dataesr/scientific-paragraphs-categorization huggingface dataset:
Cloud radio access network is one of the most promising cellular networks for the next generation of mobile networks. The basic idea of cloud RAN (radio access network) is virtualizing and centralizing the intelligent part of the base station, the base band unit, and keeping remote radio heads on cell site enabling a centralized processing and management. Offloading data computation to edge cloud was proposed as a solution to deal with resource limitation while keeping a good quality of service. In this paper, we propose a strategy to jointly handle offloading decision and offloading request scheduling in cloud RAN. We aim to improve network quality of service while reducing the scheduling cost expressed in terms of overload, network delay, and migration cost. Numerical results show that the proposed approach is able to reduce the response time of the applications, mobile terminal energy consumption, and total execution cost.
Introduction With the success of smartphones, mobile developers are becoming more ingenious in creating sophisticated applications to attract users such as face recognition, interactive gaming, and augmented reality applications. However, these applications are resource-intensive, i. e. , they require high processing and energy capabilities to be executed. Finding a tradeoff between limited resources and battery lifetime of mobile devices and resource-intensive applications is a big challenge for next-generation mobile platform development  [1]. Mobile cloud computing was proposed as a solution to tackle this challenge  [2]. Offloading of total or part of application workflow to a resource-rich cloud infrastructure helps to increase mobile devices capabilities. However, computation offloading to remote central cloud will not solve the problem while mobile users may experience long latency for data exchange with the central cloud through the wide area network.
Using this infrastructure will allow to reduce latency by deploying a fiber transport network between base stations and edge cloud data centers. Endowing base stations with additional computation and storage resources is expected to enhance mobile users' QoS anywhere and at any time  [5]. In a C-RAN (cloud radio access network) infrastructure, base band units are moved from cell site to a central data central. With this approach, all the radio access network functionalities are centralized in the cloud. Centralized processing enables indeed more advanced and efficient network coordination and management. In the cell site, RRH (radio remote head) is still responsible for transmitting radio signal, amplification of signal power and analog to/from digital conversion, while all the base band signal processing parts, including physical, mac, and upper layers, which require higher processing resources, are relocated from the cell site to a centralized BBU (base band unit) in the operator cloud infrastructure.
The interface between the numerous RRH and BBU is named CPRI (common public radio interface). This interface supports a bidirectional constant bit rate protocol that requires accurate synchronization and strict latency control. Other protocols have also been proposed for this interface such as OBSAI (Open Base Station Architecture Initiative) and ORI (open radio equipment interface). The general C-RAN architecture is illustrated in Fig. 1. In this paper, we propose a novel strategy for data offloading in cloud RAN-based mobile edge cloud computing. We aim to fully leverage the potential of such infrastructure throughout joint offloading decision and offloading requests scheduling in the edge cloud. One of the most critical issues impacting data offloading performances is task scheduling in cloud resources.
For example, when too many applications are offloaded to the same edge cloud, if all of them are executed on the same container, it will be highly overloaded. In return, this situation will lead to high energy consumption and long application response time. Therefore, in order to efficiently benefit from data computation offloading, we need to address two key challenges: (1)How to choose between local execution on mobile device and offloading to the cloud? (2)Once an application is offloaded to the cloud, how to schedule it among the available resources? The rest of the paper is organized as follows: In the next section, different related works are discussed. In Section 3, we explain our strategy for joint offloading decision and offloading request scheduling. Then, in Section 4, we study the system performances and present simulation results.
Related work Internet data traffic is increasing exponentially, especially the portion of traffic going through mobile networks. That is why mobile data computation offloading has become an important issue in cellular networks. As a result, various cloud offloading systems were proposed in the literature. MAUI (Mobile Assistance Using Infrastructure)  [1]  and ThinkAir  [6]  describe hardware components and propose to offload data in order to optimize energy consumption of mobile devices. However, they ignore other aspects of offloading. CloneCloud  [7]  proposes to improve application partitioning between the device and the cloud with the purpose of reducing energy consumption or execution time. In addition to mobile cloud frameworks, many other research works have focused on offloading decisionmaking issue in MCC (mobile cloud computing) based systems.
