cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3607304
title: Livorno, Highway pilot, only connected cars

description: <strong>Scenario description</strong>: Precondition: A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed. Actions or events: 1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone. 2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed. 3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed. Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles). <strong>Session description</strong>: Test session with only connected cars, lap of 12,3 km on the highway. The goal is to check all the systems and data management before the next test session with AD cars. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607305
title: Livorno, Highway pilot, one automated and connected car

description: <strong>Scenario description</strong>: Precondition: A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed. Actions or events: 1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone. 2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed. 3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed. Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles). <strong>Session description</strong>: Test session with one AD+connected car, lap of 12,3 km on the highway. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607306
title: Livorno, Highway pilot, one automated and connected car

description: <strong>Scenario description</strong>: Precondition: A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed. Actions or events: 1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone. 2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed. 3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed. Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles). <strong>Session description</strong>: Test session with one AD+connected car, lap of 12,3 km on the highway. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607294
title: Livorno, Highway pilot, connected car system and data management

description: <strong>Scenario description</strong>: Precondition: 1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud. Actions or events: 1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform. 2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc. 3 At the same time, the CONTI cloud is subscribed to the oneM2M platform; it receives and shares with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car 4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane-change maneuvers Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles). <strong>Session description</strong>: pre-test session with only connected cars, lap of 11,6 km on the highway. Goal is to check all the system and data management. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607295
title: Livorno, Highway pilot, connected car system and data management

description: <strong>Scenario description</strong>: Precondition: 1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud. Actions or events: 1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform. 2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc. 3 At the same time, the CONTI cloud is subscribed to the oneM2M platform; it receives and shares with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car 4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane-change maneuvers Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles). <strong>Session description</strong>: pre-test session with only connected cars, lap of 11,6 km on the highway. Goal is to check all the system and data management. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607287
title: Livorno, Highway pilot, data management connected car

description: <strong>Scenario description</strong>: Dynamic speed adaptation due to puddle on the road Precondition: A vehicle is driving in the first lane of a “smart highway" at 90 km/h with all the devices working correctly and connected to all services needed. Actions or events: 1 The puddle monitoring system of the highway trigger a puddle hazard warning for a specific extended zone. 2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed. 3 At the end of dangerous area, as notified by the «smart road», the vehicle will recover the legally allowed cruise speed. Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles). <strong>Session description</strong>: pre-test session with only connected cars, lap of 12,3 km on the highway. Goal is to check all the system and data management. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3607288
title: Livorno, Highway pilot, data management connected car

description: <strong>Scenario description</strong>: Precondition: A vehicle is driving in the first lane of a “smart highway" at 90 km/h with all the devices working correctly and connected to all services needed. Actions or events: 1 The puddle monitoring system of the highway trigger a puddle hazard warning for a specific extended zone. 2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed. 3 At the end of dangerous area, as notified by the «smart road», the vehicle will recover the legally allowed cruise speed. Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles). <strong>Session description</strong>: pre-test session with only connected cars, lap of 12,3 km on the highway. Goal is to check all the system and data management. <strong>Datasets description</strong>: <strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data <strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno. <strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.
DOI: 10.5286/isis.e.rb1910581
title: Study of a novel skyrmion phase in MnSi

description: We propose to study a novel skyrmion phase in the prototypical skyrmion material MnSi. Whereas standard skyrmions, which are found in a narrow pocket in the phase diagram at finite field below the phase transition temperature, require fluctuations for their stabilisation, the new phase is stabilised by anisotropy. This new phase has been experimentally identified in Cu2OSeO3 using SANS. The fact that anisotropy, which is a designable materials parameter, plays a crucial role in obtaining the phase, together with the observed wide field and temperature range of existence, make this new skyrmion phase extremely promising for applications. However, the proof that this new phase is universally observable in B20 crystals is still outstanding and our aim is to explore this part of phase space in full detail.
DOI: 10.5286/isis.e.rb1910581-1
title: Study of a novel skyrmion phase in MnSi

description: We propose to study a novel skyrmion phase in the prototypical skyrmion material MnSi. Whereas standard skyrmions, which are found in a narrow pocket in the phase diagram at finite field below the phase transition temperature, require fluctuations for their stabilisation, the new phase is stabilised by anisotropy. This new phase has been experimentally identified in Cu2OSeO3 using SANS. The fact that anisotropy, which is a designable materials parameter, plays a crucial role in obtaining the phase, together with the observed wide field and temperature range of existence, make this new skyrmion phase extremely promising for applications. However, the proof that this new phase is universally observable in B20 crystals is still outstanding and our aim is to explore this part of phase space in full detail.
DOI: 10.5281/zenodo.3607417
title: Visual documentation for "Runes and Early Slavs: the oldest inscription among Slavs reveals their interaction with Germanic peoples"

description: Fig. S1. Břeclav-Lány (CZ). Six runs of the older fuþark on the bone from settlement pit No. 25. Photo by Vojtěch Nosek. <br> Fig. S2. Břeclav-Lány (CZ). The rune-inscribed bone. Rune line above, root etching trace bellow. Dorsal surface. Reflected light microscope, magnification 200x. Photo by Ludmila Kaňáková. Fig. S3. Břeclav-Lány (CZ). The rune-inscribed bone. Rune line (vertical) and stave line (horizontal), both red-coloured. Dorsal face surface. Reflected light microscope, magnification 50x. Photo by Ludmila Kaňáková. Fig. S4. Břeclav-Lány (CZ). The rune-inscribed bone. Stratigraphy of traces – rune lines damaged by cross line (on the left) and by root etching (on the right). Dorsal surface. SEM by Jindřich Štelcl. Fig. S5. Multiple plot. Grouping (Bayesian modelling - overlapping phases, OxCal - v 4.3) of radiocarbon data from Lombard and Slavic sites excavated in the Czech Republic, Austria and Pannonia (Slovakia and Hungary) and radiocarbon dating of the rune-inscribed cattle bone from Břeclav-Lány.
DOI: 10.5281/zenodo.3607418
title: Visual documentation for "Runes and Early Slavs: the oldest inscription among Slavs reveals their interaction with Germanic peoples"

