Suchergebnisse

Bathymetric data in coastal area are important for marine sciences, hydrological applications and even for transportation and military purposes. Compare to traditional sonar and recent airborne bathymetry LIDAR systems, optical satellite images can provide information to survey a large area with single or multiple satellite images efficiently and economically. And it is especially suitable for coastal area because the penetration of visible light in water merely reaches 30 meters. In this study, a three-layer back propagation neural network is proposed to estimate bathymetry. In the learning stage, some training samples with known depth are adopted to train the weights of the neural network until the stopping criterion is satisfied. The spectral information is sent to the input layer and fits the true water depth with the output. The depths of training samples are manually measured from stereo images of the submerged reefs after water refraction correction. In the testing stage, all non-land pixels are processed. The experiments show the mean square errors are less than 3 meters.

Bathymetric data in coastal area are important for marine sciences, hydrological applications and even for transportation and military purposes. Compare to traditional sonar and recent airborne bathymetry LIDAR systems, optical satellite images can provide information to survey a large area with single or multiple satellite images efficiently and economically. And it is especially suitable for coastal area because the penetration of visible light in water merely reaches 30 meters. In this study, a three-layer back propagation neural network is proposed to estimate bathymetry. In the learning stage, some training samples with known depth are adopted to train the weights of the neural network until the stopping criterion is satisfied. The spectral information is sent to the input layer and fits the true water depth with the output. The depths of training samples are manually measured from stereo images of the submerged reefs after water refraction correction. In the testing stage, all non-land pixels are processed. The experiments show the mean square errors are less than 3 meters.

Abstract. The relationship between variations in surface latent heat flux (SLHF) and marine earthquakes has been a popular subject of recent seismological studies. So far, there are two key problems: how to identify the abnormal SLHF variations from complicated background signals, and how to ensure that the anomaly results from an earthquake. In this paper, we proposed four adjustable parameters for identification, classified the relationship and analyzed SLHF changes several months before six marine earthquakes by employing daily SLHF data. Additionally, we also quantitatively evaluate the long-term relationship between earthquakes and SLHF anomalies for the six study areas over a 20 yr period preceding each earthquake. The results suggest the following: (1) before the South Sandwich Islands, Papua, Samoa and Haiti earthquakes, the SLHF variations above their individual background levels have relatively low amplitudes and are difficult to be considered as precursory anomalies; (2) after removing the clustering effect, most of the anomalies prior to these six earthquakes are not temporally related to any earthquake in each study area in time sequence; (3) for each case, apart from Haiti, more than half of the studied earthquakes, which were moderate and even devastating earthquakes (larger than Mw = 5.3), had no precursory variations in SLHF; and (4) the correlation between SLHF and seismic activity depends largely on data accuracy and parameter settings. Before any application of SLHF data on earthquake prediction, we suggest that anomaly-identifying standards should be established based on long-term regional analysis to eliminate subjectivity. Furthermore, other factors that may result in SLHF variations should also be carefully considered.

Reliable and objective data regarding building stock is essential for predicting and analyzing energy demand and carbon emission. However, China's building stock data is lacking. This study proposes a set of China building floor space estimation method (CBFSM) based on the improved building stock turnover model. Then it measures China's building stocks by vintage and type from 2000 to 2015, as well as building energy intensity (national level and provincial level) and energy-efficient buildings. Results showed that total building stocks increased significantly, rising from 35.2 billion m2 in 2000 to 63.6 billion m2 in 2015, with the average growth rate 4.0%. The deviations were well below 10% by comparing with China Population Census, which validated the reliability of CBFSM and the results. As for energy intensity, urban dwellings and rural dwellings showed relatively stable and increasing trend respectively. The commercial building energy intensity saw a downward trend during "12th Five Year Plan" period. This indicated the effectiveness of building energy efficiency work for commercial buildings since 2005.38.6 billion m2 residential dwellings and 5.7 billion m2 commercial buildings still need to be retrofitted in future. CBFSM can overcome shortages in previous studies. It can also provide Chinese government with technical support and data evidence to promote the building energy efficiency work.

China's energy consumption in the building sector (BEC) is not counted as a separate type of energy consumption, but divided and mixed in other sectors in China's statistical system. This led to the lack of historical data on China's BEC. Moreover, previous researches' shortages such as unsystematic research on BEC, various estimation methods with complex calculation process, and difficulties in data acquisition resulted in "heterogeneous" of current BEC in China. Aiming to these deficiencies, this study proposes a set of China building energy consumption calculation method (CBECM) by splitting out the building related energy consumption mixed in other sectors in the composition of China Statistical Yearbook-Energy Balance Sheet. Then, China's BEC from 2000 to 2014 are estimated using CBECM and compared with other studies. Results show that, from 2000 to 2014, China's BEC increased 1.7 times, rising from 301 to 814 million tons of standard coal consumed, with the BEC percentage of total energy consumption stayed relatively stable between 17.7% and 20.3%. By comparison, we find that our results are reliable and the CBECM has the following advantages over other methods: data source is authoritative, calculation process is concise, and it is easy to obtain time series data on BEC etc. The CBECM is particularly suitable for the provincial government to calculate the local BEC, even in the circumstance with statistical yearbook available only.

