This is Chromatographic Data of some Transformer

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Using Wi-Fi IEEE 802.11 standard, radio frequency waves are mainly used for communication on various devices such as mobile phones, laptops, and smart televisions. Apart from communication applications, the recent research in wireless technology has turned Wi-Fi into other exploration possibilities such as human activity recognition (HAR). HAR is a field of study that aims to predict motion and movement made by a person or even several people.

Instructions: 

Please download and unzip the following files corresponding to the three experiments described in our paper https://doi.org/10.3390/app11198860:

  1. Experiment-1.zip
  2. Experiment-2.zip
  3. Experiment-3.zip

and follow the instructions in the ReadMe.pdfs.

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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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Industrial Internet of Things (IIoTs) are high-value cyber targets due to the nature of the devices and connectivity protocols they deploy. They are easy to compromise and, as they are connected on a large scale with high-value data content, the compromise of any single device can extend to the whole system and disrupt critical functions. There are various security solutions that detect and mitigate intrusions.

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Spreadsheet use for conversion of visible light lux measurements to irradiance.

Back up for manuscript: Calculation of Visible Light Irradiance from Lux Illuminance and Relative Spectral Illuminance

Instructions: 

References shown in spreadsheet tabs

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IEEE Collaboration N entities System Dynamics simulation model

Instructions: 

README.txt for IEEE_Collaboration_model_2021.mdl

 

This is a README.txt for the model published on the paper titled:

Stabilising Internet of Things Federations with Distributed Ledger Technology, 2021, Elo T, et al.

 

This readme describes how to replicate the main results from the paper using a Vensim

model file. The model file has been generated using the Vensim DSS Macintosh Version 

8.0.7 Double Precision x64.

 

To replicate the results of the associated paper do the following:

  1. Open the provided model file (“IEEE_collaboration_model.mdl”) with Vensim DSS Macintosh Version 8.0.7 Double Precision x64, or similar
  2. Push “Simulate” to obtain the baseline simulation for the federation of 10 members
  3. To obtain the spread graph around this push “Sensitivity”
  4. Input “0.119935” to the “Minimum”. Input “0.119939” to the “Maximum”.
  5. Choose “RANDOM_UNIFORM” as “Distribution”.
  6. Input “500” to “number of rounds”.
  7. Push “Parameter”
  8. Choose “fixes effect on harm multiplier”. Push OK.
  9. Push “Next”.
  10. Answer “Yes” to the question: “Do you want to incorporate your current editing?”
  11. Choose “Finnish” at “Savelist control” dialog.
  12. A Sensitivity Simulation begins.
  13. Choose “Federation health” from the model by left clicking it.
  14. Choose Sensitivity Graph from the left button menu.
  15. A Window appears replicating the result for Fig. 5 of the publication.

 

To replicate the results of the 3 member federation do the following

  1. Right click “fixes effect on harm multiplier” from the model.
  2. Push “Equation”.
  3. Edit the value in “Equations”. It reads: “0.119935”.
  4. Change it to “0.5”.
  5. Push “OK”.
  6. Push “Subscripts”.
  7. Push “Edit…”.
  8. Edit the value in “Equations”. It currently reads: “(f1-f10)”.
  9. Change it to “(f1-f3)” to simulate 1 a three member federation.
  10. Push “OK”.
  11. Push “Close” in “Subscript Control” dialog.
  12. Push simulate to get a new baseline for a three member federation.
  13. Push “Sensitivity”.
  14. Check that “Number of” reads “500”.
  15. Input “0.500” to the “Minimum”.
  16. Input “0.502” to the “Maximum”.
  17. Check that “Distribution” is “RANDOM_UNIFORM”.
  18. Push “Parameter”.
  19. Choose “fixes effect on harm multiplier”. Push OK.
  20. Push “Next”.
  21. Answer “Yes” to the question: “Do you want to incorporate your current editing?”
  22. Choose “Finnish” at “Savelist control” dialog.
  23. A Sensitivity Simulation begins.
  24. This simulation is visible faster due to 3 member federation being much more simple to simulate that the 10 member federation.
  25. Choose “Federation health” from the model by left clicking it.
  26. Choose Sensitivity Graph from the left button menu.
  27. A Window appears replicating the result for Fig. 4 of the publication.
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This is a CSI dataset towards 5G NR high-precision positioning,

which is fine-grainedgeneral-purpose and 3GPP R16 standards-complied.

 

5G NR is normally considered to as a new paradigm change of integrated sensing and communication (ISAC).

Possessing the advantages of wide-range-coverage and indoor-outdoor-integration, 5G  NR hence becomes a promising way for high-precision positioning in indoor and urban-canyon environment.

Instructions: 

 

The dataset_[SNR]_[date]_[time].mat contains: 

1) a 4-D matrix, features, representing the feature data, and

2) a structure array, labels, labeling the ground truth of UE positions.

[SNR] is the noise level of features, [date] and [time] tell us when the dataset was generated.

The labels is a structure array. labels.position records the three-dimensional coordinates of UE (meters).

