dataesr/scientific-paragraphs-categorization huggingface dataset:
Solid targets We measured the PG energy spectra of pure solid 63 Cu and 89 Y targets irradiated with protons at 25 and 70 MeV. 31 P could not be studied because of the safety constraints related to the production of a solid target only composed of this material. During the experimental campaign, liquid water was also characterized to simulate the background produced by the tissues. The results are reported in Figure  7  together with TOPAS predictions. The two energy spectra acquired with 63-Copper (Figures  7A, B ) both show characteristic peaks at 0. 96 MeV, 1. 17 MeV, and 1. 33 MeV, which are the first excited states of 63 Cu and 62 Ni in agreement with TALYS simulations and NUDAT data (Figure  5C ; Table  2 ). Of the two additional peaks predicted by TALYS at 1 MeV and 2. 5 MeV, only the low-energy one can be identified in the experimental data.
In the calculation, we used 10 9 protons, as this value represents a typical order of magnitude delivered for a single energy slice in a two-fields treatment plan with a prescribed dose of 2 Gy per fraction  [57]. The detector solid angle was estimated assuming a PETlike detection system, i. e. , a cylindrical geometry with 30 cm radius and 20 cm length  [58]. To calculate the PG yield at a 0. 4 mM concentration, we performed a linear regression on the curves of Figure  9 , and used the fit parameters to extrapolate the desired gamma count. We could apply this method for the 70 MeV protons dataset but not at the lower energy, where we measured the spectra with only one element concentration. The linear parameters obtained from the fit are reported in Table  3 , together with the R 2 values.
For each element, the curves are very close in the first part independently of the uncertainty percentage, but then they deviate in the second part and indicate a stronger dependence of the range accuracy on the PGs count error for longer proton ranges. In fact, independently of the gamma count error, the range accuracy becomes worse for beams with enough energy to traverse the entire tumor and stop downstream of it. The datasets of 31 P and 63 Cu reach a maximum value downstream of the tumor edge, while the 89 Y curves keep growing. These results suggest that the presented methodology can match the range resolution of 2-3 mm currently obtained on patients with other PG-based methodologies  [22, 59] , if signature PGs are acquired with an uncertainty below 10% for 31 P and 63 Cu, and 20% for 89 Y.
The background depends on several factors, including the tumor type and location, as well as the patient's anatomy, and for this reason, is highly variable from patient to patient. We propose two strategies to address this issue: • We will build a database of background measurements. It will include data collected on patients that are treated for different malignancies, as well as a library of the PG spectrum emitted by different tissues. We will use all these inputs to train a machine-learning model that can predict the background on the base of the patient's CT scan. • During the first treatment faction, we will not administer the label element to the patient, and measure the background. We will use the experimental spectrum to tune that predicted by the machine learning model. This approach will allow having an accurate and personalized characterization of the background of each patient.
Acknowledgments LP acknowledges support from the  National Research Foundation of South Africa. The authors thank  M. Pellicioli ,  J. Schuler ,  C. Haas , and  N. Dick-Schuler  for their technical support and valuable help with the Cyrcé cyclotron. Thank also to professor  F. Camera  for providing the LaBr 3 (Ce) detector and its contribution in the characterization. Thanks to  E. Verroi  for the fundamental technical support at Trento protontherapy center. The authors also thank  Professor G. Guella  and  F. Trenti  for the preparation of water solutions for liquid targets.
Optoelectronic Motion Capture Systems Camera-based optoelectronic motion capture systems are recognized as a gold standard to capture kinematic data  [28]. Traditional 3D-based systems are made of a set of infrared cameras carefully positioned to cover an volume of interest  [28]. Calibration procedures allow us to determine the relative position and orientation of all cameras and to anchor this information into a reference frame based on the lab. Markers are then affixed onto specific anatomical reference points on the participant. Through a triangulation process, the 3D position of each marker is determined by the system. An anatomical model can then be applied to link the different markers to segments of interest, allowing us to capture kinematics. Analysis of kinematic variations over time provides information on postural control.
