cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/a7v0-nz60
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has includedperformance comparison using Sliding Mode Observer (SMO) and flux observer isPWMCOmpensCLosed loopAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice. The author has also other 2-3 different complex models, may be uploaded later.
DOI: 10.18112/openneuro.ds003392.v1.0.1
title: NeuroSpin hMT+ Localizer DATA (MEG aMRI)
DOI: 10.21227/vdqr-e886
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has includedperformance comparison using Sliding Mode Observer (SMO) and flux observer isPWMCOmpensCLosed loopAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice. The author has also other 2-3 different complex models, may be uploaded later.
DOI: 10.21227/wwmj-1546
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has includedperformance comparison using Sliding Mode Observer (SMO) and flux observer PWM switching frequency is varied 44 times from 20 kHz to 2 MHzOpen LoopTransfer Function CompensationRoot Loci,Closed loopAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice for industries and also for academia purposes. The author has used these results in designing new 2-3 different complex models(incomplete), may be uploaded later.
DOI: 10.21227/ha8m-e233
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has includedperformance comparison using Sliding Mode Observer (SMO) and flux observerPWM switching frequency is varied 44 times from 20 kHz to 2 MHzOpen LoopTransfer Function CompensationRoot Loci,Closed loopAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice for industries and also for academia purposes. The author has used these results in designing new 2-3 different complex models(incomplete), may be uploaded later.
DOI: 10.21227/at3b-pt51
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has includedperformance comparison using Sliding Mode Observer (SMO) and flux observerPWM switching frequency is varied 44 times from 20 kHz to 2 MHzOpen LoopTransfer Function CompensationRoot Loci,Closed loopAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice for industries and also for academia purposes. The author has used these results in designing new 2-3 different complex models(incomplete), may be uploaded later.
DOI: 10.21227/g1rc-yv59
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer with FW

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has includedperformance comparison using Sliding Mode Observer (SMO) and flux observerPWM switching frequency is varied 44 times from 20 kHz to 2 MHzOpen LoopTransfer Function CompensationRoot Loci,Closed loopAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice for industries and also for academia purposes. The author has used these results in designing new 2-3 different complex models(incomplete), may be uploaded later.
DOI: 10.5281/zenodo.4295976
title: The gamaproteobacterium Achromatium forms intracellular amorphous calcium carbonate and not (crystalline) calcite- dataset

description: This is the dataset accompanying the paper published in geobiology and titled <em>The gamaproteobacterium Achromatium forms intracellular amorphous calcium carbonate and not (crystalline) calcite</em> It comprises SEM and light microscopy images, Raman spectra (txt files) and one excel files containing the results of Raman spectra fits.
DOI: 10.5281/zenodo.4295975
title: The gamaproteobacterium Achromatium forms intracellular amorphous calcium carbonate and not (crystalline) calcite- dataset

description: This is the dataset accompanying the paper published in geobiology and titled <em>The gamaproteobacterium Achromatium forms intracellular amorphous calcium carbonate and not (crystalline) calcite</em> It comprises SEM and light microscopy images, Raman spectra (txt files) and one excel files containing the results of Raman spectra fits.
DOI: 10.21227/ak09-1a75
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer with FW

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has includedperformance comparison using Sliding Mode Observer (SMO) and flux observerPWM switching frequency is varied 44 times from 20 kHz to 2 MHzOpen LoopTransfer Function CompensationRoot Loci,Closed loopField WeakeningAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice for industries and also for academia purposes. The author has used these results in designing new 2-3 different complex models(incomplete), may be uploaded later.
DOI: 10.5281/zenodo.4284380
title: 3D models of mouse embryonic development (stl, converted from EMAP)

description: A collection of readily-usable volumetric models for all tissues and stages of mouse peri-implantation development as extracted from the eMouse Atlas Project (E5.0 to E9.0), as well as custom-made models of all pre-implantation stages (E0 to E4.0). These models have been converted to a commonly used 3D format (.stl), and are provided in ready-made files for digital exploration and illustration. Specifically: individual .stl models of embryonic subcomponents for each developmental stage (e.g. epiblast, visceral endoderm; 1 folder per EMAP embryonic stage) ready-made .blend files where such components have been reassembled as a full embryo model, and in a scene with pre-prepared light sources and aligned camera (1 file per embryonic stage) a .blend file with all embryonic models provided arranged in a temporal lineup; and a .blend file with a "starter-pack" of pre-made materials to use for easy illustration. Also included are two data tables: "EMAP_key.xlsx" indicating the source of its model, and the embryonic stage corresponding to each Theiler Stage "EMAP_partslist.xlsx" containing the original name of each embryonic component provided (as used in EMAP), and the corresponding name used in this dataset.
DOI: 10.5281/zenodo.4284379
title: 3D models of mouse embryonic development (stl, converted from EMAP)

