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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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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1493 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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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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669 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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2774 Views

This dataset is in support of my 4 Research papers, initially submitted to different journals

  1. 2
  2. 3
  3. 4
  4. 5
  5. 6

Related Reseach Papers :

  1. Novel ß-Bio Model (Mathematics Foundation)
  2. ß-Model of  (Preprint:      )
  3.           and Humans Body - Part I (Preprint:      )
  4.           and Humans Body - Part II (Preprint:      )
Instructions: 

Read Me

(1) This is an open access ,so everything  can be downloaded after login (free signup). You have to click on 'Title'.

(2) Data which was  earlier uploaded in 2021 under this same DOI  'Electro-Magnetic Radiations and Human Body' is explained in ' Experimental Physical Recording’.  That data is as it is. Neither earlier  data is removed nor it is modified, it is as it was earlier submitted. No additions are even done.

(3)  The main paper which has my scientific analysis on 'Electro-Magnetic Radiations and Human Body'  is ‘ and Humans Body’. This paper is used as the foundation because of the accepted facts by WHO, ICNIRP, IARC, NIH,medical doctors, and biomedical engineers. In this paper, I have claimed and proved something.

(4) Zip do not contain any simulation project folder.

(5) Extra Libraries created, modified , other scripts , not shared, as very elementary for any graduate,degree holder, so only results given in research paper.

(6) For  details like model block diagram, parameters, analysis, interpretation, mathematical formulae used to obtain these results etc. please refer "Research Paper".

(7) Radiation patterns - If you expecting the patterns are something easy to understand or decode, but they cannot easily interpreted. For this, pls. refer either textbook or research paper.

(8) The mobile tower installation/distance parameters are also taken according to 'Ministry of Communications, Department of Telecommunications,GoI.

(9) All operating frequency ranges are not mentioned for each 2G,3G,4G,5G,6G. For complete operating frequencies, pls refer your country or search on net. For other details, pls see Research paper.

(10)  This work has undergone complete revisions, loss of data many times,  and many computer crashes. 

(11) This is the last version in those datasets. Only update will be related to ß-Bio models which I

(12)  All work is simple , on basic and elementary concepts, can be easily copied, remade and understood.

(13) The dataset has been checked by the 'Data or Code or model Inspector' before uploading.

(14)  If any problem in creating or copying, pls contact your university professor or board or any of the companies engineer.

(15) As such, No other question or email will be replied. I may have left completely R&D or other reason.

 

Dataset Files

All the following 25 folders are zipped.

1)  2G

  • 2G_800 is CDMA 800MHz or 0.8 GHz
  • 2G_900 is GSM 900MHZ or 0.9 GHz
  • 2G_1800 is GSM1800MHz or 1.8 GHz

2) 3G 

  • 3G_1900 is 1900  MHz or 1.9 GHz
  • 3G_2100 is 2100 MHz or 2.1 GHz

3)  4G

  • 4G_2300 is 2300 MHz or 2.3 GHz
  • 4G_2400 is 2400 MHz or 2.4 GHz
  • 4G_2600  is 2600 MHz or 2.6 GHz

4) Low/Mid 5G FR1

  • 5G_3300 is 3300 MHz or 3.3 GHz
  • 5G_3500 is 3500 MHz or 3.5 GHz
  • 5G_5200  is 5200 MHz or 5.2 GHz
  • 5G_5900  is 5900 MHz or 5.9 GHz
  • 5G_6000 is 6000 MHz or 6 GHz
  • 5G_6200  is 6200 MHz or 6.2 GHz

Here 5G_3500 is n78 C-Band but 5G_6000, 5G_6200 are TDD, n96, n102  UNII defined by  US FCC. For details, pls refer Research paper.

5) High 5G  FR2

  • 5G_26000  is 26000 MHz or 26 GHz
  • 5G_28000  is 28000 MHz or 28 GHz
  • 5G_39000  is 39000 MHz or 39 GHz
  • 5G_41000  is 41000 MHz or 41 GHz
  • 5G_47000 is 47000 MHz or 47 GHz

6) 6G

  • 6G_90000  is 90,000 MHz or 90 GHz
  • 6G_150000 is 150 GHz
  • 6G_220000 is 220 GHz
  • 6G_500000 is 500 GHz
  • 6G_750000 is  750 GHz
  • 6G_1100000 is 1100 GHz, that is, 1.1 Terahertz (THz)

8) Each of the above zip has following datasets. The plots, images can be seen in IEEE CodeOcean DOI.

