The experiment here was to demonstrate that we can reliably measure the Reference Waveforms designed in the IEEE P1765 proposed standard and calculate EVM along with the associated uncertainties. The measurements were performed using NIST's calibrated sampling oscilloscope and were traceable to the primary standards.We have uploaded the following two datasets. (1) Table 3 contains the EVM values (in %) for the Reference Waveforms 1--7 after performing the uncertainty analyses. The Monte Carlo means are also compared with the ideal values from the calculations in the IEEE P1765 standard.(2) Figure 3 shows the complete EVM distribution upon performing uncertainty analysis for Reference Waveform 3 as an example. Each of the entries in Table 3 is associated with an EVM distribution similar to that shown in Fig. 3.

We present an experimental approach to design an over-the-air (OTA) millimeter-wave system for measuring error vector magnitude (EVM) with associated uncertainties that include correlations and nonlinearities. Our approach uses a variable waveguide attenuator at the output of a modulated-signal source at 44 GHz and provides traceable measurements on a calibrated equivalent-time sampling oscilloscope. The conductor-based EVM measurements and associated uncertainties presented here serve as a baseline for the eventual OTA-based EVM. We also discuss a noise based EVM estimation technique as a simple tool for planning OTA EVM measurements, but without a complete knowledge of measurement uncertainties.

We present an experimental approach to design an over-the-air (OTA) millimeter-wave system for measuring error vector magnitude (EVM) with associated uncertainties that include correlations and nonlinearities. Our approach uses a variable waveguide attenuator at the output of a modulated-signal source at 44 GHz and provides traceable measurements on a calibrated equivalent-time sampling oscilloscope. The conductor-based EVM measurements and associated uncertainties presented here serve as a baseline for the eventual OTA-based EVM. We also discuss a noise based EVM estimation technique as a simple tool for planning OTA EVM measurements, but without a complete knowledge of measurement uncertainties.

The data used to generate the graphs in figures 2, 4, 5 and 6 of the paper "Evaluating Uncertainty of Nonlinear Microwave Calibrations from Regression Residuals". The full reference is D. F. Williams, B. Jamroz and J. D. Rezac, "Evaluating Uncertainty of Nonlinear Microwave Calibration Models With Regression Residuals," in IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 9, pp. 3776-3782, Sept. 2020, doi: 10.1109/TMTT.2020.3005170. 0. The file with data for Fig. X is named FigX.zip. 1. The file Fig X guide.txt in the top directory of each zip file describes which EasyPlot file was used to create the graph(s) in the figure. 2. The EasyPlot files were made with EasyPlot V 4.0.4, and document the data locations, legends, etc. EasyPlot column-selection format is as follows: "xyiiyy" or "xy..yy" means that column 1 was used for x axis, column 2 for first curve y values, column 5 for second curve y values, column 6 for third curve y values

The data used to generate the graphs in figures 1-9 of the paper "Evaluating Uncertainty of Microwave Calibrations from Regression Residuals".The full citation is D. F. Williams, B. F. Jamroz, J. D. Rezac and R. D. Jones, "Evaluating Uncertainty of Microwave Calibration Models With Regression Residuals," in IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 6, pp. 2454-2467, June 2020, doi: 10.1109/TMTT.2020.2983358.The files are named as follows: 1. CI_figX.plt - Contains EasyPlot V 4.0.4 file used to create the plot, columns used in each file, legend, etc. 2. FigX_FY_name - Contains TAB-delimited data file Y used to construct figure X with original file name "name". First two lines repeats key information found in EasyPlot file. First line specifies columns used in EasyPlot column-selection format. Second line contains original location.EasyPlot column-selection format is as follows: "xyiiyy" or "xy..yy" means that column 1 was used for x axis, column 2 for first curve y values, column 5 for second curve y values, column 6 for third curve y values

As a National Metrology Institute (NMI), the National Institute of Standards and Technology (NIST) maintains traceable measurement capabilities for a variety of quantities, including microwave power. At NMIs and calibration laboratories, traceable microwave power measurements often rely on the principle of dc substitution. This approach involves a power meter that provides dc power to a sensor under test. DC substitution power meters are typically implemented by analog electronics, making them difficult to maintain. Here, we explore programmable source measure units as an alternative implementation of the power meter. We offer a preliminary uncertainty analysis and describe a method to reduce measurement uncertainty due to the accuracy of the measurement equipment. This is data for the manuscript "Using Commercial Source Measure Units for Traceable RF Power Measurements" for the 2024 ARFTG conference.

