cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4298987
title: Upper-body movements: precise tracking of human motion using inertial sensors

description: The <em>Upper-body movements: precise tracking of human motion using inertial sensors</em> is a dataset composed of 11 participants' IMU data (5 women + 6 men). This collection is divided into 6 motion sets containing different motions for the upper-body. <strong>Folder Structure</strong> subject -&gt; set -&gt; IMU position -&gt; file e.g. subject01 -&gt; set6 -&gt; forearm -&gt; Accelerometer.txt <strong>IMU placement </strong> For data collection participants wore 4 IMUs: 1 on the chest 1 on the right arm 1 on the right forearm 1 on the right hand. <strong>Sets</strong> Each set includes: set1 - flexion/extension of the forearm; abduction/adduction of the arm; anatomical position set2 - flexion/extension of the wrist; radial/ulnar deviation of the wrist; anatomical position set3 - flexion/extension and lateral flexion of the torso; anatomical position set4 - flexion/extension of the arm; flexion/extension of the torso; anatomical position set5 - flexion/extension of the arm; anatomical position; anatomical position set6 - flexion/extension of the torso; flexion/extension of the arm; anatomical position <strong>Annotations</strong> This dataset is accompanied by the<em> annotations.csv</em> file.<br> Each file row present "Set,Subject,Category,Segment,Type,Init,End": Set - sets 1-6 Subject - participant ID Category - relative or absolute. Refers to the joint angle. Absolute if the angle is obtained considering an anatomical plane as reference. Relative if the angle is obtained from one segment in relation to another. Type - segment at action (torso; right_arm_forearm; wrist; right_arm_sagittal) Init/End - time in seconds, describing the begin and end of the motion, respectively.
DOI: 10.5281/zenodo.4029126
title: Upper-body movements: precise tracking of human motion using inertial sensors

description: The <em>Upper-body movements: precise tracking of human motion using inertial sensors</em> is a dataset composed of 11 participants' IMU data (5 women + 6 men). This collection is divided into 6 motion sets containing different motions for the upper-body. <strong>Folder Structure</strong> subject -&gt; set -&gt; IMU position -&gt; file e.g. subject01 -&gt; set6 -&gt; forearm -&gt; Accelerometer.txt <strong>IMU placement </strong> For data collection participants wore 4 IMUs: 1 on the chest 1 on the right arm 1 on the right forearm 1 on the right hand. <strong>Sets</strong> Each set includes: set1 - flexion/extension of the forearm; abduction/adduction of the arm; anatomical position set2 - flexion/extension of the wrist; radial/ulnar deviation of the wrist; anatomical position set3 - flexion/extension and lateral flexion of the torso; anatomical position set4 - flexion/extension of the arm; flexion/extension of the torso; anatomical position set5 - flexion/extension of the arm; anatomical position; anatomical position set6 - flexion/extension of the torso; flexion/extension of the arm; anatomical position <strong>Annotations</strong> This dataset is accompanied by the<em> annotations.csv</em> file.<br> Each file row present "Set,Subject,Category,Segment,Type,Init,End": Set - sets 1-6 Subject - participant ID Category - relative or absolute. Refers to the joint angle. Absolute if the angle is obtained considering an anatomical plane as reference. Relative if the angle is obtained from one segment in relation to another. Type - segment at action (torso; right_arm_forearm; wrist; right_arm_sagittal) Init/End - time in seconds, describing the begin and end of the motion, respectively.
DOI: 10.5281/zenodo.4298972
title: Series arcing faults voltage and current signatures in a DC low power network.

description: The dataset contains series arc faults voltage and current signatures in a DC low power network. The first apparatus performs a series arc fault by opening contacts (ReadMe_test 1). After breaking, copper electrodes are maintained at a constant gap. The measurements are performed with direct current and resisve/Inductive loads. The second apparatus performs contact breaking under chosen voltage and current supply condions (ReadMe_test2). After breaking, electrodes are maintained at a constant gap during electrical measurement. The measurements are performed with direct current and resisve loads by separating electrodes.<br>
DOI: 10.5281/zenodo.4298971
title: Series arcing faults voltage and current signatures in a DC low power network.

description: The dataset contains series arc faults voltage and current signatures in a DC low power network. The first apparatus performs a series arc fault by opening contacts (ReadMe_test 1). After breaking, copper electrodes are maintained at a constant gap. The measurements are performed with direct current and resisve/Inductive loads. The second apparatus performs contact breaking under chosen voltage and current supply condions (ReadMe_test2). After breaking, electrodes are maintained at a constant gap during electrical measurement. The measurements are performed with direct current and resisve loads by separating electrodes.<br>
DOI: 10.5281/zenodo.4298977
title: WRF data for downscaling, used in Learned multi-resolution dynamical downscaling for precipitation

description: This study uses regional climate model (RCM) simulated precipitation at low and high spatial resolution, to develop convolution neural network (CNN) based approaches, that can emulate high resolution modeled data using low resolution modeled data with cheaper computational resource than running dynamical downscaling at the high spatial resolution. Specifically, we define two types of CNNs, one that stacks variables directly and one that encodes each variable before stacking, and train each CNN type both with a conventional loss function, such as Mean Square Error (MSE), and with a conditional generative adversarial network (CGAN), for a total of four CNN variants. We compare the four new CNN-derived high resolution precipitation with precipitation generated from a bi-linear interpolater and the state-of-the-art CNN-based super-resolution (SR) technique, using the original high resolution precipitation from the RCM as ground truth. We find that SR technique produces similar results to the interpolator with smoother spatial and temporal distributions and smaller data variabilities and extremes than ground truth shows. While the new CNNs trained by MSE generate better results over some regions than the interpolator and SR technique, their predictions are still not as close as ground truth. The CNNs trained by CGAN generate more realistic and physically reasonable results. This advanced technique improves not only the data variability in time and space, and but also the extremes, such as intense and long-lasting events, based on event-feature tracking algorithm.
DOI: 10.6084/m9.figshare.13308428
title: Emergence and evolution of social networks through exploration of the Adjacent Possible

description: The APS, APS_subsamples, MPC and TWT folders in the out directory contain the aggregated data used to produce all the figures of the paper. The TMN_data_01_09 and APS_aff_data_ISI_original contain the raw data of the interactions on the two platforms.<br><br><br>The other_measures contains the pickles and data used to produce the topology and community figures. <br>To find the code and the notebooks used to generate the figures please refer to https://github.com/ubi15/pyUrns
DOI: 10.21227/21sw-na98
title: 1.88kW BLDC Sensorless FOC with Adaptive Sliding Mode Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".The results contained in this dataset are graphs which give intutive idea on the control characteristics, whether the pole/zero is on the imaginary axis or the right half plane. The switching frequency for these controllers is generally 20 kHz or more.In this dataset, switching frequency has been increased to 1 MHz and the comparison is drawn.BLDC motors ae used in medical industry ,ventillators, solar trackers, CNC servodrives, industrial robots, automative, electric vehicles- autos, rickshaws etc.Using this dataset, the industrial engineer can decide whether to run at that frequency or not. So, this study comes in handy to decide when designing in practice for industries and also for academia purposes.In this particular dataset, MOSFET is switched from 20 kHz to 1MHZ , at 26 different PWM frequencies and the resulting characteristics.The speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) with the addition of SMO observer which gives the estimation of speed and the sensorless rotor position. 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 included following , with analysis which can be drawn from seeing attached graphs -performance comparison using Sliding Mode Observer (SMO) at different frequencies.PWM switching frequency is varied 26 times from 20 kHz to 1 MHz , - will help decide MOSFETs switching frequency.Of course, the Rdson, dc link inverter voltage ,other practical parameters etc has been taken into consideration (not submitted here) bu some analysis related to it,you may find in the authors Transaction paper.Separate results at all these 46 frequenciesStability Margins - Gain/PhaseOpen Loop Control PerformanceTransfer Function CompensationClosed loop TuningRoot Locus of Uncompensated System and Compensated SystemBode Plot - magnitude and phasePole Zero Map of Uncompensated System and Compensated SystemUnit-Step Response of Uncompensated System and Compensated SystemNyquist plot of Uncompensated System and Compensated SystemNichols chart of Uncompensated System and Compensated SystemImpulse Response of Uncompensated System and Compensated SystemSinusoidal ExcitationAll 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.
