Reliability of Atmospheric Radio Noise Predictions L

Total Page:16

File Type:pdf, Size:1020Kb

Reliability of Atmospheric Radio Noise Predictions L JOURNAL OF RESEARCH of the National Bureau of Standards- D. Radio Propagation Vol. 650 , No.6, November- December 1961 Reliability of Atmospheric Radio Noise Predictions l John R. Herman Contribution from AVCO Corporation, Research and Advanced Development Division, Geophysics Section, 201 Lowell Street, Wilmington, Mass. (Received May 15, 1961) Measured radio noise values are compared with the corresponding International Radio Consultative Committee [C.C.I.R., 1957) predicted values at four noise measuring stations. Five freq uencies between 0.013 and 10.0 Mc/s are considered. The stations selected for this ~t ud y include Balboa, Panama, near t wo major radio noise centers, and Byrd Station, Antarctic, remote from. atmosp heric radio noise sources. It is fouod that the predicted and measured noise levels are in good agreement except at some places and times, where large discrepancies occur. :Most of the disagreements are found at places where the predi ctions are based on extrapolations of data measured at other stations. R easons for the disagree­ men ts are disc u sed. 1. Introduction Becausc the success or failure of communications by radio waves depends upon the signal-to-noise i ratio at the receiver, it has become necessary to obtain some knowledge of the noise levels to be expected. Atmospherically-generated radio noise levels vary with time of day, scason, and geographi­ cal lo cation. Prediction of tbe variations on a worldwide basis have been published in Radio Propagation Unit (R.P.U.) Technical R eport No.5 [1945 1947] and National Bureau of Standards Circular 462 [1948] in terms of t he minimum field stl' e ngth~ r ~ quir e ~ to maintain int ell i~ibl e. voi ce commUlllcatlOns nmety percent of the tlme m the presence of noise. Subsequently, the prediction curves were revised in NBS Circular 557 [Crichlow, 1955] to show expected median levrls of radio noise power during four-hour time blo c~{s [or each season. FIGURE 1. Map showing location of noise measul·ing stations As more recent data accumulated It became apparent (reproduced from jVB S Tech. Note 18). that large discrepancies existed between the ill.easured and predicted values at some locatLOns and tImes, so tions where the noise level eould only be interpolated that an entirely new set of predictions was prepared when the predictions were es tablished. and published in International Radio Consultative In this paper comparisons are made of the pre­ Committee (C.C.I.R.) Report No. 65 [1957 ]. The dicted and measured nois0 values at the four sta­ C.C.I.R. predietions closely follow those in Circular tions listed in table 1 for the frequencies (in M c/s) 557, but are based on a wider selection of compatible shown. The letter X indieates the frequencies data, and should thus be more useful than the analyzed at each station. These pal'ticular stations previous publications. .. were selected because their nois; grades range from Cooperative measurements of ~ecelv c d J?-0ISC power high to very low. That is, the noise grade at Balboa at 16 widely separated geographlCal locatlOns (fig. 1) is high beeause it is near two mftjor noise centers in are currently being conducted on a routine basis Central and South Amel'iea, while that at Byrd Sta- under the direetion of the National Bureau of Stand­ ards Central Radio Propagation Laboratory. Since T ABLE 1 the issuance of the C.C.I.R. predictions, several additional noise measuring stations have been estab­ Frequency (M c/s) lished and approximately 5 years of data have been collected. Thus it is now possible to check the ac­ Station and location.. _________ .. 0.013 0.051 0.246 0.495 0.545 2.5 5.0 10.0 Balboa~Panama,9°N,800 W - -. __ . ___ X X X X X X euraey of the predictions during a time of .declining Byrd :;ta., Antarctica, 80° S, 120° W ______ • _______________ ______ . _ X ______ ______ X X X X sunspot activity (1960), and at geographlCal loea- Ellkoping,Swcden,GO oN, 17° E .. _____ X ____________ X X X X K ekaba, H awaii,22° N,160oW.. X _.. _. _______ X . _____ X X X I This work was supported by the U.S. Air Force CaJ1lbridge R esearch Labora· tories under Contract No. AF19(604)-4092. 