dataesr/scientific-paragraphs-categorization huggingface dataset:
Algorithm 1 : 1 Generating output streams from input streams Data: Input offers store  -, Previous output offers  + , Similarity function  , Minimum similarity for offer creation   , Limit of combination exploration   , and Limit of offer creation. Result: Updated output offers  +. ← ∅ // Find producer IDs in incomplete single offers from neighbors and their requirements 4 𝑝 ∈ 𝐴𝑆, 𝑟 𝑝 = 𝑟 // Store individual requirements 5 𝑛𝑂 𝑐𝑜𝑢𝑛𝑡𝑒𝑟 ← 0 ∧ 𝑛𝐶 𝑐𝑜𝑢𝑛𝑡𝑒𝑟 ← 0 // Initialize counters of offer creation and combination exploration 6 foreach (𝐴𝑆, 𝑟) ∈ 𝑂 + do // Begin expanding offers 7 𝐴𝑆  *  ← 𝐴𝑆 // Copy starting set of producers to expand on 8 foreach 𝑝 ∈ 𝑖𝐴𝑆 in increasing order of requirements do 9 if |𝐴𝑆  * | + 1 ≥ 𝑟 𝑝 then // If set is large enough for next producer ID, 10 if 𝑝 ∉ 𝐴𝑆  *  ∧ 𝛥𝑓 (𝐴𝑆  *  , {𝑝}) ≥ 𝛥 𝑚𝑖𝑛 then // datasets are disjoints, and similar enough 12 else 13 break // Stop adding producers if set has not grown enough 14 if 𝐴𝑆  18 if 𝑛𝑂 𝑐𝑜𝑢𝑛𝑡𝑒𝑟 > 𝑛𝑂 𝑚𝑎𝑥 ∨ 𝑛𝐶 𝑐𝑜𝑢𝑛𝑡𝑒𝑟 > 𝑛𝐶 𝑚𝑎𝑥 then // Stop iteration if any limit is reached 19 break 20 return {(𝐴𝑆, 𝑟) ∈ 𝑂 + | ≤ ∨ is local data} // Only announce complete offers among producers in 𝐴𝑆 are above a certain minimum threshold value, 𝛥 1 2 foreach (, ) ∈  -|  > 1 ∧ || = 1 do 3  ←  ∪  11  * ←  * ∪ {} * ≠  then 15  + ←  + ∪ {( * , | * |)} // Newoffer from extended set of producers 16   ←   + 1 17   ←   + 1  , i.
Sun et al. (2019b)  also considered LOS/NLOS signals and the multipath. This is conducted by using C/N0, satellite elevation, and PR, while the ML technique used is the DT. Furthermore,  Tomohiro and Nobuaki (2022)  used the SVM ML technique with unique features calculated by using receiver-independent exchange format-based information and pseudorange residual checks. One of the main restrictions of  Yozevitch et al (2016) ,  Xu et al (2020), and Sun et al (2019b)  is that the authors assume that low-elevation satellites always imply NLOS signals. Although lowelevation satellites are more likely to cause NLOS signals, this is not always the case. Moreover, the decision of an algorithm using the low-elevation satellite/NLOS signal feature is going to be restricted and conditioned by the elevation of the obstacles present in the training dataset.
(2016) , is that C/N0 is strongly dependent on the installation used for data recording. In other words, the results are degraded when the installation used for training differs from the later installation. Lastly, a shortcoming of the state-of-the-art approach is the use of single-frequency features. Exploitation of multiple frequencies can enhance the NLOS detection capability. One of the reasons that the literature is limited to single frequency may be the difficulty to handle the intermittent presence of secondary frequencies like L2 or L5/E5 in the training and validation process. As the choice of the relevant features, the choice of the ML algorithm is an important component of the proposals. Xu et al. (2018)  compared the SVM method with other ML methods such as k-nearest neighbors (KNN), neural network (NN), and DT and concluded that SVM offers the best performance with commercial GNSS and decent generalization ability.
Current limitations If ML and deep learning are promising tools to model complex phenomena as the GNSS local effects in urban areas, the state-of-the-art algorithm is quite recent and still needs to be completed with deeper analyses and better accuracy of the models. With respect to the selection of features for the ML/AI algorithms, we see the possible limitations and challenges of the state-of-the-art algorithms: • Training dataset availability: The limit of supervised learning algorithms is the availability of labeled datasets for training or their building. Indeed, the knowledge with certainty of the satellite state of reception LOS or NLOS still remains an issue  (Xia et al. , 2020) , in particular in kinematic measurement campaigns. In the literature, some datasets are labeled, thanks to 3D models and comparison of the satellite positions    2016 ), the unsupervised EM method shows much larger NLOS error rates than the supervised DT.
