The “Fused Image dataset for convolutional neural Network-based crack Detection” (FIND) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types.

The images contained in this dataset were collected from multiple bridge decks and roadways under real-world conditions. A laser scanning device was adopted for data acquisition such that the captured raw intensity and raw range images have pixel-to-pixel location correspondence (i.e., spatial co-registration feature). The filtered range data were generated by applying frequency domain filtering to eliminate image disturbances (e.g., surface variations, and grooved patterns) from the raw range data [1]. The fused image data were obtained by combining the raw range and raw intensity data to achieve cross-domain feature correlation [2,3]. Please refer to [4] for a comprehensive benchmark study performed using the FIND dataset to investigate the impact from different types of image data on deep convolutional neural network (DCNN) performance.

If you share or use this dataset, please cite [4] and [5] in any relevant documentation.

In addition, an image dataset for crack classification has also been published at [6].

References:

[1] Shanglian Zhou, & Wei Song. (2020). Robust Image-Based Surface Crack Detection Using Range Data. Journal of Computing in Civil Engineering, 34(2), 04019054. https://doi.org/10.1061/(asce)cp.1943-5487.0000873

[2] Shanglian Zhou, & Wei Song. (2021). Crack segmentation through deep convolutional neural networks and heterogeneous image fusion. Automation in Construction, 125. https://doi.org/10.1016/j.autcon.2021.103605

[3] Shanglian Zhou, & Wei Song. (2020). Deep learning–based roadway crack classification with heterogeneous image data fusion. Structural Health Monitoring, 20(3), 1274-1293. https://doi.org/10.1177/1475921720948434

[4] Shanglian Zhou, Carlos Canchila, & Wei Song. (2023). Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Automation in Construction, 146. https://doi.org/10.1016/j.autcon.2022.104678

[5] (This dataset) Shanglian Zhou, Carlos Canchila, & Wei Song. (2022). Fused Image dataset for convolutional neural Network-based crack Detection (FIND) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6383044

[6] Wei Song, & Shanglian Zhou. (2020). Laser-scanned roadway range image dataset (LRRD). Laser-scanned Range Image Dataset from Asphalt and Concrete Roadways for DCNN-based Crack Classification, DesignSafe-CI. https://doi.org/10.17603/ds2-bzv3-nc78

Dataset

This dataset was created by Bency Sherin

Released under MIT

Contents

About this dataset

Context

The dataset tabulates the population of South Range by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for South Range. The dataset can be utilized to understand the population distribution of South Range by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in South Range. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for South Range.

Key observations

Largest age group (population): Male # 20-24 years (49) | Female # 20-24 years (50). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the South Range population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the South Range is shown in the following column.
• Population (Female): The female population in the South Range is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in South Range for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range Population by Gender. You can refer the same here

File name definitions:

'...v_50_175_250_300...' - dataset for velocity ranges [50, 175] + [250, 300] m/s

'...v_175_250...' - dataset for velocity range [175, 250] m/s

'ANNdevelop...' - used to perform 9 parametric sub-analyses where, in each one, many ANNs are developed (trained, validated and tested) and the one yielding the best results is selected

'ANNtest...' - used to test the best ANN from each aforementioned parametric sub-analysis, aiming to find the best ANN model; this dataset includes the 'ANNdevelop...' counterpart

Where to find the input (independent) and target (dependent) variable values for each dataset/excel ?

input values in 'IN' sheet

target values in 'TARGET' sheet

Where to find the results from the best ANN model (for each target/output variable and each velocity range)?

open the corresponding excel file and the expected (target) vs ANN (output) results are written in 'TARGET vs OUTPUT' sheet

Check reference below (to be added when the paper is published)

https://www.researchgate.net/publication/328849817_11_Neural_Networks_-_Max_Disp_-_Railway_Beams

About this dataset

Context

The dataset tabulates the population of Grass Range by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Grass Range across both sexes and to determine which sex constitutes the majority.

Key observations

There is a considerable majority of female population, with 71.13% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Grass Range is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Grass Range total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Grass Range Population by Race & Ethnicity. You can refer the same here

The Long-range Pedestrian Dataset is curated for the visual entertainment sector, featuring a collection of outdoor-collected images with a high resolution of 3840 x 2160 pixels. This dataset is focused on long-distance pedestrian imagery, with each target pedestrian precisely labeled with a bounding box that closely fits the boundary of the pedestrian target, providing detailed data for scene composition and character placement in visual content.

