dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental results on cross-dataset scenario In order to compare different segmentation architectures, it is important to investigate their generalization capabilities in crossdatabases scenarios. Indeed, cross-database experiments play a crucial role in evaluating the effectiveness of each architecture in real-world scenarios. Tables  6  and 7  show the cross-dataset results of the trained models on Dataset_2 (Radiologists, 2019) and Dataset_3  (Ma et al. , 2021) , respectively. On the other hand, F1 score (F1-S), Dice score (D-S), and Intersection over Union (IoU) are used as evaluation metrics. For the cross-dataset experiments, Dataset_2 (Radiologists, 2019) is used as training dataset and Dataset_1 (Radiologists, 2019), Dataset_3  (Ma et al. , 2021)  and Dataset_4  (Zhang et al. , 2020)  are used as test datasets.
From the results of Table  6 , the proposed PDEAtt-Unet architecture performs better than the state-of-the-art architectures and the baseline architectures in the three test datasets. For the cross-dataset experiments with Dataset_3  (Ma et al. , 2021)  as the training dataset, Dataset_1 (Radiologists, 2019), Dataset_2 (Radiologists, 2019), and Dataset_4  (Zhang et al. , 2020)  are used as test datasets. Based on the results of Table  7 , we can notice that the proposed PDEAtt-Unet outperforms all competing architectures in the three cross-dataset scenario. From Tables  6  and 7 , the comparison between the state-of-the-art architectures and the baselines shows that for each experiment, the best architecture among these architectures changes. More specifically, the best competing architecture for Experiments II, III and VI is the SCOATNet architecture.
Ablation study As shown in Fig. 3 , our proposed approach consists of a pyramid encoder input, a dual decoders, and a hybrid loss function. To test the effectiveness of each component, we perform an ablation study using both intra-database and inter-database evaluation scenarios by discarding some components of the proposed PDEAtt-Unet. Table  8  shows the ablation study for intra-dataset experiments. From this table, it can be seen that using Attention Pyramid paths improves the results of the three intra-dataset experiments compared to using the standard Att-Unet. For Dataset_2, the PAtt-Unet architecture improves Att-Unet results by 4%, 3. 1%, and 4. 4% for F1 score, Dice score, and IoU, respectively. Similarly, experiments with Dataset_3 and Dataset_4 show the efficiency of using the encoder Attention Pyramids.
For Dataset_3 and Dataset_4, the F1 score is improved by 3. 1% and 0. 5%, respectively, the Dice score is improved by 3. 6% and 0. 7%, respectively, and the IoU is improved by 4. 8% and 0. 7%, respectively. The second component of our proposed approach is the use of a dual decoders. The results compared to using Att-Unet show the superiority of the proposed DAtt-Unet architecture on three datasets (Dataset_2, Dataset_3, and Dataset_4) and three metrics (F1 score, Dice score, and IoU). Moreover, the combination of Attention Pyramid input and dual decoders (PDAtt-Unet) improves the results even more. As can be F. Bougourzi et al. Table  3  Experimental results of intra-dataset scenario using Dataset_2  (Radiologists, 2019). The following architectures are evaluated for Covid-19 infection segmentation: InfNet  (Fan et al.
, 2020) , SCOATNet  (Zhao et al. , 2021a) , nCoVSegNet  (Liu et al. , 2021)  Experimental results of intra-dataset scenario using Dataset_3  (Ma et al. , 2021). The following architectures are evaluated for Covid-19 infection segmentation: InfNet  (Fan et al. , 2020) , SCOATNet  (Zhao et al. , 2021a) , nCoVSegNet  (Liu et al. , 2021)   IoU, respectively. On the other hand, training the proposed PDAtt-Unet architecture with the proposed hybrid loss (PDEAtt-Unet) achieves better performance than with the global loss, as shown in Table  8. Tables  9  and 10  show the ablation study for cross-dataset experiments where Dataset_2 and Dataset_3 are used as training data, respectively. From the results of Tables  9  and 10 , we notice that both PAtt-Unet and DAtt-Unet architectures outperform the baseline result of Att-Unet by a significant margin for all cross-dataset experiments, as shown by the three evaluation metrics.