• Generalization ability is another challenge. Indeed, as the NLOS effects are directly dependent on the close surroundings of the receiver, one needs to ensure that the training dataset can represent the different cases and configurations that will be encountered while using the model  (Xia et al. , 2020). Independently of the feature choice, some other issues need to be considered in the following. A rigorous analysis of the performance of such algorithms is still difficult for LOS/NLOS detection because of the difficulty to have a ground truth, and for the deep learning algorithms, it is difficult because of hidden layers. Lastly, in all these satellite states of reception prediction or measurement predictions, the transitions over time are neglected and do not consider the filtering techniques used by the receiver that will introduce latency on the GNSS feature variation.
5 can be used to determine NLOS reception and Pr(x) ≥ 0. 5 for the LOS reception. The training of the algorithm is usually conducted by fitting the parameters β with the following mean square error cost function: J (β) = 1 M M ∑ i=1 (Pr (x i ; β) -y i ) 2 , ( 7 ) where M is the size of the training dataset and y i is the label of the data. The typical approach to tackle this optimization problem is to use a gradient descent algorithm, as in  Mitchell (1997). In challenging GNSS scenarios where the reception of NLOS signals is expected, the tracking of the GNSS signals is stressed by multiple possible effects like multipath and intermittent shadowing. In this situation, it is common for the receiver to only be able to provide measurements in some of the received signal frequencies. It is found in general that the tracking of L1/E1 signals (and therefore the computation of code and carrierphase measurements) is normally the most available, while the tracking of L2 and L5/E5 suffers for intermittent availability in urban scenarios.
In particular, we consider, in total, five different models for the GPS and Galileo constellation with the following number of features: • Model A: x GPS,A = [C/N0 L1 , C/N0 L2 , C/N0 L5 , PRC L1 , T ′ lock,L1 ]. • Model B: x GPS,B = [C/N0 L1 , C/N0 L2 , PRC L1 , T ′ lock,L1 ]. • Model C: x GPS,C = [C/N0 L1 , C/N0 L5 , PRC L1 , T ′ lock,L1 ]. • Model D: x GAL,D = [C/N0 E1 , C/N0 E5a , C/N0 E5b , PRC E1 , T ′ lock,E1 ]. • Model E: x GAL,E = [C/N0 E1 , PRC E1 , T ′ lock,E1 ]. For the validation (and real application) of the proposed approach, only one model per constellation is used at a time. This would be the one that uses features from more frequencies depending on the available data provided by the GNSS receiver in a given epoch.
Measurement setup and campaign The training and validation of the algorithm in this work was performed with measurements collected with a DLR-designed antenna and Septentrio mosaic-X5 receiver. The antenna was placed on a tripod, while the receiver and power source were placed in a plastic box to be protected from weather conditions, as shown in Figure  3. The measurement campaign took place for several days and in a total of three measuring locations. In order to ensure a full repeating orbit cycle (of at least the GPS constellation), more than 24 consecutive hours were recorded at every location. The sampling rate was set to 10 Hz. All locations used were within the premises of the DLR facility, as shown in Figure  4. The first dataset was collected in an open-sky environment to support the derivation of the nominal reference C/N0 models (on Figure  4  labeled as "Open sky location").
The location was chosen for its good visibility of satellites for all elevation and azimuth calculations. Some pre-screening has been applied as the data preparation pre-processing step to avoid low-elevation effects. The two other datasets were collected under locations chosen because of the challenging scenarios with surrounding buildings that blocked parts of the sky visibility (on Figure  4  labeled as "Training location" and "Validation location"). The panoramic view from the standpoint of the receiver for both the training and validation locations is shown in Figure  5. The following Table  2  gives an overview of the used datasets. The locations are used to collect them, the usage, and their total length.
Results This section provides information about the labeling and normalization data results and the performance of the NLOS   detector in comparison with other state-of-the-art algorithms. The detector is ultimately a classification algorithm whose performance is evaluated by means of a confusion matrix where the accuracy of true(t)-false(f) and positive(p)-negative(n) classifications is computed normalized with respect to the label category. Note that in terms of the confusion matrix, a positive classification is defined as the classification of a signal as LOS. The overall accuracy is defined as Accuracy = tp + tn tp + fp + tn + fn. ( 8 ) 6. 1 Pre-normalization evaluation    In order to highlight the importance of normalization, we compare the detector with and without normalization in an opensky scenario where the LOS probability should be always p LOS > 0.
