dataesr/scientific-paragraphs-categorization huggingface dataset:
14. It can be seen that acquisition 02CE is considerably more saturated than the other datasets, accounting for 50. 6% of the total saturated pixels. Since significant feature misalignment between the bands can affect the quality of the NN's inference, the inter-band alignment precision was also assessed. To quantify the precision, key points were identified with the ORB algorithm  [20]. The distance between corresponding key points was then calculated and averaged for each band set. At least 100 suitable key point pairs were found for each acquisition except for 038B/Tumbarumba, where only 33 pairs were identified due to the lack of sharp features in the image. All key point pairs with distances of over ten pixels were discarded because these pairs were observed to be the result of incorrectly identified key points, and misalignments of this magnitude were not visually observed.
B. Synthesized Dataset Quality Performance In order to train an NN capable of segmenting the images produced by the HyperScout 2 camera, a dedicated dataset reflecting the characteristics of the sensor itself is needed. As described in Section IV-A, the dataset has been synthesized from Sentinel-2 images, which, although similar, differ in some aspects, namely: 1) radiance versus reflectance and 2) relative SNR per band. Simulating the behavior of radiance from reflectance images may introduce additional noise. This noise is important for the NN which uses the intrinsic characteristics to classify pixel values. To this aim, for each image from the generated synthetic radiance images, we added and removed 5% of the noise from the nominal noise expected for the selected band in HyperScout-2.
The process involved adding an additive white Gaussian noise (AWGN) to increase the baseline value, and a denoiser algorithm to reduce it. Increasing the SNR values allows us to train a more robust NN capable of working correctly in different situations, and possibly with different bands. Furthermore, to challenge the network and avoid wasting potential good images, some images containing clouds over salt lakes, snow, etc. were included in the synthetic dataset. These images, even if they do not represent the main goal of the Sat-1 mission, lead to a reduction of the overall accuracy in the classification of the synthetic dataset. On the other hand, these challenging images improve the ability of the network to recognize boundary situations. In operational missions, improvements can be easily obtained adding special location information.
C. Segmentation Network Performance The CloudScout segmentation has gone through two completely different testing phases. The first was conducted during the design phase at the end of the training and validation stages, using the synthetized dataset, and the second one was conducted in-flight using the available images acquired by HyperScout-2 during the -Sat-1 mission. While the first testing phase aimed to assess the network's capabilities and performances using the synthetic dataset to validate the inference against the requirements, the second phase was a direct evaluation of the performance exploiting the hypercubes acquired in-flight during the -Sat-1 mission by the HyperScout-2 camera. The results of the first phase have been already presented and summarized in the confusion matrix in Table  III.
In particular, the HyperScout 2 acquisitions have been planned and executed according to requirements related to EO applications. All the acquisitions have been used for the PhiSat in orbit demonstration. This implies, for example, that no images of clouds on snow or clouds on Salt Lake have been acquired, while the training and synthetic test datasets randomly chosen from the Sentinel-2 archive contained many configurations quite challenging for the NN. This is also observable in the overall accuracy obtained by the NN model against the.
D. EoT Inference Engine In-Flight Performance In addition to the CloudScout results acquired during -Sat-1's mission, in-flight performance data was also acquired for the EoT inference engine. Four separate EoT hardware test phases were executed over a 70-day period of the mission. During each phase, the EoT BIST routine was initiated, which performed both EoT chip-level and board-level diagnostics and reported back results. Chip-level tests coverage encompassed memories, caches, interfaces, and functional tests to dynamically exercise the SHAVEs and multiported memory. Board-level diagnostics evaluated the PMU, flash and serial device (SD) card. The executed diagnostics and their results are summarized in Table  VI. It is seen that every diagnostic test passed at each phase, with the exception of the SD card test.
