The dataset attached is recordings done for 5 parameters to ascertain physical soil composition. Data was collected between March 2021 and April 2021. This dataset is the raw data.

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More than 40% of energy resources are consumed in the residential buildings, and most of the energy is used for heating. Improving the energy efficiency of residential buildings is an urgent problem. The collected data is intended to study a dependence of the dynamics heat energy supply from outside temperature and houses characteristics, such as walls material, year of construction, floors amount, etc. This study will support the development of methods for comparing thermal characteristics of residential buildings and carry out recommendations for the energy efficiency increases.

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More than 40% of energy resources are consumed in the residential buildings, and most of the energy is used for heating. Improving the energy efficiency of residential buildings is an urgent problem. The collected data is intended to study a dependence of the dynamics heat energy supply from outside temperature and houses characteristics, such as walls material, year of construction, floors amount, etc. This study will support the development of methods for comparing thermal characteristics of residential buildings and carry out recommendations for the energy efficiency increases.

Instructions: 

Dataset "teplo.csv" is a simple text file. Each heating meter forms one daily record. The dataset has been collected during eight heating seasons in houses of Tomsk (Russia).

All table rows are the following.

Date - date in Windows format.
M1 - the mass of the input water (heat carrier) per day.
M2 - the mass of the output water. If the residential building has an open heating system (hot water flows from the heating system), M2 is less than M1.
Delta_M = difference M2-M1. It is the technological parameter that allows the equipment observation for buildings with the closed system.
T1 - the average temperature of the heating carrier in the input of the heating system. It is the independent variable from home characteristics.
T2 - the average temperature of the heating carrier in the output. It is the dependent variable both from T1 and heating consumption at the building.
Delta_T = difference T2-T1.
Q =M1*(T2-T1) - amount of the consumed heating in Gcal.
USPD - ID of the heating meter. Some residential buildings have not the only one heating meters.
YYYYMM - date in the format year-month YYYYMM.
Registrated - heating or heating plus hot water that under registration.
Scheme - the type of the heating system (opened or closed).
Type - code system-load (4 digits). First digit 1 is opened system, 2 is a closed system. The second digit 0 is heating, 1 is heating and hot water supply. The third and fourth digits are floor amount (01, 02, 03, ..., 17).
Area - the area of building that heating meter is served.
Floors - the amount of building floors.
Walls_material - walls material.
Year_of_construction - the year of building construction.
Area_of_building - total area of the building.
Temperature - outdoor temperature by RosHydromet website.
Inhabitants - the amount of inhabitants in the house.

The Python program "viborka_house.zip" allows you to select from the file "teplo.csv" rows that belongs to the same heating meter USPD. This allows receiving of heat consumption series from a particular house and the outside air temperature in this day. After "viborka.py" starting the user enters the USPD number, names of the input, and output files.

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DYB-PlanktonNet is a dataset contains marine plankton and suspension particles ROI images recorded from the Daya Bay (DYB), an inner bay of the South China Sea close to Shenzhen City, China.

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Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

Instructions: 

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

4) Leaf Spot

5) Downy Mildew

The Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo,  TensorFlow, OpenCV, deep learning, CNN

I had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.

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Wildfires are one of the deadliest and dangerous natural disasters in the world. Wildfires burn millions of forests and they put many lives of humans and animals in danger. Predicting fire behavior can help firefighters to have better fire management and scheduling for future incidents and also it reduces the life risks for the firefighters. Recent advance in aerial images shows that they can be beneficial in wildfire studies.

Instructions: 

The aerial pile burn detection dataset consists of different repositories. The first one is a raw video recorded using the Zenmuse X4S camera. The format of this file is MP4. The duration of the video is 966 seconds with a Frame Per Second (FPS) of 29. The size of this repository is 1.2 GB. The first video was used for the "Fire-vs-NoFire" image classification problem (training/validation dataset). The second one is a raw video recorded using the Zenmuse X4S camera. The duration of the video is 966 seconds with a Frame Per Second (FPS) of 29. The size of this repository is 503 MB. This video shows the behavior of one pile from the start of burning. The resolution of these two videos is 1280x720.

The third video is 89 seconds of heatmap footage of WhiteHot from the thermal camera. The size of this repository is 45 MB. The fourth one is 305 seconds of GreentHot heatmap with a size of 153 MB. The fifth repository is 25 mins of fusion heatmap with a size of 2.83 GB. All these three thermal videos are recorded by the FLIR Vue Pro R thermal camera with an FPS of 30 and a resolution of 640x512. The format of all these videos is MOV.

The sixth video is 17 mins long from the DJI Phantom 3 camera. This footage is used for the purpose of the "Fire-vs-NoFire" image classification problem (test dataset). The FPS is 30, the size is 32 GB, the resolution is 3840x2160, and the format is MOV.

The seventh repository is 39,375 frames that resized to 254x254 for the "Fire-vs-NoFire" image classification problem (Training/Validation dataset). The size of this repository is 1.3 GB and the format is JPEG.

The eighth repository is 8,617 frames that resized to 254x254 for the "Fire-vs-NoFire" image classification problem (Test dataset). The size of this repository is 301 MB and the format is JPEG.

The ninth repository is 2,003 fire frames with a resolution of 3480x2160 for the fire segmentation problem (Train/Val/Test dataset). The size of this repository is 5.3 GB and the format is JPEG.

The last repository is 2,003 ground truth mask frames regarding the fire segmentation problem. The resolution of each mask is 3480x2160. The size of this repository is 23.4 MB.

The published article is available here:

https://www.sciencedirect.com/science/article/pii/S1389128621001201

The preprint article of this dataset is available here:

https://arxiv.org/pdf/2012.14036.pdf

For more information please find the Table at: 

https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle

To find other projects and articles in our group:

https://www.cefns.nau.edu/~fa334/

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Sugarcane vegetation on path-loss between CC2650 and CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)".

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on path-loss between CC2650 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy rice crop monitoring from period 03/07/2019 to 18/11/2019.

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy Rice crop monitoring from period 01/07/2020 to 03/11/2020.

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