dataesr/scientific-paragraphs-categorization huggingface dataset:
For forested scenes over relatively flat terrain, oblique solar zenith angles (50 -70 ∘ ) yielded more consistent DSMs with better accuracy, whereas images with a hotspot configuration generated elevations that were closer to the DTM. Image pairs with smaller ground sample distance (GSD) improved the DSM accuracy, and combinations of small (nadir) and large (off-nadir) GSDs had accuracy between those derived from homogeneous GSDs. These simulation results suggest that available global archives of DSMs from VHR stereo imagery collected under a range of acquisition configurations will yield inconsistent estimates of canopy surfaces. This study also provides a benchmark dataset and configuration guide for 1) selecting existing data to retrieve the forest canopy surface shape, and 2) defining requirements for future satellite missions to characterize the forest canopy surface using stereogrammetry.
, 2020). Improvements in spatial resolution and global data coverage may yield important advances in the availability of DSMs from VHR stereogrammetry. However, other factors also influence the accuracy of derived surface heights. The accuracy of the surface elevations derived from VHR stereogrammetry depends on both reconstruction algorithms and acquisition configurations  (Lazaros et al. , 2008; Qin, 2019). The algorithms and their parameter settings are usually predefined for global-scale DSM production, such as the algorithm configurations of the NASA Ames Stereo Pipeline (ASP,  Beyer et al. , 2018; Moratto et al. , 2010; Shean et al. , 2016)  that are implemented on the NASA Center for Climate Simulation's Advanced Data Analytics Platform (ADAPT)  (Montesano et al. , 2017 (Montesano et al.
, , 2019)). The acquisition configurations vary for each satellite instrument (e. g. , resolution, field of view, time of acquisition, and convergence angle), and the optimal configurations differ based on landscape complexity (e. g. , topography and landcover characteristics). Several past efforts have studied the influence of stereo image acquisition characteristics on the accuracy of VHR-derived DSMs in urban settings, due in part to greater stereopair data availability over urban areas, such as the multi-view benchmark dataset from Johns Hopkins University Applied Physics Lab  (Bosch et al. , 2016). These studies have considered multiple factors, including spatial resolution, base-to-height (B/H) ratio, convergence angle (CA), and solar incident angle  (Qin, 2019; Toutin, 2004). However, the optimal configuration for stereo image acquisition in urban settings may not be suitable for forested landscapes.
3) the lack of contemporaneous high-fidelity reference surface elevation data to evaluate the VHR stereo results. These challenges must be addressed to collect datasets for a comprehensive study of the factors associated with optimal vegetation surface structure retrieval, and for designing future VHR stereo satellite missions. Physical-based radiative transfer models (RTM) can accurately simulate high-resolution remote sensing measurements over forests across the full range of configurations. In a previous study,  Yin et al. (2023)  enhanced the discrete anisotropic radiative transfer (DART) model  (Gastellu-Etchegorry et al. , 1996 , 2015 , 2017)  to simulate VHR stereopairs by implementing sensor models described by rational polynomial coefficients (RPCs)  (Fraser and Hanley, 2003). In that work, DSMs derived from simulations were comparable to those derived from WorldView stereopairs for two forested sites, confirming DART's capability to simulate realistic VHR imagery over various forest types.
Here, we conduct a comprehensive sensitivity analysis to identify optimal configurations for along-track VHR stereopair acquisitions to capture closed-and open-canopy forest surface structures. We begin with an analysis of the geometric configuration of image acquisition, including solar zenith angle (SZA), sun-view2 angle (SV2A), and CA based on stereopair combinations from thousands of simulated images with a 0. 5 m ground sample distance (GSD). Subsequently, we considered the influence of spatial resolution (0. 5, 1. 0, and 2. 0 m), and geolocation offsets typical of RPC replacement from pushbroom jitter or T. Yin et al. associated with data acquisitions from different orbits (averaged 0. 5 and 5 m, respectively) on the derived DSM from simulated stereopairs. These simulation data and results provide a relatively complete understanding of the tradeoffs in stereo image acquisition over forests and serve as a benchmark for conducting further studies, including an in-depth understanding of VHR stereo capacity, fusion with other sensors and platforms, and advanced algorithm development (e.
