<p>The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.

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Tweets related to 10 different types of disasters were monitored from 28 September 2021 till 6 October 2021. 67528 rows containing 16 fields were extracted using Artificial Intelligence and Natural Language Processing Services of Microsoft.

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98 Views

This dataset contains actual field/experimental data for the following environmental engineering applications, namely:

  • Concentration data generated from filtration systems which treat influents, having contaminant materials, via adsorption process.
  • Streamflow height data collated for 50 states/cities in America for the historical period between 1900-2018.
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ABSTRACT

Europe is covered by distinct climatic zones which include semiarid, the Mediterranean, humid subtropical, marine,

humid continental, subarctic, and highland climates. Land use and land cover change have been well documented in the

past 200 years across Europe1where land cover grassland and cropland together make up 39%2. In recent years, the

agricultural sector has been affected by abnormal weather events. Climate change will continue to change weather

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As Science and technology evolve, the environment is getting affected daily. These cause major environmental issues like Global Warming, Ozone layer depletion, Natural resource depletion, etc. These are measured and regulated by local bodies. The data given by the local bodies are average values for a large area, those data might be inaccurate for a small sector or isolated zone. However, there are few techniques such as WSN (Wireless Sensor Networks), IoT (Internet of things) which measures and updates real-time data to a cloud server to overcome the trouble.

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385 Views

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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1209 Views

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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144 Views

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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544 Views

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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1969 Views

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