cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17028/rd.lboro.27204567
title: Supplementary information for Distributed dynamic resource management and pricing in the IoT systems with blockchain-as-a-service and UAV-enabled mobile edge computing

description: Article abstractIn this article, we study the pricing and resource management in the Internet of Things (IoT) system with blockchain-as-a-service (BaaS) and mobile-edge computing (MEC). The BaaS model includes the cloud-based server to perform blockchain tasks and the set of peers to collect data from local IoT devices. The MEC model consists of the set of terrestrial and aerial base stations (BSs), i.e., unmanned aerial vehicles (UAVs), to forward the tasks of peers to the BaaS server. Each BS is also equipped with an MEC server to run some blockchain tasks. As the BSs can be privately owned or controlled by different operators, there is no information exchange among them. We show that the resource management and pricing in the BaaS-MEC system are modeled as a stochastic Stackelberg game with multiple leaders and incomplete information about actions of leaders/BSs and followers/peers. We formulate a novel hierarchical reinforcement learning (RL) algorithm for the decision makings of BSs and peers. We also develop an unsupervised hierarchical deep learning (HDL) algorithm that combines deep Q -learning (DQL) for BSs with the Bayesian deep learning (BDL) for peers. We prove that the proposed algorithms converge to stable states in which the peers' actions are the best responses to optimal actions of BSs.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.17028/rd.lboro.27204567.v1
title: Supplementary information for Distributed dynamic resource management and pricing in the IoT systems with blockchain-as-a-service and UAV-enabled mobile edge computing

description: Article abstractIn this article, we study the pricing and resource management in the Internet of Things (IoT) system with blockchain-as-a-service (BaaS) and mobile-edge computing (MEC). The BaaS model includes the cloud-based server to perform blockchain tasks and the set of peers to collect data from local IoT devices. The MEC model consists of the set of terrestrial and aerial base stations (BSs), i.e., unmanned aerial vehicles (UAVs), to forward the tasks of peers to the BaaS server. Each BS is also equipped with an MEC server to run some blockchain tasks. As the BSs can be privately owned or controlled by different operators, there is no information exchange among them. We show that the resource management and pricing in the BaaS-MEC system are modeled as a stochastic Stackelberg game with multiple leaders and incomplete information about actions of leaders/BSs and followers/peers. We formulate a novel hierarchical reinforcement learning (RL) algorithm for the decision makings of BSs and peers. We also develop an unsupervised hierarchical deep learning (HDL) algorithm that combines deep Q -learning (DQL) for BSs with the Bayesian deep learning (BDL) for peers. We prove that the proposed algorithms converge to stable states in which the peers' actions are the best responses to optimal actions of BSs.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.15468/dl.89b2nb
title: Occurrence Download

description: A dataset containing 728 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oxynotus centrina (Linnaeus, 1758)",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 728 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007777-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13918809
title: NATIONAL ASPECTS IN THE WORLD LINGUISTIC LANDSCAPE AS AN OBJECT OF THE STUDY OF LINGUISTICS
DOI: 10.5281/zenodo.13918810
title: NATIONAL ASPECTS IN THE WORLD LINGUISTIC LANDSCAPE AS AN OBJECT OF THE STUDY OF LINGUISTICS
DOI: 10.15468/dl.ujx7ut
title: Occurrence Download

description: A dataset containing 4302 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Zelkova serrata (Thunb.) Makino"
 ]
}

 The dataset includes 4302 records from 144 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007778-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n5yed7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Centrina paradoxa (Frade, 1929)",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007779-241007104925546/datasets/export for details.
DOI: 10.5518/411
title: High-speed Imaging of Ice Nucleation in Water Proves the Existence of Active Sites - dataset

description: This is the data for High-speed Imaging of Ice Nucleation in Water Proves the Existence of Active Sites, where we demonstrate that specific locations on the surface of feldspar and quartz are repeatedly the area in which ice growth originates in freezing experiments, i.e. the nucleation active sites. This means that heterogeneities on the surfaces are of first order importance in determining the temperature at which liquid water freezes. The data contained in this repository consists of the ice nucleation experimental data, used to construct the figures in the main text and supplementary materials. These show that nucleation is site specific for both quartz and feldspar. We also present WDS mapping results, where no chemical differences were found at the active sites within the resolution of the technique, leading to our hypothesis that topography is an important factor in determining ice nucleation effectiveness of a site.
DOI: 10.17028/rd.lboro.27204573
title: Supplementary information for Bayesian reinforcement learning and Bayesian deep learning for blockchains with mobile edge computing

