dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimation We now turn to the practical matter of estimation of the TOF spectrum from the measured data 𝐶 𝑖𝑗 , in which case we must consider background effects. In the simplest case of constant background, 𝑏(𝑡) = 𝑏, one obtains 𝑐 𝑖𝑗 ≈ 𝑏 𝛥𝜏 + 𝛼 -1 𝑠(𝑇 𝑖 , 𝜏 𝑗 ) , (18) which may be compared with  (14). Another contribution to background can come from a ''leak'' in the chopper, i. e. , nonzero UCN transmission through the chopper even when it is geometrically closed. Although in general such a leak may be velocity-dependent or change in time, to first order we analyze it by writing the transmission function as 𝓁 + ∑ 𝑖=0,1,… 𝑓 (𝑡 -𝑇 𝑖 ) , ( 19 ) where 0 ≤ 𝓁 ≤ 1 is a constant. From (  1 ) and (  2 ), the excess counts in the 𝑗th bin due to leakage are 𝓁𝛥𝜏 ∫ 𝜏 max 𝜏 min d𝜏 𝑠(𝑡 𝑗 -𝜏, 𝜏). ( 20 ) If  (17)  holds, 𝑡 𝑗 -𝜏 may be replaced by 𝑡 𝑗 , so that, with  (12) , this becomes approximately 𝓁𝛥𝜏 ∫ 𝜏 max 𝜏 min d𝜏 𝑠(𝑡 𝑗 , 𝜏) = 𝓁𝛥𝜏𝑅(𝑡 𝑗 ) , (21) where 𝑅(𝑡) = ∫ 𝜏 max 𝜏 min d𝜏 𝑠(𝑡, 𝜏) (22) is the total rate of UCN incident on the chopper at time 𝑡.
By transforming these TOF spectra to others independent of 𝐿, it becomes more apparent that the two datasets correspond to the same UCN distribution. For example, Fig. 8  shows the two estimates transformed into velocity spectra 𝑠 𝑣 (𝑣), with bin edges determined by 𝑉 (𝜏) in (49). One sees that now the two spectra occupy the same region in 𝑣, and have very similar shapes. The disagreement in spectral weights between the two estimates is considered in Section 4. We note that in transforming spectra, only TOF 0 < 𝜏 ≤ 𝜏 max is physically meaningful. It is therefore reassuring that the points having 𝑣 < 0, which corresponds to 𝜏 > 𝜏 max , are consistent with zero. Fig. 9  shows the same estimates transformed into energy spectra 𝑠 𝜀 (𝜀), with bin edges determined by (𝜏) in (53). Only those data corresponding to 𝜏 < 𝜏 max are plotted.
Conclusion In this paper, we have discussed the analysis of UCN TOF data in a general way that is applicable to both time-dependent and stationary spectra in either horizontal or vertical configurations. In this section, we summarize the main ideas presented in this paper. The formulas in Section 2 connecting measured data to spectral estimates are good approximations provided that two conditions hold. The first is that the transmission function 𝑓 (𝑡) does not depend on the UCN velocity, which is equivalent to the assumption of a perfectly absorbing chopper that has zero thickness in the direction of the beam. This is almost certainly not true for the experimental data presented here. The second condition is that the TOF spectrum does not vary in time or in TOF quickly compared to the opening time of the chopper 𝛥𝑇 , in which case it is not necessary to deconvolve 𝑓 (𝑡) from the measured data.
In order to study the time evolution of TOF spectra, a periodic chopper function can be employed, in which case it is necessary to ensure that the chopper repetition time 𝑇 is long enough to avoid frame overlap. In addition, 𝑇 should be large enough to adequately sample the background; one convenience of vertical TOF compared to horizontal TOF is that the maximum TOF 𝜏 max = √ 2𝐿∕𝑔 is well-defined in the case of the former. In our consideration of background effects, we made the approximation that the chopper leakage does not depend on UCN velocity, and is constant in time. Provided that this is the case and additionally that (17) holds, it is then possible to estimate the leakage fraction using (32) and (33). While the leakage fraction determined by this method is in reasonable agreement with independent measurements, the spread in the data in Fig.
Acknowledgments The authors would like to thank  Dr. Tobias Jenke  for supplying the chopper used in these experiments and for providing the optical characterization data of its transmission function. This work was supported by the  European Union 's  Horizon 2020 research and innovation program  under the  EURIZON  project, grant agreement No. 871072 , and by the  Excellence Cluster ORIGINS  which is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under Germany's  Excellence Strategy  - EXC-2094 -390783311.