Funding: The APC was funded by a grant from the  "programme collectif "  Agence Universitaire de la Francophonie  (project n • 3294, AUF, given to T. L. ). T. L. , G. L. and H. M. received financial support from the  AUF  for travel grants. G. L. received salary support from the  Fonds de recherche du Québec-Santé  (FQRS,  Senior Clinical Research Scholar ). K. L. received grants from the  Chaires de recherche of Canada.
Samples were characterized by complementary investigation techniques. A Phenom Pro-X scanning electron microscope equipped with an energy-dispersive X-ray spectrometer was utilized for the preliminary characterization of electrospun materials. A detailed investigation was performed through a high-resolution scanning electron microscope, Jeol JSM-7900F, specialized in low accelerating voltages studies and low vacuum measurements. The system was coupled with a chemical microanalysis system using X-ray spectrometry (BRUKER Esprit). Materials crystalline structure was investigated using a Bruker D2 Phaser with CuK𝛼 radiation at 30 kV and 20 mA. Peaks attribution was made according with COD (Crystallographic Open Database). The diffraction angles 2𝜃 were varied between 10°and 80°in steps of 0. 02°and a count time of 5 s per step.
Acknowledgments This research benefited from  Norwegian Research Council  funding to the  Centre for Earth Evolution and Dynamics (223272)  and to TR (PLATONICS, 276032). MA benefited from  CNRS-INSU-PNP  and  UCBL-BQR  fundings. Computations were performed on Stallo and  Saga Uninett Sigma2  facilities (nn9010k, ns9010k). Crameri et al. (2020) authored Figure 4d colormap.
Scaling laws in the wind tunnel The data used in this study is obtained experimentally, working at reduced scale. The scaling laws applied to model the atmospheric boundary layer in the wind tunnel, as well as the reasoning behind the motion regimes employed and the data acquisition are discussed in this section. In order to maintain comparability between the different motion regimes tested, a reduced frequency (f red ) is introduced: f red = f • D U hub (1) where f is the imposed motion frequency, U hub is the wind velocity at hub height h (considered as the physically representative reference velocity for the wake flow scales) and D is the turbine's diameter. From this definition, a higher f red can represent an increased motion frequency, as investigated in the scope of this study, or an increased wind turbine rotor diameter D.
The motion system can reproduce the demanded motions with a temporal resolution of 1 ms and an average precision of 1% for the longitudinal and rotational motions with regard to the commanded position. The average precision is 2% for the vertical axis. A delay of 4 ms is found between the commanded position and the real position of the motion system. The system is controlled using Motion Perfect v5. 0. 3 software. The system is installed below the wind tunnel floor in a sealed wooden box. No impact of the opening in the floor on the flow within the test section is detected. No propagation of vibrations from the motion system to the measurement equipment is detected.
Acknowledgments The authors are very grateful to  Edoardo Datteri , logician and philosopher of science of the  University of Milano-Bicocca , for the long and intriguing talks on the topics of this work. Although the authors are solely responsible for any excessive simplification of the theories they cite regarding that broad and complex area of study, most of the good ideas that this contribution contains would never have come to light without Edoardo's generous contribution and knowledgeable suggestions. Parts of this work have been funded by the  Austrian Science Fund (FWF) , Project:  P-32554 ''explainable Artificial Intelligence ''. All authors read and approved the final version of the manuscript.
europa. eu/doi/10. 2759/177365. l https://eur-lex. europa. eu/eli/reg/2016/679/oj. m https://blogs. microsoft. com/wp-content/uploads/2018/02/The-Future-Computed_2. 8. 18. pdf. n https://www. ibm. com/uk-en/watson/explainable-ai. o https://doi. org/10. 1109/ACCESS. 2018. 2870052. p https://doi. org/10. 5281/zenodo. 3240529. q https://www. europeanlawinstitute. eu/projects-publications/completed-projects-old/ai-and-public-administration/. r https://doi. org/10. 1016/j. ijinfomgt. 2022. 102479. s https://arxiv. org/abs/1711. 01134.
