cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10346302
title: HazGTSimulator

description: ## An introduction to the files.


 


### 1. Dataset


    * UniformSampling: the result data of uniform sampling experiment.


    * UniformSampling/Scene*/Hazard/UniformSampling.png: the curves of recall and precision.


    * UniformSampling/Scene*/Hazard/WrongLabelsNumber.png: the curve of the number of perception errors.


    * UniformSampling/Scene*/Hazard/Hazard_Value/GroundtruthLabels: the detection files of the groundtruth of a simulation.


    * UniformSampling/Scene*/Hazard/Hazard_Value/labels: the detection files of yolo model.


    * UniformSampling/Scene*/Hazard/Hazard_Value/flabels: the detection files of frcnn model.


    * UniformSampling/Scene*/Hazard/Hazard_Value/Source: the images captured in a simulation.


    * UniformSampling/Scene*/Hazard/Hazard_Value/Paint: the images with groundtruth labels.


    * UniformSampling/Scene*/Hazard/Hazard_Value/Yolov7: the images detected by yolov7.


    * UniformSampling/Scene*/Hazard/Hazard_Value/FRCNN: the images detected by frcnn.


    * UniformSampling/Scene*/Hazard/Hazard_Value/Hazards.json: the attributes file used in a simulation.


    * Optimization: the result data of optimization.


    * Optimization/Yolo: the result data of optimization using yolo as perception model.


    * Optimization/Frcnn: the result data of optimization using frcnn as perception model.


    * Optimization/F1SUM.pdf: the outcome picture about F1-type failures.


    * Optimization/F2SUM.pdf: the outcome picture about F2-type failures.


    * Optimization/*/*/GA.json: the result data of optimization using GA.


    * Optimization/*/*/RA.json: the result data of optimization using random algorithm.


    * Optimization/*/*/NH.json: the result data of control group in whose scenario there is no hazard.



 


### 2. Tool


    * HazGTSimulator: the simulator we used to generate traffic videos and images


        * Click HazGTSimulator.exe.


        * Use the toggles or sliders located at the top left of the screen to set the scenario. Alternatively, use the panel located at the bottom left of the screen, where you can choose to import settings using files in three formats: hazs, hazgt, and json.


        * Click the button Start Simulation which is located at the bottom of the screen. Before you start a simulation, make sure you know where the output files are, which can be modified through the panel mentioned at RQ2.


        * Click the button Uniform Sampling and select an output folder with enough space.
DOI: 10.5281/zenodo.10958151
title: HazGTSimulator

description: An introduction to the files.

NOTE1: To save space, we deleted most of the images in the dataset. But we still kept the first sample of each sub-experiment for the convenience of readers to conduct inspections.

NOTE2: Although he test dataset used in the ChatGPT4 experiment is the same as the test dataset used in the random-sampling experiment, we still listed 3 extra samples for comparison to demonstrate the results of the ChatGPT4 experiment.

1. Dataset

|--RQ1-UniformSampling: the data obtained from the uniform-sampling experiment in RQ1.

|--RQ1-RQ3-RandomSampling: the data obtained from the random-sampling experiment in RQ1 and the random-sampling experiment in RQ3.

|--RQ1-RandomSampling_ChatGPT4: the data obtained from the ChatGPT4 experiment in RQ1.

|--RQ2: the data obtained from the experiment in RQ2.

2. Tool

HazGTSimulator: the simulator we used to generate hazardous traffic scenarios.

Steps to generate a scenario:





Double click Tool\HazGTSimulator\HazGTSimulator.exe.




Use the toggles or sliders located at the top left of the screen to set the scenario and the images directory.




Click the button Start Simulation which is located at the bottom of the screen.



3. Details-of-Hazards.pdf

We have defined a total of 70 hazards, which fall into four categories. This document shows the details of the hazards.
DOI: 10.5281/zenodo.10963863
title: HazGTSimulator

description: An introduction to the files.

1. Details-of-Hazards.pdf

We have defined a total of 70 hazards, which fall into four categories. This document shows the details of the hazards.