description: Article abstractWe present a novel game-theoretic, Bayesian reinforcement learning (RL) and deep learning (DL) framework to represent interactions of miners in public and consortium blockchains with mobile edge computing (MEC). Within the framework, we formulate a stochastic game played by miners under incomplete information. Each miner can offload its block operations to one of the base stations (BSs) equipped with the MEC server. The miners select their offloading BSs and block processing rates simultaneously and independently, without informing other miners about their actions. As such, no miner knows the past and current actions of others and, hence, constructs its belief about these actions. Accordingly, we devise a Bayesian RL algorithm based on the partially-observable Markov decision process for miner's decision making that allows each miner to dynamically adjust its strategy and update its beliefs through repeated interactions with each other and with the mobile environment. We also propose a novel unsupervised Bayesian deep learning algorithm where the uncertainties about unobservable states are approximated with Bayesian neural networks. We show that the proposed Bayesian RL and DL algorithms converge to the stable states where the miners' actions and beliefs form the perfect Bayesian equilibrium (PBE) and myopic PBE, respectively.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.17028/rd.lboro.27204573.v1
title: Supplementary information for Bayesian reinforcement learning and Bayesian deep learning for blockchains with mobile edge computing

description: Article abstractWe present a novel game-theoretic, Bayesian reinforcement learning (RL) and deep learning (DL) framework to represent interactions of miners in public and consortium blockchains with mobile edge computing (MEC). Within the framework, we formulate a stochastic game played by miners under incomplete information. Each miner can offload its block operations to one of the base stations (BSs) equipped with the MEC server. The miners select their offloading BSs and block processing rates simultaneously and independently, without informing other miners about their actions. As such, no miner knows the past and current actions of others and, hence, constructs its belief about these actions. Accordingly, we devise a Bayesian RL algorithm based on the partially-observable Markov decision process for miner's decision making that allows each miner to dynamically adjust its strategy and update its beliefs through repeated interactions with each other and with the mobile environment. We also propose a novel unsupervised Bayesian deep learning algorithm where the uncertainties about unobservable states are approximated with Bayesian neural networks. We show that the proposed Bayesian RL and DL algorithms converge to the stable states where the miners' actions and beliefs form the perfect Bayesian equilibrium (PBE) and myopic PBE, respectively.© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.15468/dl.d7xxhn
title: Occurrence Download

description: A dataset containing 562 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Somniosus microcephalus (Bloch & Schneider, 1801)",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 562 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007782-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/9d74337b-f44a-4d68-82db-c3b88662268d
title: Data Underlying the publication: Characterization of extracellular vesicles from Streptococcus thermophilus 065 and their potential capacity as immune modulators

description: <br>This data set corresponds to the proteome of <em>Streptococcus thermophilus</em> 065 and corresponding extracellular vesicles (EVs) which were cultivated in M17 broth with lactose as carbon source. The bacteria were cultivated at 40°C for approximately 6 hours with and without Mitomycin C addition in the beginning of the exponential phase. For the proteome analysis, samples were collected at the end on the cultivation time in three biological replicates. EVs were collected by ultra-centrifugation. Bacterial cells were collected by centrifugation for 15 minutes at 6000×<em>g</em>, samples were frozen at -80°C until further use. Relative protein quantification was performed by Proteome Discoverer based on peptide intensity signals using default settings. <br>
DOI: 10.4121/9d74337b-f44a-4d68-82db-c3b88662268d.v1
title: Data Underlying the publication: Characterization of extracellular vesicles from Streptococcus thermophilus 065 and their potential capacity as immune modulators

description: <br>This data set corresponds to the proteome of <em>Streptococcus thermophilus</em> 065 and corresponding extracellular vesicles (EVs) which were cultivated in M17 broth with lactose as carbon source. The bacteria were cultivated at 40°C for approximately 6 hours with and without Mitomycin C addition in the beginning of the exponential phase. For the proteome analysis, samples were collected at the end on the cultivation time in three biological replicates. EVs were collected by ultra-centrifugation. Bacterial cells were collected by centrifugation for 15 minutes at 6000×<em>g</em>, samples were frozen at -80°C until further use. Relative protein quantification was performed by Proteome Discoverer based on peptide intensity signals using default settings. <br>
DOI: 10.5281/zenodo.13893724
title: MEDp

description: The dataset provides processed and regridded precipitation data for the Mediterranean region, harmonized onto a common grid for consistent analysis. The data covers multiple sources, including gauge-based, satellite-based, and reanalysis products, facilitating comparative studies of precipitation patterns. Temporal coverage and spatial resolution have been standardized, enabling users to analyze precipitation trends and variations across the Mediterranean region with a focus on inter-comparability between different datasets. This resource supports research into climate variability, hydrological modeling, and the impacts of climate change on regional precipitation.
DOI: 10.5281/zenodo.13893601
title: MEDp

description: The dataset provides processed and regridded precipitation data for the Mediterranean region, harmonized onto a common grid for consistent analysis. The data covers multiple sources, including gauge-based, satellite-based, and reanalysis products, facilitating comparative studies of precipitation patterns. Temporal coverage and spatial resolution have been standardized, enabling users to analyze precipitation trends and variations across the Mediterranean region with a focus on inter-comparability between different datasets. This resource supports research into climate variability, hydrological modeling, and the impacts of climate change on regional precipitation.
DOI: 10.5281/zenodo.13893602
title: MEDp

