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6,321 to 6,330 of 8,062 Results
Experiment_positions_CADTrained.tab
Jun 3, 2021 - Replication Data for: Deeply Sub-Wavelength Non-Contact Optical Metrology of Sub-Wavelength Objects
Tabular Data - 158.2 KB - 101 Variables, 84 Observations - UNF:6:7ounWXLCkJg0iCHBHdr+QQ==
Experiment_widths_CADTrained.tab
Jun 3, 2021 - Replication Data for: Deeply Sub-Wavelength Non-Contact Optical Metrology of Sub-Wavelength Objects
Tabular Data - 161.9 KB - 101 Variables, 84 Observations - UNF:6:QKPKuw4H9O92ibb6qcAZjA==
Experiment_widths_SEMTrained.tab
Jun 3, 2021 - Replication Data for: Deeply Sub-Wavelength Non-Contact Optical Metrology of Sub-Wavelength Objects
Tabular Data - 162.0 KB - 101 Variables, 84 Observations - UNF:6:NKMVwf3QaHmWOABtuPUTEA==
Experiment_widths_SEMTrained_TotalIntensity.tab
Jun 3, 2021 - Replication Data for: Deeply Sub-Wavelength Non-Contact Optical Metrology of Sub-Wavelength Objects
Tabular Data - 161.9 KB - 101 Variables, 84 Observations - UNF:6:7OceWn+2KKi5TI1zAV6Vtg==
Simulation_widths_ASMTrained-1.tab
Jun 3, 2021 - Replication Data for: Deeply Sub-Wavelength Non-Contact Optical Metrology of Sub-Wavelength Objects
Tabular Data - 162.0 KB - 101 Variables, 84 Observations - UNF:6:zPO8+k+sWwHaru8GA7b1Dw==
Simulation_widths_ASMTrained.xlsx
Jun 3, 2021 - Replication Data for: Deeply Sub-Wavelength Non-Contact Optical Metrology of Sub-Wavelength Objects
MS Excel Spreadsheet - 307.1 KB - MD5: a18f872d5dbb2c53860af0534abcc36c
slit_names_all.txt
Jun 3, 2021 - Replication Data for: Deeply Sub-Wavelength Non-Contact Optical Metrology of Sub-Wavelength Objects
Plain Text - 8.7 KB - MD5: 7ab3cfadfd1f0dce3b7d63a5b2829fc6
slit_names_validation.txt
Jun 3, 2021 - Replication Data for: Deeply Sub-Wavelength Non-Contact Optical Metrology of Sub-Wavelength Objects
Plain Text - 888 B - MD5: cea4abdf892d76ab6c42e0216aa9194d
Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan
Jun 1, 2021 - CHEONG Siew Ann
Cheong, Siew Ann, 2021, "Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan", https://doi.org/10.21979/N9/JSUTCD, DR-NTU (Data), V1
(C, V, S, K) index time series data generated using 0.5-Hz GEMS time series data from Taiwan. In this data set, C is a modified autocorrelation function, V is the variance, S the skewness, and K the kurtosis of the GEMS geo-electric field time series.
D_CHCH_ti2_FCW500S2_BW0813_RWday0.02_05JAN.pickle
Jun 1, 2021 - Hidden-State Modelling of a Cross-section of Geoelectric Time Series Data Can Provide Reliable Intermediate-term Probabilistic Earthquake Forecasting in Taiwan
Unknown - 10.6 MB - MD5: 3a285c247f80e8855c600dcf58ba954e
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