dataesr/scientific-paragraphs-categorization huggingface dataset:
qc_flag The overall success of a sounding is recorded as values of good, bad or ugly in the qc_flag parameter and is determined by the combination of success through the three QC tests, as shown in Table  5. Table  6  summarizes the statistics of the QC tests for HALO and P3. Although the process of classifying the sondes can be simplified by other combinations of the sat_test and low_test values, the method we present ensures no good sondes are omitted, and no bad sondes are admitted. The rest of the sondes, the ugly sondes, still have data that can be salvaged and, after some additional QC, can be combined with the other good sondes depending on the user's objective. JOANNE provides a status file per platform, which stores the results for each individual test and group of tests mentioned above, as well as the final qc_flag classification for each sounding.
sonde_id is a variable available in JOANNE products from Level 2 onwards. This is a unique, immutable identifier and is meant to identify exactly one dropsonde which corresponds to exactly one sounding profile. Note that the identifier variable sounding_id in the EUREC 4 A radiosonde dataset  (Stephan et al. , 2021)  identifies sounding trajectory and not instrument, since one instrument can have upward and downward trajectories. The JOANNE variable sonde_id functions solely as an identifier, and no information should be interpreted from the semantics of this variable. The Level-2 product consists of individual files for every sounding with the file structure as shown in Table  7. All files also include flight information such as position, height and speed as attributes. These are saved by the AVAPS aircraft computer in the sonde A files (see Table  4 ) and is input from the aircraft system itself.
Gridding The primary objective behind the Level-3 product is gridding all soundings on a common vertical grid, thus making it easier to use the soundings for different analyses. The vertical grid spacing for the dataset is kept at 10 m, up to an altitude of 10 km. In the case of a regular drop, i. e. , if there are no issues like a fast fall, or a failed parachute, the average descent rate of the dropsondes is ∼ 21 m s -1 at 12 km altitude and ∼ 11 m s -1 close to the surface. The PTU sensors have a measurement frequency of 2 Hz, while the GPS has a 4 Hz measurement frequency. This would translate to a vertical sampling of roughly 9-10 m at HALO's flight altitude and 5-6 m close to the surface for the PTU values and correspondingly finer vertical sampling for the GPS-based measurements. Hence the data are slightly coarsened, and only for PTU values in the upper to mid-troposphere do the interpolated values exceed the resolution of the measurements.
The gridding is carried out through the following steps. 1. Variables q (specific humidity), theta (potential temperature), u (eastward wind) and v (northward wind) are computed and added to the dataset (for details, see Sect. 5. 2. 2). 2. All variables along the height coordinate in the dataset are averaged on 10 m bins up to 10 km altitude. In cases where no data are available in the altitude bin, a linear interpolation from neighboring measurements along the height dimension is used to estimate the value in the altitude bin, with the restraint that the neighboring measurements are not further apart than 50 m. If data are not available within 50 m of the desired height level, values at that height level are assigned _FillValue. While this still allows for a few missing values (∼ 2-3 considering a fall speed of 15-20 m s -1 ), it does not lead to substantial artificial information created by the smoothened interpolation between points relatively farther away.
3. Pressure values are interpolated logarithmically, and these values replace the linearly interpolated pressure values. 4. Temperature (T ) and relative humidity (RH) are the originally measured properties by the dropsonde sensors. However, for interpolation q and θ are preferred, as these variables are conserved. After interpolation, T and RH are recomputed from the interpolated values of θ and q. The recomputed values for T and RH replace the previously interpolated T and RH variables from the sounding. 5. Wind speed and wind direction are computed from the interpolated values of u and v and added to the interpolated dataset.
Level 4 (circle products) As discussed in Sect. 2. 2, the estimation of area-averaged mesoscale properties, such as divergence, was the primary objective behind the sondes' deployment over circular patterns. The Level-4 product provides these circle products as gradient terms estimated by regressing the parameters at each level for a set of sondes comprising a circle. Level 4 also includes terms of divergence, vorticity, vertical velocity and pressure velocity, which are subsequently computed from the gradient terms. The input data are from the gridded dataset in Level 3.
