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The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth's microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator's visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories. [ABSTRACT FROM AUTHOR]

The aim of this work was to study the impact of electron‐beam (e‐beam) irradiation (0.5–2 kGy) on microbiological, physicochemical, and sensory quality of raspberries, over a storage period of 14 days at 4°C. Results showed that irradiation had a significant effect (p <.05) on the reduction of the microbial population, weight loss, and the firmness of raspberries by slightly affecting the pH, TA, and TSS during storage. This effect depends on the used dose. The brightness of irradiated raspberries increased significantly by e‐beam doses. The panel preferred raspberries irradiated with 0.5 and 1 kGy. In fact, untreated and treated at 2 kGy samples, showed a sensory degradation of stored raspberries. These results pointed out that e‐beam irradiation can be considered as an environmentally friendly postharvest treatment suitable for conservation and preserving the quality of raspberries during storage. Novelty impact statement: There is an ever‐increasing consumer demand for food obtained by low‐processed, more environmentally friendly, and greener preservation techniques to ensure food safety, guarantee high nutritional and organoleptic quality.E‐beam irradiation at 0.5 and 1 kGy improved the color and some quality characteristics of raspberries better than control.E‐beam irradiation at low doses is a method that may be a substitute and could be used along with conventional methods, providing raspberries quality. [ABSTRACT FROM AUTHOR]

The instrument package SEIS (Seismic Experiment for Internal Structure) with the three very broadband and three short‐period seismic sensors is installed on the surface on Mars as part of NASA's InSight Discovery mission. When compared to terrestrial installations, SEIS is deployed in a very harsh wind and temperature environment that leads to inevitable degradation of the quality of the recorded data. One ubiquitous artifact in the raw data is an abundance of transient one‐sided pulses often accompanied by high‐frequency spikes. These pulses, which we term "glitches", can be modeled as the response of the instrument to a step in acceleration, while the spikes can be modeled as the response to a simultaneous step in displacement. We attribute the glitches primarily to SEIS‐internal stress relaxations caused by the large temperature variations to which the instrument is exposed during a Martian day. Only a small fraction of glitches correspond to a motion of the SEIS package as a whole caused by minuscule tilts of either the instrument or the ground. In this study, we focus on the analysis of the glitch+spike phenomenon and present how these signals can be automatically detected and removed from SEIS's raw data. As glitches affect many standard seismological analysis methods such as receiver functions, spectral decomposition and source inversions, we anticipate that studies of the Martian seismicity as well as studies of Mars' internal structure should benefit from deglitched seismic data. Plain Language Summary: The instrument package SEIS (Seismic Experiment for Internal Structure) with two fully equipped seismometers is installed on the surface of Mars as part of NASA's InSight Discovery mission. When compared to terrestrial installations, SEIS is more exposed to wind and daily temperature changes that leads to inevitable degradation of the quality of the recorded data. One consequence is the occurrence of a specific type of transient noise that we term "glitch". Glitches show up in the recorded data as one‐sided pulses and have strong implications for the typical seismic data analysis. Glitches can be understood as step‐like changes in the acceleration sensed by the seismometers. We attribute them primarily to SEIS‐internal stress relaxations caused by the large temperature variations to which the instrument is exposed during a Martian day. Only a small fraction of glitches correspond to a motion of the whole SEIS instrument. In this study, we focus on the detection and removal of glitches and anticipate that studies of the Martian seismicity as well as studies of Mars's internal structure should benefit from deglitched seismic data. Key Points: Glitches due to steps in acceleration significantly complicate seismic records on MarsGlitches are mostly due to relaxations of thermal stresses and instrument tiltWe provide a toolbox to automatically detect and remove glitches [ABSTRACT FROM AUTHOR]