About this dataset

Context

The dataset tabulates the South Range population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of South Range across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of South Range was 740, a 0.00% decrease year-by-year from 2021. Previously, in 2021, South Range population was 740, an increase of 0.54% compared to a population of 736 in 2020. Over the last 20 plus years, between 2000 and 2022, population of South Range increased by 16. In this period, the peak population was 760 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the South Range is shown in this column.
• Year on Year Change: This column displays the change in South Range population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range Population by Year. You can refer the same here

Overview

This dataset contains 1,000 retail transaction records after cleaning and preprocessing.

This synthetic dataset has been meticulously crafted to simulate a dynamic retail environment, providing an ideal playground for those eager to sharpen their data analysis skills through exploratory data analysis (EDA). With a focus on retail sales and customer characteristics, this dataset invites you to unravel intricate patterns, draw insights, and gain a deeper understanding of customer behaviour.

It includes customer demographics, product categories, transaction details, and derived analytics, such as the daily percentage change in sales.

Original dataset (Uncleaned):- https://www.kaggle.com/datasets/mohammadtalib786/retail-sales-dataset

The dataset can be used for:

• Sales trend analysis
• Customer segmentation
• Revenue forecasting
• Data visualisation projects
• Teaching SQL, Pandas, or AWS analytics pipelines

File Information

• Filename: cleaned_retail_sales_dataset.csv
• Records (rows): 1,000
• Columns (features): 10
• Missing values: Minimal (only 1 missing in Daily Percent Change)

Column Descriptions

• Transaction ID – Unique identifier for each transaction (range: 1–1000).
• Date – Purchase date in DD-MM-YYYY format (345 unique dates).
• Customer ID – Unique identifier for each customer (1,000 unique customers).
• Gender – Customer gender: Male / Female (~51% Female, ~49% Male).
• Age – Customer’s age (range: 18–64, average ≈ 41 years).
• Product Category – Purchased product category (Clothing, Electronics, Groceries).
• Quantity – Number of items purchased per transaction (range: 1–4, average ≈ 2.5).
• Price per Unit – Price of a single item (range: ₹25 – ₹500, average ≈ ₹180).
• Total Amount – Transaction value = Quantity × Price per Unit (range: ₹25 – ₹2000, average ≈ ₹456).
• Daily Percent Change – Day-over-day percentage change in sales (range: -98.75% to 7900%).

Features

• Transaction ID: Unique identifier for each transaction.
• Date: Purchase date in DD-MM-YYYY format.
• Customer ID: Unique identifier for each customer.
• Gender: Customer gender (Male / Female).
• Age: Customer’s age.
• Product Category: Purchased product category (Clothing, Electronics, Groceries).
• Quantity: Number of items purchased in the transaction.
• Price per Unit: Price of a single item.
• Total Amount: Transaction value (Quantity × Price per Unit).
• Daily Percent Change: Day-over-day percentage change in sales.

**💬 Feedback & Suggestions ** If you find this dataset helpful for your research or projects, feel free to upvote and share your feedback or suggestions. Your support is appreciated — thank you! 😉

This dataset tracks annual distribution of students across grade levels in Range View Elementary School

Serviciul de vizualizare INSPIRE (WMS INSPIRE) pentru setul de date Romanian Species Range Dataset (art.17) - sensitive

**Trending on YouTube ** Trending helps viewers see what’s happening on YouTube and in the world. Trending aims to surface videos and shorts that a wide range of viewers would find interesting. Some trends are predictable, like a new song from a popular artist or a new movie trailer. Others are surprising, like a viral video.

Trending isn't personalized and displays the same list of trending videos to all viewers in the same country, which is why you may see videos in Trending that aren’t in the same language as your browser. However, in India, Trending displays a list of results for each of the 9 most common Indic languages.

