dataesr/scientific-paragraphs-categorization huggingface dataset:
Before running the streak-linking algorithms, we apply another iteration of the static-streak removal, now to the streaks merged with RNN. Almost all static streaklets are removed already after the sliding-window CNN stage, but a small number A135, page 7 of 18  Pöntinen, M. , et al. : A&A, 679, A135 (2023)  of full streaks (0. 11% out of all streaks) can only be removed after the merging with RNN. To link streaks by the same SSO in multiple exposures, we first tested the so-called multi-streak analysis, which was used in  Pöntinen et al. (2020)  to decrease the number of false-positive streaks. We adapted the algorithm to use the CNN+RNN catalog data and focused on the following parameters: minimum number of streaks in a multi-streak combination; maximum length difference between streaks; maximum and minimum distance between streaks compared to their lengths; maximum angle difference between streaks; maximum angle difference between the single streaks and the common multi-streak line; and maximum difference in median pixel flux values of the streaks.
To optimize the XGBoost hyperparameters, we ran the Ran-domizedSearchCV function of scikit-learn  (Pedregosa et al. 2011 ) over eight hyperparameters with five-fold cross-validation. After optimization, the best model consisted of 644 gradientboosted trees, each with a maximum depth of 11. Because all permutations of multi-streak candidates are generated, many duplicates remain after the classification. For example, if the ground-truth multi-streak contains members in all four exposures, there are different permutations of it containing members in only two or three exposures. Therefore, after the XGBoost classification, the program checks if all members of some positively classified multi-streak are included in another positive multi-streak having members in more exposures. If so, the multi-streak with fewer members is removed.
CNN Our deep-learning pipeline outperforms StreakDet in terms of speed, especially when parallelization is not considered. Running a single instance of StreakDet on a field of 144 CCDs takes over 2 h, not including post-processing steps. The nonmachine-learning processes in the deep-learning pipeline have much room for optimization to achieve even faster execution. Conversely, optimizing the runtime of StreakDet is more challenging as it is already written in well-optimized C++ code. It is possible to parallelize StreakDet by running multiple instances of the software simultaneously on different CPUs, with each instance analyzing different CCD exposures, which reduces the total running time. Comparably, the deep-learning pipeline can be parallelized easily with Tensorflow to utilize multiple GPUs.
8 ). The reason is that a fast-moving object's flux is divided among a larger number of pixels than the flux of a slow-moving object, making fast objects appear fainter and thus   23 15 8 10 7 3 0 5  62 100 90 100 85 65 87 44 33 34 8 5 9 4 8 3 1 1 2 1  40 94 97 83 71 57 61 20 11 11 7 4 4 4 1 4 0 1 0 0  80 90 87 50 53 21 9 3 12 1 3 1 0 1 0 0 2 0 0 1 100 85 67 30 25 21 0 2 2 0 0 1 0 0 0 2 0 0 4 0 64 100 35 18 4 2 2 0 0 4 1 0 0 5 1 0 2 0 5 0 67 44 8 0 0 0 0 0 0 1 0 3 0 3 0 0 0 0 3 0 0 11 2 0 0 0 1 0 0 0 0 1 1 1 2 0 0 1 1 0 7 10 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0   harder to detect. On the other hand, the slowest-moving objects in our dataset resemble point sources, making it difficult to differentiate them from stars and resulting in lower completeness compared to faster-moving objects.
8. The achieved completeness appears to be relatively close to the practical limit with the dataset used, judging by how faint different streaks appear in the images. The purity at this point is only 6. 4% (3956 true positives and 345 duplicates out of 66 798 total detections). In other words, there are over 15 times more false positives than true positives. The sharp decrease in purity from the CNN cutout training and test phases is explained by the fact that, during the slidingwindow analysis, there is a much larger number of negative windows compared to positive ones, and therefore to achieve high purity during the sliding-window analysis, the achieved purity during the cutout CNN training stage should be practically 100%. However, focusing on achieving extremely high purity during training inevitably diminishes completeness, typically quite drastically.
