dataesr/scientific-paragraphs-categorization huggingface dataset:
Results For evaluation, our test dataset contains images with varying noise levels (4. 1. 2). To perform a quantitative comparison, we use the Normalised Mean Squared Error (NMSE) and the Structural Similarity Index Measure (SSIM), two commonly used metrics to quantify image-reconstruction quality. To measure the NMSE and SSIM, we weighted all images by a Gaussian window centered around the galaxy. The FWHM for each object was obtained from its catalog and used for the Gaussian weighting, thus ensuring that the window would only encircle the object and discard the noise present in the background of the target images. An illustration of the same along with the noisy Tikhonov output We observe that Tikhonet is mostly able to reconstruct pixels around the central region. Consequently, it is able to deconvolve   small objects but is unable generalize well to extended objects.
Contrastingly, the Learnlet and Unet-64 residuals contain minimal structure with no orientational preference, which indicates that the reconstruction is better regardless of the size of the galaxy. Residual = y -h * N θ ( x ) (9) In Figure  5 , we show the trends obtained for windowed NMSE and SSIM on our test dataset images binned according to their magnitudes. All networks perform better on low magnitude (or high SNR) images, and there is a decrease in performance with an increase in the magnitude (or decrease in SNR). Furthermore, for the range of magnitudes and metrics considered, Unet-64 outperforms all other methods, followed by Learnlet. The mean NMSE improvement is 11. 4% when going from Tikhonet to Learnlet, and 2. 2% when going from Learnlet to Unet-64. Similarly, the mean SSIM improvements are 10% and 1.
As seen in Table  1 , although Tikhonet has the smallest runtime, it does not perform well on extended objects. Furthermore, Unet-64 has a smaller runtime per image compared to Learnlet, making it more efficient. This makes it an ideal candidate to be used for astrophysical image deconvolution. The strategy we proposed in this paper for building the training dataset seems very efficient and could easily be applicable to Euclid images by using the deep field survey to build a training dataset that could eventually be used for the wide field survey. More experiments will however be required to check more in details the generalization properties. It would also be interesting to compare all the networks with the shape constraint investigated in  Nammour et al. (2022). For Learnlets, as in the case of wavelets, various different kinds of noise could be considered on a single model with an undecimated implementation by adjusting the thresholding function according to the noise.
Our spectroscopic observations with iSHELL are presented in Section 2. We derive stellar parameters for the stars using the radiative transfer code MoogStokes  (Deen 2013)  in Section 3. In Section 4, we combine optical and infrared temperatures with the magnetic stellar evolutionary models of  Feiden (2016)  to derive stellar masses. The model-derived masses are then compared to independently measured stellar masses from interferometric observations. We discuss our results in Section 5 and summarize our findings in Section 6.
Stellar Parameter Measurements We derive stellar parameters for the 40 young stars using a synthetic stellar spectrum technique that combines MARCS stellar atmospheric models  (Gustafsson et al. 2008) , the magnetic radiative transfer code MoogStokes (Deen 2013), along with atomic and molecular lines from the VALD3  (Ryabchikova et al. 2015; Pakhomov et al. 2019 ) and HITEMP databases  (Rothman & Gordon 2010). More details of the modeling technique can be found in  Flores et al. (2019 Flores et al. ( , 2020) )  but, in brief, we took a two-step approach to fit the observations. First, we varied seven stellar parameters: temperature, gravity, magnetic field strength, projected rotational velocity, K-band veiling, microturbulence, and CO abundance. We performed the fitting using eight wavelength regions, one of which contains the CO overtone starting at 2.
294 μm (see Figures  in Appendix A ). In the second step, we excluded the CO region to avoid potential CO contamination from the circumstellar material (as seen, for example, in  Doppmann et al. 2005 and Flores et al. 2020). We anchored the projected rotational velocity to the best value found in the previous step and recalculated the temperature, gravity, magnetic field strength, K-band veiling, and microturbulence. In both steps, we fitted the spectra with a Markov Chain Monte Carlo (MCMC) method using the emcee package (Foreman-Mackey et al. 2013). We ran a total of 50 individual chain "walkers" advancing a total of 500 steps. Examples of how sensitive the stellar spectra are to parameters such as surface gravity and magnetic field strengths are depicted in Appendixes B and C.
