dataesr/scientific-paragraphs-categorization huggingface dataset:
Tracking Tracking in ORBSLAM2 is performed in two ways, either with or without a motion model. The motion model represents the previous transformation between frames, the assumption is that it is still valid, given the short physical and temporal distances between frames. When a motion model exists, it is used to project the previous frame's keypoints to the current one. A search around the projections is carried out. If the search fails (too few matches), the window of the search is enlarged and a second search is performed. If the second search also fails, tracking without the motion model is launched. In the absence of a motion model, the current frame is condensed into a visual bag of words and compared with the current keyframe. If the search fails to reach a number of matches (threshold fixed by the user), the relocalization module is launched.
In this section, the results of the evaluation of the proposed system are presented. The evaluation is performed in two parts. The first part is the evaluation of the proposed system's camera trajectory against those obtained from the three benchmark systems (LCSD  [32] , ORBSLAM2  [5]  and LSD-SLAM  [6] ). LCSD is used as a benchmark given its similarity to the proposed method. In the compiled version of LCSD, we added input/output wrappers to process the data from the used databases, given that the developers of LCSD have made the choice in the code to only process two datasets  [32]. Camera trajectories are computed on sequences belonging to three databases: TUM  [42] , KITTI  [43]  and EuRoC  [44]. The TUM database represents an interior, desk space. The KITTI database represents an exterior, car view of a road.
The EuRoC database represents a highly textured industrial scene. As a metric to compare the performance of these systems, we chose to rely on the root mean squared error (RMSE). RMSE provides an idea on the typical error on a single point. For AR applications, the quality of the augmentations is more related to individual errors than to the mean or median errors. For that reason, we believe RMSE represents a better metric for our evaluation. Since our final objective is to be able to use our system for AR purposes, the second part of the evaluation compares visual augmentations obtained by our proposed system with augmentations obtained via ORBSLAM2 and LSD-SLAM.
Trajectory Comparison In this section, we compare camera trajectories obtained with our proposed system, LCSD, ORBSLAM2 and LSD-SLAM on three databases. The trajectories present the mean of five passes of each algorithm. In order to further clarify the evaluation process, a single scene is presented in detail. Table  1  presents the RMSE error of the tested systems on nine sequences of the TUM database. Table  1. RMSE of the four systems on sequences of the TUM database (best results in bold). N/A means the system has failed to initialize or was lost for more than 50% of the scene. The values represent the mean over five passes of each algorithm. As can be seen from Table  1 , our system has better performance on five out of nine sequences on this metric. LCSD outperforms the proposed system on f r2/xyz, this scene is a static view of a desk, this is coherent with the hypothesis of this work given that indirect methods perform better with linear movement (especially for initialization).
Sequence Table  2  presents the results of comparison between the systems on the KITTI dataset. Results show that, on this database, the proposed system performs equally to the best performing algorithm or better on four out of seven sequences. On this dataset, it can be noted that the results can be classified into three categories. The first category is where the proposed system outperforms the other three algorithms (seq_00, seq_04). The second category is where the proposed system has a equal performance to the best of the three other algorithms (seq_03, seq_06). The third and last category is the one where one (and only one) of the systems outperforms the proposed system (seq_01, seq_02 and seq_05). 3  presents the results of comparison between the four systems on the EuRoC dataset. On this dataset, ORBSLAM2 outperforms the other three algorithms.
This can be explained by the fact that the EuRoC dataset offers sequences which are highly textured and varying levels of lighting, which violates the photometric consistency hypothesis made by direct algorithms such as LSD-SLAM. It must be noted, even though our system comes in the second place, both our proposal and ORBSLAM2 are (one or two) orders of magnitude better than the two other algorithms. The next part focuses on a single sequence. This represents one pass of the algorithms to show the consistency of the results over the sequences. This sequence is detailed in order to present other metrics than the RMSE. We chose to focus on the f r1/xyz sequence of the TUM dataset. It represents a scene with both texture and untextured parts. To compare the performance of the four systems, the x, y and z components of the trajectory can be visualized separately, see Figure  4.