5 magnitudes fainter would increase the number of detected asteroids by approximately 50%. StreakDet's advantage in the angle and coordinate accuracies over the deep-learning pipeline is explained mainly by StreakDet's dedicated PSF fitting procedures, which the software uses to estimate the streak parameters precisely. In contrast, the deep-learning pipeline does not explicitly incorporate such PSF fitting techniques. In addition to more accurate coordinate prediction, StreakDet has a few other advantages over deeplearning methods. Firstly, StreakDet can more readily be used for other surveys without retraining. In other words, it exhibits relatively high robustness, although some parameter tuning is still typically required to reach optimal performance on specific data. In contrast, the performance of deep-learning models often suffers in the case of a domain shift, which occurs when the model is tested on data following a different distribution than the training set.
In other words, deep-learning models often have low robustness. We expect that the robustness of our deep-learning model is not very good and that the model will require at least some real training data in addition to the simulated data to perform well for real Euclid images. Secondly, StreakDet has a more interpretable decision-making process than deep learning. Deep learning models typically work as black boxes whose inner workings are hard to interpret. One thing to note is that the test set used in this work is more challenging in terms of linking streaks between exposures than the data used by  Pöntinen et al. (2020). For the StreakDet pipeline, all the simulated data could be used for testing. Here, we had to divide it into training, validation, and test sets. To have test-set images from all 11 simulated datasets, we chose to use certain CCDs from all the datasets for the test set.
This effect is visible in Table  3 , where the completeness is 61. 2% for multistreaks with three ground-truth parts and 60. 3% for four parts, whereas for multi-streaks with two parts, it is only 55. 4%. In other words, the deep-learning pipeline surpasses the completeness of the StreakDet pipeline even with a more demanding test set. Therefore, these estimates offer a kind of lower bound, and the deep-learning pipeline is expected to perform better on complete CCD mosaics. Qualitative comparison to other object-detection pipelines tested on other relevant datasets shows that the performance of our pipeline appears quite strong. Also, choosing a customized streak-focused object-detection approach instead of a boundingbox-based one seems better for streak detection. Varela et al. (2019)  tested the YOLOv2 object-detection model  (Redmon & Farhadi 2017)  to find satellite streaks in the data of their Wide-Field-of-View (WFoV) system.
YOLOv2 reached a completeness of only 40. 4% on their human-labeled dataset. As humans labeled the dataset, the upper limit for completeness was realistically 100% because all the streaks were visible to the human eye. Furthermore, the authors also commented on the limitations caused by the rectangular bounding boxes on the accuracy of streak coordinates. Similarly,  Kruk et al. (2022)  tested a bounding-box-based approach using Google Cloud AutoML to detect asteroids in Hubble images. Their AutoML model achieved 58. 2% completeness and 73. 6% purity when detecting asteroid streaks, again on a dataset where humans had reached 100% completeness. Also, due to the bounding-boxbased model, they had to develop an additional processing step to find the streak coordinates from the bounding box area.
2022). Therefore, a sensible approach would be first to use StreakDet to find initial asteroid streaks from real Euclid images, add them to our simulated training set, train the models, and then use them to find more asteroids from the images. Furthermore, it might be advantageous to train the deeplearning pipeline with a dataset following more realistic streak angle and length distributions, which depend on the SSO orbits and the observational geometry. The deep-learning pipeline operates in pixel coordinates, so getting the training distribution right requires considering further factors, such as the telescope orientation at different pointings and the fact that long streaks appear in more CNN cutout images than short streaks. The streak lengths in our simulated dataset cover most of the expected range of SSO streak lengths, but fast near-Earth asteroids can form longer streaks than are included in our training set, so some training examples of them should probably be added to the training set before deploying the models for the real data.