As part of the -Sat-1 mission it has been demonstrated that it is possible to run the full preprocessing chain before inference onboard the HyperScout OBDH, including computing spectral data cubes at pixel accuracy without relying on platform ADCS data but instead solely on machine vision techniques, allowing robustness and independence from small satellites' performance. As shown in Fig. 19 , at the bottom of its structure, there is the EoT hardware accelerator, developed by Ubotica (Section III-B), latch-up protected and controlled by cosine's HyperScout subsystems. The EoT, powered by the Myriad 2 VPU, accelerates both CV and AI while operating in a low power envelope. Acceleration is achieved via a unique combination of parallel processing and high-bandwidth multiported memory on the multicore Myriad 2 SoC.
The CloudScout NN exploits only three of the ten bands available from the HyperScout 2 for two main reasons: memory constraints on the Myriad 2, and power limits derived from the satellite power budget. Hence, to perform the NN training, a synthetic dataset was developed by Sinergise (Section IV-A), starting from the Sentinel 2 images. The dataset was built following three phases: 1) PCA to select the best bands combination for our goal; 2) re-binning the Sentinel-2 images from 10 to 70 m GSD; and 3) using the Sinergise s2cloudless algorithm to construct a label/ground truth mask for the input images. The training of the network aimed to obtain the highest accuracy while maintaining a low number of false positives. Maintaining a low false-positive rate is of paramount importance for the application, as images wrongly classified as cloudy would be not transmitted to the ground, resulting in a potential loss of interesting data.
This kind of approach was proposed in the early eighties  (Berger et Oliger, 1984)  and has already been implemented in either academic works  (Debreu et al. , 2012; Penven et al. , 2006)  or for large-scale realistic applications  (Biastoch et al. , 2009; Marchesiello et al. , 2011)  to improve the resolution and hence the realism of the local dynamics. Such an approach, the Adaptive Grid Refinement In Fortran (AGRIF)  (Debreu et al. , 2008) , is based on domain decomposition and allows partial overlapping of the grids. It is thus well suited for coastal applications and is expected to facilitate the offshore continuum. Consequently, the AGRIF software has been introduced into a coastal numerical model, namely MARS3D  (Lazure and Dumas, 2008). Implemented along the coasts of the north western European shelf and over coastal bays, the mesh refinement should allow the representation of a large spectrum of spatial (small embayments and large areas) and temporal (fast as tides or and surges and slow as mesoscale or frontal instabilities) scales.
The AGRIF model: Mesh refinement and grid interactions AGRIF (Adaptive Grid Refinement In Fortran from  Debreu et al. (2008) ) is a package for the integration of Structured Adaptive Mesh Refinement (SAMR) features within a multidimensional finite difference model. Its main objective is to simplify the integration of SAMR potentialities within an existing model, whilst making minimal changes. In particular, it includes a lexicographic analyser of Fortran code that generates, at the compilation step, the data structures required for running the same code on any grid hierarchy. In addition to MARS3D  (Dufois et al. , 2014; Muller et al. , 2009) , AGRIF is currently used in the following ocean models: ROMS-AGRIF  (Debreu et al. , 2012; Penven et al. , 2006)  a regional model developed jointly at Rutgers and UCLA universities; NEMO ocean modelling system  (Biastoch et al.
The main hydrological runoffs are set in the zooms (96 rivers accounted for) from different source databases (Spain, English, French and Netherlands). The atmospheric forcings are picked up in the Météo France ARPEGE High Resolution (0. 1°) analysis with hourly data for the coarser grids. For the child grids, the atmospheric forcings rely on the Météo France AROME (2. 5 km) analysis with hourly data. The discretisation over the vertical axis is performed with 40 generalized 𝜎-layers with a stretching function that induces refinement above 150m depth, next to the surface. A space refinement factor of 5 is used between each grid level but the time refinement is adapted according to the maximum velocity encountered locally. Thus, the time refinement coefficient is heterogenous: it is either 3 (over areas with rather slow flows) or 5 (in very energetic areas such as in the middle of the English Channel).