PCR, amplicon sequencing and recA phylogenetic analysis The strains were subjected to PCR with primers specific for the gene gapA (gapAF and gapAR, 0. 8 kb amplicon)  (Cigna et al. , 2017). The PCR products were sequenced in both directions with the same set of primers, using a commercial service (Microsynth France, Vaulx-en-Velin France). These gapA sequences were used for strain identification using BlastN comparison and construction of phylogenetic trees including the type strains. In addition, several recA and 16S rRNA sequences were retrieved from NCBI databases and used to define or verify the species affectation of the strain. Several strains firstly identified as E. chrysanthemi or Dickeya sp. were assigned to species that were not recognized at the time of their isolation. The phylogenetic tree based on the recA gene was constructed using the pipeline Phylogeny.
fr (http://www. phylogeny. fr/ phylogeny. cgi); the nucleotide sequences are aligned with MUSCLE  (Edgar, 2004) , the phylogenetic tree is reconstructed using the maximum likelihood method implemented in the PhyML program  (Guindon and Gascuel, 2003)  and graphical representation is performed with TreeDyn  (Dereeper et al. , 2008). The recA sequences were retrieved from the NCBI database; accession numbers are given in the tree.
, 2021). Most D. paradisiaca strains were isolated from banana trees (Musa paradisiaca) in tropical or subtropical countries  (Dickey and Victoria, 1980)  (Table  1 ). The species previously named D. paradisiaca was considered as the earliest branching lineage in the Dickeya genus in previous evolution studies  (Duprey et al. , 2019). However, data on the genetic diversity of this species was scarce, and it was poorly characterized at the genomic level. Only two genome sequences were available, including those of the type strain CFBP 4178 T  (Pritchard et al. , 2013). Calculation of aligned fraction values between Dickeya and Musicola genomes showed that less than 33% of the genomes of these two genera could be aligned together  (Hugouvieux-Cotte-Pattat et al. , 2021). A new Musicola strain was identified among poorly characterized strains of the CFBP collection.
D. dadantii, a wide host range, the model strain 3937 Noticeably, the D. dadantii species includes the strain 3937 that has been used for a long time as a model of the genus Dickeya for genetic studies leading to the characterization of virulence factors, including the different enzymes constituting the PCWDE equipment, and the regulatory network controlling expression of the virulence genes  (Hugouvieux-Cotte-Pattat et al. , 1996; FIGURE 2  Phylogenetic position of strain Ca3a among the Dickeya species, based on partial recA gene sequences. The tree was constructed using the pipeline Phylogeny. fr (http://www. phylogeny. fr/phylogeny. cgi). The nucleotide sequences are aligned with MUSCLE, the phylogenetic tree is reconstructed using the maximum likelihood method implemented in the PhyML program and graphical representation is performed with TreeDyn.
D. dianthicola, a potato pathogen known since 1950s The two species D. dianthicola and D. solani deserved special attention because they caused severe damages in the important crop potato. Most analyzed genomes are from strains isolated from diseased potatoes introducing a severe bias for diversity analyses. Despite its reputation of potato pathogen, D. dianthicola has a broad host range  (van der Wolf et al. , 2021). It was first described in For each species, are given the number of available genomes, their size range and their GC% range (data from the NCBI genome database, February 2023) and the intraspecies ANI and dDDH values. an outbreak on carnation in Europe in the 1950s. Nearly 300 strains have now been characterized, including about 80% potato isolates (Table  4 ; Table  S1 ). Other isolates were reported from vegetables, such as chicory or artichoke, and ornamentals, frequently carnation, kalanchoe, begonia and dahlia (Table  4 ).
The Dickeya zeae complex: D. oryzae, D. parazeae, D. zeae and maybe others Since classification of the strains previously classified as D. zeae has recently evolved, the term "D. zeae complex" was used to describe all these isolates (Hugouvieux-Cotte-Pattat and Van Gijsegem, 2021). In addition to the species D. zeae, the D. zeae complex comprises the species D. oryzae, initially described for a rice isolate  (Wang et al. , 2020) , and D. parazeae whose type strain is a water isolate (Hugouvieux-Cotte-Pattat and Van Gijsegem, 2021). Several genome sequences of strains belonging to the D. zeae complex are avaible in public databases but most of the corresponding strains have not been deposited in collections and are not publicly available for phenotypic analyses, restraining their microbiological characterization.
Acknowledgments We thank  Veronique Utzinger  for medium preparation and contribution to phenotypic analysis, and the members of the team MTSB (MAP, UMR 5240) for sharing of unpublished information. We also thank  Sebastien Santini  ( CNRS   UMR7256 ) and the  PACA Bioinfo  platform for the availability and management of the phylogeny. fr website used to construct the recA tree.