2. 智能网联汽车预期功能安全场景库建设报告.pdf

(Research on the Construction of a Scenario Library for the Safety Of The Intended Functionality(SOTIF) in Connected and Autonomous Vehicles)

This research report studies the scenario framework and architecture for SOTIF testing needs of intelligent driving, aiming to provide a reference for the development of simulation and testing software for SOTIF of intelligent driving, as well as the design of testing procedures and test cases.

Pages 7 to 22 of the document provide a detailed overview of the external hazards that lead to perception limitations, i.e., the details of visual hazards.

3. Data

NOTE1: To save space, we deleted most of the images in the dataset. But we still kept the first sample of each sub-experiment for the convenience of readers to conduct inspections. 

NOTE2: Although he test dataset used in the ChatGPT4 experiment is the same as the test dataset used in the random-sampling experiment, we still listed 3 extra samples for comparison to demonstrate the results of the ChatGPT4 experiment. 

|--RQ1-UniformSampling: the data obtained from the uniform-sampling experiment in RQ1. 

|--RQ1-RQ3-RandomSampling: the data obtained from the random-sampling experiment in RQ1 and the random-sampling experiment in RQ3. 

|--RQ1-RandomSampling_ChatGPT4: the data obtained from the ChatGPT4 experiment in RQ1. 

|--RQ2: the data obtained from the experiment in RQ2. 

4. Tool

|--Hazard Generator

|--Perception Reward Evaluator

NOTE: the ChatGPT4 experiment is conducted manually. 

|--Parameter Optimizer

Steps to generate a scenario:





Double click Tool\HazGTSimulator\HazGTSimulator.exe.




Use the toggles or sliders located at the top left of the screen to set the scenario and the images directory.




Click the button Start Simulation which is located at the bottom of the screen.
DOI: 10.5281/zenodo.14064355
title: HazGTSimulator

description: An introduction to the files.

1. Details-of-Hazards.pdf

We have defined a total of 70 hazards, which fall into four categories. This document shows the details of the hazards.

2. 智能网联汽车预期功能安全场景库建设报告.pdf

(Research on the Construction of a Scenario Library for the Safety Of The Intended Functionality(SOTIF) in Connected and Autonomous Vehicles)

This research report studies the scenario framework and architecture for SOTIF testing needs of intelligent driving, aiming to provide a reference for the development of simulation and testing software for SOTIF of intelligent driving, as well as the design of testing procedures and test cases.

Pages 7 to 22 of the document provide a detailed overview of the external hazards that lead to perception limitations, i.e., the details of visual hazards.

3. Conflicting Hazard Parameter Assignment Scheme (CHPAS).pdf

We have defined an assignment scheme for assigning values to conflicting hazards. By assigning values to hazards in order through this scheme, we avoid some unnatural phenomena, such as the combination of dense fog and strong light.

4. Data

NOTE1: To save space, we deleted most of the images in the dataset. But we still kept the first sample of each sub-experiment for the convenience of readers to conduct inspections.

NOTE2: Although he test dataset used in the ChatGPT4 experiment is the same as the test dataset used in the random-sampling experiment, we still listed 3 extra samples for comparison to demonstrate the results of the ChatGPT4 experiment.

|--RQ1-UniformSampling: the data obtained from the uniform-sampling experiment in RQ1. 

|--RQ1-RQ3-RandomSampling: the data obtained from the random-sampling experiment in RQ1 and the random-sampling experiment in RQ3. 

|--RQ1-RandomSampling_ChatGPT4: the data obtained from the ChatGPT4 experiment in RQ1. 

|--RQ2: the data obtained from the experiment in RQ2. 

5. Tool

|--Hazard Generator

|--Perception Reward Evaluator

NOTE: the ChatGPT4 experiment is conducted manually. 

|--Parameter Optimizer

Steps to generate a scenario:





Double click Tool\HazGTSimulator\HazGTSimulator.exe.




Use the toggles or sliders located at the top left of the screen to set the scenario and the images directory.




