artificial intelligence

A dataset of Global Positioning System (GPS) spoofing attacks is presented in this article. This dataset includes data extracted from authentic GPS signals collected from different locationsto emulate a moving and a static autonomous vehicle using a universal software radio peripheral unit configured as a GPS receiver. During the data collection, 13 features are extracted from eight-parallel channels at different receiver stages (i.e., acquisition, tracking, and navigation decoding).
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These datasets contain the database of the local television channels in the greater manila area (GMA) in the Philippines as of 2020. There are 8 databases corresponding to 8 eight channels namely Channel4.xlsx, Channel5.xlsx, Channel7.xlsx, Channel9.xlsx, Channel11.xlsx,
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This benchmark dataset accompanies an article paper titled ``Learning to Reuse Distractors to support Multiple Choice Question Generation in Education''. It contains a test of 298 educational questions covering multiple subjects & languages and a 77K multilingual pool of distractor vocabulary. The goal is for a given question to propose a list of relevant candidate distractors from the pool of distractors.
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This dataset includes real-world Channel Quality Indicator (CQI) values from UEs connected to real commercial LTE networks in Greece. Channel Quality Indicator (CQI) is a metric posted by the UEs to the base station (BS). It is linked with the allocation of the UE’s modulation and coding schemes and ranges from 0 to 15 in values. This is from no to 64 QAM modulation, from zero to 0.93 code rate, from zero to 5.6 bits per symbol, from less than 1.25 to 20.31 SINR (dB) and from zero to 3840 Transport Block Size bits.
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The Baseline set described in the submitted IEEE article as Baseline_set contains 1442450 rows, where the number of rows varied between 15395 and 197542 for the 16 subjects; the average per subject being 69095 rows. The data set is filtered and standardized as described in III.C in the submission . The other data sets used in the article are derived from Baseline set.
The data set in .csv format contains the columns timestamp, user_id, session_id, acc_var, hr, rmssd, sdnn, st, eda, eda_freq, bm, location, and concentration. The time step is 5 seconds.
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Visual perception can be used by robotic leg prostheses and exoskeletons to improve the accuracy and speed of transitions between different locomotion mode controllers (e.g., level-ground walking to stair ascent) by sensing the walking environment prior to physical interactions. Here we developed the StairNet dataset to support the development of vision-based stair recognition systems.
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