cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/0wv2-9446
title: MATLAB Source Code: Frequency-Partitioning Fitting Method for Linear Equivalent Identification from Frequency Response Data

description: OverviewIt is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems. The main applications are frequency-dependent transmission-line modeling and frequency-dependent network modeling, but they should be applicable to various engineering and scientific problems where a pole-residue representation of a linear multi-input multi-output (MIMO) system has to be identified from its frequency response data.Here, MATLAB code of the FpF method is provided. Ref. [1] below describes the code in detail and can be used as an instruction manual. The code is fully functional and can be used as a functioning software package of the FpF method. The file "test_fit.m", which is executed as default when you run this Compute Capsule, performs the fitting of the verification case shown in Section III of ref. [1]. For this case, the frequency-response data of the three-by-three admittance matrix of a 500-kV transmission network is provided as "test_data.mat" (for details, see ref. [2]). The mathematical descriptions of the FpF method are given in refs. [2]-[4], and its applications to the frequency-dependent modeling of overhead and underground-cable transmission lines are shown in refs. [5] and [6] respectively.References[1] Taku Noda, "Implementation of the frequency-partitioning fitting method for linear equivalent identification from frequency response data," Proc. of 2016 IEEE Power Energy Society General Meeting (PESGM), Boston, MA. USA, July 2016.[2] Taku Noda, "Identification of a multiphase network equivalent for electromagnetic transient calculations using partitioned frequency response," IEEE Trans. on Power Delivery, vol. 20, no. 2, Apr. 2005.[3] Taku Noda, "A binary frequency-region partitioning algorithm for the identification of a multiphase network equivalent for EMT studies," IEEE Trans. on Power Delivery, vol. 22, no. 2, Apr. 2007[4] Juan A. Martinez-Velasco (Editor), "Power System Transients: Parameter Determination," CRC Press, Oct. 2009, ISBN-10: 1420065297, ISBN-13: 978-1420065299. (See Appendix A)[5] Taku Noda, "Application of frequency-partitioning fitting to the phase-domain frequency-dependent modeling of overhead transmission lines," IEEE Trans. on Power Delivery, vol. 30, no. 1, Feb. 2015.[6] Taku Noda, "Application of frequency-partitioning fitting to the phase-domain frequency-dependent modeling of underground cables," IEEE Trans. on Power Delivery, vol. 31, no. 4, Aug. 2016.
DOI: 10.21227/1ejg-br14
title: MATLAB Source Code: Frequency-Partitioning Fitting Method for Linear Equivalent Identification from Frequency Response Data

description: OverviewIt is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems. The main applications are frequency-dependent transmission-line modeling and frequency-dependent network modeling, but they should be applicable to various engineering and scientific problems where a pole-residue representation of a linear multi-input multi-output (MIMO) system has to be identified from its frequency response data.Here, MATLAB code of the FpF method is provided. Ref. [1] below describes the code in detail and can be used as an instruction manual. The code is fully functional and can be used as a functioning software package of the FpF method. The file "test_fit.m", which is executed as default when you run this Compute Capsule, performs the fitting of the verification case shown in Section III of ref. [1]. For this case, the frequency-response data of the three-by-three admittance matrix of a 500-kV transmission network is provided as "test_data.mat" (for details, see ref. [2]). The mathematical descriptions of the FpF method are given in refs. [2]-[4], and its applications to the frequency-dependent modeling of overhead and underground-cable transmission lines are shown in refs. [5] and [6] respectively.References[1] Taku Noda, "Implementation of the frequency-partitioning fitting method for linear equivalent identification from frequency response data," Proc. of 2016 IEEE Power Energy Society General Meeting (PESGM), Boston, MA. USA, July 2016.[2] Taku Noda, "Identification of a multiphase network equivalent for electromagnetic transient calculations using partitioned frequency response," IEEE Trans. on Power Delivery, vol. 20, no. 2, Apr. 2005.[3] Taku Noda, "A binary frequency-region partitioning algorithm for the identification of a multiphase network equivalent for EMT studies," IEEE Trans. on Power Delivery, vol. 22, no. 2, Apr. 2007[4] Juan A. Martinez-Velasco (Editor), "Power System Transients: Parameter Determination," CRC Press, Oct. 2009, ISBN-10: 1420065297, ISBN-13: 978-1420065299. (See Appendix A)[5] Taku Noda, "Application of frequency-partitioning fitting to the phase-domain frequency-dependent modeling of overhead transmission lines," IEEE Trans. on Power Delivery, vol. 30, no. 1, Feb. 2015.[6] Taku Noda, "Application of frequency-partitioning fitting to the phase-domain frequency-dependent modeling of underground cables," IEEE Trans. on Power Delivery, vol. 31, no. 4, Aug. 2016.
DOI: 10.21227/1p9t-fv87
title: MATLAB Source Code: Frequency-Partitioning Fitting Method for Linear Equivalent Identification from Frequency Response Data