Acknowledgements. We wish to thank  Marcel Popescu  for offering constructive comments. We acknowledge funding from the  Vilho ,  Yrjö , and  Kalle Väisälä Foundation  and the  Academy of Finland  (projects # 316292  and # 299543 ). We thank the  Finnish Computing Competence Infrastructure (FCCI)  for supporting this project with computational and data storage resources. The  Euclid Consortium  acknowledges the  European Space Agency  and a number of agencies and institutes that have supported the development of Euclid, in particular the  Academy of Finland , the  Agenzia Spaziale Italiana , the  Belgian Science Policy , the  Canadian Euclid Consortium , the  French Centre National d'Etudes Spatiales , the  Deutsches Zentrum für Luft-und Raumfahrt , the  Danish Space Research Institute , the  Fundação para a Ciência e a Tecnologia , the  Ministerio de Ciencia e Innovación , the  National Aeronautics and Space Administration , the  National Astronomical Observatory of Japan , the  Netherlandse Onderzoekschool Voor Astronomie , the  Norwegian Space Agency , the  Romanian Space Agency , the  State Secretariat for Education, Research and Innovation (SERI)  at the  Swiss Space Office (SSO) , and the  United Kingdom Space Agency.
Computational Method and Details The energetic and structural characteristics of the short-period superlattices were investigated using the Quantum Espresso ab initio package  [25, 26]  employing exchange correlation energy functionals with local density approximation with modified pseudopotentials (MPP-LDA)  [27]. This method comprises the estimation of both the structural and electronic properties (energy gaps and densities of states-DOS) with good agreement to their experimental values. The calculations were made with the Broyden-Fletcher-Goldfarb-Shanno minimization algorithm  [28] , and cutoffs were set at E c = 140 Ry for the kinetic energy of plane waves and r c = 560 Ry for the charge density. The following convergence criteria were used: δE < 10 -10 Ry for energy, δF < 10 -10 Ry/Bohr for forces, and δσ < 10 -10 kbar for stress.
Aims. Models built to explain blazar flares display a broad variety of results. In this work, we study the flare profile induced by a moving and expanding blob, with a special emphasis on γ-γ pair production. Methods. We first developed a simple semi-analytical model to study the evolution of the particle distribution in the expanding blob and show the influence of the pair production. In a second step, we produced a realistic simulation using the OneHaLe code based upon the parameters of PKS 1510-089. Results. The applied semi-analytical model shows that the pair production significantly influences the flare evolution, while the opening angle and the expansion can prolong flares considerably. The simulation based on PKS 1510-089 indicates that flares of a moving, expanding blob result in strongly wavelength-dependent light curves that may include delayed, secondary flares.
A simple electron evolution model We derived simple semi-analytical models for the timedependent evolution of the electron distribution and while ignoring all energy dependencies. This is indeed a major simplification, however, it allows us to study three distinct cases with a linear cascade (at most). Using the time-dependent, one-zone radiation code OneHaLe, we reproduced two of the three cases well. We discuss the failure of the third case, as well as the influence of the choice of parameters on the solutions.
Simulations We use the OneHaLe code  (Zacharias 2021; Zacharias et al. 2022 , see the appendix for a brief description) to model the timedependent evolution of the blob. In order to comply with some of the assumptions above, we have to slightly tweak the code. The γ-γ pair production injection rate is given by  Aharonian et al. (1983) , Cerruti et al. (  2021 ) Q γγ (γ) = 2 3σ T c 32 ∞ γ d n ph ( ) 3 1 4γ( -γ) d˜ n ph (˜ ) ˜ 2 × 4 2 γ( -γ) ln 4γ˜ ( -γ) -8 ˜ + 2 2 (2 ˜ -1) γ( -γ) -1 - 1 ˜ 2 γ( -γ) 2        , (22) with the photon distribution, n ph , photon energies, and ˜ , normalized to the electron rest energy, and the electron Lorentz factor, γ. Equation (  22 ) requires 1/˜ , ensuring, together with ˜ < 1, an absorber with low photon energies for the γ rays. In order to suppress severe cooling (due to the extreme parameter settings; see Table  1 ), we switch off electron IC cooling.
Conclusions In this paper, we have discussed the particle evolution within a moving, expanding emission region within the jet of a blazar  (Boula & Mastichiadis 2022; Tramacere et al. 2022). With a simple semi-analytical model, we have shown that the opening angle has an important influence on the particle distribution and the electron-positron cascade. With an increasing opening angle the high-state (including the cascade) may last much longer owing to the increased escape timescale for both the particles and photons. With the help of the OneHaLe code  (Zacharias 2021; Zacharias et al. 2022) , we have shown that the simple model can be reproduced for linear cascades induced by external photon fields, while the linear cascade resulting from internal processes was not reproduced successfully.
