IEEE 5G/6G Testbed

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

Annotating the scene text in the PRIVATY-TEXT-IMAGE dataset was done in Adobe Photoshop.   To maintain the rationality of the annotation operation, the images' aesthetics, and the textures' consistency around the deleted text areas, we utilized the content-aware fill feature of Photoshop.   This feature can enhance intelligent editing and modification capabilities during the image processing, automatically analyze the image content around the private text areas, and generate matching filling content to make the images look more natural and complete.  

Online Quiz System is a web-based quiz system for accessing students. It is a system by which students can

sit in a quiz which need no pencil and paper. Nowadays, students take quiz manually .Lecturers need spend

more time on grading. Other than that, the quiz paper maybe will be missing. Students need to wait for

lecturers finish grading to get their result. Therefore, this system will help lecturers save their time because

of automated marking. Lecturers can set up a quiz which is it will auto-grade itself. Students can answer the

El dataset desarrollado integra múltiples dimensiones relevantes para el análisis académico y profesional de los estudiantes.

Overview

This dataset contains detailed experimental data from a series of tests conducted to evaluate the performance of a pulsed water jet ablation system. The experiments aim to investigate the effects of various parameters on the ablation process when cutting through material composites such as PLA/Bone Cement and Bone. The experiments involve layers of different materials, including metal, plastic, and bone cement. The primary objective is to understand the material differentiation.

Dataset Content

We created this dataset to study Outdoor-to-Indoor (O2I) signal propagation using four UAV transmitters and 17,485 receivers positioned inside the building. For each receiver location and transmitter, we generated up to 25 multipath components by simulating six transmissions, six reflections, one diffraction, and diffused multipath (comprising two transmissions and one diffraction) using Remcom's Wireless InSite.

This dataset provides the foundational resources for evaluating and optimizing Formula L , a novel mathematical framework for semantic-driven task allocation in multi-agent systems (MAS) powered by large language models (LLM). The dataset includes Python code and both empirical and synthetic data, specifically designed to validate the effectiveness of Formula L in improving task distribution, contextual relevance, and dynamic adaptation within MAS.

The dataset comprises: