Abstract
This communication explores an optical extreme learning architecture to unravel the impact of using a nonlinear optical media as an activation layer. Our analysis encloses the evaluation of multiple parameters, with special emphasis on the efficiency of the training process, the dimensionality of the output space, and computing performance across tasks associated with the classification in low-dimensionality input feature spaces. The results enclosed provide evidence of the importance of the nonlinear media as a building block of an optical extreme learning machine, effectively increasing the size of the output space, the accuracy, and the generalization performances. These findings may constitute a step to support future research on the field, specifically targeting the development of robust general-purpose all-optical hardware to a full-stack integration with optical sensing devices toward edge computing solutions.

Abstract
Lately, the field of optical computing resurfaced with the demonstration of a series of novel photonic neuromorphic schemes for autonomous and inline data processing promising parallel and light-speed computing. We emphasize the Photonic Extreme Learning Machine (PELM) as a versatile configuration exploring the randomness of optical media and device production to bypass the training of the hidden layer. Nevertheless, the implementation of this framework is limited to having the output layer performed digitally. In this work, we extend the general PELM implementation to an all-optical configuration by exploring the amplitude modulation from a spatial light modulator (SLM) as an output linear layer with the main challenge residing in the training of the output weights. The proposed solution explores the package pyTorch to train a digital twin using gradient descent back-propagation. The trained model is then transposed to the SLM performing the linear output layer. We showcase this methodology by solving a two-class classification problem where the total intensity reaching the camera predicts the class of the input sample.

Abstract
Portable X-Ray Fluorescence (XRF) has become increasingly popular where traditional laboratory methods are either impractical, time consuming, and/or too costly. While the Limit of Detection (LOD) is generally poorer for XRF compared to laboratory-based methods, recent advances have improved XRF LODs and increased its potential for field-based studies. Portable XRF can be used to screen food products for toxic elements such as lead (Pb), cadmium (Cd), mercury (Hg), arsenic (As), manganese, (Mn), zinc (Zn), and strontium (Sr). In this study, 23 seafood samples were analyzed using portable XRF in a home setting. After XRF measurements were completed in each home, the same samples were transferred to the laboratory for re-analysis using microwave-assisted digestion and Inductively Coupled Plasma Tandem Mass Spectrometry (ICP-MS/MS). Four elements (Mn, Sr, As, and Zn) were quantifiable by XRF in most samples, and those results were compared to those obtained by ICP-MS/MS. Agreement was judged reasonable for Mn, Sr, and As, but not for Zn. Discrepancies could be due to (1) the limited time available to prepare field samples for XRF, (2) the heterogeneous nature of real samples analyzed by XRF, and (3) the small beam spot size (similar to 1 mm) of the XRF analyzer. Portable XRF is a cost-effective screening tool for public health investigations involving exposure to toxic metals. It is important for practitioners untrained in XRF spectrometry to (1) recognize the limitations of portable instrumentation, (2) include validation data for each specific analyte(s) measured, and (3) ensure personnel have some training in sample preparation techniques for field-based XRF analyses.

Abstract
DAS technology has emerged as a transformative technology with a vast range of applications, both on land and at sea. These applications span from oil and gas exploration to geophysical data collection, infrastructure monitoring, security, and environmental hazard monitoring, including earthquake and tsunami early warning systems (Landrø et al., 2022; Gorshkov et al., 2022). The unique properties of DAS systems can bring high benefits to the demanding field of seismology, as it provides a significant increment in the spatial information that can be obtained from a seismic event. Moreover, the widespread deployment of optical fiber across the Earth's surface, coupled with the relatively low cost per monitoring point for extended distances, has rendered DAS an appealing alternative to traditional seismographs (Li et al., 2023). This is especially true for subsea applications, where the capability of remote sensing is particularly attractive. Remote sensing enables the placement of systems far from harsh environments, often difficult to access, enhancing the feasibility and effectiveness of monitoring efforts. In this work, it was employed a DAS equipment on a dark telecommunication fiber was installed exclusively for research purposes, named GEOLAB, located on the island of Madeira. This fiber spans approximately 50 km, where the initial tests were conducted using a DAS from January 31 to February 14, 2023. The equipment utilized is the HDAS provided by the IO-CSIC. The signal of the fiber was collected with a spatial resolution (or gauge length) of 10 m, resulting in total of 5000 channels, with a temporal acquisition with a frequency of 50 Hz. The DAS system has a chirped pulsed laser as the optical source, generating pulses with a width of 100 ns. These pulses were then amplified using a semiconductor optical amplifier to mitigate intra-band coherent noise. A total of 19 seismic events were detected, and then characterized by performing two-dimensional linear bandpass filtering. We will present the initial findings, particularly the seismic activity resulting from the earthquakes with epicenters near the city of Gaziantep, located in Turkey. These events occurred on February 6, 2023, with magnitudes of 7.5 and 7.8 on the Richter scale.

Abstract
This work presents an implementation of a reference optical cavity based on parasitic cavities on a low coherence interferometric system. This method allows a maximization of the number of sensors to be implemented without occupying additional reading channels.

Abstract
The domain of Intelligent Transportation Systems (ITS) is becoming a key candidate to enable safer and efficient mobility in IoT enabled smart cities. Several recent research in cooperative autonomous systems are conducted over simulation frameworks as real experiments are still too costly. In this paper, we present a platooning robotic test-bed platform with a 1/10 scale robotic vehicles that functions based on the input front commercially off the shelf technologies (COTS) such as Lidars and cameras. We also present an in-depth analysis of the functionalities and architecture of the proposed system. We also compare the performance of the aforementioned sensors in some real-life emulated scenarios. From our results, we were able to concur that the camera based platooning is able to perform well at partially observable scenarios than its counterpart.