The Europa lander is a concept for a potential future planetary exploration mission which purpose is to characterize the icy shell of Europa and to search for organics. To achieve this objective, the current concept of the lander includes a Raman spectrometer, such as RLS instrument, that could be able to analyze (sub) surface targets in their solid and liquid form. Knowing that ice and brines of Europa are potentially enriched by sulfate and chlorides, this work seeks to evaluate if Raman spectroscopy could be used to semi quantify the saline content of water solutions using space-like instrumentation. To do so, MgSO4 and MgCl2 were used to prepare three sets of water solutions. Raman analyses were then performed by the laboratory simulator of the ExoMars Raman Laser Spectrometer (RLS), which has been defined as the threshold system for the Europa Lander. After data analysis, two different semi-quantification approaches were tested, and their results compared. Although univariate calibration curves proved to successfully quantify the content of SO4 2- and Cl- anions dissolved in mono-analyte water solutions, this strategy provided very poor results when applied to binary saline mixtures. Overcoming this issue, the non-linearity prediction ability of Artificial Neural Networks (ANNs) in combination with bandfitting allows to successfully resolve the complexity of the vibrational perturbation suffered by the OH region, which is caused by the cross interaction of H2O molecules with different anions.

The combined analysis of geological targets by complementary spectroscopic techniques could enhance the characterization of the mineral phases found on Mars. This is indeed the case with the SuperCam instrument onboard the Perseverance rover. In this framework, the present study seeks to evaluate and compare multiple machine learning techniques for the characterization of carbonate minerals based on Raman‐LIBS (Laser‐Induced Breakdown Spectroscopy) spectroscopic data. To do so, a Ca‐Mg prediction curve was created by mixing hydromagnesite and calcite at different concentration ratios. After their characterization by Raman and LIBS spectroscopy, different multivariable machine learning (Gaussian process regression, support vector machines, ensembles of trees, and artificial neural networks) were used to predict the concentration ratio of each sample from their respective datasets. The results obtained by separately analyzing Raman and LIBS data were then compared to those obtained by combining them. By comparing their performance, this work demonstrates that mineral discrimination based on Gaussian and ensemble methods optimized the combine of Raman‐LIBS dataset outperformed those ensured by Raman and LIBS data alone. This demonstrated that the fusion of data combination and machine learning is a promising approach to optimize the analysis of spectroscopic data returned from Mars.

The SuperCam and SHERLOC instruments onboard the NASA/Perseverance rover are returning the first Raman spectra to be ever collected from another planet. Similarly, the RLS instrument onboard the ESA/Rosalind Franklin rover will collect Raman spectra from powdered rocks sampled from the subsurface of Mars. To optimize the scientific exploitation of Raman spectra returned from planetary exploration missions, tailored chemometric tools are being developed that take into account the analytical capability of the mentioned Raman spectrometers. In this framework, the ERICA research group is using laboratory simulators of SuperCam and RLS to perform representative laboratory studies that will enhance the scientific outcome of both Mars2020 and ExoMars missions. On one hand, preliminary studies proved the chemometric analysis of RLS datasets could be used to obtain a reliable semi‐quantitative estimation of the main mineral phases composing Martian geological samples. On the other hand, it was proved the data fusion of Raman and LIBS spectra gathered by SuperCam could be used to enhance the discrimination of mineral phases from remote geological targets. Besides describing the models developed by the ERICA group, this work presents an overview of the complementary chemometric approaches so far tested in this field of study and propose further improvements to be addressed in the future.

The Raman laser spectrometer (RLS) instrument onboard the Rosalind Franklin rover of the ExoMars 2022 mission will analyze powdered samples on Mars to search for traces of life. To prepare for the mission, the RLS scientific team has developed the RLS ExoMars Simulator (RLS Sim), a flexible model of RLS that operates similarly to the actual instrument, both in laboratory and field conditions, while also emulating the rover operational constraints in terms of sample distribution that are relevant to the Raman analysis. This system can operate autonomously to perform RLS-representative analysis in one or several samples, making it very useful to perform heavy experimental tasks that would otherwise be impossible using a flight-representative model of the instrument. In this work, we introduce the current configuration of the RLS Sim that has incorporated new hardware elements such as the RAman Demonstrator 1 (RAD1) spectrometer with the objective of approaching its performance to that of the actual RLS instrument. To evaluate the scientific capability of the RLS Sim, we have compared it with a replica model of RLS, the RLS Flight Spare (FS). Several acquisition aspects have been evaluated based on the analysis of select samples, assessing the performance in terms of spectral range and resolution and also studying several issues related to the evolution of signal-to-noise ratio (SNR) with different acquisition parameters, especially the number of accumulations. This performance analysis has shown that the RLS Sim in its updated configuration will be a key model to perform support science for the ExoMars mission and the RLS instrument on the Rosalind Franklin rover. Designed to work intensively, the use of the RLS Sim in combination with the RLS FS will facilitate maximizing the scientific return of the RLS spectrometer during Martian operations.