Acknowledgements. I would like to thank  Andreas Zech ,  Markus Böttcher ,  Anton Dmytriiev , and  Stella Boula  for fruit-and beerful discussions on the content of the manuscript. I extend my gratitude to the anonymous referee for valuable comments that helped to improve the manuscript. M. Z. acknowledges postdoctoral financial support from  LUTH, Observatoire de Paris. Simulations for this work have been performed on the  TAU-cluster of the Centre for Space Research at North-West University, Potchesftroom, South Africa. The OneHaLe code is available upon request to the author.
ACKNOWLEDGMENTS S. D. C. and V. L. acknowledge support by the  ANR-18-CE30-0028-01  Grant  LABS , the  EverEvol CNRS MITI  project and an  IXXI  project. The research was supported by the  National Research Fund Luxembourg  (project  CORE ThermoComp C17/MS/11696700 ) and by the  European Research Council , project  NanoThermo  ( ERC-2015-CoG  Agreement No. 681456 ). S. D. C. is grateful to  Emanuele Penocchio  and  Gianmaria Falasco  for helpful discussions on chemical reaction networks. M. P. thanks  Daniele De Martino ,  Alexander Skupin , and  Susan Ghaderi  for useful insights and discussions on metabolic reconstruction issues. We warmly thank  Muhittin Mungan  for a critical read of a first version of this manuscript,  Delphine Ropers  for discussions and references on metabolic reconstruction, and  Pierre Recho  for stimulating discussions that inspired this work.
This systematic change is probably related to the surface adjustement after holography measurements at the end of 2021. The whole set of data has been recalibrated and co-added to produce the QUIJOTE line survey used for this work. The new line intensities, and hence the column densities, are ∼15% larger than those previously reported. The data for TMC-1 and IRC +10216 presented here correspond to 758 h and 696 h of on-source telescope time, respectively. All data were analysed using the GILDAS package 3.
Acknowledgements. We thank  ERC  for funding through grant  ERC-2013-Syg-610256-NANOCOSMOS. M. A. thanks  MICIU  for grant  RyC-2014-16277. We also thank  Ministerio de Ciencia e Innovación of Spain (MICIU)  for funding support through projects  PID2019-106110GB-I00 ,  PID2019-107115GB-C21/AEI/10. 13039/501100011033 , and  PID2019-106235GB-I00.
Principal Component Analysis PCA is a dimensionality reduction technique which converts an n-dimensional data cloud into orthogonal principal components (PCs) sorted from high to low percentages of the variance in the data set that can be summarized with only a few axes to aid in analysis  (Pearson, 1901). PCA was conducted on the passive spectral data from ChemCam using the scikit-learn library in Python  (Pedregosa et al. , 2012). To understand the influence of compositional features on the data set and subsequent major PCs, the PCs were plotted against each other, spectral parameters (as described in Section 3. 2), and LIBS elemental abundances. Correlations were reported using Spearman's rank correlation coefficient, which describes how well the relationship between two variables can be expressed as a monotonic function  (Spearman, 1904).
Sharp group rocks  (Jacob et al. , 2020; Wellington et al. , 2017). However, the 14 samples of Murray and Carolyn Shoemaker formation bedrock analyzed by CheMin that had corresponding Chem-Cam passive observations did show a correlation between the strength of these absorption features and total ferric phases in the sample (Figure  15 ), demonstrating that the abundance of ferric phases is an important driver in the presence and depth of these absorptions. The target with the highest abundance of ferric phases as measured by CheMin, Sebina, had a deep 535 nm absorption and strong NIR slope, while the target with the lowest abundance of ferric phases, Groken, had very little BD535 and flat NIR slopes (Figure  15 ). These findings are consistent with complementary Curiosity data sets. Spectral variations in Mastcam multispectral data are dominated by spectral differences that are attributed to hematite and other oxides  (Jacob et al.
g. ,  Dehouck et al. , 2022). Each scenario predicts a different regional distribution of Hutton interval type material with respect to the Siccar Point unconformity, which Curiosity will have a chance to test to if it revisits the unconformity at a higher elevation. If the Hutton interval is related to the diagenesis along the unconformity, rocks with similar compositional characteristics will reappear. If the interval is related to depositional environments, the rocks near the Siccar Point unconformity to the south may be different. The discovery that the Hutton interval unconformity has unique spectral properties will provide additional criteria to test these hypotheses from Curiosity remote sensing data (whose acquisition is less resource intensive than drill sample collection for CheMin).
