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We present the discovery of highly-collimated radio jets spanning a total of 355 kpc around the nearby elliptical galaxy NGC 2663, and the possible first detection of recollimation on kiloparsec scales. The small distance to the galaxy (~28.5 Mpc) allows us to resolve portions of the jets to examine their structure. We combine multiwavelength data: radio observations by the Murchison Widefield Array (MWA), the Australian Square Kilometre Array Pathfinder (ASKAP) and the Australia Telescope Compact Array (ATCA), and X-ray data from Chandra, Swift and SRG/eROSITA. We present intensity, rotation measure, polarisation, spectral index and X-ray environment maps. Regions of the southern jet show simultaneous narrowing and brightening, which can be interpreted as a signature of the recollimation of the jet by external, environmental pressure, though it is also consistent with an intermittent Active Galactic Nuclei (AGN) or complex internal jet structure. X-ray data suggest that the environment is extremely poor; if the jet is indeed recollimating, the large recollimation scale (40 kpc) is consistent with a slow jet in a low-density environment., 16 pages, 14 figures, Accepted for publication in MNRAS

We report on multi-wavelength observations of the tidal disruption event (TDE) candidate eRASSt J074426.3+291606 (J0744), located in the nucleus of a previously quiescent galaxy at $z=0.0396$. J0744 was first detected as a new, ultra-soft X-ray source (photon index $\sim 4$) during the second SRG/eROSITA All-Sky Survey (eRASS2), where it had brightened in the 0.3--2~keV band by a factor of more than $\sim$160 relative to an archival 3$\sigma$ upper limit inferred from a serendipitous Chandra pointing in 2011. The transient was also independently found in the optical by the Zwicky Transient Factory (ZTF), with the eRASS2 detection occurring only $\sim$20 days after the peak optical brightness, suggesting that the accretion disc formed promptly in this TDE. Continued X-ray monitoring over the following $\sim$400 days by eROSITA, NICER XTI and Swift XRT showed a net decline by a factor of $\sim$100, albeit with large amplitude X-ray variability where the system fades, and then rebrightens, in the 0.3--2~keV band by a factor $\sim$50 during an 80 day period. Contemporaneous Swift UVOT observations during this extreme X-ray variability reveal a relatively smooth decline, which persists over $\sim$400 days post-optical peak. The peak observed optical luminosity (absolute $g$-band magnitude $\sim -16.8$ mag) from this transient makes J0744 the faintest optically-detected TDE observed to date. However, contrasting the known set of `faint and fast' TDEs, the optical emission from J0744 decays slowly (exponential decay timescale $\sim$120~days), making J0744 the first member of a potential new class of `faint and slow' TDEs., Comment: 17 pages, 14 figures. MNRAS accepted

SMSS\,J114447.77-430859.3 ($z=0.83$) has been identified in the SkyMapper Southern Survey as the most luminous quasar in the last $\sim 9\,\rm Gyr$. In this paper, we report on the eROSITA/Spectrum-Roentgen-Gamma (SRG) observations of the source from the eROSITA All Sky Survey, along with presenting results from recent monitoring performed using Swift, XMM-Newton, and NuSTAR. The source shows a clear variability by factors of $\sim 10$ and $\sim 2.7$ over timescales of a year and of a few days, respectively. When fit with an absorbed power law plus high-energy cutoff, the X-ray spectra reveal a $\Gamma=2.2 \pm 0.2$ and $E_{\rm cut}=23^{+26}_{-5}\,\rm keV$. Assuming Comptonisation, we estimate a coronal optical depth and electron temperature of $\tau=2.5-5.3\, (5.2-8)$ and $kT=8-18\, (7.5-14)\,\rm keV$, respectively, for a slab (spherical) geometry. The broadband SED is successfully modelled by assuming either a standard accretion disc illuminated by a central X-ray source, or a thin disc with a slim disc emissivity profile. The former model results in a black hole mass estimate of the order of $10^{10}\,M_\odot$, slightly higher than prior optical estimates; meanwhile, the latter model suggests a lower mass. Both models suggest sub-Eddington accretion when assuming a spinning black hole, and a compact ($\sim 10\,r_{\rm g}$) X-ray corona. The measured intrinsic column density and the Eddington ratio strongly suggest the presence of an outflow driven by radiation pressure. This is also supported by variation of absorption by an order of magnitude over the period of $\sim 900\,\rm days$., Comment: Accepted for publication in MNRAS

