dataesr/scientific-paragraphs-categorization huggingface dataset:
Training and evaluation of ssDLA and DLA-Mutation We trained and validated ssDLA on the PDBInter database. The protein complexes in the train set do not share any family level similarity with the 142 complexes from S2003, according to the SCOPe hierarchy. We generated 247 662 input samples (interfacial cubes) from the train set and 34 174 from the validation set. Amino acids are not equally distributed in these sets; leucine is the most frequent one, while cysteine is the rarest (Fig. S  3). To compensate for such imbalance and with the aim of penalizing more those errors that are made for the less frequent amino acids, we assigned a weight to the loss of each amino acid type that is inversely proportional to its frequency of occurrence (Table  S 4 ). We trained ssDLA for 50 epochs with the Adam optimiser in TensorFlow  [1]  at a learning rate of 0.
S 5A ). We explored different hyperparameter values by varying the learning rate, applying different normalisation schemes, changing the compensation weights, etc. We retained the hyperparameters leading to the best performance on the validation set. The trained ssDLA model extracts embedding vector e k of size 200 (Fig. 1B ) for a given cube. We used S2003 to train and test DLA-Mutation. We set the learning rate at 0. 001 and we initialized the weights of the network with those of the pre-trained ssDLA model. We first evaluated DLA-Mutation through a 10-fold cross validation performed at the mutation level. This evaluation procedure, which is widely used in the literature  [20, 59, 60, 73, 78] , considers each sample independently when splitting the data between train and test sets (mutation-based split).
However, this assumption is problematic since the same complex or even the same wild-type residue may be seen during both the training and the testing phases. These cases are expected to be "easy" to deal with. For a more challenging and realistic assessment, we held out 32 complexes displaying 391 mutations for the testing phase, and trained DLA-Mutation on the rest of the dataset (complex-based split). For the comparison with iSEE, we used the same train and test procedure as that reported in  [20]  (Table  S  2), using the wild-type and mutant 3D models produced by HADDOCK  [69]  and available from  [20]. For the comparison with * The input 3D models, generated by HADDOCK  [69] , were taken from  [20]. the other predictors, we defined the test set from the intersection between S2003 and the benchmark set used in  [20].
It amounted to 112 mutations from 17 complexes. We defined a new training set comprising 945 mutations from S2003 coming from complexes sharing less than 30% sequence identity with those from this test set. In the case of GraphPPI  [42]  and TopNetTree  [73] , the comparison remains qualitative due to the lack of software package from GraphPPI and the function-specific model for TopNetTree that is trained only on antibody-antigen complexes from the AB-Bind database  [63].
Evolutionary information and pre-training matter To build the DLA-Mutation model, we fine-tuned the weights of the pre-trained ssDLA model (Fig. 1B ) to predict ∆∆G bind values in a supervised fashion (Fig. 1C ). For each mutation, we combined information coming from the local 3D environments of the wild-type and mutant residues extracted from 3D models of the corresponding complexes (S2003-3D database, see Methods and Fig. S 1 ) with information about the structural region of the wild-type residue (Fig. 1C , SR), as well as other geometrical (CV), physico-chemical (PC), and evolutionary (GEMME, T jet ) descriptors. We performed an ablation study to assess the contribution of these descriptors and of the pre-training step (Fig. S 7  and Table  1 ). In the baseline configuration, the only auxiliary feature we used was SR.
793). Finally, the predictions are robust to variations in the sequence identity between the test and train complexes (Fig. 4D ). The model relies on fine-tuned weights, starting from those of the pre-trained ssDLA, and exploits all auxiliary features. B. Test set of 391 mutations coming from 32 complexes that were not seen during training and were randomly selected from the S2003 dataset. C-D. DLA-Mutation (green) and iSEE (blue) scores versus experimental ∆∆G bind values for the test set of 487 mutations from 56 complexes (S487 dataset). The input 3D models and training and evaluation procedure were directly taken from  [20]   We report values for 112 mutations coming from 17 protein complexes not seen during the training or optimisation of any of the predictors. A. DLA-Mutation was trained on 945 mutations from S2003 coming from complexes sharing less than 30% sequence identity with those from this test set.
