dataesr/scientific-paragraphs-categorization huggingface dataset:
WuR idea is simple and the behavior studied in this work is described in Section II. A complete description of the state-of-the-art of this technology is provided in  [2]. In a few words, WuR adds a secondary ultra-low-power radio (the Wake-Up Radio) that stays on all the time, while the high power main radio (MR) stays sleeping. When a source node wants to initiate the communication it sends a WuR packet, called Wake-Up Signal (WuS), towards the destination. Upon This project is funded by the French National Research Agency (ANR) reception, the destination issues an interrupt to wake up the MR. Finally, the source and the destination can exchange data and acknowledgment through their MR. This way the idle listening is reduced because the MR is sleeping the whole time and wakes up asynchronously.
B. Wake-up radio systems The idea behind Wake-Up Radios is to reduce idle-listening, overhearing and latency. To do so, a secondary ultra-lowpower radio (WuR) is added to the node, as shown in Fig. 1. This WuR is always on, listening for WuS. Before sending a data packet via the MR, a source node sends a WuS towards the destination. The reception of WuS triggers the receiving node to wake up its MR, now waiting for the reception of the pending data packet. Upon data and acknowledgment exchange, the destination puts back its MR into sleep in order to save energy. The modulation used commonly to send WuS is On-Off-Keying (OOK) because of the easiness of its implementation in hardware. Essentially, the WuR block consists of a receiver, a transmitter and an optional module to decode the WuS. Both the transmitter and the decoding module are common chips (such as CC1101 or CC2420).
C. Single WuS In this article, we begin with a simple scenario where we have 4 nodes in a row. Main sender (node 4) and destination (node 1) are placed at the opposite borders of the line-shaped network. The main radio range is large enough so that both are connected through a single hop. However, the WuR range is small enough so that only adjacent nodes are connected. That means that for 4 to wake up 1 it needs to send a WuS to 3, and 3 needs to retransmit it to 2, and finally, 2 needs to retransmit it to 1. This operation is illustrated in Fig. 3. Such a situation should be very common in real life deployments due to the range mismatch between MR and WuR. However, analyzing the impact of relaying WuS has never been investigated before. A short time after the WuS was sent, the main sender transmits the data packet over the main radio.
D. WuS ACK The usage of WuS Acknowledgments (WuS ACKs) is not clearly pointed out in the literature. It is in some cases used as a response of a potential next hop in reactive protocols  [7] , but there is no indication on an end-to-end acknowledgment of the WuS through several hops. This way it would be possible to avoid the retransmissions problem related to a long WuS path and late destination wake up. For this, we introduce WuS ACK packets. The WuS ACK is sent by the main destination when it receives a WuS REQ and wakes up. This type of packet is relayed in the same fashion as the WuS REQ but in the opposite direction, towards the main sender. Upon reception of the WuS ACK, the sender is assured that the final destination is ready to receive the pending packet. This means that, in our example, firstly there is a WuS sequence from node 4 to node 1, secondly a WuS ACK sequence from node 1 to node 4, and only after both are successful, the main data and data ACK are exchanged between main node 4 and node 1.
III. SIMULATION FRAMEWORK There are lots of simulators and simulation frameworks for WSN in general  [8]  [9], but not so much has been done in the area of multiple radios. Several authors have been working with GreenCastalia  [10] , an extension of OMNet++  [8] , that allows to easily model and simulate networks of embedded devices with energy-harvesting capabilities. However, this software cannot reproduce exactly the firmware that runs on the real devices and it does not consider the entire stack. In this work, we used WaCo  [11] , a COOJA extension that models a WuR prototype proposed in  [6] , and implements a WuR MAC protocol called W-MAC. COOJA is a software that simulates    [11]. Although this software provides the basic elements to handle the WuR, there is no support for relaying WuS packets or changing the RX success probability of the WuR medium.
