pinecone/core-2020-05-10-deduplication huggingface dataset:
Economic growth and development with low-carbon energy

Energy is needed for economic growth, and access to cheap, reliable energy is an essential development objective. Historically most incremental energy demand has been met through fossil fuels; however, in future that energy will have to be low carbon and ultimately zero‐carbon. Decarbonization can and needs to happen at varying speeds in all countries, depending on national circumstances. This article reviews the implications of a transition to low‐carbon energy on economic growth and development in current low‐income countries. It sets out empirical findings about trajectories for energy intensity and emissions intensity of economic growth; explores pathways to accelerate decarbonization; reviews the theoretical and empirical literature on economic costs and co‐benefits of energy decarbonization; and assesses analytical approaches. It discusses the opportunities that might arise in terms of a cleaner, more dynamic and more sustainable growth model, and the options for developing countries to implement a less‐carbon intensive model of economic developmen
Overcoming public resistance to carbon taxes

Carbon taxes represent a cost-effective way to steer the economy towards a greener future. In the real world, their application has however been limited. In this paper, we address one of the main obstacles to carbon taxes: public opposition. We identify drivers of and barriers to public support, and, under the form of stylized facts, provide general lessons on the acceptability of carbon taxes. We derive our lessons from a growing literature, as well as from a combination of policy “failures” and “successes”. Based on our stylized facts, we formulate a set of suggestions concerning the design of carbon taxes. We consider the use of trial periods, tax escalators, environmental earmarking, lump-sum transfers, tax rebates, and advanced communication strategies, among others. This paper aims to contribute to the policy debate, ideally leading to more success stories, and less policy failures
Big Data Approaches for coastal flood risk assessment and emergency response

Big Data Approaches (BDAs) refers to the combined use of historic datasets, incoming data streams, and the array of related technologies designed to shed new light on societal and environmental complexities through novel organizational, storage, and analytical capabilities. Despite widespread recognition of the commercial benefits of BDAs, application in the environmental domain is less well articulated. This represents a missed opportunity given that the dimensions used to characterize BDAs (volume, variety, velocity, and veracity) appear apt in describing the intractable challenges posed by global climate change. This paper employs coastal flood risk management as an illustrative case study to explore the potential applications in the environmental domain. Trends in global change including accelerating sea level rise, concentration of people and assets in low‐lying areas and deterioration of protective coastal ecosystems are expected to manifest locally as increased future flood risk. Two branches of coastal flood risk management are considered. First, coastal flood risk assessment, focusing on better characterization of hazard sources, facilitative pathways, and vulnerable receptors. Second, flood emergency response procedures, focusing on forecasting of flooding events, dissemination of warnings, and response monitoring. Critical commentary regarding technical, contextual, institutional, and behavioral barriers to the implementation of BDAs is offered throughout including a discussion of two fundamental difficulties associated with applying BDAs to coastal flood risk management: the role of BDAs in the broader flood system and the skill requirements for a generation of data scientists capable of implementing Big Data Approaches
Effects of instrumentation changes on ocean surface temperature measured in situ

Measurements of ocean surface temperature are an important climate record, complementing terrestrial air temperature observations, records of marine air temperature, ocean subsurface temperatures and ocean heat content.  Sea-surface temperature (SST) has been measured since the mid-18th Century, although observations are sparse in the early period. Historically, marine observing systems relied on observations made by seafarers and necessary information on measurement methods is often not available. There are many historical descriptions of observing practice and instrumentation, some including quantification of biases between different methods.  This documentation has been used, with the available observations, to develop models for the expected biases which vary according to how the measurement were made, over time and with the environmental conditions.  Adjustments have been developed for these biases and some gridded SST datasets adjust for these differences and provide uncertainty estimates, including uncertainties in the bias adjustments. The modern in situ SST observing system continues to evolve and now includes many observations from moored and drifting buoys which must be characterised relative to earlier observations to provide a consistent record of multi-decadal changes in SST
Resilience isn't the same for all: comparing subjective and objective approaches to resilience measurement