Acknowledgements We thank  Christophe Guilluy  for support and encouragements,  A. Wong  ( MBI, Singapore ) for expert help in paper editing,  Diego Pitta de Araujo  ( MBI, Singapore ) for artistic design of the figure 6,  Bryant L. Doss  for advices with the TFM experiments,  Yukako Nishimura  ( Hokkaido University ) for advices with the MY-134 pillars technology,  M. Davidson  fluorescence protein collection (The  Florida State University, Tallahassee, USA ), the  SIMBA microscopy facility  and nanofabrication core facility at the  Mechanobiology Institute  for technical help. The research is supported in part by the  Singapore Ministry of Education Academic Research Fund Tier 2  ( MOE  Grant No:  MOE2018-T2-2-138 , awarded to A. D. B;  MOE2019-T2-1-099  and  MOE2019-T2-02-014 ; awarded to P.
The Artemis exploration zone is a topographically complex impact-cratered terrain. Steep undulating slopes pose a challenge for walking extravehicular activities (EVAs) anticipated for the Artemis III and subsequent missions. Using 5 m/pixel Lunar Orbiter Laser Altimeter (LOLA) measurements of the surface, an automated Python pipeline was developed to calculate traverse paths that minimize metabolic workload. The tool combines a Monte Carlo method with a minimum-cost path algorithm that assesses cumulative slope over distances between a lander and stations, as well as between stations. To illustrate the functionality of the tool, optimized paths to permanently shadowed regions (PSRs) are calculated around potential landing sites 001, nearby location 001(6), and 004, all within the Artemis III 'Connecting Ridge' candidate landing region.
Path-finder algorithm Route design for EVAs has typically been completed manually by geology experts. Owing to recent lunar orbital missions, topographic data is now available at high resolution (up to 5 m/pixel) for elevation Fig. 2. Interpreted cross-section of the 'Connecting Ridge' (center left) and Shackleton crater. Shackleton impact ejecta blankets the ridge. Drawing upon the Apollo 16 landing site, which also occurred in a feldspathic terrain, one infers a series of lithologies (pictured) might be found in proximity to the 'Connecting Ridge'. Elevation and distances extracted from LOLA and NAC data. Photographs courtesy of LPI Lunar Sample Atlas and Washington University in St. Louis; section developed from Ref. [11] ; SPA basin profile from Ref. [25]. and slope. This accuracy allows the implementation of minimum-cost calculation algorithms to obtain optimized traverses that reduce both distance traveled and the risk from high slopes.
Therefore, our priority is to identify paths that strike a balance between minimizing the accumulated absolute slope and achieving the shortest distance possible. By prioritizing both flat terrains and shorter routes, we aim to optimize efficiency while reducing the physical exertion required during the traverses. We developed a Python pipeline called MoonPath that, given a starting point, a maximum slope, and a distance threshold, automatically computes all optimized traverses to nearby accessible PSRs. At the same time, it also evaluates bearing capacity, temperature, astronaut sunlight incidence (for a given date), traverse walking time, and astronaut-to-lander view opportunity.
Processing pipeline The data processing pipeline requires a series of baseline data products, which we developed using primary and secondary Lunar Reconnaissance Orbiter (LRO) data products. The topography of the region is assembled using an enhanced 5 m/pixel LOLA data product  [46]  derived from LOLA Digital Elevation Counts (LDEC), a LOLA Digital Elevation Model (LDEM), and a LOLA Digital Slope Model (LDSM). The published geolocation uncertainty is ~10-20 cm horizontally and ~2-4 cm vertically for each pixel. Note, however, that ~90 % of the 5 m/pixels are interpolated, i. e. , do not contain physically measured data. For visual inspection of the region and to obtain detailed images of the PSRs, we use a 1 m/pixel mosaic of the south polar region  [47]  which was generated from the 0. 5 m/pixel Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) data  [48] , orthorectified using a 5 m/pixel DEM and registered horizontally to the LOLA global DEM  [49].
An assemblage of NAC images downloaded and calibrated using Integrated Software for Imagers and Spectrometers (ISIS) was also mosaiced to create base maps. The data was georeferenced and mosaiced using ENVI and ArcGIS 10. 6 software  [50]. To determine the mean bolometric brightness temperatures, ~10 years of nadir-pointing Diviner Radiometer Experiment observations were compiled  [51]  from 7 IR channels over a wavelength range of 7. 55 μm-400 μm. For the present study, we use the Level 4 gridded 240 m/pixel Polar Cumulative Products described in Ref. [52] , focused on the southern summer season as indicative of minimum PSR extent and for more favorable exploration conditions at high latitude. A bearing capacity map with 5 m/pixel resolution was produced following  [53]  and a variation of Hansen's formula.