The Raman Laser Spectrometer (RLS) is one of three key analytical instruments incorporated within the body of the ExoMars 2022 rover. The rover will collect samples from different sites on the Oxia Planum plain, using a drill capable of penetrating the near subsurface and rocky outcrops to a depth of 2 m. Samples are passed to the Analytical Laboratory Drawer (ALD) in the heart of the rover vehicle, where the Sample Preparation and Distribution System (SPDS) processes and transports the crushed material into a refillable container (RC), which is then presented to the analytical instruments for exobiology and geological investigation. The final sample grain distribution of the powder sample following the crushing and flattening processes is a critical aspect of the RLS instrument that has a direct impact on its overall performance, related to its mineral identification and operational capabilities. This paper provides a comparative overview of the performance of a set of Raman instruments, the RLS micro-Raman Laboratory Equipment, the RLS ExoMars Simulator, and the RLS Engineering and Qualification Model (EQM) using Martian representative crushed samples, along with an evaluation of instrument performance as a function of the operational scenario. The results from the work performed by the RLS team confirm the capability of the RLS instrument performances, by acquiring good-quality spectra from crushed samples provided by the SPDS, whose science return can be further optimized when improving the RLS instrument operation sequence.

Ambient intelligence pursues the integration of intelligent approaches on an IoT infrastructure, mainly using everyday objects of the environment. The main hypothesis of the work is that the way in which a user interacts with a door handle is suitable to be used in the identification task. Our proposal contributes with a new method to identify persons in a seamless and unobstrusive way, suitable to be used in a smart building scenery without the need to bring any additional device. In this case, we embed accelerometers and gyroscopes in a door handle in order to obtain a data set comprising samples of 47 individuals. A parametric approximation is adopted to reduce each sample to a feature vector by using a dynamic time warping technique. A study has been made of the outcomes of different classification techniques over six different feature sets in order to assess the feasibility of this identification challenge. The AUC values observed with the selected feature set show promising results above 0.90 using neural networks and SVM classifiers.

The Spectral Line Shapes in Plasmas (SLSP) code comparison workshop series [...]

In this paper we describe a classical experiment with an air track in which smartphones are used as experimental devices to obtain physical data. The proposed experiment allows users to easily observe and measure relationships between physical magnitudes, conservation of momentum in collisions and friction effects on movement by utilizing the users' own mobile devices.

An open-source platform to be used in high school or university laboratories has been developed. The platform permits the performance of dynamics experiments in a simple and affordable way, combining measurements of different sensors in the platform. The sensors are controlled by an Arduino microcontroller, which can be wirelessly accessed with smartphones or tablets. The platform constitutes an economical sensing alternative to commercial configurations and can easily be extended by including new sensors that broaden the range of covered experiments.

Most initiatives about embedded sensing capabilities in computational systems lead to devise an ad hoc sensor platform, usually poorly reusable, as a first stage to prepare a data corpus or production prototype. In this paper, an open source hardware platform for sensing is described. This platform was intended to be used in data acquisition for gait identification, and is designed in a way general enough so many other projects could reuse the design to accelerate prototyping. The platform is based on popular open source hardware and software like Arduino and Raspberry Pi using well known languages and libraries. Some experimental results about the throughput of the overall system are reported showing the feasibility of acquiring data from up to 6 sensors with a sampling frequency no less than 118 Hz.

A simple experiment on the determination of the coefficient of restitution of different materials is taken as the basis of an extendable work that can be done by students in an autonomous way. On the whole, the work described in this paper would involve concepts of kinematics, materials science, air drag and buoyancy, and would help students to think of physics as a whole subject instead of a set of, more or less, isolated parts. The experiment can be done either in teaching laboratories or as an autonomous work by students at home. Students' smartphones and cheap balls of different materials are the only experimental materials required to do the experiment. The proposed work also permits the students to analyse the limitations of a physical model used in the experiment by analysing the approximations considered in it, and then enhancing their critical thinking.

This paper describes an open source hardware and software platform devised specifically to allow high school and undergraduate instructors to prepare new laboratory demonstrations and projects for kinematics and dynamics in an easy, affordable and extensible way. The platform consists of a computing portable station with static sensors and a microcontroller based mobile device small sensors embedded. The whole platform rely heavily on wireless technologies so the students could operate the platform with their own tablets or smartphones.To meet the open source requirements, all the components comply with open source restrictions and also the design documents and the source code are made publicly available to the education community. Therefore, there is not only an economical sensing alternative to current configurations of laboratories but an extensible system in which new requirements and sensors could be added covering new areas such as electronics, magnetism and thermodynamics. A user friendly web client permits the students to configure and perform measurements using an Internet connected smartphone or any portable computing device in a way that makes possible to the instructor to broaden the type and intensity of experiments.

Modern smartphones have calculation and sensor capabilities that make them suitable for use as versatile and reliable measurement devices in simple teaching experiments. In this work a smartphone is used, together with low cost materials, in an experiment to measure the frequencies emitted by vibrating rods of different materials, shapes and lengths. The results obtained with the smartphone have been compared with theoretical calculations and the agreement is good. Alternatively, physics students can perform the experiment described here and use their results to determine the dependencies of the obtained frequencies on the rod characteristics. In this way they will also practice research methods that they will probably use in their professional life.

Nearly every practical improvement in modeling human motion is well founded in a properly designed collection of data or datasets. These datasets must be made publicly available for the community could validate and accept them. It is reasonable to concede that a collective, guided enterprise could serve to devise solid and substantial datasets, as a result of a collaborative effort, in the same sense as the open software community does. In this way datasets could be complemented, extended and expanded in size with, for example, more individuals, samples and human actions. For this to be possible some commitments must be made by the collaborators, being one of them sharing the same data acquisition platform. In this paper, we offer an affordable open source hardware and software platform based on inertial wearable sensors in a way that several groups could cooperate in the construction of datasets through common software suitable for collaboration. Some experimental results about the throughput of the overall system are reported showing the feasibility of acquiring data from up to 6 sensors with a sampling frequency no less than 118 Hz. Also, a proof-of-concept dataset is provided comprising sampled data from 12 subjects suitable for gait analysis.