Discussions and conclusions This sensitivity study evaluated the optimal acquisition configurations for retrieving surface models over open and closed-canopy forests using DART-simulated VHR stereopairs. We found that, without a steep topography, a more oblique SZA (50 -70 ∘ ) and smaller CA (< 35 ∘ , optimal angles varied depending on GSD) provided better accuracy for retrieval of the forest canopy surface. These findings are broadly consistent with previous research, e. g. , the optimal SZA from 55 ∘ to 65 ∘ suggested by  Montesano et al. (2017)  and the fact that the DSM accuracy of a small CA of 12 ∘ outperformed that of a larger CA of ∼ 25 ∘  (Piermattei et al. , 2018). In addition, the simulation methods can consider a broader range of image acquisition conditions in the model environment, compared to the actual VHR stereo data with limited configurations.
Stereopairs with a hotspot configuration generated elevations close to the DTM for open forests, which is consistent with the World-View data over GPNA as demonstrated in  (Yin et al. , 2023). In general, smaller GSD improved the accuracy of the DSM for simulations with the same geometric configurations. In addition, for small areas, a consistent geolocation offset between the two simulated images of a stereopair mainly induced a vertical bias in the generated DSM without apparent influence on accuracy (RMSE' estimated after co-registration). These results are significant for several reasons. First, they provide a benchmark for conducting further analyses or developing stereo processing algorithms for canopy surface elevations. Second, these results and the developed tools provide guidance for future VHR satellite acquisition strategies that focus on capturing vegetation structure, given the broad interest in the potential use of stereogrammetry for the surface topography and vegetation targeted observable identified in the 2017 Decadal Survey (NASA, 2021).
This is consistent with the findings of  Piermattei et al. (2018) : a small CA of 12 ∘ from the tri-stereo configuration of Pléiades-HR (0. 5 m resolution) was found to be favorable in terms of accuracy and completeness of forest canopy shape, compared to the CA of ∼ 25 ∘ of another dataset. Owing to the tri-stereo property of Pléiades, the forward-nadir and backwardnadir CA of ∼ 5 ∘ generated less accurate results compared to the forward-backward CA of 12 ∘ , which is also partially consistent with our simulation results. Indeed, 5 ∘ and 12 ∘ are distributed on the opposite sides of the global minimum of the optimal CA for the VHR stereo of 0. 5 m GSD. However, the optimal CA for Pléiades may not be the same as the optimal CA derived from our paired simulation results, because the resolution is not conceptually equivalent to GSD and the optimal CA varies with different GSD.
Nevertheless, it is essential to note that CAs larger than the optimal value increase the RMSE' from the simulation results. Therefore, the selection of any CA between the optimal value and a larger CA boundary at 35 ∘ , for example, can persistently generate Fig. 8. Influence of increasing ground sampling distance (GSD) from 0. 5 to 2. 0 m on the RMSE' of the generated DSM against the reference DSM for a range of convergence angles at GPNA. (a) RMSE' of generated DSMs from images with homogeneous GSD for both view1 and view2 (b) RMSE' of generated DSMs from images with mixed GSD for view1 and view2. better results than a CA of 35 ∘. This finding offers guidance to support future stereo mission design and operations to improve the stereogenerated DSM accuracy over forests. Furthermore, commercial VHR satellite vendors should consider offering smaller along-track stereo CA in their standard data acquisition modes.
Convergence angle influence on generated DSMs: the tradeoff between height accuracy and completeness The vegetation canopy shape, height, density and overall arrangement on the landscape are important factors in the accuracy of canopy surface estimates from stereogrammetry. Considering the similar structural complexity of protruding shapes between high-rise urban buildings and forest trees, similar suggestions for small CA stereopairs can be found in past work on urban infrastructure height retrieval  (Carl et al. , 2013; d'Angelo et al. , 2014). In addition, the DSM accuracy relies on the reconstruction algorithms, evaluation criteria, and quality of reference datasets. However, occlusions induced by a large CA for twoimage stereo can not be easily corrected using traditional stereo processing algorithms, although a systematic evaluation of available algorithm performance as a function of CA is an important area for future study.