Because an analysis of Apollo and Lunokhod data recommended bearing capacities of >7 kNm -2  [54] , calculated traverses are subsequently restricted to those surfaces. An analysis of Apollo walking EVA speeds  [55]  was expanded with a new non-linear analysis using transcripts of Apollo 12 (EVA 2) and Apollo 14 (EVA 2) traverses  [56]. Of these two missions, the latter covered terrain more akin to upcoming surfaces and astronauts also hauled a Modular Equipment Transporter (MET), analogous to the tool cart/carrier being designed for Artemis III. The locations of PSRs were mapped by applying a process similar to that of  [2] , who use 240 m/pixel LOLA data, albeit with the 5 m/pixel LOLA equivalent and the simplification of not considering backscattered (secondary or reflected) illumination.
PSRs with less than 5 % coverage or fewer than 3 LDEC elevation data points were automatically excluded from the analysis. Additionally, we manually removed PSRs that conflicted with the NAC imagery, specifically targeting those situated in illuminated areas. We used Dijkstra's algorithm, as justified, to produce a shortest-path tree from the starting point (landing site or crater rim) to the endpoint (PSR edge or center), allowing both lateral and diagonal movements between adjacent pixels  [63, 64]. The 5 m/pixel LDSM data is employed as a cost map, using cumulative slope as the optimization metric. The 'best' traverse is chosen by evaluating the lowest cumulative slope result. The algorithm intrinsically considers distance and height. By utilizing the LDSM raster as a cost map, the algorithm rewards shorter paths with lower slopes, as longer distances with lower slopes or shorter distances with higher slopes would result in a worse cumulative slope.
Apollo traverse speed By comparing maps of traced traverses to transcripts from Apollo 12 and 14 (both EVA 2) missions, way points were generated that link discrete times with spatial positions for a given astronaut (Figs. 7  and 8 ). Only way points separated by > 25 m were used to ensure traverse speed calculations encompass periods that were predominantly walking, rather than those involving geological activities at stations or short bursts of speed between points of interest. Apollo 12 (EVA 2) had regular stops, and averages contain the time for geological observations and photographs. Apollo 14 (EVA 2) was characterized by 12 stations for data collection separated by long walks. Data collected on astronaut walking speed provide operational times included in traverse models calculated for this ROI.
Atlas All traverses from potential landing sites, along with the optimal crater descent, have been compiled in an atlas found in the Supplementary Material. Distance, elevation change, slope, bearing capacity, temperature, illumination, direct view from the HLS, and duration (including the return, 'bck') are evaluated for each traverse. Atlas images were generated with Python using the Mayavi package  [74]  and Matplotlib  [75]. For 3D renderings the scene illumination was selected to best convey the topography. The atlas includes the 521 PSRs identified within the ROI, accompanied by host crater and PSR properties, such as shadowed area for 2 • and 5 • Sun elevation, temperature, coverage (percentage of data points not interpolated), and radius and depth of the host crater (calculated by fitting a plane to the rim for slope correction, prior to computing maximum elevation change).
Integrating these variables could enhance traverse optimization. Illumination, communication constraints, and mission goals are also relevant considerations. Sun position and local topography affect visibility and power generation, while communication constraints impact real-time support. We acknowledge the importance of these factors and their prospective integration into future software iterations. Both the MoonPath tool and manual mappers need to download suitable orbital data, demanding a comparable time investment. Once the data is downloaded, our pipeline produces results within seconds or minutes, whereas manual mapping may extend to hours or days. Crucially, this expedited automated method enables mission planners to explore a broader array of quantitatively optimized options.
Conclusions The Artemis program seeks to return humans to the lunar surface and specifically the south polar region, where PSRs may host near-surface ice, making them critical targets for sample collection and scientific analysis. To design astronaut EVAs, we developed a Python pipeline that automatically models and evaluates the optimized path to all accessible PSRs given a starting point within the vicinity of the 'Connecting Ridge' region. Using high-resolution LOLA data, we identified and mapped 521 PSRs that maintain a minimum area of 75 m 2 nearby potential landing sites 001 and 004, and a flat landing zone 001  (6). Our MoonPath pipeline combines a Monte Carlo approach with Dijkstra's minimum cost path algorithm to compute the optimal route for EVAs with the most efficient workload criterion, i.
