dataesr/scientific-paragraphs-categorization huggingface dataset:
2009) , light saturated CO2 assimilation (or maximal photosynthesis Amax, in µmol/m 2 /s), nitrogen content per unit leaf area Na (g/m²) and leaf mass per area LMA (g/m 2 ) [from the CANTRIP database,  Keenan & Niinemets (2016) ]. The final trait database and associated references are reported in Appendix E. For each of the selected ForCEEPS parameters, we tested the Pearson's correlations between the ForCEEPS parameters and some of the traits at the interspecific level. Note that the consistence of the results across both Angiosperms and Gymnosperms was taken into account to assess the robustness of the congruence of species parameters with functional traits.
Validation against forest growth data Forest growth dataset. The validation of simulated annual productivity at the tree and stand levels was conducted using a dataset independent from the one used in the calibration process. Following  Guillemot et al. (2017) , we primarily relied on the RENECOFOR permanent forest plot network  (Ulrich 1997 ) that includes 103 half-hectare plots in even-aged managed forests covering most of the main tree species and climate conditions in France. After excluding the plots that had experienced a natural or anthropic disturbance (e. g. , thinning) less than 4 years before the last diameter inventory, 77 plots remained. Most of the stands included in the validation dataset are monospecific or strongly dominated by one species The RENECOFOR network does not include forests growing under Mediterranean conditions.
Therefore, we completed the validation by using data from the long term experimental sites of Puéchabon (Quercus ilex,  Rambal et al. 2014)  and Font Blanche (mixed forest dominated by Pinus halepensis,  Simioni et al. 2016). Diameter inventories were used to estimate the tree and stand basal area increment (BAI) in all validation plots. The time interval between the initial and final inventories in RENECOFOR plots varied between 4 and 14 years, while they were of 14 and 10 years for the Puéchabon and Font Blanche sites, respectively (see further details about the validation datasets in Appendix D). The BAI data recorded over contrasted time intervals were normalized to mean annual BAI. Local measurements of soil water holding capacity (SWHC) were available for all plots, and climate time-series were obtained from the SAFRAN atmospheric reanalysis  (Vidal et al.
5000 m²). To simulate the patches, trees were randomly sampled in the inventory dataset of a given plot until the stand basal area per square meter of the simulated patch was comparable to the observed stand basal area per square meter. Local measurements of SWHC and local climate time-series were used as inputs. ForCEEPS simulations were run over the time period for which BAI measurements were available in each plot (i. e. from 4 to 14 years), and subsequently normalized to mean annual BAI. As the results were very consistent across the five repetitions carried out per plot (as shown in Fig. S3  for the RENECOFOR plots), we only present the results for one repetition at the tree level for the sake of clarity (the results for each repetition are shown in supplementary material -Table  S4  and Fig.
Quantifying the importance of the hypotheses embodied in ForCEEPS for forest growth After simulating BAI for each plot using the full model, we carried out three types of simulations to quantify the importance of some hypotheses and ecological processes embedded in ForCEEPS. First, we ran simulations without the new module for competition for light, to test whether an explicit representation of individual crown lengths in the vertical canopy space increased the prediction accuracy of stand growth (Test 1). Second, we ran simulations without considering the limiting effect of drought stress and thermal constraints on tree growth, i. e. under optimal climatic conditions (Test 2). Third, we aimed at testing the importance of the species-specific tolerance to shade in ForCEEPS (Test 3), as it has been shown to be a key parameter driving diversity effects in ForClim 2.
e. across a few years) was conducted using forest growth data from monospecific stands. The rationale for this choice was to evaluate its behavior and predictive ability in a context with low influence of complex interspecific interactions. Because gap models are often validated using species composition of PNV at selected sites, their validation is actually conducted in mixed forests in most cases  (Bugmann 2001 ). Thus, this test of the ability of gap models to accurately simulate the functioning of monospecific stands in various environmental conditions and for a wide range of species has been very rarely assessed. Yet, monocultures are often compared to mixed stands to quantify biodiversity effects in forests (e. g. , as in  Morin et al. 2011). Ensuring that the functioning of monospecific stands is well reproduced by a gap model is thus a sine qua non condition to simulate non-biased biodiversity effects in tree communities.