Execution Time Our proposal is a fusion of two existing algorithms. In Section 4. 1, the results show the system is generally better or comparable to the best algorithm in each type of scenes (direct or indirect). To study the added computational cost of the fusion, we chose a sequence out of every database and calculate the mean and median time per frame on our system and ORBSLAM2 as can be seen in Table  6. Table  6. Processing times of frames in seconds is presented by subscript t. The inverse is presented as frequency with a subscript f. "Fus. " represents the results of the fusion system. All the results are a mean for five passes. For each scene, the "media" and "mean" columns represent the median and mean of ORBSLAM2 system. "Adj. " represents the adjusted times/frequencies as explained in Section 4.
Augmentations Since our end goal for the proposed system is to use it for AR applications, another set of evaluations involving augmentations on a set of data is presented. The augmentations are achieved by displaying a 3D cube into known coordinates of the scene ( f r1/xyz). The quality of the augmentations are judged based on the stability of the virtual object with respect to the scene. Figure  5  represents two frames from an augmented scene. During these two frames the proposed system runs ORBSLAM2. At the beginning of the sequence, ORBSLAM2 could not initialize, where LSD-SLAM does initialize from the second frame. Therefore the system uses LSD-SLAM to initialise ORBSLAM2, where the system adjusts the poses and map of ORBSLAM2 by the poses and map provided by LSD-SLAM as explained in Section 3.
Discussion The proposed system offers similar or better performances on the majority of sequences. The TUM dataset offers photometrically consistent scenes with textured and untextured parts-this favors direct methods. Both KITTI and EuRoC datasets are more textured and less photometrically consistent, which favors indirect methods. The proposed system has adapted to each type and performed similar or better on the three datasets. In cases where the scene has textured and untextured parts, ORBSLAM2 has difficulties initializing while LSD-SLAM and can do so more easily; However, once all the systems are initialized, then ORBSLAM2 is generally more precise. These types of scenes are more representative of the real world, which is usually a mix of textured and untextured parts. The cases where our proposed system has a weaker performance, generally happen on scenes where one of the sub-systems (ORBSLAM2 or LSD-SLAM) fails to initialize or loses tracking.
This generally happens on homogeneous (i. e. , fully textured or fully untextured) sequences. LCSD is a method quite similar to our proposal. The main difference is that LCSD uses a direct vSLAM (DSO) as a base algorithm. This reduces the performance on scenes that are more favorable to indirect vSLAM methods (i. e. , textured and photometrically inconsistent scenes). This explains the performance of LCSD (and LSD-SLAM) on the EuRoC dataset. Furthermore, the published results of LCSD (cf. [32] ) with respect to the EuRoC dataset confirms this. The proposed system has shown that it can adapt to different types of scenes, in Section 4. 2, we show that the added cost in terms of processing time is under 7. 5%. Section 4. 3 presents augmentations based on the proposed system. In the augmented sequence, given that the system initialized immediately (thanks to LSD-SLAM) and the robustness of the tracking (thanks to ORBSLAM2), the virtual cube is stable from the start and does not present any jitter.
Data Availability Statement: The data used in this work consist of three databases, which are available at http://www. cvlibs. net/datasets/kitti/eval_odometry. php, https://vision. in. tum. de/data/datasets/ rgbd-dataset and https://projects. asl. ethz. ch/datasets/doku. php?id=kmavvisualinertialdatasets. The databases were accessed on the 17 May 2020.
Community profiling based on amplicon sequencing Reads from amplicons were processed using R (version 3. 2. 2) and the package DADA2  [32]  as already described elsewhere  [33] , following the SOPs as recommended by the developers. Merged sequences were trimmed to a consistent length of ~270 bp ("universal" primer set) and ~140 bp ("Archaea" primer set). Thereafter, the sequences were assigned to a taxonomy using the RDP training set classifier v. 14 and the SILVA v. 123 classifier. Ribosomal sequence variants (RSVs) which were overlapping in negative controls and samples were removed from the datasets. All RSV tables are available in the Additional file 2 (Supplementary Tables  2 3 4 5 ). An additional data quality check was performed by visualization of rarefaction curves (richness vs. reads sampled), which confirmed sufficient sampling depth by reaching plateaus in each sample.