description: OverviewIt is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems. The main applications are frequency-dependent transmission-line modeling and frequency-dependent network modeling, but they should be applicable to various engineering and scientific problems where a pole-residue representation of a linear multi-input multi-output (MIMO) system has to be identified from its frequency response data.
DOI: 10.21227/3s3w-b250
title: MATLAB Source Code: Frequency-Partitioning Fitting Method for Linear Equivalent Identification from Frequency Response Data

description: It is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems. The main applications are frequency-dependent transmission-line modeling and frequency-dependent network modeling, but they should be applicable to various engineering and scientific problems where a pole-residue representation of a linear multi-input multi-output (MIMO) system has to be identified from its frequency response data.
DOI: 10.21227/yk8m-bm86
title: MATLAB Source Code: Frequency-Partitioning Fitting Method for Linear Equivalent Identification from Frequency Response Data

description: OverviewIt is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems. The main applications are frequency-dependent transmission-line modeling and frequency-dependent network modeling, but they should be applicable to various engineering and scientific problems where a pole-residue representation of a linear multi-input multi-output (MIMO) system has to be identified from its frequency response data.Here, MATLAB code of the FpF method is provided. Ref. [1] below describes the code in detail and can be used as an instruction manual. The code is fully functional and can be used as a functioning software package of the FpF method. The file "test_fit.m", which is executed as default when you run this Compute Capsule, performs the fitting of the verification case shown in Section III of ref. [1]. For this case, the frequency-response data of the three-by-three admittance matrix of a 500-kV transmission network is provided as "test_data.mat" (for details, see ref. [2]). The mathematical descriptions of the FpF method are given in refs. [2]-[4], and its applications to the frequency-dependent modeling of overhead and underground-cable transmission lines are shown in refs. [5] and [6] respectively.
DOI: 10.21227/trw2-3y50
title: MATLAB Source Code: Frequency-Partitioning Fitting Method for Linear Equivalent Identification from Frequency Response Data

description: It is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems. The main applications are frequency-dependent transmission-line modeling and frequency-dependent network modeling, but they should be applicable to various engineering and scientific problems where a pole-residue representation of a linear multi-input multi-output (MIMO) system has to be identified from its frequency response data.
DOI: 10.5519/qd.mi5xy6gg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.21227/52b4-rz66
title: MATLAB Source Code: Frequency-Partitioning Fitting Method for Linear Equivalent Identification from Frequency Response Data

description: It is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems. The main applications are frequency-dependent transmission-line modeling and frequency-dependent network modeling, but they should be applicable to various engineering and scientific problems where a pole-residue representation of a linear multi-input multi-output (MIMO) system has to be identified from its frequency response data.
DOI: 10.21227/cetz-pj51
title: MATLAB Source Code: Frequency-Partitioning Fitting Method for Linear Equivalent Identification from Frequency Response Data

description: It is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems. The main applications are frequency-dependent transmission-line modeling and frequency-dependent network modeling, but they should be applicable to various engineering and scientific problems where a pole-residue representation of a linear multi-input multi-output (MIMO) system has to be identified from its frequency response data.
DOI: 10.21227/q27x-h650
title: MATLAB Source Code: Frequency-Partitioning Fitting Method for Linear Equivalent Identification from Frequency Response Data