5. Table  3  exhibits the confusion matrix for GPS in this scenario whereas Figure  9  shows its respective skyplot. As expected, the not normalized version considers NLOS satellites as those with low elevation. This is caused by the reduction of C/N0 just because of the elevation. As shown numerically in Table  3 , the normalized branched LR clearly outperforms the not normalized LR. The difference in terms of accuracy could potentially be even higher, but in practice, it is difficult that the scenario for recording the data is perfectly open sky.
Training and validation evaluations In order to make a comparison between the state-of-theart algorithm and the design presented in this work, we have trained a detector according to the algorithms and features that can be found in the literature. In particular, the most used ML algorithms in the state-of-the-art algorithm are the DT and the  linear SVM, as shown in Table  1 , and they are, here, used as a reference for comparison. These algorithms do not consider either the normalization step or the use of measurements from multiple frequencies. Therefore, an adapted model and feature vector have been used for these algorithms. Details about the design of the chosen state-of-the-art algorithms can be found in Supplementary Appendix A. Table  4  shows the performance of the DT and SVM algorithms over the training dataset for GPS and Galileo, respectively.
The DT is slightly better than the SVM in terms of accuracy. However, the SVM has a more balanced distribution of error. This is more noticeable for the case of GPS. Table  5  presents the confusion matrices for GPS and Galileo for the proposed algorithm in this work for the training and validation datasets. The results show that the inclusion of C/N0 from additional bands clearly outperforms the state-of-the-art algorithm results in Table  4. Figure  10  provides further insight about the performance of the LOS probability determination by showing the skyplot results in the training scenario. Figure  11  illustrates the skyplot results of the LOS probability determination of the predictions for GPS and Galileo over the validation dataset. The algorithm outperforms in the state-of-the-art algorithm.
The accuracy obtained over the validation scenario is lower than the scenario used to train the model. Although this is typically the normal situation with ML algorithms, it could also be caused by additional factors such as a higher error labeling, the data due to the difficulties for modeling, and the validation scenarios. Despite the efforts made in the location choice, some complex elements to model such as trees remained were not taken into account for the horizon determination. Finally, we show the classification results over two C/N0 features of interest to get an insight about the regions in which the classification considers LOS or NLOS. We consider Eq. 6 with the threshold probability of the classification on the left-hand side of p LOS = 0. 5. Under that restriction, we solve Eq.
6 leading β T x = 0. (9)   Eq. 9 represents a hyper-surface with respect to the input features (over training dataset) given in the model parameters β. We fix all the input features to the mean values from the datasets except for the two features of interest. Figure  12  depicts the point cloud of two C/N0 features along with the detection regions separated by a black line of probability of 0. 5. In particular, Figure  12A  shows the features of the branch tracking only L1 and L2 (Model B), while Figure  12B  considers the case of only L1 and L5 (Model C). The figure shows how, despite some outliers, most of the points are correctly split into two regions. Moreover, observing the slant of the p LOS = 0. 5 line, it is appreciated that all C/N0 values are useful for differentiating between LOS and NLOS signals.
B Decision Trees The decision tree is a supervised algorithm for classification or regression. It is widely used because the final flowchart of the trained model can be visually analyzed. This allows extracting insights about the behavior of the algorithm. As its name indicates, the structure of this algorithm is a tree where each leaf node represents a condition or decision rule. This decision rule is generally a threshold for a feature fitted as a function of the dataset. The more depth the tree has, the better the tree will be fitted. However, a very deep tree could cause overfitting to the training dataset losing generalization and, therefore, accuracy in the validation dataset. The main advantages of decision trees are not only their visual representation but also their robustness against outliers.
Several reports have provided transcriptomic information on the global organ response  (11) (12) (13) , but so far not at the cell type level. The single-cell RNA-sequencing (scRNA-seq) technique allows to simultaneously evaluate the gene response of multiple cell types in a tissue. Therefore its implementation for EVLP would both advance our knowledge on the biological effects of EVLP and provide an investigating platform to test therapeutic strategies aiming at reducing inflammation and potentially reconditioning initially damaged organs. In this study, we generated scRNA-seq datasets using two human lungs undergoing prolonged EVLP up to 10 h. Despite the scarcity of donor lungs, these lungs, although well-functional, were declined for transplantation due to lack of recipient of compatible ABO blood group or due to age, therefore such biological material is rare for its availability to research.
