In this paper, the security-aware robust resource allocation in energy harvesting cognitive radio networks is considered with cooperation between two transmitters while there are uncertainties in channel gains and battery energy value. To be specific, the primary access point harvests energy from the green resource and uses time switching protocol to send the energy and data towards the secondary access point (SAP).

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This dataset keeps inequivalent collections of (from 1 to 9) ternary $(9,3^6,3)_3$ codes that are subsets o the all-parity-check $(9,3^6,3)_3$ code. The equivalence is understood in the sense of the automorphisms of the Hamming graph $H(9,3)$. There are 4 equivalence classes of such codes; 141 equivalence classes of pairs of disjoint codes; 10956 equivalence classes of triples; 118388 classes for 4 disjoint codes; 501915 for 5; 945965 for 6; 755066 for 7; 314833 for 8; 65436 equivalence classes of partitions of the all-parity-check $(9,3^6,3)_3$ code into 9 distance-3 codes.

Instructions: 

Each text file in the dataset archived in H93.zip keeps collections of disjoint $(9,3^6,3)_3$ codes, coded in the following manner (the file names are coded with equivalence classes of the corresponding codes, from 0 to 3; some files are empty, for example, "n00123", because two codes from equivalence class 0, one code from class 1, one code from class 2, and one code from class 3 cannot be packed in a disjoint manner). Each line corresponds to one representative of such a collection. If the number of codes is M, then the line contains M records like "N T PPPPPPPPP". "N" denotes the number of a code in the list 7codes.py of seven permutably inequivalent codes (note that "permutably equivalent" is not the same as "equivalent"; only first 4 are inequivalent in the sense of $Aut(H(9,3))$, the last 3 are equivaleng to the code number 3). "T" is the number of a translation vector in the list 0: 000000000, 1: 120000000, 2: 102000000, 3: 100200000, 4: 100020000, 5: 100002000, 6: 100000200, 7: 100000020, 8: 100000002, and "PPPPPPPPP" is a coordinate permutation. To reconstruct the corresponding code, one should take the code number "N" from 7codes.py, apply the coordinate permutation "PPPPPPPPP" in the following manner: $$(x_0,x_1,x_2,x_3,x_4,x_5,x_6,x_7,x_8) \to (x_{P^{-1}(0)},x_{P^{-1}(1)},x_{P^{-1}(2)},x_{P^{-1}(3)},x_{P^{-1}(4)},x_{P^{-1}(5)},x_{P^{-1}(6)},x_{P^{-1}(7)},x_{P^{-1}(8)}),$$ and add the translation vector number "T" to all codewords.  After M records about the codes, each line contains: (ii) the record "-A", where A is the order of the automorphism group of the collection of codes; (iii) the record "+R", where 6+R is the rank of the collection, that is, the dimension of the union of the (non-translated) codes (the minimum rank is 6 and the maximum is 8=9-1 because of the all-parity check, so R is 0, 1, ro 2); (iv) the record "~" or "|", where "~" means that the collection can be continued to a collection of M+1 codes and "|" means that no $(9,3^6,3)_3$ code (satisfying the all-parity check) can be added to the complement (this can only happen when M is 6 or 9).

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The dataset corresponds the measurement that was implemented on the control accuracy of a mixed reality application for digital twin based crane. 

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3D-FDTD simulation results included in the manuscript "End-fire Optical Phased Array Designs with Multimode Interference Structures on InP, SiN and Polymer Platforms"

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Some 6G use cases include augmented reality and high-fidelity holograms, with this information flowing through the network. Hence, it is expected that 6G systems can feed machine learning algorithms with such context information to optimize communication performance. This paper focuses on the simulation of 6G MIMO systems that rely on a 3-D representation of the environment as captured by cameras and eventually other sensors. We present new and improved Raymobtime datasets, which consist of paired MIMO channels and multimodal data.

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LATIC is focusing on non-native Mandarin Chinese learners. It is an annotated non-native speech database for Chinese, which is fully open-source can get online for any purpose use. The related using area can be automatic speech scoring, evaluation, derivation—L2 teaching, Education of Chinese as Foreign Language, etc. We are aiming to provide a relatively small-scale and highly efficient training deviation dataset. For this target, four chosen non-native Chinese speaker participated in this project, and their mother tongue (L1s) varies from Russian, Korean, French and Arabic.

Instructions: 

 

Motivation

As we know, since 1997, although the non-native speaker corpus has made a promising breakthrough from the durations' length and the diversities of the non-native speakers for the Mandarin speech scoring task. But the primary target language still focused more on English. After Chen et al. (2015) released the iCall corpus, the non-native Chinese speech corpus's shortage got relieved, but the open source dataset still has not appeared. Still, there is no total opensource (as far as the best we know). And this will be a big burden for new-learners or researchers to do the further research. In the future, we have a strong belief that the open resource datasets target for non-native Chinese speakers will grow significantly after LATIC released.

 

 Description of LATIC

Here is the description of our validation corpus; we will introduce it from these two parts: speaker's statistics and annotation.

Speaker's statistics: first, to initialize, the default output pinyin representation size is 1424, that is, 1423 pinyin + 1 blank block. We have defined a total of 1423 pinyin in dict.txt.

Currently, there are 4 participants in our dataset, which includes two males and two females, and their age varies from 19 to 30. The average age is about 24. Our dataset contains 4 hours of speech files, 2,579 audio samples, and the average length is about 9-10secs. 

Annotation: we set three script notations from three levels add to each waveform file. Two Chinese major students play the role as protocols, who are very proficient in the Chinese L2 language, and Mandarin Chinese is their native tongue. After listening to the recording for each file, they recorded the "closest" transcript followed by the modern Mandarin annotations.

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The presented data contain recordings of underwater acoustic transmissions collected from a field experiment whose goal was to characterize self-interference for in-band full-duplex underwater acoustic communications. The experiment was conducted in the Lake of Tuscaloosa in July 2019. A single transmission-receiving line was deployed off a boat that was moored in the center of the lake. The transmission-receiving line had one acoustic transmitter and eight hydrophone receivers.

Instructions: 

1) User Guide for In-Band Full-Duplex Underwater Acoustic Communication Measurements Self-interference.pdf

2) readme_BPSK.txt

3) readme_OFDM.txt

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The channel path data (signal strength, delay, angle of departure and angle of arrival, etc. of each path for each Tx-Rx pair) calculated by a ray-tracing model in an industrial warehouse, via Wireless InSite
https://www.remcom.com/wireless-insite-em-propagation-software

For more details, please download the scripts and .zip to access the instructions and the data files that contain the path information.

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This data is related to the submitted paper "Optimal Power Allocation in Poisson Shot-Noise-Limited M-ary PPM Photon-Counting NOMA Systems" by Yongkang Chen, Xiaolin Zhou, Haitao Zhou, Dailing Shen, Chongbin Xu, Xin Wang, and Lajos Hanzo. We formulate the power allocation optimization problem of nonlinear discrete-time Poisson (DTP) multiple-access channels (MAC), which is easier than reconfiguring the modulation mode or the duty cycle of signaling.

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Supplementary Data for "Beam and Bandwidth Design of Conformal Dielectric Resonator Antenna Employing SIE-Based Substructure Characteristic Mode Analysis"

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