dataesr/scientific-paragraphs-categorization huggingface dataset:
Insight from REVEALS dataset analysis -comparison with previous studies Previously, land-cover naturalness and anthropogenic impact on vegetation were assessed through analysing changes in forested areas  (Strona et al. , 2016; Kaplan et al. , 2017; Pirzamanbein and Lindström, 2022; Woodbridge et al. , 2018)  and via cultural indicator-types in pollen sequences  (Deza-Araujo et al. , 2020 , 2022; Behre, 1981; Mercuri et al. , 2013; Gaillard, 2013). Apart from known cultivars, many cultural indicator pollen types are components of natural vegetation, characteristic of open conditions, natural grazing dynamics or disturbed soils, particularly in southern Europe  (Fyfe et al. , 2019; Roberts et al. , 2019)  and thus require careful interpretation. Within the framework of the LANDCLIM project  (Gaillard et al.
, 2010)  anthropogenic land-cover changes were commonly expressed as vegetation openness indicated in the REVEALS model results  (Trondman et al. , 2015). Recently, REVEALS estimates were compared with DVM-simulated vegetation with a goal of assessing land-cover change in Europe and analysing underlying processes behind the observed changes  (Dallmeyer et al. , 2023). Similar to our study,  Dallmeyer et al. (2023)  attributed varying discrepancy over time between the two datasets to human activity. Here we applied a more complex approach that takes into account vegetation composition in the form of fractions of plant functional types (PFTs) and their evolution through time, and used more spatially explicit REVEALS estimates. We identified that in addition to vegetation openness (expressed as fraction of herbaceous cover), anthropogenic changes affected distribution of other PFTs, such as broadleaved summergreen boreal/temperate cold trees (BSBTT), broadleaved summergreen temperate cool trees (BSTCT), broadleaved evergreen boreal/temperate cold shrubs (BEBTS), and broadleaved evergreen temperate warm shrubs (BETWS) (Fig.
1 ). Lechterbeck et al. (2014)  also found that early agriculture resulted in compositional changes in forests in southern Germany, rather than increased openness. Analysing the REVEALS dataset, Marquer et al. (  2017 ) indicated a decrease of broadleaved forest and an expansion of coniferous woodland during the Mid-Holocene. Our findings support the hypothesis about the anthropogenic nature of changes in broadleaved summergreen PFTs (BSBTT and BSTCT) (Fig. 1 ). However, contrastingly to  Marquer et al. (2017) , we observed no significant variation in MR of needleleaved evergreen plant group (NBTT), which suggests that the coniferous woodland expansion reported by  Marquer et al. (2017)  is likely driven by climatic factors. In addition to changes in vegetation openness and herbs (such as Filipendula, Artemisia, Plantago and Rumex acetosa t.
Reported increase in HPI agrees with the ALCC estimates and FAO land use statistics (FAO, 2022), which reports that 52. 3% of the area of European Union was 1961 classified as agricultural. Similarly, the ALCC scenarios suggest that for the study area at TW1 (modern time) nearly 56% of land-cover was transformed by anthropogenic activity accordingly to KK10, and HYDE 3. 2 estimates suggested nearly 45% of land-cover to be impacted by agriculture (18% grazing and 27% cropland) (Fig. 6 ). ALCC estimates agree with the FAO land use statistics (FAO, 2022), since they are partially based on FAO statistics (in case of HYDE 3. 2). In this study we analysed data for continental Europe, contrastingly to FAO land use statistics, which is constrained by the EU borders. Additionally, the REVEALS dataset is not spatially continuous.
Methodological limitations It should be noted that the overestimation in HPI could be partially attributed to methodology limitations. Such limitations were caused by data availability in the REVEALS dataset, as well as by unequal reliability of the REVEALS grid cells  (Serge et al. , 2023). The intercomparison between reconstructed and modelled vegetation was also previously reported to be challenging  (Dallmeyer et al. , 2023). In addition, to correct biases of the used climate model we used the CDF-t approach  (Vrac, 2018; Zapolska et al. , 2023) , but no bias correction was performed for the vegetation model (CARAIB). For the intercomparison PNV data was resampled and reclassified to be compatible with the REVEALS dataset, which in turn led to simplifications and resampling biases. For example, as in this work we assigned each taxon to a unique PFT category, the taxon Ericaceae was only included in broadleaved evergreen boreal/temperate cold shrubs (BEBTS) PFT (Table  1 ).