Results Identification of lung cell types from scRNA-seq data that display important transcriptomic changes upon EVLP at 4 h and 10 h In order to implement a scRNA-seq workflow for analyzing human lungs undergoing acellular EVLP, we collected lung tissue samples from two donor lungs declined for transplantation. Donor 1's lung was declined due to absence of suitable recipient of the ABO blood group, and donor 2's lung was declined due to age. These lungs did not show initial macroscopic inflammatory lesions nor any signs of injury, their respiratory functions were both very good during the whole duration of EVLP (> 400 mmHg) and of a "transplantable" quality on that basis, as shown in Additional file 1. The samples were collected at 0 h (just before EVLP initiation, i. e. "untreated"), and after 4 h and 10 h EVLP from the two donors, with care to take similar upper lung zones.
The scRNA-seq (6 in total) were conducted on 2x10 4 loaded cells from each sample using 10X Genomics 3'end RNA-seq V3 chemistry. The work flow of the analysis of the scRNA-seq data sets is presented in Additional file 2 and extensively detailed in the Methods. High-quality transcriptomes from 43,569 cells were generated upon removal of mitochondrial genes and cell doublets (Additional files 3, 4). The clustering of the scRNA-seq data of each donor in "time-combined UMAPs" showed that EVLP induced major transcriptomic changes (Additional file 5). In order to identify clusters corresponding to the same cell types across donors and timings, we integrated our six samples using a batch correction algorithm, generating an initial "integrated UMAP" with 17 clusters that are populated by cells from both donors at all timings (Additional file 6).
In order to determine the cell identity, we applied Azimuth to map our scRNAseq data onto the Human Lung Cell Atlas (Human lung reference v2). Upon filtration of the data based on Azimuth scores and exclusion of minor identities (see Additional files 2, 6), we generated an "integrated UMAP-filtered" (Figure  1A ) encompassing 23 cell identities based on cluster (Cx) belonging and Azimuth annotation, i. e. C0-alveolar macrophages (AMs), C1monocyte-derived macrophage (MoMacs), C2-alveolar type 2 Single cell RNA-seq analysis of lung samples at 3 EVLP timing (0, 4, 10 h) and definition of cell identities. (A) The scRNA-seq data of the 6 samples (2 donors, 3 timings) were processed for high quality transcriptomes, integrated, clustered and submitted to cell annotation analysis with the Azimuth package (see Additional file 2 and Methods).
e. epithelial (AT1s, AT2s, t-Club-AT2s, ciliated), endothelial (blood and lymph ECs), myeloid (AMs, MoMacs, cMos, ncMos, mast cells, cDC2s, pDCs), and lymphoid cell type families (CD4 + and CD8 + T cells, NK cells, B cells). The gene expression across cell type identities and clusters can be visualized with an interactive viewer (https:// applisweb. vim. inrae. fr/Human_EVLP). For each of the 19 cell identities obtained by our strategy based on Azimuth scoring and exclusion of minor identities and ambiguous clusters, we extracted differentially expressed genes (DEGs, adjusted p-value < 0. 05, absolute fold change > 0. 4 (Log2), Additional file 10) for each donor between 0 versus 4 h and 0 versus 10 h, for downstream functional enrichment analyses. DEGs were processed through Ingenuity Pathways Analysis (IPA), which predicts modulation of functions and pathways by integrating the orientation of gene expression modulation and a scientific literature-based curated database.
We selected the meaningful pathways and functions that were predicted to be modulated similarly in both donors at least at one time point, and we systematically added the IPA functions designated as Inflammatory Responses, Inflammation of Lung, Apoptosis and Cell Viability. No consistent pathways/functions were found for stromal cells, t-Club-AT2s, ciliated cells, B-cells and pDCs, that were thus not considered for further analyses. In order to confirm and possibly complete the IPA analysis, we performed a highthroughput Gene Set Enrichment Analysis (GSEA) using BubbleMap applied to the Hallmark collection from the Molecular Signatures Database (MsigDB). GSEA performs statistical enrichment analyses using genesets mapped onto the gene expression comparison between two timings, and hence is not restricted to the DEGs; in addition, IPA and GSEA use different knowledge-based annotations for a given function/pathway, leading to often similar but sometimes different results between the two approaches (see Discussion).