System model 3. 1. 1 Computation offloading system model The scenario is depicted in Fig. 2. We consider a H-CRAN (heterogeneous cloud radio access network) composed of H-RRHs (high remote radio heads) that act as macro-cells and L-RRHs (low remote radio heads) that act as small cells. In our previous works, we proposed to add an edge cloud, the Cloud-RRH  [19]. It represents additional cloud resources close to the mobile end user. While in traditional cloud RAN architecture all RAN functionalities are centralized in the cloud, we proposed to flexibly split RAN functionalities. Besides, the Cloud-RRH contains additional computation and storage resources for data offloading. We also make the following assumptions: (i) mobile applications that are utilized by the mobile user are installed on the mobile device, on the cloud server, and also on the Cloud-RRH; (ii) even if the interface between mobile terminal and clouds may provide different rate and delay values, mobile broadband connectivity does not change during the application processing time.
Note that the second assumption means that we considered applications with no large processing time. This condition has also been assumed by most of the related works  [5, [20] [21] [22] [23]. The system is composed of M mobile users that can be served by either a high or a low RRH. We consider uplink. The time needed to transfer S up bits in the UL (uplink) connection between mobile device and the serving RRH t up depends only on the uplink data rate r up and the number of bits to be transmitted, i. e. , t up = S up / r up. Similarly, for DL (downlink) transmission, after remote processing, from Cloud-RRH to the serving RRH, the time required is t dl = S dl /r dl , with S dl the number of bits to transmit and r dl the downlink data rate. The measurements provided in  [22]  proved that the power consumed by the mobile device in UL increases with the uplink transmission power, p tx , while a baseline power is consumed just for having the transmission chain switched on, whereas the power consumed in DL increases with the downlink data rate, r dl , and a baseline power is consumed just for having the reception chain switched on.
Performances evaluation In order to test our resource management scheme, we considered an urban environment simulation scenario. Our heterogeneous C-RAN infrastructure is composed of seven H-RRHs and four L-RRHs per cell. H-RRHs have a coverage of 500 m and L-RRHs 30 m radius  [23]. We have aligned values of ε 0 and t 0 with the measurements given in  [24]  for energy and frequency characteristics of local computing in commercial mobile handsets, as well as computation of data ratios in practical applications. As in an urban area, we have considered users with random velocity from 3 to 120 km/h. In the Cloud-RRH, containers have a computing capacity from 25 to 100. The memory varies from 100 to 200 KB and the network bandwidth is set from 1 to 2 Kbps. The number of tasks T varies from 20 to 140, and they have heterogeneous requirements.
Thanks to cloud edge introduction and network flexibility, the proposed scheme has the lowest response time especially for big data size values. Thus, the proposed offloading decision algorithm can be useful for high resource demand applications. Figure  8  shows the simulation results of the energy spent by the MT under the data size (ranging from 1 to 100 Mb). We can see that the proposed offloading decision algorithm can make the mobile handset consume less energy. The difference is more important when the data size is big. Therefore, the proposed algorithm is able to augment the mobile handset battery lifetime while executing complex program applications compared to local execution and total offloading. When a task is offloaded to Cloud-RRH, we have evaluated the scheduling efficiency in terms of execution cost under a varying number of associated tasks.
Conclusions Data computation offloading enables intelligent mobile devices to run greedy resource applications. However, in order to fully exploit computation offloading benefits, we have to deal with two key challenges. The first one is about offloading decision, and the second one is about offloading request scheduling among available cloud resources  [18]. The main contribution of this paper is the development of a whole offloading strategy for H-CRAN composed of offloading decision and task scheduling in order to improve network performances and user QoE. Therefore, we jointly handle offloading decision and offloading request scheduling in Cloud-RRH. First, we proposed a dynamic multiparameter offloading decision scheme in order to adapt offloading decision to the current network state and application characteristics.
