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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In this data set, a power transistor’s uncertainty added S-parameters, its estimated S-parameters, uncertainty added S-parameters of a power amplifier circuit, uncertainty added S-parameters of a cascaded amplifier, and amplifier’s design files are presented. Sumitomo’s GaAs-FET FLL57MK is used for the measurements and design. Cadence AWR is used for the power amplifier design. The power amplifier is designed for 2.4 GHz, using S-parameters.

Uncertainty added S-parameters can be seen and analyzed with Maury Microwave’s Insight software or VNA Tools. Estimated S-parameters of the transistor and matching networks can be analyzed with S-parameter viewer or EDA tools. The circuit is designed with Cadence AWR. Matching networks are cascaded with uncertainty added S-parameters’ of the transistor using VNA Tool software to find the final uncertainty of the power amplifier and cascaded amplifier. The same amplifier is used to form the cascaded amplifier since it is a 50 ohm matched amplifier.

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S-parameter data with uncertainty information are presented in the data set. High power RF transistors are measured with a regular measurement setup, consisting of a VNA, coaxial cables, bias tees, and fixture. Measured transistors are FLL57MK (GaAs-FET), LP601 (LDMOS-FET), CLF1G0060S-10 (GaN-HEMT), T2G6000528-Q3 (GaN-HEMT), CGH40010-F (GaN-HEMT). The data can be used to analyze for uncertainty studies and designing with uncertainty added S-parameters.

There are several software tools available to analyze uncertainty added S-parameters. For example, Maury Microwave’s Insight and VNA Tools are useful software tools. Uncertainty added S-parameters can not be used directly for design. Estimated s-parameters (s1p or s2p files) can be extracted and used to design circuit.

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Dataset to accompany the manuscript "Adaptive Content Seeding for Information-Centric Networking under High Topology Dynamics".

Dataset to accompany the manuscript "Adaptive Content Seeding for Information-Centric Networking under High Topology Dynamics".

(Version 2)

Instructions:

The files contained in this dataset measure the following metrics:

Cache miss ratio: this metric, computed by every requester, is calculated as the ratio between the number of non-satisfied content requests (i.e., interests sent via \ac{D2D} links) and the total number of interest messages sent. The metric is recorded in the following files: CacheMissRatio_Low.csv (low density), CacheMissRatio_Medium.csv (medium density), CacheMissRatio_High.csv (high density).

Content hop count: this metric is computed by every requester and measures the number of hops to reach the content via \ac{D2D} communication links. The metric is recorded in the following files: CacheHitHops_Low.csv (low density), CacheHitHops_Medium.csv (medium density), CacheHitHops_High.csv (high density).

Sent control messages: this metric is computed by every vehicle by counting the total number of sent interest and acknowledgment messages. The metric is recorded in the following files: SentCtrlMsgs_Low.csv (low density), SentCtrlMsgs_Medium.csv (medium density), SentCtrlMsgs_High.csv (high density).

Content downloads: this metric is computed by the centralized controller and counts the number of content downloads from the backend service via RAN for all vehicles during one simulation run. The metric is recorded in the following files: ContentDownloads_Low.csv (low density), ContentDownloads_Medium.csv (medium density), ContentDownloads_High.csv (high density).

Channel busy ratio: this metric is computed by every vehicle and measures the ratio of time the channel is sensed busy over the total active simulation time. The metric is recorded in the following files: CBR_Low.csv (low density), CBR_Medium.csv (medium density), CBR_High.csv (high density).

Every file in the dataset has the following columns: <"Strategy","Clustering","reqProb","Value","sd","se","ci">

- "Strategy" contains the employed seeding algorithm

- "Clustering" contains the employed community detection algorithm

- "reqProb" contains the request probability p

- "Value" contains the mean value of the actual metric that is being measured, which is defined in the file name

- "sd","se","ci" are the standard deviation, standard error of the mean, and confidence interval (95%)

Changes with respect to the previous version:

- We repeated every simulation run using 100 different seeds, instead of 50 as in the previous version of the dataset

- We deleted the "Number of clusters" metric, as it is not being used in the manuscript

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The dataset includes both the software and the results for the calculation of large Minimum Redundancy Linear Arrays (MRLA, also known as sparse rulers).

The software uses an exhausive search to find perfect and optimal sparse rulers. The results include all rulers up to 213 and the combination of length and marks of the rulers up to a length of 244.

The source code contains readme files on how to compile, install and execute the program.

The combinations of array length (L) and mark count (M) for perfect rulers are listed in combinations.txt. The results found for each L/M combination are presented in seperate files each, containing the rulers encoded in differences of positions of the marks. An exponent defines how many times this distance occurs in sequence. E.g. {1², 4², 3} would expand to the ruler xxxoooxoooxoox, where x represents a mark and o represents the absecense of a mark.

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Decision-makers across many professions are often required to make multi-objective decisions over increasingly larger volumes of data with several competing criteria. Data visualization is a powerful tool for exploring these complex ‘solution spaces’, but there is little research on its ability to support multi-objective decisions. In this paper, we explore the effects of visualization design and data volume on decision quality in multi-objective scenarios with complex trade-offs.

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Secure cryptographic protocols are indispensable for modern communication systems. It is realized through an encryption process in cryptography. In quantum cryptography, Quantum Key Distribution (QKD) is a widely popular quantum communication scheme that enables two parties to establish a shared secret key that can be used to encrypt and decrypt messages.

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The Bebop2 drone is controlled by the follow-the-carrot category to follow the planned path in a windy outdoor environment.

Constrained by the hardware, the drone can only be controlled via WIFI and works at a slow control rate.

Its localization is realized based on the IMU and ultrasonic altimeter because the accuracy of GPS is unsatisfactory.

However, the experiment result is still sufficient to support the validity of our path planning algorithm which is mainly concerned in our present researches.

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This dataset is very vast and contains tweets related to COVID-19. There are 226668 unique tweet-ids in the whole dataset that ranges from December 2019 till May 2020 . The keywords that have been used to crawl the tweets are 'corona', , 'covid ' , 'sarscov2 ', 'covid19', 'coronavirus '. For getting the other 33 fields of data drop a mail at "avishekgarain@gmail.com". Twitter doesn't allow public sharing of other details related to tweet data( texts,etc.) so can't upload here.

Read the documentation properly and use the code snippet written in python to load data.

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