Identifying circles and corresponding sondes To aid in processing EUREC 4 A data from aircraft, flight tracks for HALO and P3 were "segmented" into different standard categories such as circles and cloud modules. The flight phase segmentation (FPS) is described in more detail in  Konow et al. (2021). We use these FPS files to identify the circles and the dropsondes corresponding to these circle segments. To facilitate ease of working with JOANNE and the FPS files, the circle segments in JOANNE Level 4 have been tagged with the same segment IDs as those in the FPS files. Moreover, the FPS files include a list of dropsondes associated with every flight segment, and this list is comprised of sonde IDs that are the same as that in the JOANNE Level-3 gridded product.
1 x k y k     , x =   φ o ∂ x φ ∂ y φ   and b =     φ 1 φ 2. φ k     , we solve for x and compute the regression estimates as where A + is the Moore-Penrose pseudo-inverse. This pseudo-inverse is obtained from the components of singular value decomposition (SVD) of A. If the SVD of A is written as U • • V T , then A + is estimated from the inverse of the SVD components as V • + • U T. Here, U and V are unitary matrices, is a rectangular diagonal matrix with A's singular values and + is a rectangular diagonal matrix with the reciprocal of A's singular values. We use the linalg. pinv function from the numpy Python library (v1. 18. 3) to calculate A +. x = A + b, (4) As a sanity check, we tested the Moore-Penrose pseudoinverse method of least-squares fitting against the ordinary least-squares fitting by  Bony and Stevens (2019) , and we found no difference between the solutions (not shown).
The advantage with incorporating SVD in the regression is that it significantly reduces computing time, because of the availability of vectorized functions in the numpy library. The Level-4 product includes the eastward (zonal) and northward (meridional) gradients of temperature, pressure, specific humidity, and u and v winds. Derived from these, Level 4 also provides area-averaged mesoscale divergence (D), vorticity (ζ ), vertical velocity (W ) and pressure velocity (ω), following  Bony and Stevens (2019). The dataset also provides the standard error of each of these regressed estimates as ancillaries to the corresponding variables, thus establishing an extent of confidence in the calculation of these mesoscale properties. Derived variables in Level 4 are at the same vertical grid of 10 m spacing as in Level 3, and the number of sondes regressed at every level is provided as a variable (sondes_regressed).
Summary The EUREC 4 A field campaign took place in January-February 2020 over the North Atlantic trade-wind region. The campaign employed a multitude of platforms measuring a range of atmospheric and oceanographic variables with the objective of understanding shallow clouds and processes that influence them. A core part of the campaign was the deployment of dropsondes to characterize the thermodynamic and dynamic structure of the atmospheric environment. Here, we present JOANNE, the dataset that provides these dropsonde data and additional derived products. JOANNE presents measurements from 1215 dropsondes launched during EUREC 4 A by the German research aircraft HALO and the NOAA WP-3D. Dropsondes were primarily released in groups of 12, circumscribing a mesoscale ∼ 222 km diameter circle centered near 13.
3 • N, 57. 7 • W, which we call the EUREC 4 A-circle. A total of 85 circle patterns were flown with dropsonde launches, 73 being flown by HALO over the EUREC 4 A-circle along patterns that were not biased toward particular meteorological conditions. In addition, sondes were launched on circular flight patterns centered elsewhere, along lawn-mower flight patterns coinciding with AXBT drops and in a variety of other locations to provide context or calibration for other measurements. Data presented in JOANNE have been quality controlled to eliminate sondes with no, or partially corrupted, data. A total of 51 of the 1215 sondes did not provide usable data, and another 98 provided only partial data and are not included in data products from Level 2 onwards. A comparison of the HALO dropsondes with radiosondes intensively launched from the R/V Meteor close to the western (upwind) edge of the EUREC 4 A-circle and with radiosondes launched from the downwind Barbados Cloud Observatory suggests a dry bias.