Gene-centric data analysis of metagenomic reads We used FastQC v. 0. 11. 5  [36]  to determine the base quality throughout the 250 bp MiSeq-generated pairedend reads. Identified adapter and overrepresented homooligonucleotides were removed using cutadapt v 1. 14  [37]  and retained reads were further trimmed using Prinseqlite v. 0. 20. 4 [38]  and following parameters: "-min_len 100 -trim_qual_right 20 -trim_qual_left 20 -trim_left 8". BBMap short read aligner v. 37. 61 was used to remove bacteriophage PhiX174 contaminants from trimmed high-quality reads by mapping them against the respective genome. Matching reads were not included in further analysis. Quality-filtered reads were then compared against the NCBI non redundant database using DIAMOND BLASTx v 0. 9. 10  [39]  and default parameters.
Data availability Sequence datasets obtained for microbial community data analysis were submitted to EBI and are publicly available (study project number: PRJEB18706). Shotgun datasets and binned genomes, as well as the genomes of the isolates Buttiauxella MASE-IM-9, Yersinia MASE-LG1, Halanerobium MASE-Boulby, are available through Bioproject number PRJEB28336.
It should be noted that no archaeal RSVs were observed from the samples obtained from the glacier (PMA treated and untreated), which indicates either a low amount of archaeal 16S rRNA genes (below detection limit) or an insufficient primer match  [51, 52]. A bar chart displaying the archaeal community composition (phylum level) of all samples (PMA treated and untreated) is given in Additional file 1: Supplementary Figure  S3. In the PMA dataset, which included the PMA-untreated hypersaline environment sample  [7] , the majority of RSV counts were assigned to the phyla Euryarchaeota (39. 3%), Thaumarchaeota (22. 7%), unassigned Archaea (20. 8%) and Woesearchaeota (DHVEG-6; 10. 9 %; Fig. 1a ). Signatures of Euryarchaeota were detected throughout all Archaeapositive samples, except for the acidic lake SS1* sample.
1b, c ). Signatures of Proteobacteria were present throughout all sampling sites, but in different proportions. Similarly, signatures of Actinobacteria were detected in each environment with the exception of the "hypersaline environment". This biotope also revealed a minor proportion of Firmicutes signatures, which were completely absent in samples from the acidic lake* environment, but were present in all other samples. In the datasets, we identified the signatures of a broad diversity of facultatively or obligately anaerobic and extremotolerant/extremophilic microorganisms with relevance for analogue research and astrobiology  [56] [57] [58]. Those included methanogens (e. g. Methanobacterium), halophiles (e. g. archaeal Halobacteria, Halothiobacillaceae), acidophiles (e. g. Acidithiobacillaceae) and a number of taxa associated with psychrotolerance (e.
80 (mean)). With respect to archaeal richness and diversity, PMA-treated and -untreated samples (all sites) did not differ significantly (Additional file 2: Supplementary Table  6 ). The samples from sulfidic springs also showed the highest diversity when observing all microbial signatures (universal dataset, "microbiome"; PMA-treated samples: observed species: 2589 (mean), Shannon index: 6. 51 (mean); PMA-untreated samples: observed species: 2438 (mean), Shannon index: 6. 28 (mean); Additional file 2: Supplementary Table  7 ), whereas the lowest diversity was observed in samples from the hypersaline environment (observed species: 155, Shannon index: 3. 8). Alpha diversity information of all PMA-treated samples is displayed in Fig. 2. The lowest proportion of RSVs from intact cells was detected in samples from permafrost (15.