We present the non-local thermodynamic equilibrium (NLTE) abundances of up to 10 chemical species in a sample of 59 very metalpoor (VMP, -4 ≤ [Fe/H] -2) stars in seven dwarf spheroidal galaxies (dSphs) and in the Milky Way (MW) halo. Our results are based on high-resolution spectroscopic datasets and homogeneous and accurate atmospheric parameters determined in Paper I. We show that once the NLTE effects are properly taken into account, all massive galaxies in our sample, that is, the MW halo and the classical dSphs Sculptor, Ursa Minor, Sextans, and Fornax, reveal a similar plateau at [α/Fe] 0. 3 for each of the α-process elements: Mg, Ca, and Ti. We put on a firm ground the evidence for a decline in α/Fe with increasing metallicity in the Boötes I ultra-faint dwarf galaxy (UFD), that is most probably due to the ejecta of type Ia supernovae.
2015). Whenever the spectrum was available, the Ba abundance was determined from spectral synthesis. Otherwise, the contribution of the Fe i lines to the blends at 4934 Å and 6141 Å was estimated by computing their synthetic spectra. When the blending between the Fe and Ba lines was substantial, then Ba ii 6141 Å was not used. The lines of Sr ii, Ba ii, and Eu ii are composed of multiple components because each of these chemical elements is represented by several isotopes. For Sr ii 4077 and 4215 Å, their isotopic splitting (IS) and hyper-fine splitting (HFS) structure is taken into account using the atomic data from  Borghs et al. (1983)  and the solar system Sr isotope abundance ratios from  Lodders et al. (2009). The difference between the solar system and the r-process Sr isotope mixture  (Arlandini et al.
Codes and model ingredients In this study, the synthetic spectrum calculations were performed with the codes SIU  (Reetz 1991)  and synthV_NLTE  (Ryabchikova et al. 2016)  Kochukhov, finally allowing the user to determine the best fit to the observed line profile. A line list for spectral synthesis has been extracted from the Vienna Atomic Line Database 3  (VALD3;  Ryabchikova et al. 2015). Our test calculations with the solar model atmosphere in a broad wavelength range (4209 Å to 9111 Å) proved that using two different codes, SIU and synthV_NLTE + binmag3, does not produce systematic shifts in derived abundances, namely, the abundance difference never exceeds 0. 03 dex. For all other lines we use their equivalent widths. For each line, we first calculate the LTE abundance with the code WIDTH9 4   (Kurucz 2005, modified by Vadim Tsymbal, priv.
The abundance trends deliver important clues to galaxy star formation histories. We can firmly assess the following impact of SNeIa ejecta on the chemical evolution of some of the faintest dwarfs known: -The Boötes I UFD reveals a decline in α/Fe and possesses a low [α/Fe] population, with consistent evidence from the three elements for which data are available, i. e. Mg, Ca, and Ti. This can be a signature of the SNe Ia contribution to iron and a duration of about 1 Gyr for star formation in this galaxy. We provide further evidence that the mass of a galaxy is an important driver of its chemical evolution. We bring in constraints on nucleosynthesis sites, particularly for the neutron-capture elements, and on the mixing of the SN ejecta in the galaxy interstellar medium as follows: -Inhomogeneous mixing and/or stochastic effects from small numbers of SNe II is robustly documented in the Sculptor dSph.
Acknowledgements. We thank  Judith G. Cohen ,  Rana Ezzeddine ,  Anna Frebel , and  Joshua D. Simon  for providing stellar spectra,  David Yong  for double checking the observed equivalent widths of the Ba ii lines in the Boötes I stars. The authors are indebted to the  International Space Science Institute (ISSI), Bern, Switzerland , for supporting and funding the international team "The  Formation and Evolution of the Galactic Halo ". L. M. , Y. P. , and T. S. acknowledge financial support from the grant on Leading Scientific Schools 9951. 2016. 2. We made use of the MARCS, SIMBAD, and VALD databases.
Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes at global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI land cover maps provide continuous land cover changes at 300 m resolution fro m 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas, gross and net changes of different plant functional types (PFTs) derived fro m ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs fro m ESA are 30. 4, 19. 3 and 35. 7 million km 2 in 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al. , 2011), Hansen et al. (2013) and Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al.
