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:      )

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

  •  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
  • 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



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.


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.


Leaderboard (numbers are kW MAE):

Teams with more than 5 missing submissions are eliminated from the leaderboard.


Last Updated On: 
Wed, 11/17/2021 - 21:02

This India-specific COVID-19 tweets dataset has been developed using the large-scale Coronavirus (COVID-19) Tweets Dataset, which currently contains more than 700 million COVID-19 specific English language tweets. This dataset contains tweets originating from India during the first week of each four phases of nationwide lockdowns initiated by the Government of India.


The zipped files contain .db (SQLite database) files. Each .db file has a table 'geo'. To hydrate the IDs you can import the .db file as a pandas dataframe and then export it to .CSV or .TXT for hydration. For more details on hydrating the IDs, please visit the primary dataset page.

conn = sqlite3.connect('/path/to/the/db/file')

c = conn.cursor()

data = pd.read_sql("SELECT tweet_id FROM geo", conn)


This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.


The TXT files in this dataset can be used in generating the trend graph. The peaks and drops in the trend graph can be made more meaningful by computing n-grams for those periods. To compute the n-grams, the tweet IDs of the Coronavirus (COVID-19) Tweets Dataset should be hydrated to form a tweets corpus.

Pseudo-code for generating similar trend dataset

current = int(time.time()*1000)     #we receive the timestamp in ms from twitter

off = 600*1000    #we're looking for 10-minute (600 seconds) average data (offset)

past = current - off     #getting timestamp of 10-minute past the current time

df = select recent most 60,000    #even if we receive 100 tweets per second, the no. of tweets do not cross this number in an interval of 10 minutes

new_df = df[df.unix > past]     #here "unix" is the timestamp column name in the primary tweets dataset

avg_sentiment = new_df["sentiment"].mean()    #calculate mean

store current, avg_sentiment into a database

Pseudo-code for extracting top 100 "unigrams" and "bigrams" from a tweets corpus

import nltk

from collections import Counter

#loading a tweet corpus

with open ("/path/to/the/tweets/corpus", "r", encoding="UTF-8") as myfile:'\n', ' ')

data = preprocess your data (use regular expression-perform find and replace operations)

data = data.split(' ')

stopwords = nltk.corpus.stopwords.words('english')


#removing stopwords from each tweet

for w in data:

     if w not in stopwords:


#extracting top 100 n-grams

unigram = Counter(clean_data)

unigram_top = unigram.most_common(100)

bigram = Counter(zip(clean_data, clean_data[1:]))

bigram_top = bigram.most_common(100)


Recently, the coronavirus pandemic has made the use of facial masks and respirators common, the former to reduce the likelihood of spreading saliva droplets and the latter as Personal Protective Equipment (PPE). As a result, this caused problems for the existing face detection algorithms. For this reason, and for the implementation of other more sophisticated systems, able to recognize the type of facial mask or respirator and to react given this information, we created the Facial Masks and Respirators Database (FMR-DB).


For reasons related to the copyright of the images, we cannot publish the entire database here. If you are a student, a professor, or a researcher and you want to use it for research purposes, send an email to attaching the license, duly completed, which you can find here on IEEE DataPort.



The dataset links to the survey performed on students and professors of Biological Engineering introductory course, as the Department of Biological Engineering, University of the Republic, Uruguay.


The dataset is meant for pure academic and non-commerical use.

For queries, please consult the corresponding author (Parag Chatterjee,


Urban informatics and social geographic computing, spatial and temporal big data processing and spatial measurement, map service and natural language processing.


Urban informatics and social geographic computing, spatial and temporal big data processing and spatial measurement, map service and natural language processing.


This dataset has the following data about the COVID-19 pandemic in the State of Maranhão, Brazil:

  • Number of daily cases
  • Number of daily deaths

In addition, this dataset also contains data from Google Trends on some subjects related to the pandemic, related to searches carried out in the State of Maranhão.

The data follows a timeline that begins on March 20, 2020, the date of the first case of COVID-19 in the State of Maranhão, until July 9, 2020.


The last decade faced a number of pandemics [1]. The current outbreak of COVID is creating havoc globally. The daily incidences of COVID-2019 from 11th January 2020 to 9th May 2020 were collected from the official COVID dashboard of world health organization (WHO) [2] , i.e. The data is updated with the population of the countries and further Case fatality rate, Basic Attack Rate (BAR) and Household Secondary Attack Rate (HSAR) are computed for all the countries.


The data will be used by epidemiologist, statisticians, data scientists for assessing the risk of the Covid 2019 globally and would be used as a model to predict the case fatality rate along with the possible spread of the disease along with its attack rate.Data was in raw format. A detailed analysis is carried out from Epidemiology point of view and a datasheet is prepared through the identification of the Risk Factor in a Defined Population.The daily incidences of COVID-2019 from 11th January 2020 to 9th May 2020 were collected form the official covid dashboard of world health organization (WHO), i.e. The data is compiled in Excel 2016 and a database is created. The database is updated with the population of the countries and Case fatality rate, Basic Attack Rate (BAR) and Household Secondary Attack Rate (HSAR) is computed for all the countries.