Click the button Start Simulation which is located at the bottom of the screen.
DOI: 10.5281/zenodo.8378848
title: HazGTSimulator

description: ## An introduction to the files. ### 1. Dataset * UniformSampling: the result data of uniform sampling experiment. * UniformSampling/Scene*/Hazard/UniformSampling.png: the curves of recall and precision. * UniformSampling/Scene*/Hazard/WrongLabelsNumber.png: the curve of the number of perception errors. * UniformSampling/Scene*/Hazard/Hazard_Value/GroundtruthLabels: the detection files of the groundtruth of a simulation. * UniformSampling/Scene*/Hazard/Hazard_Value/labels: the detection files of yolo model. * UniformSampling/Scene*/Hazard/Hazard_Value/flabels: the detection files of frcnn model. * UniformSampling/Scene*/Hazard/Hazard_Value/Source: the images captured in a simulation. * UniformSampling/Scene*/Hazard/Hazard_Value/Paint: the images with groundtruth labels. * UniformSampling/Scene*/Hazard/Hazard_Value/Yolov7: the images detected by yolov7. * UniformSampling/Scene*/Hazard/Hazard_Value/FRCNN: the images detected by frcnn. * UniformSampling/Scene*/Hazard/Hazard_Value/Hazards.json: the attributes file used in a simulation. * Optimization: the result data of optimization. * Optimization/Yolo: the result data of optimization using yolo as perception model. * Optimization/Frcnn: the result data of optimization using frcnn as perception model. * Optimization/F1SUM.pdf: the outcome picture about F1-type failures. * Optimization/F2SUM.pdf: the outcome picture about F2-type failures. * Optimization/*/*/GA.json: the result data of optimization using GA. * Optimization/*/*/RA.json: the result data of optimization using random algorithm. * Optimization/*/*/NH.json: the result data of control group in whose scenario there is no hazard. <br> ### 2. Tool * HazGTSimulator: the simulator we used to generate traffic videos and images * Click HazGTSimulator.exe. * Use the toggles or sliders located at the top left of the screen to set the scenario. Alternatively, use the panel located at the bottom left of the screen, where you can choose to import settings using files in three formats: hazs, hazgt, and json. * Click the button Start Simulation which is located at the bottom of the screen. Before you start a simulation, make sure you know where the output files are, which can be modified through the panel mentioned at RQ2. * Click the button Uniform Sampling and select an output folder with enough space.
DOI: 10.5281/zenodo.8385571
title: HazGTSimulator

description: ## An introduction to the files. ### 1. Dataset * UniformSampling: the result data of uniform sampling experiment. * UniformSampling/Scene*/Hazard/UniformSampling.png: the curves of recall and precision. * UniformSampling/Scene*/Hazard/WrongLabelsNumber.png: the curve of the number of perception errors. * UniformSampling/Scene*/Hazard/Hazard_Value/GroundtruthLabels: the detection files of the groundtruth of a simulation. * UniformSampling/Scene*/Hazard/Hazard_Value/labels: the detection files of yolo model. * UniformSampling/Scene*/Hazard/Hazard_Value/flabels: the detection files of frcnn model. * UniformSampling/Scene*/Hazard/Hazard_Value/Source: the images captured in a simulation. * UniformSampling/Scene*/Hazard/Hazard_Value/Paint: the images with groundtruth labels. * UniformSampling/Scene*/Hazard/Hazard_Value/Yolov7: the images detected by yolov7. * UniformSampling/Scene*/Hazard/Hazard_Value/FRCNN: the images detected by frcnn. * UniformSampling/Scene*/Hazard/Hazard_Value/Hazards.json: the attributes file used in a simulation. * Optimization: the result data of optimization. * Optimization/Yolo: the result data of optimization using yolo as perception model. * Optimization/Frcnn: the result data of optimization using frcnn as perception model. * Optimization/F1SUM.pdf: the outcome picture about F1-type failures. * Optimization/F2SUM.pdf: the outcome picture about F2-type failures. * Optimization/*/*/GA.json: the result data of optimization using GA. * Optimization/*/*/RA.json: the result data of optimization using random algorithm. * Optimization/*/*/NH.json: the result data of control group in whose scenario there is no hazard. <br> ### 2. Tool * HazGTSimulator: the simulator we used to generate traffic videos and images * Click HazGTSimulator.exe. * Use the toggles or sliders located at the top left of the screen to set the scenario. Alternatively, use the panel located at the bottom left of the screen, where you can choose to import settings using files in three formats: hazs, hazgt, and json. * Click the button Start Simulation which is located at the bottom of the screen. Before you start a simulation, make sure you know where the output files are, which can be modified through the panel mentioned at RQ2. * Click the button Uniform Sampling and select an output folder with enough space.
DOI: 10.5281/zenodo.8386380
title: HazGTSimulator

