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This reprint focuses on a very important topic in metrology, which is represent by measurement uncertainty. Any good metrologist or scientist in engineering knows that no measurement makes sense without an associated uncertainty value: without an uncertainty value, no decision can be taken; no comparisons can be made; no conformity can be assessed. Any decision, comparison or conformity assessment made without considering the measurement uncertainty affecting the measurement value is completely useless and meaningless. Stated that, it becomes very clear that uncertainty in measurement plays indeed a very important rule in our everyday life. This is the reason why there is a great need to have a fruitful academic and scientific discussion on this topic. We have been speaking about measurement uncertainty for less than 30 years, since the concept of "measurement uncertainty" has been introduced in 1995 by the "Guide to the expression of uncertainty in measurement" (GUM). Thirty years seems to be many, but still the concept of measurement uncertainty has not been spread worldwide and the GUM is a document that is not known everywhere. On the other hand, this document should be considered not only in academic scenario, but also in any technical and industrial scenario, where it is pivotal to know the meaning of measurement uncertainty, identify the uncertainty contributions and know how these contributions affect the final measurement result.

Measurement uncertainty (Statistics) --- Measurement

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This reprint focuses on a very important topic in metrology, which is represent by measurement uncertainty. Any good metrologist or scientist in engineering knows that no measurement makes sense without an associated uncertainty value: without an uncertainty value, no decision can be taken; no comparisons can be made; no conformity can be assessed. Any decision, comparison or conformity assessment made without considering the measurement uncertainty affecting the measurement value is completely useless and meaningless. Stated that, it becomes very clear that uncertainty in measurement plays indeed a very important rule in our everyday life. This is the reason why there is a great need to have a fruitful academic and scientific discussion on this topic. We have been speaking about measurement uncertainty for less than 30 years, since the concept of "measurement uncertainty" has been introduced in 1995 by the "Guide to the expression of uncertainty in measurement" (GUM). Thirty years seems to be many, but still the concept of measurement uncertainty has not been spread worldwide and the GUM is a document that is not known everywhere. On the other hand, this document should be considered not only in academic scenario, but also in any technical and industrial scenario, where it is pivotal to know the meaning of measurement uncertainty, identify the uncertainty contributions and know how these contributions affect the final measurement result.

Measurement uncertainty (Statistics) --- Measurement

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Optical measurement methods are becoming increasingly important for high-precision production of components and quality assurance. The increasing demand can be met by modern imaging systems with advanced optics, such as light field cameras. This work explores their use in the deflectometric measurement of specular surfaces. It presents improvements in phase unwrapping and calibration techniques, enabling high surface reconstruction accuracies using only a single monocular light field camera.

Optical measurement.

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Measurement uncertainty (Statistics)

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This paper uses satellite readings of nitrogen (NO2) air pollution, a byproduct of combustion, to improve the measurement of global economic activity. The proposed approach improves upon night light measures for countries where data manipulation, conflict, or other factors have led to poor national accounts. The paper also shows that existing country rankings of gross domestic product accuracy over the past 15 years are unreliable, even among advanced economies. For example, the paper shows that during COVID, in France, the UK and Spain gross domestic product in 2020 was underreported by 76, 181, and 205 basis points respectively. The methodological contribution extends previous Error-Measurement frameworks which, suffer from error-in-variables biases, with an objective, data-driven identification strategy exploiting the plausibly orthogonal measurement errors between nitrogen dioxide and night lights, which are measured at different times.

Air --- Measurement. --- Pollution.