Robust resilience measurement can improve our understanding of how people and societies respond to climate risk. It also allows for the effectiveness of resilience‐building interventions to be tracked over time. To date, the majority of measurement tools use objective methods of evaluation. Broadly speaking, these relate to approaches that solicit little, if any, judgment on behalf of the subject in question. More recently, subjective methods of evaluation have been proposed. These take a contrasting epistemological view, relying on people's self‐assessments of their own capacity to deal with climate risk. Subjective methods offer some promise in complementing objective methods, including: factoring in people's own knowledge of resilience and what contributes to it; more nuanced contextualization; and the potential to reduce survey length and fatigue. Yet, considerable confusion exists in understanding subjectivity and objectivity. Little is also known about the merits and limitations of different approaches to measurement. Here, I clarify the conceptual and practical relationships between objective and subjective forms of measuring resilience, aiming to provide practical guidance in distinguishing between them. In reviewing existing toolkits, I propose a subjectivity–objectivity continuum that groups measurement approaches according to two core tenets: (a) how resilience is defined and (b) how resilience is evaluated. I then use the continuum to explore the strengths and weaknesses of different types of toolkits, allowing comparison across each. I also emphasize that there is no one‐size fits all approach to resilience measurement. As such, evaluators should carefully consider: their epistemology of resilience; core objectives for measurement; as well as resource and data constraints, before choosing which methods to adopt
Prospects for decadal climate prediction

During the last decade, global surface temperatures did not increase as rapidly as in the preceding decades. Although relatively small compared to the observed centennial scale global warming, it has renewed interest in understanding and even predicting climate on time scales of decades, and sparked a community initiative on near‐term prediction that will feature in the fifth intergovernmental panel on climate change assessment report. Decadal prediction, however, is in its infancy, with only a few publications existing. This article has three aims. The first is to make the case for decadal prediction. Decadal fluctuations in global climate similar to that of recent decades were observed during the past century. Associated with large regional changes in precipitation and climate extremes, they are of socioeconomic importance. Climate models, which capture some aspects of observed decadal variability, indicate that such variations might be partly predictable. The second aim is to describe the major challenges to skilful decadal climate prediction. One is poor understanding of mechanisms of decadal climate variability, with climate models showing little agreement. Sparse observations in the past, particularly in the ocean, are also a limiting factor to developing and testing of initialization and prediction systems. The third aim is to stress that despite promising initial results, decadal prediction is in a highly experimental stage, and care is needed in interpreting results and utilizing data from such experiments. In the long‐term, decadal prediction has the potential to improve models, reduce uncertainties in climate change projections, and be of socioeconomic benefit. Copyright © 2010 John Wiley & Sons, Ltd
Optimal and suboptimal receivers for code-multiplexed transmitted-reference ultra-wideband systems

Cataloged from PDF version of article.In this study, optimal and suboptimal receivers are investigated for code-multiplexed transmitted-reference (CM-TR)
ultra-wideband systems. First, a single-user scenario is considered, and a CM-TR system is modeled as a generalized
noncoherent pulse-position modulated system. Based on that model, the optimal receiver that minimizes the bit error probability
is derived. Then, it is shown that the conventional CM-TR receiver converges to the optimal receiver under certain
conditions and achieves close-to-optimal performance in practical cases. Next, multi-user systems are considered, and the
conventional receiver, blinking receiver, and chip discriminator are investigated. Also, the linear minimum mean-squared
error (MMSE) receiver is derived for the downlink of a multi-user CM-TR system. In addition, the maximum likelihood
receiver is obtained as a performance benchmark. The practicality and the computational complexity of the receivers are
discussed, and their performance is evaluated via simulations. The linear MMSE receiver is observed to provide the best
trade-off between performance and complexity/practicality. Copyright © 2011 John Wiley & Sons, Lt
Reduced Complexity Sphere Decoding

In Multiple-Input Multiple-Output (MIMO) systems, Sphere Decoding (SD) can
achieve performance equivalent to full search Maximum Likelihood (ML) decoding,
with reduced complexity. Several researchers reported techniques that reduce
the complexity of SD further. In this paper, a new technique is introduced
which decreases the computational complexity of SD substantially, without
sacrificing performance. The reduction is accomplished by deconstructing the
decoding metric to decrease the number of computations and exploiting the
structure of a lattice representation. Furthermore, an application of SD,
employing a proposed smart implementation with very low computational
complexity is introduced. This application calculates the soft bit metrics of a
bit-interleaved convolutional-coded MIMO system in an efficient manner. Based
on the reduced complexity SD, the proposed smart implementation employs the
initial radius acquired by Zero-Forcing Decision Feedback Equalization (ZF-DFE)
which ensures no empty spheres. Other than that, a technique of a particular
data structure is also incorporated to efficiently reduce the number of
executions carried out by SD. Simulation results show that these approaches
achieve substantial gains in terms of the computational complexity for both
uncoded and coded MIMO systems.Comment: accepted to Journal. arXiv admin note: substantial text overlap with
  arXiv:1009.351
Modeling and Performance Evaluation of Advanced Diffusion with Classified Data in Vehicular Sensor Networks