Multiplying relative humidity values by 1. 06 appears to largely correct the bias and therefore has been applied from Level 2 onwards to relative humidity and variables derived from it. We found no evidence of such a bias in the P3 sondes, and the reason for the dry bias in HALO seems attributable to a lack of reconditioning of the HALO sondes. JOANNE is divided into five levels of data products, with increasing order of processing and product retrieval. Level 0 comprises the raw measurement data from the dropsondes collected by AVAPS on the aircraft. Level 1 provides data processed using ASPEN -a state-of-the-art tool for processing raw dropsonde data files. Level 2 consists of individual sounding files that passed through the QC check, but with redundant quantities removed and no derived variables added.
5. 3. 2). JOANNE's immediate usefulness lies in aiding the calibration of or processing the data from remote-sensing instruments on board HALO as well as creation of derived products, e. g. , a dataset of radiative profiles from EUREC 4 A soundings  (Albright et al. , 2021). Furthermore, the dataset potentially has applications in furthering the understanding of processes in the trades, e. g. , the influence of mesoscale circulation on clouds  (George et al. , 2021a)  or the changes in atmospheric properties within a cold pool  (Touzé-Peiffer et al. , 2021). The vertical profiles and histogram of flight altitude for dropsonde launches shown in Fig. 6  provide an overview for a subset of the atmospheric observations that JOANNE provides. While reaffirming the typical steadiness in the thermodynamic structure of the trades, JOANNE also confirms the high variability in mesoscale vertical motion found by  Bony and Stevens (2019)  compared to the mean over longer timescales.
Acknowledgements. A great number of people contributed to the launching of the sondes, as recognized in the EUREC 4 A overview paper. In particular, the authors thank Friedhelm Jansen for his organization and provision of the sondes, Mario Mech and Lutz Hirsch for logistical and technical support, and Angela Gruber for administrative support. Aboard HALO, in addition to the authors, Felix Ament, Jude Charles, Tim Cronin, André Ehrlich, Kerry Emanuel, Florian Ewald, David Farrell, Marvin Forde, Silke Groß, Martin Hagen, Marek Jacob, Theresa Mieslinger, Ann Kristin Naumann, Theresa Lang, Veronica Pörtge, Sabrina Schnitt, Eleni Tetoni, Ludovic Touze-Peiffer, Jessica Vial, Raphaela Vogel, Antone Wiltshire, Allison Wing and Kevin Wolf contributed to the launching of sondes. Akshar Patel launched the sondes from the P3.
The authors are also indebted to the ground and flight crews of both aircraft as well as the civil aviation facility and air traffic control for their efforts to facilitate the measurements. Special thanks to Holger Vömel for advice on the possible cause behind the HALO dry bias. GG thanks Anna Lea Albright, Florent Beucher, Xuanyu Chen, Thibaut Dauhut, Geiske de Groot and Louise Nuijens in addition to some names already mentioned for feedback on early versions of the dataset and Ann Kristin Naumann for her comments on the manuscript. Financial support. This research was made possible through generous public support provided to, and managed by, the Max Planck Society (DE), DFG HALO SPP 1294 (DE), CNRS (FR) and NOAA (USA). The project also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (EUREC4A advanced grant no.
If no integral remains, all the computed values Y i 's are returned. As illustrated in Example 4. 1 below, this allows to select a specific linear extension in the initial partial ordering. The justification of the algorithm is given by the following theorem. ( v) The Python/SageMath implementation of the random sampler is available at the following location: https://gitlab. com/ ParComb/combinatorics-barrier-synchro/blob/master/code/RandLinExtSage. py Theorem 4. 1. Algorithm 1 uniformly samples a point of the order polytope with a Opnq complexity in the number of integrations. Proof: The problem is reduced to the uniform random sampling of a point p in the order polytope P. This is a classical problem about marginal densities that can be solved by slicing the polytope and evaluating incrementally the n continuous random variables associated to the coordinates of p.
The Multi-BSP model defines a tree of nested computational components: the leaves are the processors and the inner vertices are computers and more. For example, a height 4 tree would be a data center (the root of the tree), composed of server racks (depth 1), each composed of servers (depth 2) with several multi-core processors (depth 3). Then the Multi-BSP model sets that each vertex obey to the original BSP model. The BSP model states that processing units computations are divided in superstep composed of (asynchronous) computations, communications requests (between processing units) and ending by a barrier synchronization during which the communications are processed. So supersteps at depth i correspond to fork-join processes where i barriers names are visible, put another way it corresponds to sub-DAGs of depth i from the root.