Acknowledgements. This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche. This work was supported by the  Programme National de Planétologie (PNP)  of  CNRS/INSU , co-funded by  CNES. G. T. acknowledges the support from the  ANR   COLOSSe  project. RL acknowledges the support of the  Dragonfly  project, via  NASA  contract  NNN06AA01C  to the  Johns Hopkins Applied Physics Laboratory. We are grateful to  Henrik Kahanpää  for a useful discussion about Huygens' pressure measurements, and we thank the referee,  Tetsuya Tokano , for his thorough review.
The error bars represent Type B uncertainties relating to measurement. A Monte-Carlo approach  [8]  was used to decompose the mass spectrum. Fragmentation data under EI 70 eV of 118 molecules were collected from the NIST Chemistry WebBook. The database was then narrowed by removing the molecules whose parent peak does not coincide with a signal on the experimental spectrum. The Monte-Carlo algorithm then managed to fit the fragmentation patterns of the dabatase compounds with the experimental spectrum. The deconvolution was performed 100,000 times to obtain a statistical distribution and the calculation uncertainties were plotted.
III. Results The mass spectrometry analysis (Fig. 1 ) shows a notable increase in intensity for many peaks of the C1 and C2 groups under irradiation. For higher m/z, most peaks only appear at a significant intensity when the tholins are irradiated. No signal was detected over m/z 53 in either experiment. The deconvolution (Fig. 2 ) permitted identification of the volatiles released. First, we report a clear increase in desorption of nitriles and unsaturated hydrocarbons under heavy ion irradiation. When the tholins were irradiated, hydrogen cyanide production was 20 times higher and it is observed that cyanogen and propiolonitrile formation and fragmentation shaped the significant C4-group signals (m/z 50, 51, 52). Allene and acetylene production also appeared under irradiation. Second, the Monte-Carlo calculation did not manage to fit the database's compounds to the experimental results for m/z 12, 26, 29, 36, 38.
Methods and Materials A MEDLINE-based literature review of available data regarding the lymph node drainage pathways of prostate cancer at the time of initial diagnosis and in case of postoperative biochemical relapse was performed by 3 radiation oncologists from the GFRU committee (ODH, PS, SPanonymized for review). Keywords included "prostate cancer," "radiotherapy," "lymphadenectomy," "lymphography," "sentinel node(s)," "magnetic resonance imaging," "computed tomography," "PET," "lymph node dissection," "lymph node drainage," and "nodal relapse. " The research algorithm used by the GETUG-AFU group  6  was updated for publications between 2015 and 2021. Original articles, consensus publications, reference articles, and review papers about lymph node drainage, imaging, and pelvic CTV contouring in prostate cancer were selected.
Papers published in any language other than English, editorials, case reports, and letters to the editor were excluded from this literature selection and review. Additional references were identified from other sources. Based on this literature review, a reference contour (CRef) volume was generated for PLNRT using the images from a 68-year-old patient with localized T2b N0 M0, International Society of Urological Pathology grade group 3 prostate adenocarcinoma, with an initial prostate-specific antigen level of 24 ng/mL. This CRef was compared with participants' individual contours performed at the time of 3 consecutive GFRU workshops by radiation oncologists with an interest in prostate cancer. During these workshops, the same clinical case was presented. On the anonymized DICOM (Digital Imaging and Communications in Medicine) data set of the patient, the participants were requested to delineate a prophylactic nodal CTV for this case of localized high-risk prostate cancer without delineating neither the prostate or seminal vesicles nor the organs at risk (OARs).
The delineation of the pelvic CTV includes both the vein and the artery with an additional margin of 7 mm around the blood vessels, according to magnetic resonance imaging (MRI) data obtained from the use of lymphotropic nanoparticles. 14  CTV margins are cropped for fixed and impassable anatomic barriers such as muscles and bones which are always excluded for the perivascular expansion margin (Fig. 2A ). The interface between CTV margins and OARs was addressed during the workshops and remained a point of discussion. For instance, in the case of the bowel, some radiation oncologists would limit the CTV margin to the bowel bag (such as the GFRU experts) while others would allow the margin to extend within this mobile structure or would use a planning OAR volume. A certain degree of freedom should remain at the discretion of the radiation oncologist, depending on local practices and on the anatomy and comorbidities of the individual patient.