However the Ericaceae family includes species that live both in the north and at high elevations (herbs and low shrubs, i. e. Vaccinium, Arctostaphylos uva-ursi), and in the South (trees, i. e. Arbutus unedo, Erica arborea)  (Serge et al. , 2023). We hypothesise that the descending temporal trend and high HPI values in northern latitudes were mainly caused by the aforementioned methodology biases, such as PFT classification limitations and differences in representation of bare ground in studied datasets. Moreover, the trees at high latitudes might be confined in small topographic depressions that induce a local micro-climate that we cannot reproduce at our spatial scale. However, the rising HPI values through the studied period in most of continental Europe (south of 54. 5 • N) were mainly associated with human activity, which was previously reported to shift north throughout the second half of the Holocene  (Goldewijk et al.
Quantifying uncertainties It should be noted that the absolute HPI values (Figs. 2, 3  and 5 ) should be treated with caution, as they are affected by various methodology biases discussed in this study. To our knowledge, there is no approach to quantify given uncertainties. However, the study of  Zapolska et al. (2023)  estimated methodological uncertainties of the used approach by comparing PNV simulations from bias-corrected CARAIB and statistically modelled PNV distribution of  Levavasseur et al. (2012) , based on BIOME6000 pollen reconstructions. Their findings indicate that methodological biases can constitute up to nearly 50% when analysing matching ratios between PNV datasets of a different origin, which would place a lower boundary on the HPI absolute value at 6000 BP (Fig. 5 ) at about 30%, but likely more if median taken.
To eliminate the impact of these biases on our conclusions, we emphasize the importance of changes from one TW to another, rather than absolute differences between the datasets, as the impact of methodology biases remains constant in all TWs (assuming their stationarity). Hence, our conclusions are largely based on the analysis of temporal changes in HPI values and trends that appear in these changes. To verify our findings, further studies aimed at evaluation of human pressure on land-cover could compare the results using several vegetation models (i. e. Dallmeyer et al. , 2023) , undertake a detailed intercomparison with established and emerging pollen-indicator type approaches (e. g. Deza-Araujo et al. , 2022) , draw on more extensive compilations of pollen records, as well as investigate potential impact of bare ground in pollen-based reconstructions (e.
reveals ÷ frac PFT,caraib × 100, reveals × MR PFT , where Nb. PFTmaximum number of PFTs, frac PFT,reveals -fraction of a PFT in REVEALS dataset, frac PFT,caraib -fraction of corresponding PFT in CARAIB dataset, MR PFT -matching ratio per PFT, WMRweighted matching ratio of REVEALS and CARAIB.
Acknowledgements The authors would like to thank  Louis M. François  for providing the CARAIB global dynamic vegetation model and his help in running it. We would also like to express our sincere appreciation to  Emily Vella ,  Kailin Hatlestad ,  Alexandre Martinez  and  Anastasia Nikulina  for their invaluable contributions and expertise during the review process, and to two anonymous reviewers for valuable comments that improved the quality of the paper.
Goals and highlights The goal of the present work is to develop an efficient continuous thermal adjoint solver for the multi-objective optimization of coupled thermal-fluid problems, where penalization is used to model material transition. The coupling with an Adaptive Mesh Refinement (AMR) strategy is presented and its application to mitigate typical problems of topology optimization, as the high computational expense and the mesh dependency, is discussed. We describe the development of an incompressible, singlephase, multi-dimensional Finite Volume solver, that has been implemented as an open-source C++ code in the OpenFOAM Technology. Two-and three-dimensional numerical experiments are used for validation. The field of application for which the solver has been conceived characterizes some of its features that are here summarized: -a semi-implicit method for pressure linked equations (SIMPLE)  [38]  is applied to couple pressure and velocity in the primal and in the adjoint problem; -the objective function is defined by a weighted combination of recoverable thermal and dissipated power through the boundaries; -a density-based approach for the optimization problem is combined with the Rational Approximation of Material Properties (RAMP) method  [39]  to model the porosity and material properties between the fluid and the solid; -sensitivity is computed by solving the adjoint equations and used with the Method of Moving Asymptotes  [17]  to update the design variables.