g. Maréchaux and Chave 2017, Martin-StPaul et al. 2017)  using functional traits available from global databases  (Kattge et al. 2011). Although these models provide a unique insight on the physiological mechanism driving forest growth and survival, they are not aimed to describe the long-term ecological processes shaping forest composition on the long-term. In this study, we evidence that the processes embodied in gap models to simulate long-term forest succession can also predict annual forest growth in species with contrasted ecology and under various climate conditions, making them an important tool to study forest responses to climate change. ForCEEPS requires a rather small number of parameters to describe a species, allowing both a straightforward calibration of some parameters using forest inventory data and an a priori-calibration of the other parameters relying on literature and ecological knowledge.
Interestingly, as for Sh_tolS, DrTolS was only weakly correlated with wood density and LMA, which is probably related to their poor mechanistic relevance in the species resistance to drought  (Chave et al 2009 ; Bartlett et al 2012). Although performed on a relatively small number of species, these results nevertheless pave the way for potential improvement of the representation of drought tolerance in ForCEEPS, for instance by implementing an hydraulic failure module that mechanistically integrate multiple traits (e. g. ,  Martin-StPaul et al 2017). More generally, exploring the mechanistic relevance of gap model parameters allows using functional trait databases to constrain them within realistic values and avoid equifinalities issues.
Acknowledgements The authors wish to thank the  Institut Géographique National  for providing the national forest inventory data, and the  Office National des Forêts  and the RENECOFOR team, particularly  Manuel Nicolas  and  Marc Lanier , for providing their database. In both cases, these long-term monitoring networks are invaluable tools. The SAFRAN database was provided by  Météo-France. The authors are also grateful to the CAPSIS modelling platform (http://www7. inra. fr/capsis/). They thank  Maude Toigo ,  Georges Kunstler  and  Patrick Vallet  for helpful discussions about the study, and  Gregor Cresnar  from the  Noun Project. This study was funded by the projects  BIOPROFOR  ( ANR-11-PDOC-030-01 ), DISTIMACC (ECOFOR-.
WiFi-Based Device-Free Indoor Localization The existing device-free indoor localization systems can be categorized as fingerprintbased and geometric mapping-based. Dang et al. [25]  uses the post-processed CSI amplitude and phase as fingerprint data to build the fingerprint database. Xiao et al. [37]  use the distribution of the CSI amplitudes over the all sub-carriers and the multiple antennas as fingerprint of corresponding positions, in order to improve the localization accuracy. Most of fingerprint-based systems are difficult to deploy since these systems need labor intensive offline training and are sensitive to the changes of the environment. Unlike fingerprint-based method, geometric mapping-based method does not need to build fingerprint database. MaTrack  [17]  employ the angle-of-arrival (AoA) information to locate the user.
So the thresholds for area detection of Wiborder will change even in the silence environment, thus Wiborder is not suitable for long-term area detection. In order to evaluate the effectiveness of our proposed method for remove the noise of AGC, we collect CSI data for a long time (about 1. 5 h) in the silence environment. Figure  3  shows the impact of AGC and environmental noise on the amplitude. Figure  3a  shows the CSI amplitudes of two antennas, which contain the environmental noise and the noise of AGC. From Figure  3a , we can find besides some fluctuations which caused by environmental noise, the amplitude is divided into two segments at about 5000 second, as marked by red rectangle. Figure  3b, c  shows the coefficients agc and γ(agc), respectively. There is also has a similar but smaller jump at around 3500 s in the Figure  3a , but the agc of the corresponding CSI amplitude remains relative stable except for some outliers.