Flores et al. (2019)  demonstrated that iSHELL K-band spectroscopic observations combined with the same radiative transfer code used here, provide accurate effective temperatures for main-and post-main-sequence stars (see Figure  6  of their paper). We thus argue that the observed temperature differences displayed in Figure  1  cannot be solely accounted for by Note. The reported uncertainties correspond to 3σ deviations from the median value obtained from the MCMC distributions. (This table is available in machine-readable form. ) uncertainties in the techniques used at the two different wavelengths. Instead, we propose that they reveal a true physical peculiarity of the stellar surfaces in low-mass young stars. Furthermore, since young stars evolve almost isothermally during their first few million years of evolution  (Hayashi 1961) , the mass of a star can be almost directly inferred from its effective temperature using a "theoretical" HR diagram ( g log versus T eff , see Figure  2 ).
This assumption makes sense from a physical perspective, as starspots should be in hydrostatic equilibrium with the ambient photosphere, but, in practice, the gravities and optical temperatures were calculated independently from different data sets, instruments, and techniques. Second, the dynamical mass values adopted in our computations could be more uncertain than what was originally reported by  Simon et al. (2019). Braun et al. (2021)  used archival ALMA data to measure dynamical masses of young stars in Taurus. Their sample overlapped for eight sources with the Taurus sources analyzed by  Simon et al. (2019) , and discrepancies of dynamical mass measurements were as high as 46%. As explained in  Braun et al. (2021) , these stellar-mass differences mainly arise due to the difference in the adopted disk-inclination angles (since stellar masses scale as µ M  i 1 sin 2 ).
This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This work has made use of the VALD database, operated at  Uppsala University , the  Institute of Astronomy RAS in Moscow , and the  University of Vienna. Facilities:  IRTF, ALMA, PdBI. Software: astropy (Astropy Collaboration et al. 2013), MoogStokes (Deen 2013), emcee (Foreman-Mackey et al. 2013), Spextools (Cushing et al. 2004), xtellcor (Vacca et al. 2003) ORCID iDs C. Flores https:/ /orcid. org/ 0000-0002-8591-472XM. S. Connelley https:/ /orcid. org/ 0000-0002-8293-1428B. Reipurth https://orcid. org/ 0000-0001-8174-1932G. Duchêne https:/ /orcid. org/ 0000-0002-5092-6464.
However, C-SDMs do not take into account biological factors that are considered important for estimating species distributions, such as population dynamics and dispersal capacity  (Guisan and Thuiller, 2005; Elith et al. , 2010; Fordham et al. , 2013; Melo-Merino et al. , 2020). On the other end of the spectrum, mechanistic species distribution models (M-SDMs) explicitly account for these dynamics by incorporating how both abiotic and biological processes constrain the distribution and abundance of a species  (Kearney and Porter, 2009; Singer et al. , 2016). However, M-SDMs require greater computer processing power and more detailed datasets to parameterize  (Fordham et al. , 2018) , and so have generally only been calibrated for well-studied or commercially exploited species for which physiological or ecological constraints are known  (Kearney and Porter, 2009; Cheung et al.
, 2014; Perälä and Kuparinen, 2017) , and climate change is anticipated to strengthen these effects  (Berec, 2019; Winter et al. , 2020). The primary objective of this study was to develop an original H-SDM framework for diadromous species that incorporated dispersal and population dynamics coupled with a habitat suitability module based on environmental predictor variables from both marine and freshwater habitats. This new modeling framework, named HyDiaD for "Hybrid approach for Diadromous species Distribution", could be utilized for various diadromous species despite differences in distributions and lifehistory types. These models were intended to improve upon the existing C-SDMs developed for European diadromous species by  Lassalle et al. (2008b)  and  Lassalle and Rochard (2009) , which assumed unlimited dispersal (i.
