Gaming consoles are very common connected devices which have evolved in functionality and applications (games and beyond) they support. This diversity of traffic generated from these consoles has diverse quality of service (QoS) requirements. However, in order to offer diverse QoS, ISPs and operators must be able to classify this traffic. To enable research in traffic classification (Machine Learning based or other), we have generated and collected this dataset. This is a labelled dataset collected from a gaming console, PlayStation 4.

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Download Microsoft Network Monitor (at the following link: https://www.microsoft.com/en-us/download/details.aspx?id=4865) to be able to access the data. Open the capture file and then wait for all the collected frames to be loaded. The data set was collected using Microsoft Network Monitor 3.4. The traffic is Labelled by number, time and day, Source and Destination IP, Protocol, length and description. Using Microsoft Network Monitor, there is a way to Filter by Media type (check the following link: https://docs.microsoft.com/en-us/archive/blogs/netmon/intro-to-filtering...). To navigate the data easily, you can apply a filter on the media type by putting it Ethernet meaning that only the data exchanged between the Laptop and the PlayStation will show. The Excel sheet included with the dataset contains the date and the time of each capture and also when each activity was running and when it was stopped making it easy to identify the data. Refer to the time delay report attached for more information about the time synchronization aspects between the data capture and the PlayStation.

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This document shows time-domain thermoreflectance (TDTR) measurements on blanket phase change multilayers [Fig. S1] and electro-thermal simulations of confined PCM cells [Fig. S2]. The extracted thermal resistance per unit area obtained from our measured TDTR signal ratio vs. time delay in Fig. S1 reveals that Bi2Te3 layer only introduces ~13% additional thermal resistance in the PCM stack. On the other hand, Fig. S2 shows the electro-thermal simulation of confined cells with 4 nm Bi2Te3 and 50 nm GST layer with a BE (TiN) diameter of 150 nm in Fig.

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Text Book from Univ of Buenos Aires - Facultad de Ingeniería

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Libro de texto para cursos universitarios de grado en ingeniería eléctrica

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The following pages show axial T2-weighted MRI obtained at 24 hours and at 3-15 months after MRgFUS. The images shown here were registered to the same reference frame that was used in the thermal simulations; every third image is shown. To segment the bone marrow lesions, the registered images were toggled back and forth between the two time points to detect obvious changes. The lesion segmentations were completed before the acoustic and thermal simulations were performed. They were originally done on the native T2-weighted images acquired at 3-15 months after FUS.

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Accurate information about crop rotation is essential for administrators, managers and various government departments for assessment, monitoring, and management of various resources for crop escalation. Radar remote sensing, because of its all-weather capability and assured uninterrupted data supply can show a substantial part in the evaluation of crop rotation.

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This data set containts the detailed parameters of the modified Kundur's two-area system that is used in the manuscript "Hybrid Symbolic-Numeric Library for Power System Modeling and Analysis"

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These datasets are for requirement specification of the real projects in the software engineering scope.

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New concept high quality high performance hearing aid

 

A typical hearing aid can't be distinguished by mixing sounds in noisy places or in a multi-person voice environment.

This is due to the limited performance of the amplifier inside the hearing aid. This may be due to the lack of linearity of the amplifier, or because of transistor-specific characteristics.

By applying high-quality amplifier technology to these hearing aids, several sounds can be separated and listened well.

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The electric power system of a deep space vehicle is mission-critical, and needs to respond to faults intelligently and autonomously. 

An algorithm to determine the optimal operation of such a system is presented in the paper: P. Kulkarni, D. Aliprantis, B. Loop and N. Wu, 'Autonomous Power Dispatch for a Deep Space Vehicle Power System,' Proc. 2020 IEEE Power and Energy Conf. at Illinois (PECI), Champaign, IL.

This dataset contains the complete information about the system used in the case studies in this paper. 

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