description: A collection of readily-usable volumetric models for all tissues and stages of mouse peri-implantation development as extracted from the eMouse Atlas Project (E5.0 to E9.0), as well as custom-made models of all pre-implantation stages (E0 to E4.0). These models have been converted to a commonly used 3D format (.stl), and are provided in ready-made files for digital exploration and illustration. Specifically: individual .stl models of embryonic subcomponents for each developmental stage (e.g. epiblast, visceral endoderm; 1 folder per EMAP embryonic stage) ready-made .blend files where such components have been reassembled as a full embryo model, and in a scene with pre-prepared light sources and aligned camera (1 file per embryonic stage) a .blend file with all embryonic models provided arranged in a temporal lineup; and a .blend file with a "starter-pack" of pre-made materials to use for easy illustration. Also included are two data tables: "EMAP_key.xlsx" indicating the source of its model, and the embryonic stage corresponding to each Theiler Stage "EMAP_partslist.xlsx" containing the original name of each embryonic component provided (as used in EMAP), and the corresponding name used in this dataset.
DOI: 10.34740/kaggle/dsv/1696816
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.21227/a90k-ta18
title: 1.88kW BLDC Sensorless FOC - Sliding Mode vs Flux Observer with FW

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) and observer. The implementation method is the known method of vector control, so any textbook can be referred, with the addition of SMO or flux observer which acts as the "Adaptive Controller" in the estimation of speed. The switching pattern of the 3-phase inverter is implemented using space vector modulation.DIfferences in this paper dataset can be seen as the author has includedperformance comparison using Sliding Mode Observer (SMO) and flux observerPWM switching frequency is varied 44 times from 20 kHz to 2 MHzOpen LoopTransfer Function CompensationRoot Loci,Closed loopField WeakeningAll this is implemented on 32-bit Real-Time microcontroller. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.PFC is not included in this simulation as it is assumed that PF = 1.There is related dataset "200W BLDC Sensorless FOC - Sliding Mode vs Flux Observer" ,DOI: https://dx.doi.org/10.21227/8rz1-p666These brushless motors and controllers are used in many industries including medical e.g. in Positive Airway Pressure respirators,ventilator.This study comes in handy to decide when designing in practice for industries and also for academia purposes. The author has used these results in designing new 2-3 different complex models(incomplete), may be uploaded later.
DOI: 10.6084/m9.figshare.12155553.v11
title: A global dataset for crop production under conventional tillage and no tillage systems

description: Author: <br>Yang Su 1, Benoit Gabrielle 1, David Makowski 2,3 <br>Affiliations: <br>1. UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 78850 Thiverval-Grignon, France 2. UMR Agronomie, INRAE AgroParisTech, Université Paris-Saclay, 78850 Thiverval-Grignon, France<br>3. Applied mathematics and computer science (MIA 518), INRAE AgroParisTech, Université Paris-Saclay, 75005 Paris, France.<br><br>This dataset includes the results extracted from 413 papers (published between 1983 to 2020), 4403 paired yield observations from CA and CT for 8 major crop species (370 observations for barley (232 for spring barley and 138 for winter barley), 94 observations for cotton, 1690 observations for maize, 195 observation for rice, 160 observations for sorghum, 583 observations for soybean, 61 observations for sunflower, 1250 observations for wheat (209 for spring wheat and 1041 for winter wheat)) in 50 countries from 1980 to 2017. <br>For more information, please contact with author through: yang.su@inrae.fr<br><br>For cite this dataset, please use:Su, Y., Gabrielle, B. & Makowski, D. A global dataset for crop production under conventional tillage and no tillage systems. <i>figshare</i> https://doi.org/10.6084/m9.figshare.12155553 (2020).<br><br><br>
DOI: 10.6084/m9.figshare.12687893.v1
title: Rayleigh wave phase velocity dispersions and 3D S-wave velocity model of the South China Sea and adjacent regions

description: Rayleigh wave phase velocity dispersions and 3D S-wave velocity model of the South China Sea and adjacent regions from Chen et al. (2020).
DOI: 10.5281/zenodo.4296031
title: Anthropometric data set

description: The data set describes anthropometric data in extreme positions: standard, relaxed posture, self-hug, leaning, tilting, arms raised, lingering. Data were analyzed in tight clothing. The protective clothing used during the Covid-19 pandemic was analyzed in two positions for the same test subject (relaxed and raised arms). The data show the fit of the suit to the body. And changes in the body's anthropometric data as a result of various movements.
description: Data are list of vertices and frames, therefore not language dependent. Data set created as a result of project VPP-COVID-2020/1-0004
DOI: 10.5281/zenodo.4296030
title: Anthropometric data set