This study aims at identifying luminous quasars at $z>5.7$ among X-ray-selected sources in the eROSITA Final Equatorial-Depth Survey (eFEDS) in order to place a lower limit on black hole accretion well into the epoch of re-ionisation. We confirm the low significance detection with eROSITA of a previously known, optically faint $z=6.56$ quasar from the Subaru High-z Exploration of Low-luminosity Quasars (SHELLQs) survey. We obtained a pointed follow-up observation of the source with the Chandra X-ray telescope in order to confirm the eROSITA detection. Using new near-infrared spectroscopy, we derived the physical properties of the super-massive black hole. Finally, we used this detection to infer a lower limit on the black hole accretion density rate at $z>6$. The Chandra observation confirms the eFEDS source as the most distant blind X-ray detection to date. The derived X-ray luminosity is high with respect to the rest-frame optical emission of the quasar. With a narrow MgII line, low derived black hole mass, and high Eddington ratio, as well as its steep photon index, the source shows properties that are similar to local narrow-line Seyfert 1 galaxies, which are thought to be powered by young super-massive black holes. In combination with a previous high-redshift quasar detection in the field, we show that quasars with $L_{2-10 \, \mathrm{keV}} >10^{45} \, \mathrm{erg \, s^{-1}}$ dominate accretion onto super-massive black holes at $z\sim 6$., Accepted for publication in A&A

Studies indicate strong evidence of a scaling relation in the local Universe between the supermassive black hole mass ($M_\rm{BH}$) and the stellar mass of their host galaxies ($M_\star$). They even show similar histories across cosmic times of their differential terms: star formation rate (SFR) and black hole accretion rate (BHAR). However, a clear picture of this coevolution is far from being understood. We select an X-ray sample of active galactic nuclei (AGN) up to $z=2.5$ in the miniJPAS footprint. Their X-ray to infrared spectral energy distributions (SEDs) have been modeled with CIGALE, constraining the emission to 68 bands. For a final sample of 308 galaxies, we derive their physical properties (e.g., $M_\star$, $\rm{SFR}$, $\rm{SFH}$, and $L_\rm{AGN}$). We also fit their optical spectra for a subsample of 113 sources to estimate the $M_\rm{BH}$. We calculate the BHAR depending on two radiative efficiency regimes. We find that the Eddington ratios ($\lambda$) and its popular proxy ($L_\rm{X}$/$M_\star$) have 0.6 dex of difference, and a KS-test indicates that they come from different distributions. Our sources exhibit a considerable scatter on the $M_\rm{BH}$-$M_\star$ relation, which can explain the difference between $\lambda$ and its proxy. We also model three evolution scenarios to recover the integral properties at $z=0$. Using the SFR and BHAR, we show a notable diminution in the scattering between $M_\rm{BH}$-$M_\star$. For the last scenario, we consider the SFH and a simple energy budget for the AGN accretion, obtaining a relation similar to the local Universe. Our study covers $\sim 1$ deg$^2$ in the sky and is sensitive to biases in luminosity. Nevertheless, we show that, for bright sources, the link between SFR and BHAR, and their decoupling based on an energy limit is the key that leads to the local $M_\rm{BH}$-$M_\star$ scaling relation., Comment: Accepted for publication in A&A

Quasi-periodic eruptions (QPEs) are recurrent X-ray bursts found so far in the nuclei of low-mass galaxies. Their trigger mechanism is still unknown, but recent models involving one or two stellar-mass companions around the central massive ($\approx10^5-10^6$ solar masses) black hole have gathered significant attention. While these have been compared only qualitatively with observations, the phenomenology of QPEs is developing at a fast pace, with the potential to reveal new insights. Here we report two new observational results found in eRO-QPE1, the brightest QPE source discovered so far: i) the eruptions in eRO-QPE1 occur sometimes as single isolated bursts, and at others as chaotic mixtures of multiple overlapping bursts with very different amplitudes; ii) we confirm that QPEs peak at later times and are broader at lower energies, with respect to higher energies while, for the first time, we find that QPEs also start earlier at lower energies. Furthermore, eruptions appear to undergo an anti-clockwise hysteresis cycle in a plane of hardness ratio versus total count rate. Behavior i) was not found before in any other QPE source and implies that if a common trigger mechanism is in place for all QPEs, it must be able to produce both types of timing properties, regular and complex. Result ii) implies that the X-ray emitting component does not have an achromatic evolution even during the start of QPEs, and that the rise is harder than the decay at a given total count rate. This specific energy dependence could be qualitatively compatible with inward radial propagation during the rise within a compact accretion flow, the presence of which is suggested by the stable quiescence spectrum observed in general for QPE sources., 11 pages. Accepted for publication in A&A

Femtosecond (fs) laser pulsed excitation of plasmonic nanoparticle (NP)-biomolecule conjugates is a promising method to locally heat biological materials. Studies have demonstrated that fs pulses of light can modulate the activity of DNA or proteins when attached to plasmonic NPs; however, the precision over subsequent biological function remains largely undetermined. Specifically, the temperature the localized biomolecules "experience" remains unknown. We used 55 nm gold nanoparticles (AuNPs) displaying double-stranded (ds) DNA to examine how, for dsDNA with different melting temperatures, the laser pulse energy fluence and bulk solution temperature affect the rate of local DNA denaturation. A universal "template" single-stranded DNA was attached to the AuNP surface, and three dye-labeled probe strands, distinct in length and melting temperature, were hybridized to it creating three individual dsDNA-AuNP bioconjugates. The dye-labeled probe strands were used to quantify the rate and amount of DNA release after a given number of light pulses, which was then correlated to the dsDNA denaturation temperature, resulting in a quantitative nanothermometer. The localized DNA denaturation rate could be modulated by more than threefold over the biologically relevant range of 8-53 °C by varying pulse energy fluence, DNA melting temperature, and surrounding bath temperature. With a modified dissociation equation tailored for this system, a "sensed" temperature parameter was extracted and compared to simulated AuNP temperature profiles. Determining actual biological responses in such systems can allow researchers to design precision nanoscale photothermal heating sources.