565 tion is very low because it is remote from all at­ The estimated manmade noise levels shown in mospheric noise sources. The noise grades at Kekaha figures 2 to 5 were also obtained from tbe C.C.LR. and Enkoping are between these two extremes; that report. The values represent what should be ex­ at Kekaha being greater than at Enkoping. pected at r eceiving sites located several miles from populated areas and at least a quarter of a mile 2. Noise Level Predictions from elevated power lines and electrical machinery. The e.C.LR. predictions are presented as world­ 3 . Noise Level Measurements wide noise level contour maps for 1.0 Me/s in 4-hr time blocks for each season. Curves are given to 3.1. Seasonal Time-Block Measured Values determine the noise levels at other frequencies be­ The noise power is r ecorded for 15 min per hI' for tween 10 kc/s and about 30 Mc/s. The magnitude each frequency, and each sampling is assumed to of the noise is in terms of a basic parameter repre­ represent the noise level for the full hour. From senting the median value of atmospheric noise power these data the month-hour medians, Fam, are ob­ passed by a narrow bandwi.dth, expressed for a 4- tained in terms of decibels above kTB and are com­ hour time block. The seasons of the year for the piled in the NBS Technical Note 18 Series [Crichlow, present purpose are defined as in table 2. 1959 a and b] . Also given arc the seasonal 4-hr time The basic parameter, Fa [Crichlow, 1955], used to block values, averaged over each 4-hr period for the describe the noise is defined as the noise power avail­ 3 mon ths of the season in accordance with C.C.I.R. able from a short, vertical, grounded, loss-free recommendations. The tabulations include the up­ antenna in decibels above kTB, where kTB is the per and lower decile values of Fa to give an indica­ terminal noise power in watts, availa.ble from a pas­ tion of the exten t of th e day to day variations from sive resistance (dummy antenna). Here, the median, Fam. The appropriate seasonal time­ k = Boltzmann's constant= 1.38X 10- 23 joules per block noise power averages as reported by Crichlow degree K elvin. [1 959b] are plotted as dashed lines in figures 2 to 5. T=absolute room temperature= 288 ° K. Since the noise-measuring equipment approxi­ B =effective noise bandwidth in cycles per second mates, insofar as practicable, the idealization of the (c/s). system for which the predictions were gen erated, I and corrections have been made in the measured TABLE 2. Seasons of the Year (in accordance with C.C. l .R. R eport No. 65 and NBS Technical Note 18) data for antenna losses and the like, the measured and predi cted values of noise power given in figures Season 2 to 5 can be compared direc tly . Months Northern Southern 3.2. Comparison of Measured and Predicted Sea- H emisphere H emisphere sonal Time-Block Values -----------------------------1--------1-------- D ecember, January, February _ _ _____ _____ __ ______ \\Tinter __ ____ Summer. Mareh, April, May _ ___ _________________ __________ Spring _ _____ Autu mn. As is evident from the curves of figures 2 to 5, a June, July, AugusL _______________________________ Summer _ ___ W inter. gr eat number of discrepancies exist between the September, October, N ovemheL _________________ _ Autumn __ __ Spring. predicted and measured values of noise power. In ' several cases there is very good agreement, but in 2.1. Prediction Curves for Each Station others the predicted noise level is as much as 40 db lower or 24 db higher than that actually measured. The expected atmospheric noise level at each of the However, it should be noted that in most of the four noise measuring stations listed in table 1 are disagreements the predicted noise level is on the low shown in figures 2 to 5 by solid lines for each season side, and a predicted value 24 db too high occurs of the year and each frequency. The corresponding only once (winter 1600- 2000 hour time block at measured noise values are shown in the figures by Byrd Station on 0.051 Mc/s) in the data analyzed dashed lines, and the estimated manmade noise levels here. Furthermore, most of the too-high predicted at the stations are depicted as broken lines. (All values occur at Byrd Station and Enkoping, where times referred to in this paper are local times.) the prediction curves are based on scanty data or More u,bout the measured values is given in the next extrapolation techniques. section. M easured values that are much higher than the The expected atmospheric noise levels shown in corresponding predicted values can be attributed to figures 2 to 5 were prepared from the noise grade maps a number of causes. One of the most important given in the e.C.I.R. report. Because the noise causes is manmade noise ncar the receiving station. maps are for 1.0 MC/8 only, the predIcted values This can arise from automobile ignition systems, of Fa for the frequencies considered here were ex­ power lines, and electrical machinery, which is prop­ trapolated using the curves of noise power as a func­ agated primarily over power lines or by ground tion of frequency given in figures 21 and 22 of that waves.