, 2011), in which cropland area is fro m HYDE3. 2 (Klein Go ldewijk et al. , 2016). Gross forest loss and gain during 1992-2015 are 1. 5 and 0. 9 million km 2 respectively, resulting in a net forest loss of 0. 6 million km 2 , main ly occurring in South and Central A merica. The magnitudes of gross changes of forest, cropland and grassland PFTs in ESA CCI are s maller than those in other datasets. The magnitude of g lobal net cropland gain for the whole period is consistent with HYDE3. 2 (Klein Go ldewijk et al. , 2016), but most of the increases happened before 2004 in ESA while after 2007 in HYDE3. 2. Brazil, Bo liv ia and Indonesia are the countries with the largest net forest loss fro m 1992 t o 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images.
Gross PFT changes and transitions Gross changes need to be considered differently because it is only possible to derive the net change by comparing the annual maps sequentially. Gross changes may be far larger than the net changes, and thus may show different magnitudes or even directions of LULCC flu xes when simulated in LSMs. To document all the b idirectional LC t ransitions at 0. 5° × 0. 5° resolution, high-resolution LC transitions data are needed. Therefore, the annual ESA CCI LC maps are co mpared year by year at 300 m resolution to record the gross loss and gain of each original LC class over the whole period fro m 1992 to 2015. There are 23 original LC classes that experienced gross changes (classes with stars in Table  S1 ). In order to derive the gross transitions, all possible transitions (506 in total) between the 23 original LC classes with gross changes were calculated at 300m resolution.
Comparison with other datasets Three land-use and land-cover datasets (Table  1 ) were used for comparison, namely, forest, grassland and cropland area fro m Land Use Harmonizat ion (LUH2v2h ) data  (Hurtt et al. , 2011) , forest cover data fro m  Hansen et al. (2013)   It should be noted that land use data are not necessarily the same as land cov er, and the exact definitions and categorization of forest (cropland and grassland) are different for each dataset (see details in Discussion). Nevertheless, these represent the best datasets available for co mparison, and we have tried to harmonize the def initions where possible (see belo w), but to some degree this is an ongoing discussion between the modeling and data communit ies. Furthermore, all the LSMs have to use these datasets for deriving PFT changes back through time, so it is a very worthwhile exe rcise to determine if the broad groupings differ, and to what extent.
Absolute areas, net changes and gross transitions from 1992 to 2015 in the LUH2v2h dataset  (Hurtt et al. , 2011)  were used for co mparison. Forest used in this study fro m LUH2v 2h  (Hurtt et al. , 2011)  refers to the total o f primary and secondary forest; cropland refers to all crop types; grassland refers to the total of pasture and rangeland. Because LUH2v 2h data use cropland and grazing land areas fro m HYDE3. 2 as an input  (Hurtt et al. , 2011) , the spatial d istributions are main ly determined by HYDE3. 2. The gross transitions in LUH2v2h data are calculated fro m the Global Land use Model  (Hurtt et al. , 2006)  that tracks sub-grid cell loss and gain in land use categories. They first determined the urban area in each grid c ell proportionally fro m cropland, pasture and secondary lands, and if these areas cannot fulfill the urban increase, primary land s were cleared.
The min imu m transition rates between cropland, pasture and other (sum of primary and secondary lands) were then calculated to identify the gross transitions between these land use categories  (Hurtt et al. , 2011). Transitions related to shifting cultivation and wood harvest were determined last  (Hurtt et al. , 2011). Only annual gross forest loss each year during 2000-2014 and total gross forest gain during 2000-2012 are available in the dataset of  Hansen et al. (2013). Thus, the net forest area change from this dataset only refers to the period of 2000-2012. The national forest area data fro m 1992 to 2015 in the dataset of  Houghton and Nassikas (2017)  were used to calculate the fo rest area changes. A land mask with nine regions (Figure  1 ) defined by  Houghton (1999)  was used to derive the regional values.
Data availability The ESA CCI LC maps can be viewed online using http://maps. elie. ucl. ac. be/ CCI/viewer/index. ph, and the data products can be download from http://maps. elie. ucl. ac. be/CCI/viewer/download. php. After entering some basic information, the land cover maps with a specific version nu mber are available for download in the Clima te Research Data Package (CRDP) section. In this study, we used the version: "Land Cover Maps -v2. 0.