DOI: 10.21227/3p32-rc31
title: 1.88kW BLDC Sensorless FOC with Adaptive Sliding Mode Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".The results contained in this dataset are graphs which give intutive idea on the control characteristics, whether the pole/zero is on the imaginary axis or the right half plane. The switching frequency for these controllers is generally 20 kHz or more.In this dataset, switching frequency has been increased from 20 kHz to 1MHZ , at 26 different PWM frequencies and the comparison of the resulting characteristics are drawn.BLDC motors ae used in medical industry ,ventillators, solar trackers, CNC servodrives, industrial robots, automative, electric vehicles- autos, rickshaws etc.Using this dataset, the industrial engineer can decide whether to run at that frequency or not. So, this study comes in handy to decide when designing in practice for industries and also for academia purposes.The speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) with the addition of SMO observer which gives the estimation of speed and the sensorless rotor position. 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 included following , with analysis which can be drawn from seeing attached graphs -performance comparison using Sliding Mode Observer (SMO) at different frequencies.PWM switching frequency is varied 26 times from 20 kHz to 1 MHz , - will help decide MOSFETs switching frequency.Of course, the Rdson, dc link inverter voltage ,other practical parameters etc has been taken into consideration (not submitted here) bu some analysis related to it,you may find in the authors Transaction paper.Separate results at all these 26 frequenciesStability Margins - Gain/PhaseOpen Loop Control PerformanceTransfer Function CompensationClosed loop TuningRoot Locus of Uncompensated System and Compensated SystemBode Plot - magnitude and phasePole Zero Map of Uncompensated System and Compensated SystemUnit-Step Response of Uncompensated System and Compensated SystemNyquist plot of Uncompensated System and Compensated SystemNichols chart of Uncompensated System and Compensated SystemImpulse Response of Uncompensated System and Compensated SystemSinusoidal ExcitationAll 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.
DOI: 10.21227/88sd-sx57
title: 1.88kW BLDC Sensorless FOC with Adaptive Sliding Mode Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".The results contained in this dataset are graphs which give intutive idea on the control characteristics, whether the pole/zero is on the imaginary axis or the right half plane. The switching frequency for these controllers is generally 20 kHz or more.In this dataset, switching frequency has been increased from 20 kHz to 1MHZ , at 26 different PWM frequencies and the comparison of the resulting characteristics are drawn.BLDC motors ae used in medical industry ,ventillators, solar trackers, CNC servodrives, industrial robots, automative, electric vehicles- autos, rickshaws etc.Using this dataset, any industrial engineer can decide whether to run at that frequency or not. So, this study comes in handy to decide when designing in practice for industries and also for academia purposes.The speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) with the addition of SMO observer which gives the estimation of speed and the sensorless rotor position. 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 included following , with analysis which can be drawn from seeing attached graphs -performance comparison using Sliding Mode Observer (SMO) at different frequencies.PWM switching frequency is varied 26 times from 20 kHz to 1 MHz , - will help decide MOSFETs switching frequency.Of course, the Rdson, dc link inverter voltage ,other practical parameters etc has been taken into consideration (not submitted here) bu some analysis related to it,you may find in the authors Transaction paper.Separate results at all these 26 frequenciesStability Margins - Gain/PhaseOpen Loop Control PerformanceTransfer Function CompensationClosed loop TuningRoot Locus of Uncompensated System and Compensated SystemBode Plot - magnitude and phasePole Zero Map of Uncompensated System and Compensated SystemUnit-Step Response of Uncompensated System and Compensated SystemNyquist plot of Uncompensated System and Compensated SystemNichols chart of Uncompensated System and Compensated SystemImpulse Response of Uncompensated System and Compensated SystemSinusoidal ExcitationAll 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.
DOI: 10.17632/ymx7mjwpkp.5
title: Data for: New Nanocarried Phenobarbital Formulation: Maintains Better Control of Pentylenetetrazole-Induced Seizures.