Moreover, the combination of the encoder Attention Pyramids and the dual encoders further improves all results. Similar to the results in Table  8 , using the proposed hybrid loss function to train PDAtt-Unet achieves better performance than using the global loss. On the other hand, comparing the intra-dataset results within Tables 4, 3, 5, and 8 shows that the performance of PAtt-Unet, DAtt-Unet, and PDAtt-Unet is better than the baseline architectures in the three datasets. Moreover, PAtt-Unet, DAtt-Unet, and PDAtt-Unet perform better or close to better than the best state-of-the-art approach of InfNet  (Fan et al. , 2020) , SCOATNet  (Zhao et al. , 2021a) , and nCoVSeg-Net  (Liu et al. , 2021). Similarly, the comparison of cross-dataset results shows that PAtt-Unet, DAtt-Unet, and PDAtt-Unet perform better than the state-of-the-art approaches in most experiments (Tables 6, 7, 9, and 10).
Conclusion In this work, we proposed an approach to segment Covid-19 infections from CT scans. Our proposed PDEAtt-Unet is an encoder-decoder CNN architecture. The encoder is a Unet-like encoder with pyramid paths and attention gates aimed to preserve the general structure awareness for all encoder layers. The decoder consists of two Att-Unetlike decoders that simultaneously segment the Covid-19 infection and lung regions. To circumvent the limitations of the BCE loss function, edge loss is proposed. To prove the efficiency of our approach, we evaluated its performance in both scenarios; within a dataset and across data. Moreover, we compared the performance of our proposed approach with three baseline architectures (Unet, Att-Unet, Unet++) and three stateof-the-art architectures for Covid-19 segmentation (InfNet, SCOAT-Net, and nCoVSegNet).
(Liu et al. , 2021)0) of intra-dataset scenario using Dataset_4(Zhang et al. , 2020). The following architectures are evaluated for Covid-19 infection segmentation: InfNet(Fan et al. , 2020), SCOATNet(Zhao et al. , 2021a), nCoVSegNet(Liu et al. , 2021), Unet, Unet++, and Att-Unet and the proposed PDEAtt-Unet.
54 Experiment VI: Dataset_4 (Zhang et al. , 2020) SCOATNet (Zhao et al. , 2021a) nCoVSegNet (Liu et al. , 2021) Unet Unet++ Att-Unet 63. 77 60. 03 41. 54 39. 55 53. 13 34. 22 32. 90 16. 54 16. 84 23. 78 46. 81 42. 89 26. 22 24. 65 36. 17 PDEAtt-Unet 66. 74 38. 90 50. 08.
Results and discussion A global simulation of our proposed system is carried out using MATLAB/OptiSystem co-simulation platform proposed and used in our previous works  [20, 21]. The electrical up-conversion and the optical link are implemented in OptiSystem, while all other processes are performed using MATLAB. Data bits are generated and encoded according to the 5G NR specifications for multiplexing and channel coding  [24]. The encoding processing includes cyclic redundancy check (CRC) attachment, code block segmentation, LDPC coding and rate matching. The encoded bits are mapped using QPSK, 16-QAM, and 64-QAM. The complex symbols are fed to the OFDM modulator to generate four baseband MIMO signals. Channel estimation pilots and Cyclic Prefix (CP) are added to the signals, then a Digital-to-Analogue Conversion (DAC) is performed.
We have used the parameters defined in Table  2  to generate the OFDM MIMO signal, which occupies the 7,2 GHz bandwidth. The resulting signals fed our optical architecture specified above. The main parameters of this architecture used for simulation are shown in Table  3. The 60 GHz signals at the output of PDs are transmitted over the 4×4 MIMO channel. To achieve this, a channel matrix is generated according to the 4×4 MIMO extension of the TSV model. At the receiver, the inverse processes are done. Channel estimation at pilot positions with arithmetic averaging is implemented according to the Least Squares (LS) criterion. The obtained channel coefficients are then interpolated to obtain the estimated channel coefficients at data positions. To reduce MIMO channel effects, Zero-Forcing Successive Interference Cancellation (ZF-SIC) detector is implemented.
