Original SJC value test data for papers.
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy Rice crop monitoring from period 01/07/2020 to 03/11/2020.
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Millet vegetation on path-loss between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for millet crop monitoring from period 03/06/2020 to 04/10/2020.
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Steel studs, HVAC systems, rebar, and many other building components produce spatially varying magnetic fields. Magnetometers can measure these fields and can be used in combination with inertial sensors for indoor positioning of robots and of handheld devices like smartphones. Current methods of localization and mapping with magnetometers are often based on the simplifying assumption that magnetic fields do not vary with height.
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Steel studs, HVAC systems, rebar, and many other building components produce spatially varying magnetic fields. Magnetometers can measure these fields and can be used in combination with inertial sensors for indoor positioning of robots and of handheld devices like smartphones. Current methods of localization and mapping with magnetometers are often based on the simplifying assumption that magnetic fields do not vary with height.
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This dataset is created with the usage of Galvanic Skin Response Sensor and Electrocardiogram sensor of MySignals Healthcare Toolkit. MySignals toolkit consists of the Arduino Uno board and different sensor ports. The sensors were connected to the different ports of the hardware kit which was controlled by Arduino SDK.
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DataSet used in learning process of the traditional technique's operation, considering different devices and scenarios, the proposed approach can adapt its response to the device in use, identifying the MAC layer protocol, perform the commutation through the protocol in use, and make the device to operate with the best possible configuration.
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The rise of the Internet of Things (IoT) has opened new research lines that focus on applying IoT applications to domains further beyond basic user-grade applications, such as Industry or Healthcare. These domains demand a very high Quality of Service (QoS), mainly a very short response time. In order to meet these demands, some works are evaluating how to modularize and deploy IoT applications in different nodes of the infrastructure (edge, fog, cloud), as well as how to place the network controllers, since these decisions affect the response time of the application.
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The data set contains electrical and mechanical signals from experiments on three-phase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.
Experimental Setup:
The experimental workbench consists of a three-phase induction motor coupled with a direct-current machine, which works as a generator simulating the load torque, connected by a shaft containing a rotary torque wrench.
- Induction motor: 1hp, 220V/380V, 3.02A/1.75A, 4 poles, 60 Hz, with a nominal torque of 4.1 Nm and a rated speed of 1715 rpm. The rotor is of the squirrel cage type composed of 34 bars.
- Load torque: is adjusted by varying the field winding voltage of direct current generator. A single-phase voltage variator with a filtered full-bridge rectifier is used for the purpose. An induction motor was tested under 12.5, 25, 37.5, 50, 62.5, 75, 87.5 and 100% of full load.
- Broken rotor bar: to simulate the failure on the three-phase induction motor's rotor, it was necessary to drill the rotor. The rupture rotor bars are generally adjacent to the first rotor bar, 4 rotors have been tested, the first with a break bar, the second with two adjacent broken bars, and so on rotor containing four bars adjacent broken.
Monitoring condition:
All signals were sampled at the same time for 18 seconds for each loading condition and ten repetitions were performed from transient to steady state of the induction motor.
- mechanical signals: five axial accelerometers were used simultaneously, with a sensitivity of 10 mV/mm/s, frequency range from 5 to 2000Hz and stainless steel housing, allowing vibration measurements in both drive end (DE) and non-drive end (NDE) sides of the motor, axially or radially, in the horizontal or vertical directions.
- electrical signals: the currents were measured by alternating current probes, which correspond to precision meters, with a capacity of up to 50ARMS, with an output voltage of 10 mV/A, corresponding to the Yokogawa 96033 model. The voltages were measured directly at the induction terminals using voltage points of the oscilloscope and the manufacturer Yokogawa.
Data Set Overview:
- Three-phase Voltage
- Three-phase Current
- Five Vibration Signals
Acknowledgements:
The database was acquired in the Laboratory of Intelligent Automation of Processes and Systems and Laboratory of Intelligent Control of Electrical Machines, School of Engineering of São Carlos of the University of São Paulo (USP), Brazil.
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The global system for mobile communications (GSM) supports mobile operators for cellular networks. Huge devices are connected to obtain services through the internet. To avoid failures when connecting IoT devices to mobile networks, GSM has provided two datasets: IoT device connection efficiency and Mobile IoT (MIoT) common test cases (TCs) and guidelines as per the IoT systems specifications. GSM produces TCs at least each year since 2015 till present.
This dataset is based on the GSM 2014, GSM 2015 and GSM 2017 PDFs found as:
GSM, 2014. IoT Device Connection Efficiency Guidelines 1–73.
GSM, 2015. IoT Device Connection Efficiency Common Test Cases 30 January 2015 1–51.
GSM, 2017. MIoT Test Requirements 1–24. TC, G., 2017. MIoT Test Cases 1–40.
The dataset consists of 13 files as follows:
1.IoT-system-requirements.xlsx: it contains the IoT system specifications of GSM 2014 and 2015 in excel format.
2.IoT-system-requirements-MIoT.xlsx: it contains the IoT system specifications of GSM 2017 in excel format.
3.IoT-system-test-cases.xlsx: it contains the IoT test cases of GSM 2014 and 2015 in excel format.
4.IoT-system-test-cases-MIoT.xlsx: it contains the IoT test cases of GSM 2017 in excel format.
5. IoT-system-traceability-matrix.xlsx: it contains the traceability matrix we created for GSM 2014 and 2015 system requirements with their related test cases in excel format.
6. IoT-system-traceability-matrix-MIoT.xlsx: it contains the traceability matrix we created for GSM 2017 system requirements with their related test cases in excel format.
7. IoT-system-test-cases-attributes-extraction.xlsx: it contains the test cases attributes we extracted from GSM 2014 and 2015 test cases using our developed IoT-CIRTF in excel format, in terms of coverage rate, fault detection rate and execution time.
8. IoT-system-test-cases-attributes-extraction-MIoT.xlsx: it contains the test cases attributes we extracted from GSM 2017 test cases using our developed IoT-CIRTF in excel format, in terms of coverage rate, fault detection rate and execution time.
9. IoT-system-selected-prioritized-integration-test-cases.xlsx: it contains the test cases selected and prioritized for IoT integration testing we generated for GSM 2014 and 2015, using our developed IoT-CIRTF in excel format.
10. IoT-system-selected-prioritized-integration-test-cases-MIoT.xlsx: it contains the test cases selected and prioritized for IoT integration testing we generated for GSM 2017, using our developed IoT-CIRTF in excel format.
11. IoT-system-selected-prioritized-regression-test-cases.xlsx: it contains the test cases selected and prioritized for IoT regression testing we generated GSM 2014 and 2015, using our developed IoT-CIRTF in excel format.
12. IoT-system-selected-prioritized-regression-test-cases-MIoT.xlsx: it contains the test cases selected and prioritized for IoT regression testing we generated GSM 2017, using our developed IoT-CIRTF in excel format.
13. IoT-CIRTF Demo.mp4: A demoenstration video for the runtime execution of our developed IoT-CIRTF.
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