description: ## An introduction to the files. ### 1. Dataset * UniformSampling: the result data of uniform sampling experiment. * UniformSampling/Scene*/Hazard/UniformSampling.png: the curves of recall and precision. * UniformSampling/Scene*/Hazard/WrongLabelsNumber.png: the curve of the number of perception errors. * UniformSampling/Scene*/Hazard/Hazard_Value/GroundtruthLabels: the detection files of the groundtruth of a simulation. * UniformSampling/Scene*/Hazard/Hazard_Value/labels: the detection files of yolo model. * UniformSampling/Scene*/Hazard/Hazard_Value/flabels: the detection files of frcnn model. * UniformSampling/Scene*/Hazard/Hazard_Value/Source: the images captured in a simulation. * UniformSampling/Scene*/Hazard/Hazard_Value/Paint: the images with groundtruth labels. * UniformSampling/Scene*/Hazard/Hazard_Value/Yolov7: the images detected by yolov7. * UniformSampling/Scene*/Hazard/Hazard_Value/FRCNN: the images detected by frcnn. * UniformSampling/Scene*/Hazard/Hazard_Value/Hazards.json: the attributes file used in a simulation. * Optimization: the result data of optimization. * Optimization/Yolo: the result data of optimization using yolo as perception model. * Optimization/Frcnn: the result data of optimization using frcnn as perception model. * Optimization/F1SUM.pdf: the outcome picture about F1-type failures. * Optimization/F2SUM.pdf: the outcome picture about F2-type failures. * Optimization/*/*/GA.json: the result data of optimization using GA. * Optimization/*/*/RA.json: the result data of optimization using random algorithm. * Optimization/*/*/NH.json: the result data of control group in whose scenario there is no hazard. <br> ### 2. Tool * HazGTSimulator: the simulator we used to generate traffic videos and images * Click HazGTSimulator.exe. * Use the toggles or sliders located at the top left of the screen to set the scenario. Alternatively, use the panel located at the bottom left of the screen, where you can choose to import settings using files in three formats: hazs, hazgt, and json. * Click the button Start Simulation which is located at the bottom of the screen. Before you start a simulation, make sure you know where the output files are, which can be modified through the panel mentioned at RQ2. * Click the button Uniform Sampling and select an output folder with enough space.
DOI: 10.6084/m9.figshare.28586555
title: <b>蛋白磷酸酶 2A 介导的 </b><b>PPAR-α</b><b>脂质积累和线粒体功能障碍加剧保留肾单位手术诱导的急性肾损伤 </b><b>dysfunction</b>

description: Nephron-sparing surgery (NSS), the gold standard for the management of technically feasible localized renal tumors, may cause acute kidney injury (AKI). However, the precise etiology, intrinsic mechanisms, and effective interventions are not sufficiently understood.To investigate the molecular basis, we carried out metabolomic sequencing using two distinct mouse models: Sham and NSS.
DOI: 10.6084/m9.figshare.28586555.v1
title: <b>蛋白磷酸酶 2A 介导的 </b><b>PPAR-α</b><b>脂质积累和线粒体功能障碍加剧保留肾单位手术诱导的急性肾损伤 </b><b>dysfunction</b>

description: Nephron-sparing surgery (NSS), the gold standard for the management of technically feasible localized renal tumors, may cause acute kidney injury (AKI). However, the precise etiology, intrinsic mechanisms, and effective interventions are not sufficiently understood.To investigate the molecular basis, we carried out metabolomic sequencing using two distinct mouse models: Sham and NSS.
DOI: 10.15468/dl.9z3gj6
title: Occurrence Download

description: A dataset containing 11772 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Thailand",
 "TaxonKey is Plantae",
 "Year is 2024"
 ]
}

