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Although the optical cometary database is extensive, the radio database is limited. The 18-cm OH maser observations of comets allow us to determine (i) the production rate of OH ($Q_{\rm{OH}}$) and (ii) the water expansion velocity, for each comet. To reveal the physical properties of the periodic comet 12P/Pons-Brooks, we conducted the OH maser observations of the comet using the 40-m TNRT (Thai National Radio Telescope) on March 22nd, 27th and 29th, 2024 before the perihelion passage on April 21st, 2024. We successfully detected 1665 and 1667 MHz OH maser emissions from the comet. The average OH production rates of 12P/Pons-Brooks were determined as 4.28$\pm$0.30 $\times$ 10$^{29}$ sec$^{-1}$, 5.21$\pm$0.42 $\times$ 10$^{29}$ sec$^{-1}$, and 3.36$\pm$0.43 $\times$ 10$^{29}$ sec$^{-1}$ for March 22nd, 27th and 29th, respectively. Combining our results with previous estimates, we find that the OH production rate of 12P/Pons-Brooks shows some fluctuations on timescales of a few days, but gradually increases on longer timescales as the comet approaches the last perihelion. The water expansion velocities of the comet were determined to be 1.55$\pm$0.14 km s$^{-1}$, 1.55$\pm$0.35 km s$

This document contains the lecture notes for a mini-course on special functions from a complex viewpoint given at the OPSFA Summer School OPSFA-S10 2024, in the period July 29th -- August 2nd, 2024. The summer school was hosted by Section of Mathematics -- Luciano Modica at the Uninettuno University.

Although it has been more than four decades that the first components-based software development (CBSD) studies were conducted, there is still no standard method or tool for component selection which is widely accepted by the industry. The gulf between industry and academia contributes to the lack of an accepted tool. We conducted a mixed methods survey of nearly 100 people engaged in component-based software engineering practice or research to better understand the problems facing industry, how these needs could be addressed, and current best practices employed in component selection. We also sought to identify and prioritize quality criteria for component selection from an industry perspective. In response to the call for CBSD component selection tools to incorporate recent technical advances, we also explored the perceptions of professionals about AI-driven tools, present and envisioned.

This work presents a simulator designed for the validation, evaluation, and demonstration of flying adhoc networks (FANETs) using 5G vehicle-to-everything (V2X) communications and the named-data networking (NDN) paradigm. The simulator integrates the ns-3 network simulator and the Zenoh NDN protocol, enabling realistic testing of applications that involve the multi-hop communication among multiple unmanned aerial vehicles (UAVs).

Unlike code generation, which involves creating code from scratch, code completion focuses on integrating new lines or blocks of code into an existing codebase. This process requires a deep understanding of the surrounding context, such as variable scope, object models, API calls, and database relations, to produce accurate results. These complex contextual dependencies make code completion a particularly challenging problem. Current models and approaches often fail to effectively incorporate such context, leading to inaccurate completions with low acceptance rates (around 30\%). For tasks like data transfer, which rely heavily on specific relationships and data structures, acceptance rates drop even further. This study introduces CCCI, a novel method for generating context-aware code completions specifically designed to address data transfer tasks. By integrating contextual information, such as database table relationships, object models, and library details into Large Language Models (LLMs), CCCI improves the accuracy of code completions. We evaluate CCCI using 289 Java snippets, extracted from over 819 operational scripts in an industrial setting. The results demonstrate that CC

Object detection has recently seen an interesting trend in terms of the most innovative research work, this task being of particular importance in the field of remote sensing, given the consistency of these images in terms of geographical coverage and the objects present. Furthermore, Deep Learning (DL) models, in particular those based on Transformers, are especially relevant for visual computing tasks in general, and target detection in particular. Thus, the present work proposes an application of Deformable-DETR model, a specific architecture using deformable attention mechanisms, on remote sensing images in two different modes, especially optical and Synthetic Aperture Radar (SAR). To achieve this objective, two datasets are used, one optical, which is Pleiades Aircraft dataset, and the other SAR, in particular SAR Ship Detection Dataset (SSDD). The results of a 10-fold stratified validation showed that the proposed model performed particularly well, obtaining an F1 score of 95.12% for the optical dataset and 94.54% for SSDD, while comparing these results with several models detections, especially those based on CNNs and transformers, as well as those specifically designed to d

This report presents the results of the 13th Video Browser Showdown, held at the 2024 International Conference on Multimedia Modeling on the 29th of January 2024 in Amsterdam, the Netherlands.

In this contribution to the proceedings of the 29th Solvay Conference on Physics I will give an overview of some key challenges in our theoretical understanding of the rheology of glasses, focussing on (i) steady shear flow curves and their relation to the glass and jamming transitions, (ii) ductile versus brittle yielding in shear startup and (iii) yielding under oscillatory shear. I will also briefly discuss connections to the reversible-irreversible and random organization transitions as well as to the broad field of memory formation in materials.