ACKNOWLEDGMENTS Authors gratefully acknowledge the  Laboratory Computing Resource Center (LCRC)  of the  Argonne National Laboratory (Lemont, US)  for the computing resources provided and  Vitesco Technologies SAS  for provididng data and geometries for validation and testing. Algorithmic developments have been implemented and tested by the authors at the  Dept. of Aerospace Science and Technology (DAER), Politecnico di Milano  and are based on the most recent OpenFOAM Technology available at the time the draft was submitted.
Sizing purpose Implications on model causality When it comes to sizing a system based on a desired trajectoryhere, imposed by the optimization algorithm -, it is often necessary to reverse the power chain. This is also known as bicausality. A performance simulation of the drone model would typically require a known load at the propellers and would compute the resulting speed of the drone, for a given command. On the contrary, a sizing scenario would provide both the load at the rotors and the drone speed. These two variables define the required power output, which can be propagated upstream on the power chain to compute the require power at the power generation or storagehere, the battery. As the design validation would require a performance simulation, the acausality of the Modelica language is clearly a benefit to solve sizing problems.
Optimization purpose Most optimization algorithms rely on gradients to define on which direction they should perform the next step. This means that the variables of the model should, at least, be continuous and derivable. A special effort is made in the Modelica code to respect this constrain. In addition, minimum, maximum and nominal values of the design variables are provided to allow respectively to bound and normalize the variables and equationskey for optimization convergence. Finally, dynamic optimization benefits from having both an initial and a nominal trajectory that match the specified requirementswithout necessarily be optimal. This is easily achieved by simulating first the drone behavior with a smooth trajectory command. Here, the acausality of the Modelica language becomes once more convenient.
11867𝛽𝛽 + 0. 27334𝛽𝛽 2 -0. 28852𝛽𝛽 3 + 0. 18677𝛽𝛽𝐽𝐽 2 (5) 𝐶𝐶 𝑃𝑃 ≈ 0. 01813 -0. 00343𝐽𝐽 -0. 12350𝐽𝐽 2 + 0. 06218𝛽𝛽 + 0. 35712𝛽𝛽 2 -0. 23774𝛽𝛽 3 + 0. 07549𝛽𝛽𝐽𝐽 The surface responses and the corresponding datasets are presented below for both coefficients. This gives us the performance model of the propeller, that needs to be completed by its sizing model. For the propeller, that parameters of interest are the mass and the inertia. These are computed based on scaling laws. The propeller mass Mprop is found to be proportional to the cube of its diameter. From the mass, the inertia Iprop can be computed. Note that scaling laws require similar reference data to scale on to. 𝑀𝑀 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 = M ref (𝐷𝐷 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 /𝐷𝐷 𝑝𝑝𝑟𝑟𝑟𝑟 ) 2 (7) I prop = M prop (𝐷𝐷 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 /2) 3 /3 (.
Optimica Implementation Modelon Impact (Coïc 2020-b) is a state of the art, cloudbased modeling and simulation environment, relying on open standards such as Modelica, FMI and Python. Modelica models can be developed within Modelon Impact by composition (drag and drop and connect) of existing models from available Modelica libraries, or by writing Modelica code within the code editor. Modelon Impact compiler, Optimica Compiler Toolkit (OCT), compiles the modelseither in steady-state or dynamic simulation mode. As many engineering problems can be cast as optimization problemsincluding optimal control, minimum time problems, optimal design, and model calibration -Modelon Impact compiler supports optimization of dynamic and steady state models. This is achieved relying on the extension of Modelica language for optimization: OPTIMICA  (Åkesson 2008).