72 m for GPNA). DART will be enhanced to eliminate or mitigate these aspects in a future release. In addition, the default aerosol model of 23 km RURAL visibility could be different from reality. Furthermore, the simulations for these study sites (~400 m) are small compared to the larger swath dimensions of many linescan stereo datasets (~10-20 km wide, ~15-110 km long). This could be solved by adapting to the latest DART-Lux implementations  (Wang et al. , 2022) , which have less memory usage to allow a larger scene. Also, the study areas have small slopes, and the topography can impact the stereo reconstruction. This T. Yin et al. will be studied in the future selecting a mountainous study area with G-LiHT data coverage. This study also showed many advantages of combining PVlad with DART.
Acknowledgments This research was supported by ( 1)  NASA Goddard Space Flight Center 's  Internal Research and Development Program  (2)  NASA 's  Commercial SmallSat Data Acquisition Program (CSDA) augmentation  to the  Terrestrial Ecology Program  [grant number  NNH16ZDA001N-CARBON ] and (3)  NASA 's  Earth Science Technology Office  [grant number  NNH21ZDA001N-DSI ]. Maxar data were provided by  NASA 's Commercial Archive Data for NASA investigators under the  National Geospatial-Intelligence Agency 's NextView license agreement. The authors are also thankful to  Guoqing Sun  at the  University of Maryland  and  Oleg Alexandrov  at  NASA Ames Research Center  for their valuable suggestions. The authors also thank the anonymous reviewers for their valuable suggestions.
Acknowledgments We thank  Dr. Tom Baldock  and  Dr. Marissa Yates  for their valuable comments which helped to improve the manuscript. The experiments described in this work were funded by the  European Community 's  Horizon 2020 Programme  through the grant to the budget of the  Integrated Infrastructure Initiative HYDRALAB+ , Contract no. 654110 , and were conducted as part of the transnational access project RESIST. FG acknowledges funding from the  Agency for Management of University and Research Grants (AGAUR). DH acknowledges funding from the  French DGA  funded  ANR   ASTRID Maturation project MESURE  ( ANR-16-ASMA-0005-01 ). JA acknowledges funding from the  Serra Húnter Programme (SHP). We wish to thank fellow RESIST researchers and the CIEM staff ( Joaquim Sospedra ,  Oscar Galego ,  Dr.
Material and procedures of the gait analysis The 3D gait analysis was performed using an optoelectronic system OptiTrack (NaturalPoint, Corvallis, OR, USA) comprising 15 optoelectronic cameras. The trajectories of 58 reflective markers positioned on anatomical landmarks according to the Conventional Gait Model were recorded  [18]. Participants performed 8 barefoot gait trials at a comfortable pace over a 10 m long walkway. QTM 2020. 2 and MOKKA Biomechanical ToolKit 0. 6. 2 were used to respectively label markers and define gait cycle phases. All spatiotemporal and joint kinematic gait parameters were post-processed with PyCGM 2. 5, an open-source Python implementation of the Con-ventional Gait Model  [18]. The same experimenters performed all the analyses to ensure reliability.
STATISTICAL METHODS Data were analyzed using JASP® version 0. 16. 4 with an alpha risk of 5%. Means, standard deviations and medians were calculated. The standard deviations of the gait parameters were calculated across each gait cycle for each participant and then the average variability for each group was calculated. Before identifying the determinants of locomotor alterations, we compared gait and muscle strength variables between LOPD and control participants using the nonparametric Mann-Whitney U. test (p < 0. 05) and calculated the ␣ rank biserial correlation to indicate the effect size. For our primary objective, determinants were identified using forward stepwise multiple regression models. We submitted to the model all muscle strength parameters (MVT) and altered spatiotemporal gait parameters (highlighted by significant intergroup comparisons).
Limitations and perspectives The small sample size of this study, owing to the low prevalence of adults with LOPD, could appear as a limitation. However, 9. 1% of the French adult population with LOPD were included in our study, and the large effect sizes of the different variables measured ensuring confidence in our results. The sample could be considered heterogeneous since 3 of the 18 participants were not on ERT treatment. However, analyses performed without these 3 subjects (data not shown) did not alter the results, and the duration of the treatment was not correlated with locomotor parameters or the number of falls. Future longitudinal follow-up studies and studies on the new therapies should investigate changes in locomotor performance, as well as gait stability and hip abductor strength, which have a major impact on these impairments.