Water body changes are relatively s mall co mpared to other PFTs. In addition to the urban area increase over cropland in Europe and North China Plain, there is also urban expansion to cropland in United States (Figure  S1 ). The magnitudes of net forest area change from LUH2v2h  (Hurtt et al. , 2011) ) are much smaller than those from ESA CCI, mainly because the forest area decrease between 1992 and 2009 (Figure  4 ) is not re flected in the LUH2v2h dataset  (Hurtt et al. , 2011). A lthough the net cropland area increases from 1992 to 2015 are similar between ESA CCI and LUH2v 2h  (Hurtt et al. , 2011) , the temporal trajectories are rather different. The increase of cropland in ESA CCI data happened between 1992 and 2004, while cropland area in LUH2v 2h  (Hurtt et al. , 2011)  mainly increased since 2007 (Figure  4 ).
(2013)  is much larger than that from ESA CCI. Net forest loss from Houghton and Nassikas (  2017 ) is always higher than the loss from other datasets in the all these countries except in Ch ina and Russia where a net forest gain was found in  Houghton and Nassikas (2017). The overall net cropland gain fro m 1992 to 2015 between ESA CCI and LUH2v2h  (Hurtt et al. , 2011)  is similar in Bolivia but is rather different in all the other countries in Figure  5. Larger cropland gain fro m LUH2v2h  (Hurtt et al. , 2011)  compared to ESA CCI was found in Brazil, Indonesia, Argentina, and Paraguay while lo wer cropland gain was found in Cambodia and Congo, DRC. The cropland area change in China and Russia fro m LUH2v2h  (Hurtt et al. , 2011)  shows even a net loss rather than gain. Grassland area increased in Argentina, Paraguay, Russia, Cambodia and Congo, DRC in LUH2v 2h  (Hurtt et al.
, 2011) , which was not captured by ESA CCI maps. The magnitudes of forest change in the countries with the largest forest gain in Figure  6  are much s maller than those with largest forest loss (Figure  5 ). For example, the net forest gain from 1992 to 2015 is 0. 019 million km 2 in Canada, co mpared with a forest loss of 0. 28 million km 2 in Brazil. In these largest forest gain countries, forest area change from  Hansen et al. (2013)  indicates a net forest gain only in Uruguay, and a net loss or stable in other countries. Again, contrary to ESA CCI PFTs,  Houghton and Nassikas (2017)  forest area data show large magn itudes of net forest loss in Myanmar, Sudan and Nigeria, and greater magnitudes of net forest gain than other datasets in Uruguay. Cropland changes from LUH2v2h  (Hurtt et al.
Ho wever, this level of knowledge is not available for all LC classes. This is particularly true for mosaic and sparse vegetation classes (e. g. classes "100", "110" and "150") that span different regions/biomes that may contain different fractional coverage of vegetation. Likewise, an exp licit regional classification is required for cropland. For examp le, class "10" (cropland, rainfed) is separated well in North A merica, i. e. , main ly part itioning into class "11" (herbaceous cover), and thus the cropland area in this regio n is highly consistent with LUH2v2h data  (Hurtt et al. , 2011)  (Figure  1 ). In tropical Africa where class "10" is not separated into a more detailed classification, the difference in cropland areas between these two datasets are large (Figure  1 ). This is because if most of the cropland in this region belongs to class "12", using the corresponding value for class "10" in the cross-walking table (90% for class "10" vs.
Conclusions In this study, we compare the absolute areas and areal changes between PFTs from annual ESA CCI LC products and other datasets. In the intensive LULCC regions like South and Central A merica, both forest area and net forest change are consistent with those from other datasets. The detection of LC changes has significantly imp roved fro m the last version of five-year epoch ESA CCI maps  (Li et al. , 2016). The detailed annual cropland changes fro m 1992 to 2000 fill the gaps of HYDE 3. 2 data for this period, in which only decadal changes are available  (Klein Goldewijk et al. , 2016). Considering the discrepancies, advantages and defects among different datasets (Table  1 ), we p ropose different choices of these datasets for the applications in LSMs depending on our research purposes.