47,346 research outputs found
From MFN to SFN: Performance Prediction Through Machine Learning
No full textIn the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97
Three-stages concatenated Machine Learning model for SFN prediction
No full textThe single frequency network (SFN) has been assumed worldwide by telecommunication operators to save radio frequency resources and homogenize the network. Its applications have transcended the digital terrestrial television and digital radio to become part of the key techniques of the broadband and broadcast convergence for LTE-A, 5G and beyond. However, the transition from a multi frequency network (MFN) to an SFN involves multiple measurement campaigns and tuning of the network to achieve the expected up-performance and quality of service. This paper aims to propose a machine learning model to predict the SFN performance from the legacy MFN parameters. The model is based on regression and classification machine learning algorithms concatenated in three consecutive stages to predict SFN electric-field strength, modulation error ratio and gain. The training and test processes are performed with a dataset of 389 samples from an SFN/MFN trial in Ghent, Belgium. The best performance is obtained with concatenating gradient boosting, random forest, and linear regression, which allows predicting the SFN electric-field strength with an R2 of 92%, the modulation error ratio with 95%, and SFN gain with 87% from only MFN and position data. Besides, the model allows classifying the data points according to positive or negative SFN gain with an accuracy of 93%
A 2 h periodic variation in the low-mass X-ray binary Ser X-1
No full textSpectroscopy of the low-mass X-ray binary Ser X-1 using the Gran Telescopio Canarias have revealed a ?2 h periodic variability that is present in the three strongest emission lines. We tentatively interpret this variability as due to orbital motion, making it the first indication of the orbital period of Ser X-1. Together with the fact that the emission lines are remarkably narrow, but still resolved, we show that a main-sequence K dwarf together with a canonical 1.4 M? neutron star gives a good description of the system. In this scenario, the most likely place for the emission lines to arise is the accretion disc, instead of a localized region in the binary (such as the irradiated surface or the stream-impact point), and their narrowness is due instead to the low inclination (?10°) of Ser X-1
Extracting Boer-Mulders functions from p+D Drell-Yan processes
No full textWe extract the Boer- Mulders functions of valence and sea quarks in the proton from unpolarized p + D Drell- Yan data measured by the FNAL E866 Collaboration. Using these Boer- Mulders functions, we calculate the cos2 phi asymmetries in unpolarized pp Drell- Yan processes, both for the FNAL E866/ NuSea and the BNL Relativistic Heavy Ion Collider experiments. We also estimate the cos2 phi asymmetries in the unpolarized p (P) over bar Drell- Yan processes at GSI.Astronomy & AstrophysicsPhysics, Particles & FieldsSCI(E)37ARTICLE5null7
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Measurement of the D+/- production asymmetry in 7 TeV pp collisions
No full textThe asymmetry in the production cross-section \sigma of D+/- mesons, A_P = (\sigma(D+) - \sigma(D-))/(\sigma(D+) + \sigma(D-)), is measured in bins of pseudorapidity \eta and transverse momentum p_T within the acceptance of the LHCb detector. The result is obtained with a sample of D+ -> K_S pi+ decays corresponding to an integrated luminosity of 1.0 fb^-1, collected in pp collisions at a centre of mass energy of 7 TeV at the Large Hadron Collider. When integrated over the kinematic range 2.0 K_S pi+ decay is negligible. No significant dependence on \eta or p_T is observed
1ST MEASUREMENT OF GAMMA(D(S)(+)-]MU+NU)/GAMMA(D(S)(+)-]PHI-PI+)
No full textComplete Author List:
ACOSTA D, ATHANAS M, MASEK G, PAAR H, BEAN A, GRONBERG J, KUTSCHKE R, MENARY S, MORRISON RJ, NAKANISHI S, NELSON HN, NELSON TK, RICHMAN JD, RYD A, TAJIMA H, SCHMIDT D, SPERKA D, WITHERELL MS, PROCARIO M, YANG S, BALEST R, CHO K, DAOUDI M, FORD WT, JOHNSON DR, LINGEL K, LOHNER M, RANKIN P, SMITH JG, ALEXANDER JP, BEBEK C, BERKELMAN K, BESSON D, BROWDER TE, CASSEL DG, CHO HA, COFFMAN DM, DRELL PS, EHRLICH R, GALIK RS, GARCIASCIVERES M, GEISER B, GITTELMAN B, GRAY SW, HARTILL DL, HELTSLEY BK, JONES CD, JONES SL, KANDASWAMY J, KATAYAMA N, KIM PC, KREINICK DL, LUDWIG GS, MASUI J, MEVISSEN J, MISTRY NB, NG CR, NORDBERG E, OGG M, PATTERSON JR, PETERSON D, RILEY D, SALMAN S, SAPPER M, WORDEN H, WURTHWEIN F, AVERY P, FREYBERGER A, RODRIGUEZ J, STEPHENS R, YELTON J, CINABRO D, HENDERSON S, KINOSHITA K, LIU T, SAULNIER M, SHEN F, WILSON R, YAMAMOTO H, ONG B, SELEN M, SADOFF AJ, AMMAR R, BALL S, BARINGER P, COPPAGE D, COPTY N, DAVIS R, HANCOCK N, KELLY M, KWAK N, LAM H, KUBOTA Y, LATTERY M, NELSON JK, PATTON S, PERTICONE D, POLING R, SAVINOV V, SCHRENK S, WANG R, ALAM MS, KIM IJ, NEMATI B, ONEILL JJ, SEVERINI H, SUN CR, ZOELLER MM, CRAWFORD G, DAUBENMIER CM, FULTON R, FUJINO D, GAN KK, HONSCHEID K, KAGAN H, KASS R, LEE J, MALCHOW R, MORROW F, SKOVPEN Y, SUNG M, WHITE C, WHITMORE J, WILSON P, BUTLER F, FU X, KALBFLEISCH G, LAMBRECHT M, ROSS WR, SKUBIC P, SNOW J, WANG PL, WOOD M, BORTOLETTO D, BROWN DN, FAST J, MCILWAIN RL, MIAO T, MILLER DH, MODESITT M, SCHAFFNER SF, SHIBATA EI, SHIPSEY IPJ, WANG PN, BATTLE M, ERNST J, KROHA H, ROBERTS S, SPARKS K, THORNDIKE EH, WANG CH, DOMINICK J, SANGHERA S, SHELKOV V, SKWARNICKI T, STROYNOWSKI R, VOLOBOUEV I, ZADOROZHNY P, ARTUSO M, HE D, GOLDBERG M, HORWITZ N, KENNETT R, MONETI GC, MUHEIM F, MUKHIN Y, PLAYFER S, ROZEN Y, STONE S, THULASIDAS M, VASSEUR G, ZHU G, BARTELT J, CSORNA SE, EGYED Z, JAIN V, SHELDON P, AKERIB DS, BARISH B, CHADHA M, CHAN S, COWEN DF, EIGEN G, MILLER JS, OGRADY C, URHEIM J, WEINSTEIN A
Universal Statistical Properties of Inertial-particle Trajectories in Three-dimensional, Homogeneous, Isotropic, Fluid Turbulence
No full textWe obtain new universal statistical properties of heavy-particle trajectories in three-dimensional, statistically steady, homogeneous, and isotropic turbulent flows by direct numerical simulations. We show that the probability distribution functions (PDFs) P(Φ), of the angle Φ between the Eulerian velocity u and the particle velocity v, at a point and time, scales as P(Φ) ∼Φ−, with a new universal exponent ≃ 4
- …