ACKNOWLEDGMENTS We would like to thank all the participants included in this study and the  ARS Ile de France : call for projects "support towards the  emergence of teacher-researchers " 2018,  Fonds d'Intervention Regional N  •  C 2018DOSRHS001. We thank the  French National Research Agency (ANR)  for supporting our project, which was led by  Helge Amthor , and  Nawal Derridj  for coordinating with the  Direction of Clinical Research, Innovation and Relations with Universities and Research Organizations (DRCI). We also thank  Johanna Robertson  for copyediting and constructive criticism.
For this purpose, the dissolution stages were combined into two groups: no to slight dissolution (DS-0, 1 and 2) and moderate to severe dissolution (DS-3, 4 and 5). These two groups were then compared between each of the three stations, and between the different depth levels (0-2 / 6-8 / 40-50 mm depth) for each station. Statistics were carried out using the R software using the "stats" and "rstatix" packages. and 20 s at 72 C (amplification). Afterwards, the melting curve was obtained by 30 s at 95C, 30 s at 60 C, and 30 s at 95 C. Finally, the temperature was held for 5 min at 40 C to terminate the analysis. The results are reported as the unit gene copies. (g wet sediment) -1. CB filament density were calculated as in  Geelhoed et al. (2020) , using data of wet sediment density from a previous campaign in 2019 (Table  1 ), and expressed in m.
, 2017) , subtidal mudflats (van de  Velde et al. , 2016)  or intertidal salt marshes  (Larsen et al. , 2015). The geochemical signature at station 1 is less clear regarding Cable Bacteria Activity (CBA). There was no pH peak in the oxic zone and only a moderate acidification within the suboxic zone (pH < 0. 1). This acidification was lower than expected if only driven by sulphate reduction and too high to be explained by iron reduction  (van Cappellen and Wang, 1996; Soetaert et al. , 2007). Therefore, we suggested that it was driven by low CBA. The qPCR data indicated very low CB filament density about 7 m. cm -3. The filament density was in the low range of the in situ densities reported from the Baltic sea  (Marzocchi et al. , 2018; Hermans et al. , 2019). The diversity of pH microprofiles observed between the three  Currently, the control factors of spatial and temporal variability of the CB density and the CBA on mudflats are still unresolved.
The relative abundance of calcareous specimens over agglutinated (C/T) is very stable along depth at station 1 (0. 78 ± 0. 07; Appendix) whereas this ratio is more variable at stations 2 and 3 (0. 65 ± 0. 17 and 0. 73 ± 0. 15 respectively), confirming that pH conditions affect assemblage composition through the under representation of calcareous foraminifera although species diversity is never affected (Figure  5 ). Assuming that acidification intensity in the suboxic zone is due to cable bacteria activity, our data suggest that CB have a drastic effect on the integrity of shells from living benthic foraminifera and potentially on their assemblages. The magnitude of this effect may depend in the CBA intensity and duration throughout the life cycle of foraminifera. Since the dataset of the present study is rather limited, one can examine literature data that provides together oxygen and pH microprofiles with sub-centimetre vertical distribution of living foraminifera in intertidal mudflats first and other benthic environments then.
Acknowledgments. The authors are grateful to  Sophie QUINCHARD  for assistance with the foraminifera picking,  Sophie SANCHEZ  for sample splitting (LPG,  Université d'Angers ) and  Romain MALLET  ( SCIAM, Université d'Angers ) for the achievement of a part of the SEM imaging. Susanne NIELSEN  and  Ian MARSHALL  ( Aarhus University ) are thanked for assistance with the qPCR analysis. The authors are grateful to  Frans JORISSEN  for field work, and his advice for the writing process (LPG,  Université d'Angers ). The authors thank the participants to the field trips (2019 and 2020). Financial support. The authors received funding from the  CNRS-INSU  (program  LEFE-CYBER , project  CB-FOR ), from the  Angers University  and from the  OFB ( project  FORESTAT ).