Chemical solution deposition (CSD) is a versatile and cheap technique widely used for both buffer layer and YBa2Cu3O7-δ (YBCO) epitaxial film growth. One of the main limits hindering the widespread use of the CSD technique in the field of coated conductors is the limited film thickness that can be reached without degradation. Among the different strategies adopted to obtain thicker film by CSD, increase of precursor solution concentration and viscosity can be mentioned. Another interesting approach is the use of multilayer deposition in order to overcome the limits of CSD technique. Multilayer deposition has been so far realized by performing a complete heat treatment - or at least a pyrolysis step - after each deposited layer, resulting in increased processing time and overall cost. In this contribution, we show that an epitaxial multilayer buffer can be easily obtained by means of multiple successive depositions, in which after each layer only a fast drying step of the precursor solution is carried out. In this way, only a single conversion heat treatment can be used. This approach not only allows a significant reduction on the processing time but also it is more suitable for the growth on metallic template. The viability of this approach is shown by using multilayer Zr-doped CeO2 buffer deposition on single crystalline YSZ substrate. It is shown that, up to 8 layers, no significant structural and morphological degradation of the buffer layer occurs. Further, YBCO film grown by CSD was used to test the suitability of multilayer buffer. YBCO film grown on multilayer buffer shows excellent superconducting properties, with zero-resistance critical temperature up to 92 K and a critical current density exceeding 1 MA/cm2 at 77 K in self field.

The Cosmic Evolution Survey (COSMOS) has become a cornerstone of extragalactic astronomy. Since the last public catalog in 2015, a wealth of new imaging and spectroscopic data has been collected in the COSMOS field. This paper describes the collection, processing, and analysis of this new imaging data to produce a new reference photometric redshift catalog. Source detection and multi-wavelength photometry is performed for 1.7 million sources across the $2\,\mathrm{deg}^{2}$ of the COSMOS field, $\sim$966,000 of which are measured with all available broad-band data using both traditional aperture photometric methods and a new profile-fitting photometric extraction tool, The Farmer, which we have developed. A detailed comparison of the two resulting photometric catalogs is presented. Photometric redshifts are computed for all sources in each catalog utilizing two independent photometric redshift codes. Finally, a comparison is made between the performance of the photometric methodologies and of the redshift codes to demonstrate an exceptional degree of self-consistency in the resulting photometric redshifts. The $i, Comment: 39 pages, 21 figures, accepted for publication in The Astrophysical Journal Supplement Series. Production of IRAC mosaics are presented in Moneti et al. "Euclid Preparation: XVIII. Cosmic Dawn Survey. Spitzer observations of the Euclid deep fields and calibration fields" which has been posted simultaneously. Catalogs can be accessed online now at https://cosmos2020.calet.org

We present the first look at the spectral and timing analysis of Narrow-Line Seyfert 1 Galaxies (NLS1s) with the extended ROentgen Survey with an Imaging Telescope Array (eROSITA) on board the Spectrum-Roentgen-Gamma (SRG) mission. The sample of about 1,200 NLS1s was obtained via cross-match of the first eROSITA All-Sky Survey (eRASS1) catalogue with the catalogue of spectroscopically selected NLS1s from the Sloan Digital Sky Survey (SDSS) DR12 by Rakshit et al. [ApJS, 229, 39 (2017)]. The X-ray spectral analysis is based on a simple power-law fit. The photon index distribution has a mean value of about 2.81+-0.03, as expected from previous X-ray studies of NLS1s. Interestingly, it is positively skewed, and about 10 percent of the sources are located in the super-soft tail of photon indices larger than 4. These sources are of further interest as their source counts run into the X-ray background at values at around 1 keV. We argue that ionised outflows have been detected by eROSITA and may account for some of the extreme spectral steepness, which is supported by correlations found between the photon index and optical outflows parameters. We analysed the intrinsic X-ray variability of the eRASS1 to eRASS3 light curves of the sample but do not find significant variability neither during the individual survey scans nor between them., Comment: 11 pages, 10 figures, accepted for publication in Section 4. Extragalactic astronomy of Astronomy and Astrophysics

A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.