description: It is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems. The main applications are frequency-dependent transmission-line modeling and frequency-dependent network modeling, but they should be applicable to various engineering and scientific problems where a pole-residue representation of a linear multi-input multi-output (MIMO) system has to be identified from its frequency response data.
DOI: 10.21227/q8a4-0275
title: MATLAB Source Code: Frequency-Partitioning Fitting Method for Linear Equivalent Identification from Frequency Response Data

description: It is often the case that the frequency response data of a system or a network is available and one wants to identify a linear equivalent from the frequency response data for time-domain simulations and other purposes. To this end, a variety of methods have been proposed, and the vector fitting (VF) method and the frequency-partitioning fitting (FpF) method are often used for electromagnetic transient (EMT) simulations of power systems. The main applications are frequency-dependent transmission-line modeling and frequency-dependent network modeling, but they should be applicable to various engineering and scientific problems where a pole-residue representation of a linear multi-input multi-output (MIMO) system has to be identified from its frequency response data.
DOI: 10.5281/zenodo.3334830
title: Top performer companies in the Dow Jones Market and the Dow Jones Islamic Market

description: Top performer companies in the Dow Jones Market and the Dow Jones Islamic Market on 14/07/2019.
DOI: 10.5281/zenodo.3334831
title: Top performer companies in the Dow Jones Market and the Dow Jones Islamic Market

description: Top performer companies in the Dow Jones Market and the Dow Jones Islamic Market on 14/07/2019.
DOI: 10.5281/zenodo.3241602
title: FT29 Scherer (2) 4-key fagottino: measurements, photos, endoscopic video

description: Dataset of FT29 Scherer (2) 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.
DOI: 10.5281/zenodo.3241603
title: FT29 Scherer (2) 4-key fagottino: measurements, photos, endoscopic video

description: Dataset of FT29 Scherer (2) 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.
DOI: 10.5281/zenodo.3276352
title: FT28 Scherer (1) 4-key fagottino: measurements, photos, endoscopic video

description: Dataset of FT28 Scherer (1) 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.
DOI: 10.5281/zenodo.3276353
title: FT28 Scherer (1) 4-key fagottino: measurements, photos, endoscopic video

description: Dataset of FT28 Scherer (1) 4-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.
DOI: 10.5281/zenodo.3241608
title: FT30 Scherer (4) 5-key fagottino: measurements, photos, endoscopic video

description: Dataset of FT30 Scherer 5-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.
DOI: 10.5281/zenodo.3241607
title: FT30 Scherer (4) 5-key fagottino: measurements, photos, endoscopic video

description: Dataset of FT30 Scherer 5-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.
DOI: 10.25345/c5t94k
title: MassIVE MSV000084087 - GNPS-digupi-calystegines-2019-07-13
title: Cortex Lycii, the root barks of Lycium barbarum or chinense, known as di gu pi in traditional Chinese herbal drugs, are important ingredient of formulations used for treating variety of diseases. During the last three decades, more than seventy chemical entities have been separated and purified from the water or EtOH aqueous extracts of Cortex Lycii.
DOI: 10.5281/zenodo.3334842
title: Software Deployment on Heterogeneous Platforms: A Systematic Mapping Study

description: Complete dataset of the study
DOI: 10.5281/zenodo.3334887
title: DWD European Weather 2

description: opendata.dwd.de - OpenData by Deutscher Wetter Dienst Conditions: https://www.dwd.de/EN/service/copyright/copyright_node.html dates and times are UTC.
DOI: 10.25919/5d2bc41139cd7
title: Parkes observations for project PX500 semester 2019APRS_10

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.5281/zenodo.3334018
title: Raw Data of 10th July

description: Raw Data of 10th July. See log.
DOI: 10.5281/zenodo.3334019
title: Raw Data of 10th July

description: Raw Data of 10th July. See log.
DOI: 10.5281/zenodo.3334354
title: Codes and datasets associated with the paper "Dynamic LES modeling of a diurnal cycle"