Playback noise The playback was generated in R (v. 4. 1. 2, The R Foundation for Statistical Computing) with the package seewave (v. 2. 1. 8). We synthetized 40 s of white noise (0-22 kHz, sample rate of 44. 1 kHz, 16 bits accuracy) followed by 40 s of silence. We chose a duration of 40 s because this is longer than typical male Canary songs in the breeding season  (Voigt and Leitner 2008)  and theoretically would allow the birds to insert their songs into the silent periods between the noise. The noise had a 0. 1-s fade-in and 0. 1-s fade-out to prevent the loudspeaker of producing a clicking sound due to a sudden signal onset. We further band pass-filtered the noise (0. 10-10. 0 kHz) to restrict it to the spectral range of Canary songs. In addition, we used a band-stop filter to make a small spectral "window" between 3.
Statistical analysis All subsequent statistical analyses were performed in R (v. 4. 1. 2, The R Foundation for Statistical Computing). We investigated the impact of noise level and the total duration of intermittent noise exposure (number of experimental days) on the onset of songs using four generalised linear mixed models and one linear mixed model (Table  1 ), built with the package lme4 (v. 1. 1. 29). In model 1. 1 and model 1. 2, we investigated the probability of birds to start singing during a 40-s noise burst or in the silent periods between the noise burst over the course of the experiment (fixed effect: experimental day, from 1 to 5) and according to the noise level [fixed effect: playback noise level in dB(A)]. To account for circadian changes in their song activity, we included the time since the start of playback (in hours) as a fixed effect.
Renal cancer is one of the ten most common cancers in the population that affects 65,000 new patients a year. Nowadays, to predict pathologies or classify tumors, deep learning (DL) methods are effective in addition to extracting high-performance features and dealing with segmentation tasks. This review has focused on the different studies related to the application of DL techniques for the detection or segmentation of renal tumors in patients. From the bibliographic search carried out, a total of 33 records were identified in Scopus, PubMed and Web of Science. The results derived from the systematic review give a detailed description of the research objectives, the types of images used for analysis, the data sets used, whether the database used is public or private, and the number of patients involved in the studies.
The advent of graph processing units and large training datasets have proven the capabilities of Convolutional Neural Networks (CNNs) to extract high-throughput features, deal with segmentation tasks, also to building models (V-Net, U-Net, ANU-Net, ImageNet, ResNet. ) to predict pathology or classify the tumor  [8]. Unfortunately, medical data is not always adequate or easily available, for that reason Transfer Learning (TL) using a data-augmentation strategy is used to tackle them  [9]. To summarise, the purpose of this study is to better understand the predictive models based on DL in renal tumors to be used in other types. Specifically, one of them is the diagnosis of adrenal tumors that have been covered until now by only a few studies.
Search Strategy Search keywords were selected according to the review framework. As primary concepts for the search, "renal tumor", "deep learning" and "image" were selected. In addition to them, synonyms of the term "renal tumor" were also considered, namely "renal carcinoma", as they are often used interchangeably in this area of research. The exclusion criteria applied to the search were by language (English). Below are the resulting queries that are ultimately run on Scopus, PubMed and Web of Science as they are the world's two main databases for bibliographic references and periodical citations.
Data Origin In the 18 articles selected, there are 5 different origins of the image databases used in the studies (Fig. 4 ). The most common data source is from hospitals or departments with a total of 61. 11% (11/18)  [1, 3, 10, 11, 14, 15, 18-20, 22, 23]. The public databases used are The 2019 renal and renal Tumor Segmentation Challenge (KiTS19) with a total of 16. 67% (3/18)  [12, 16, 17] , followed by ImageNet with a total of 11. 11% (2/18)  [2, 9]. Only one study each used The 2021 renal and renal Tumor Segmentation Challenge (KiTS21)  [13]  and The Cancer Imaging Archive (TCIA) (1/18)  [21].
Considering our results, there are only two objectives in the selected papers: renal tumor segmentation and classification of benign or malignant renal cyst, without having a significant difference in the number of articles that have each objective. The image most analysed by the studies is computed tomography (hereinafter, CT). The reason for this may be the availability in all hospitals of CT scan used to supplement conventional X-ray imaging and ultrasonography. This machine allows obtaining detailed internal images of the body of the patients with metallic implants or pacemakers in contrast to MRI which is contraindicated. The vast majority of studies use private hospital or departmental databases (61%). This may be due to the fact that usually when such data are obtained, they are protected by the ethics committee, which does not allow these data to be published openly.