cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3630464
title: Livorno, Highway pilot, one automated and two 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 one AD+connected car and two connected cars, 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 Pilot session 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 Highway Pilot sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Pilot session 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.3630465
title: Livorno, Highway pilot, one automated car, two connected cars, smart highway

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 share 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>: Test session with one AD+connected car and two connected cars, lap of 11,6 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 platooning 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.3630239
title: Population assessment and foraging ecology of the rare solitary bee Anthophora retusa at Seaford Head Nature reserve

description: <em>Anthophora retusa</em> is a rare solitary bee which has declined throughout Britain and other European countries since the 1990s. It is thought to be restricted to five sites in Britain. However, information on these remaining populations is limited. Knowledge on population size, dispersal distance, habitat and forage requirements are important for successful conservation of species. The population of <em>A. retusa</em> at the Seaford Head Nature reserve in East Sussex was surveyed. Using mark recapture the population was estimated in 2018 and 2019 to be fewer than 200 individuals, with the male population increasing from 47 in 2018 to 167 in 2019. The female population was 44 in 2018 but due to zero recaptures in 2019, no population estimate was possible. Bees seem to be geographically restricted to a 25ha area within the reserve. The most visited flower species by females was <em>Glechoma hederacea </em>(66% of visits) but flower preference changed throughout the flight season, shifting to Fabaceae species and <em>Iris foetidissima</em> with 16 plant groups identified in pollen samples. Although the exact location of nesting sites was not determined with certainty it’s thought they are nesting in the loess deposits at the top of the inaccessible sea cliff face. The average distance between recaptures was 122m, perhaps indicating low dispersal. This project suggests the presence of appropriate nesting sites, potentially soft exposed soil, may be limiting <em>A. retusa</em> distribution as they appear to forage on common plant species. More research is needed on the exact nesting requirements of the species.
description: Funded by Gatwick Airport and University of Sussex Doctoral School
DOI: 10.21227/4ba2-tg21
title: RSRPSet: test dataset of the 16th CNGMCM

description: All data used for model evaluation and training in this letter are from the $16^{th}$ Chinese National Graduate Mathematical Contest in Modeling (CNGMCM). The dataset is measured by HUAWEI TECHNOLOGIES CO., LTD, which includes engineering parameter data, map data and RSRP label data of multiple communities. And the dataset used in this letter is from the public dataset of CNGMCM, which is sponsored by the Ministry of Education's Degree and Graduate Education Development Center. The purpose of building the dataset is to find the mapping model between engineering parameters, geographical environment and RSRP, so as to quickly predict the RSRP value of a specific geographical location in the new environment.ATTENTION: This is Not the orginal dataset of CNGMCM. The data have beenPreprocessed. More detail please see "Received Signal Power Estimation by Using the Deep Learning Algorithm".
DOI: 10.21227/j9z7-ra93
title: RSRPSet: test dataset of the 16th CNGMCM

description: All data used for model evaluation and training in this letter are from the $16^{th}$ Chinese National Graduate Mathematical Contest in Modeling (CNGMCM). The dataset is measured by HUAWEI TECHNOLOGIES CO., LTD, which includes engineering parameter data, map data and RSRP label data of multiple communities. And the dataset used in this letter is from the public dataset of CNGMCM, which is sponsored by the Ministry of Education's Degree and Graduate Education Development Center. The purpose of building the dataset is to find the mapping model between engineering parameters, geographical environment and RSRP, so as to quickly predict the RSRP value of a specific geographical location in the new environment.ATTENTION: This is Not the orginal dataset of CNGMCM. The data have beenPreprocessed. More detail please see "Received Signal Power Estimation by Using the Deep Learning Algorithm".
DOI: 10.5281/zenodo.3630238
title: Population assessment and foraging ecology of the rare solitary bee Anthophora retusa at Seaford Head Nature reserve

description: <em>Anthophora retusa</em> is a rare solitary bee which has declined throughout Britain and other European countries since the 1990s. It is thought to be restricted to five sites in Britain. However, information on these remaining populations is limited. Knowledge on population size, dispersal distance, habitat and forage requirements are important for successful conservation of species. The population of <em>A. retusa</em> at the Seaford Head Nature reserve in East Sussex was surveyed. Using mark recapture the population was estimated in 2018 and 2019 to be fewer than 200 individuals, with the male population increasing from 47 in 2018 to 167 in 2019. The female population was 44 in 2018 but due to zero recaptures in 2019, no population estimate was possible. Bees seem to be geographically restricted to a 25ha area within the reserve. The most visited flower species by females was <em>Glechoma hederacea </em>(66% of visits) but flower preference changed throughout the flight season, shifting to Fabaceae species and <em>Iris foetidissima</em> with 16 plant groups identified in pollen samples. Although the exact location of nesting sites was not determined with certainty it’s thought they are nesting in the loess deposits at the top of the inaccessible sea cliff face. The average distance between recaptures was 122m, perhaps indicating low dispersal. This project suggests the presence of appropriate nesting sites, potentially soft exposed soil, may be limiting <em>A. retusa</em> distribution as they appear to forage on common plant species. More research is needed on the exact nesting requirements of the species.
description: Funded by Gatwick Airport and University of Sussex Doctoral School
DOI: 10.5281/zenodo.3630473
title: Microsecond ALEX FRET analysis notebook using FRETbursts - corrections, FRET burst analysis of recurring molecules, FCS, 2CDE & BVA

