Machine Learning

We used  Sentinel-2 images to create the dataset In order to estimate sequestered carbon in the above-ground forest Biomass.  Moreover, fieldwork was completed to gather related forest biomass volume. The clipped image has a size of 1115 × 955 pixels and consists of bands 3, 4, and 8, which correspond to green, red, and near-infrared.

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This bundle contains 4 well known and established causal inference benchmark datasets in order to evaluate the performance of causal/treatment effect estimation methods. These datasets are: IHDP, Jobs, Twins and News. All datasets are already publicly available. This bundle merely collects them in a single location for ease of replication.

IHDP is based on Infant Health Development Program (IHDP) clinical trial. Goal: predict the effect of receiving specialised childcare on cognitive test score of the infants. Introduced by [1].

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A new design and implementation of a control system for an anthropomorphic robotic hand has been developed for the Bioinformatics and Autonomous Learning Laboratory (BALL) at ESPOL. Myoelectric signals were acquired using a bioelectric data acquisition board (CYTON BOARD) with six out of the available eight channels. These signals had an amplitude of 200 [uV] and were sampled at a frequency of 250 [Hz].

 

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Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long TermEvolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In this paper, we propose a cross-band DSS scheme that utilizes the Multimedia BroadcastMulticast Service over a Single Frequency Network (MBSFN) feature of an LTE network and theMulticast Broadcast Service (MBS) feature of an NR network.

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577 Views

Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long TermEvolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In this paper, we propose a cross-band DSS scheme that utilizes the Multimedia BroadcastMulticast Service over a Single Frequency Network (MBSFN) feature of an LTE network and theMulticast Broadcast Service (MBS) feature of an NR network.

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360 Views

The dataset that we published in this data repository can be used to build neural networks-based inverse kinematics for NAO robot arms. This dataset is named ARKOMA. ARKOMA is an acronym for ARif eKO MAuridhi, all of whom are the creators of this dataset. This dataset contains input-output data pairs. In this dataset, the input data is the end-effector position and orientation, while the output data is a set of joint angular positions. For further applications, this dataset was split into the training dataset, validation dataset, and testing dataset.

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<p>Ten individuals in good health were enlisted to execute 16 distinct movements involving the wrist and fingers in real-time. Before commencing the experimental procedure, explicit consent was obtained from each participant. Participants were informed that they had the option to withdraw from the study at any point during the experimental session. The experimental protocol adhered to the principles outlined in the Declaration of Helsinki and received approval from the local ethics committee at the National University of Sciences and Technology, Islamabad, Pakistan.

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This dataset contains MRI data acquired approximately 20 minutes before ("Pre-ablation") and 20-40 minutes after ("Post-ablation") MR-guided focused ultrasound thermal ablations in the muscle tissue (quadriceps) in four (n=4) New Zealand white rabbits. MR images include MR thermometry acquired with the proton resonance frenquency method, ADC maps (b=0,400), T2-weighted, and pre- and post-contrast enhanced T1-weighted images. All MRI acquisitions were 3D except for the ADC acquisition, which was 2D multi-slice.

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150 Views

As communications service providers ponder ways to cater to the diverse traffic requirements of mobile applications that range from the classic telephony to modern augmented reality (AR)-related use cases, the traditional quality of service (QoS)-based radio resource management (RRM) techniques for RAN slicing that are agnostic to the intrinsic workings of applications can result in a poor quality of experience (QoE) for the end-user. We argue that in addition to QoS, RAN slicing strategies should also consider QoE for efficient resource utilization.

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321 Views

 

The uploaded dataset appears to be related to various composite materials, and it includes the following columns:

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