Recommended publications
  • Vertical Magnetic Noise in the Voice Frequency Band Within and Above Coal Mines
    Report of Investigations 8828 Vertical Magnetic Noise in the Voice Frequency Band Within and Above Coal Mines By John Durkin UNITED STATES DEPARTMENT OF THE INTERIOR William P. Clark, Secretary BUREAU OF MINES Robert C. Horton, Director Library of Congress Cataloging in Publication Data: Durkin, John Vertical magnetic noise in the voice frequency band within and above coal mines. (Report of investigations ; 8828) Bibliography: p. 21-22. Supt. of Docs. no.: I 28.23:8828. 1. Coal mines and mining-Communication systems. 2. Electromag- netic noise. 3. Voice frequency. I. Title. 11. Series: Report of in- vestigations (United States. Bureau of Mines) ; 8828. TN23.U43 [TN344] 622s [622] 83-600 164 CONTENTS Page Abstract ....................................................................... 1 Introduction ................................................................. 2 EM atmospheric noise ........................................................... 2 Noise source ................................................................. 2 Earth-ionosphere waveguide ................................................... 5 Prior atmospheric noise measurements ......................................... 8 Surface vertical magnetic noise .......................................... 9 Underground vertical magnetic noise ...................................... 11 EM vertical magnetic noise field tests ......................................... 12 Noise measuring instrumentation.............................................. 12 Noise data ..................................................................
    [Show full text]
  • Demodulation, Bit-Error Probability and Diversity Arrangements
    RADIO SYSTEMS – ETIN15 Lecture no: 6 Demodulation, bit-error probability and diversity arrangements Anders J Johansson, Department of Electrical and Information Technology [email protected] 5 April 2017 1 Contents • Receiver noise calculations [Covered briefly in Chapter 3 of textbook!] • Optimal receiver and bit error probability – Principle of maximum-likelihood receiver – Error probabilities in non-fading channels – Error probabilities in fading channels • Diversity arrangements – The diversity principle – Types of diversity – Spatial (antenna) diversity performance 5 April 2017 2 RECEIVER NOISE 5 April 2017 3 Receiver noise Noise sources The noise situation in a receiver depends on several noise sources Noise picked up Wanted by the antenna signal Output signal Analog Detector with requirement circuits on quality Thermal noise 5 April 2017 4 Receiver noise Equivalent noise source To simplify the situation, we replace all noise sources with a single equivalent noise source. Wanted How do we determine Noise free signal N from the other sources? N Output signal C Analog Detector with requirement circuits on quality Noise free Same “input quality”, signal-to-noise ratio, C/N in the whole chain. 5 April 2017 5 Receiver noise Examples • Thermal noise is caused by random movements of electrons in circuits. It is assumed to be Gaussian and the power is proportional to the temperature of the material, in Kelvin. • Atmospheric noise is caused by electrical activity in the atmosphere, e.g. lightning. This noise is impulsive in its nature and below 20 MHz it is a dominating. • Cosmic noise is caused by radiation from space and the sun is a major contributor.