description: The dataset provides processed and regridded precipitation data for the Mediterranean region, harmonized onto a common grid for consistent analysis. The data covers multiple sources, including gauge-based, satellite-based, and reanalysis products, facilitating comparative studies of precipitation patterns. Temporal coverage and spatial resolution have been standardized, enabling users to analyze precipitation trends and variations across the Mediterranean region with a focus on inter-comparability between different datasets. This resource supports research into climate variability, hydrological modeling, and the impacts of climate change on regional precipitation.
DOI: 10.5281/zenodo.13897331
title: MEDp

description: The dataset provides processed and regridded precipitation data for the Mediterranean region, harmonized onto a common grid for consistent analysis. The data covers multiple sources, including gauge-based, satellite-based, and reanalysis products, facilitating comparative studies of precipitation patterns. Temporal coverage and spatial resolution have been standardized, enabling users to analyze precipitation trends and variations across the Mediterranean region with a focus on inter-comparability between different datasets. This resource supports research into climate variability, hydrological modeling, and the impacts of climate change on regional precipitation.
DOI: 10.17028/rd.lboro.27204564
title: Supplementary information for Learning-based mobile edge computing resource management to support public blockchain networks

description: Article abstractWe consider a public blockchain realized in the mobile edge computing (MEC) network, where the blockchain miners compete against each other to solve the proof-of-work puzzle and win a mining reward. Due to limited computing capabilities of their mobile terminals, miners offload computations to the MEC servers. The MEC servers are maintained by the service provider (SP) that sells its computing resources to the miners. The SP aims at maximizing its long-Term profit subject to miners' budget constraints. The miners decide on their hash rates, i.e., computing powers, simultaneously and independently, to maximize their payoffs without revealing their decisions to other miners. As such, the interactions between the SP and miners are modeled as a stochastic Stackelberg game under private information, where the SP assigns the price per unit hash rate, and miners select their actions, i.e., hash rate decisions, without observing actions of other miners. We develop a hierarchical learning framework for this game based on fully-and partially-observable Markov decision models of the decision processes of the SP and miners. We show that the proposed learning algorithms converge to stable states in which miners' actions are the best responses to the optimal price assigned by the SP.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.17028/rd.lboro.27204564.v1
title: Supplementary information for Learning-based mobile edge computing resource management to support public blockchain networks

description: Article abstractWe consider a public blockchain realized in the mobile edge computing (MEC) network, where the blockchain miners compete against each other to solve the proof-of-work puzzle and win a mining reward. Due to limited computing capabilities of their mobile terminals, miners offload computations to the MEC servers. The MEC servers are maintained by the service provider (SP) that sells its computing resources to the miners. The SP aims at maximizing its long-Term profit subject to miners' budget constraints. The miners decide on their hash rates, i.e., computing powers, simultaneously and independently, to maximize their payoffs without revealing their decisions to other miners. As such, the interactions between the SP and miners are modeled as a stochastic Stackelberg game under private information, where the SP assigns the price per unit hash rate, and miners select their actions, i.e., hash rate decisions, without observing actions of other miners. We develop a hierarchical learning framework for this game based on fully-and partially-observable Markov decision models of the decision processes of the SP and miners. We show that the proposed learning algorithms converge to stable states in which miners' actions are the best responses to the optimal price assigned by the SP.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
DOI: 10.5518/412
title: Laboratory data and simulation files for partially-confined gravity currents

description: Velocity data from experiments of partially-confined gravity currents conducted in the Sorby Fluid Dynamics Laboratory. Both ADV and UVP data is listed. Additionally, simulation input files for ANSYS CFX are available for both simulations of laboratory scale and larger scale flows.
DOI: 10.5281/zenodo.13918794
title: Measurement report: The influence of particle number size distribution and hygroscopicity on the microphysical properties of cloud droplets based on  a mountain site field campaign
DOI: 10.17028/rd.lboro.27178341
title: Supplementary information for Secure and efficient coded multi-access edge computing with generalized graph neural networks

description: Article abstract:We formulate a novel framework to improve security and utility of the coded multi-access edge computing (MEC) network for Internet of Things (IoT) applications where multiple edge servers (ESs) jointly process raw IoT data to obtain the final network output. To correctly recover the final output even when some processing outputs produced by malicious or malfunctioning ESs are erroneous, the network utilizes coded distributed computing (CDC) that enhances security by adding computational redundancy to the data processed by ESs. Within the framework, we propose an advanced approach to address limitations of contemporary CDC-based systems related to their inability to guarantee security when the number of malicious ESs is large and reduced network utility due to redundant computations. In this approach, the processing loads are allocated to ESs based on deep learning (DL) algorithms to identify the unknown ESs' types (faithful or malicious) and minimize the load of malicious ESs, thereby optimizing security and utility. The proposed DL algorithms adopt the message passing neural network (NN) - a generalized graph NN with lower complexity and faster convergence than conventional NNs. We prove that our framework yields the optimal security and utility, and verify its superior performance compared with the state-of-the-art schemes.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.