2) Single WuS: In this protocol, only the WuS REQ is sent to wake-up the main destination. The main data is sent after the sync delay described in Section II. We implement 3 versions of this protocol: no sync delay (data transmission as soon as possible), medium sync delay and long sync delay. In the long case, the delay is long enough so that the WuS REQ can hop up to 9 times and arrive successfully at the main destination before the main data is sent. 3) WuSACK: Here when the main destination receives a WuS REQ, it transmits a WuS ACK back to the main sender. The sender will not transmit the main data until it receives the WuS ACK back. For all of them, we use the Contiki implementation of CSMA on top of them without backoff exponential increments. RIME is used as the network layer for the main node in all scenarios because it is a lightweight protocol that let us focus on the MAC layer.
This is also reflected in Fig. 8 , where we see clearly that the total power consumption is commanded mainly by the destination node. Whereas when the network is larger, there are several nodes in the middle and it becomes very hard to get all the WuS sequence successfully to wake-up the final destination, so there is less listening wastage. In addition, it is even more difficult to get the full WuS + WuS ACK sequence successfully to get to send a main data packet. So in the end, there is less listening on the destination and less number of main data packets transmitted by the sender. This behavior is because main radio packets are the ones that impact more on the overall power consumption while WuR packets retransmissions are insignificant. 3) 2-nodes ContikiMAC comparison: We turn our attention now to figure  9 , where we do consider the CPU and LPM to compare the total power consumption of the network.
As we have seen in Section IV, the reliability of the WuR medium significantly impacts on the performance of WuR. Simulations in OMNeT++  [8]  have been done in  [5]  in order to prove that WuR constantly allows for substantial energy savings, higher PDR, lower latency and less complicated software implementations. In spite of the fact that the authors provide several variations of the network type and details on how to reproduce the analysis, the comparison considers neither the relay of the WuS to reach a destination nor acknowledging WuS packets. T-ROME  [16]  is one of the few solutions that propose to use intermediate nodes to retransmit the WuS between the source and the destination. The reactive protocol operates dynamically choosing the most appropriate stopover nodes in case the sink is not reachable within one communication hop.
Current Land Surface Models (LSMs) typically represent soils in a very simplistic way, assuming soil organic carbon (SOC) as a bulk, thus impeding a correct representation of deep soil carbon dynamics. Moreover, LSMs generally neglect the production and export of dissolved organic carbon (DOC) from soils to rivers, leading to overestimations of the potential carbon sequestration on land. These common oversimplified processing of SOC in LSMs is partly responsible for the large uncertainty in the predictions of the soil carbon response to climate change. In this study, we present a new soil carbon module called ORCHIDEE-SOM, embedded within the land surface model ORCHIDEE, which is able to reproduce the DOC and SOC dynamics in a vertically discretized soil to two meters. The model includes processes of biological production and consumption of SOC and DOC, DOC adsorption on-and desorption from soil minerals, diffusion of SOC and DOC and DOC transport with water through and out of the soils to rivers.
The purpose of this paper is to describe the new soil C module ORCHIDEE-SOM, embedded in the LSM ORCHIDEE, which is able to reproduce the SOC and DOC dynamics in a vertically discretized soil down to two meters, consistent with water transport and soil thermics. We also perform a first evaluation of the new soil module ability to reproduce SOC stocks and DOC concentrations dynamics by comparing model predictions with 25 respective field observations at four European experimental sites with different vegetation covers and soil properties. If the model structure is valid, ORCHIDEE-SOM should be able to reproduce, not only the values of DOC and SOC concentrations within the range of the observations, but also the internal soil processes that drive the site-specific differences in SOC stocks following differences in soil texture, vegetation and climate and the decrease in SOC and DOC down the soil profile.
30 2 Model developments ORCHIDEE-SOM is an extension to the soil module in ORCHIDEE, based on the ORCHIDEE version SVN r3340. ORCHIDEE represents the principal processes influencing the carbon cycle (photosynthesis, ecosystem respiration, soil carbon dynamics, fire, etc. ) and energy exchanges in the biosphere  (Krinner et al. , 2005). It consists of two modules: SECHIBA, describing the fast processes of energy and water exchanges between the atmosphere 35 and the biosphere at a time step of 30 minutes  (de Rosnay et al. , 2002)  and STOMATE, which calculates the phenology and carbon dynamics of the terrestrial biosphere at a time step of one day. ORCHIDEE represents vegetation globally using 13 Plant Functional Types (PFT): one PFT for bare soil, eight for forests, two for grasslands, and two for croplands  (Krinner et al.