Carbon Fiber reinforced polymer (CFRP) bonding is a paramount asset for enabling the transition towards a green aircraft based transport industry. Guaranteeing the quality of the bonding is crucial for ensuring its strength and its robustness to mechanical stresses. Here, we present the results of an Ion Mobility Spectrometer based no-contact methodology to detect the presence of two contaminants that are usually present on surfaces before bonding. False negative rate was found to be less than 5% at an accuracy level of 92% for detection which look promising for further development aiming at field deployment.

The eROSITA Final Equatorial-Depth Survey (eFEDS), covering ~140 square degrees, was performed as part of the performance verification phase of the eROSITA telescope on board of the Russian-German satellite Spectrum-Roentgen-Gamma (SRG). In this paper we present the optical follow-up of 542 X-ray extent selected galaxy group and cluster candidates providing redshifts and cluster confirmation for the full sample. We use optical imaging data from the Hyper Suprime-Cam Subaru Strategic Program and from the Legacy Survey to run the cluster redshift and confirmation tool MCMF as well as the optical cluster finder CAMIRA at the location of the X-ray candidates. While providing redshift estimates for all 542 candidates, we construct an optically confirmed sample of 477 clusters and groups with a residual contamination of 6%. Of these, 470 (98.5%) are confirmed using MCMF and 7 systems are added through cross matching with spectroscopic group catalogs. Using observable to observable scaling and the applied confirmation threshold, we predict 8 +/- 2 real systems have been excluded with the MCMF cut required to build this low contamination sample. This number is in good agreement with the 7 systems recovered through cross matching. Thus, we expect those 477 systems to include >99% of all true clusters in the candidate list. Using an MCMF independent method, we confirm the catalog contamination of the confirmed subsample to be 6 +/- 3% and find 17 +/- 3% contamination for the full X-ray sample. The estimated contamination of the fulls sample is in agreement with MCMF dependent estimate of ~17% and the expectation from dedicated X-ray simulations of ~20%. We further present a sample of optically selected merging cluster candidates., 24 pages, 26 figures, accepted to appear on A&A, Special Issue: "The Early Data Release of eROSITA and Mikhail Pavlinsky ART-XC on the SRG Mission". Catalog available at: https://erosita.mpe.mpg.de

The eROSITA Final Equatorial-Depth Survey has been carried out during the PV phase of the SRG/eROSITA telescope and completed in November 2019. This survey is designed to provide the first eROSITA-selected sample of galaxy clusters and groups and to test the predictions for the all-sky survey in the context of cosmological studies with clusters. In the 140 deg$^2$ area covered by eFEDS, 542 candidate clusters and groups are detected as extended X-ray sources, down to a flux of $\sim10^{-14} $erg/s/cm$^2$ in the soft band (0.5-2 keV) within 1'. In this work, we provide the catalog of candidate galaxy clusters and groups in eFEDS. We perform imaging and spectral analysis on the eFEDS clusters with eROSITA X-ray data, and study the properties of the sample. The clusters are distributed in the redshift range [0.01, 1.3], with the median redshift at 0.35. We obtain the ICM temperature measurement with $>2\sigma$ c.l. for $\sim$1/5 (102/542) of the sample. The average temperature of these clusters is $\sim$2 keV. Radial profiles of flux, luminosity, electron density, and gas mass are measured from the precise modeling of the imaging data. The selection function, the purity and completeness of the catalog are examined and discussed in detail. The contamination fraction is $\sim1/5$ in this sample, dominated by misidentified point sources. The X-ray Luminosity Function of the clusters agrees well with the results obtained from other recent X-ray surveys. We also find 19 supercluster candidates in eFEDS, most of which are located at redshifts between 0.1 and 0.5. The eFEDS cluster and group catalog provides a benchmark proof-of-concept for the eROSITA All-Sky Survey extended source detection and characterization. We confirm the excellent performance of eROSITA for cluster science and expect no significant deviations from our pre-launch expectations for the final All-Sky Survey., Comment: Submitted to A&A for the Special Issue: The Early Data Release of eROSITA and Mikhail Pavlinsky ART-XC on the SRG Mission. 25 pages, 13 figures

This chapter introduces various extended non-destructive testing (ENDT) techniques for surface quality assessment, which are first characterized, then enhanced, and finally applied to assess the level of pre-bond contaminations intentionally applied to carbon fiber reinforced plastic (CFRP) adherends following the procedures described in the previous chapter. Based on two user cases comprising different scenarios that are characteristic of either aeronautical production or repair, the detailed tests conducted on two types of sample geometry, namely flat coupons and scarfed pilot samples with a more complex shape, form the basis for applying the advanced ENDT procedures for the monitoring of realistic and real aircraft parts, as will be described in Chap. 10.1007/978-3-319-92810-4_5. Specifically, the reported investigations were performed to assess the surface quality of first ground and then intentionally contaminated CFRP surfaces using the following ENDT tools: the aerosol wetting test (AWT), optically stimulated electron emission (OSEE), two differently implemented approaches based on electronic noses, laser-induced breakdown spectroscopy (LIBS), Fourier-transform infrared (FTIR) spectroscopy, laser-induced fluorescence (LIF), and laser vibrometry.