Case scenario For the regional Bay of Biscay configuration, two realistic hindcasts have been realized over several years (2010-2020 for the two-way and 2016-2019 for the one-way) to demonstrate and to characterize nesting benefits. For each hindcast, a spin-up of 3 months has been performed. These two common years have been selected upon the available datasets for several validation parameters (tide gauge, temperature and salinity data). For the coastal Bay of Brest configuration, two realistic hindcasts have been realized over 2017 up to 2019 for both one-way and two-way nesting techniques. As Brest is located at mid-latitude, the bay is subjected to highly variable and sometimes intense meteorological forcings. Therefore, various numerical experiments have been performed to catch these conditions and estimate a realistic renewal indicator.
Satellite data Besides, satellite data are used for sea surface temperature validation at two different horizontal scales: The first one is based on SST fields extracted from the global Advanced Very High Resolution Radiometer (AVHRR) Pathfinder V5 daily dataset. The ODYSSEA chain has been modified by  Saulquin and Gohin, (2010)  to use optimal interpolation for the reconstruction of gap-free and using the previous analysis as a first guess. The product is gridded at a 0. 02° spatial resolution and freely available at https://resources. marine. copernicus. eu  (Autret and Piollé, 2018). The second one is based on the Thermal InfraRed Sensor (TIRS) from the Landsat 8 satellite. As it orbits the Earth in a sun-synchronous, near-polar orbit inclined at 98. 2 degrees, one gets a track over our area of interest (Iroise Sea in paragraph 3.
Hydrodynamic impact over open boundary conditions The ongoing issue for nesting models relies on open boundary conditions. For the AGRIF configuration, the tidal propagation is performed using sea surface elevation and fluxes interpolation. For the regional Bay of Biscay configuration, the initial tide forcing is imposed at the mother grid's boundary with FES model. It is composed of 12 tidal harmonic components whereas SHOM CST-France model used to force the child grid in one-way nesting contains 112 components. Moreover, this latter model has been accurately validated throughout the French tidal gauge network RONIM. The modelled tidal signal is compared to hourly validated data from four tidal gauge available in the studied area. The main statistics are estimated over one year are given in the Table 3 for the regional Bay of Biscay configuration.
(21%) This comparison has been also done for the barotropic currents. The differences between both models are higher for the minor waves O1 and K1 with difference of 5 cm. s -1 and relative difference of 30% for some points. However, the validation of the PREVIMER atlas currents was not available all over the area due to a lack of long time series data over the French coast. In the same idea, barotropic currents of both configurations have been compared to different available datasets. The recent ADCP data have been recorded in the Bay of Brest or in Molène archipelago. As they are mainly situated over shallow waters, there have been no significant difference between nesting techniques.
Hydrologic validation For both regional and coastal configurations, the qualification of the results has been assessed over a two-year period from 2018 to 2019. Regarding the regional Bay of Biscay configuration, the evaluation focuses on the Iroise Sea zoom. Figure  6  displays a Taylor diagram for temperature (Fig. 6a ) or salinity (Fig. 6b ) and another diagram that represents bias values and root mean squared errors (RMSE). These graphs summarize the comparisons between the available datasets and both nesting methods (one-way vs two-way) for the two different configurations. It appears that the nesting impact is not homogenous over the whole domain. First, for the regional Bay of Biscay configuration, the two-way nesting technique improves the overall performance of the model compared to oceanic datasets (see green points for one-way and yellow points for two-way).
The simulated temperature offsets are noticeably reduced. The main favourable impact is the drop of the root mean square error by 0. 4°C for the COAST-HF MAREL Iroise point. For salinity comparisons, the RMSE and the bias exhibit to be in the same order of magnitude. The major improvement relies on the enhancement of correlation for the COAST-HF ASTAN dataset. This could be due to the vicinity of this point to the eastern border of the zoom. The update ability of AGRIF two-way enables more realistic incoming fluxes at high temporal resolution. The relative standard deviation of the simulated salinity at MAREL Iroise buoy is also considerably reduced. As this point is located in the strait of Brest, the tidal variations of salinity front are somehow better simulated for the same reason. As previously stated, the one-way nesting solution is already in good agreements with the dataset (see blue points for one-way and red points for two-way) for the coastal Bay of Brest configuration.