International audienceIn this paper, we propose a newly distributed protocol called ADCD to manage information harvesting and distribution in Vehicular Sensor Networks (VSN). ADCD aims at reducing the generated overhead avoiding network congestions as well as long latency to deliver the harvested information. The concept of ADCD is based on the characterization of sensed information (i.e. based on its importance, location and time of collection) and the diffusion of this information accordingly. Furthermore, ADCD uses an adaptive broadcasting strategy to avoid overwhelming users with messages in which they have no interest. Also, we propose in this paper a new probabilistic model for ADCD based on Markov chain. This one aims at optimally tune the parameters of ADCD, such as the optimal number of broadcaster nodes. The analytical and simulation results based on different metrics, like the overhead, the delivery ratio, the probability of a complete transmission and the minimal number of hops, are presented. These results illustrate that ADCD allows to mitigate the information redundancy and its delivery with an adequate latency while making the reception of interesting data for the drivers (related to their location) more adapted. Moreover, the ADCD protocol reduces the overhead by 90% compared to the classical broadcast and an adapted version of MobEyes. The ADCD overhead is kept stable whatever the vehicular density
Application of vehicular communications for improving the efficiency of traffic in urban areas

This paper studies the impacts of vehicular communications on efficiency of traffic in urban areas. We consider a Green Light Optimized Speed Advisory application implementation in a typical reference area and present the results of its performance analysis using an integrated cooperative intelligent transportation systems simulation platform. In addition, we study route alternation using vehicle-to-infrastructure and vehicle-to-vehicle communications. Our interest was to monitor the impacts of these applications on fuel and traffic efficiency by introducing metrics for average fuel consumption, average stop time behind a traffic light and average trip time, respectively. For gathering the results, we implemented two traffic scenarios defining routes through an urban area including traffic lights. The simulations are varied for different penetration rates of application-equipped vehicles, driver's compliance to the advised speed and traffic density. Our results indicate that Green Light Optimized Speed Advisory systems could improve fuel consumption, reduce traffic congestion in junctions and the total trip tim
On the performance of multi-hop wireless relay networks

User cooperation has evolved as a popular coding technique in wireless relay networks (WRNs). Using the neighboring nodes as relays to establish a communication between a source and a destination achieves an increase of the diversity order. The relay nodes can be seen as a distributed multi-antenna system, which can be exploited for transmit diversity by using distributed space–time block coding (STBC). In this paper, we investigate the bit error rate (BER) of multi-hop WRNs employing distributed STBC at the relay nodes. We develop the general model of WRNs using distributed STBC, and we derive the pairwise error probability and an approximation of the BER.We examine the impact of several parameters, such as distributed STBC at the relays, the number of relays, the distances between the nodes, and the channel state information available at the receivers, on the BER performance of the multi-hop WRN. The obtained results provide guidelines about the expected error performance and the design of channel estimation for these networks
Advanced mobile network monitoring and automated optimization methods

The operation of mobile networks is a complex task with the networks serving a large amount of subscribers with both voice and data services, containing extensive sets of elements, generating extensive amounts of measurement data and being controlled by a large amount of parameters. The objective of this thesis was to ease the operation of mobile networks by introducing advanced monitoring and automated optimization methods. In the monitoring domain the thesis introduced visualization and anomaly detection methods that were applied to detect intrusions, mal-functioning network elements and cluster network elements to do parameter optimization on network-element-cluster level. A key component in the monitoring methods was the Self-Organizing Map. In the automated optimization domain several rule-based Wideband CDMA radio access parameter optimization methods were introduced. The methods tackled automated optimization in areas such as admission control, handover control and mobile base station cell size setting. The results from test usage of the monitoring methods indicated good performance and simulations indicated that the automated optimization methods enable significant improvements in mobile network performance. The presented methods constitute promising feature candidates for the mobile network management system.reviewe
Inband Relaying in Long Term Evolution-Advanced Networks

The set of stringent requirements for 4G radio access networks has triggered the embodiment of new small low-power nodes, e.g. relay, Femto and Pico access nodes, as part of the network infrastructure. Various types of relay nodes are currently supported in IEEE 802.16m and 3GPP LTE-Advanced, e.g. inband Layer 2 or Layer 3 nodes and outband nodes, considering different functional capabilities and backhauling characteristics. In general, relay nodes are characterized by compact physical characteristics, low power consumption, a wireless backhaul link to the core network, and relaxed installation guidelines with respect to radiation and planning regulation. In specific, inband relay nodes, the matter of this study, are Layer 3 access nodes with time-multiplexed transmission and reception on their wireless backhaul and access links, which operate on the same frequency band. These characteristics impose serious challenges on one hand, but allow for significant improvements on the other hand.

