Sensors
During Printed Circuit Board (PCB) manufacturing, it is critical to dispense the correct amount of conductive glue on the substrate LCP surface before die attachment, as the dispensing of excessive or insufficient glue may cause defects through short circuits or weak die bonding. Therefore it is critical to monitor the amount of the dispensed glue during production.
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This work investigates an innovative magnetic field probe especially suitable but not limited to the characterization of insertion devices for light sources such as undulators and wigglers. Examples of such systems include the undulators planned for the Advanced Light Source Upgrade (ALS-U), where a complete magnetic characterization of the device is an integral part of their construction and certification. Current magnetic field measurement technologies for such hardware include Hall effect probes, wire-based systems, and sensing coils.
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Dataset contains all raw data measured with our developed sensor and two other for evaluation.
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This data set contains data collected from an overhead crane (https://doi.org/10.1109/WF-IoT.2018.8355217) OPC UA server when driving an L-shaped path with different loads (0kg, 120kg, 500kg, and 1000kg). Each driving cycle was driven with an anti-sway system activated and deactivated. Each driving cycle consisted of repeating five times the process of lifting the weight, driving from point A to point B along with the path, lowering the weight, lifting the weight, driving back to point A, and lowering the weight.
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This handbook and dataset provides a large set of ferromagnetic saturation curves with Hysteresis based on JA model and nonlinear reluctance of Matlab/Simulink/Simscape.
The saturation curves are available for different materials, but for simulations, the JA parameters are also needed which are not available to the public. Therefore, this handbook will help researchers to find the best fit between their specimen and curves provided in this handbook to find the approximate JA parametes to use, or start fiding accurate JA using them as the initial values.
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Three real geological sensor data with missing values (namely, 45710421 x, 45710421 y, and 45710422 x).
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This dataset accurately models the internal behavior of an IoT spectrum sensor (belonging to the ElectroSense platform and consisting of a Raspberry Pi 3 with a software-defined radio kit) when it is functioning normally and under attack. To accomplish it, the system calls of the IoT sensor are monitored under normal behavior, gathered, cleaned, and stored in a centralized directory. Then, the device is infected with current malware affecting IoT devices, such as the Bashlite botnet, Thetick backdoor, Bdvl rootkit, and a Ransomware proof of concept.
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The accelerometer data has been collected using one smartphone carried by subjects, which are caregivers and nurses, when they were conducting daily works at a healthcare facility. The smartphone was carried in an arbitrary position such as a pocket. There are a total of 27 activities divided into 4 groups.
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