description: The herein Python notebook uses FRETbursts (download from here: https://github.com/tritemio/FRETBursts) to show how to analyze microsecond alternating laser excitation (usALEX) confocal-based FRET measurements of freely diffusing single molecules. It includes a step-by-step calculation and implementation of correction factors, donor fluorescence leakage to the acceptor detection channel (Lk), acceptor fluorescence caused by acceptor excitation by the laser intended for donor excitation (Dir), the imbalance in acceptor/donor fluorecence quantum yields and detection efficiencies (Gamma) and the imbalance in donor/acceptor excitation yields (Beta). The notebook implments a global Gamma correction, assuming the Gamma correction factor is constant for all FRET populations, based on the procedure from Lee et al. 2005. Burst search for showing the FRET population is a dual-channel burst search. After correction, the corrected FRET histogram is presented (after burst selection takes into account Beta & Gamma corrected burst sizes). We also present analysis of bursts from recurring molecules, as well as the FCS (a bit irrelevant here, due to the lasr alternation in microeconds), 2CDE & BVA plots, helping in identifying whether a FRET population is a time average of FRET states, occurring faster then molecular diffusion time, or wheather the FRET population is static and represents a single conformational state. The sample data is a result of measurement of 50 pM of hairpin 3 presented in Tsukanov et al. 2013, labeled with ATTO dyes (ATTO 550 & ATTO 647N as donor and acceptor dyes, respectively) - 532 & 640 nm cw excitation, with an alternation period of 50 microseconds.
DOI: 10.5281/zenodo.3630474
title: Microsecond ALEX FRET analysis notebook using FRETbursts - corrections, FRET burst analysis of recurring molecules, FCS, 2CDE & BVA

description: The herein Python notebook uses FRETbursts (download from here: https://github.com/tritemio/FRETBursts) to show how to analyze microsecond alternating laser excitation (usALEX) confocal-based FRET measurements of freely diffusing single molecules. It includes a step-by-step calculation and implementation of correction factors, donor fluorescence leakage to the acceptor detection channel (Lk), acceptor fluorescence caused by acceptor excitation by the laser intended for donor excitation (Dir), the imbalance in acceptor/donor fluorecence quantum yields and detection efficiencies (Gamma) and the imbalance in donor/acceptor excitation yields (Beta). The notebook implments a global Gamma correction, assuming the Gamma correction factor is constant for all FRET populations, based on the procedure from Lee et al. 2005. Burst search for showing the FRET population is a dual-channel burst search. After correction, the corrected FRET histogram is presented (after burst selection takes into account Beta & Gamma corrected burst sizes). We also present analysis of bursts from recurring molecules, as well as the FCS (a bit irrelevant here, due to the lasr alternation in microeconds), 2CDE & BVA plots, helping in identifying whether a FRET population is a time average of FRET states, occurring faster then molecular diffusion time, or wheather the FRET population is static and represents a single conformational state. The sample data is a result of measurement of 50 pM of hairpin 3 presented in Tsukanov et al. 2013, labeled with ATTO dyes (ATTO 550 & ATTO 647N as donor and acceptor dyes, respectively) - 532 & 640 nm cw excitation, with an alternation period of 50 microseconds.
DOI: 10.5281/zenodo.3630478
title: Livorno, Urban driving, detection of fallen bicycle

description: <strong>Scenario description</strong>: Test session for fallen bicycle with AD and connected vehicle <strong>Session description</strong>: The fallen bicycle use case aims to demonstrate the possibility for a vehicle to detect in advance, using V2X communication, the presence of a fallen bicycle on the road. In case of fall, the bicycle signals its presence to the other vehicles using DENM messages. The AD car safely reduces its speed and stops. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving 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_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3630479
title: Livorno, Urban driving, Automated vehicle detects fallen bicycle

description: <strong>Scenario description</strong>: Test session for AD+connected car and connected cars approaching a fallen bicycle. <strong>Session description</strong>: The fallen bicycle use case aims to demonstrate the possibility for a vehicle to detect in advance, using V2X communication, the presence of a fallen bicycle on the road. In case of fall, the bicycle signals its presence to the other vehicles using DENM messages. The AD car publishes the detected event to the oneM2M and safely reduces its speed until to stop. Goal is to record data for the technical evaluation. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving 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_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3630480
title: Livorno, Urban driving, pothole detection

description: <strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU. The information is sent to the cloud and can be sent back to other connected vehicles for warning. The information is also transmitted via V2V to AD cars that can automatically adapt the speed. <strong>Session description</strong>: Test session with only a connected car with 6LoWPAN based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving 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_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3630481
title: Livorno, Urban driving, IMU pothole detection

description: <strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed. <strong>Session description</strong>: Test session with only a connected car with IMU based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving 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_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3630484
title: Livorno, Urban driving, Automated car detects pothole

description: <strong>Scenario description</strong>: Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed. <strong>Session description</strong>: Test session with only a connected car with smartphone based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving 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_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.
DOI: 10.5281/zenodo.3630487
title: Livorno, Urban driving, autonomous speed adaptation approaching intersection

description: <strong>Scenario description</strong>: Test session for AD+connected car and connected cars approaching an intersection regulated by a "smart" traffic light with a stereocamera able to detect jaywalking. <strong>Session description</strong>: A "smart" traffic light (with stereocamera) sends SPaT and MAP messages describing the topology and the actual status of the traffic light. If a jaywalking occurrence is detected (pedestrian crossing with the red light) a DENM message is sent to warn vehicles of the presence of the pedestrian to other connected vehicles. The the hazard warning is also sent to the oneM2M platform on the cloud. An AD vehicle consumes the information and autonomously adapts its speed in order to cross the intersection without violating the traffic light phases, or even stop to avoid collision with pedestrian. The influence of other vehicles moving in front is considered too. Goal is to record data for the technical evaluation. <strong>Datasets descriptions</strong>: <strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving 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_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno. <strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.