    [Show full text]
  • Signal to Noise Ratio (SNR Or S/N) SNR Is the Ratio of the Signal Power to Noise Power
    JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA KAKINADA – 533 001 , ANDHRA PRADESH GATE Coaching Classes as per the Direction of Ministry of Education GOVERNMENT OF ANDHRA PRADESH Analog Communication 26-05-2020 to 06-07-2020 Prof. Ch. Srinivasa Rao Dept. of ECE, JNTUK-UCE Vizianagaram Analog Communication-Day 7, 01-06-2020 Presentation Outline Noise: • Noise and its types • Thermal Noise • Parameters of Noise • Noise in Baseband Communication systems • Gaussian process and NB Noise • Problems 02-06-2020 Prof.Ch.Srinivasa Rao, JNTUK UCEV 2 Learning Outcomes • At the end of this Session, Student will be able to: • LO 1 : Fundamental aspects of Noise • LO 2 : Different Noise types and its Paraneters • LO 3 : Gaussian Process and ND Noise 02-06-2020 Prof.Ch.Srinivasa Rao, JNTUK UCEV 3 Noise Definitions of Noise: • Noise is unwanted signal that affects wanted signal. • Noise can broadly be defined as any unknown signal that affects the recovery of the desired signal. • Noise is an unwanted signal, which interferes with the original message signal and corrupts the parameters of the message signal. This alteration in the communication process, leads to the message getting altered. It most likely enters at the channel or the receiver. Effect of noise • Degrades system performance (Analog and digital) • Receiver cannot distinguish signal from noise • Efficiency of communication system reduces Most common examples of noise are: • Hiss sound in radio receivers • Buzz sound amidst of telephone conversations • Flicker in television receivers, Noise Sources External Internal Noise Noise Atmospheric Industrial Extraterrestrial Solar Noise Cosmic Noise External Noise : • It is due to Man- made and natural resources • Sources over which we have no control such as thunders, snow fall, lightning etc.
    [Show full text]
  • Rolling Bearings Fault Diagnosis and Classi Cation Based on Optimal Wavelet Subband Selection and Deep Neural Network
    Rolling bearings fault diagnosis and classication based on optimal wavelet subband selection and deep neural network Rakesh Kumar Jha ( [email protected] ) Rajiv Gandhi Proudyogiki Vishwavidyalaya https://orcid.org/0000-0002-6568-8083 Preety D Swami Rajiv Gandhi Proudyogiki Vishwavidyalaya Research Article Keywords: Autoencoder, fault diagnosis, Shannon entropy, softmax classier, stationary wavelet transform. Posted Date: March 23rd, 2021 DOI: https://doi.org/10.21203/rs.3.rs-342393/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Rolling bearings fault diagnosis and classification based on optimal wavelet subband selection and deep neural network Rakesh Kumar Jha1, a *, Preety D Swami1,b 1 Department of Electronics & Communication Engineering, RGPV, Bhopal- 462033, India *a email-address: [email protected] b email-address: [email protected] Abstract Time-frequency analysis plays a vital role in fault diagnosis of nonstationary vibration signals acquired from mechanical systems. However, the practical applications face the challenges of continuous variation in speed and load. Apart from this, the disturbances introduced by noise are inevitable. This paper aims to develop a robust method for fault identification in bearings under varying speed, load and noisy conditions. An Optimal Wavelet Subband Deep Neural Network (OWS-DNN) technique is proposed that automatically extracts features from an optimal wavelet subband selected on the basis of Shannon entropy. After denoising the optimal subband, the optimal subbands are dimensionally reduced by the encoder section of an autoencoder. The output of the encoder can be considered as data features. Finally, softmax classifier is employed to classify the encoder output.