The features is a matrix, Ns-by-Nc-by-Ng-by-Nu, where Ns is the number of samples, Nc is the number of MIMO channels, Ng is the number of gNBs and the Nu is the number of UEs.

The value of Ng corresponds to the number of UEs in labels.

 

 Colsed beta test is running.

In the first phase, we plan to provide three researchers (groups) with a full version of dataset generation and 864 core/hours of computing resources. You can use CAD software to make custom map files and save them in '.stl' format. Supported scenarios include, but are not limited to, typical 5G positioning scenarios such as enclosed indoors, city canyons, etc., which should not exceed 1,000 square meters in area.

 

In addition, you can customize the location, number, and other specific parameters of the base stations and UEs in the map, such as carrier frequency, number of antennas, and bandwidth. If you don't know the specific parameters, you can just submit the map file, and we'll generate your custom dataset based on the default parameters.

 

Customized datasets with fine-grained CSI for each point and their detailed documentation will be returned after they are generated.

To get your dataset for 5G NR Positioning, please contact us by email. We will start your dataset-generation after confirming your identity and requirements.

 

 Release note 

2021-07-23 :

1) Recruit participants for colsed beta test.

2021-07-22 :

1)Expend our dataset with more CSI data with low SNR levels noise.

2)We set up an open system for researchers to upload their own scene maps to obtain customized data sets.

Closed beta test will start after suggestion collection.

2021-07-18 :

1)Expend our dataset with more CSI data with different SNR levels noise.

2)Publish map files for Scenario 1 indoor office.

 

 

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The uploaded data are for the paper: "A Wearable Skin Temperature Monitoring System for Early Detection of Infections". Baseline kin temperature measurement data from all 5 volunteers (subjects) who wore the wearable band for 3-5 days are included along with 5-day temperature measurement data with anomalies of one volunteer who wore both the smart band and a heating pad. Augmented data generated using the methods described in the paper for COVID-19 infection anomaly detection are also included 

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This dataset includes FTM WiFi measurements made with several ESP32-S2 devices in different indoor and outdoor environments. The measurements include the actual distance between devices as well as the RTT (Round Trip Time) values generated by the module.

Instructions: 

# ESP32 S2 FTM Measurements

=========================

 

FTM measurements were created using several ESP32-S2 devices. They are presented in two different formats: rosbag (http://wiki.ros.org/rosbag) format and matlab format.

 

## ROS BAG records

 

The measurements can be found in the directories:

 

- AC

- Indoor

- Outdoor

 

The ROS messages are of type ESP32S2FTMRangingExtra and ESP32S2FTMRanging. These message types can be found in the following repository: https://github.com/valentinbarral/rosmsgs

 

The following fields are included within each message:

 

- anchorId: Identifier of the module that acted as beaconin the measurement.

- rtt_raw:   RTT   value   averaged   among   the   differentframes sent. In nanoseconds.

- rtt_est:  RTT  estimation  created  by  the  ESP32-S2firmware. In nanoseconds.

- dist_est:  Distance  estimation.  Internally,  the  rttestvalue is used to calculate this value. In meters.

- num_frames: Number of frames successfully sent dur-ing the RTT communication.

- frames: A list of all successfully sent frames.

 

Each individual frame includes the following information:

- rssi: Received signal strength. In dBm.

- rtt: RTT value in that frame. In nanoseconds.

- t1:  Outgoing  timestamp  of  the  first  packet  from  thesender. In picoseconds.

- t2: Timestamp of reception of the ranging request at thereceiver. In picoseconds.

- t3: Timestamp of the response message at the receiver.In picoseconds.

- t4:  Timestamp  of  reception  of  the  response  messagefrom the receiver at the sender. In picoseconds.

 

## Matlab logs

 

The measurements in matlab format are in the .mat file. This file includes four 1x1 struc elements:

 

- indoor

- outdoor20

- outdoor40

- sa

 

Each of these structures has the following fields:

 

- actualDist: Actual distance.

- rttRaw: RTT   value   averaged   among   the   differentframes sent. In nanoseconds.

- estDistRaw: Distance estimate using rttRaw.

- absErrRaw: Absolute distance error of estDistRaw.

- rttEst: RTT  estimation  created  by  the  ESP32-S2firmware. In nanoseconds.

- estDistEst: Distance estimate using rttEst.

- absErrEst: Absolute distance error of estDistEst.

- varRtt: variance of RTT

- meanRtt: mean of RTT

- countRtt: count of RTT

- meanRss: mean RSSI

- distEst: Distance estimate using own algorithm.

 

 

More info about this measurements can be found in the next paper (under review):

 

Fine Time Measurement in low-cost microprocessors for the Internet of Things

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The given Dataset is record of different age group people either diabetic or non diabetic for theie blood glucose level reading with superficial body features like body temperature, heart rate, blood pressure etc.

The main purpose of the dataset is to understand the effect of blood glucose level on human body. 

The different superficial body parameters show sifnificant variation according to change in blood glucose level.

Instructions: 

The use of dataset to be done for machine learning analysis or study purpose only. No medical implementations to be claimed using the given dataset.

 

 

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