Presence/absence data For both shad species, presence/absence data to calibrate the C-SDM were available from the EuroDiad 4. 0 database (https://data. inrae. fr /dataset. xhtml?persistentId=doi:10. 15454/IVVAIC; Barber-O'Malley et al. (  2022 )). This database stores information about the presence/absence and functionality (i. e. , reproductive capacity) of diadromous species populations in selected catchments in Europe, the Middle East, and North Africa from 1750 to present time  (Béguer et al. , 2007; Lassalle et al. , 2008b; Lassalle and Rochard, 2009). EuroDiad 4. 0 stores data for 350 catchments and three time periods, though the precision of information varies and not every species has information for each time period. This database underwent two validation processes by local experts from various organizations involved in fisheries sciences and environmental management.
The 292 catchments with presence/absence data available represented all of the large (> 50,000 km 2 , N = 37) and medium-sized (>10,000 km 2 , N = 48) catchments present in Europe, the Middle East, and North Africa, as well as 207 small-sized catchments (<10,000 km 2 ) that were specifically chosen to prevent any geographical bias in the database  (Lassalle and Rochard, 2009). However, this represents only a proportion of the small-size catchments that exist within the study area. For shads, calibration was performed using all catchments with available presence/absence data for each species (N = 254 and 243 for A. alosa and A. fallax, respectively). This included 37 large, 47 medium and 159 small-sized catchments for A. fallax and 37 large, 48 medium, and 169 small-sized catchments for A. alosa.
Environmental predictor variables Environmental variables used in C-SDMs were chosen to balance data availability with the potential to affect diadromous species distribution across Multiple species  (Bradie and Leung, 2017). As these species utilize both freshwater and marine habitats, both continental and marine environmental variables were used for the first time to predict species distributions (Table  1 ). Continental variables included elevation at the headwater, length of the main watercourse, surface area of the drainage basin, and precipitation (see  Lassalle et al. (2008)  for their ecological meaning). The first three variables were provided for each catchment by EuroDiad 4. 0. Precipitation was calculated as an annual value across the surface area of each catchment. These values represent the physical aspect of each catchment, and taken together can represent the range of hydrologic conditions that diadromous species experience Fig.
, 2016; Erauskin-Extramiana et al. , 2019). Marine variables were calculated for each catchment using a buffer (of 0. 5 • side) around the outlet of the river to take into account the dilution of the river plume. Observed atmospheric data came from the Climate Research Unit time-series dataset (CRU T. S. 4. 03; crudata. uea. ac. uk)  (Mitchell and Jones, 2005) , which includes monthly climate observations measured at meteorological stations throughout Europe and interpolated between stations with a resolution of 0. 5ᵒ (~ 62 km) from 1901 to 2018. Observed data for the marine variables came from the CHORE_AS (CMCC Historical Ocean Reanalysis) dataset, which is a reanalysis performed by the CMCC (Centro Euro-Mediterraneosui Cambiamenti Climatici; http://c-glors. cmcc. it/) (Yang et al. , 2017)  reconstructing ocean variables from 1900 to 2010 at a resolution of 0.
5 • degree (Table  1 ). When using climate projections it is important to include the projections from multiple Global Climate Models (GCMs) in order to account for possible discrepancies between model estimates  (Harris et al. , 2014) , in the kind of strategy commonly known as the use of ensembles. For marine environmental variables, we have used data from three different Global Climate Models (GCMs) that are detailed in Table  2  with trends presented in Fig. 2  for RCP scenario 8. 5 and in Appendix C Figure  C1  for RCP scenario 4. 5. For atmospheric variables we have used data from three models from the EURO-CORDEX project (http://www. euro-cordex. net/), where several GCMs have been dynamically downscaled to achieve better spatial resolution and better representation of local atmospheric processesthese three Regional Climate Models (RCMS) (Table  3 ; Fig.