    [Show full text]
  • Dr. Dharmendra Kumar Assistant Professor Department of Electronics and Communication Engineering MMM University of Technology, Gorakhpur–273010
    Dr. Dharmendra Kumar Assistant Professor Department of Electronics and Communication Engineering MMM University of Technology, Gorakhpur–273010. Content of Unit-3 Noise: Source of Noise, Frequency domain, Representation of noise, Linear Filtering of noise, Noise in Amplitude modulation system, Noise in SSB-SC,DSB and DSB-C, Noise Ratio, Noise Comparison of FM and AM, Pre- emphasis and De-emphasis, Figure of Merit. Claude E. Shannon conceptualized the communication theory model in the late 1940s. It remains central to communication study today. Noise is random signal that exists in communication systems Channel is the main source of noise in communication systems Transmitter or Receiver may also induce noise in the system There are mainly 2-types of noise sources Internal noise source (are mainly internal to the communication system) External noise source Noise is an inconvenient feature which affects the system performance. Following are the effects of noise Degrade system performance for both analog and digital systems Noise limits the operating range of the systems Noise indirectly places a limit on the weakest signal that can be amplified by an amplifier. The oscillator in the mixer circuit may limit its frequency because of noise. A system’s operation depends on the operation of its circuits. Noise limits the smallest signal that a receiver is capable of processing The receiver can not understand the sender the receiver can not function as it should be. Noise affects the sensitivity of receivers: Sensitivity is the minimum amount of input signal necessary to obtain the specified quality output. Noise affects the sensitivity of a receiver system, which eventually affects the output.
    [Show full text]
  • The Response of an ENSO Model to Climate Noise, Weather Noise and Intraseasonal Forcing
    GEOPHYSICAL RESEARCH LETTERS, VOL. 27, NO. 22, PAGES 3723-3726, NOVEMBER 15, 2000 The Response of an ENSO Model to Climate Noise, Weather Noise and Intraseasonal Forcing Mark S. Roulston 1 Division of Geological and Planetary Sciences, Caltech 150-21, CA 91125 J. David Neelin Department of Atmospheric Sciences, UCLA, CA 90095-1565 Abstract. The response of an intermediate coupled model to distinguish between these two types of stochastic forcing. of the tropical Pacific to different forms of stochastic wind The term “weather noise” has been used in the ENSO liter- forcing is studied. An estimate of observed Pacific wind ature to refer to effects of atmospheric internal variability. variance that is unrelated to Pacific sea surface temperature The term “climate noise” has been used in some earlier liter- (SST) has a red spectrum, inconsistent with standard defini- ature [Leith, 1978] to refer to weather noise, but we propose tions of “weather noise”. The reddening is likely due to SST that it is more usefully reserved for noise effects that include outside the basin; we propose a definition of “climate noise” some reddening by oceanic or other slow components of the for such reddened variance. Effects are compared for (i) red climate system outside the domain of study. climate noise; (ii) the corresponding white weather noise This paper investigates the impact of these different types estimate; (iii) intraseasonal and interannual components of of noise on an intermediate coupled model (ICM) of the the white noise (to test frequency response); and (iv) a noise tropical Pacific. product with extra power in the 30-60 day range.
    [Show full text]
  • ECE 6640 Digital Communications
    ECE 6640 Digital Communications Dr. Bradley J. Bazuin Assistant Professor Department of Electrical and Computer Engineering College of Engineering and Applied Sciences Chapter 5 5. Communications Link Analysis. 1. What the System Link Budget Tells the System Engineer. 2. The Channel. 3. Received Signal Power and Noise Power. 4. Link Budget Analysis. 5. Noise Figure, Noise Temperature, and System Temperature. 6. Sample Link Analysis. 7. Satellite Repeaters. 8. System Trade-Offs. ECE 6640 2 Sklar’s Communications System Notes and figures are based on or taken from materials in the course textbook: ECE 6640 Bernard Sklar, Digital Communications, Fundamentals and Applications, 3 Prentice Hall PTR, Second Edition, 2001. What is a Link Budget • An analysis of the entire communications path – signal, noise, interference, ISI contributions, etc. – Include gains and losses • Link Budget – An estimate of the input to output system performance – Will the message get communicated? – What trade-offs can be made and what effect will they have? ECE 6640 4 The Channel • The propagation medium of the communicated signal • Between the transmitting device and the receiving device (e.g. RF antennas, cable modems, fiber optic transceivers) • For RF we think of “Free Space” – An ideal approximation for near-ground, atmospheric RF transmissions. – Non ideal atmospheric impairments include: absorption, reflection, diffraction, scattering. ECE 6640 5 Error-Performance Degradation • Established in Chapter 3 – Loss of SNR – Intersymbol interference • For Digital Communications E S W b N0 N R – The relationship between SNR and Eb/No – SNR relates the average signal power and average noise power – Eb/N0 relates the energy per bit to the noise energy – Loss: refers to a loss in signal energy – Noise: refers to an increase in noise or interference energy ECE 6640 6 Sources of Signal Loss and Noise 1.