description: Here, you will find some of the codes, images, and datasets utilized in the article: Basu et al. (2008). "Dynamic LES modeling of a diurnal cycle", Journal of Applied Meteorology and Climatology, 47, 1156-1174. (https://journals.ametsoc.org/doi/pdf/10.1175/2007JAMC1677.1) Matlab codes: MatlabCodes.tar.gz (Contains all the analyses and visualization codes used in the paper) Observational data: Wangara_Observations.tar.gz (These data were manually typed directly from the original Wangara document) LES data (binary): LES_160x160x160.tar.gz and LES_80x80x80.tar.gz
DOI: 10.5281/zenodo.3334355
title: Codes and datasets associated with the paper "Dynamic LES modeling of a diurnal cycle"

description: Here, you will find some of the codes, images, and datasets utilized in the article: Basu et al. (2008). "Dynamic LES modeling of a diurnal cycle", Journal of Applied Meteorology and Climatology, 47, 1156-1174. (https://journals.ametsoc.org/doi/pdf/10.1175/2007JAMC1677.1) Matlab codes: MatlabCodes.tar.gz (Contains all the analyses and visualization codes used in the paper) Observational data: Wangara_Observations.tar.gz (These data were manually typed directly from the original Wangara document) LES data (binary): LES_160x160x160.tar.gz and LES_80x80x80.tar.gz
DOI: 10.5281/zenodo.3334379
title: Evaluation Data of the Implementation of the Approach for Automatic Test Generation for Information-Flow Properties

description: This data set contains the programs for which the automatic test generation approach of the KeY theorem prover was used to automatically generate noninterference tests. The approach is described in http://dx.doi.org/10.1145/3297280.3297500 DATA<br> ---------<br> The data folder contains the secure and insecure programs which were evaluated and the tests which were generated for them. Each program is in the folder "program" and is written in Java and specified in an extended version of the JML specification language. Check out http://dx.doi.org/10.5445/IR/1000046878 for a reference on the used specification language. For each example we provide the tests that were generated. For the insecure examples we provide the tests generated with each of the two options of our approach. The tests generated with the option for searching for counterexamples is in the folder "WithPost" of each insecure example.
DOI: 10.5281/zenodo.3334380
title: Evaluation Data of the Implementation of the Approach for Automatic Test Generation for Information-Flow Properties

description: This data set contains the programs for which the automatic test generation approach of the KeY theorem prover was used to automatically generate noninterference tests. The approach is described in http://dx.doi.org/10.1145/3297280.3297500 DATA<br> ---------<br> The data folder contains the secure and insecure programs which were evaluated and the tests which were generated for them. Each program is in the folder "program" and is written in Java and specified in an extended version of the JML specification language. Check out http://dx.doi.org/10.5445/IR/1000046878 for a reference on the used specification language. For each example we provide the tests that were generated. For the insecure examples we provide the tests generated with each of the two options of our approach. The tests generated with the option for searching for counterexamples is in the folder "WithPost" of each insecure example.
DOI: 10.25919/5d2aea89582e4
title: Parkes observations for project P958 semester 2019APRS_45

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until a few months ago, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last year searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 28 bursts, two of which have been localised to host galaxies at redshifts &gt; 0.3. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. Over the next six months we expect to detect another 12 FRBs, which will also have sub-arcsecond localisations. Here we propose to search this population for repeats. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections, will either confirm the presence of additional repeating FRBs or the uniqueness of FRB 121102 amongst the population.
DOI: 10.5281/zenodo.3334679
title: Audio Spatialisation for Headphones - Impulse Response Dataset

description: The Audio Spatialisation for Headphones Impulse Response Dataset (ASH IR Dataset) is a set of impulse responses that can be used for binaural synthesis of spatial audio systems on headphones. It includes binaural room impulse responses (BRIRs), headphone compensation filters (HpCFs) and configuration files for Equalizer APO. The dataset can be used to create spatial surround sound on headphones by convolving an audio stream with a set of BRIRs and a HpCF. The dataset contains: A set of equalised binaural room impulse responses that can be used to simulate a variety of rooms including control rooms, listening rooms, seminar rooms, studios, and more A set of compensation filters that can be used to equalise a wide range of headphones to the diffuse-field target frequency response A set of configuration files for Equalizer APO that can be used to simulate common speaker configurations (Stereo, 5.1 surround, 7.1 surround) within the rooms More information can be found at: https://github.com/ShanonPearce/ASH-IR-Dataset