Genes associated to endothelial cell activation (19), such as ICAM1 and VCAM1 were upregulated and their protein shedding also increased with time in the perfusion liquid (Figure  3B ). The Endothelin-1 and Signaling Apelin pathways, that are implicated in the control of vascular permeability  (20) , were also predicted to be modulated (Figure  3A ). The GSEA analysis (Figure  3C ) confirms the enrichment of TNFa Signaling via NFkB and Inflammatory Responses in blood ECs and also in lymph ECs, although no clear prediction could be inferred from IPA in the latter cell type. As in the case of epithelial cells, no consistent conclusion from IPA and GSEA could be deduced regarding Apoptosis. These data indicate that the gene expression profiles of blood ECs upon EVLP are associated with activation of inflammatory response and of Endothelin/Apelin pathways.
Relative contribution of cell types to the cytokine and chemokine transcript synthesis during EVLP The inflammatory response revealed to be a dominant function induced upon EVLP in several cell types. This activated function was reflected by the production of cytokines and chemokines in the perfusion liquid (IL-1b, CXCL8, TNFa, IL-6), that accumulated B A FIGURE 4 Computational inference of the changes induced by EVLP in lymphoid cells. (A) The differentially expressed genes (DEGs, adjusted p-values p < 0. 05, Log2FC > 0. 4) in the C7-CD4 + T cells, CD8 + T cells and NK cells between 4 h and 10 h vs 0 h were submitted to Ingenuity Pathways Analysis. The predicted pathways and functions are illustrated as in Figure  2A. Functions/pathways in bold fonts are mentioned in the main text. The modulated expressions of the contributing genes to the functions/pathways marked with a star (*) are provided in Additional file 14.
This latter result may explain the reduction of passenger leukocyte transfer encountered after transplantation of lungs treated with EVLP  (23). As rapid transfer of passenger leukocytes is known to promote acute cellular rejection  (23) , this reduction of migration, together with the global downmodulation of activation in cDC2s and lymphoid cells, might stand as a positive effect of EVLP. Indeed several papers suggest that LT with EVLP leads to better clinical outcomes, although this statement needs to be confirmed with additional clinical studies  (24) (25) (26). In most instances, the IPA and GSEA approaches gave consistent results except in the case of Apoptosis, a form of noninflammatory and controlled cell death. The discrepancy can be explained by i) the differences (in terms of numbers and identities) of the Apoptosis genes found in the IPA-knowledge database (4597 genes) and in MSigDB (161 genes) and ii) the difference of methodologies that involves DEGs in IPA and ranked gene expression in GSEA (see Additional file 16).
The cytokine/chemokine expression at 4 h that is specific to a cell type family (myeloid, epithelial, stromal/ endothelial) is highlighted by a blue rectangle with dash lines. The cytokine/chemokine expression that switches to dominance in MoMacs versus AMs at 10 h is highlighted by a green rectangle. Note that in the samples of donor 1 at 10 h, pDCs were absent (pDC none). Reciprocally in AT2s and blood ECs at 4 h EVLP, a majority of the genes found in the GSEA Apoptosis leading edges with positive enrichment (about 60%) are not DEGs and thus not taken into account in the IPA analysis. Finally IPA results are expected to be more robust because, at the difference with the Hallmark Apoptosis gene set that includes both pro-apoptosis (BAX, CASP1 to 9) and anti-apoptosis (BCL2L1, BCL2L10, BCL10) genes, IPA takes into account the pro-and anti-apoptotic functions and their orientation of expression in our data set.
Detection of cytokines, VCAM and ICAM in perfusion liquid Perfusates were thawed and briefly centrifuged. VCAM1 was measured by a Human VCAM-1 Picokine ™ ELISA kit (Boster Biological Technology Co. , Ltd. , Pleasanton, CA), ICAM1 by the Human ICAM-1/CD54 allele-specific Quantikine ELISA kit (R&D Systems, Inc. , Minneapolis, MN) and IL-6 by the LEGEND MAX ™ Human IL-6 ELISA kit (BioLegend, San Diego, CA) according to each manufacturer's instructions. IL-1b, CXCL8, CXCL10 and TNFa were assessed with a Human ProcartaPlex ™ Mix&Match 4-plex (ThermoFischer Scientific, Waltham, MA) using a MagPix instrument (Luminex, Austin, TX) and the data were analyzed with the Bio-Plex Manager software (Bio-Rad, Hercules, CA).