This observation was confirmed by the detection of twenty overlapping taxa, of which eight were resolved on genus level, namely Paludibacter, Aquabacterium, Geobacter, Sulfurovum, Beggiatoa, Thiothrix, Spirochaeta_2 and Opitutus. Notably, Halobacteriaceae were found in both environments, as well as Acidobacteriales. Most of the core genera of the hypersaline environment and acidic sites were, however, affiliated to Proteobacteria (Additional file 1: Supplementary Information Figure  6 ). (See figure on previous page. ) Fig. 1  Archaeal and bacterial profiles of intact microbial communities from MASE environments. Panel a shows the taxonomic profile obtained using the Archaea-targeting primer, on phylum (upper bar chart) and family level (below). Phyla names are followed by relative abundances (all datasets); no archaeal signatures were obtained from the glacier samples.
Panel b shows the bacterial composition of all samples on class level (only those classes with > 0. 1% are shown). Panel c displays the most abundant phyla of the microbial community (based on "universal" primer set, "microbiome"). For all panels, the relative abundance of each taxon is shown on the y-axis. The total relative abundance, summed up for all samples, is given in brackets behind the taxa names in the legends. For unclassified RSVs ("unassigned") the highest assigned taxonomic level is given. For instance, "Archaea, not assigned" reflects all RSVs which were classified on Archaea level, but could not be assigned to other taxonomic levels. Full detail on the archaeal and universal dataset is given in Additional file 2: Supplementary Tables  2  and 3. • • • • • • • • • • • • • 0. 2 0.
Seven of them were resolved at the genus level (Bryobacter (35 different RSVs), Candidatus Solibacter  (22) , Acidocella (9), Bdellovibrio  (34) , Aquicella (16), Opitutus (34) and Paludibacter  (14) ; Additional file 1: Supplementary Information Figure  8b ), and 20 common taxa could be resolved on the order level (Additional file 1: Supplementary Information Figure  9 ). Most RSVs belong to the family Anaerolinacaea (239 different RSVs), followed by members of the order Gaiellales (92) and the family Planctomycetaceae (88). Notably, we could not detect any archaeal core taxa on any taxonomic level. RSVs assigned to Paludibacter and members of the family Opitutacea were also identified in the shotgun metagenomics dataset (in PMA and PMA untreated sulfidic spring sample, abundance < 0. 1%; see below).
PMA treatment affects the abundance of Eukaryota in the metagenomics datasets Samples from three representative sampling sites (i. e. sulfidic spring SM, acidic lake SS3 and permafrost SOB) were selected for metagenomic sequencing. For each sample, we processed two parallel fractions, one PMA treated, the other untreated. The other samples of the MASE sites could not be included in the metagenomic approach, as obtained DNA concentrations were too low for a proper analysis. Overall, the taxonomic information derived from the metagenomics dataset (Additional file 2: Supplementary Table  8 ) was congruent with the findings from amplicon sequencing approach. However, the permafrost sample SOB showed a higher contribution of Acidobacteria and the archaeal TACK superphylum (i. e. Thaumarchaeota), when PMA treatment of the sample was performed.
The functional profiles of the MASE microbial communities mirror the extreme physical and chemical characteristics of the MASE sites As mentioned above, metagenomic analyses could only be performed for three representative MASE sites (each with and without PMA). However, for the sake of completeness, we performed comparative analysis of all samples using the in silico tool Tax4fun  [65]  to infer the estimated microbial functions from amplicon-based information. The methodology and results of this approach are provided in Additional file 1: Supplementary Figure  11  and its legend. (See figure on previous page. ) Fig. 2  Microbial diversity and network analysis of microbial communities in samples from MASE sites (only PMA treated samples shown). The left panel gives information on the general microbiome (universal dataset), whereas the right panel displays results from the archaeome analyses (Archaea-targeted approach).
The alpha diversity (a, d), beta diversity (PCoA plots; unweighted Bray-Curtis distance; b, e) and the connections between in the sampling sites (networks; c, f) are displayed separately. The connection (based on Bray-Curtis similarity) of samples is visualized by the number of straight lines in-between, reflecting a distance of 0. 4 or below (see text)   In the metagenomics dataset, the diversity of microbial functions was found to be significantly higher (Shannon index, p = 0. 027, ANOVA) in the permafrost sample (SOB), compared to SM and SS3, where it was found to be the lowest diversity. While the grouping of microbial functions according to location was found to be significant (p = 0. 01, redundancy analysis), the grouping according to PMA treatment was not found to be significant. The most abundant enzymes/functions detected in the shotgun metagenome dataset were (i) an integrase (involved in phage integration and excision), (ii) glycosyltransferase (biosynthesis of galactoglacans and related lipopolysaccharides), (iii) cation efflux system protein CusA (cobalt zinc cadmium resistance), (iv) long-chain fatty acid CoA ligase (fatty acid degradation regulons) and (v) decarboxylase (serine glyoxylate cycle) (additional information is given in Fig.