 The dataset includes 11772 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006414-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q8ytd8
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Vachellia viguieri (Villiers & Du Puy) Boatwr."
 ]
}

 The dataset includes 27 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006416-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/6trt-0533
title: Full Selfish Mining Attack on Blockchain

description: As the number of blockchain miners increases, mining profits tend to stabilize, leading to more rational behavior among miners. Mining attacks, particularly the influential selfish mining (SM) attack, seek to unfairly boost adversary profits by strategically withholding and releasing discovered blocks, thereby wasting the computational resources of other miners. Existing SM methods primarily focus on adversary strategies, assuming that honest miners continue mining regardless of the circumstances. However, our research reveals that honest miners can optimize their profits by adopting intermittent mining strategies—mining during non-fork states and halting during equal-length fork states. This finding forms the basis of our proposed theory of rational miners' intermittent mining. Building on this insight, we propose Full Selfish Mining (FSM), a novel attack strategy in which the adversary maximizes profits by maintaining the blockchain in equal-length fork states. Experimental results demonstrate that FSM consistently yields higher expected profits than SM, enabling 51\% attacks with just 29\% of the computing power.
DOI: 10.15468/dl.bxyn8f
title: Occurrence Download

description: A dataset containing 422 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Antarctic Krill Euphausia superba"
 ]
}

 The dataset includes 422 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006417-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/0ytn-n006
title: Full Selfish Mining Attack on Blockchain

description: As the number of blockchain miners increases, mining profits tend to stabilize, leading to more rational behavior among miners. Mining attacks, particularly the influential selfish mining (SM) attack, seek to unfairly boost adversary profits by strategically withholding and releasing discovered blocks, thereby wasting the computational resources of other miners. Existing SM methods primarily focus on adversary strategies, assuming that honest miners continue mining regardless of the circumstances. However, our research reveals that honest miners can optimize their profits by adopting intermittent mining strategies—mining during non-fork states and halting during equal-length fork states. This finding forms the basis of our proposed theory of rational miners' intermittent mining. Building on this insight, we propose Full Selfish Mining (FSM), a novel attack strategy in which the adversary maximizes profits by maintaining the blockchain in equal-length fork states. Experimental results demonstrate that FSM consistently yields higher expected profits than SM, enabling 51\% attacks with just 29\% of the computing power.
DOI: 10.15468/dl.jebtqt
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Helianthus verticillatus Small"
 ]
}

 The dataset includes 87 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006420-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n3u7py
title: Occurrence Download

description: A dataset containing 234 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Antarctic Krill occurrence data from Australian expeditions"
 ]
}

 The dataset includes 234 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006419-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.czcung
title: Occurrence Download

description: A dataset containing 3148 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sclerocarya birrea (A.Rich.) Hochst.",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Occurrence evidence)"
 ]
}

 The dataset includes 3148 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006421-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b4gyrt
title: Occurrence Download

description: A dataset containing 346 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Antarctic Krill occurrence data from BAS expeditions"
 ]
}

 The dataset includes 346 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006422-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y5hsgh
title: Occurrence Download

description: A dataset containing 3499 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cicinnurus regius (Linnaeus, 1758)"
 ]
}

 The dataset includes 3499 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006424-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8rhrd3
title: Occurrence Download

description: A dataset containing 667 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Antarctic Krill occurrence data from Discovery expeditions"
 ]
}

 The dataset includes 667 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006425-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.49a4gq
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Antarctic Krill occurrence data from Japanese expeditions"
 ]
}

 The dataset includes 19 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006426-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.