description: The data set describes anthropometric data in extreme positions: standard, relaxed posture, self-hug, leaning, tilting, arms raised, lingering. Data were analyzed in tight clothing. The protective clothing used during the Covid-19 pandemic was analyzed in two positions for the same test subject (relaxed and raised arms). The data show the fit of the suit to the body. And changes in the body's anthropometric data as a result of various movements.
description: Data are list of vertices and frames, therefore not language dependent. Data set created as a result of project VPP-COVID-2020/1-0004
DOI: 10.5281/zenodo.4295829
title: English Vaccine-related Twitter data (Tweet IDs) 2018-07-01 to 2020-09-30

description: Twitter data collected through the Crowdbreaks platform [1]. Between July 1st, 2017 and October 1st, 2020 a total of 57.5M tweets (including 39.7M retweets) in English language by 9.9M unique users were collected using the public filter stream endpoint of the Twitter API. The tweets matched one or more of the keywords "vaccine", "vaccination", "vaxxer", "vaxxed", "vaccinated", "vaccinating", "vacine", "overvaccinate", "undervaccinate", "unvaccinated". The data can be considered complete with respect to these keywords. The files contain a single column which corresponds to the Tweet ID (one file per day). Using the IDs the full tweet objects can be restored.
DOI: 10.5281/zenodo.4295828
title: English Vaccine-related Twitter data (Tweet IDs) 2018-07-01 to 2020-09-30

description: Twitter data collected through the Crowdbreaks platform [1]. Between July 1st, 2017 and October 1st, 2020 a total of 57.5M tweets (including 39.7M retweets) in English language by 9.9M unique users were collected using the public filter stream endpoint of the Twitter API. The tweets matched one or more of the keywords "vaccine", "vaccination", "vaxxer", "vaxxed", "vaccinated", "vaccinating", "vacine", "overvaccinate", "undervaccinate", "unvaccinated". The data can be considered complete with respect to these keywords. The files contain a single column which corresponds to the Tweet ID (one file per day). Using the IDs the full tweet objects can be restored.
DOI: 10.5281/zenodo.4292150
title: Math anxiety mediates the link between number sense and math achievements in high math anxiety young adults

description: The data are organized into three subfolders. One subfolder for each task: <strong>MOT:</strong> Each file contains a 3x4 matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a condition. The columns contain the following information: 1<sup>st</sup>: Condition 2<sup>nd</sup>: Number of response correct 3<sup>rd</sup>: Total number of trials 4<sup>th</sup>: proportion between column 2 and 3 <strong>Numerosity discrimination: </strong> Each file contains a matrix called “a”. Each row of the matrix “a” is a trial. The columns contain the following information: 1<sup>st</sup>: Test Numerosity 2<sup>nd</sup>: Subject response 3<sup>rd</sup>: Reference position: 1= left; 2=right <strong>Size discrimination:</strong> Each file contains a matrix called “MATR”. Each row of the matrix “MATR” is a trial. The columns contain the following information: 1<sup>st</sup>: Delta (%) 2<sup>nd</sup>: Subject response 3<sup>rd</sup>: Test stimulus size (deg) 4<sup>th</sup>: Probe stimulus size (deg) 5<sup>th</sup>: Delta (%) 6<sup>th</sup>: delta (deg)
DOI: 10.5281/zenodo.4292149
title: Math anxiety mediates the link between number sense and math achievements in high math anxiety young adults

description: The data are organized into three subfolders. One subfolder for each task: <strong>MOT:</strong> Each file contains a 3x4 matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a condition. The columns contain the following information: 1<sup>st</sup>: Condition 2<sup>nd</sup>: Number of response correct 3<sup>rd</sup>: Total number of trials 4<sup>th</sup>: proportion between column 2 and 3 <strong>Numerosity discrimination: </strong> Each file contains a matrix called “a”. Each row of the matrix “a” is a trial. The columns contain the following information: 1<sup>st</sup>: Test Numerosity 2<sup>nd</sup>: Subject response 3<sup>rd</sup>: Reference position: 1= left; 2=right <strong>Size discrimination:</strong> Each file contains a matrix called “MATR”. Each row of the matrix “MATR” is a trial. The columns contain the following information: 1<sup>st</sup>: Delta (%) 2<sup>nd</sup>: Subject response 3<sup>rd</sup>: Test stimulus size (deg) 4<sup>th</sup>: Probe stimulus size (deg) 5<sup>th</sup>: Delta (%) 6<sup>th</sup>: delta (deg)