The per-class tendencies are consistent with those observed for the pre-training task (Fig. 5 , compare the two panels). Specifically, the best performances are observed for the aromatic (ARO) and positively charged (POS) classes, with more than 70% recall, while the CAST class is the most difficult to identify. Conformational sampling influences the results. We observed improved performances when dealing with 3D models compared to experimental structures (Fig. S 10). We may hypothesize that the backbone  The predicted and experimental values are reported for 391 mutations coming from 32 complexes not seen during training (randomly selected from S2003 dataset). We used weight fine-tuning and all auxiliary features. The overall PCC is 0. 720 (Table  1 ). The dots are colored with respect to the structural region where the mutated residue lies (A), the complex's biological function (B), the amino acid size change upon mutation (C) and the minimum sequence identity shared with any training complex (D).
Overall, these results suggest that DLA-Mutation would benefit from a simplified version of the architecture for performing computational alanine scans, which relies only on X-ray crystal structure. Combining DLA-Mutation with alanine scans performed on the wild-type complex would open the way to systematically assess mutational outcomes on protein-protein interactions at a proteome-wide scale. • a numerical score (a float value) estimating the functional impact of point mutations from multiple sequence alignments computed for single (monomeric) proteins by GEMME  [38]. To do this estimation, GEMME combines the conservation levels T jet with amino acid frequencies and the minimum evolutionary distance between the protein sequence and an homologous protein presenting the mutation. We built the input multiple sequence alignments for JET and GEMME by performing five iterations of the profile HMM homology search tool Jackhmmer  [34, 16]  against the UniRef100 database of non-redundant proteins  [66]  using the EVcouplings framework  [27].
Experimental values for ∆∆G bind We used SKEMPI v2. 0  [31] , the most complete source for experimentally measured binding affinities of wild-type and mutated protein complexes. It includes the smaller databases AB-Bind, PROXiMATE, and dbMPIKT  [21]. In total, it reports measurements for over 7 000 single and multiple point mutations coming from 345 protein complexes, including antibody-antigen (AB/AG) and protease-inhibitor (Pr/PI) assemblies, and assemblies formed between major histocompatibility complex proteins and T-cell receptors (pMHC-TCR). For each entry, corresponding to a single or multiple mutation, the database provides the PDB structure of the wild-type complex, the names of the partners, the binding affinities of the wild-type and mutated complexes, some related experimental measurements, details about the experimental method and conditions, and the structural region of the mutation site(s), either INT, SUR, SUP, COR or RIM  [39].
Figure4: Influence of different residue-based and complex-based properties on DLA-Mutation accuracy. The predicted and experimental values are reported for 391 mutations coming from 32 complexes not seen during training (randomly selected from S2003 dataset). We used weight fine-tuning and all auxiliary features. The overall PCC is 0. 720 (Table1). The dots are colored with respect to the structural region where the mutated residue lies (A), the complex's biological function (B), the amino acid size change upon mutation (C) and the minimum sequence identity shared with any training complex (D). We calculated the change of amino acid size as a volume difference (δV ) between wild-type and mutant following [25]. A mutation was classified as size-neutral if |δV | < 10Å.
Satellites enable the provision of global 5G coverage utilizing an customized protocol stack for direct access of 5G devices. This paper reports the results of a field trial based on an Open Source Software 5G protocol stack modified for Satellite Communications. 5G New Radio functions have been adapted to support 3GPP Non-Terrestrial Networks standardization for Release 17. Some of the currently discussed enhancements for 5G New Radio direct access for Non-Terrestrial Networks were evaluated in this study over a geosynchronous spacecraft. Non-Terrestrial Networks devices and base station were software-defined solutions communicating via satellite through a standardized 5G New Radio waveform. The results show that Open Source Software implementations running on general purpose platforms greatly support the research and standardization process for Non-Terrestrial Networks in 5G-Beyond.