20  Similarly, in the postoperative setting, the NRG Oncology/RTOG 0534 SPPORT trial  2, 21  showed a disease progression benefit at 5 years by adding WPRT to prostate bed irradiation in combination with short-term androgen deprivation therapy, while no differences were observed in terms of metastasis-free, cancer-specific, and overall survivals. Although pending trials will help to define the real clinical role of WPRT for prostate cancer (NCT01952223, NCT01368588, ISRCTN80146950), the use of a homogenous and reproducible delineation of pelvic lymph nodes among centers is of significance. At the time of the GFRU contouring workshops, the updated NRG contouring guidelines were not published yet. 9  It is therefore very interesting to compare our results to these NRG guidelines (Table  2 ).
Acknowledgements S. W. acknowledges financial support from  Labex FIRST-TF  under contract number  ANR-10-LABX-48-01. The work is supported by the  French ANR  project " SPINNET "  ANR-18-CE24-0012  and the  Hori-zon2020 Framework Program  of the  European Commission , under  FET-Proactive  Grant agreement No. 899646  (k -NET ).
, 2010)  and, after sampling, stored at -20 • C. Conductivity, temperature and depth were measured with a Sea-Bird CTD (SBE 911plus) installed on the trace metal clean rosette frame. Macronutrient data (NO - 3 , NO - 2 , PO 4 3-, Si(OH) 4 ) obtained from the trace metal clean rosette were reported by  Graeve et al. (2019).
The measurements were performed using a Hanging Mercury Drop Electrode (VA663 stand Metrohm), an Ag/AgCl reference electrode with a KCl salt bridge (3 M) and a glassy carbon counter electrode. Following 120 s nitrogen gas purge, a deposition potential of -0. 2 V was applied for 180 s. After 10 s of equilibration, a differential pulse scan was applied from -0. 2 to -0. 5 V, with a step potential 3 mV, modulation amplitude 49. 95 mV, interval time 0. 1 s and modulation time of 0. 05 s. Data fitting was performed with PromCC software  (Omanović et al. , 2015)  using the "complete fitting" for one ligand (Supplementary material Fig. S1 ). For each titration curve, the sensitivity was determined by internal calibration, from the last titration points (4 or 5) which presented linearity. This method assumes that in the linear portion of the titration curve all ligands were titrated.
Acknowledgements The authors would like to thank captain  Schwarze  and his crew of the  RV Polarstern , the chief scientist  Torsten Kanzow  and all other participants, for their effort and support during sample collection. We also acknowledge the financial support for the  ATOPFe  project ( CTM2017-83476-P ) from the  Ministerio de Ciencia e Innovación (Spain). VA participation was funded by the  PhD grant  ( PRE 2018-084476 ). AGG participation was partially funded by  LabexMER International Postdoctoral Program  for providing fellowship and  Laboratoire d'Excellence LabexMer. PL also thank the support from the  Ministerio de Ciencia, Innovación y Universidades of Spain  and  University of Lleida  ( Beatriz Galindo Senior  award number  BG20/00104 ).
We used R version 4. 1. 2 (R Core Team, 2021) with R-package Rclustercpp. hclust (version 0. 2. 6) for large datasets with standard settings, i. e. using euclidean distances and the ward method. For describing the characteristics of the individual clusters, we make use of the median and interquartile range of each RRs distribution within each cluster. tograms in figures and we thus present results of variation in median and skewness values in Tables  2  and 3. Table  2  shows the range of median RR values across GGCMs, crops and driver combinations. Median values range between 0. 06 (importance of C in comparison to N for maize in GEPIC data) and 1. 0 (importance of C in comparison to N for soybean in GEPIC data). For all crops analyzed here, many RRs show a very broad range across GGCMs with differences between min and max values often well above 0.
Acknowledgments J. J. was supported by the  NASA GISS Climate Impacts Group  and the  Open Philanthropy  Project. J. F. was supported by the  NSF   NRT program  (grant  DGE-1735359 ) and the  NSF   Graduate Research Fellowship   Program  (grant  DGE-1746045 ). A. C. R. received support from the  NASA Earth Sciences Division  via the  GISS Climate Impacts Group. S. O. acknowledges support from the Swedish strong research areas BECC and MERGE, together with support from  LUCCI  ( Lund University Centre  for studies of  Carbon Cycle and Climate Interactions ). manuscript submitted to Earth's Future Acknowledgments J. J. was supported by the  NASA GISS Climate Impacts Group  and the  Open Philanthropy Project. J. F. was supported by the  NSF   NRT program  (grant  DGE-1735359 ) and the  NSF   Graduate Research Fellowship   Program  (grant  DGE-1746045 ).