Climate Change/Environmental
The raw data are collected from the websites of EPD (Environmental Protection Department, Hong Kong) and HKO (Hong Kong Observatory). Marine water quality data is provided by EPD and climatological data is provided by HKO. The data is interpolated by SAS “proc expand” and aligned to the beginning of each month.
The raw data used to produce this dataset are extracted from the following URL.
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The large variability of system and types of heating load is a feature of the commercial metering of thermal energy. Heating consumption depends on many factors, for example, wall and roof material, floors number, system (opened and closed) etc. The daily data from heating meters in the residential buildings are presented in this dataset for comparing the thermal characteristics. These data are supplemented by floors number, wall material and year of construction, as well as data on average daily outdoor temperatures.
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This data package is parepared by Dr. Jianguo Niu (IMSG at NOAA NESDIS/STAR) on
March 18, 2020
The purpose of this OMPS LFSO2 retrieval products package is in support the paper:
"Evaluation and Improvement of the Near-real-time Linear Fit SO2 retrievals from Suomi NPP (S-NPP) Ozone Mapping & Profiler Suite"
This package includes LFSO2 V8TOS retrievals of:
1. "logic swith on" (original set as described by th paper 01824) products
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The dataset is mainly used for leak detection and localization.
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The target scene consists of a black card with six cocoa beans of three different fermentation levels (High, correct, and low fermentation), two beans for each class, whose false-color composite is shown in the provided Figure (a), ground-truth map is shown in Fig. (b), and Fig. (c) presents its representative spectral signatures. The spectral image was acquired by the AVT Stingray F-080B camera by acquiring one band each time from 350 - 950 nm. The acquired image has a spatial resolution of 1096x712 pixels and 300 spectral bands of 2 nm width.
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This is an observation data for water quality monitoring.
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Attempts to prevent invasion of marine biofouling on marine vessels are demanding. By developing a system to detect marine fouling on vessels in an early stage of fouling is a viable solution. However, there is a lack of database for fouling images for performing image processing and machine learning algorithm.
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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the
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As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed.
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