dataesr/scientific-paragraphs-categorization huggingface dataset:
In  [25] , the authors compared single tree detection from six different methods applied in forests of Germany, Norway, Sweden and Brazil, but the Brazilian forest was a monospecific Eucalyptus plantation which is not representative of natural tropical forest. Moreover, the focus was on tree detection, not crown segmentation. In the present study, we focused on the comparison of different segmentation methods on the same study site in French Guiana, to compare performances of the methods in a dense and highly diverse forest. Six methods were compared, based on the CHM or on the entire point cloud. To evaluate the ability of these methods to segment realistic crowns, we first compared the segmented crowns with manually delineated crowns from a reference dataset, and then paired each segmented crown with a tree from the field inventory and checked the allometric relation between the crowns size and the trunk diameter.
In this study, three types of plots were selected (Table  1 ): two control plots (control), two plots which had been selectively logged for 58 commercial species (Treatment 1, removing about 10 trees/ha of DBH ≥ 50-60 cm), and two plots which had been selectively logged for both commercial and non-commercial species (Treatment 2, removing all trees of 40-50 cm DBH , followed by poison-girdling of all non-commercial species of DBH ≥ 50 cm (20 trees/ha) and poison-girdling of commercial species when trees had major defects). A detailed description of the site can be found in  [26]. Airborne laser scanning data were acquired on October 20th 2015 by the ONF-CEBA consortium, operating a RIEGL LMS-Q780 sensor. The scan frequency was 400 kHz and the final point density 55-112 points/m 2 , the flying altitude was 800 m and the scan angle +/-25 •.
Validation Dataset The most clearly visible crowns were manually segmented using colorized ALS points obtained by back-projecting lidar points into the image plane  [27]. Manually segmented crowns were then checked in the field, shapes and precise locations of the crowns were corrected when necessary and each crown was associated with a tree from the inventory, leading to a dataset of 1598 manually segmented crowns with a mean crown area of 69 m 2 , used in this analysis as references (Figure  1 ). The precision of this manual segmentation does not allow to segment smaller subcanopy crowns, and the reference dataset is therefore, biased toward big emergent crowns.
ITC Delineation Algorithms Five different algorithms developed by different research teams were compared in this study (Table  2 ). ALS data from the six plots and one RGB image (only for the plot number 15) were sent to the teams in charge of running the algorithms. Moreover, a reference segmentation was also conducted using both the Canopy Surface Model (derived from the lidar point cloud) and high-resolution RGB-NIR imagery using the e-cognition software [28].
This procedure is repeated for all the neighbours of cells now included in the region, and so on iteratively until no further pixels are added to the region. (iv) From each region that has been identified, the first-return ALS points are extracted (having first removed low elevation points). (v) A 2D convex hull is applied to these points, and the resulting polygons become the final ITCs. This method has been applied using the R package itcSegment  [34]. The itcSegment method has been tested with different allometries, and we selected the allometry producing the better congruence between segmented crowns and the reference dataset in plot number 15.
Graph-Cut In the Multi-Class Graph Cut (MCGC) approach, a normalized graph cut is applied to the raw 3D point cloud to produce 3D segments corresponding to tree crowns. The point cloud is first converted to a graph by computing similarity weights based on horizontal and vertical distances between points, as well as incorporating 'prior' data on tree tops derived from a local window maxima finder. A normalized graph cut is then used to form clusters of well-connected points, which are in turn less-strongly linked to other clusters. The number of clusters is automatically set based upon the structure of the graph and information from the prior tree tops. Candidate clusters are then tested for allometric feasibility based upon a specified height to crown radius relationship (with height computed as aboveground height, based on a model of the ground surface).
Feasible clusters are collected for the segmentation, and those clusters that fail the testing have their points rejected and left unassigned. This process is applied in a gridded approach to the full dataset, where sections of the data are sequentially clustered and then the results are merged. A second pass of the MCGC algorithm is then applied to the remaining points that are unassigned, with the goal of finding lower canopy crowns now that the more dominant trees have been segmented. The same process is applied, including allometric testing. After that, the trees from both applications of MCGC are collected and any points left unassigned after the second pass of graph cut are excluded from the list of valid crowns  [30].