The eROSITA Final Equatorial-Depth Survey (eFEDS), executed during the performance verification phase of the Spectrum-Roentgen-Gamma (SRG)/eROSITA telescope, was completed in Nov. 2019. One of the science goals of this survey is to demonstrate the ability of eROSITA to detect samples of clusters and groups at the final depth of the eROSITA all-sky survey. Because of the sizeable point-spread function of eROSITA, high-redshift clusters of galaxies or compact nearby groups hosting bright active galactic nuclei (AGN) can be misclassified as point sources by the source detection algorithms. A total of 346 galaxy clusters and groups in the redshift range of 0.1, Comment: 31 pages, 13 figures, 3 tables. Accepted for publication in A&A for the Special Issue: The Early Data Release of eROSITA and Mikhail Pavlinsky ART-XC on the SRG Mission

In-plane, or azimuthal, photo-reorientation of azodye films using polarized exposure makes them promising alignment layers for a host of liquid crystal (LC) applications beyond displays including beam steering, q-plates, liquid crystal elastomer origami, and control of active matter. Out-of-plane, or polar, reorientation of azodye films, which dictates the liquid crystal pretilt, has received far less attention. Spatial control over the full polar and azimuthal orientation enables the generation of complex patterns that have broad interests and applications. In this paper, we describe an enhanced out-of-plane reorientation in Brilliant Yellow films utilizing a two-step exposure and demonstrate a liquid crystal pretilt angle that is tunable over a range of 0-33° with the associated anchoring strength of the alignment layer being unaffected by the inclusion of a pretilt. We report an order of magnitude increase in both amplitude and tunability of the pretilt angle in terms of previous results for single photoalignment films. This is a significant result for liquid crystal applications because it offers a simple, scalable, single-component solution with the potential to provide three-dimensional (3-D) patternability of the LC director at the surface.

This work presents the results of the crowdfunding campaign devised for MONICA, an air quality monitoring portable device. The initiative is strongly focused on the increased awareness and involvement of citizens in the air pollution issue solution. Specifically, MONICA is an architecture composed by a portable device based on an array of commercial electrochemical sensors calibrated in lab, an Android App for smartphone, a web portal (MENA) and a NOSQL backend. This infrastructure is able to manage data communication/storage and map visualization of personal exposure to air pollutants. Two associated calibration procedures are depicted, one based on in-lab recordings while the second, based on the emerging on field calibration paradigm, will refine the performance of the node. The successful, both in financial and participatory terms, campaign has reached a crowdfunded contribution of 8000 € (145% of expected 6000 €) by 102 supporters. Among them, 44 users, have opted to become part of a small fleet of human-sensors able to produce air quality data during their daily mobility routine.

Air quality is a source of increasing concern in several cities, due to the adverse health effects of significant pollution levels. For this reason, the need to assess the concentration of pollutants at high temporal and spatial resolution is perceived as very urgent. As such, there is a growing interest in building pervasive networks integrating different sensing technologies to achieve this capability. In this view, low cost smart sensors data could be fused together with fixed but more accurate multisensor devices and certified analyzers data to build hi-res maps (

Artificial olfaction is an emerging application field for machine learning practitioners. In this paper, we propose a holistic approach to pattern classification in electronic noses applications. In particular, we show how classification results based on a complete measurement cycle can be combined with an assessment provided by real-time classifiers acting on the single instantaneous measurement sample. A running classification confidence measure allows for obtaining fast and reliable outcomes. A safety critical scenario has been selected for the testing of the proposed pattern analysis strategy involving the identification and discrimination of surface contaminants on composite panels in pre-bonding nondestructive tests during lightweight aircraft assembly. A reject option has been introduced to refuse low classification confidence panels improving both FP and FN rates. Results show how this strategy can efficiently exploit two different views of the electronic nose olfactive fingerprinting process that is currently seen as alternative.

This work report the preliminary results of crowdfunding/crowdsensing campaign run in Italy aimed to functional test of a smart air quality monitoring infrastructure. Design and implementation of the cooperative monitoring infrastructure are described along with details of crowdfunding campaign. Participating users received, for a whole month, a field validated electrochemical sensors based air quality monitoring node and a companion APP capable of reporting sophisticated concentration estimations. Calibration functions are actually based on machine learning components correcting for environmental and non target gas interferences. Data gathered in the cloud allowed for evaluation of acceptability and reliability of the node as well as for mapping concentrations measurements inside city landscape through an ad-hoc GUI.