This dataset contains results represented in the work titled 'A Robust, Over-the-Air Test Bed for Radio-Frequency Fingerprinting of Cellular Devices', whose abstract sample is below. We present a characterized test bed and algorithms for non-destructive, over-the-air fingerprinting of commercial cellular user equipment (UE). This test bed is designed to repeatably collect radiated fields from cellular devices in a 4G long term evolution (LTE) network configuration. We describe a straightforward classification algorithm to determine the model of each cellular device that allows for a direct correlation between input data from test cellular phones and identification efficacy. Additionally, by controlling the radio channel conditions, we provide a framework for transparently studying dominant uncertainties and sensitivities in data-driven cellular device fingerprinting. The algorithm performs classification with either the error vector magnitude, a quantity derived from demodulated data, or the out-of-band frequency domain response of the cellular devices. We have investigated the robustness over time of this fingerprinting method and show over 95% accuracy in identifying UE models from different manufacturers and gaining insight into parameters that can cause a reduction in this level of accuracy and in data-driven approaches in general. This work is part of a larger effort to identify and create a database of genuine off-the-shelf cellular devices to help mitigate counterfeiting and hardware security tampering using RF fingerprinting. As such, the raw data are text files in comma separated value (CSV) format. The text files have varying numbers of columns depending on the figure it is attributed to.

This dataset contains results represented in the work titled 'A Robust, Over-the-Air Test Bed for Radio-Frequency Fingerprinting of Cellular Devices', whose abstract sample is below. We present a characterized test bed and algorithms for non-destructive, over-the-air fingerprinting of commercial cellular user equipment (UE). This test bed is designed to repeatably collect radiated fields from cellular devices in a 4G long term evolution (LTE) network configuration. We describe a straightforward classification algorithm to determine the model of each cellular device that allows for a direct correlation between input data from test cellular phones and identification efficacy. Additionally, by controlling the radio channel conditions, we provide a framework for transparently studying dominant uncertainties and sensitivities in data-driven cellular device fingerprinting. The algorithm performs classification with either the error vector magnitude, a quantity derived from demodulated data, or the out-of-band frequency domain response of the cellular devices. We have investigated the robustness over time of this fingerprinting method and show over 95% accuracy in identifying UE models from different manufacturers and gaining insight into parameters that can cause a reduction in this level of accuracy and in data-driven approaches in general. This work is part of a larger effort to identify and create a database of genuine off-the-shelf cellular devices to help mitigate counterfeiting and hardware security tampering using RF fingerprinting. As such, the raw data are text files in comma separated value (CSV) format. The text files have varying numbers of columns depending on the figure it is attributed to.

This dataset includes Level 1B (L1B) data products from the MODIS/ASTER Airborne Simulator (MASTER) instrument. The spectral data were collected during a single flight aboard a DOE B-200 aircraft over California and Nevada, U.S., on 1998-12-02. A primary objective of this deployment was instrument validation. This deployment was coordinated by the U.S. Department of Energy's Remote Sensing Laboratory (RSL) located at Nellis Air Force Base near Las Vegas, Nevada. Data products include L1B georeferenced multispectral imagery of calibrated radiance in 50 bands covering wavelengths of 0.460 to 12.879 micrometers at approximately 10-meter spatial resolution. The L1B file format is HDF-4. In addition, the dataset includes flight paths, spectral band information, instrument configuration, ancillary notes, and summary information for each flight, and browse images derived from each L1B data file.