These two algorithms can, therefore, segment overlapping crowns and the method described before will trim the crowns that are partially masked from above. Therefore, for these two algorithms, each point cloud segmented as a unique crown was rasterized individually to capture the full extent of the crown. Crowns that are composed of only few points (less than 40 points) or that are too short (<15 m. ) are not considered. Moreover, only crowns at least partially visible from the top were considered. Indeed firstly the reference dataset is composed only of dominant or emergent trees (visible from above) and secondly the comparison with the other algorithms for completely masked crowns would not have been possible. For each machine-segmented crown intersecting with a manually delineated crown, the Jaccard Index was calculated.
ln(DBH i ) = α sp i + β sp i ln(H i × CD i ) + ε ε ∼ N (0, σ 2 0 ) (2) where DBH i is the diameter at breast height (DBH, in cm) of tree i from species sp i , H i is its height, CD i its crown diameter, and ε the error term. Crown diameter is computed here from irregular polygons as the diameter of a disk of equivalent surface. In our case, tree species identification of the reference data is also available, and can be used to refine this model as allometries can vary among species. To take advantage of the botanical identification of the species, the parameters of the model (α and β) were modelled as random variables for each species, genus and families, with a hierarchical structure (Figure  2 ). α sp ∼ N (α g , σ 2 α sp ) β sp ∼ N (β g , σ 2 β sp ) (3) where α g and β g are genus-specific parameters sampled from the following normal distribution: α g ∼ N (α f , σ 2 α g ) β g ∼ N (β f , σ 2 β g ) (4) data The family parameters α f and β f are sampled in the prior distribution: H i DBH i CD i α sp β sp α g β g σ 2 α sp σ 2 β sp α f β f σ 2 α g σ 2 β g α 0 β 0 σ 2 α f σ 2 α f ∼ N (α 0 , σ 2 α f ) β f ∼ N (β 0 , σ 2 β f ) (5) To select the level of taxonomic information to use, we built the same model with different taxonomic information for parameters α and β: species, genus, or family level, and a model without any taxonomic information (same parameters for all trees).
The deviance information criterion (DIC) was then computed for each model to compare them  [38]. The model with the most information (species level) had the lowest DIC (Table  3 ). The allometric model was built and parameters estimated using the package R2jags  [39, 40]. To pair the segmented crowns with the tree dataset, we took two kinds of information into account. First crown center (computed as the 2D-centroid of crown projection) and trunk location have to be close enough, and second the tree should respect the allometric relation between the crown size and the trunk diameter. We considered those two rules to apply independently, neglecting a possible dependency of crown trunk distance to tree size. To pair crowns with neighboring trees, we computed the distances between each crown of the reference 1598 crowns and all trunks of the inventory.
Moreover, the RMSE was also computed using only crowns and trees that were matched with a ∆ p in the 40% highest ∆ p of the pairs for the 5000 largest stems. Additionally, the RMSE was also computed for the trees that belonged to the reference dataset. The pairing algorithm and validation were realized using the packages raster, rgdal and rgeos on R  [40] [41] [42] [43].
Paired Tree Validation When applied to the reference dataset, the pairing algorithm paired 76% of the crowns with the right stems. For the remaining 24%, the algorithm tended to attribute stems with a smaller DBH than the reference stem DBH (Figure  6 ). The pairing algorithm was then applied to the segmented crowns to attribute trees from the field inventories to each crown. Then the allometric model was applied to the resulting pairs (Equation (  2 )). The RMSE was computed for the 5000 biggest trees to make the comparison between algorithms possible. The lowest RMSE was obtained with the Graph-Cut segmentation (7. 64), followed by AMS3D, itcSegment, SEGMA, Profiler, E-cognition (Table  6 ) in that order. All methods had lower RMSE when considering only the 40% with the highest pairing confidence, and results for all pairs or considering only the 40% with the highest pairing confidence were roughly consistent among methods.
