Abstract
This work provides a method that combines graphene oxide coating and self-image theory to improve the sensitivity of optical sensors. The sensor is designed specifically to measure the amount of glucose present quantitatively in aqueous solutions that replicate the range of glucose concentrations found in human saliva. COMSOL Multiphysics 6.0 was used to simulate the self-imaging phenomenon using a coreless silica fiber (CSF). For high-quality self-imaging, the second and fourth self-imaging points are usually preferred because of their higher coupling efficiency, which increases the sensor sensitivity. However, managing the fourth self-image is more difficult because it calls for a longer CSF length. As a result, the first and second self-image points were the focus of the simulation in this work. After the simulation, using the Layerby-Layer method, the sensor was constructed to a length that matched the second self-image point (29.12 mm) and coated with an 80 mu m/mL graphene oxide layer. When comparing uncoated and graphene oxide-covered sensors to measure glucose in liquids ranging from 25 to 200 mg/dL, one bilayer of polyethyleneimine/graphene demonstrated an eight-fold improvement in sensitivity. The final sensor, built on graphene oxide, showed stability with a low standard deviation of 0.6 pm/min. It also showed sensitivity at 10.403 +/- 0.004 pm/(mg/dL) with a limit of detection of 9.15 mg/dL.

Abstract
Combining data from different sensing modalities has been a promising research topic for building better and more reliable data-driven models. In particular, it is known that multimodal spectral imaging can improve the analytical capabilities of standalone spectroscopy techniques through fusion, hyphenation, or knowledge distillation techniques. In this manuscript, we focus on the latter, exploring how one can increase the performance of a Laser-induced Breakdown Spectroscopy system for mineral classification problems using additional spectral imaging techniques. Specifically, focusing on a scenario where Raman spectroscopy delivers accurate mineral classification performance, we show how to deploy a knowledge distillation pipeline where Raman spectroscopy may act as an autonomous supervisor for LIBS. For a case study concerning a challenging Li-bearing mineral identification of spodumene and petalite, our results demonstrate the advantages of this method in improving the performance of a single-technique system. LIBS trained with labels obtained by Raman presents an enhanced classification performance. Furthermore, leveraging the interpretability of the model deployed, the workflow opens opportunities for the deployment of assisted feature discovery pipelines, which may impact future academic and industrial applications.

Abstract
Distributed acoustic sensing (DAS) is a sensing technique that allows continuous data acquisition of strain rate and temperature with exceptional spatial resolution, up to few meters, for extensive lengths up to 100 km. The ubiquitous nature of optical fiber cables rendered DAS an appealing alternative for geophysical sensing, allowing cost-effective data collection with extensive spatial coverage leveraging existing infrastructure. This study presents findings from the deployment of a DAS system on a dark fiber located on the Madeira Island, Portugal. Through the implementation of 2D filtering, simultaneous analysis of data from road traffic, ocean waves, and seismic activity was achieved.

Abstract
Spectral Imaging techniques such as Laser-induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy (RS) enable the localized acquisition of spectral data, providing insights into the presence, quantity, and spatial distribution of chemical elements or molecules within a sample. This significantly expands the accessible information compared to conventional imaging approaches such as machine vision. However, despite its potential, spectral imaging also faces specific challenges depending on the limitations of the spectroscopy technique used, such as signal saturation, matrix interferences, fluorescence, or background emission. To address these challenges, this work explores the potential of using techniques from conventional RGB imaging to enhance the dynamic range of spectral imaging. Drawing inspiration from multi-exposure fusion techniques, we propose an algorithm that calculates a global weight map using exposure and contrast metrics. This map is then used to merge datasets acquired with the same technique under distinct acquisition conditions. With case studies focused on LIBS and Raman Imaging, we demonstrate the potential of our approach to enhance the quality of spectral data, mitigating the impact of the aforementioned limitations. Results show a consistent improvement in overall contrast and peak signal-to-noise ratios of the merged images compared to single-condition images. Additionally, from the application perspective, we also discuss the impact of our approach on sample classification problems. The results indicate that LIBS-based classification of Li-bearing minerals (with Raman serving as the ground truth), is significantly improved when using merged images, reinforcing the advantages of the proposed solution for practical applications.