    [Show full text]
  • TI-TRNG: Technology Independent True Random Number Generator
    TI-TRNG: Technology Independent True Random Number Generator Md. Tauhidur Rahman, Kan Xiao, Domenic Forte, Xuhei Zhang, Jerry Shi, and Mohammad Tehranipoor ECE Department, University of Connecticut {tauhid, kanxiao, forte, xuz09001, zshi, tehrani}@engr.uconn.edu ABSTRACT and customer-facing web access [1-8]. A quality RNG gen- True random number generators (TRNGs) are needed for erates statistically independent and unpredictable sequences a variety of security applications and protocols. The qual- of random numbers. Compromising an RNG often means ity (randomness) of TRNGs depends on sensitivity to ran- compromising an entire system. dom noise, environmental conditions, and aging. Random A true RNG (TRNG) translates random physical phenom- sources of noise improve TRNG quality. In older or more ena such as thermal noise, atmospheric noise, shot noise, ra- mature technologies, the random sources are limited result- dio noise, flicker noise, clock jitter, phase noise, noise in a ing in low TRNG quality. Prior work has also shown that compact memory etc. into random digits [1-8]. A TRNG attackers can manipulate voltage supply and temperature must have uniform statistics; non-uniform statistics due to to bias the TRNG output. In this paper, we propose bias biased random sequences help attackers to guess the ran- detection mechanisms and a technology independent TRNG dom numbers. Generally, random numbers are generated by (TI-TRNG) architecture. The TI-TRNG enhances power comparing two symmetric devices which possess some pro- supply noise for older technologies and uses a self-calibration cess variation (PV) and random inner noise. Variations in mechanism that reduces bias in TRNG output due to ag- the inherent process, operating temperature, VDD , and ag- ing and attacks.
    [Show full text]
  • Detecting Ground Deformation in the Built Environment Using Sparse Satellite Insar Data with a Convolutional Neural Network
    This is a repository copy of Detecting Ground Deformation in the Built Environment Using Sparse Satellite InSAR Data With a Convolutional Neural Network. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/165027/ Version: Accepted Version Article: Anantrasirichai, N, Biggs, J, Kelevitz, K et al. (5 more authors) (2020) Detecting Ground Deformation in the Built Environment Using Sparse Satellite InSAR Data With a Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing. ISSN 0196-2892 https://doi.org/10.1109/tgrs.2020.3018315 © 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. Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including
    [Show full text]
  • Implications of Increasing Man Made Noise Floor Levels on Radio Broadcasting
    Implications Of Increasing Man Made Noise Floor Levels On Radio Broadcasting Charles W. Kelly, Jr. Nautel Limited Halifax, NS Canada 1 Noise is Everywhere • Noise has been a fact of life since Marconi first complained about ignition noise from early cars. • With the state of the art of receivers, the ambient noise floor, rather than the receiver sensitivity determines the receiving threshold in the AM & FM bands. • There are three basic types of radio noise: Natural, Unintentional, and Intentional. 2 Natural Noise: Atmospheric • Atmospheric Noise: Atmospheric noise is primarily caused by lightning, and as such, varies due to the proximity to storms, and the time of the year. The noise amplitude caused declines roughly 50 dB per frequency decade from 10 kHz to 10 MHz. • Atmospheric noise is the dominant natural noise source in the AM band. Atmospheric noise is more problematic in the night time hours because distant lightning storms can propagate long distances via sky wave. 3 Natural Noise: Thermal • Thermal Noise: Also known as Johnson–Nyquist noise. Caused by thermal agitation of electrons, and is roughly linear with respect to frequency. • Because atmospheric noise declines so dramatically with frequency, thermal noise is the dominant natural noise source on the FM broadcast band. 4 Man Made: Power Line Noise • Power line noise often is caused by arcing across power line equipment. It declines in amplitude with frequency, and is typically more troublesome in rainy and windy conditions. • Affects AM and FM but is most often noticed on AM as the recognizable buzz is demodulated in an AM receiver more readily than on FM.