We implemented all the algorithms in Python 3, and we did not optimize for efficiency, hence the numbers we obtain only give a rough idea of their performances. For the sake of reproducibility, the whole experimental setting is available in the companion repository, with explanations about the required dependencies and usage. The computer we used to perform the benchmark is a standard laptop PC with an I7-8550U CPU, 8Gb RAM running Manjaro Linux. As an initial experiment, the example of Fig. 2  is BIT-decomposable, so we can apply the BIT and CFTP algorithms. The counting (of its 1975974 possible executions) takes about 0. 3s and it takes about 9 milliseconds to uniformly generate an execution with the BIT sampler, and about 0. 2s with CFTP. For "small" state spaces, we observe that BIT is always faster than CFTP.
We give two illustrations in the paper with the fork-join processes and promises. This is complementary to definitions wrt. some combinatorial properties, such as the "BITdecomposable" sub-classes. The class of arch-processes (which we study in  [10]  and the promise processes introduced here) is also interesting: it is a combinatorially-defined sub-class of the inductivelydefined asynchronous processes with promises. We see as quite enlightening the meeting of these two (ix) Arbogen is uniform random generation for context-free grammar structures: cf. https://github. com/fredokun/ arbogen. Proof: This is trivial given the similarity of the definitions of ctg and ectg. As long as only the atomic actions (and not the barrier events) are considered, the definition generate exactly the same depedencies, although it might be the case that many barrier events must be traversed from α in order to reach β.
00013 for control vs grade I) or between grade I-grade II discrimination (student test: p value = 0. 0077 for grade I vs grade II). The increase of fluorescence lifetime can be correlated with an increase of NADH concentration in tumor tissues, confirmed through 810 nm spectral analysis, and considered as a reliable discrimination threshold criterion in a diagnosis rapid test. So a high NADH level with a lifetime value lower than 1. 9 ns leads to a grade II meningioma diagnosis; a low level of NADH and a lifetime value higher than 2. 1 ns leads to a non-tumoral control brain tissue obtained in epilepsy surgery patients zone diagnosed while if these two criteria were in between, a grade I could be diagnosed. Phasor FLIM technique was also used to analyze FLIM data acquired at 810 nm to study FAD and its two components, free and protein-bound, lifetime values in the NIR domain where lifetime values varies in each type as well the phasor cloud shape.
Conclusion We presented in this study an advanced multimodal approaches to analyze and track the molecular variation between control, grade I and grade II meningioma tumor. We can conclude at the end this work, that the tracking of several molecules individually (collagen, tryptophan, Lipopigments, NADH, FAD) and their analysis through multiscale and multimodal analysis could lead to better extraction of discriminative parameters tissue differeciantion. The combination of these parameters with qualitative analysis allows reaching our to increase the reliability of the future tissue differenciation based on this database and to render our tissue database more robust. The molecular changes and the significant discrimination quantitative factors between control and meningioma tumor tissues that were extracted in this study could be enumerated as: (1) increase of collagen fluorescence signal with the tissue grade: a tissue with a collagen contribution (Coll-Int) lower than 28 is considered as control, while a tissue with a Coll-Int higher than 28 is considered as tumor.
However, the obtained results should be strengthened with more statistics by increasing the samples number. In addition, while control samples group of this study consisted of brain parenchyma tissues extracted through epileptic surgery, a second control group should be added in the future that consist of healthy dural matter since that residual meningioma tissues are usually found in the surrounding meninges. The clinical relevance of such multimodal optical analysis, which can be easily applied to a neurosurgical purpose was confirmed. Each specific tissue signature, established in our database, will be implemented in the under-development endomicroscope to give it the ability to establish a reliable real-time diagnosis response. More technical detailed information (pulse duration, pulse compression system, endoscopic fiber) about this endomicroscope characteristics are already published in previous works of our team  38, 39.
