Bats are ecologically vital mammals, serving as pollinators, seed dispersers, and bioindicators of ecosystem health. Many species inhabit natural caves, which offer optimal conditions for survival but present challenges for direct ecological monitoring due to their dark, complex, and inaccessible environments. Traditional monitoring methods, such as mist-netting, are invasive and limited in scope, highlighting the need for non-intrusive alternatives. In this work, we present a portable multisensor platform designed to operate in underground habitats. The system captures multimodal data, including near-infrared (NIR) imagery, ultrasonic audio, 3D structural data, and RGB video. Focusing on NIR imagery, we evaluate the effectiveness of the YOLO object detection framework for automated bat detection and counting. Experiments were conducted using a dataset of NIR images collected in natural shelters. Three YOLO variants (v10, v11, and v12) were trained and tested on this dataset. The models achieved high detection accuracy, with YOLO v12m reaching a mean average precision (mAP) of 0.981. These results demonstrate that combining NIR imaging with deep learning enables accurate and non-invasive monitoring of bats in challenging environments. The proposed approach offers a scalable tool for ecological research and conservation, supporting population assessment and behavioral studies without disturbing bat colonies.

This paper describes the implementation of a wavelength division multiplexing (WDM) network controller that accounts for the varying conditions of the network physical layer in real-time. A newly added software module is used to estimate the physical layer quality of transmission (QoT-E) in the form of a single transmission figure of merit for every line span in the network, i.e., the optical signal-to-noise ratio (OSNR) degradation. The estimated OSNR degradation is used to verify lightpath feasibility in the network, and produce optimal routing and wavelength assignment solutions in real-time, without the need for a separate network planning tool. The OSNR degradation is estimated without requiring detailed knowledge of the optical components deployed in the network, and consequently the approach is vendor-agnostic. The accuracy of the estimated OSNR degradation is assessed experimentally, and its impact on two network-wide performance indicators is studied through simulation.