Conclusions Passive relative reflectance spectra of bedrock targets taken by ChemCam in the Murray and Carolyn Shoemaker formations were analyzed using PCA, spectral parameters, and compared to their corresponding LIBS elemental abundances. PCA of ChemCam passive spectra proved useful in highlighting geochemically distinct regions of the traverse. The Blunts Point-Sutton Island transition, which contains magnesium sulfate rich targets and has been proposed as an area of low lake level by  Rapin et al. (2019) , was found to have zero 750-840 nm spectral slope (PC4) and weak 535 nm band depth (PC2), in agreement with Mastcam data from this interval  (Haber et al. , 2022). The Hutton interval (sol 2750-2800) was found to be spectrally distinct from the rest of the Mt. Sharp group, having strong 750-840 nm spectral slope slopes but little change in 535 nm band depth.
This indicates either a change in alteration conditions or enhanced diagenesis in this region. Comparisons between spectral parameters and LIBS data in Blunts point also suggest influences from calcium cements. The correlation coefficient between FeO T reported by LIBS and BD535 from passive spectra was investigated as a tool for understanding the changing oxidation conditions across Curiosity's traverse. Higher than average correlations between FeO T and BD535 were found in the Blunts Point-Sutton Island transition and Knockfarril Hill, regions known to be rich in MnO, but this signature of oxidizing conditions was not found to rise to significance on the scale of the traverse.
Acknowledgments The authors would like to thank  Deanne Rogers ,  Ed Cloutis , and  Suniti Karunatillake  for their helpful feedback on this manuscript. A portion of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). This work was funded by the  JPL   SURF Program , the  NASA   MSL Participating Scientist Program , and  NASA 's  Mars Exploration Program  support to the  Mars Science Laboratory Mission. JF acknowledges the support from the  Carlsberg Foundation. OG work on the ChemCam instrument supported by the  CNES.
Data Availability Statement The derived data used to generate figures featured in this article are available on Mendeley Data  (Manelski, 2023). The MSL ChemCam Passive Surface Spectra bundle  (Johnson, 2022)  was obtained from the Planetary Data System (PDS). CheMin and LIBS data were also obtained from the PDS  (Vaniman, 2022; Wiens, 2022). MSL Localizations for mapping were acquired on the PDS  (Deen, 2015).
The zero-power reactor MINERVE (CEA Cadarache) was designed to perform reactivity worth measurements by the oscillation technique. The various experimental programs, undertaken for the last thirty years, involved cylindrical samples with a diameter of about 1 cm and a height ranging from a few cm to 10 cm. Most of the samples are composed of UO 2 pellets mixed with a high neutron absorbing nuclide, i. e. fission product, actinide, in a double-sealed Zry-4 container. An experimental program started in 2015 in collaboration with the Joint Research Centre of Geel to study the MINERVE samples at the time-of-flight facility GELINA by the neutron transmission technique. The two main objectives consist of checking both the composition of the MINERVE samples provided by the manufacturer and the quality of the resonance parameters recommended in the evaluated neutron data library JEFF-3.
3. The pioneer experiments on MINERVE samples containing 107 Ag and 109 Ag revealed a substantial Tungsten contamination that was not reported by the manufacturer. Such a Tungsten contamination is related to the manufacturing process of the sample pellets. The observed Tungsten contaminations lead to non-negligible increases of the C/E ratios up to a few percent. second experimental campaign on MINERVE samples containing 99 Tc provided useful insight on the quality of the 99 Tc resonance parameters measured at the GELINA facility at the end of the 90s. The ongoing program continuing through 2022 will deliver data for samarium (natural, 147, 149, 152), neodymium (natural,143,145), gadolinium (natural, 155), europium (151, 153), rhodium (103), cesium (133), hafnium (180), dysprosium (160, 161, 162,  163, 164) and erbium (168, 170).