SOURCE The data has been scrapped from "Mendeley.com". The source of this file ishttps://data.mendeley.com/datasets/7pkbvjtnxm/1/files/e7763107-45e9-4613-8c81-146e6a272266 Converted the data to csv file to use it in kaggle ../input/youtube-vdos/youtube trending videos dataset.csv

The data contains following columns . * ) Position (int type) - An index column which gives the position of the channel in youtube channel 1) Channel Id ( Stirng ) - ID of the youtube channel 2) Channel Title ( String ) - Youtube channel title 3) Video Id (String) - ID of video in the youtube channel 4) Published At (String) - date of the video published at 5) Video Title (String ) - Title of the video 6) Video Description (String) - Description of the video(what the video is about) 6 Video Category Id ( int type) - Category of the video in youtube channel 7 Video Category Label (String) - type of category the video belongs
8 Duration (String ) - duration of the video 9 Duration Sec ( int type) - Duration of video in seconds 10 Dimension (String) - Dimension of the video (2D , Hd) 11 Definition (String) - Defining the video 12 Caption (bool ) - Boolean type caption (True or False) 13 Licensed Content (float Type) 14 View Count ( int type) - number of people viewed the video
15 Like Count (float) - Number of likes the channel got 16 Dislike Count (float) - Number of dislikes the channel got 17 Favorite Count ( int type) - Number of people marked as favourite 18 Comment Count (float) - Number of people commented on the video

This dataset provides information about the number of properties, residents, and average property values for Range View Road cross streets in Valier, MT.

This dataset tracks annual total students amount from 2013 to 2023 for Range View Elementary School

This point feature class contains 81,481 points arranged in a 270-meter spaced grid that covers the Spring Mountains and Sheep Range in Clark County, Nevada. Points are attributed with hydroclimate variables and ancillary data compiled to support efforts to characterize ecological zones.

General

For more details and the most up-to-date information please consult our project page: https://kainmueller-lab.github.io/fisbe.

Summary

• A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains
  • 30 completely labeled (segmented) images
  • 71 partly labeled images
  • altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)
• To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects
• A set of metrics and a novel ranking score for respective meaningful method benchmarking
• An evaluation of three baseline methods in terms of the above metrics and score

Abstract

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.

Dataset documentation:

We provide a detailed documentation of our dataset, following the Datasheet for Datasets questionnaire:

>> FISBe Datasheet

Our dataset originates from the FlyLight project, where the authors released a large image collection of nervous systems of ~74,000 flies, available for download under CC BY 4.0 license.

Files

• fisbe_v1.0_{completely,partly}.zip
  • contains the image and ground truth segmentation data; there is one zarr file per sample, see below for more information on how to access zarr files.
• fisbe_v1.0_mips.zip
  • maximum intensity projections of all samples, for convenience.
• sample_list_per_split.txt
  • a simple list of all samples and the subset they are in, for convenience.
• view_data.py
  • a simple python script to visualize samples, see below for more information on how to use it.
• dim_neurons_val_and_test_sets.json
  • a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.
• Readme.md
  • general information

How to work with the image files

Each sample consists of a single 3d MCFO image of neurons of the fruit fly.
For each image, we provide a pixel-wise instance segmentation for all separable neurons.
Each sample is stored as a separate zarr file (zarr is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").
The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.
The segmentation mask for each neuron is stored in a separate channel.
The order of dimensions is CZYX.

We recommend to work in a virtual environment, e.g., by using conda:

conda create -y -n flylight-env -c conda-forge python=3.9
conda activate flylight-env

How to open zarr files

1. Install the python zarr package:

   pip install zarr

2. Opened a zarr file with:
   

   import zarr
raw = zarr.open(
seg = zarr.open(

   # optional:
import numpy as np
raw_np = np.array(raw)

Zarr arrays are read lazily on-demand.
Many functions that expect numpy arrays also work with zarr arrays.
Optionally, the arrays can also explicitly be converted to numpy arrays.

How to view zarr image files

We recommend to use napari to view the image data.

1. Install napari:

   pip install "napari[all]"

2. Save the following Python script:
   

   import zarr, sys, napari

   raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")
gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")

   viewer = napari.Viewer(ndisplay=3)
for idx, gt in enumerate(gts):
viewer.add_labels(
gt, rendering='translucent', blending='additive', name=f'gt_{idx}')
viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')
viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')
viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')
napari.run()

3. Execute:

   python view_data.py 

Metrics

• S: Average of avF1 and C
• avF1: Average F1 Score
• C: Average ground truth coverage
• clDice_TP: Average true positives clDice
• FS: Number of false splits
• FM: Number of false merges
• tp: Relative number of true positives

For more information on our selected metrics and formal definitions please see our paper.

