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Having ubiquitous optical monitors in dense wavelength-division multiplexing (DWDM) or flex-grid networks allows the estimation in real time of crucial parameters. Such monitoring would be even more important in disaggregated optical networks, to inspect performance issues related to inter-vendor interoperability. Several important parameters can be retrieved using optical spectrum analyzers (OSAs). However, omnipresent OSAs represent an infeasible solution. Nevertheless, the advent of new, relatively cheap, compact and medium-resolution optical channel monitors (OCMs) enable a more intensive deployment of these devices. In this paper, we identify two main scenarios for the placement of such monitors: at the ingress and at the egress of the optical nodes. In the ingress scenario, we can directly estimate the parameters related to the signals, but not those related to the filters. On the contrary, in the egress scenario, the filter-related parameters can be easily detected, but not those related to amplified spontaneous emission. Therefore, we present two methods that, leveraging a curve fitting and a machine learning regression algorithm, allow detection of the missing parameters. We verify the proposed solutions with spectral data acquired in simulation and experimental setups. We obtained good estimation accuracy for both setups and for both studied placement scenarios. It is noteworthy that in the experimental assessment of the ingress scenario, we achieved a maximum absolute error (MAE) lower than 1 GHz in filter bandwidth estimation and a MAE lower than 0.5 GHz in filter frequency shift estimation. In addition, by comparing the relative errors of the considered parameters, we identified the ingress scenario as the more beneficial. In particular, we estimated the filter central frequency shift with 84% and the filter 6 dB bandwidth with 75% higher accuracy, with respect to datasheet/reference values. This translates into a total reduction of the estimated signal-to-noise ratio (SNR) penalty, introduced by a single optical filter, of 0.24 dB. ; Funding: Horizon 2020 Framework Programme (765275). This work is part of the Future Optical Networks for Innovation, Research and Experimentation (ONFIRE) project (https://h2020-onfire.eu), which is supported by the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Action. ; Peer Reviewed ; Postprint (author's final draft)

Spectral images were captured of paper samples varnished with two water-soluble materials: gum arabic and egg white. The samples were submitted to degradation processes that partially or totally eliminated the varnish from the substrate (water immersion and ageing). The spectral information was used to obtain average color data and to characterize the spatial and color inhomogeneity across pixels, showing that the pixel spectral data are critical for an accurate characterization of the degradation process of the varnishes. Since the varnishes typically become yellower with ageing, this study introduces two novel and simple-to-compute yellowness indices based on the spectral information, which are validated against a standard colorimetric index (ASTM-E313 2015). The potential uses of spectral information are demonstrated with several pieces of a real antique map sample by comparing the spectral information measured before and after cleaning the sample. To sum up, the main contributions of this study are the characterization of the spatial homogeneity through pixel-based spectral and color information and the proposal of spectral-based yellowing indices for two critical applications (ageing process follow-up and effect of cleaning), as demonstrated with synthetic and historical samples of varnished paper respectively. ; Spanish Ministry of Economy and Competitiveness, under research Grant DPI2015-64571-R. Spanish State Agency of Research (AEI) and the Ministry for Economy, Industry and Competitiveness (MIMECO) by means of the Grant Number FIS2017-89258-P with European Union FEDER (European Regional Development Funds) support.

AbstractMachine learning has been extensively used for analyzing spectral data in food quality management. However, collecting high-quality spectral data from miniature spectrometers outside the laboratory is challenging due to various factors such as distortions, noise, high dimensionality, and collinearity. This paper presents an in-depth analysis of food datasets collected from miniature spectrometers to evaluate the data quality and characteristics, by focusing on a case study of olive oil quality check, where various machine learning models were applied to differentiate pure and adulterated olive oil. Furthermore, the impact of pre-processing techniques on data distortions was studied. It presents a comprehensive pipeline, including data pre-processing, dimension reduction, classification, and regression analysis, and deploys different algorithms for comparative classification and regression analysis. The model performances were assessed using 2 separate methods: tenfold cross-validation on an entire dataset with 10% random testing, and an entire test set collected in different environments (multi-session validation). The first validation approach reached classification rates of up to 96.73%, while the second achieved 83.32%. These results demonstrate that cost-effective miniature spectrometers augmented with a suitable machine learning pipeline could execute classification tasks on par with non-portable and more expensive spectrometers. Furthermore, the study highlights the requirement of specialized algorithms to handle different ambient conditions affecting data acquisition and to eliminate performance gaps, making miniature spectrometers suitable for in situ scenarios. This work extends previous research to enable consumers becoming the first line in the defense against food fraud.

Abstract. — Using a newly proposed method of analysis of the solar photometric spectrum in the range λ 6105 - λ 6150, with the spectral data published by Delbouille, Neven and Roland, all lines mentioned in the S.R.R.T. are confirmed with the exception of the eight weak lines mentioned in table I.
Eleven weak lines quoted in the R.R.T. but rejected in the S.R.R.T., reappear in our analysis (column λ0 of Table II). Tentative identifications are proposed for five new lines while two identifications given in the R.R.T. are confirmed.