The EuNetAir Joint Exercise focused on the evaluation and assessment of environmental gaseous, particulate matter (PM) and meteorological microsensors versus standard air quality reference methods through an experimental urban air quality monitoring campaign. This work presents the second part of the results, including evaluation of parameter dependencies, measurement uncertainty of sensors and the use of machine learning approaches to improve the abilities and limitations of sensors. The results confirm that the microsensor platforms, supported by post processing and data modelling tools, have considerable potential in new strategies for air quality control. In terms of pollutants, improved correlations were obtained between sensors and reference methods through calibration with machine learning techniques for CO (r = 0.13–0.83), NO (r = 0.24–0.93), O (r = 0.22–0.84), PM10 (r = 0.54–0.83), PM2.5 (r = 0.33–0.40) and SO (r = 0.49–0.84). Additionally, the analysis performed suggests the possibility of compliance with the data quality objectives (DQO) defined by the European Air Quality Directive (2008/50/EC) for indicative measurements.

This electronic Air quality is nowadays a primary concern in several cities. More generally pollutant and toxic gases continuously, incidentally or purposedly released are now considered to be extremely relevant for their severe adverse health effects to exposed citizens. In this view, low cost smart chemical multisensors are arising as a reliable source for indicative data on Air Quality. Low cost UAVs are versatile platforms capable to host playloads integrating multi sensor technologies for short term, mobile monitoring tasks. The recently proposed tethered UAV platforms can be deemed as interesting solutions to build and rapidly deploy impromptu networks of air quality analyzers for short-term and focused environmental monitoring actions. In this work, we propose the integration of the ENEA MONICA multisensor platform as a measurement payload for the TopView SAV-ES UAV. The prototype has been flown on board of the SAV-ES platform for a test flight targeting the measurement of a plume generated with a brushwood controlled fire. The functional proof of concept flight have confirmed the possibility to use MONICA as a measurement payload for the targeted platform. © 2018 IEEE.

Recently, the interest in the development of new pervasive or mobile implementations of air quality multisensor devices has significantly grown. New application opportunities appeared together with new challenges due to limitations in dealing with rapid pollutants concentrations transients both for static and mobile deployments. Sensors dynamic is one of the primary factor in limiting the capability of the device of estimating true concentration when it is rapidly changing. Researchers have proposed several approaches to these issues but none have been tested in real conditions. Furthermore, no performance comparison is currently available. In this contribution, we propose and compare different approaches to the calibration problem of novel fast air quality multisensing devices, using two datasets recorded in field. Machine learning architectures have been designed, optimized and tested in order to tackle the cross sensitivities issues and sensors inherent dynamic limitations to perform accurate prediction and uncertainty estimation. Comparison results shows the advantage of dynamic non linear architectures versus static linear ones with support vector regressors scoring best results. © Springer International Publishing AG 2018.

Lightweight green aircraft development goals rely on the massive use of CFRP - Carbon Fiber Reinforced Polymers - for aircraft assembly. However, the lack of a validated NDT methodology for bond quality assurance is slowing down their adoption in primary structures. In particular, during pre-bond phase it is necessary to check for surface contaminations that could hamper the effectiveness of the adhesive bond. Among different techniques under screening, electronic noses (e-noses) remain a very promising tool for detection and identification of surface contaminants. In this work, we report the results of a test campaign conducted in the framework of COMBONDT project. An ad-hoc designed platform is tested in realistic conditions, i.e. using curved samples or real aircraft parts for assessing the tool capability. Design of the e-nose architecture as well as data processing techniques are also presented. © 2018 IEEE.

The adoption of on field calibration for pervasive air quality monitors, is increasing significantly in the last few years. The sensors data, recorded on the field, together with co-located reference analyzers data, allow to build a knowledge base that is more representative of the real world conditions and thus more effective. However, on field calibration precision may fade in time due to change in operative conditions, due to different drivers. Among these, relocation is deemed among the most relevant. In this work, for the first time, we attempt to assess the robustness of this approach to relocation of the sensor nodes. We try to evaluate the impact on performance of the so called locality issue by measuring the changes in the performance indicators, when a chemical multisensory system operates in a location that differs from the one in which it was on field calibrated. To this purposes, a nonlinear multivariate approach with Neural Networks (NN) and a suitable dataset, provided by NILU (the Norwegian Institute for Air Quality), have been used. The preliminary results show a greater influence of seasonal forcers distribution with respect to the relocation issues. © Springer International Publishing AG 2018.

Modern aircraft structure, by making use of lightweight composite materials based on carbon fiber reinforced plastics (CFRP), succeeds in reducing CO2 emissions and transport fuel costs. Nevertheless, its usage cannot leave Non Destructive Tests out to consideration in order to set up a quality assurance procedure of surfaces’ contamination status. Here, we show and compare two different e-nose solutions able to detect and quantify hydraulic-oil fingerprint contamination at significantly low contamination levels occurring during aircraft maintenance operations.