When performing in-place activities, the torso is the main reflection body part. As we all know, the torso of human body has a larger reflection area than other body parts such as arms and legs, so the signal energy reflected from torso is stronger than that reflected from arms and legs  [44, 45] , so the amplitude of the tangent vector can represent the signal energies reflected from different body parts. Suppose the CSI Ratio has M complex data samples within a short time window (such as 2 s), and the tangent vector of dynamic component also has M complex data sam- ples. [ ] 2 0, , M t t Δ Δ  is the sampling interval of each sample with respect to the first sample at t0, where Δt1 = 0. We can construct the sample distance vector from tangent vector: ( ) ( ) ( ) ( ) 0 0 0 2 0 , , , M A t t t t t t α α α = +Δ +Δ       , ( 23 ) where α(t0) is the amplitude of tangent vector data sample at time t0.
We let a person perform in-place activities at first, and then wave his arm while standing still. Figure  9  shows the distances between consecutive samples when the person doing these two actions. From Figure  9 , we can find that the distances between consecutive samples are larger in case of person performing in-place activities than waving his arm. When performing in-place activities, the torso is the main reflection body part. As we all know, the torso of human body has a larger reflection area than other body parts such as arms and legs, so the signal energy reflected from torso is stronger than that reflected from arms and legs  [44, 45] , so the amplitude of the tangent vector can represent the signal energies reflected from different body parts. Suppose the CSI Ratio has M complex data samples within a short time window (such as 2 s), and the tangent vector of dynamic component also has M complex data sam- ples.
[ ] 2 0, , M t t Δ Δ  is the sampling interval of each sample with respect to the first sample at t0, where Δt1 = 0. We can construct the sample distance vector from tangent vector: ( ) ( ) ( ) ( ) 0 0 0 2 0 , , , M A t t t t t t α α α = +Δ +Δ       , ( 23 ) where α(t0) is the amplitude of tangent vector data sample at time t0. Let α(t0 + Δtk) represent the k th element of vector ( ). In order to find the statistical properties of α(t0 + Δtk), we have collected 2 s tangent vector data samples generated by a Suppose the CSI Ratio has M complex data samples within a short time window (such as 2 s), and the tangent vector of dynamic component also has M complex data samples. [0, ∆t 2 , • • • ∆t M ] is the sampling interval of each sample with respect to the first sample at t 0 , where ∆t 1 = 0.
Statistical Analysis of 22 Days Human Vitality Information Since WiMonitor system recorded 22 days of continuous 24 h of human vitality information data from four volunteers, in this subsection, we can analyze these data over 24-h from different perspective to capture the living habit patterns of different volunteers. It is well known that in the smart home environment, physical activities are arearelated, such as sleeping usually takes place in the bedroom, and cooking usually takes place in the kitchen. Analyzing the area occupation rate during 24 h of the elder, could help us to find out the living habits of the elder. Figure  18  shows the area occupation rate of volunteer 1 during 24 h. From Figure  18 , we can find that the volunteer 1 spends most of the time in bedroom in one day, and spends the least time in kitchen since the volunteer 1 only enters kitchen to wash dishes after lunch and supper.
1 m is achieved in  [49]. Unlike fingerprint-based method, geometric mapping-based method does not need to build fingerprint database. MaTrack  [16]  employ the angle-ofarrival (AoA) information to locate the user. The median localization accuracy of MaTrack is about 0. 6 m. SiFi  [19]  uses a single AP to locate the target. The median localization accuracy is about 0. 93 m. Though the above two types of localization approaches are very useful in many applications, such as indoor navigation, augmented reality, disaster rescue, etc. However, in a typical home environment, the rooms are usually divided by walls, detecting which room a person is staying is essential for daily life monitoring. This requires the system to detect the precise sensing boundary between different rooms. However, the above two types of localization approaches couldn't fulfill this requirement, because 20 cm localization error could make wrong judgement of room-level localization when a person is close to the room door.
Conclusions In this paper, we propose a continuous long-term human vitality sensing system called WiMonitor using commodity Wi-Fi devices. For an elder who lives alone, the WiMonitor system could capture vitality information continuously in real-time without any human effort on offline-training. WiMonitor could achieve continuous long-term accurate area detection by removing the noise of AGC. Moreover, we construct a CSI metric for estimate the instantaneous activity intensity real time. We recruit 4 volunteers to conduct a continuous 22-days experiment, and record the long-term vitality data. Through analysis, we can conclude that the recorded long-term vitality information can be used to obtain the routine habits of the user and detect any abnormality. As the next step, we are going to use our system to track vitality information over a longer term, with the hope that we could observe the long-term routine change trends using the data from several months.
