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412 results for “verification”

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zenodo44/100

Machine Learning Features from Proton Therapy Treatment Simulations with the Bergen DTC Prototype for Range Verification

Extracted features from the simulation data found at DOI: <a href="https://doi.org/10.5281/zenodo.8192778">10.5281/zenodo.8192778</a> Each simulation constitutes a single data sample. The following features were extracted. Detector features: <ul> <li>Total number of active pixels</li> <li>Total number of clusters (hits)</li> <li>Number of clusters over threshold (5, 20 pixels)</li> <li>Mean and standard deviation of cluster sizes</li> <li>The number of clusters of any given size (1–72)</li> <li>Mean and standard deviation of x- and y-coordinates over each layer (0–42), and the entire detector</li> <li>Number of active pixels in each layer (0–42)</li> <li>Number of clusters (hits) in each layer (0–42)</li> <li>Total energy deposition of the hits in each layer (0–42)</li> </ul> Higher-level detector features, i.e., function fits (linear, cubic, exponential) with their mean squared residuals over the following quantities: <ul> <li>Active pixels over layer</li> <li>Number of clusters over layer</li> <li>Total deposited energy over layer</li> </ul> 201 RSP features extracted from the beam spot, the phantom rotation, and its 3D RSP image. Two datasets are included in two separate archive files: <ul> <li>features.tar.gz: 715-HN phantom by CIRS Inc. (Norfolk, VA, United States), digitized by Giacometti et al. (2017).</li> <li>features-vhf.tar.gz: The Visible Human Female (VHF) Head phantom (Ackermann et al. 1995), courtesy of the U.S. National Library of Medicine, resampled to 1 mm voxels and scaled down to 80% size in the simulation.</li> </ul> After extracting features, some outliers were removed from the datasets: 14 samples for 715-HN and 3 samples for VHF. The rest of the samples were split into train (70%), validation (10%), and test (20%) sets, for both phantoms separately, which can be found in separate CSV files: features_train.csv, features_val.csv, features_test.csv (715-HN) and features-vhf_train.csv, features-vhf_val.csv, features-vhf_test.csv (VHF). The last file (features_shifted_test.csv (715-HN) and features-vhf_shifted_test.csv (VHF)) contains 40 additional samples for each data point in the respective test set, representing a simulated lateral shift between 1 mm and 10 mm in 1 mm intervals in all directions along the x- and y-axis of the beam.

opencc-by-4.0Jun 2023View details →

zenodo44/100

Proton Therapy Treatment Simulations with the Bergen DTC Prototype for Range Verification

This dataset contains proton therapy Monte Carlo simulations using GATE (Jan et al. 2004) version 9.2 and Geant4 (Agostinelli et al. 2003; Allison et al. 2006; Allison et al. 2016) version 11.0.0. The treatment targets are the pediatric head phantom 715-HN by CIRS Inc. (Norfolk, VA, United States), digitized by Giacometti et al. (2017), and The Visible Human Female (VHF) head, courtesy of the U.S. National Library of Medicine, resampled to 1 mm voxels and scaled down to 80%. Distal to the phantom, is a simplified geometry of the digital tracking calorimeter designed by the <a href="https://www.uib.no/en/ift/142356/medical-physics-bergen-pct-project">Bergen pCT collaboration</a> for proton computed tomography (Alme et al. 2020). <ul> <li>treatment_simulations.tar.gz: 36258 simulations with the 715-HN phantom</li> <li>treatment_simulations_vhf.tar.gz: 35673 simulations with the VHF phantom</li> </ul> Each simulation is a single pencil beam with \(10^7\) primary protons. To determine valid spots to use, probing simulations with \(10^5\) primaries were conducted, where the beam position, energy, and phantom rotation were varied: 10 mm interval in the xy-plane for the beam position, 3 mm water range interval for the beam energy, and 30° rotations of the phantom. If no primary was found in the detecter after the probing simulation, it is a valid spot for the dataset. Resulting hit files, containing simulated detector readout, can be found in the archive files for the respective phantom. Each simulation is accompanied by a metafile in JSON format, specifying all parameters used to produce the output. E.g., beam position and energy can be found in "parameters" -> "beam_spot_x", "beam_spot_y", "beam_energy" and the phantom rotation can be found in "parameters" -> "phantom_rotation_angle". Simulation data has been pre-processed with two steps. First, hits for the same track in the same layer are binned together into a single entry by averaging the positions (posX, posY, posZ) and summing the energy depositions (edep). Second, the energy deposition values are discretized into a cluster size and back into MeV, to represent the energy resolution of the ALPIDE chip (Alme et al. 2020). Clusters of size 0 are removed. Additionally, some of the unused GATE output columns are removed and a column specifying the detector layer is created through the formula layer = 2*volumeID[2] + volumeID[3]. Details about the meaning of the other output columns can be found in the <a href="https://opengate.readthedocs.io/en/latest/data_output_management.html">GATE documentation</a>.

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Dataset results

Machine Learning Features from Proton Therapy Treatment Simulations with the Bergen DTC Prototype for Range Verification

Proton Therapy Treatment Simulations with the Bergen DTC Prototype for Range Verification

Experiments for "Automata Theoretic Approach to Verification of MPLS Networks under Link Failures"

Results of the 10th Intl. Competition on Software Verification (SV-COMP 2021)

Verification Witnesses from Verification Tools (SV-COMP 2021)

baofeng-apm/DATAforAGU: First release of data for Love verification

Datasets used in the Lab Validation of the RADON Verification Tool

Dataset for Close Miking Empirical Practice Verification: A Source Separation Approach

Fig. 1 in Spore Dimorphism in Nosema pyrausta (Microsporidia, Nosematidae): from Morphological Evidence to Molecular Genetic Verification

Efficient Techniques for Formal Verification of C Programs: SV-COMP 2022 dataset

Sustainable Smart Tags with Two-Step Verification for Anticounterfeiting Triggered by the Photothermal Response of Upconverting Nanoparticles

GNSS Location Verification in Connected and Autonomous Vehicles Using in-Vehicle Multimodal Sensor Data Fusion (presentation recording)

A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects: dataset

Measurement verification data for AutoMorph vs. hand measurements

Data from: Empirical verification of feeding selectivity of larval and juvenile pelagic fishes using in situ zooplankton communities

Fig. 4 in Verification of Natural Marking for Individual Identification Using a Duplex Marking Approach in Ijima's Sea Snakes, (Reptilia: Elapidae).

2 3 in Verification of Natural Marking for Individual Identification Using a Duplex Marking Approach in Ijima's Sea Snakes, (Reptilia: Elapidae).

Fig. 11 in Fig. 4 in Verification of Natural Marking for Individual Identification Using a Duplex Marking Approach in Ijima's Sea Snakes, (Reptilia: Elapidae).

Fig. 10 in Fig. 4 in Verification of Natural Marking for Individual Identification Using a Duplex Marking Approach in Ijima's Sea Snakes, (Reptilia: Elapidae).

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