1 ). ORCHIDEE-SOM upgrades the trunk version of ORCHIDEE to simulate carbon dynamics in the soil column 10 down to two meters depth, partitioned in 11 layers following the same scheme as in the hydrological module ORC11  (Campoy et al. , 2013; Guimberteau et al. , 2014). ORCHIDEE-SOM mechanistically models the concentration of DOC in each soil layer and its transport between layers. Moreover, the upgraded module links SOC decomposition with the amount of fresh organic matter as a way of accounting for the priming effect  (Guenet et al. , 2017). 15 In short, ORCHIDEE-SOM represents four litter pools (metabolic aboveground litter, metabolic belowground litter, structural aboveground litter and structural belowground litter) and three SOC pools based on their turnover rate (active, slow and passive).
Adsorbed DOC is assumed 25 to be protected and thus it is neither decomposed nor transported within the soil column. Free DOC is subject to transport with the water flux between layers calculated by the soil hydrological module of ORCHIDEE, i. e. by advection. Also, SOC and DOC are subject to diffusion, that is represented using the second Fick's law of diffusion. All the described processes occur within each soil layer. At the end of every time step, the flux of DOC (expressed in g C m -2 d -1 ) leaving the soil with runoff (upper layer) and drainage (bottom layer) is calculated by 30 multiplying DOC concentrations in the solution with the runoff and drainage flux calculated by the hydrological module ORC11 (Fig. 1 ). This section presents the new ORCHIDEE-SOM formulations in more detail, focusing first on the vertical discretization scheme, followed by the newly implemented biological and physical processes affecting soil carbon (i.
Vertical discretization of the soil carbon module For mathematical reasons, ORCHIDEE SVN r3340 has two different discretized schemes for soil physics: one for energy and other for hydrology. Since ORCHIDEE-SOM requires the transport of water between layers and drainage for the calculation of DOC concentrations and fluxes, we adopted the discretization used for the soil hydrology scheme whose performance has already been tested against tropical  (Guimberteau et al. , 2014) , boreal  (Gouttevin et al. , 2012)  and temperate datasets  (Campoy et al. , 2013). Therefore, ORCHIDEE-SOM represents a two-meter soil column with 11 discrete layers of geometrically increasing thicknesses with depth. This kind of geometric configuration is used in most LSMs describing the vertical soil water fluxes based on the Richards 5 equation, such as ORCHIDEE  (Campoy et al.
, 2013). More information on the hydrological formulation of ORCHIDEE is given in section 2. 2. 3. The midpoint depths (in meters from the surface) of the layers in the discretized soil column are: 0. 00098, 0. 00391, 0. 00978, 0. 02151, 0. 04497, 0. 09189, 0. 18573, 0. 37341, 0. 74878, 1. 49951, respectively. The first layers in the soil hydrology discretization scheme are thinner (1 mm) than needed in terms of biological and pedogenetic process 10 representation. Nevertheless, we decided to integrate the 11-layers scheme for technical reasons: it simplifies the coding and the understanding of the code for the users. At every time step, each soil layer is updated with all the sources and sinks of DOC due to the represented biological and physical processes. The new 11-layers scheme applies to the soil carbon in the mineral soil and for the belowground litter (see Section 2.
Advection Like most models, ORCHIDEE-SOM represents the transport of carbon with the liquid phase (only DOC in our case) by means of advection (e. g. ,  Braakhekke et al. , 2011; Futter et al. , 2007). The calculation of advection fluxes in ORCHIDEE-SOM relies on the flux of water between soil layers as calculated by the soil hydrology module 5 ORC11, which is briefly described hereafter. The soil hydrology module is based on the two-meters vertical discretization of the soil column (see section 2. 1. ). A physically-based description of the unsaturated water flow was introduced in ORCHIDEE by de  Rosnay et al. , (2002). Soil water fluxes calculation relies on a one-dimensional Fokker-Planck equation, combining the mass and momentum conservation equations using volumetric water content as a state variable  (Campoy et al.