description: Other files were not accepted for upload, and a message appears: “Something went wrong”.
DOI: 10.5281/zenodo.4284472
title: 96 Long Term Solar System Integrations

description: We share 96 long term Solar System integrations. The simulations are in the REBOUND Simulation Archives format. Please see the companion paper for details.
DOI: 10.21227/4gam-0p75
title: 1.88kW BLDC Sensorless FOC with Adaptive Sliding Mode Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".The results contained in this dataset are graphs which give intutive idea on the control characteristics, whether the pole/zero is on the imaginary axis or the right half plane. The switching frequency for these controllers is generally 20 kHz or more.In this dataset, switching frequency has been increased from 20 kHz to 1MHZ , at 26 different PWM frequencies and the comparison of the resulting characteristics are drawn.BLDC motors ae used in medical industry ,ventillators, solar trackers, CNC servodrives, industrial robots, automative, electric vehicles- autos, rickshaws etc.Using this dataset, any industrial engineer can decide whether to run at that frequency or not. So, this study comes in handy to decide when designing in practice for industries and also for academia purposes.The speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) with the addition of SMO observer which gives the estimation of speed and the sensorless rotor position. 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 included following , with analysis which can be drawn from seeing attached graphs -performance comparison using Sliding Mode Observer (SMO) at different frequencies.PWM switching frequency is varied 26 times from 20 kHz to 1 MHz , - will help decide MOSFETs switching frequency.Of course, the Rdson, dc link inverter voltage ,other practical parameters etc has been taken into consideration (not submitted here) bu some analysis related to it,you may find in the authors Transaction paper.Separate results at all these 26 frequenciesStability Margins - Gain/PhaseOpen Loop Control PerformanceTransfer Function CompensationClosed loop TuningRoot Locus of Uncompensated System and Compensated SystemBode Plot - magnitude and phasePole Zero Map of Uncompensated System and Compensated SystemUnit-Step Response of Uncompensated System and Compensated SystemNyquist plot of Uncompensated System and Compensated SystemNichols chart of Uncompensated System and Compensated SystemImpulse Response of Uncompensated System and Compensated SystemSinusoidal ExcitationAll 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.
DOI: 10.21227/0g5v-ma61
title: 1.88kW BLDC Sensorless FOC with Adaptive Sliding Mode Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".The results contained in this dataset are graphs which give intutive idea on the control characteristics, whether the pole/zero is on the imaginary axis or the right half plane. The switching frequency for these controllers is generally 20 kHz or more.In this dataset, switching frequency has been increased from 20 kHz to 1MHZ , at 26 different PWM frequencies and the comparison of the resulting characteristics are drawn.BLDC motors ae used in medical industry ,ventillators, solar trackers, CNC servodrives, industrial robots, automative, electric vehicles- autos, rickshaws etc.Using this dataset, any industrial engineer can decide whether to run at that frequency or not. So, this study comes in handy to decide when designing in practice for industries and also for academia purposes.The speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) with the addition of SMO observer which gives the estimation of speed and the sensorless rotor position. 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 included following , with analysis which can be drawn from seeing attached graphs -performance comparison using Sliding Mode Observer (SMO) at different frequencies.PWM switching frequency is varied 26 times from 20 kHz to 1 MHz , - will help decide MOSFETs switching frequency.Of course, the Rdson, dc link inverter voltage ,other practical parameters etc has been taken into consideration (not submitted here) bu some analysis related to it,you may find in the authors Transaction paper.Separate results at all these 26 frequenciesStability Margins - Gain/PhaseOpen Loop Control PerformanceTransfer Function CompensationClosed loop TuningRoot Locus of Uncompensated System and Compensated SystemBode Plot - magnitude and phasePole Zero Map of Uncompensated System and Compensated SystemUnit-Step Response of Uncompensated System and Compensated SystemNyquist plot of Uncompensated System and Compensated SystemNichols chart of Uncompensated System and Compensated SystemImpulse Response of Uncompensated System and Compensated SystemSinusoidal ExcitationAll 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.
DOI: 10.5281/zenodo.4298605
title: Pankti - Production Monitoring to Improve Test Suites - Replication Data

description: This package contains experimental data for <strong>PANKTI</strong>, a test improvement tool introduced in the paper titled <strong>Production Monitoring to Improve Test Suites</strong> by Deepika Tiwari, Long Zhang, Martin Monperrus, and Benoit Baudry, at KTH Royal Institute of Technology, Stockholm, Sweden.
DOI: 10.18112/openneuro.ds003404.v1.0.0
title: Brain Network Mechanisms of Visual Shape Completion
DOI: 10.21227/jh0r-ar24
title: 1.88kW BLDC Sensorless FOC with Adaptive Sliding Mode Observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".The results contained in this dataset are graphs which give intutive idea on the control characteristics, whether the pole/zero is on the imaginary axis or the right half plane. The switching frequency for these controllers is generally 20 kHz or more.In this dataset, switching frequency has been increased from 20 kHz to 1MHZ , at 26 different PWM frequencies and the comparison of the resulting characteristics are drawn.BLDC motors ae used in medical industry ,ventillators, solar trackers, CNC servodrives, industrial robots, automative, electric vehicles- autos, rickshaws etc.Using this dataset, any industrial engineer can decide whether to run at that frequency or not. So, this study comes in handy to decide when designing in practice for industries and also for academia purposes.The speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC) with the addition of SMO observer which gives the estimation of speed and the sensorless rotor position. 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 included following , with analysis which can be drawn from seeing attached graphs -performance comparison using Sliding Mode Observer (SMO) at different frequencies.PWM switching frequency is varied 26 times from 20 kHz to 1 MHz , - will help decide MOSFETs switching frequency.Of course, the Rdson, dc link inverter voltage ,other practical parameters etc has been taken into consideration (not submitted here) bu some analysis related to it,you may find in the authors Transaction paper.Separate results at all these 26 frequenciesStability Margins - Gain/PhaseOpen Loop Control PerformanceTransfer Function CompensationClosed loop TuningRoot Locus of Uncompensated System and Compensated SystemBode Plot - magnitude and phasePole Zero Map of Uncompensated System and Compensated SystemUnit-Step Response of Uncompensated System and Compensated SystemNyquist plot of Uncompensated System and Compensated SystemNichols chart of Uncompensated System and Compensated SystemImpulse Response of Uncompensated System and Compensated SystemSinusoidal ExcitationAll 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.