Algorithm choice for habitat suitability models Boosted regression trees (BRTs) were chosen to produce quantitative measures of habitat suitability because they can handle different types of predictor variables and missing values  (De'ath, 2007). BRTs combine multiple, simple decision trees to optimize a model's predictive performance  (Elith et al. , 2008). Boosting involves growing a large group of trees in sequence and combining the averaged predictions  (De'ath, 2007). The predictive error of this method is improved and over-learning is reduced using stochastic gradient boosting, which uses least squares regression trees and subsamples the training data to include randomness  (Friedman, 2001; De'ath, 2007). Due to the limited data set, subsampling was accomplished through a cross-validation method with smaller datasets  (De'ath, 2007).
Using the "gbm" package in R  (Greenwell et al. , 2020) , the AUC score was calculated for each subset of data used for cross-validation (k = 10). The standard error of the AUC was also calculated for these subsets. An AUC value of 0. 5 or lower indicates a classification no better than random, while a value of 1 indicates all modeled predictions were correctly classified  (Swets, 1988).
0) from the period 1851-1950. This time period was chosen to represent species distributions right before what is called the "Great Acceleration" in human activities during the Anthropocene  (Steffen et al. , 2015) , but was also a compromise with environmental data availability. This initial value of habitat suitability, along with population and dispersal dynamics described in the next section, were used for a burn-in period of 50 years, i. e. outputs were discarded. After the burn-in period, annual predictions of habitat suitability from 1951 to 2100 were estimated using modeled projections of the same environmental predictor variables averaged across multiple GCMs and RCP scenarios (as described below). ǂ  Fielding and Bell (1997).
A value close to 1 indicates that the abundance of the population is close to its maximum as defined by the habitat suitability. A value close to 0 indicates that the population abundance is much smaller than it could be. Therefore, the saturation rate informs on the interest to run a hybrid model compared to a strictly correlative approach. A low SR value indicates the high contribution of dispersal and demography in the population dynamics and thus the added value of considering a more complex approach when modeling a species distribution. The HyDiaD model was run for each shad species from 1951 to 2100, and N i,t was calculated annually for all catchments in the Atlantic Area. Annual spawner abundance was used to calculate annual saturation rate. Results were presented as heat plots that were created in the package "ComplexHeatmap" in R  (Gu et al.
HyDiaD evaluation based on the saturation ratio metric To further demonstrate the advantage of increasing the model complexity with a population dynamics and dispersal module, and to validate HyDiaD estimations with observations, two analyses were performed. First, the distribution of decadal SR values was inspected across all the catchments included in the HyDiaD physical environment. For a given year between 2041 and 2050, and a specific catchment, SR values were averaged across the three GCMs under the most pessimistic scenario RCP 8. 5. Then, the decadal means from 2041 to 2050 were calculated and plotted together as a histogram. The skewness and spread of the distribution were examined to assess the relative contribution of the population dynamics and dispersal module compared to a strictly correlative approach.
(2018)  for hybrid species distribution models, several novel approaches were developed to address difficulties related to knowledge gaps and the fact that dispersal for diadromous species was measured between catchments that were different sizes and distances apart rather than through a continuous landscape, meaning it could not be estimated using the grid-cell approach often used for terrestrial species  (De Cáceres and Brotons, 2012; Visintin et al. , 2020). Even if this model is difficult to calibrate since it simulates a potential rather than an observable abundance, the confusion matrix between observed abundances provided by the Euro-Diad database and classes of simulated saturation ratios with HSI=1 showed a limited number of discrepancies (Table  10 ), leading to a quite good confidence in the model.
For data-poor species, the availability of species-specific data related to biological processes can be a major knowledge gap, and expert knowledge elicitation has been suggested as a possible solution  (Gallien et al. , 2010; Singer et al. , 2018). The current study successfully used this approach on two aspects: i) to parameterize the HyDiaD model for two shad species (Section 2. 3. 4. ), and ii) to fill data gaps related to both historical presences/absences and physical characteristics of catchments (Section 2. 2. 1). Lastly, given that marine predictor variables have been neglected in previous studies on diadromous species range-shift responses, the abiotic niche for the two shad species was estimated using a suite of environmental predictor variables that covered the entire species life cycle.