In this context, the deployment flexibility of relay nodes simplifies the network planning procedure and reduces deployment costs. On the other hand, low power transmission and limited antenna capabilities result in small relay cell coverage areas which will lead to load imbalances. Besides, multiplexing backhaul and access communications on different subframes implies the need for suitable two-hop resource allocation and scheduling. Further challenges are attributed to increased interference levels compared to macrocell deployments, as well as the introduction of a new interference type known as relay-to-relay interference resulting from the misalignment of access and backhaul link dedicated subframes at different relay nodes.

The research towards this thesis has addressed these challenges within 3GPP LTE-Advanced context. A feasibility study of different relaying modes is provided and the performance of relay deployments is evaluated in different propagation environments. Thereafter, simple network planning techniques are proposed to alleviate the limitations of the inband backhaul link. Further, novel techniques are investigated to address resource allocation and scheduling, load balancing and interference coordination. The performance of proposed techniques along with the energy efficiency of relay nodes is evaluated. Results show in general significant gains and validate relaying as an efficient enhancement technology
Goodput and throughput comparison of single-hop and multi-hop routing for IEEE 802.11 DCF-based wireless networks under hidden terminal existence

We investigate how multi-hop routing affects the goodput and throughput performances of IEEE 802.11 distributed coordination function-based wireless networks compared with direct transmission (single hopping), when medium access control dynamics such as carrier sensing, collisions, retransmissions, and exponential backoff are taken into account under hidden terminal presence. We propose a semi-Markov chain-based goodput and throughput model for IEEE 802.11-based wireless networks, which works accurately with both multi-hopping and single hopping for different network topologies and over a large range of traffic loads. Results show that, under light traffic, there is little benefit of parallel transmissions and both single-hop and multi-hop routing achieve the same end-to-end goodput. Under moderate traffic, concurrent transmissions are favorable as multi-hopping improves the goodput up to 730% with respect to single hopping for dense networks. At heavy traffic, multi-hopping becomes unstable because of increased packet collisions and network congestion, and single-hopping achieves higher network layer goodput compared with multi-hop routing. As for the link layer throughput is concerned, multi-hopping increases throughput 75 times for large networks, whereas single hopping may become advantageous for small networks. The results point out that the end-to-end goodput can be improved by adaptively switching between single hopping and multi-hopping according to the traffic load and topology
A coalitional game-based relay load balancing and power allocation scheme in decode-and-forward cellurlar relay networks

In this paper, a game theoretic relay load balancing and power allocationscheme is proposed for downlink transmission in
a decode-and-forward orthogonal frequency division multiple access-based cellular relay network. A system with a base
station communicating with multiple users via multiple relays is considered. The relays have limited power, which must be
divided among the users they support. In traditional scheme, each relay simply divides its transmit power equally among all
its users. Moreover, each user selects the relay with the highest channel gain. In this work, we do not apply the traditional
relay scheme. It is because the users are distributed randomly, and by applying the traditional relay selection scheme, it
may happen that some relays have more users connected to them than other relays, which results in having unbalanced
load among the relays. In order to avoid performance degradation, achieve relay load balancing, and maximize the total
data rate of the network, a game theoretic approach is proposed, which efficiently assigns the users to relays. The power of
each relay is wisely distributed among users by the efficient power allocation scheme. Simulation results indicate that the
proposed game-based scheme can considerably improve the average sum-spectral efficiency. Moreover, it shows that by
applying the game, users who can connect to uncongested relays join them as opposed to connecting to congested relays.Grant from the Natural Sciences and Engineering Research Council
(NSERC) IRC and Bell Canada.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1530-86772017-06-30hb2016Electrical, Electronic and Computer Engineerin
Opportunistic distributed channel access for a dense wireless small-cell zone

This paper considers uplink channel access in a zone of closed access small-cells that is deployed in a macrocell service area. All small-cell user equipments (SUEs) have access to a common orthogonal set of channels, leading to intercell interference. In addition, each channel forms a separate collision domain in each cell, thus can be successfully used only by one SUE of that cell. This paper proposes two noncooperative Bayesian games, G1 and G2, that are played among the SUEs. G1 assumes the availability of channel state information (CSI) at the transmitters while G2 assumes the availability of only the distribution of the CSI. Each SUE can choose to transmit over one of the channels or not to transmit. The emphasis of the paper is on the set of symmetric threshold strategies where the Nash equilibrium is fully determined by a single parameter. The existence and uniqueness of pure Bayesian-Nash symmetric equilibrium (BNSE) of G1 in threshold strategies and mixed\ud
BNSE of G2 in uniformly distributed threshold strategies are proven. Numerical results corroborate the theoretical findings and benchmark against another decentralized scheme