Recent work has already shown first efforts in terms of deriving first concepts and exploration of new technologies  [6] ,  [7] ,  [8]. It is expected that NTNs will be a main service provider in the overall 6G ecosystem. Early prototypes of the NTN technology are important for the 6G community and the Release 18 standardization process. Open Source Software (OSS) can make an important contribution to new NTN innovations, since New Space companies, can rely on a basic framework for their future software development. Porting the 5G/6G protocol stack on-board will be an unprecedented innovation. The integration becomes even more important, in the light of satellites connecting with each other via inter-satellite links. Implementing base station functions on-board the satellite is therefore only logical in future 6G networks.
Afterwards UE and gNB exchanged Message 3 in the UL and Message 4 in the DL to resolve collisions with potential other UEs. In addition, the RA procedure was also used to reestablish UL synchronization if the UE lost synchronization due to a too long period without any UL transmission. Over the course of the tests, the satellite drift of the GEO around its orbital station-keeping box was also compensated by the NTN TA procedure. By means of a Linux ping command, we measured a RTT between gNB (IP address: 10. 1. 10. 1) and UE (IP address: 10. 1. 10. 2) ranging between 530 and 570 milliseconds. The RTT measurements with our prototype, shown in Fig. 11 , are comparable with COTS satellite modems. The two diagrams of the PUSCH and PDSCH shown in Fig. 9  and 10 show that there is negligible phase rotation of the Quadrature Phase Shift Keying (QPSK) constellation points.
In this work, such unification of multiple data sets is presented. This was done for the validation of the ESA OC-CCI ocean colour products, but with the intent to also serve the broader user community. A merged data set is not without drawbacks: it is likely to be large (with hundreds of thousands of observations) and so not always easy to manipulate; because the merging is done on pre-existing, processed databases, it is not possible to have full control of the whole processing chain. Hence, the data set would be a collection of observations collected by several investigators using different instruments, sampling methods, and protocols, which might eventually have been modified by the processing routines used by the repositories or archives. To minimize these potential drawbacks, we have, for the most part, incorporated only data sets that have emerged from the long-term efforts of the ocean colour and biological oceanographical communities to provide scientists with high-quality in situ data, and implemented additional quality checks on the data to enhance confidence in the quality of the merged product.
One requirement for "chla_fluor" measurements was that they were made using in vitro methods (i. e. based on extractions of chlorophyll-a). Although this severely decreased the number of observations, since in vivo fluorometry (e. g. fluorometers mounted on CTDs) is widely available in oceanographic databases, it was decided to exclude such data because of potential problems with the calibration of in situ fluorometer data. The variable "chla_hplc" was calculated by summing all reported chlorophyll-a derivatives, including divinyl chlorophyll-a, epimers, allomers, and chlorophyllidea. The two chlorophyll variables are retained separately in the database to facilitate their use. HPLC measurements could be considered of higher quality, but fluorometric measurements are more numerous. Thus, one option for users is to use "chla_fluor" only when there are no "chla_hplc" measurements available.
g. original flags) or method of observation that did not meet the criteria for the data set (e. g. in situ fluorescence for chlorophyll concentration); and (3) spuriously high or low data. For the last, the following limits were imposed: for "chla_fluor" and "chla_hplc" [0. 001-100] mg m -3 ; for "rrs" [0-0. 15] sr -1 ; for "aph", "adg", and "bbp" [0. 0001-10] m -1 ; for "tsm" [0-1000] g m -3 ; and for "kd"  [(aw(λ) -10] m -1 , where "aw" is the pure water absorption coefficients derived from  Pope and Fry (1997). Also, during this stage, three metadata strings were attributed to each observation: "dataset", "subdataset", and "contributor". The "dataset" contains the name of the original set of data and can only be one of the following: "aoc", "boussole", "mermaid", "moby", "nomad", "seabass", "hot", "ices", "amt", "gepco", "arcsspp", "awi", "barentssea", "bats", "biochem", "bodc", "calcofi", "cc", "ccelter", "cimt", "estoc", "imos", "maredat", "palmer", "seadatanet", "tpss", and "tara".