, 2013). Due 10 to the large scale at which ORCHIDEE is applied, the lateral fluxes between adjacent grid cells are neglected. Also, all variables are assumed to be horizontally homogeneous. The flux field q along the soil profile comes from the equation of motion known as  Darcy (1856) In this equation, z is the depth (in m) below the soil surface, t (in s) is the time, K(θ) (in m s -1 ) is the hydraulic conductivity and D(θ) (in m 2 s -1 ) is the diffusivity. The soil hydrological module counts with the following boundary conditions at the soil surface and at the bottom 20 layer. First, the water flux at the soil surface is defined by the difference between infiltration into the soil and soil evaporation. Precipitation is partitioned between surface runoff and infiltration into the soil, by characterizing the wetting front speed through the top soil layers (d  'Orgeval et al.
, 2008). Soil evaporation is calculated assuming that it can proceed at the potential rate, unless water becomes limiting. Second, ORCHIDEE assumes conditions of free gravitational drainage at the soil bottom. This boundary condition implies that soil moisture is constant 25 below the lower node, which is not always the case in nature. In particular, when a shallow water table is present, water saturation within the soil column cannot be modelled within ORCHIDEE. More information on the calculation of the water flux, runoff and drainage can be found in  Campoy et al. , (2013). The transport of DOC within the liquid phase is assumed to occur due to advection flux and it is modelled as the flow of water calculated by the hydrology module multiplied by the concentration of DOC at each layer according 30 to Eq.
Model parameterization The soil carbon stocks in a LSM such as ORCHIDEE-SOM depend on the primary production  (Todd-Brown et al. , 2013). Consequently, prior to the evaluation of the soil module in ORCHIDEE-SOM, we used gross primary 35 production (GPP) measurements from the FLUXNET network to optimize the GPP-related parameters (V cmax , Surface leaf area, maximum leaf area index, minimum leaf area index to start photosynthesis and minimum and maximum photosynthesis temperature sensitivity) in ORCHIDEE in order to assure that model inputs coming from plant production are correct (Table  S1 ). The ORCHIDEE data assimilation system, based on a Bayesian optimization scheme, was used for the optimization  (MacBean et al. , 2016). The optimization approach relies on the iterative minimization of the mismatch between the set of experimental observations and corresponding model outputs by adjusting the model driving parameters using L-BFGS-B algorithm  (Byrd et al.
Model simulations on site The performance of the model was tested using data of DOC and SOC stocks at the four selected sites. Since these sites are all part of the FLUXNET network  (Baldocchi et al. , 2001) , the in-situ measured meteorological variables were available to be used as forcing for the simulations in ORCHIDEE. For Carlow grassland, only one year 20 (2008) of flux measurements was available. However, Carlow grassland and cropland sites are located very close to each other and we therefore used the meteorological measurements of Carlow cropland as forcing for Carlow grassland. For all sites, the in-situ meteorological data was gap-filled using the ERA-interim 3-hourly product, following the methodology in  Vuichard and Papale (2015). Before the model application, we ran the model over approximately 14,000 years iteratively using the 25 meteorological data for the available period for each site until all the soil variables reached a steady state (spinup).
GPP Accurate soil DOC simulations rely on the correct simulation of productivity, which in this study was approximated by optimizing the GPP modelled by ORCHIDEE-SOM at the study sites. After optimization of the GPP-related parameters (Table  S1 ), modelled and measured GPP were in good agreement for Brasschaat forest 5 (modelled GPP of 1350 ± 50 g C m -2 yr -1 versus measured GPP of 1240 ± 130 g C m -2 yr -1 ), Hainich forest (  1410 ± 70 g C m -2 yr -1 versus 1520 ± 110 g C m -2 yr -1 ) and Carlow Cropland (1250 ± 180 g C m -2 yr -1 versus 820 ± 90 g C m -2 yr -1 ) (Fig. 2 ). The GPP of Carlow grassland simulations could not be optimized due to limited data availability since GPP measurements were available only for the year 2008. Consequently, modelled GPP in 2008 was 40% higher than measured GPP for the single year of measurement, mainly due to a longer growing season 10 modelled by ORCHIDEE-SOM.