Ensuring timing guarantees for every individual tasks is critical in real-time systems. Even for periodic tasks, providing timing guarantees for tasks with segmented self-suspending behavior is challenging due to timing anomalies, i.e., the reduction of execution or suspension time of some jobs increases the response time of another job. The release jitter of tasks can add further complexity to the situation, affecting the predictability and timing guarantees of real-time systems. The existing worst-case response time analyses for sporadic self-suspending tasks are only over-approximations and lead to overly pessimistic results. In this work, we address timing anomalies without compromising the worst-case response time (WCRT) analysis when scheduling periodic segmented self-suspending tasks with release jitter. We propose two treatments: segment release time enforcement and segment priority modification, and prove their effectiveness in eliminating timing anomalies. Our evaluation demonstrates that the proposed treatments achieve higher acceptance ratios in terms of schedulability compared to state-of-the-art scheduling algorithms. Additionally, we implement the segment-level fixed

Satellite Communications (SatCom) are a backbone of worldwide development. In contrast with the past, when the GEO satellites were the only means for such connectivity, nowadays the multi-orbital connectivity is emerging, especially with the use of satellite constellations. Simultaneously, SatCom enabled the so-called In-Flight Connectivity, while with the advent of 5G-NTN, the development of this market is being accelerated. However, there are still various missing points before such a technology becomes mainstream, especially in the case of Rotary Wing Aircraft (RWA). Indeed, due to their particular characteristics, such as the low altitude flights and the blade interference, there are still open challenges. In this work, an End-to-End (E2E) analysis for the performance of SatCom under 5G-NTN for manned and unmanned RWA is performed. Various scenarios are examined, and related requirements are shown. The effects of blades and other characteristics of the RWA are established, and simulations for these cases are developed. Results along with related discussion are presented, while future directions for development are suggested. This work is part of the ESA ACROSS-AIR project.

We illustrate the main points behind the computation of the gravitational wave spectrum originated from a phase in the early universe where the energy density is dominated by highly excited fundamental string degrees of freedom - a Hagedorn phase. This phase is described using the Boltzmann equation approach to string thermodynamics, which we illustrate via a toy model which permits analytic computations for the evolution of perturbations. This window into the out-of-equilibrium regime allows us to compute equilibration rates and conclude that, in realistic scenarios, long, open strings dominate the ensemble, source gravitational waves and provide a succesful reheating. Furthermore, we compare the results against the Standard Model prediction for a gravitational wave background of thermal origin and conclude that the string prediction is larger. This is one of the few cases in which a signal of stringy origin dominates over the field theory analogue. This work is based on arXiv:2310.11494 and arXiv:2408.13803 and is a contribution to the Proceedings of the 29th symposium on Particles, Strings and Cosmology (PASCOS 2024).

This document represents the proceedings of the 2023 XCSP3 Competition. The results of this competition of constraint solvers were presented at CP'23 (the 29th International Conference on Principles and Practice of Constraint Programming, held in Toronto, Canada from 27th to 31th August, 2023).

In the past few years, there has been a growing significance of interactions between human workers and automated systems throughout the factory floor. Wherever static or mobile robots, such as automated guided vehicles, operate autonomously, a protected environment for personnel and machines must be provided by, e.g., safe, deterministic and low-latency technologies. Another trend in this area is the increased use of wireless communication, offering a high flexibility, modularity, and reduced installation and maintenance efforts. This work presents a testbed implementation that integrates a wireless framework, employing IO-Link Wireless (IOLW) and a private 5G cellular network, to orchestrate a complete example process from sensors and actuators up into the edge, represented by a programmable logic controller (PLC). Latency assessments identify the systems cycle time as well as opportunities for improvement. A worst-case estimation shows the attainable safety function response time for practical applications in the context of functional safety.

Analyzing building models for usable area, building safety, or energy analysis requires function classification data of spaces and related objects. Automated space function classification is desirable to reduce input model preparation effort and errors. Existing space function classifiers use space feature vectors or space connectivity graphs as input. The application of deep learning (DL) image segmentation methods to space function classification has not been studied. As an initial step towards addressing this gap, we present a dataset, SFS-A68, that consists of input and ground truth images generated from 68 digital 3D models of space layouts of apartment buildings. The dataset is suitable for developing DL models for space function segmentation. We use the dataset to train and evaluate an experimental space function segmentation network based on transfer learning and training from scratch. Test results confirm the applicability of DL image segmentation for space function classification. The code and the dataset of the experiments are publicly available online (https://github.com/A2Amir/SFS-A68).