In this case, the participant was asked to only respond to an image that met two criteria (the other color from the one-choice task and an animate or inanimate object, chosen randomly). The computer recorded reaction time (RT) and response quality (the number and type of errors) for each trial. RT was divided into three main components: simple reaction time (SRT), executive speed (ES), and reaction to difficulty (RD). HRV Heartbeat interval data (RR) were recorded using the goldstandard ECG data in an integrated system and software package (Biopac MP160 System Inc. , California, USA) at a sampling frequency of 2,000 Hz. Each participant wore a 3lead ECG (BN-EL45-LEADS3) configuration placed on the right chest, the left chest, and the lower left chest. An alcohol pad was used to remove the top layer of dead skin cells.
Three self-adhesive disposable electrodes (2. 5 × 2. 5 cm) were placed on the abovementioned areas. Electrodes were connected to a Biopac BIONOMADIX ECG2-R. The positive lead was attached to the left chest electrode, the negative lead was attached to the right chest electrode (lead II derivation), and the ground electrode was attached to the lower left chest. RR data were recorded for a 10-min period in a sitting position at each intervention, except during the first and last interventions, in which a 10-min period in a standing position was added. The HRV analysis followed guidelines reported in previous studies  (38, 78) , which take into account potential circadian variation, and used the PyHRV Python library  (79). The following data were recorded: weight, height, waist-to-hip ratio, smoking habits, time of most recent alcohol intake (>24 h), time of most recent caffeine (coffee/tea) intake (>2 h), time of most recent meal (>2 h), time of most recent physical activity (>24 h), and quality of sleep on the day of the experiment and the preceding day.
Raw ECG data were filtered between 3 and 45 Hz using a finite impulse response band-pass filter. The order of the filter was set at 600 (0. 3 times the sampling frequency). R peaks were automatically detected using the BioSPPy Python library  (80). Hamilton segmentation was performed on the filtered signal, followed by R-peak correction with tolerance set to 0. 05. R waves were manually examined to ensure correct detection. If an ECG sequence was overly noisy when visualizing the superposition of all QRS complexes, the time interval was manually removed to improve data quality. RR intervals were automatically detected with the hrvanalysis module using linear interpolation and manually corrected for artifacts and ectopic beats.
Statistical methods Statistics were computed for all outcome measures. Data analyses were performed with JASP (Amsterdam, version 0. 16. 3 ), an open-source software package that is used for both classical and Bayesian analyses. Descriptive statistics were expressed as mean ± SD. The Shapiro-Wilk test was used to determine whether the data were normally distributed. When the analysis was significant, effect sizes were reported. To compare the single and repeated sessions, deltas between the first and last sham and taVNS stimulations were calculated for each cognitive, psychological, and physiological variable. ANOVA twoway or Kruskal-Wallis signed rank analyses were used according to the normal distribution to evaluate the impact of the intervention (i. e. , sham and taVNS) and session (i.
Limitations This study has several methodological shortcomings. The first is the small sample size and an imbalance between male and female participants and right-and left-handed subjects. Second, our study does not include a control group that does not belong to any of the taVNS or sham conditions. A larger study should include this third control group to monitor the impact of intervention responses. Third, our results are dependent on the conditions in which our participants found themselves at each stage. The lack of control over life events between each of the sessions can constitute a bias. Fourth, psychological and interoceptive data (i. e. , collected through questionnaires) are subjective measures. Intelligent sensors would provide more objective measures of subjects' adaptation. Finally, no longitudinal monitoring was carried out outside the intervention period.
Acknowledgments The authors wish to thank the volunteers who took part in this study and the data collection team. The authors also would like to thank the  French Space Agency (CNES)  and the  French Government Defense (AID)  for their support. We are grateful to the  Human Adaptation Institute  for their support and thank  MindPulse  for providing us with their technology. The data used in this study are the property of the  French Health Service. Any requests must be filed with and evaluated by the  French Military Staff.
arcgis. com. The parameters are defined per one TEU container, the most common unit load for multimodal transportation. Example problems were solved using GAMS version 25. 1 using the ODHCPLEX solver 1  for the exact MIP solution and MATLAB for the GA, both on a computer with 16. 0 GB RAM and a Core i7 CPU. Note that when solving the MIP using GAMS/ODHCPLEX, priorities for branching are also set to make the model solve faster. The priority was given first to the flow variables of    , then to the intermodal terminal selection variables of    , and then to the delay time variables of. The model was initially validated using a set of small-size problems (with four nodes, 18 links, three modes of transportation, and four containers that move from Node 1 to Node 4). Results were compared to the optimal answer with complete answer enumeration.