Conclusion In this paper, we presented a new 4×4 MIMO RoF architecture for 5G indoor mmWave applications. The proposal allows the transmission of 60 GHz OFDM MIMO signal over the optical fiber and performs heterodyne detection at the RAP level to reduce its cost and complexity. The optical architecture is based on DP-MZM for data optical modulation and DD-MZM for optical subcarrier generation by exploiting its optical sidebands. To evaluate this scheme under a realistic indoor scenario, we have proposed a 4×4 MIMO extension of the well-known TSV channel model. For performance evaluation purposes, we have configured the optical power injected in the optical modulators and the MIMO antennas separation based on simulation results. The global RoF system is investigated in terms of BER versus of Eb/No for uncoded QPSK, 16-QAM, and 64-QAM schemes.
The development of efficient and sustainable marine energy converter systems is a great challenge, especially in remote areas such as oceanic islands. This work proposes a numerical modeling methodology to assess the mechanical behavior of a wave energy converter (WEC) to be applied outside Fernando de Noronha Island (Pernambuco, Brazil). First, oceanographic data collected in situ were analyzed to determine different sea state scenarios in the region. The Airy theory and second-order Stokes' theory were used to obtain the velocity profiles for the maximum and operational swells. These profiles were then implemented in a flow model developed in COMSOL Multiphysics software (Burlington, MA, USA) to calculate the wave distributions of pressure on the WEC structure. Finally, wave pressure distributions obtained from simulations were implemented in a static analysis of the system by the finite element method using SolidWorks (France).
The development and installation of marine energy conversion systems to supply the needs of Fernando de Noronha Island would be a major achievement. In this work, an original study involving a coupling methodology using oceanographic data, mathematical theories and finite element (FE) modeling was applied to evaluate the possibility of installing a WEC system in the island. This would bring massive environmental benefits in terms of electric power generation, limiting the use of fossil fuels to produce energy in Fernando de Noronha. In this context, this paper proposes a preliminary methodology to model and simulate a wave energy converter (WEC) system adapted to the conditions of the study area, in order to give a first approach to wave loading in the system. For this investigation, oceanographic data obtained during surveys were first analyzed to characterize the operational and extreme states of the sea in the study region.
To carry out this work, several steps were followed: (1) the treatment of oceanographic data measured in the platform of Fernando de Noronha Island, highlighting the different types of swells; (2) the use of linear wave theories (Airy and second-order Stokes) to determine the velocity profiles of the different types of waves observed in the region; (3) the development of a numerical model of the submersed WEC system using COMSOL Multiphysics software to obtain the pressure distributions on the system due to the waves; (4) the development of a numerical model using SolidWorks software to perform a static mechanical study of the WEC system under extreme conditions.
Oceanographic Data The study area for a future deployment of an energy conversion plant using sea waves was the Fernando de Noronha archipelago, located 345 km from the closest point to the Brazilian coast. Oceanographic survey data were used for the identification and characterization of the operational swell, as well as extreme scenarios that may take place in the study area. The data were provided by the Oceanography Department at Universidade Federal de Pernambuco (UFPE, Brazil) and were obtained using an acoustic wave and current meter system (AWAC) with a transmission frequency of 600 kHz. Depths at the site are around 17 m for coordinates 03 • 49 S and 32 • 24 W (Figure  1 ). The site is in an area open to tourism and oceanographic surveys. The instrumentation was operated in stand-alone mode over one year (May 2013-June 2014) and the following were measured: (i) currents every 30 min; (ii) waves with 2 h bursts; and (iii) tidal elevation every 2 h with an absolute accuracy of 0.
1% of full scale for the wave measuring equipment. In this study, the oceanographic data used hereafter were obtained during the whole time frame of the survey (1 year data). kHz. Depths at the site are around 17 m for coordinates 03°49′ S and 32°24′ W (Figure  1 ). The site is in an area open to tourism and oceanographic surveys. The instrumentation was operated in stand-alone mode over one year (May 2013-June 2014) and the following were measured: (i) currents every 30 min; (ii) waves with 2 h bursts; and (iii) tidal elevation every 2 h with an absolute accuracy of 0. 1% of full scale for the wave measuring equipment. In this study, the oceanographic data used hereafter were obtained during the whole time frame of the survey (1 year data).
Mass Balance The weight of each component was calculated by using the volume of each component and the densities from Beirão and Malça  [37]. According to the balance sheet presented in Table  2 , the panel weighs about 10 tons while the base weighs nearly 290 tons. The volume of water displaced by the fully submerged panel is of approximately 150 m 3. Based on Archimedes' principle, since the tendency is to lift the panel to the surface, the calculated Archimedes thrust on the panel is approximately 1500 kN, providing a positive buoyancy to the panel. Thus, the base allows the anchoring of the system to the bottom by compensating for almost twice the pressure of Archimedes applied to the panel. A FE model of the simplified WEC structure enclosed in the volume of an external fluid was developed in COMSOL Multiphysics software.