There is indeed a trade-off between the number of crowns actually paired with stems from the inventories and the quality of the pairing evaluated with the RMSE (see Table  4 ). Table  6. Results (RMSE) of the allometric model applied to dendrological field data and crowns segmented by the different algorithms. For the two first lines, the RMSE was computed for the 5000 largest stems which were automatically paired with a segmented crown, and for the last line RMSE ref was computed with the pairs that are part of the reference dataset (the proportion of the reference dataset crowns that are segmented is added in parenthesis). 6.
Discussion This study focuses on the application of ITC delineation methods in a tropical, dense and diverse forest. Our results confirm that different segmentation methods lead to highly different results, and that the use of the entire 3D point cloud, instead of the CHM derived from this point cloud, is clearly beneficial in dense forests (Figures  A2  and A3 ). The performance of CHM-based methods is usually much better in coniferous forests than in deciduous or mixed forest stands  [9, 13, 14, 21, 44] , and 3D methods have shown better results in "flat crowns" forests  [45]  that are common in windswept regions or during secondary succession. Our benchmark identified the AMS3D method developed in  [3, 29]  as the best method to segment crowns, it clearly has the highest number of crowns with a match, but also the highest mean Jaccard index when comparing the segmented crowns with the reference dataset (Table  5 ).
These differences can also be related to the size of the crowns segmented (Figure  5 ); Profiler and SEGMA are the only methods segmenting some very large crowns (area > 300 m 2 ), and are also the one over-segmenting the less (Table  5 ). On the other hand, even though the AMS3D method is the only one segmenting small crowns, it tends to over-segment a lot. AMS3D is also the only method in this benchmark using the normalized point cloud. Normalizing the point's heights does not change the point clouds geometry too much in the forest of Paracou because the terrain is even, without steep slopes. But this could clearly affect results in hilly or mountainous forests  [9]. Moreover, differences of performances are based on the projected crowns, and on the comparison with a reference dataset that contains mostly tall canopy trees, the manually segmented crowns being preferably selected among the largest since they need to be clearly visible from above to be segmented (Figure  8 ).
This estimation relies on the pairing method, which is slightly biased toward small trees (Figure  6 ). Indeed, crowns are often paired with trees of smaller DBH than the reference tree validated in the field, but reference trees are tall and large, which may explain this bias (Figure  8 ). Moreover, we made the assumption of independence between the trunk-crown distance and the tree DBH, but large trunks are potentially further from their crown centroid than small trees. Again, our reference dataset does not allow a complete study of this relation because it predominantly includes fairly large dominant trees. The limitations of our benchmarking approach largely stem from the multi-layered structure of the tropical forest: small trees are difficult to see in the airborne imagery and therefore, can hardly be included in the reference dataset.
Raman mapping (Fig. 1f , inset) of the D/G band intensity ratio spans from 1. 01 (namely pure GO) to 1. 52 (namely pure rGO), in agreement with the literature  [41]. The Raman mapping indicates a clear distinction between the GO and rGO regions over the chip, with the accentuated presence of rGO within the IDE channel. Alongside, unambiguous evidence of the absence of film discontinuity, especially at the IDE edges responsible for electrical charge injection/withdrawal, has been observed. Surface Raman mapping also corroborated the above-mentioned GO film thickness extracted from AFM providing information of the film homogeneity over large areas (see Fig. S5 ). XPS data revealed a substantial decrease in the C-O content after the GO electrochemical reduction (Fig. S4 ). In addition to the general spectroscopic and morphological characterizations, the electrical characteristics of the coated test patterns have been assessed by fabricating twoterminal devices and testing them when operating in either dry state or in bi-distilled water.
After the GO deposition, all coated substrates were copiously rinsed in bi-distilled water and blown dry in N 2. acquisition time. Raman images were obtained using a Raman point-mapping method with a 100× objective (N. A. 0. 85), which provided a spatial resolution of about 0. 46 μm 2. It creates a spectral image by measuring the Raman spectrum of each pixel of the image, one at a time. The Raman images of each well were decoded using the characteristic peak intensities of the GO using WiRE software V 4. 2 (Renishaw, U. K. ). AFM characterization was carried out in a Bruker Dimension Icon microscope, in intermittent contact mode using a TESPA-V2 silicon tip with a 42 N/m force constant. SEM imaging was conducted using an FEI Quanta 250 FEG instrument operated in high vacuum, with accelerating voltages of 2-5 kV for the incident beam.