Acknowledgments A special thanks go to  Mr. Scott Marion  and the team from Motor Carrier Services of the  Missouri Department of Transportation (MoDOT)  and  Ms. Shelley Latham ,  Business Development Manager  of  Perkins Specialized Transportation , for all their kind support in different ways of this project. Also, we would like to thank many individuals from Specialized Carriers and  Rigging Association (SC&RA)  who supported our information-gathering process, especially  Mr. Steven Todd , the  Vice President , and  Mr. Doug Goldzwig  from  Crane Rigging. We also would like to thank the editor,  Ms. Carla Roberts  and the reviewers for their edits and insightful comments on the paper that have improved the overall clarity and contribution of this research.
A total of 73,744 images (256 × 256 pixels in size) were visually interpreted and manually labeled with three semantic classes (logging, fire, and roads) to train/validate a U-Net model. We predicted the three classes over the study area for all dates, producing accumulated degradation maps biannually. Estimates of accuracy and areas of degradation were performed using a probability design-based stratified random sampling approach (n = 2678 samples) and compared it with existing operational data products at the state level. DL-DEGRAD performed significantly better than all other data products in mapping logging activities (F 1 -score = 68. 9) and forest fire (F 1 -score = 75. 6) when compared with the Brazil's national maps (SIMEX, DETER, MapBiomas Fire) and global products (UMD-GFC, TMF, FireCCI, FireGFL, GABAM, MCD64).
Pixel-based spatial comparison of degradation areas showed the highest agreement with DETER and SIMEX as Brazil official data products derived from visual interpretation of Landsat imagery. The U-Net model applied to NICFI data performed as closely to a trained human delineation of logged and burned forests, suggesting the methodology can readily scale up the mapping and monitoring of degraded forests at national to regional scales. Over the state of Mato Grosso, the combined effects of logging and fire are degrading the remaining intact forests at an average rate of 8443 km 2 year -1 from 2017 to 2021. In 2020, a record degradation area of 13,294 km 2 was estimated from DL-DEGRAD, which was two times the areas of deforestation.
Current initiatives are based on Landsat satellite data (30-m spatial resolution) in conjunction with machine learning methods and time series analysis to create global-scale maps of annual forest cover change that includes mostly clear cuts. Examples are the Global Forest Change (GFC)  (Hansen et al. , 2013)  and the Tropical Moist Forests (TMF)  (Vancutsem et al. , 2021). The GFC product detects forest loss from clearing but also captures some levels of intense forest degradation  (Cunningham et al. , 2019; Kinnebrew et al. , 2022). The TMF product includes separate information on degradation and deforestation, but it does not attribute or label the causes of degradation  (Vancutsem et al. , 2021). A newly derived product from GFC, called Global Forest Loss by Fire (here called FireGFL), disentangles the effects of forest fire and should be further tested over tropical regions  (Tyukavina et al.
77 m) from Planet's satellite constellation for the entire tropics at bi-annual (twice a year) to monthly time scales  (NICFI, 2021). A recent local-scale study exploring logging detection at multiple scales showed that the spatial resolution brought by PlanetScope data shows logging at low to medium intensity with success  (Aquino et al. , 2022). Another recent study explored the use of PlanetScope to map logging and fire-related degradation using traditional machine learning and texture metrics at local scale  (Pinagé et al. , 2023). Moreover, Planet NICFI mosaics aggregate data into a bi-annual scale from 2016 to 2020, and offer monthly mosaics from September 2020 until the present day through selecting the best cloud-free images. The high temporal resolution of the raw PlanetScope image acquisitions (close to daily) increases the chance of obtaining cloud-free data for the image composites generated, thereby increasing the possibility of mapping degradation in tropical forests.