Mesh Properties The meshes used for the modeling are detailed in Table  4  and presented in Figure  8. The system mesh was refined to increase the accuracy of the pressure calculation over the surface of the panel. A convergence study was previously performed on the same system with another FE software, ANSYS (Canonsburg, PA, USA), and then compared to COMSOL Multiphysics simulations, in order to find the adequate meshes of the fluid and the solid  A convergence study was previously performed on the same system with another FE software, ANSYS (Canonsburg, PA, USA), and then compared to COMSOL Multiphysics simulations, in order to find the adequate meshes of the fluid and the solid WEC structure. For the same study configuration, 6 different mesh sizes were tested, and the pressure obtained on the panel was studied to verify the convergence of the results.
The characteristics of this preliminary study are presented in Table  5. The results highlight that the maximum pressure applied by the water on the panel increased by 16. 6% between the coarsest and the finest mesh. On the other hand, the maximum pressure was the same for the 0. 2 m and 0. 15 m mesh while the computational time increased by 2 h. The performance of the 0. 2 m mesh was, therefore, satisfactory for a much shorter computational time. Figure  9  shows the evolution of the pressure applied by the fluid on the panel as a function of x, with the convergence of the solutions. Figure  10  represents the pressure distribution on the panel. A convergence study was previously performed on the same system with another FE software, ANSYS (Canonsburg, PA, USA), and then compared to COMSOL Multiphysics simulations, in order to find the adequate meshes of the fluid and the solid WEC structure.
The same model was run with ANSYS and COMSOL Multiphysics to compare the results with different input velocities (Table  6 ). The same model was run with ANSYS and COMSOL Multiphysics to compare the results with different input velocities (Table  6 ). The results show that the pressures simulated by COMSOL Multiphysics were all lower than the pressures obtained with ANSYS. This slight difference can be explained by the difference in the mesh size between the two software. Indeed, with ANSYS it is possible to make very fine meshes but requiring quite long calculation times (4 h 30 min of calculation for ca. 16,000,000 elements). With COMSOL, it was difficult to create an extremely fine mesh because the solution did not converge beyond a certain mesh fineness. On the other hand, COMSOL has the advantage of being able to insert functions more easily than ANSYS and without an associated programming language, e.
• The pressure distribution on the panel due to the maximum swell scenario; • The hydrostatic pressure; • Gravity. This static investigation was carried out with SolidWorks software for the five cases of inclination. Thus, it was possible to study the behavior of the structure facing the loading of the maximum swell and identify possible critical points of the structure. An example of a simulation result is presented in Figure  18 , showing the distribution of the von Mises stresses in the WEC system for the panel inclined at 30 pressures in SolidWorks on the right and left edges of the panel were larger and the results regarding the behavior of our structure were slightly overestimated.
• The pressure distribution on the panel due to the maximum swell scenario; • The hydrostatic pressure; • Gravity. This static investigation was carried out with SolidWorks software for the five cases of inclination. Thus, it was possible to study the behavior of the structure facing the loading of the maximum swell and identify possible critical points of the structure. An example of a simulation result is presented in Figure  18 , showing the distribution of the von Mises stresses in the WEC system for the panel inclined at 30°. The results show that the stress values obtained in the HDPE panel are higher the reinforcements than on the rest of the panel. This is due to the fact that the panel is pressed against the reinforcement by the pressure of the wave. Simulation results also show that for the HDPE panel and steel reinforcements, the 0° inclination is the most Table  10  presents the maximum von Mises stress values obtained for each panel inclination, for both the HDPE panel and the reinforcements made of stainless steel.
Conclusions The main objective of this work was to propose a numerical modeling methodology to assess the mechanical behavior of a WEC system to be applied offshore the Fernando de Noronha Island (Pernambuco, Brazil). In order to evaluate the different wave conditions under which the system would operate, oceanographic data collected in situ on the platform of Fernando de Noronha were analyzed. Different types of swells were highlighted: an operational swell (likely to be formed more frequently) for the energy potential of the area and a maximum swell (arising during storms) to verify the structure. Thereafter, linear wave theories (Airy and second-order Stokes) were used to determine the velocity profiles of the types of waves. The respective wave velocity profiles were calculated as a function of the depth as well as of their oscillation.