Air-sea flux of carbon dioxide (CO 2 ) is a critical component of the global carbon cycle and the climate system with the ocean removing about a quarter of the CO 2 emitted into the atmosphere by human activities over the last decade. A common approach to estimate this net flux of CO 2 across the air-sea interface is the use of surface ocean CO 2 observations and the computation of the flux through a bulk parameterization approach. Yet, the details for how this is done in order to arrive at a global ocean CO 2 uptake estimate vary greatly, enhancing the spread of estimates. Here we introduce the ensemble data product, SeaFlux (Gregor and Fay, 2021, https://doi. org/10. 5281/zenodo. 5482547, https://github. com/luke-gregor/pySeaFlux, last access: 9 September 2021); this resource enables users to harmonize an ensemble of products that interpolate surface ocean CO 2 observations to near-global coverage with a common methodology to fill in missing areas in the products.
Further, the dataset provides the inputs to calculate fluxes in a consistent manner. Utilizing six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN), the SeaFlux ensemble approach adjusts for methodological inconsistencies in flux calculations. We address differences in spatial coverage of the surface ocean CO 2 between the mapping products, which ultimately yields an increase in CO 2 uptake of up to 17 % for some products. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55). Application of a scaled gas exchange coefficient has a greater impact on the resulting flux than solely the choice of wind product. With these adjustments, we present an ensemble of global surface ocean pCO 2 and air-sea carbon flux estimates.
In this work, we assess the impact of wind speed product choice and scaling on six pCO 2 products' calculated air-sea flux estimates. By applying a consistent flux calculation methodology to each pCO 2 product, we minimize the methodological divergence of fluxes within the ensemble. Here, we present SeaFlux, a dataset that provides a consistent approach specifically targeting the most commonly used pCO 2 data products to deliver an end-product for intercomparisons within assessment studies such as the Global Carbon Budget  (Friedlingstein et al. , 2020 ) and the Regional Carbon Cycle Assessment and Processes (RECCAP). The SeaFlux dataset is accompanied by a Python package, called py-SeaFlux (https://github. com/lukegre/pySeaFlux, last access: 9 September 2021), that enables users to calculate fluxes for other configurations, use cases, and resolutions.
Specifically, by first addressing differences in spatial coverage between the observation-based products we can better present a true global pCO 2 estimate for each product. SeaFlux also provides gas transfer velocities scaled to independent estimates of the gas transfer velocity based on the ocean 14 C inventory. Further, the dataset includes estimates of atmospheric pCO 2 and the solubility of CO 2 in seawater. Finally, by calculating fluxes using multiple scaled gas transfer velocities for different wind products, we present a methodologically consistent database of air-sea CO 2 fluxes calculated from available pCO 2 products. SeaFlux is thus an ensemble data product with documented code (pySeaFlux) allowing the community to reproduce consistent flux calculations from various databased pCO 2 reconstructions now and in the future.
Methods SeaFlux is based on six observation-based pCO 2 products and spans the years 1990-2019 (Table  1 ). These six products include three neural-network-derived products (CMEMS-FFNN, MPI-SOMFFN, NIES-FNN), a mixed layer scheme product (JENA-MLS), a multiple linear regression (JMA-MLR), and a machine learning ensemble (CSIR-ML6). These products are included as they have been regularly updated to extend their time period and incorporate additional data that come with each annual release of the SOCAT database. All of these methods provide three-dimensional fields (latitude, longitude, time) of the sea surface pCO 2 and the airsea CO 2 flux. In their original form, each product may utilize different choices for the inputs to Eq. (1) (Table  A1 ). While the choices made by each product's creator, listed in Table  A1 , are not incorrect, by utilizing a uniform methodology in flux calculation, provided by pySeaFlux, the differences in the resulting flux estimate can be attributed to the pCO 2 mapping method itself.