description: Fig. S1. Břeclav-Lány (CZ). Six runs of the older fuþark on the bone from settlement pit No. 25. Photo by Vojtěch Nosek. <br> Fig. S2. Břeclav-Lány (CZ). The rune-inscribed bone. Rune line above, root etching trace bellow. Dorsal surface. Reflected light microscope, magnification 200x. Photo by Ludmila Kaňáková. Fig. S3. Břeclav-Lány (CZ). The rune-inscribed bone. Rune line (vertical) and stave line (horizontal), both red-coloured. Dorsal face surface. Reflected light microscope, magnification 50x. Photo by Ludmila Kaňáková. Fig. S4. Břeclav-Lány (CZ). The rune-inscribed bone. Stratigraphy of traces – rune lines damaged by cross line (on the left) and by root etching (on the right). Dorsal surface. SEM by Jindřich Štelcl. Fig. S5. Multiple plot. Grouping (Bayesian modelling - overlapping phases, OxCal - v 4.3) of radiocarbon data from Lombard and Slavic sites excavated in the Czech Republic, Austria and Pannonia (Slovakia and Hungary) and radiocarbon dating of the rune-inscribed cattle bone from Břeclav-Lány.
DOI: 10.17632/83tc4n2r73.1
title: Data for: Three-dimensional simulation of two-phase flow in a complex gallery and telescopic pipe coupled system
DOI: 10.17632/yrgb2c6gwh.1
title: Data for: Development and application of a solid-state fan for enhanced heat dissipation

description: Brief data for 'Development and application of a solid-state fan for enhanced heat dissipation'
DOI: 10.5281/zenodo.3607598
title: Experimental and theoretical study of wind turbine wakes in yawed conditions

description: Hub-height horizontal-plane PIV measurement data of the wake behind a stand-alone yawed wind turbine. HDF5 data structure: Yaw_0_Lambda_o: yaw angle (0, 10, 20, 30 degree) case at the optimal tip-speed ratio u_avg: mean of u (v, w) component. u_std: standard deviation of the u (v, w) component. x: x grid point. y: y grid point. Reference Bastankhah, Majid, and Fernando Porté-Agel. "Experimental and theoretical study of wind turbine wakes in yawed conditions." <em>Journal of Fluid Mechanics</em> 806 (2016): 506-541.
DOI: 10.5281/zenodo.3607597
title: Experimental and theoretical study of wind turbine wakes in yawed conditions

description: Hub-height horizontal-plane PIV measurement data of the wake behind a stand-alone yawed wind turbine. HDF5 data structure: Yaw_0_Lambda_o: yaw angle (0, 10, 20, 30 degree) case at the optimal tip-speed ratio u_avg: mean of u (v, w) component. u_std: standard deviation of the u (v, w) component. x: x grid point. y: y grid point. Reference Bastankhah, Majid, and Fernando Porté-Agel. "Experimental and theoretical study of wind turbine wakes in yawed conditions." <em>Journal of Fluid Mechanics</em> 806 (2016): 506-541.
DOI: 10.5281/zenodo.3607486
title: Correlation of Prevotella abundancy with a particular carbohydrate-active CAZy enzyme profile in healthy gut microbiota

description: Data_microbiota
DOI: 10.5281/zenodo.3607485
title: Correlation of Prevotella abundancy with a particular carbohydrate-active CAZy enzyme profile in healthy gut microbiota

description: Data_microbiota
DOI: 10.5286/isis.e.rb2010573
title: Structure of Multicomponent Gelators: Effect of Dynamic Covalent Chemistry

description: Supramolecular gels form via the self-assembly of low molecular weight gelators (LMWGs) into complex, hierarchical structures. Here we propose a SANS study of our multicomponent gels prepared using two or more different gelators. Our group has used rheology, NMR and SEM to characterise these gels. These techniques, while useful, lack the necessary microscopic insight to relate chemical changes to structure and macroscopic behaviour. The SANS experiment aims to further our understanding of multicomponent gels through characterisation of primary fibre structures. By exploiting contrast variation we study the self-assembly mechanism. We also compare two distinct synthetic routes to prove our hypothesis of a co-assembly mechanism. By measuring SANS intensities in both the solution and gel phase we hope to learn more about the assembly process, which is currently poorly understood.
DOI: 10.5286/isis.e.rb2010573-1
title: Structure of Multicomponent Gelators: Effect of Dynamic Covalent Chemistry

description: Supramolecular gels form via the self-assembly of low molecular weight gelators (LMWGs) into complex, hierarchical structures. Here we propose a SANS study of our multicomponent gels prepared using two or more different gelators. Our group has used rheology, NMR and SEM to characterise these gels. These techniques, while useful, lack the necessary microscopic insight to relate chemical changes to structure and macroscopic behaviour. The SANS experiment aims to further our understanding of multicomponent gels through characterisation of primary fibre structures. By exploiting contrast variation we study the self-assembly mechanism. We also compare two distinct synthetic routes to prove our hypothesis of a co-assembly mechanism. By measuring SANS intensities in both the solution and gel phase we hope to learn more about the assembly process, which is currently poorly understood.
DOI: 10.7910/dvn/d5h063/8jgmss
title: jharkhand_access_data.tab

description: :unav
DOI: 10.7910/dvn/d5h063/t6ll4l
title: JharkhandSurvey_v8.pdf

description: :unav