As the OPTIMICA language extends the Modelica language, it is convenient to opt for a similar approach when formulating an OPTIMICA optimization problem. Thus, the optimization model could be built as follow: 1. Create an optimization class and provide modifiers to set up the objective and time constraints. 2. Extend the Modelica model and provide modifiers to fix or relax parameters as well as minimum, maximum and nominal values. 3. Optionally add more variables and equations. 4. Add the constraints of the optimization problem. The Optimica code of the drone optimization is listed in Listing 1. It appears that the OPTIMICA language is straightforward for a person used to the Modelica languagewhich itself is quite straightforward for many engineers. This way, the threshold to develop an optimization model is minimized.
The Benefits of Modelon Impact There are several advantages in using Modelon Impact compared to a separate optimization with a Python package or in a dedicated optimization platform such as OpenMDAO, even if relying on a FMU for the plant model. A key advantage of using Modelon Impact is that it relies on its OPTIMICA compiler and the OPTIMCA language, as mentioned previous, extends the Modelica language. Hence, a fair amount of the benefits listed below are Modelica features serving the optimization purposes.
Solving Initialization Problem The Modelica language can deal with both Initial Value Problem (IVP) and Boundary Value Problem (BVP) for the model initialization. In the former case, the user provides the initial values for every state. In the latter, the number of independent initial values should match the number of states but is not necessarily their initial values. Therefore, Modelon Impact compiler solves an initial problemdifferent from the dynamic problemto resolve the BVP. In this process, the compiler can solve linear and non-linear systems, which is often not tolerated out of the box with a different solution. For example, in the Python optimization, it was necessary to adapt the code, define iteration variables (coefficient factors) and residuals (equations that should tend to zero) to solve these nonlinear problems.
Acausality -One Model Several Purposes A second advantage is that the Modelica language is acausal. This way, defining a Drone model based on the equations of the physics makes it useful for several use cases: position, speed or torque command. In our example, we want to optimize the torque trajectory while sizing the drone. Nevertheless, it is convenient to provide the optimizer a start trajectory not too far from our constraints. This is easily achieved by simulating the same.
Simpler, Faster and More Robust Modelon Impact solution also proved to be much simpler than the python with FAST-OAD approach. The Modelica code wasn't written differently for performance simulation and for optimization purposes as it was the case for its Python version. Also, the optimization problem formulation in OPTIMICA language is very simply expressed, as mentioned above (see Listing 1), and does not diverge much from the Modelica languagewhich makes it really easy for a Modelica developer to ramp up on OPTIMICA The same optimization problem was solved in Modelon Impact and using FAST-OAD. For this optimization problem that consists of 10 design variables, 7 inequality and 1 equality constraint, Modelon Impact could solve the problem in less than 30 seconds while it took more than 2 minutes to FAST-OAD.
This can be explained by the different level of information on the model the optimizer hasby default FAST-OAD does not have access to the internal derivatives of the model. Finally, and this might sound unfortunately qualitative, the Modelon Impact solution was more robust, more straightforward to converge. While it took several hours of debugging to get the Python code running and optimization solving with FAST-OAD, it appeared to work almost directly with Modelon Impact. In all transparency, the first attempt did not involve normalization of the added variables for inequality constraints, and it failed to converge. Normalizing solved the issue.
Conclusion This paper uses a multi-rotor drone (pre-)sizing and trajectory optimization to illustrate the needs for solving such a problem. The model fidelity is not necessarily the highest but constrains on the numerical aspect of the code are highlightede. g. acausality, smoothness, etc. The models shall be "optimization-friendly". The propeller model is detailed to emphasize the level of details sufficient for such a purpose. In a second step, the optimization problem has been expressed, first analytically and then in OPTIMICA languagesupported by Modelon Impact. The solving of the problem is achieved, and the benefit of this solution are discussed, in a generic way, and in comparison with another implementation using Python and FAST-OAD. The results proved that solving the sizing problem in combination with the trajectory optimization leads to a better design, compared to solving both problems sequentially.