3a, b ). In a following step, we wanted to understand the differences between MASE microbial communities in taxonomic and functional composition when compared to metagenomic datasets from moderate environments, namely soil (Hungary, Finland, South Africa), groundwater (Tulsa, USA), lake-(Switzerland, Greece) and seawater (Mediterranean Sea, Indian Ocean). For comparison, we also included other extreme environments, namely brines (Spain), Antarctic lake (Antarctica) and permafrost (Axel Heiberg Island, Canada) (Additional file 2: Supplementary Table  9 ). Amongst all these environments, the permafrost samples were characterized by the lowest taxonomic diversity (assessed on genus level, Shannon index, p = 0. 0016, ANOVA), whereas the samples from lakes, groundwater and sulfidic spring showed the highest diversity.
Notably, intactness and activity of the MASE-associated microorganisms were also supported by calculations of their replication rates with iRep. This software uses the coverage information of high quality MAGs to determine copy numbers between the origin of replication and the terminus to define iRep values. These values can be further interpreted as a trade-off between the proportion of the population involved and the number of replication events (further details on interpreting iRep values are available at  [66] ). According to this analysis, at least one representative draft genome was actively replicating per MASE site. The highest activity (iRep 2. 05, could refer to 25-100% of the population was actively replicating 1-4 times) was observed for Acidimicrobium ferooxidans from the Icelandic lake.
Planetary analogues are selected on the basis of geology, mineralogy, topography and environmental conditions, depending on the planet or body of interest, such as Mars or the Icy Moons. One of the most obvious model ecosystems are permafrost environments  [58] , deserts and high-salt biotopes, as well as Antarctic and subsurface environments  [3]. It has been discussed that these analogue environments are incapable of combining all chemical and physical characteristics of interest; however, these environments were considered good models to analyze the impact of one or a few extreme conditions on the biology therein. In this study, we performed one further step in optimizing research for Mars-analogue sites, by the exclusion of oxygenated model environments. Samples from selected analogue sites were collected under anoxic conditions, and the living microbial community thriving therein was analyzed by a broad combination of anaerobic cultivation and molecular methodology to address the following questions: Which Bacteria and Archaea are alive under the extreme, anoxic, environmental conditions? Which functions do they possess? Can we cultivate them? Is there a core microbiome of all sites? Are there general principles of anaerobic microbial communities with impact on the habitability assessment and the search for life on Mars and beyond? The retrieved data were intended to optimize the definition of habitable extraterrestrial environments and to deliver important information for future life detection missions.
Conclusions and outlook Our study has contributed novel insights into the microbiology of analogue sites. In particular, a number of highly valuable model organisms has been retrieved, which directly feeds into the other goals of the MASE project, namely studying the limits of growth of selected isolates, deciphering the molecular principles of resistances  [78] , analysing the genomic and metabolomic inventory of representative microbes  [75] , studying the fossilization processes and detectability of biomarkers during artificial fossilization  [85]  and the optimization of automated life detection  [1, 86]. However, numerous tasks remain to be accomplished in future. These include (i) a comprehensive re-evaluation of the potential impact of the terrestrial organic load on the biology of analogue environments for space research, (ii) the extension of the dataset with additional microbiome data from other extreme environments, (iii) further testing of the hypothesis that a core microbiome in extreme anoxic environment exists, (iv) further identification of so-far unknown microbial taxa found in our molecular survey (v) and the improvement of (targeted) cultivation strategies to increase the available culture collection of microorganisms thriving in extreme, astrobiology-relevant terrestrial sites.