
This dataset is the supplementary material of an IEEE RAL paper named "Design of Fully Controllable and Continuous Programmable Surface Based on Machine Learning". It includes the z-displacement data derived from the FEA simulation, voltage input data derived from Matlab, and dataset for inverse application. The detailed description can be found in that paper.
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Biomechanics has predominantly relied upon the trajectory optimization method for the analysis and prediction of the movement of the limbs. Such approaches have paved the way for the motion planning of biped and quadruped robots as well. Most of these methods are deterministic, utilizing first-order iterative gradient-based algorithms incorporating the constrained differentiable objective functions.
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In this data file are the ratings made by 36 subjects during an experiment evaluating 3 different products (desktop telephones) in three different media: photorealistic renders, augmented reality and virtual reality. In addition, their opinion about the overall evaluation of the product ("Like"), security in response and purchase decision was collected. The presence data collected is equivalent to the application of the SUS presence test.
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CONTENTS OF THIS FILE
- Data Analysis
- Experimental Design
Data Analysis
This folder consists of data obtained from the experiments and the materials we use for analysis. Here we provide the following documents:
01-The folder "Class diagrams"
This folder contains class diagrams realized by participants in three experiments.
02-Code.R
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Abstract—Chipless RFID tag decoding has some inherent degrees of uncertainty because there is no handshake protocol between chipless tags and readers. This paper initially compares the outcome of different pattern recognition methods to decode some frequency-based tags in the mm-wave spectrum. It will be shown that these pattern recognition methods suffer from almost 2 to 5% false decoding rate. To overcome this mis-decoding problem, two novel methods of making images of the chipless tags are presented.
This is the S11 parameters collected for 27 alphanumeric tags, with background noise deleted. Tag to antenna is 5 cm, a horn antenna (A-info antenna in the paper reference)
Tags are scanned in three positions, perpendicular to the antenna broadside, and +/-1.5cm away as right of left. Noise is already deducted from the data.
As data is huge (<1M rows), it has been put into two sheets.
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This sheet contains the answers from our european Cyber Security MSc Education Survey. The data shows which knowledge units various educations in Europe cover and to which extend. We drew conclusions in the paper "Are We Preparing Students to Build Security In? A Survey of European Cybersecurity in Higher Education Programs". The present dataset is newer and therefore extends the one we based our paper on.
The file is an excel .xlsx file, so you can open it in Excel, LibreOffice, OpenOffice or a similar spread sheet tool. The file has 3 sheets:
- Universities: Contains all the raw data
- KAs and KUs: Contains the mapping of each knowledge unit to a knowledge area
- Explanation: Contains an explanation of the data. It also contains a few errata for our paper based on a previous version of the data.
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