9) 3G has addition dataset

10) Following datasets are based on ß-Bio

 \

 Experimental Physical Recording

The folder 'PhysicalRecording_2021.zip ' has recordings of Magnetic fields in the year 2021 measured using  Magnetic Sensor,  mobile app(software) and mobile phone

  •  Recordings.zip     
  •  14.mp4     
  •  327uT at 0:19/00:20   .   At 0:19/0:20 of the recording, 327 uT reading
  •  11uT at 0:04/0:05     .  At 0:04/0:05 of the recording, 11 uT reading
  •  5gproof.zip has screenshots from wifi detection   
  •  479uT at 0:42/0:44  .  At  0:42/0:44  of the recording, 479 uT reading

 Area: Delhi,NCR,India 

  • Physical Magnetic Sensor(hardware)

                  Resolution of the sensor is 0.0976 uT    &   Maximum Range of the sensor : 3000.0044 uT

  • Physical orientation and angular velocity  Sensor  (hardware)

                Resolution of the sensor is 0.0012216975 rad/s  &   Maximum Range of the sensor : 34.90549 rad/s

  •  Physical Proximity Sensor (hardware)

                    Resolution of the sensor : 1.0 cm    &   Maximum Range of the sensor : 5.0 cm

  •  Physical Gravity Sensor (hardware)

                    Resolution of the sensor :  0.01 m/s2  & Maximum Range of the sensor :156.98999 m/s2

Experimental Result   -   Lowest Recorded Reading : 11 uT

Highest Recorded Reading : 479 uT

Around 300 uT was measured anywhere, if nearby has 5G equipment ( fluctuates to 50 uT then 111, then 200 , 286,  ...) .   More details in paper.

Reading of 479 was measured, as few people were feeling unwell and when I checked, it was 420 uT, stationary and fluctuating to it around but that is not recorded.   So after some time, this was recorded.

 But later, this reading went to below 200 uT ? And even from 30 uT to 150 uT ,  how come

Experimental Result   - 24 April 2022, See Corona cases, rising, reading which was 29uT to 150uT is 243.95 uT

 

Scripts

For scripts of IEEE Codeocean (Rstudio & Matlab). To see colored plots and images, pls. read details given in ReadMe.txt.

  • Capsule : Plots of EM Fields in 2G              , DOI :
  • Capsule : Plots of EM Fields in 3G            , DOI :
  • Capsule : Plots of EM Fields in 4G             , DOI :
  • Capsule : Plots of EM Fields in 5G             , DOI :
  • Capsule : Plots of EM Fields in 6G             , DOI :

Paper Citing : If want to cite this in paper etc. ,please refer DoI and/or this url.

Funding: There are no funders for this submission. The  author has himself fully self-financed (for his passion).I expect all these papers, would be nice Shroud for the passion and the price paid.

Acknowledgement : The author has generated this on Linux and had even used IEEE partner- Code Ocean - Python,C, Matlab ,Cloud Workstation, Jupyter Notebook,Rstudio,stata,julia,Tensorflow, pandas,trial (evaluation) of many proprietary softwares. No paid research, personal R&D work with no support, wastage of time in self teaching.Few gave trial (evaluation) sw with 2-5 months with even willing for 3-6 months further extension but didnt accepted hire contract request (the names cannot be disclosed & word of acknowledging expired in duration). No industry or academic will use their time only doing this work, even if given free unless financed or top MNC.  The author does not have any special name to be acknowledged.

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1238 Views

This dataset is in support of my research paper 'ElectroMagnetic Fields in Wireless Charging of Electric Vehicles '.

Preprint :

This is useful for industries, manufacturers,doctors,environmentalists, who are curious to see and know.

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994 Views

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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15151 Views

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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385 Views

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