SingularLogic (Greece)

The application of unmanned aerial vehicles (UAVs) in improving the coverage of terrestrial networks have been established as an important field in wireless communications. The realization of these systems is dependent on the positioning of the aerial base stations (ABSs) due to their high mobility in three dimensions (3D), which is complicated by the random measurement noise at the receivers. This paper estimates the ABS’s position using the golden section (GS) method and compares the throughput gain to terrestrial users served by the ABS, with baseline methods for the same system model. The ABS is associated with users with low downlink throughput from the stationary access point (AP), which is used by default. Additionally, the relationship between the throughput and user density is explored. System throughput of up to 110 Mbps and an average improvement to users’ data rate of 125%, are achieved.

Agricultural data have great potential to improve decision-making, enhance operational efficiency, and drive innovation. Despite the growing acknowledgment of their value, there remains a gap in understanding how data value creation is perceived and implemented in agriculture. This study addresses this gap by investigating data value creation mechanisms, targets, and impacts through a structured literature review of 80 articles, including 13 core articles retrieved via targeted database searches and 67 additional articles identified through cross-reference snowballing. Key “value creation mechanisms” are categorized as transparency and access, discovery and experimentation, prediction and optimization, customization and targeting, learning and crowdsourcing, and monitoring and adaptation. The value creation mechanisms aim to enhance key “targets”, namely organizational performance, business process improvement, product and service innovation, and consumer and market experience. Organization performance was the most frequently addressed value target, appearing in approximately 85% of the core articles, followed by business process improvement, highlighted in approximately 77% of the articles. Together, the mechanisms and targets create “impact”, constructing the value of data. The findings reveal that all core articles (100%) emphasize the functional value of agricultural data, while 54% also explore their symbolic value, which enhances reputation and market positioning. A key takeaway is that, unlike many other assets, the value of agricultural data increases with reuse, which calls for a shift in focus from data ownership to ownership of the value derived from them. This study highlights the need for robust frameworks to fully realize the potential of agricultural data and calls for future research to further characterize and assess this value. These insights are essential for developing tools and methodologies that enhance productivity, sustainability, and profitability in agriculture.

Telecommunication systems utilize several mechanisms to collect data from 5G/6G-enabled IoT. In the 5G/6G community, various AI techniques and tools are applied to 5G/6G data to monitor, predict, and make decisions. Therefore, 5G/6G data must be interoperable for monitoring, prediction, and decision support systems. However, 5G/6G data are typically mapped in local data models for local applications, which poses challenges to using them in different or cross-domain applications due to a lack of interoperability issues. In this paper, we propose an approach to support and enhance the interoperability of 5G/6G data through NLP and Semantic Web technologies to achieve 5G/6G data harmonization.

The collection and interpretation of electroencephalogram (EEG) signals are laborious and time-consuming activities, requiring a trained specialist to perform them. Automatic detection of epilepsy may be a solution. However, research on the subject has focused on detecting specific, non-generalized epilepsies in a larger patient population. Decomposition of signals, through singular spectrum analysis, of records of patients with epilepsy for subsequent verification of the energy limit. These records were available in a publicly accessible signal bank. The use of different weights to calculate means and standard deviations of the energy series and different sample sizes contributed to improve the diagnosis.

As a consequence of numerous wars and forced migrations, the Phuan kingdom, which once flourished on the Plain of Jars in Laos, was obliterated during the 19th century. Much of the population was force-marched down to the Mekong Valley and into northeastern and central Thailand. One of the last contingents settled in central Thailand’s Lopburi province, in the district of Ban Mi. After nearly 150 years of exile there, only two living traditional singers of khap phuan, both around 90 years of age, could be found and were recorded in 2012 and 2013. Since our initial documentation of them and their khaen mouth organ accompanist, all have passed away, leaving no one to carry on the tradition. This article examines these musical fragments and compares them to the living music found in the old Phuan area. Due to the stark differences between Ban Mi singing and modern khap phuan, we aimed to identify what was preserved in Thailand and what this reveals about Phuan history and migration.

In this work, we implement multiple techniques for predicting users viewing directions while watching 360° videos. We utilize historical viewing traces to forecast future directions based on a real-life head tracking dataset. We compare the performance of linear regression (LR), artificial neural networks (ANN), long short-term memory (LSTM), and convolutional neural networks (CNN). We assess their efficiency in terms of viewing angles prediction errors. We also investigate tile viewing prediction in tile-based 360° video transmission scenarios. We built two classifiers based on ANN and LSTM to predict watched tiles and provide an evaluation of their performance in this article.