In the simulation experiment, OPNET simulation software was used to simulate the Space-Integrated Ground Information Intelligent Network environment to obtain the data. A satellite network composed of six satellites was used to simulate the data flow formed by the ground communication traffic through this network. The simulated time was one week and the traffic data information of a node was randomly collected for analysis and learning.

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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 contains all inequivalent concatenated ternary 1-perfect codes of length 13. Additionally, it contains some components necessary to obtain such concatenated codes, namely, collections of disjoint ternary distance-3 Reed-Muller-like codes of length 9, see p.(1) below.

The dataset keeps:

Instructions: 

(1) 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).

(2) The file "concat13" (arxived to concat13.xz; the original file is 1.7GiB) contains the list of all inequivalent concatenated ternary 1-perfect codes of length 13 in the following form. Each length-9 partition in the form described in p.(1) (the order of records is (i), (iii), (ii), and the last record contains generators of the group of permutations of 9 codes induced by the automorphism group of the partition) is followed by the list of concatenated codes obtained with this partitions. The records for each code are the following:

(i) "A" or "B" indicate the partition of $H(4,3)$ into 1-perfect codes.

(ii) The next 8 numbers show the permutation $p$ of the codes of the partition.

(iii) The record "+R" means that the rank of the code is 10+R (10, 11, or 12).

(iv) The record ":K" means that the dimension of the kernel of the code is K (the kernel is the set of all periods of the code, that is, the translations that send the code to itself).

(v) The record "/S" means that the code can be represented as a concatenated code in S different ways (partitions 9+4 of coordinates); for the rank-10 Hamming code this number is 13; for rank-12 codes it is 1; for rank-11 codes it is from 1 to 4.

(vi) The record "-A*K" means that the order of the automorphism group of the code is A*K, while A is the number of authomorphisms that keep the coordinate partition 9+4, i.e., the current concatenation structure (if (iv) is "/1", then K necessarily equals 1).

If the record (v) is "/1" or "/13", then there are no more records, because this guarantees that an equivalent code cannot be obtained by concatenation in another way (in particular, from a different partition).

If the record (v) is "/2", "/3", or "/4", then such codes were checked for isomorphism, and one more record is given:

(vii) This record contains a symbol "n" or "o" and some number. "n" means that the code is new, and it is not equivalent to any of the codes above, and the following number is its unique number (among the codes with symbol "n" in the record). "o" means that the code is not new and it is equivalent to one of the codes above, namely, the one with symbol "n" followed by the same number.

So (ATTENTION!!!), some lines of the database correspond to equivalent codes; to read only inequivalent codes, one should ignore the lines with symbol "o".

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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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293 Views

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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