Adaptive bitrate (ABR) algorithms are used in streaming media to adjust video or audio quality based on the viewer's network conditions to provide a smooth playback experience. With the rise of virtual reality (VR) headsets, 360°video streaming is growing rapidly and requires efficient ABR strategies to also adapt the video quality to the user's head position. However, research in this field is often difficult to compare due to a lack of reproducible simulations. To address this problem, we provide SMART360, a 360°streaming simulation environment to compare motion prediction and adaptive bitrates strategies. We provide sample inputs and baseline algorithms along with the simulator, as well as examples of results and visualizations that can be obtained with SMART360. The code and data are made publicly available.
Adaptive streaming of 360°videos has been the subject of many research works in the past few years  [2, 7, 8]. While these works present new approaches and methods to improve the quality of experience (QoE) or bandwidth usage of adaptive streaming systems, it is often difficult to compare them fairly, as the code for simulating them is not always provided. We present SMART360, a 360°streaming simulation environment that can be used to compare head motion prediction and ABR algorithms. Our contributions are the following: • we provide a new simulator  1  , equipped with large datasets and baseline algorithms that builds upon the existing solutions, with explanations about the code structure and logic; • we also make the preprocessing pipeline available  2  for transparency and to give the ability to easily create new input configurations for the simulator; • we explain in detail how SMART360 can be used by researchers to implement and compare existing and new motion prediction and adaptive bitrate strategies and show examples of metrics and visualizations that can readily be used to evaluate their algorithms.
Simulator inputs All the input data for the SMART360 simulator is provided in the config/ directory of the simulator repository. The input data uses the same JSON format as Sabre360  [12]. The data provided in the SMART360-simulator/config/ directory is split in two types: real and synthetic data. The real data is extracted from multiple public datasets and is described in the following subsections. The synthetic data contains simple cases of network traces with constant bandwidth and manifests describing uniformly-sized 360°videos. The user head motion traces found in the synthetic directory are copied from real data.
Network traces. The network traces describe the available bandwidth and the latency over time in different situations. They allow for realistic simulations where the bandwidth is highly variable. The network traces provided in the real input data are the same as the ones used in Sabre360, and come from the 4G/LTE dataset published by van der Hooft et al. [14]. They are made of 40 traces of bandwidth measurements along several routes in the city of Ghent, Belgium. For the comparisons between ABR algorithms to be relevant, we need to be in a situation where the algorithm has to adapt to the network constraints. On the one hand, if the bandwidth is very high relative to the video bitrate, there is no need for ABR streaming, as we can just download everything in the highest quality without any rebuffering (stall) event.
On the other hand, if the bandwidth is very low relative to the video bitrate, ABR streaming is not so useful either, as we can only download everything in the lowest quality. To make for a relevant comparison between ABR algorithms, we provide a Jupyter notebook to scale the network traces relatively to the video bitrates, as illustrated in Fig. 1. This notebook is available in the SMART360-simulator/notebooks/ directory. 3. 1. 2 User head motion traces. The user head motion traces describe the behavior of people watching 360°videos. They contain the coordinates of the head orientation over time. This allows calculating which tiles are visible to the user at any given time during the video. We provide 3518 head motion traces from users watching 94 different videos, extracted from three of the datasets used by Romero et al.
ACKNOWLEDGMENTS This work has been partly supported by the  French government , through the  UCA JEDI  and  EUR DS4H Investments in the Future  projects  ANR-15-IDEX-0001  and  ANR-17-EURE-0004. This work was partly supported by  EU Horizon 2020  project  AI4Media , under contract no. 951911  (https://ai4media. eu/).
Background: Measurement of cross-sectional muscle area (CSMA) at the mid third lumbar vertebra (L3) level from computed tomography (CT) images is becoming one of the reference methods for sarcopenia diagnosis. However, manual skeletal muscle segmentation is tedious and is thus restricted to research. Automated solutions are required for use in clinical practice. Purpose: The aim of this study was to compare the reliability of two automated solutions for the measurement of CSMA. Methods: We conducted a retrospective analysis of CT images in our hospital database. We included consecutive individuals hospitalized at the Grenoble University Hospital in France between January and May 2018 with abdominal CT images and sagittal reconstruction. We used two types of software to automatically segment skeletal muscle: ABACS, a module of the SliceOmatic software solution "ABACS-SliceOmatic,"and a deep learning-based solution called "Auto-MATiCA.