This is explained by the well-suited parametrisation performed with the bottom drag coefficient spatialisation and the vertical turbulence closure. It is also due to the fact that the flows are driven by the tide whose large-scale features are easily captured  (Petton et al. , 2020)  in this semi-enclosed area. The two-way nesting technique barely enhances the performance of the model. For temperature, the main improvement is the reduction of bias. This could be explained by the nesting feedback that enables a more accurate global temperature budget in the mother grid. For salinity, the model also reduces the relative standard deviation for the MAREL Iroise and ECOSCOPA datasets. As the water runoffs are identical, this amelioration might be hard to explain. It may result in a better simulation of current flows with nonlinear effects.
Discussion The present study demonstrates the capabilities of MARS3D/AGRIF two-way nesting compared to classical one-way offline embedding; it is based on gradually more complex examples from local to global issues. The very first objective of grid refinement is either to tackle a local stationary problem or to follow a single dynamical structure along to its displacements  (Blayo and Debreu, 1999). The zoom is there to reach a relevant resolution (e. g. , commensurable with the Rossby internal radius or with the geometry of a given structure or set of structures such as islands, capes or peninsulas) raising the major question which lies in the downscaling of global and coarse information to the resolution of the targeted area at least at the open boundaries of the considered domain. Despite the regular improvement in the computational power available for high-performance scientific computing, the computational cost and the storage of huge datasets remain major issues for long-term numerical oceanic simulations, on behalf of various reasons such as green computing considerations.
Once the bathymetry consistency is performed along the boundaries between the mother and child grids, the user can launch the runs without additional task. The initial conditions for the new zoom are estimated online at the first timestep by the AGRIF library. This capability has been used to study more precisely the deep convection in the northwestern mediterranean sea  (Garreau and Garnier, 2015)  or to better identify shear stress over future renewable marine energy structures (Maisondieu, Pers. comm). In situation where the user needs to remove a specific child grid, the bathymetry consistency has just to be checked once offline before launching the model. This software also has the capability to perform the opposite, that is to say grid coarsening. It is a relevant capability for online physical-biogeochemical coupling which was used for example by  Lévy et al.
Conclusions Algorithms for the implementation of two-way structured mesh refinement in an implicit free surface ocean model are described in this paper. For coastal applications, the correct treatment of wetting and drying requires a careful design of the grid interactions. Applications of such a mesh refinement technique allows to reach very high-resolution all along the French coast of the Bay of Biscay featured by a macro-tidal regime. The present analyses demonstrate the positive impact of the twoway refinement against simple one-way refinement. In addition to the applications presented here, these mesh refinement capabilities have now been validated on a series of realistic configurations. The computational aspects of grid refinement are managed by the AGRIF software. In the context of sustainable green computing, this nesting technique saves tedious file processing and interpolation steps.
It is well suited for large configurations with several zoom models, for long-term hindcast or operational forecast simulations to monitor marine environment. The AGRIF implementation in the MARS3D model give the opportunity to the users to easily activate or deactivate a given child grid. Adding one additional zoom level is a quick process which actually relies more on the accuracy of the available bathymetric data than on technical issues. In coastal areas, the two-way nesting solution enables the correct estimation of environmental indicators calculated for highly different hydrologic and tidal forcing in order to characterize the general mixing of the bay. It allows the fine simulation of biological processes that require mass conservation at large scales with different horizontal resolution.
Code availability Last version of MARS3D is freely available on request at https://wwz. ifremer. fr/mars3d/. The AGRIF library is freely available at under CECILL license (http://www. cecill. info). Both codes are written in Fortran-90/95 and figures are displayed from python scripts or with QGIS software for both configuration presentations. All the model code, bathymetric grid files, namelist https://doi. org/10. 5194/egusphere-2022-337 Preprint. Discussion started: 28 June 2022 c Author(s) 2022. CC BY 4. 0 License.