Acknowledgements We acknowledge funding from the  European Commission  through the  Graphene Flagship Core 2 project  ( GA-785219 ), the  AMI  project funded by the  ERA-NET EuroNanoMed III programme , the  European Union  and the  Agence Nationale de la Recherche (ANR)   GA-ANR-17-ENM3-0001-01 , the  Marie Sklodowska-Curie  projects  ITN  project  BORGES  ( GA-813863 ), the  Agence Nationale de la Recherche  through the  Labex projects CSC  ( ANR-10-LABX-0026 CSC ) and  NIE  ( ANR-11-LABX-0058 NIE ) within the  Investissement d'Avenir program  ( ANR-10-120 IDEX-0002-02 ), and the  International Center for Frontier Research in Chemistry (icFRC). We aknowledge  Dr. T. Cramer  ( University of Bologna/Italy ) for support with accurate potentiometric measurements.
In this paper, a performance analysis is reported for optical code division multiplexing (OCDM) system for long-reach passive optical network (LR-PON) systems by taking into account multiple access interference (MAI), single-mode fiber (SMF) channel effects and receiver noise. The mathematical model representing the 2-D optical code parameters for different receiver structures used in optical code division multiplexing access (OCDMA) are developed, optimized and implemented using Matlab simulations, where channel imperfections, such as attenuation losses and chromatic dispersion have been considered. In the proposed system configuration, we have investigated the probability of error for Back-to-Back (B2B) with conventional correlation receiver (CCR), SMF with CCR receiver and SMF channel with successive interference cancelation (SIC) receiver.
e. , namely multiple access interference (MAI)) resulting in significant system degradation. So, in order to reduce the system degradation due to the channel nonlinearity, we limit the system performance bit rate at 40 Gb/s  [10, 16]. Furthermore, for 40 Gb/s we consider that linear effects (i. e. , chromatic dispersion) is more relevant when compared to fiber nonlinearity that should be considered for higher data bit rate (>40 Gb/s) and in longhaul communications system (i. e. , more than several thousands of kilometer). In the latter case, various nonlinear physical impairments over OCDMA system should be considered, involving Kerr effect (i. e. , self-phase modulation (SPM), four waving mixing (FWM) and cross-phase modulation (CPM)), the stimulated Brillouin scattering (SBS) and stimulated Raman scattering (SRS).
In this work, we are focused on the investigations of the most important impact, such as MAI, optical channel effects and photo-detector noise on the OCDMA system performance in PON context. Single mode fibers (SMF)s, due to their small core size (less than 10 µm) allow only one mode to be transmitted with low fiber attenuation, are used to develop communication networks, such as 2D-OCDMA-WDM passive optical network (PON)  [9] , ultra-dense WDM PON (UDWDM-PON) network  [11] , cable access network using hybrid fiber-coaxial (HFC) networks  [12]  and cable access network using hybrid fiber-coaxial (HFC) networks  [13]. Furthermore, SMF can support metropolitan, long haul access, premises applications in telecommunications, Cable TV (CATV), RoF Mach-Zehnder (MZ) based link  [14] , inter-data center application  [15]  and long-reach PON (LR-PON)  [16].
Simulation results as function of BER and Q-Factor are discussed against simultaneous users and SMF fiber length, respectively. Section 6 presents the complexity study of MATALB programs in terms of processing power for CCR, SIC and PIC OCDMA receivers. Finally, conclusions are drawing in the last section. In this study, we consider a direct sequence-optical code division multiplexing access (DS-OCDMA) system where primary limitation is the MAI. Other impairments caused by the optical fiber and receiver noise are also considered. In the proposed DS-OCDMA system, illustrated in Fig. 1 , the information bits that have been transmitted have been spread using the code signature generated by the encoder and then modulated by an optical modulator. For the optical modulation, we propose an OFDM modulation technique based on quadrature amplitude modulation (QAM) harnessing higher-order modulation formats and using cost-effective intensity-modulation and direct-detection (IM/DD).
