using two kernels from real-world applications, showing that the methods work better than LLVM's default scheduler in most of the configurations.

Abstract
Developing real-time systems applications requires programming paradigms that can handle the specification of concurrent activities and timing constraints, and controlling execution on a particular platform. The increasing need for high-performance, and the use of fine-grained parallel execution, makes this an even more challenging task. This paper explores the state-of-the-art and challenges in real-time parallel application development, focusing on two research directions: one from the high- performance domain (using OpenMP) and another from the real-time and critical systems field (based on Ada). The paper reviews the features of each approach and highlights remaining open issues.

Abstract
The increasing relevance of remote sensing and artificial intelligence (AI) for archaeological research and cultural heritage management is undeniable. However, there is a critical gap in this field. Many studies conclude with identifying hundreds or even thousands of potential sites, but very few follow through with crucial fieldwork validation to confirm their existence. This research addresses this gap by proposing and implementing a fieldwork validation pipeline. In northern Portugal's Alto Minho region, we employed this pipeline to verify 237 potential burial mounds identified by an AI-powered algorithm. Fieldwork provided valuable information on the optimal conditions for burial mounds and the specific factors that led the algorithm to err. Based on these insights, we implemented two key improvements to the algorithm. First, we incorporated a slope map derived from LiDAR-generated terrain models to eliminate potential burial mound inferences in areas with high slopes. Second, we trained a Vision Transformer model using digital orthophotos of both confirmed burial mounds and previously identified False Positives. This further refines the algorithm's ability to distinguish genuine sites. The improved algorithm was then tested in two areas: the original Alto Minho validation region and the Barbanza region in Spain, where the location of burial mounds was well established through prior field work.

Abstract
Geospatial data acquisition methods like airborne LiDAR allow for obtaining large volumes of data, such as aerial and satellite imagery, which are increasingly being used in archaeology. As in other subjects, the ability to produce raw datasets far exceeds the capacity of domain experts to process and analyze them, but recent developments in image processing, Geographic Information Systems (GIS), Machine Learning (ML) and related technologies enable the transformation of large volumes of data into useful information. However, these technologies are challenging to use and not designed to interact with each other. Hence, tools are needed to efficiently manage, share, document, and reuse archaeological data. This article presents the Odyssey SDI platform, a spatial data infrastructure for annotating, validating, and visualizing data about archaeological sites. This platform is built upon GeoNode, and special-purpose modules were developed for dealing with archaeological information. The main contribution is the integration of remote sensing, GIS features and ML algorithms in a single framework. © 2024 The Author(s).

Abstract
All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller skate variant of team hockey, it is of great interest to automatically track player movements, positions, and sticks, and also to make other judgments, such as being able to locate the ball. In this work, we present a real-time pipeline consisting of an object detection model specifically designed for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and fast movements, our deep learning object detection model effectively identifies and tracks important visual elements in real time, such as: ball, players, sticks, referees, crowd, goalkeeper, and goal. Using a curated dataset consisting of a collection of rink hockey videos containing 2525 annotated frames, we trained and evaluated the algorithm's performance and compared it to state-of-the-art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80% and, according to our results, good performance in terms of accuracy and speed, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected an important event type in rink hockey games, namely, the occurrence of penalties.