The current study provides an approach for filling in knowledge gaps for data-poor diadromous species in which parameters for population and dispersal dynamics may not be readily available in the primary literature for the spatial scale used in the HyDiaD model. The use of iterative expert knowledge elicitation could be an alternative source of information in those cases. The results of the expert knowledge survey, in addition to the equations and R script for calculating the weighted group mean (Appendix A), could be used to apply the HyDiaD model to other diadromous species and geographic regions. However, the ecological processes behind the parameters being estimated are complex and the spatial scale covered by the model was large, thus extensive feedback was necessary to develop the wording of the questions used in the expert knowledge survey.
Although there is evidence for rapid adaptive evolution of species in response to climate change  (Lavergne et al. , 2010) , the hybrid model used in this study did not account for the adaptive potential of species, phenotypic plasticity, and ecological processes such as competition, positive interactions, and trophic relationship which can affect rates of species range shifts  (Lavergne et al. , 2010). For data-poor species, finding historical presence/absence information can be difficult and time-consuming. In the current study, historical presence/absence information was available for the two shad species because of an existing database (https://data. inrae. fr/dataset. xhtml?per sistentId=doi:10. 15454/IVVAIC; Barber-O'Malley et al. (  2022 )). This database required enormous effort from a large group of students and project partners to create and validate, but this work will allow the HyDiaD model to be applied to other diadromous species both within the same area used in the current study as well as other areas in Europe.
Jolly et al. (2012)  found that neighboring populations in the UK (Severn and Wye) displayed no genetic differentiation, while the Severn was genetically distinct from several populations located at a farther distance (Tywi, Usk, and Irish catchments). Davies et al. (2020)  found that 33 out of 34 A. fallax tagged in 2018 from the Severn returned to this catchment in 2019. However, in tagging studies capture location may not represent natal origin  (Nachón et al. , 2020) , and there may still be long distance dispersal between major estuaries  (Davies et al. , 2020). For shads, not all river catchments in the European Atlantic area listed in the CCM River and Catchment Database (data. europa. eu) were included in the model projections. Many small and medium-sized catchments were not incorporated into the dispersal matrix because simulations only used catchments included in EuroDiad 4.
Acknowledgements This study was funded by the  Atlantic Area Interreg Project DiadES. Many thanks go to all the experts that help with the validation of the EuroDiad 4. 0 database (see the list in Barber-O'Malley et al. (2022)). We are also grateful for the time experts spent on answering the expert knowledge survey on population dynamics and dispersal. The  World Climate Research Programme 's Working Group on Coupled Modelling is responsible for the fifth Coupled Model Intercomparison Project, and we thank the climate modeling groups for producing and making available their model output. EURO-CORDEX is the European branch of the international CORDEX initiative, which is a program sponsored by the  World Climate Research Program (WRCP)  to organize an internationally coordinated framework to produce improved regional climate change projections for all land regions world-wide.
These parameters include examples such as driver profile changes, deep learning algorithm optimizations, or parameter updates for regulatory compliance. These updates strongly condition the vehicle's behavior without requiring any software change; thus, these update types do not require the relevant software components to shut down, only the vehicle to stop. Another key update type is (2) Firmware updates Over-The-Air (FOTA), which involves installing or updating the main system software that controls the underlying hardware. Thus, to achieve these updates, a complete restart and re-flash of the ECU are required. After the update, the software must be tested entirely and, if there are any errors, switched back to the previous firmware version through techniques such as dual banking. This form of updates will be used for the Actuators and Sensors.
A third update type is (3) Software updates Over-The-Air (SOTA), involving the installation of application components. These updates can be whole (e. g. , a full software install) or partial, also known as software updates ( SOTA). Note that the size of the partial updates is typically close to the aforementioned FOTA packages. For both SOTA and SOTA, the software install process must be performed with the vehicle shut down; the process must also be tested afterward and allowed to roll back if the system does not operate properly following the update. SOTA usually take place over Unixlike systems, typically in infotainment or telematics ECUs. Finally, update type (4) comprises Media file updates Over-The-Air (MOTA) packages, which include some multi-media files such as Global Navigation Satellite Systems (GNSS) maps, custom images, sounds, or videos for the In-Vehicle Infotainment (IVI).