Waste water management process has a significant role in guarantee sea and surface water bodies water quality with direct impact on tourism based economy and public health. Protection of this critical infrastructure form illicit discharges is hence paramount for the whole society. Here, We propose a pervasive monitoring centered approach to the protection of wastewater management plant. An hybrid sensor network is actually deployed along the wastewater network including several different transducers. Incepted data are harmonized and processed with an integrated SWMM model and machine learning based approach in order to forecast water qualitative and quantitative aspects, detect and localize anomalies. An advanced WEBGIS-SOS based interface conveys relevant information to the management entity allowing it to take appropriate actions in a timely way, reducing and mitigating the impacts of illicit discharges. © Springer International Publishing AG 2018.

Chemical multisensor devices need calibration algorithms to estimate gas concentrations. Their possible adoption as indicative air quality measurements devices poses new challenges due to the need to operate in continuous monitoring modes in uncontrolled environments. Several issues, including slow dynamics, continue to affect their real world performances. At the same time, the need for estimating pollutant concentrations on board the devices, especially for wearables and IoT deployments, is becoming highly desirable. In this framework, several calibration approaches have been proposed and tested on a variety of proprietary devices and datasets; still, no thorough comparison is available to researchers. This work attempts a benchmarking of the most promising calibration algorithms according to recent literature with a focus on machine learning approaches. We test the techniques against absolute and dynamic performances, generalization capabilities and computational/storage needs using three different datasets sharing continuous monitoring operation methodology. Our results can guide researchers and engineers in the choice of optimal strategy. They show that non-linear multivariate techniques yield reproducible results, outperforming linear approaches. Specifically, the Support Vector Regression method consistently shows good performances in all the considered scenarios. We highlight the enhanced suitability of shallow neural networks in a trade-off between performance and computational/storage needs. We confirm, on a much wider basis, the advantages of dynamic approaches with respect to static ones that only rely on instantaneous sensor array response. The latter have been shown to be best choice whenever prompt and precise response is needed.

In this work, we describe a cooperative air quality sensor architecture based on crowdfunded, mobile, electrochemical sensor based, monitoring systems. The platform aims to produce enhanced information on personal pollutant exposure and enable cooperative reconstruction of high resolution pictures of air pollution in the urban landscape. The calibrated devices are connected to smartphones that provide georeferenced visualization of personal exposure and session based log capabilities. A cloud based interface provides a sensor fusion based mapping capability exploiting google maps APIs. An in-lab calibration by linear regression with temperature correction has been computed and preliminary results have been reported. A small set of calibrated devices will be shipped to crowdfunders for extended field tests in different italian cities.

We make use of the deep Karl G. Jansky Very Large Array (VLA) COSMOS radio observations at 3 GHz to infer radio luminosity functions of star-forming galaxies up to redshifts of z~5 based on approximately 6000 detections with reliable optical counterparts. This is currently the largest radio-selected sample available out to z~5 across an area of 2 square degrees with a sensitivity of rms=2.3 ujy/beam. By fixing the faint and bright end shape of the radio luminosity function to the local values, we find a strong redshift trend that can be fitted with a pure luminosity evolution L~(1+z)^{(3.16 +- 0.2)-(0.32 +- 0.07) z}. We estimate star formation rates (SFRs) from our radio luminosities using an infrared (IR)-radio correlation that is redshift dependent. By integrating the parametric fits of the evolved luminosity function we calculate the cosmic SFR density (SFRD) history since z~5. Our data suggest that the SFRD history peaks between 21000 Msol/yr) contribute an additional 4) on the order of 15-20%, owing to appreciable star formation in highly dust-obscured galaxies, which might remain undetected in such UV observations., Comment: 18 pages, 8 figures, 2 tables, accepted for publication in A&A

The full-scale adoption of light-weighted carbon fiber reinforced plastics (CFRP) in the primary aircraft structures cannot be safely achieved without a reliable quality assurance protocol. A pre-bond cleaning state inspection of adherents surfaces by means of Non-Destructive Tests (NDTs) is absolutely necessary in order to guarantee the strength of bonded joints and so the structural aircraft integrity. Hydraulic-oil fingerprint contamination mainly occurs during aircraft maintenance operations, due to wrong handling by workers. It may severely affect the mechanical properties of bonded joints. Here, an ENEA customized electronic nose solution and a suitable measurement methodology are proposed as NDT tool for pre-bond detection of the Skydrol contamination state of the involved surfaces, at least at high concentration level. The protocol provides a reliable and efficient strategy to uplift and extend the technology readiness level (TRL) of NDTs allowing to overcome existing limitations in adhesive bonding for CFRP materials.

On Field calibration is increasingly considered as the best performing approach for air quality monitor devices. Field recorded sensor data together with co-located reference data build suitable dataset that are more representative of the complexity of real world conditions. However, many researchers pointed out the possible lack of generalization due to the strong dependence on the condition encountered during the field recordings. This work, for the first time, try to assess the robustness of this approach to relocation of the sensor nodes. This is particular relevant for mobile deployments and for guaranteeing the scalability properties of this calibration approach in pervasive deployments. Neural Networks have been used to provide for a nonlinear multivariate calibration algorithm. An extensive dataset, recorded in Oslo during 2015-16, provided the ground for a multi-node/multi-weeks assessment. The observed differences account for a greater influence of seasonal changes on the performances with respect to relocation effects. © 2017 IEEE.