esrl. noaa. gov/gmd/ccgg/mbl/data. php (last access: 10 October 2020)  (Dlugokencky et al. , 2019)  multiplied by ERA5 sea level pressure  (Hersbach et al. , 2020)  at monthly resolution and applying the water vapor correction according to  Dickson et al. (2007). All of the components of Eq. (  1 ) are available in the SeaFlux dataset. Throughout this study, flux is defined as being positive when CO 2 is released from the ocean to the atmosphere and negative when CO 2 is absorbed by the ocean from the atmosphere. In the following sections, we discuss the three steps that have the greatest impact on the inconsistencies between flux calculations in the six pCO 2 products and the approach that we utilize for the SeaFlux ensemble product.
Here, we do not take such adjustments into account for two reasons. Firstly, the skin temperature correction to pCO 2 needs to be applied directly to the measurements and not the final interpolated pCO 2 from the data products. Hence, it is up to the developers of the SOCAT dataset and the developers of the pCO 2 mapping products to decide on the inclusion of this correction. It would then be up to the developers of the data products to update their mapped products. Secondly, the cool skin correction would be equally applied to all methods and would not contribute to the inconsistencies in flux calculation that we are trying to address here. As the ocean carbon community moves towards consensus on such issues, SeaFlux will be updated to include revised protocols. To compare these estimates of contemporary air-sea net flux (Fnet) from surface ocean pCO 2 with estimates of the anthropogenic carbon flux from interior data  (Mikaloff Fletcher et al.
Code and data availability Data  (Gregor and Fay, 2021)  are available on Zenodo (https://doi. org/10. 5281/zenodo. 5482547), and the software used to generate these data is available on GitHub (https://github. com/lukegre/pySeaFlux, last access: 9 September 2021). NOAA_OI_SST_V2 data are provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA  (Reynolds et al. , 2002 (Reynolds et al. , , https://doi. org/10. 1175 (Reynolds et al. , /1520 (Reynolds et al. , -0442(2002)) 015<1609:AIISAS>2. 0. CO;2).
Conclusions We introduce SeaFlux, a dataset that facilitates a standardized approach for flux calculations from observation-based pCO 2 products. Specifically, we address the two largest sources of divergence in air-sea flux calculation, namely the differences in spatial coverage between the products and the scaling of the gas transfer velocity for available wind speed products based on global 14 C-based constraints. The area adjustment is the largest contributor to the methodological discrepancies, resulting in an increase in CO 2 uptake of 0 %-17 % relative to the original, possibly incomplete coverage (depending on pCO 2 product). The global scaling of the gas transfer velocity can change the CO 2 flux on average by 5 % relative to non-standardized flux calculations. The impact of applying the appropriate gas exchange coefficient through proper scaling has a larger impact on the resulting flux time series than solely the choice of wind product.
By accounting for these sources of differences, the global mean-calculated air-sea carbon flux calculated from the six available products is adjusted by up to 21 %. The SeaFlux ensemble mean air-sea carbon flux is estimated to be -1. 97 ± 0. 45 PgC yr -1 with the spread representing 1σ as calculated from the 18 realizations. This work provides an ensemble data product of the airsea CO 2 flux predicated on observation-based pCO 2 products. This ensemble product is meant to facilitate the use of the pCO 2 observation-based ocean flux estimates in assessment studies of the global carbon cycle, such as the Global Carbon Budget or RECCAP-2. Note that the original pCO 2 products still offer additional information important in other applications, such as coverage over longer time periods, higher spatial or temporal resolution, or runs incorporating further auxiliary datasets or pCO 2 data (e.
g. , SOCCOM float data;  Bushinsky et al. , 2019). Along with the ensemble of CO 2 flux fields, we also provide a public-use coding package (pySeaFlux) allowing users to apply the presented standardized flux calculations to their own data-based pCO 2 reconstructions. (d)  Bias between panels (a) and (c) (in µatm; red colors correspond to regions in which the pCO 2 from ETHZ-OceanSODA is higher than the MPI-ULB merged product). There is good agreement between the products on a regional scale.