The "subdataset" starts with the "dataset" identifier and is followed by additional information about the data, as <dataset>_<cruise/station/site>) (e. g. "seabass_car81"). The "contributor" contains the name of the data contributor. An effort was made to homogenize the names of data contributors from the different sets of data. These three metadata are the link to trace each observation to its origin and were propagated throughout the processing. Finally, this processing stage ended with each set of data being scanned for replicate variable data and replicate station data, which when found, were averaged if the coefficient of variation was less than 50 %, otherwise they were discarded. Replicates were defined as multiple observations of the same variable, with the same date, time, latitude, longitude, and depth.
Replicate station data were defined as multiple measurements of the same variable, with the same date, time, latitude, and longitude. For the latter case, a search window of 5 min in time and 200 m in distance was given to account for station drift. A small number of observations that were identified as replicates had a different "subdataset" identifiers (i. e. different cruise names). These observations were considered suspicious if the values were different and discarded. If the values were the same, one of the observations was retained.
A. Valente et al. : A compilation of global bio-optical in situ data This possibly originated from the same group of data being contributed to an archive by two different data contributors. Once a set of data was homogenized, its data were integrated into a unique table. This final merging focused on the removal of duplicates between the sets of data. Although some duplicates are known (e. g. MOBY, BOUS-SOLE, AERONET-OC, and NOMAD data are found in SeaBASS and MERMAID), others are unknown (e. g. how many of GeP&CO, ICES, AMT, and HOT are within NO-MAD, SeaBASS, and MERMAID). Therefore, duplicates were identified using the metadata ("dataset" and "subdataset") when possible, and temporal-spatial matches, as an additional precaution. For temporal-spatial matches, several thresholds were used, but typically 5 min and 200 m were taken to be sufficient to identify most duplicated data, which reflected small differences in time, latitude, and longitude, between the different sets of data.
ARCSSPP "Arctic System Science Primary Production" database. The compiled variables were "rrs" and "chla_hplc". Remote-sensing reflectance was computed from the original "fully-normalized" water-leaving radiance ("nLw_ex"), which is the "normalized" water-leaving radiance ("nLw" previously described), with a correction for the bidirectional nature of the light field  (Morel and Gentili, 1996; Morel et al. , 2002). The solar irradiance ("Fo") was computed from two available variables in the original set of data: the normalized water-leaving radiance ("nLw") and the remote-sensing reflectance ("rrs"), using the equation "Fo = nLw/rrs". Only radiometric observations that meet the following criteria were used: (1) tilt of the buoy was less than 10 • ; (2) the buoy was not lowered by more than 2 m as compared to its nominal water line (to ensure the Es reference sensor is above water and exempt from sea spray); and (3) the solar irradiance was within 10 % of its theoretical clear-sky value (determined from  Gregg and Carder, 1990).
SeaWiFS Bio-optical Archive and Storage System (SeaBASS) SeaBASS is one of the largest archives of in situ marine biooptical data  (Werdell et al. , 2003)  with a long-established inventory  (Hooker et al. , 1994). It is maintained by NASA's Ocean Biology Processing Group (OBPG) and includes measurements of optical properties, phytoplankton pigment concentrations, and other related oceanographic and atmospheric data. The SeaBASS database consists of in situ data from multiple contributors, collected using a variety of measurement instruments with consistent, community-vetted protocols, from several marine platforms such as fixed buoys, handheld radiometers, and profiling instruments. Quality control of the received data includes a rigorous series of protocols that range from file format verification to inspection of the geophysical data values  (Werdell et al.
MERIS Match-up In situ Database (MERMAID) MERMAID provides in situ bio-optical data matched with concurrent and comparable MERIS Level 2 satellite ocean colour products  (Barker, 2013a, b). The MERMAID in situ database consists of data from multiple contributors, measured using a variety of instruments and protocols, from several marine platforms such as fixed buoys, handheld radiometers, and profiling instruments. Comprehensive quality control and protocols are used by MERMAID to integrate all the data into a common and comparable format  (Barker, 2013a, b). Access to MERMAID data is limited to the MERIS Validation Team, the MERIS Quality Working Group, and to the in situ data contributors. For this work, access has been granted to the MERMAID database through a signed Service Level Agreement.
