Global Navigation Satellite Systems (GNSS) are highly affected in equatorial regions, especially due to the formation of Equatorial Plasma Bubbles (EPBs), which cause disturbances in the ionosphere resulting in different forms of signal degradation. Despite Colombia’s privileged geographic position, its limited monitoring infrastructure hinders the detection and mitigation of these effects. This study proposes the development of a Low-Cost Scintillation Laboratory (LCSL) using a cognitive radio–based approach for real-time scintillation monitoring, aimed at improving GNSS reliability. The system was designed following a Systems Engineering methodology, defining functional architectures and constraints. A communication system model was developed to account for EPBs’ effects on GNSS signals, while cognitive radio algorithms within a Software-Defined Radio (SDR) framework enabled real-time detection, monitoring, and alert generation. To implement this approach, monitoring stations were deployed in Bogotá, Cartagena, and Santa Marta utilized low-cost GNSS receivers integrated with Machine Learning (ML) algorithms for the automatic classification of scintillation events. Additionally, the system’s accuracy was validated by comparing experimental data with historical records from the Geophysical Institute of Peru (IGP). The results demonstrated that the integration of cognitive radio and ML-based detection enhanced precision and adaptability compared to traditional methods. The network of monitoring stations effectively validated the system’s performance, providing valuable insights into equatorial ionospheric dynamics. This study contributes to the advancement of monitoring methodologies and highlights the importance of accessible infrastructure for mitigating EPB effects on GNSS, ultimately fostering more resilient navigation and communication systems.

Flutter constrains the design of modern high-aspect-ratio wings, yet the combined influence of planform geometry, aspect ratio (AR) and structural flexibility is rarely assessed within a single consistent framework. This work presents a unified, physics-based reduced-order comparison of rectangular, trapezoidal and elliptical planforms under both rigid and flexible representations, intended as a screening tool for preliminary design rather than as a quantitatively predictive methodology for specific aircraft. A two-degree-of-freedom plunge–pitch typical section with quasi-steady (Theodorsen-based) aerodynamics is cast in state-space form, and flutter is identified through eigenvalue continuation by combined frequency coalescence and damping sign change. For rectangular and trapezoidal wings, increasing AR or reducing stiffness consistently advances flutter onset: at a fixed AR of 7.1, stiffening alone raises the critical speed by approximately 43 m/s (from 105 to 148 m/s, ≈+41%). In contrast, within the reduced-order model the elliptical planform inverts this trend: with sufficient stiffness, frequency coalescence is suppressed, and from the stiffened baseline (AR ≈ 4.75) raising AR to 5.6 keeps the wing flutter-free across the investigated velocity range, whereas lowering AR to 3.9 reintroduces hard flutter at 155 m/s. These model-based comparative tendencies indicate that AR and structural flexibility are strongly coupled design drivers and that elliptical loading is comparatively flutter-resistant, providing an efficient basis for early-stage configuration screening.

This research introduces a system designed to predict three-dimensional airspace occupancy over Colombia using historical Automatic Dependent Surveillance-Broadcast (ADS-B) data and machine learning techniques. The goal is to support proactive air traffic management by estimating future aircraft positions—specifically their latitude, longitude, and flight level. To achieve this, four predictive models were developed and tested: K-Nearest Neighbors (KNN), Random Forest, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). Among them, the LSTM model delivered the most accurate results, with a Mean Absolute Error (MAE) of 312.59, a Root Mean Squared Error (RMSE) of 1187.43, and a coefficient of determination (R²) of 0.7523. Compared to the baseline models (KNN, Random Forest, XGBoost), these values represent an improvement of approximately 91% in MAE, 83% in RMSE, and an eighteen-fold increase in R², demonstrating the substantial advantage of the LSTM approach. These metrics indicate a significant improvement over the other models, particularly in capturing temporal patterns and adjusting to evolving traffic conditions. The strength of the LSTM approach lies in its ability to model sequential data and adapt to dynamic environments—making it especially suitable for supporting future Trajectory-Based Operations (TBO). The results confirm that predicting airspace occupancy in three dimensions using historical data are not only possible but can yield reliable and actionable insights. Looking ahead, the integration of hybrid neural network architectures and their deployment in real-time systems offer promising directions to enhance both accuracy and operational value.

Flapping-wing micro air vehicles (FWMAVs) have gained increasing attention due to their manoeuvrability, low acoustic signature, and suitability for confined or cluttered environments. Despite considerable progress, existing reviews treat actuation mechanisms and mechanical transmissions in isolation, leaving a gap in the comparative assessment of pulley-based and alternative flapping systems. This study provides a comprehensive and quantitative synthesis of the literature on FWMAV mechanical architectures, with particular emphasis on pulley-driven transmissions used in platforms such as the Nano Hummingbird and the Robotic Hummingbird. A structured review methodology was applied, incorporating a systematic database search, extraction of performance parameters, and cross-platform comparison of flapping frequency, lift generation, power consumption, lift-to-weight ratio, and material choices. The analysis identifies consistent scaling trends across motor-driven, piezoelectric, and hybrid actuation families and highlights the efficiency and stroke-amplification advantages of pulley-based mechanisms for centimetre-scale hovering MAVs. The review also identifies unresolved challenges, including durability of transmission materials, standardisation of performance metrics, and the need for high-fidelity aerodynamic characterisation. Overall, this work offers an integrated framework for understanding the trade-offs among actuation and transmission strategies and provides a roadmap to guide future research and the practical development of next-generation FWMAVs.

Reliable and up-to-date measurements of public space remain scarce in small and medium-sized towns (SMSTs), where conventional geospatial datasets are often outdated, inconsistent, or inaccessible. This study presents a low-cost and fully reproducible computational pipeline that integrates nadir RGB imagery captured by a DJI Mini 3 UAV with a lightweight instance-segmentation model (Ultralytics YOLOv12-seg) and GIS-based post-processing to derive class-specific surface indicators at the neighborhood scale. The workflow consists of four components: autonomous UAV acquisition over three representative zones of Sesquilé, Colombia; planar mosaic generation and georeferencing using ad hoc ground control points; fine-tuning of a YOLOv12-seg model trained on locally annotated images; and transformation of predicted masks into OSM and GeoPackage geometries for metric analysis. The trained model achieved stable convergence with mask mAP50 ≈ 0.85 and mAP50–95 ≈ 0.70, supported by balanced precision–recall behavior across classes. Spatial outputs exhibit coherent morphological contrasts between the analyzed zones. Buildings occupy 48.17% of the mapped area, vegetation 25.88%, and transport- and plaza-related public space (roadways, sidewalks, and hardscape areas) 25.95%. These proportions capture a clear gradient from a dense urban core to less consolidated peripheral sectors. Results demonstrate that very-high-resolution UAV imagery, combined with open-source deep-learning tools and structured GIS post-processing, can reliably produce operational public-space indicators for SMSTs at low cost. The methodology provides an accessible and scalable framework for evidence-based urban assessment in municipalities with limited technical and financial resources.