Visible Light Communication (VLC) and Optical Camera Communication (OCC) are recent viable technologies for wireless communication, using existing lighting infrastructure and conventional cameras. The Color-Hopping Space-Time (CHST) scheme is a promising technique for VLC/OCC, introducing permutations in the mapping between symbols and colors. This work presents the first experimental validation of CHST in an OCC system, using an RGB LED array and a camera, with an accessible implementation based on low-cost hardware. The results confirm improvements in channel conditioning, BER reduction, and increased communication range with the use of the CHST scheme.
This study validates LoRaWAN coverage in forested and sloped terrains using field tests and simulations. Heltec LoRa modules operating at 433 and 915 MHz were evaluated under varying topography, vegetation density, and environmental interference. Experimental results show that adjusting LoRa parameters (spreading factor, bandwidth, and coding rate) significantly improves communication range, even in non-line-of-sight (NLoS) conditions. Terrain-aware simulations with Radio Mobile, based on the Longley–Rice model and SRTM data, correlated well with empirical results, but highlighted limitations in predicting dynamic factors such as multipath fading and vegetation absorption. This combined approach offers practical insights for optimizing LoRaWAN network deployments in challenging natural environments, making it suitable for environmental monitoring, remote sensing, and rural IoT applications.
The increasing frequency and intensity of natural disasters in Chile demands innovative technological solutions to strengthen the timely and efficient risk management capabilities of the National Disaster Prevention and Response System (SINAPRED).Based on the guidelines and coordination plans promoted by the National Disaster Prevention and Response Service (SENAPRED), this work proposes the design and implementation of a satellite platform focused on Earth observation and geospatial analysis, supported by quantum communication capabilities. BThrough the use of data from optical sensors, radars, and LoRa satellites, the platform will integrate near real-time processing capabilities and intuitive visualization tools for institutional and technical users. This technological integration will strengthen technological autonomy and significantly improve data processing efficiency. The focus of this paper is a model designed to achieve greater efficiency in the data processing workflow.
Integrated Sensing and Communication (ISAC) has emerged as a promising solution to spectrum scarcity and interference issues in next-generation wireless networks. In particular, resource integration enables the coexistence of sensing and communication functions using a single waveform within shared spectral resources. Despite the growing theoretical interest, empirical validation of ISAC systems, especially under practical conditions, remains limited. This paper presents a proof-of-concept implementation of a wireless ISAC system based on a bistatic sensing configuration and a target. The system employs a time-division multiplexed (TDM) waveform consisting of a linear chirp for sensing and a binary phase shift keying (BPSK)-modulated signal for communication. A hardware-reconfigurable platform was developed, and the system was validated through a real-world measurement campaign. The obtained experimental results confirm the feasibility of the proposed architecture and highlight its potential for practical ISAC deployments in complex environments.
This paper introduces ModernBERT-DoS, a transformer-based system designed for the detection and classification of network traffic, with a particular focus on identifying Distributed Denial of Service (DDoS) attacks. The model is fine-tuned on the CIC-DDoS2019 dataset and an additional custom SSL dataset, enabling robust performance across a wide range of traffic categories. Leveraging a modified BERT architecture, ModernBERT-DoS achieves high accuracy, precision, recall, and F1-scores in the evaluations. The training process incorporates early stopping to optimize generalization and prevent overfitting. For transparency and reproducibility, the trained model is publicly available via the Hugging Face Hub.