The paper proposes a contemporary interdisciplinary method to identify consistent patterns within cyclical dynamics of GDP and its macroeconomics determinants in the Russian Federation. This method may contribute to better recognition of the stages of economic cycle and of potential early predicators to recessions and crises. We first identify the trend component of Russian GDP and then apply the spectral data analysis to its cyclical component which reveals its multi-frequency, and non-linear vibrations. These vibrations are then further investigated by transforming time series data on GDP and its determinants into a frequency spectrum series via Fourier transform techniques. Wavelength scanning of selected macroeconomic indicators shows the basic economic cycle of real GDP with duration time of approx. 3.13 years. Other procyclical indicators nevertheless discover asynchronous behavior towards GDP due to the relative autonomy of the sectors standing behind these indicators. Their autonomy lies behind differences in reaction forces (shifts) and periods (lags) to both internal and external shocks. We estimate differentials between the dynamics of GDP and its determinants by evaluating phase deviations of their pairwise harmonic components, mutual pairwise phase shifts, and by comparison of their pairwise cross-spectrum. The one of output is the quantification of time lags between GDP and key macroeconomic indicators of individual economic sectors. This result reveals the complexity of GDP dynamics that sends an aliased rather than a unit signal to economic agents. Our decomposition of this signal into signals from key economic sectors and quantification of phase discrepancies between sectoral signals may contribute to findings in early crisis predicators. We also estimate the depth and velocity of shocks penetrations into both economy as a whole and its particular sectors.

The Astrophysical Journal 818.2 (2016): 174 reproduced by permission of the AAS ; We study the clustering of galaxies as a function of spectral type and redshift in the range 0.35

AbstractIncreasing attention has focused in recent years on the use of spectral analytic techniques for the analysis of economic time series data. Sovereign, Nolan and Mandel [26] have recently outlined the basic concepts underlying such analysis and presented examples relating to the management science area including inventory demand, transportation simulation and stock market price behavior. The purpose of this paper is to supplement and extend their tutorial with a discussion of some of the problems involved with the application and interpretation of spectral statistics, emphasizing the problems of stationarity and detrending and data windows. Additionally, the scope of the examples is broadened to include applications in the area of monetary economics.The first part of this paper presents a brief discussion of the objectives of spectral analysis. Since this paper attempts to extend the work of Sovereign, Nolan and Mandel, only a heuristic discussion of spectral techniques of analysis is presented. This is followed by a more detailed description of the problem of stationarity and the effects of detrending upon the interpretation of spectral results. Then we consider the problem of estimating a continuous curve with finite information and the implications of both of these problems by presenting several applied examples in the area of monetary economics. The concluding section of this paper briefly summarizes some additional considerations in harmonic analysis of economic time series data and notes some potential directions for future research.

Data of optical performance, thermal stability and ageing are given for solar selective coatings (SSC) based on a novel MoSi2-Si3N4 absorbing composite. SSC have been prepared as multilayer stacks formed by silver as metallic infrared reflector, a double layer composite and an antireflective layer (doi:10.1016/j.solmat.2016.04.001 [1]). Spectroscopic reflectance data corresponding to the optical performance of samples after moderate vacuum annealing at temperatures up to 600 °C and after ageing test of more than 200 h with several heating-cooling cycles are shown here ; This work has been supported by European Commission under HITECO Collaborative project contract No. 256830 from the FP7-ENERGY program and by the CSIC PIE project E201460E108, by the Spanish MINECO through contract MAT2012-37276-C03-01 and by the Madrid Regional Government through contract P2013/MIT-2740. E.C. acknowledges MINECO for the "Formación Posdoctoral (JdC)" program (FPDI-2013)

Data of optical performance, thermal stability and ageing are given for solar selective coatings (SSC) based on a novel MoSi2–Si3N4 absorbing composite. SSC have been prepared as multilayer stacks formed by silver as metallic infrared reflector, a double layer composite and an antireflective layer (doi:10.1016/j.solmat.2016.04.001 [1]). Spectroscopic reflectance data corresponding to the optical performance of samples after moderate vacuum annealing at temperatures up to 600 °C and after ageing test of more than 200 h with several heating–cooling cycles are shown here. ; This work has been supported by European Commission under HITECO Collaborative project contract No. 256830 from the FP7-ENERGY program and by the CSIC PIE project E201460E108, by the Spanish MINECO through contract MAT2012-37276-C03-01 and by the Madrid Regional Government through contract P2013/MIT-2740. E.C. acknowledges MINECO for the "Formación Posdoctoral (JdC)" program (FPDI-2013). ; EUR 446,27 APC fee funded by the EC FP7 Post-Grant Open Access Pilot ; Peer reviewed

"This is the peer reviewed version of the following article: Perales, E., Micó-Vicent, B., Huraibat, K., Martínez-Verdú, F. M., Dietz, C., & Yamanoi, Y. (2019). Review of instrumental inter-agreement study of spectral and colorimetric data of commercial multi-angle spectrophotometersspectrophotometers. Color Research & Application, 44(2), 168-175., which has been published in final form at https://doi.org/10.1002/col.22320. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." ; [EN] Several different instruments have been introduced into the market to obtain good color characterization for different measurement configurations. These commercial instruments have different optical and working configurations, however, the measurement data provided by each instrument should be similar to have good consistency when comparing them to each other measuring the same sample material. Therefore, the purpose of this work was to apply an inter-model agreement study of spectral and colorimetric data of three instruments (CM-M6, BYK-mac-I, and MA98). Two different statistical tests were applied following ASTM recommendations. In general, the measurement geometries close to the specular direction and the flop direction showed greater deviations. In addition, the partial color differences calculated for the comparison of MA98 versus CM-M6 were larger than the BYK-mac-i versus CM-M6 comparison. Finally, it can be concluded that most of the measurement geometries were statistically significantly different from each other which means that these differences were due to systematic or bias errors and not exclusively to random errors. ; EMRP, Grant/Award Numbers: Bidirectional reflectance definitions (16NRM08), 16NRM08; Spanish Ministry of Economy and Competitiveness, Grant/Award Number: DPI2015-65814-R; Ministry of Economy and Competitiveness, Grant/Award Number: DPI2015-65814-R; European Union; EURAMET ; Perales Romero, E.; Micó-Vicent, B.; Huraibat, K.; ...