The next step consisted in performing flow simulations on the simplified WEC structure using COMSOL Multiphysics software, in order to obtain the pressure distributions due to the fluid on the panel. These simulations were carried out considering the different inclinations of the WEC system panel to subsequently determine the critical positions. At last, these pressure distributions were implemented to perform a static analysis of the system using a FE model in SolidWorks. In this way, the simulation of the behavior of the structure in the most critical case (maximum swell) and for different cases of inclination was achieved. In a first approach, the maximum stress results were below the elastic limits of the materials for all inclinations, except for the panel blocked at 0 • (in relation to the vertical axis).
Acknowledgments: The authors would like to thank  CAPES (BRAFITEC Program-Projects :  NER 155/14  and  AMINOV-MECA 253/19 ),  FACEPE  ( APQ-1361-3. 05/12 )and  CNPq  ( MCTI/CNPQ/ Universal 14/2014-460973/2014-2 ) for the financial support. M. A. thanks the  Brazilian Research Network on Global Climate Change-Rede CLIMA  ( FINEP-CNPq 437167/2016-0) Grant 01. 13. 0353-00  and the  Brazilian National Institute of Science and Technology for Tropical Marine Environments-INCT AmbTropic  ( CNPq/FAPESB 565054/2010-4  and  8936/2011 ) for their support.
Adaptive line search The main advantage of the method (3. 1) consists in its easy implementation. Indeed, in the case ψ(•) ≡ 0 with dom ψ = E, the iteration (2. 1) is reduced mainly to matrix inversion, the very standard operation of Linear Algebra, which is available in the majority of software packages. However, for the better performance of this scheme, it is necessary to apply a dynamic strategy for updating the step-size coefficient H. Let us show how this can be done. Consider the following optimization method.
Previous work has predicted spoligotypes in silico, implemented within the widely applied SpolPred software  12. With at least 20-fold more Mtb WGS available since the development of Spol-Pred, we seek to assess the consistency of spoligotypes with the sub-lineage system 1 , and determine their global distribution. This goal is achieved by developing new software to in silico genotype isolates, called "Spolpred2", which predicts spoligotypes from raw sequence reads generated by several technological platforms. We incorporate the updated barcodes for spoligotypes and imbed "Spolpred2" within the TB-Profiler tool  13  , widely used to profile MTBC sub-lineages, strain-types, and drug resistance from WGS for clinical and surveillance applications.
Results Global distribution of spoligotypes families. The dataset consisted of 28,436 M. tuberculosis isolates with WGS, drug susceptibility test and geographical source data, with lineages inferred using the TB-Profiler software (Table  1 ). The spoligotypes were predicted using the new Spolpred2 software, developed as part of   To improve the stringency of the analysis, all spoligotypes with < 5 isolates support were removed, resulting in 24,661 (86. 7%) isolates, 96 (96. 0%) unique lineages and 415 (13. 9%) distinct spoligotypes (Table  1 ; Table  2 ; Fig. 1 ). This filtering task reveals the high number of rare spoligotypes (n = 3,775; see S1 Table for a list), with representation across most lineages (L4 57. 5%; L1 19. 2%; L3 14. 6%; other 8. 7%). After filtering (n = 24,661), the most frequent spoligotype families were Beijing (7,167; 29.
Discussion Our study aimed to characterize the global distribution of spoligotypes and correlate this with the lineage system developed previously 1. To enable this work, a new rapid in silico spoligotyping software was developed with speed and flexibility in mind, and was integrated into the TB-Profiler analysis platform. The main improvements of Spolpred2 over the previous tool (SpolPred) include faster processing, with > 20-and > 500-fold speed improvements from fastq and BAM formatted files, respectively. Further, there is more flexibility with regards to input data types (e. g. , gzipped fastq and BAM), the reporting of associated metadata (e. g. , family names), and the ability to include custom spacer sets. The frequency of spoligotypes and their respective families confirm the known common spoligotype families, with representation from Beijing (SIT1), T (SIT53), LAM (SIT42), CAS (SIT26) and EAI (SIT236).