The effect of electron beam irradiation (1–3 kGy) was studied on the microbiological, physicochemical, sensory, and bioactive properties of strawberries during storage at 4 °C for 14 days. E -beam treatment reduced the population of fungi to non-detectable levels and the bacterial mesophilic population by 2 log CFU g−1. Sensory analysis highlighted the strawberries treated at 2 kGy with 7 days storage as the most appreciated. Principal Compounds Analysis between the different physicochemical parameters and sensory descriptors suggested that the attributes of the strawberries irradiated at 1 and 2 kGy were enhanced comparatively to the control. The bioactive content of strawberries was preserved or improved after irradiation and 7 days of storage. The results indicated that e-beam treatment at 2 kGy and refrigerated storage could guarantee the microbial safety and extend the shelf-life up to 7 days, while preserve and/or improve the physicochemical and sensory attributes of strawberries. Strawberries are sources of compounds with high bioactivity and health benefits, despite of being high perishable. The present work demonstrated that e-beam radiation could be used as an environmental friendly and safe post-harvest treatment of strawberries to preserve the bioactive properties in order to extend their shelf-life, thus ensuring their quality and safety, and consequently, increasing their commercial value. • Electron-beam radiation was evaluated as a post-harvest treatment of strawberries. • Strawberries treated at 2 kGy were the most appreciated by the panelists. • Physicochemical parameters and sensory descriptors were positively correlated by PCA. • Irradiation and 7 days of storage preserved the bioactive content of strawberries. • 2 kGy guaranteed the safety and quality of strawberries, extending the shelf-life up to 7 days. [ABSTRACT FROM AUTHOR]

The aim of this work was to perform a comprehensive study on the effect of gamma radiation on strawberries by evaluating its effect on physicochemical, microbiological, sensory acceptance, phenolic and flavonoid contents, and antioxidant activity over a storage period of 14 days at 4 °C. The results showed that gamma irradiation significantly decreased the proliferation of molds and yeasts at 1, 2 and 3 kGy up to seven days of storage. However, a significant decrease of firmness, redness and chroma was noticed with gamma radiation doses. Moreover, sensory scores revealed that gamma radiation had no significant effect on strawberries compared to the non-treated, the dose of 2 kGy seemed to be the best dose to maintain the acceptance sensory quality of strawberries for the panel. A positive relation between different physicochemical parameters and sensory descriptors was illustrated with Principal Compounds Analysis. The gamma radiation treatment and refrigerated storage indicated to have an increasing trend on the bioactive content of strawberries. Based on the obtained results, the dose of 2 kGy point out to be the optimal treatment to maintain the safety and global quality of strawberries with extended shelf-life. • Gamma radiation at 2 kGy ensures strawberries safety and improves sensory quality. • Gamma radiation at 2 kGy increases antioxidants of strawberries. • Data analysis shows strong correlations between sensory and physicochemical attributes. • Data analysis allows distinction of treatments resulting in different quality levels. [ABSTRACT FROM AUTHOR]

Exploring the internal structure and the dynamics of our solar system is mandatory to understand the behavior of our universe and its origin. One of the tools chosen by NASA is seismology, particularly in order to constrain the deep interior structure of the red planet via the Insight (Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport) mission that was successfully landed on November 26th, 2018 in Elysium Planitia. However, planetary seismology is still far from providing models with high precision and calculation speed. Thus, this study is one of the first attempts to model the Martian interior structure using a seismic waveform inversion technique : the Machine Learning and the Pattern Search algorithm by resolving a nonlinear global optimization problem.It's a new revolutionary mathematical tool for seismology and inverse problem in general as it gives a better understanding of blinded or burred problems like our case.For that, before applying it to the incoming InSight data provided by the seismometer SEIS (the Seismic Experiment for Interior Structure), we need to ensure its efficiency through several tests. Therefore, we will focus on the inversion sensibility error by comparing the resulting inverted crustal model obtained using this new method to the existing models provided by the SEIS team using other tools like Markov Chain Monte Carlo (McMC). This technique will be applied to the MSS blind test data computed by the InSight collaborators. The aim is to constrain the epicentral distance, the depth and origin time of the seismic event, as well as the seismic velocity profiles and the attenuation, and to compare it with existing methods developed by the InSight science team. [ABSTRACT FROM AUTHOR]