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  • br Data br Experimental design material and methods br Data

    2018-11-03


    Data
    Experimental design, material and methods
    Data The dataset contains 15 files of temporal series that represent 15 different situations related to 5 operational scenarios. Files’ duration varies depending on the situation and dysfunctional component. Accordingly, affected components are two types of depth sensor, the underlying network, or the whole subsystem. These situations can be wrongly understood by a decision maker, or only identified for instance after the malicious act was accomplished. Since wrongly managed situations might have significant adverse operational costs, it is critical to detect and analyze in real time such events. Datasets covering such situations are currently rare, because of the complexity to acquire data from cyber-physical systems. In our case, the principle of reusable experimental platform [1] was applied, to collect diverse datasets for monitoring [2] and categorization of aNomalies [3].
    Experimental design, materials and methods Two tanks of different volumes that function as storage and distribution device for water or fuel, one ultrasound depth sensor, four discrete sensors, and two pumps, were used to acquire the dataset (Fig. 1). A computer controlled the system with a PLC connected to a monitoring network. The ultrasound depth sensor on the main tank (volume of 7L) was calibrated relating the tank dimensions to 10,000 equidistant depth steps (0 corresponds to the full tank and 10,000 to the empty tank). Fig. 2 shows the tracked filling and emptying of the main tank. The four floating discrete sensors in the second tank (volume of 9L), measured levels of liquid corresponding to four volumes: 1.25L, 3.35L, 8L, and 9L. All signals – ultrasound depth sensor, pump 1, pump 2, and the four discrete level sensors – were acquired synchroNously for every situation described in Table 1, independently of the affected component, operational scenario, and duration. The Normal scenario without aNomalies serves as reference. Nine situations focus on the ultrasound depth sensor, since its high phosphodiesterase inhibitor makes it more sensitive to show aNomalies (No. 2, No. 3, and No. 4). Also, objects intentionally hidden inside the main tank modify liquid volume measurements depending on the number of pieces (No. 5 and No. 6), while surrounding humidity can block the measure (No. 7). The ultrasound depth sensor measurements also change incorrectly when the tanks are hit with different intensities (No. 13, No. 14, and No. 15). Some examples of signal alterations are represented in Figs. 3–5. Additionally, two of the discrete sensors (1 and 2) were disrupted by keeping each one at a blocked position, i.e. up when the liquid has Not reached that level yet (No. 8) and pushing randomly down once liquid overflowed it (No. 9), leaving the tank almost empty or filling up to the security aperture, respectively. Network intrusions were carried out making use of the Modbus Penetration Testing Framework, Smod, to execute a denial of service attack (No. 10) and a spoofing attack (No. 11). Finally, aNomalies can also be the result of unintentional human errors as a wrong system connection (No. 12) and more generally incorrect maintenance. Technical data sheets of the ultrasound sensor and the PLC, the network schema, the transmitted information between components, a script written in Python to read and display files, and additional details are provided with the dataset.
    Acknowledgements The authors would like to thank the Chair of Naval Cyber Defense funded and supported by École Navale, Institut Mines-Telecom Atlantique Bretagne Pays de la Loire, Thales and DCNS.
    Data The values of the materials which have been used different layers for simulation to design a CIGS solar cell are presented in Table 1. The reflectance and the recombination velocity for holes and electrons for both front and back contact layer of the cell have been listed in Table 2. The simulation was carried out under some simulation conditions that have been represented by Table 3. All of these data has been assumed from the published research articles [1–12]. Fig. 1 lays out the schematic design for ZnO:Al/i-ZnO/ZnS/CIGS structure. Consequently, Fig. 2 shows the J-V characteristic curve for optimized CIGS photovoltaic cell whereas Table 4 reports the performance parameters of the cell.