Model limitations and further work 10 In this study, ORCHIDEE-SOM was tested at four temperate ecosystems in Europe, but additional considerations need to be taken into account when applying the model to other ecosystems. For instance, the DOC coming from throughfall is not represented in ORCHIDEE-SOM, although it is a substantial source of soil DOC in tropical ecosystems  (Lauerwald et al. , 2017). Furthermore, the use of the hydrological module ORC11, that implies free drainage in the bottom layer, currently limits the representation of more humid ecosystems such as wetlands or 15 peatlands, where shallow water tables are present, and needs to be addressed in the near future. Moreover, it is important to note that the modelled DOC along the soil profile strongly depends on the downward water flux simulated by the hydrological module ORC11.
, 2014). Our findings thus point out to the necessity of a depth-and vegetation dependent parameterization of the new model parameters in the future. Finally, our results showed that the model performed worst at Brasschaat, which is the site with the most extreme 5 soil conditions (very acidic and sandy soil). It is not surprising that ORCHIDEE-SOM, which is designed for regional or global simulations, is unable to well reproduce SOC and DOC dynamics at sites with particular characteristics because it uses many default parameters based on prior knowledge (Table  1 ). Additional parameter optimization through data assimilation at multisite level is thus needed before applying ORCHIDEE-SOM to large scale simulations. Calibration of the new parameters of ORCHIDEE-SOM by 10 assimilation of DOC concentration and SOC stocks data from several sites across different biomes will not only make the model applicable to large scale simulations, but also will give insight to the relative importance of processes affecting SOC and DOC in different ecosystem types  (Braakhekke et al.
, 2013)  and will reduce the uncertainty range of the new parameters, which is an essential part of any process-based large scale model  (Zaehle et al. , 2005). 15 We present here a new soil module within the land surface model ORCHIDEE. This module keeps the pool-based structure of the CENTURY model, but is upgraded to represent the biological production and consumption, mineral sorption and transport of vertically discretized SOC and DOC. None of the existing soil models that represent all these DOC-related processes and that vertically discretized SOC are embedded within a land surface model. ORCHIDEE-SOM is an intermediate complexity model that, albeit its simplicity and generalization, has 20 proven successful in simulating soil solution DOC concentrations at four different ecosystem types (Fig.
8 ). Once the soil module in ORCHIDEE-SOM is optimized for large-scale simulations and linked to the already existing DOC river scheme  (Lauerwald et al. , 2017)  (vertical profiles of SOC) and loss of organic carbon through leaching. We evaluated the model for four European 30 sites with different vegetation covers, using input parameters that are realistic compared to prior knowledge. The modelled SOC stock profiles agree very well with the observations. Overall, the model was able to reproduce DOC concentrations at different soil depths, although DOC concentrations were overestimated in the upper horizon at the coniferous forest. Moving forward requires an exhaustive model parameterization. Our results suggest that empirical data should be 35 integrated in the SOC and DOC decomposition times and CUE DOC parameters in order to make them soil-and vegetation-dependent.
Moreover, to be able to run ORCHIDEE-SOM at regional or global scales, the new parameters should be optimized by data assimilation and the optimized model needs testing against observations of SOC and DOC at larger scales (continental or global). Tables  Table 1 - List of parameters of ORCHIDEE-SOM (the name used in the model is between brackets), with their description, value, units, the parameterization used for each parameter. Parameter.
Capturing that information is both a technical challenge-we need models, tools and automated population-and a data curation challenge. Indeed, the information or metadata about an ontology is often dispatched within web sites, scientific articles, documentation or sometimes not existing at all except in the brain of the original ontology developers. There is a need for metadata authoring guidelines and for harmonization of existing metadata vocabularies to simplify their use and enlarge their adoption. For instance, the recent Minimum Information for Reporting of an Ontology initiative (https://github. com/owlcs/miro) [6]  proposes the MIRO guidelines to ontology developers when reporting an ontology, e. g. , in a scientific article. In this paper, we adopt the perspective of designers of an ontology repository and report on our effort to develop a unified ontology metadata model for this repository.