B. Channel Model Next, the impulse response of the SMF used in the third optical transmission windows (i. e. , 1550nm), is given by  [19] : ( )         - - = L D ct j exp L D 2 c j 1 ) t ( h c 2 2 c 2 f λ π λ (2) where c is the light velocity in vacuum, Dc is the chromatic fiber dispersion coefficient, L is the fiber length and λ is the operating wavelength. The developed model has been implemented using MATLAB simulations where in Fig. 2  we present the variation of the SMF transfer function as function of frequency in the range of [0,10] GHz. It is a highly important to represent the SMF transfer function as function of the frequency due to simplifying the numerical simulation when fiber impairments are taken into considerations in the OCDMA system performance. Additionally, the transfer function versus time need convolution operator compared to a simple multiplication operator when dealing with transfer function in terms of frequency.
V. OCDMA SYSTEM SIMULATION RESULTS We have used MATLAB to calculate the main system performance parameters. Table  2  presents the simulation parameters used for the numerical results of BER and Q-factor calculation, in particular for Laser DFB transmitter (wavelength, emitted power and spectral width)  [28] , SMF specification G. 652 (length, attenuation loss and chromatic dispersion coefficient)  [29] , APD specification and code properties, respectively. In this work, the system performance is firstly investigated as function of BER. The BER as a function of the minimum probability of error is given by  [26] : where Pemin is the minimum probability of error. Additionally, the OCDMA system performance is given as function of Q-factor to simplify the calculation when performing the comparison of system performance for different codes.
e. , in km) leading to Q-Factor equal to 6 (corresponding to a BER of 10 -9 ) for the case of B2B with 3 stages SIC receiver, SMF channel with 3 stages SIC receiver, B2B with 5 stages SIC receiver and SMF channel with 5 stages SIC receiver, respectively. From the data obtained in Table  3  and Table  4 , we confirm that 2D-OCDMA PHS codes exhibit better system performance compared to 2D-OCDMA HC codes in terms of simultaneous users when 3 stages SIC receiver is employed for P equal to 7. However, 2D-OCDMA HC codes accommodate more number of simultaneous users at BER equal to 10 -9 compared to 2D-OCDMA PHS codes while using SIC receiver based on 5 stages cancellation. Our research work reveals that MAI and chromatic dispersion are the main causes leading to 2D-OCDMA system performance degradation in terms of BER and Q-factor while 100 km of fiber length with 16 simultaneous network subscribers are investigated using a probability model (Figs.
VI. COMPLEXITY STUDY In order to explain the tradeoffs between the increasing costs in terms of processing power, when the number of stage are increased, we have addressed the total CPU Time (in seconds) performed by the different OCDMA receiver program as function of the number of cancellation stage and the simultaneous user via the "cputime" Matlab function. As shown in Fig. 12 , the CPU time performed for the different OCDMA receiver (i. e. , CCR, PIC with one stage cancellation, SIC with 3 cancellation stages and SIC with 5 cancellation stages) is reported as function of increasing the number of simultaneous users in the range of  and a prime number equal to 7. As shown from Fig. 12 , the complexity of the OCDMA receiver is increased while increasing the number of stage cancellation as well as the number of simultaneous user in the network.
VII. CONCLUSION In this paper, we have developed 2D-OCDMA mathematical model, implemented and investigated system performance using MATLAB simulations. Investigation of advanced 2D-OCDMA system performance model is developed and implemented for B2B with CCR Time CPU for various receiver stucture using PHS and HC with P=7 PHS with PIC + 1 stage HC with PIC + 1 stage HC with SIC + 5 stages PHS with SIC + 5 stages receiver, SMF with CCR receiver and SMF channel with SIC receiver. In order to determine the BER and Q-factor, we have proposed and demonstrated a 2D-OCDMA system based on a probability approach by incorporating the main key system limitations, such as MAI, attenuation loss and chromatic dispersion. Our study shows that there is a tradeoff which is related to the number of simultaneous users leading to BER equal to 10 -9 for 2D-PHS codes and 2D-HC codes for 3 stages SIC receiver and 5 stages SIC receiver, respectively.