This paper introduces a low profile wideband Planar Inverted-F antenna (PIFA) for vehicular applications in the 5G systems (below 6GHz) and Vehicle-to-Everything (V2X) communications. The antenna covers a wide range of bandwidth that operates from 617MHz to 6GHz while having an acceptable filtering on the GNSS bands. This design’s physical dimensions and electrical performance makes it suitable for low profile wireless applications in the automotive field.

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'Test Dataset' folder:

-Real_sk:  random generated secret keys datasets

-Trace_data: traces generated from chosen ciphertexts

 

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 This Excel file presents the built database for the paper titled "Bridgeless PFC topology simplification and design for performance benchmarking".  

1. You can find more details about how to use the data presented here to build the component model in the paper.

2. In the "calculated results sheet", you can see our case study results by using this built database to select components and benchmark the bridgeless buck-boost PFC converters.

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Logs from running Monte Carlo simulation as serverless functions on Frankfurt, North Virginia, Tokyo regions of four FaaS systems (AWS, Google, IBM, Alibaba).

Each execution is repeated 5 times (all are warm start). 

The conducted analysis is a part of a submitted manuscript to IEEE TSC. 

Instructions: 

The zip file contains several types of datasets.

1. Logs contain details of each execution on all providers / regions. Each column has a self-descriptive title. The first 1000 functions on AWS, 200 on Alibaba, 100 on Google and 100 on IBM are all executed concurrently. The remaining functions are executed once some of the active functions finish due to concurrency limit of the provider.

2. Functions contain the Monte Carlo functions that are executed (in Python).

Based on these logs, we evaluated our xAFCL service along with our new FaaS model and the scheduler. 

3. Makespan<k> contains measured makespan for each set of experiments for scaling factor k. Experiments are denoted as N/r where N is the number of functions that are distributed across the r regions. N=k*r for weak scaling and N=12*r for strong scaling.

4. Regions are ordered in the file xAFCLModelInputs.csv. 

5. Summary presents the achieved average makespan and maximum throughput for each scaling factor k.

 

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This dataset contains a list of 284 popular websites and URLs to their privacy statements. The websites belong to the three largest South Asian economies, namely, India, Pakistan, and Bangladesh. Each website is categorized into 10 sectors, namely, e-commerce, finance/banking, education, healthcare, news, government, telecom, buy and sell, job/freelance, and blogging/discussion. We hope that this dataset will help researchers in investigating website privacy compliance.

Instructions: 

The dataset is split by country via sheets, i.e., one sheet per country. Each sheet contains five columns. The column description is as follows:Column 1 specifies the sector that a specific website belongs to.Column 2 specifies the sources that were leveraged to collect the websites belonging to a specific sector.Column 3 specifies the name of a website.Column 4 specifies the URL of a website.Column 5 specifies the privacy policy/statement URL, if provided by the corresponding website. An empty cell in this column depicts that the respective website does not provide a privacy statement.

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This dataset contains a list of 284 popular websites and URLs to their privacy statements. The websites belong to the three largest South Asian economies, namely, India, Pakistan, and Bangladesh. Each website is categorized into 10 sectors, namely, e-commerce, finance/banking, education, healthcare, news, government, telecom, buy and sell, job/freelance, and blogging/discussion. We hope that this dataset will help researchers in investigating website privacy compliance.

Instructions: 

The dataset is split by country via sheets, i.e., one sheet per country. Each sheet contains five columns. The column description is as follows:
Column 1 specifies the sector that a specific website belongs to.
Column 2 specifies the sources that were leveraged to collect the websites belonging to a specific sector.
Column 3 specifies the name of a website.
Column 4 specifies the URL of a website.
Column 5 specifies the privacy policy/statement URL, if provided by the corresponding website. An empty cell in this column depicts that the respective website does not provide a privacy statement.
In case of any questions, please email at yousra.javed@seecs.edu.pk.

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292 Views

This dataset contains a list of popular websites and their privacy statements. The websites belong to the three largest South Asian economies, namely, India, Pakistan, and Bangladesh. Each website is categorized into 10 sectors, namely, e-commerce, finance/banking, education, healthcare, news, government, telecom, buy and sell, job/freelance, blogging/discussion. We hope that this dataset will help researchers in investigating website privacy compliance.

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73 Views

A high-fidelity CarSim model is used to collect the data for almost 50 maneuvers for two different tractors with different trailer attached to them. For instance, 10 Single Lane Change (SLC) maneuvers are considered in CarSim including 5 tests with E-class SUV and 5 tests with a pick-up truck. Moreover, at each test, the trailer payload and geometry, CG location, and track width, have been changed to collect sufficient data.

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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. https://covid19.who.int/explorer. 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.

Instructions: 

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. https://covid19.who.int/explorer. 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.  

 

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