Conclusion The energy harvesting performance of a two-bladed VAT operating in a confined channel has been numerically investigated. A 2D numerical model has been implemented on OpenFOAM-v6 and validated against reference data for a similar turbine operating in unconfined flow. The model allowed for investigating the effects of the main design parametersincluding blade-channel gap ε 0 , solidity σ and tip-speed ratio λ -on the performance of the turbine.
Behavioural data. Trained observers, blind to the topic of this study, collected daily ad libitum behavioural observations and performed 5-min focal sampling on all study individuals  (Altmann, 1974). In this study, we used 2182 hours of focal data collected on 81 adult females aged ≥4 yrs (mean±SD: 26. 9±39. 3h per female) and 670 hours collected on 34 subadult and adult males aged ≥9 yrs (19. 7±29. 2h per male), collected from August 2012 to March 2020. We included subadult males (aged 9-10 yrs) because they have usually reached their full adult body size  (Setchell and Dixson, 2002)  and have started competing with other males and mate with adult females  (Setchell et al. , 2005b). During focal sampling, sexual and agonistic interactions between a focal individual and its groupmates were recorded.
Age and male immigration patterns. The exact date of birth was known for 25 individuals. For the remaining 90 individuals, the date of birth was estimated using body size, condition and patterns of tooth eruption and wear  (Galbany et al. , 2014). The error made when estimating the age of these 90 individuals was less than a year (50 individuals), two years (26 individuals), three years (13 individuals) or five years (1 individual). Long-term life-history and demographic data were also available from all individuals. Census data allowed to reconstitute patterns of male residency in the group. Here, we considered a male as resident in a given mating season when censused in the group late during the preceding birth season, between January and March. When censused for the first time during the mating season (which takes place once per year between April and September) we considered the male as immigrant.
We reran the GLMM used for testing the occurrence of intimidation, including as an explanatory variable the overall rate of aggression We ran all the above statistical tests in R version 4. 0. 3. For generalised linear mixed models (GLMMs; summarised in table  S1 ) we used the glmer function of the lme4 package (Bates et al. , 2015; binomial models) and glmmTMB from the package glmmTMB (Brooks et al. , 2017; negative binomial model). Whenever a singular fit was observed, we reran the relevant model with the bglmer function of the blme package  (Chung et al. , 2013). Whenever necessary we increased the number of iterations and/or we changed the optimiser of the model to achieve model convergence of the model and improve its fit. We used the Anova function of the car package (Fox and Weisberg, 2019) to test for the significance of fixed factors and computed their 95% confidence intervals.
We further used the vif function of the same package to detect multicollinearities. All VIFs were <2. 5 indicating no serious multicollinearities  (Zuur et al. , 2010). For multilevel categorical factors such as reproductive state, we switched the reference category sequentially  (Pinheiro and Bates, 2006)  in order to test for pairwise differences between categories. We explored the distribution of residuals to validate the models using the functions testDispersion and simulateResiduals from the DHARMa package  (Hartig, 2022).
Acknowledgements We would like to thank  Matthieu Paquet  for his constructive and thorough feedback as well as  Micaela Szykman Gunther  and 1 anonymous reviewer. The present work would not have been 12 Nikolaos Smit et al. Peer Community Journal, Vol. 2 (2022), article e36 https://doi. org/10. 24072/pcjournal. 134possible without the work of past and present field assistants of the Mandrillus Project and the logistical support of SODEPAL-COMILOG society. This is a Mandrillus Project publication number n o  26  and  ISEM 2022-132 SUD. Version 5  of a preprint of this article has been peer-reviewed and recommended by  Peer Community In Ecology  (https://doi. org/10. 24072/pci. ecology. 100099).