We examined the underground magnetic field near the lightning channel with low-frequency magnetic sensor based on SHAndong Triggering Lightning Experiment (SHATLE). Two sensors were deployed, one at 1-m height above ground and another one at 2-m depth underground at a distance of 78 m from the lightning channel, and the magnetic pulses during the initial stage of triggered lightning were recorded. The experimental results show that the microsecond-scale magnetic pulses radiated by the upward lightning leader can be detected in the subsurface space and the magnetic signal is modified by the soil medium. Specifically, the amplitude at the depth of 2 m is attenuated typically more than 55%, and the attenuation decreases as the timescale of the magnetic pulse increases; meanwhile, the peak time of the underground magnetic pulse is delayed by about 0.6 μs, and the half-peak width of the magnetic pulse is increased by 0.2–0.8 μs (namely, by 20% to 32%). The results of Fourier analysis indicate that the component with relatively high frequency is subject to more attenuation than is the component with relatively low frequency. In addition, the simulation of magnetic field with the channel-base current by using the transmission line model is consistent with the measurement, indicating that the modification on the waveform characteristics of the lightning pulse measured underground could provide valuable information for retrieving the electromagnetic parameters of soil.©2019. American Geophysical Union. All Rights Reserved. ; This work was supported by the National Key R&D Program of China (2017YFC1501501) and the National Natural Science Foundation of China (41574179 and 41622501). The work of Dongshuai Li is supported by the European Research Council (ERC) under the European Union H2020 programme/ERC grant agreement 681257. ; Peer Reviewed

Using the data sets taken at center-of-mass energies above 4 GeV by the BESIII detector at the BEPCII storage ring, we search for the reaction e(+)e(-) -> gamma(ISR) X(3872) -> gamma(ISR)pi(+)pi(-) J/psi via the Initial State Radiation technique. The production of a resonance with quantum numbers J(PC) = 1(++) such as the X(3872) via single photon e(+)e(-) annihilation is forbidden, but is allowed by a next-to-leading order box diagram. We do not observe a significant signal of X(3872), and therefore give an upper limit for the electronic width times the branching fraction Gamma B-X(3872)(ee)(X(3872) -> pi(+)pi(-) J/psi) < 0.13 eVat the 90% confidence level. This measurement improves upon existing limits by a factor of 46. Using the same final state, we also measure the electronic width of the psi(3686) to be Gamma(psi)(ee)(3686) ee = 2213 +/- 18(stat) +/- 99(sys) eV. ; Funding: The BESIII collaboration thanks the staff of BEPCII and the IHEP computing center for their strong support. This work is supported in part by the National Key Basic Research Program of China under Contract No. 2015CB856700; National Natural Science Foundation of China (NSFC) under Contract Nos. 11125525, 11235011, 11322544, 11335008, 11425524; the Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; Joint Large-Scale Scientific Facility Funds of the NSFC and CAS under Contract Nos. 11179007, U1232201, U1332201; CAS under Contract Nos. KJCX2-YW-N29, KJCX2-YW-N45; 100 Talents Program of CAS; INPAC and Shanghai Key Laboratory for Particle Physics and Cosmology; German Research Foundation DFG under Contract No. CRC-1044; Seventh Framework Programme of the European Union under Marie Curie International Incoming Fellowship Grant Agreement No. 627240; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Development of Turkey under Contract No. DPT2006K-120470; Russian Foundation for Basic Research under Contract No. 14-07-91152; U.S. Department of Energy under Contract Nos. DE-FG02-04ER41291, DE-FG02-05ER41374, DE-FG02-94ER40823, DESC0010118; U.S. National Science Foundation; University of Groningen (RuG) and the Helmholtzzentrum fur Schwerionenforschung (GSI), Darmstadt; WCU Program of National Research Foundation of Korea under Contract No. R32-2008-000-10155-0.

We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC, and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST, and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR, and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE, and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF, and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020, and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Région Auvergne, and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes cofinanced by EU-ESF and the Greek NSRF; BSF, GIF, and Minerva, Israel; BRF, Norway; Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK), and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers. Major contributors of computing resources are listed in Ref. [74]

We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain; SRC and Knut and Alice Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Sklodowska-Curie Actions, European Union; Investissements d'Avenir Labex and Idex, ANR, Région Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; Generalitat de Catalunya, Generalitat Valenciana, Spain; the Royal Society and Leverhulme Trust, United Kingdom.