For any interest in the original data, BODC is the point of contact, which ensures that if there are any updates, the most recent data are supplied. In the case of AMT 26, 27, and 28, data were provided to the OC-CCI project by Gavin Tilstone, whereas in the case of AMT 20 and 23, data were provided by Robert J. W. Brewin. The compiled variables are "chla_hplc" and "chla_fluor". ICES is a network of more than 4000 scientists from almost 300 institutes, with 1600 scientists participating in activities annually. The ICES Data Centre manages a number of large data set collections related to the marine environment covering the Northeast Atlantic, Baltic Sea, Greenland Sea, and Norwegian Sea. Most of data originate from national institutes that are part of the ICES network of member countries. Data were provided (on 28 April 2014) from the ICES database on the marine environment (Copenhagen, Denmark) following a specific request.
The Fisheries and Oceans Canada database for biological and chemical data (BIOCHEM) BioChem is an archive of marine biological and chemical data maintained by Fisheries and Oceans Canada  (DFO, 2018; Devine et al. , 2014). BODC is the designated marine science data centre for the United Kingdom. The data used in this work derive from a specific request for discrete observations of chlorophylla concentration since 1997. Initially, this request was used to compile AMT data (see Sect. 2. 2. 9). The remaining data comprising observations of chlorophyll-a concentration from fluorometric and HPLC methods, mostly sampled in the North Atlantic, were analysed and added (the "dataset" string for this data source is "bodc"). Data not flagged with highest quality or without method of measurement were discarded.
California Cooperative Oceanic Fisheries Investigations (CALCOFI) CalCOFI is a partnership of the California Department of Fish & Wildlife, National Oceanic & Atmospheric Administration Fisheries Service, and Scripps Institution of Oceanography. CalCOFI has conducted quarterly cruises off southern and central California since 1949. Data collected in the upper 500 m include temperature, salinity, oxygen, nutrients, chlorophyll, primary productivity, plankton biodiversity, and biomass. For this work, only observations of chlorophyll-a concentration derived from fluorometric methods flagged with highest quality were used. Data were acquired from the file "CalCOFI_Database_194903-201911_csv_10Jul2020. zip". The compiled variable was "chla_fluor".
CoastColour Round Robin (COASTCOLOUR) COASTCOLOUR data sets were designed to evaluate the performance of ocean colour satellite algorithms in the retrieval of water quality parameters in coastal waters  (Nechad et al. , 2015a). Three types of COASTCOLOUR data sets are available: (1) a match-up data set where in situ biooptical observations are available simultaneously with a  ("chl"), remote-sensing reflectance ("rrs"), algal pigment absorption coefficient ("aph"), detrital plus CDOM absorption coefficient ("adg"), particle backscattering coefficient ("bbp"), the diffuse attenuation coefficient for downward irradiance ("kd"), and total suspended matter ("tsm") in the final table. All chlorophyll data were considered, but for a given station, HPLC data were selected if available. Colours indicate the number of stations available for each variable as a function of month and hemisphere of data acquisition ("N" -Northern Hemisphere; "S" -Southern Hemisphere).
The empty (white) squares indicate no data for that month. cloud-free MERIS product; (2) an in situ reflectance data set where an in situ reflectance is available simultaneously with an in situ measurement of chlorophyll-a concentration and/or total suspended matter; and (3) a simulated data set where reflectances were generated by a radiative transfer model. This work used the match-up data set, which includes most of the in situ measurements, and is available at https://doi. org/10. 1594/PANGAEA. 841950  (Nechad et al. , 2015b). The match-up data set provides optical, biogeochemical, and physical data collections at 17 sites across the globe. From this data set, observations of reflectance, chlorophylla, total suspended matter, and IOPs were compiled. The remote sensing reflectances were corrected for the bidirectional nature of the light field  (Morel and Gentili, 1996; Morel et al.