These data from the laboratory tests of a prototype residential liquid-to-air ground-source air conditioner (GSAC) using CO2 as the refrigerant. The data collection and processing methods are described in detail in this report:

Report Title: "Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner", NIST Technical Note 2068
Publication Date: October 2019
DOI: https://doi.org/10.6028/NIST.TN.2068
Authors: Harrison Skye, Wei Wu

SEDCORR is an open-source Python module designed to correct for the systematic energy deficits in atom probe mass spectra of electrically insulating samples. The assumption of the algorithm is that the mass spectrum for a dataset is conserved throughout the dataset and that any changes to the peak positions arise from an unknown slowly-fluctuating accelerating voltage. For computational speed, the unknown accelerating voltage is determined using a template matching FFT-based cross correlation method.

Data files for the publication "High Accuracy Near-infrared Carbon Dioxide Intensity Measurements to Support Remote Sensing" in Geophysical Research Letters

There are two objectives of this shape retrieval contest:

a) To evaluate partial similarity between query and target objects and retrieve complete 3D models that are relevant to a partial query object.

b) To retrieve 3D models those are relevant to a query depth map. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.

Task description

The objectives of this shape retrieval contest are to evaluate the performance of 3D shape retrieval approaches on a new generic 3D shape benchmark.

Task description: In response to a given set of queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query.

The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a Generic 3D shape benchmark based on the Google 3D Warehouse.

The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.

Task description: In response to a given set of queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query. The set of query consists of range images.

SCATMECH is a library of object-oriented C++ computer codes originally developed for disseminating models for polarized light scattering from surfaces and aerosols and for diffraction from gratings. The pySCATMECH package has been developed as an interface to the SCATMECH library, simplifying use of the codes and allowing for more rapid development of software for these applications.

The objective of this shape retrieval contest is to retrieve 3D models those are relevant to a query range scan. This task corresponds to a real life scenario where the query is a 3D range scan of an object acquired from an arbitrary view direction. The algorithm should retrieve the relevant 3D objects from a database.

Task description:
In response to a given set of range scan queries, the task is to evaluate similarity scores with the target models and return an ordered ranked list along with the similarity scores for each query.

The objective of SHREC'14 Track is to evaluate the performances of different sketch-based 3D model retrieval algorithms using a large scale hand-drawn sketch query dataset on a generic 3D model dataset.