The experimental setup consists of a full-field microscope (Zeiss Axio-observer Z-1) to acquire images and a micro-spectrofluorimeter (Olympus IX71) to perform spectral measurements. For each sample, a large mosaic was selected to perform Tryptophan + Collagen full field imaging acquisitions. Two band-pass filters, (1) 323-357 nm and (2) 408-438 nm (Semrock, USA) were serially positioned in front of a CCD camera (Pixis BUV, Princeton Instrument, USA) to select respectively the fluorescence signal of tryptophan and collagen. The obtained full field images were reconstructed and merged through an open source-image-processing software "FIJI". Afterwards we used Masson's trichrome (MT3) model to generate virtual stained images from the merged Tryptophan + collagen images. This model consists of a deep-learning-based virtual histology staining method of label-free human tissue samples.
The network models for Masson's trichrome (MT3) stain alongside sample test-image data are available through a Fiji-based plugin 30. This plugin is publicly available and can be downloaded through DeepImageJ website in bundled models section: https ://deepi magej. githu b. io/deepi magej /model s. html. For spectral measurements, several Regions Of Interest (ROI) in the acquired mosaic were then selected to perform spectral measurements through the micro-spectrofluorimeter. Each ROI have a size of 160 × 160 µm with 4 µ as pixel size. From each pixel, the collected spectrum is the sum of the fluorescence emission of four endogenous fluorophores: Tyrosine, tryptophan, collagen and NADH. Multimodal NIR excitation setup. This multimodal setup is able to perform four different optical imaging modalities: (1) TPF imaging, (2) two-photon spectral imaging, (3) SHG imaging and (4) two-photon Fluorescence Lifetime Imaging (FLIM) measurements.
It consists a TCS SP8 MP confocal two-photon microscope (Leica Microsystems, Wetzlar, Germany) coupled with a FLIM module from PicoQuant (GmbH, Berlin, Germany) which uses Time Correlated Single Photon Counting (TCSPC) technique to carry out FLIM acquisition through its dedicated software (Symphotine, Picoquant, Germany). More details of this microscope are published elsewhere  11, 26, 39. The collected fluorescence signal is collected into two detection channels where in the first one (channel 1) a 448 nm centered band pass filter (FF01-448/20-25, Semrock, USA) was introduced, while in the second one a 520 nm centered band pass filter (FF01-520/35-25) was introduced. The TPF + SHG images acquisition were performed using 890 nm as excitation wavelength. Using this wavelength, the channel 1 allows to select the SHG emission signal while the channel 2 allows to select the FAD emission signal.
These images were acquired through the microscope dedicated Leica software (LAS-X) anf then reconstructed through the open source-image-processing software "FIJI". FLIM images acquisition were performed using 810 nm as excitation wavelength. Using this wavelength, the channel 1 allows to select the NADH emission signal instead of SHG, while the channel 2 selects always the FAD emission signal. For spectral imaging acquisition, and instead of using channel 1 and channel 2, an internal hybrid detector was used. It covers a detection range from 380 to 790 nm with 10 nm as chosen spectral resolution. Data analysis. DUV spectral analysis. DUV Spectral data processing was conducted using a home-developed software in DISCO beamline at Soleil synchrotron, based on matlab data processing codes used in several previous works  28.
NIR Spectral images were extracted first using the image processing software "FIJI" and then processed using a homemade Matlab interface for molecular ratios extraction and for phasor plot analysis. For spectral phasor analysis, the fluorescence spectrum of each pixel in each spectral image was recovered. Two coordinates can be extracted through the Fourier transform of each spectrum: the real and imaginary parts. These two coordinates were used to plot the corresponding "phasor" (or count) of the pixel in a histogram plot known as the phasor plot. The grid of the phasor plot covers the maximum emission peaks of the spectrum from the spectral range of 380-790 nm by 10 nm steps, and the spectral width varies from 20 to 100 nm by steps of 20 nm each. Therefore, one total histogram can be obtained for each sample, and afterwards all the histograms belonging to the samples of the same type were added together in order to plot global type histogram.