Baseline

To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely PatchPerPix (ppp), Flood Filling Networks (FFN) and a non-learnt application-specific color clustering from Duan et al..
For detailed information on the methods and the quantitative results please see our paper.

License

The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Citation

If you use FISBe in your research, please use the following BibTeX entry:

@misc{mais2024fisbe,
 title =    {FISBe: A real-world benchmark dataset for instance
         segmentation of long-range thin filamentous structures},
 author =    {Lisa Mais and Peter Hirsch and Claire Managan and Ramya
         Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena
         Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller},
 year =     2024,
 eprint =    {2404.00130},
 archivePrefix ={arXiv},
 primaryClass = {cs.CV}
}

Acknowledgments

We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable
discussions.
P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.
This work was co-funded by Helmholtz Imaging.

Changelog

There have been no changes to the dataset so far.
All future change will be listed on the changelog page.

Contributing

If you would like to contribute, have encountered any issues or have any suggestions, please open an issue for the FISBe dataset in the accompanying github repository.

All contributions are welcome!

This dataset contains ERA5 initial release (ERA5t) model level analysis parameter data. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Surface level analysis and forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.

This dataset tracks annual math proficiency from 2012 to 2023 for Range View Elementary School vs. Colorado and Weld County Reorganized School District No. Re-4

This dataset tracks annual overall school rank from 2012 to 2023 for Range View Elementary School

A LiDAR-based 3D object detection dataset.

This dataset has been generated using NYUSIM 3.0 mm-Wave channel simulator software, which takes into account atmospheric data such as rain rate, humidity, barometric pressure, and temperature. The input data was collected over the course of a year in South Asia. As a result, the dataset provides an accurate representation of the seasonal variations in mm-wave channel characteristics in these areas. The dataset includes a total of 2835 records, each of which contains T-R Separation Distance (m), Time Delay (ns), Received Power (dBm), Phase (rad), Azimuth AoD (degree), Elevation AoD (degree), Azimuth AoA (degree), Elevation, AoA (degree), RMS Delay Spread (ns), Season, Frequency and Path Loss (dB). Four main seasons have been considered in this dataset: Spring, Summer, Fall, and Winter. Each season is subdivided into three parts (i.e., low, medium, and high), to accurately include the atmospheric variations in a season. To simulate the path loss, realistic Tx and Rx height, NLoS environment, and mean human blockage attenuation effects have been taken into consideration. The data has been preprocessed and normalized to ensure consistency and ease of use. Researchers in the field of mm-wave communications and networking can use this dataset to study the impact of atmospheric conditions on mm-wave channel characteristics and develop more accurate models for predicting channel behavior. The dataset can also be used to evaluate the performance of different communication protocols and signal processing techniques under varying weather conditions. Note that while the data was collected specifically in South Asia region, the high correlation between the weather patterns in this region and other areas means that the dataset may also be applicable to other regions with similar atmospheric conditions.

Acknowledgements The paper in which the dataset was proposed is available on: https://ieeexplore.ieee.org/abstract/document/10307972

Citation

If you use this dataset, please cite the following paper:

Rashed Hasan Ratul, S. M. Mehedi Zaman, Hasib Arman Chowdhury, Md. Zayed Hassan Sagor, Mohammad Tawhid Kawser, and Mirza Muntasir Nishat, “Atmospheric Influence on the Path Loss at High Frequencies for Deployment of 5G Cellular Communication Networks,” 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2023, pp. 1–6. https://doi.org/10.1109/ICCCNT56998.2023.10307972

BibTeX ```bibtex @inproceedings{Ratul2023Atmospheric, author = {Ratul, Rashed Hasan and Zaman, S. M. Mehedi and Chowdhury, Hasib Arman and Sagor, Md. Zayed Hassan and Kawser, Mohammad Tawhid and Nishat, Mirza Muntasir}, title = {Atmospheric Influence on the Path Loss at High Frequencies for Deployment of {5G} Cellular Communication Networks}, booktitle = {2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)}, year = {2023}, pages = {1--6}, doi = {10.1109/ICCCNT56998.2023.10307972}, keywords = {Wireless communication; Fluctuations; Rain; 5G mobile communication; Atmospheric modeling; Simulation; Predictive models; 5G-NR; mm-wave propagation; path loss; atmospheric influence; NYUSIM; ML} }