Temporal weights.

<p>Average normalized relative decision weights for the four conditions showing only temporal variation, as a function of segment number. Normalization: mean of the absolute values of the ten temporal weights equals 1.0. Blue squares: low-CF noise band. Green circles: mid-CF noise band. Red triangles: high-CF noise band. Black diamonds: broadband noise. Error bars show 95% confidence intervals.</p

Stimuli with purely temporal variation.

<p>Panel A: broadband noise. Panel B: low-CF noise band. Panel C: mid-CF noise band. Panel D: high-CF noise band.</p

Temporal weights compared between the spectro-temporal condition and the single-noise-band conditions.

<p>Average normalized relative decision weights, as a function of segment number, condition, and noise band. Normalization: mean of the absolute values of the ten temporal weights equals 1.0 per noise band. Filled symbols: single-noise-band conditions (replotted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050184#pone-0050184-g005" target="_blank"><b>Figure 5</b></a>). Open symbols: spectro-temporal condition (same data as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050184#pone-0050184-g006" target="_blank"><b>Figure 6</b></a>, but different normalization). Error bars show 95% confidence intervals.</p

Stimulus presented in the spectral-weights condition.

<p>Stimulus presented in the spectral-weights condition.</p

Individual spectral weights: observed versus predicted.

<p>Average normalized relative decision weights, as a function of noise band. Black circles: weights estimated from the spectro-temporal condition. Gray squares: weights predicted from the sensitivity in the single-noise- band conditions (see text).</p

Spectro-temporal weights.

<p>Average normalized relative decision weights for the spectro-temporal condition, as a function of segment number and noise band. Normalization: mean of the absolute values of the 30 (noise band × segment) weights equals 1.0. Blue squares: low-CF noise band. Green circles: mid-CF noise band. Red triangles: high-CF noise band. Error bars show 95% confidence intervals.</p

Spectral weights.

<p>Average normalized relative decision weights as a function of noise band. Normalization: mean of the absolute values of the three weights equals 1.0. Filled symbols: spectral weights condition. Open symbols: spectro-temporal condition, spectral weights averaged across segments (see text). Error bars show 95% confidence intervals.</p

Stimulus with spectro-temporal variation.

<p>Panel A: In the spectro-temporal condition the stimulus consisted of three narrowband noises. For each noise band, ten temporal segments were presented. Independent and random level perturbations were imposed on the 3 (noise band) × 10 (segment) component levels. Panel B: temporal configuration for the mid-CF noise band. The 10 segment levels were drawn independently from a normal distribution with mean <i>µ_M</i> = 55 dB SPL and a standard deviation of 2.5 dB. With identical probability, either 0.75 dB was subtracted from or added to each segment level, in order to create “soft” and “loud” trials (see text). For the low and high noise band the same temporal configuration was used. Panel C: Spectral configuration.</p

Secure Exchange of Digital Metrological Data in a Smart Overhead Crane

Funding Information: One of the research projects covering the digitalization of metrology is the EMPIR Project 17IND02 SmartCom funded through the European Union’s Horizon 2020 Programme. The central mission of the SmartCom project is to develop and provide the basis for a secure, unambiguous, and unified exchange of data in all communication networks where metrological data are used [27,28]. To test and validate the research outcomes of SmartCom in industrial end-user applications, two demonstrators were developed as a part of the project [29]. The demonstrator presented in this paper showcases the use of DCCs, D-SI, and appropriate cryptographical methods for the secure exchange of the measurement data and relevant metadata of cargo containers. In this paper, we report the following original contributions: 1. We present a method for how digital metrological data as metadata can be used to enhance the trustworthiness IoT data; 2. We propose how to use data security technologies and cryptographical methods alongside DCC and D-SI applications; 3. We introduce a demonstrator for integrating the digital data formats and necessary se-curity technologies into IIoT systems with the use case being exchanging metrological data in a smart overhead crane similar to the ones that are used in harbors. Funding Information: Funding: This project received funding from the EMPIR program co-financed by the Participating States and from the European Union’s Horizon 2020 Research and Innovation Programme. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Digitalization and the rapid development of IoT systems has posed challenges for metrol-ogy because it has been comparatively slow in adapting to the new demands. That is why the digital transformation of metrology has become a key research and development topic all over the world including the development of machine-readable formats for digital SI (D-SI) and digital calibration certificates (DCCs). In this paper, we present a method for using these digital formats for metrological data to enhance the trustworthiness of data and propose how to use digital signatures and distributed ledger technology (DLT) alongside DCCs and D-SI to ensure integrity, authenticity, and non-repudiation of measurement data and DCCs. The implementation of these technologies in industrial applications is demonstrated with a use case of data exchange in a smart overhead crane. The presented system was tested and validated in providing security against data tampering attacks.Peer reviewe