The purpose of this study was to determine whether the distance between the setter, the middle hitter and the second tempo attacker can predict the attack zone in Complex I in elite male volleyball. A total amount of 3,936 rallies corresponding to 23 matches of the Men’s World Championship 2010 were analysed following a category system, recurring to an observational methodology. Measured variables were Availability of the middle hitter, Attack type, Latitude of the middle hitter, setter, and second tempo attacker, Second tempo attack lane and Distance between players. Results suggest that a relationship with high level of significance (p<0.01) exists between the middle hitter’s position and setter’s position, regardless of the occurrence of a first tempo attack. Using a classification tree, it was observed that the middle hitter’s position was modified according to setter’s position, normally with a distance of one or two metres on his left. Furthermore, the variable Distance from setter to middle hitter emerged as a key factor to dismiss a second tempo attack in the central lane. This study provides a dynamical perspective about the spatial relationships between players, and how these constitute a key factor to dismiss or not a specific final attack zone.

The extended H I rotation curves of galaxies are one of the most powerful tools for studying galaxy evolution and for testing different dark matter and modified gravity models. However, their sample sizes have historically been limited to a few hundred galaxies, mostly due to the survey speed of radio interferometers. Here, we present a first data release of H I kinematic models from the Apertif Wide-area Extragalactic Survey (AWES). Starting from 1924 galaxies detected in H I , we constructed 568 reliable kinematic models, which provide rotation curves, velocity dispersion profiles, surface density profiles, and H I disk geometries. This dataset constitutes the largest homogeneous H I rotation curve sample to date. The H I disks of the modeled galaxies span three orders of magnitude in H I mass, have outer rotation velocities between ∼20 and ∼300 km s ⁻¹ , and extend from 2 to more than 60. The number of data points of the rotation curves ranges from 3 to 23, with a peak value at 4 and a broad tail to larger values. The rotation curves display a variety of inner shapes, but in general, they tend to become flat at large radii. Based on the H I surface density profiles derived during the kinematic modeling, we studied the H I mass–size relation with a large homogeneous sample of 213 galaxies. The relation is very tight, with an observed scatter along the mass direction of ∼0.101 and an inferred intrinsic scatter along the same direction of only 0.067± 0.007. The best fitting slope is 1.96 ± 0.03 when fitting for mass as a function of size, indicating that H I disks have a constant average surface density.

The unprecedented volume of data from the Square Kilometre Array (SKA) telescopes will require the implementation of robust and solid strategies for efficient data processing and management. In this context, the SKA Regional Centre Network (SRCNet) -- a collaborative global infrastructure comprising multiple regional centres distributed across various geographical regions around the globe -- is poised to play a critical role. This network will be instrumental in facilitating the effective handling and analysis of extensive data streams generated by the telescopes, thereby enabling significant advancements in astronomical research and exploration. This paper introduces a semantic model implemented with JSON-LD designed specifically for the SRCNet, detailing its architecture, data distribution, and computing service. By explicitly defining nodes, resources, relationships, and workflows, this model lays a foundation for interoperability and efficient resource management within the distributed network. The model presented in this text supports two possible configurations: centralized and decentralized -- depending where data reside -- enabling a future service broker to efficiently plan workflows by querying nodes for real-time system availability. Consistency tests conducted using SPARQL queries were made on the model in order to validate and test its integrity. Therefore, this research contributes to the advancement of semantic modeling in astronomy by addressing the semantic model for the SRCNet, a topic that has not been previously explored. This semantic model serves as a precursor to the development of a precise mathematical representation of the network and establishes a foundational framework for a future service broker.

The distributed architecture of the SKA Regional Centre Network (SRCNet) aims to provide scientific communities worldwide with efficient computational and storage resources to exploit the massive data volumes produced by the SKA Observatory (SKAO). Given the amount of SKAO data, traditional data management paradigms — where data is transferred to computational resources— are no longer feasible. Instead, computational workflows must increasingly be relocated closer to data storage locations, emphasizing efficient data access strategies and avoiding unnecessary duplication or redundancy. In this context, we present PrepareData, a modular and extensible data delivery service developed within SRCNet prototyping activities. Our proposal for this service addresses the critical challenge of redundant data transfers and duplication at both node and user levels by enabling seamless delivery of requested datasets from local Rucio Storage Elements (RSEs) directly into users’ working environments. PrepareData operates as a local service within each SRCNet node and it is integrated into a broader ecosystem of federated services. Specifically, we designed and evaluated two distinct yet complementary implementations to avoid unnecessary data duplication and to enable a dynamic data bridge between the RSEs and the user storage areas, through: (1) a filesystem-based solution leveraging CephFS, which uses shared filesystem mount points and bind mounts to ensure consistent and immediate data availability of the data across computational nodes, and (2) a Kubernetes model using Persistent Volumes and Persistent Volume Claims, dynamically injecting data into a user’s areas. To tackle this work, we detail the architectural design and development, the technical implementation, the integration of both solutions with science-enabling tools such as JupyterHub, CARTA, or virtually any application, and, finally, we present a trade-off analysis based on operational preparation times, data-access implications, and orchestration overheads. This contribution provides a scalable and sustainable blueprint for data delivery in federated scientific infrastructures, supporting the broader goals of green computing and efficient resource utilisation.