    [Show full text]
  • Evolution of the Soundscape Following the Traffic Closure on the Right Bank of the Seine in Paris
    Evolution of the soundscape following the traffic closure on the right bank of the Seine in Paris Fanny Mietlicki Bruitparif, France. Matthieu Sineau Bruitparif, France.. Summary Since September 2016, by decision of the City of Paris, the right bank of the Seine river is closed to traffic along 3.3 km. Bruitparif has put in place a system for assessment of the modifications on the sound environment brought by this closure. This system was based on the implementation of noise measurements at 90 sites in and around Paris as well as modeling. At the end of a year of follow-up, it was possible to establish the following observations: 1. Partial traffic shifts to the lanes located above the banks have generated an increase in traffic noise for residents. This is more pronounced at night (up to 4 dB (A) in some areas) than during the day (less than 2 dB (A) increase). However, during daytime there is an increase of noise peaks (horns, sirens of emergency vehicles ...) related to rising congestion. 2. Other axes in Paris have also undergone a slight increase in noise likely related to the traffic reports, but in a more limited manner (of the order of 1 dB (A)). 3. Outside Paris, especially on the ring road and major roads, no significant change has been noted. 4. At the end of a year of observation, there does not seem to have been an adaptation of the behavior of drivers. 5. On the other hand, traffic noise has clearly decreased on the bank which is now pedestrianized, as well as on the façades of the first buildings located opposite the right banks of the Seine river.
    [Show full text]
  • Download Preprint
    1 This manuscriptis a preprintand has been submitted for publication in IEEE Transactions on Geoscience and Remote Sensing. Please note that, despite having undergone peer-review, the manuscript has yet to be formally accepted for publication. Subsequent versions of this manuscript may have slightly different content.If accepted, the final version of this manuscript will be available via the ‘Peer-reviewed Publication DOI’ link on the right-hand side of this webpage. Please feel free to contact any of the authors; we welcome feedback. Authors: N. Anantrasirichai, Visual Information Laboratory, University of Bristol, UK ( [email protected]) J. Biggs, School of Earth Sciences, University of Bristol, UK ([email protected]) K. Kelevitz, COMET, School of Earth and Environment, University of Leeds, UK ([email protected]) Z. Sadeghi, COMET, School of Earth and Environment, University of Leeds, UK ([email protected]) T .Wright, COMET, School of Earth and Environment, University of Leeds, UK ([email protected]) J. Thompson, School of Earth and Environment, University of Leeds, UK ([email protected]) A. Achim, Visual Information Laboratory, University of Bristol, UK ([email protected]) D. Bull, Visual Information Laboratory, University of Bristol, UK ([email protected]) September 2, 2020 DRAFT DRAFT JULY 2020 2 Detecting Ground Deformation in the Built Environment using Sparse Satellite InSAR data with a Convolutional Neural Network Nantheera Anantrasirichai, Member, IEEE, Juliet Biggs, Krisztina Kelevitz, Zahra Sadeghi, Tim Wright, James Thompson, Alin Achim, Senior Member, IEEE and David Bull, Fellow, IEEE Abstract The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services.
    [Show full text]