The full exploitation of Composite Fiber Reinforced Polymers (CFRP) in so-called green aircrafts design is still limited by the lack of adequate quality assurance procedures for checking the adhesive bonding assembly, especially in load-critical primary structures. In this respect, contamination of the CFRP panel surface is of significant concern since it may severely affect the bonding and the mechanical properties of the joint. During the last years, the authors have developed and tested an electronic nose as a non-destructive tool for pre-bonding surface inspection for contaminants detection, identification and quantification. Several sensors and sampling architectures have been screened in view of the high Technology Readiness Level (TRL) scenarios requirements. Ad-hoc pattern recognition systems have also been devised to ensure a fast and reliable assessment of the contamination status, by combining real time classifiers and the implementation of a suitable rejection option. Results show that e-noses could be used as first line low cost Non Destructive Test (NDT) tool in aerospace CFRP assembly and maintenance scenarios.

This work aims to present the results of the first phase of crowdfunding campaign devised to involve general public in the development of a portable device for air quality monitoring and the associated infrastructure. A portable device based on an array of electrochemical sensors has been developed and calibrated in lab in order to build detailed maps of personal exposure to air pollutants with the help of an Android App and a sensor NOSQL based backend. Two associated calibration procedures are depicted, one based on in-lab recordings while the second, based on the emerging on field calibration paradigm, will refine the performance of the node. Preliminary tests shows that the project is now ready for its second step in which a small fleet of devices will be shipped to the crowdfunding supporters. © 2017 IEEE.

Machine learning techniques will take an increasingly central role in the distributed sensing realm and specifically in smart cities scenarios. Pervasive air quality monitoring as one of the primary city service requires a significant effort in term of data processing for extracting the needed semantic value. In this paper, after briefly reviewing the emerging relevant literature, we compare several machine learning tools for the purpose of devising intelligent calibration components to be run on board or in cloud computing architectures for pollutant concentration estimation. Two cities field experiments provide the needed on field recorded datasets to validate the approaches. Results are discussed both in terms of performance and computational impact for the specific application. © Springer International Publishing AG 2017.

We examine the behaviour of the infrared-radio correlation (IRRC) over the range 0 2 COSMOS field. We distinguish between objects where emission is believed to arise solely from star-formation, and those where an active galactic nucleus (AGN) is thought to be present. We account for non-detections in the radio or in the infrared using a doubly-censored survival analysis. We find that the IRRC of star-forming galaxies, quantified by the infrared-to-1.4 GHz radio luminosity ratio (qTIR), decreases with increasing redshift: qTIR(z) = (2.88 ± 0.03)(1 + z)- 0.19 ± 0.01. This is consistent with several previous results from the literature. Moderate-to-high radiative luminosity AGN do not follow the same qTIR(z) trend as star-forming galaxies, having a lower normalisation and steeper decrease with redshift. We cannot rule out the possibility that unidentified AGN contributions only to the radio regime may be steepening the observed qTIR(z) trend of the star-forming galaxy population. We demonstrate that the choice of the average radio spectral index directly affects the normalisation, as well as the derived trend with redshift of the IRRC. An increasing fractional contribution to the observed 3 GHz flux by free-free emission of star-forming galaxies may also affect the derived evolution. However, we find that the standard (M82-based) assumption of the typical radio spectral energy distribution (SED) for star-forming galaxies is inconsistent with our results. This suggests a more complex shape of the typical radio SED for star-forming galaxies, and that imperfect K corrections in the radio may govern the derived trend of decreasing qTIR with increasing redshift. A more detailed understanding of the radio spectrum is therefore required for robust K corrections in the radio and to fully understand the cosmic evolution of the IRRC. Lastly, we present a redshift-dependent relation between rest-frame 1.4 GHz radio luminosity and star formation rate taking the derived redshift trend into account.

Fast response and reliability are a prerogative in non- destructive tests s p e c i f i c a l l y in aerospace industry for safety and efficiency reasons. Currently, composite panels bonding, in green aircraft concept, is lacking a validated NDT technique for the bond quality. E-noses equipped with PARC algorithms appear a promising choice to acquire speedily a complete pattern response maximizing reliability. In this paper, combining real time classifiers, we show how to obtain a rapid first- hand response with the possibility of increasing accuracy awaiting for the end of the e-nose measurement cycle. A reject option is casted on the base of classifier self-perceived reliability to nullify false negatives while keeping the false positive rate at minimum.

This paper deals with the Dirichlet problem for second order linear elliptic equations in unbounded domains of the plane in weighted Sobolev spaces. We prove an a priori bound and an existence and uniqueness result.