dataesr/scientific-paragraphs-categorization huggingface dataset:
For copy-move forgery, camera-based methods tend to be ill-suited as the duplicated element will share most of the initial image properties. For this reason, there exist many algorithms targeting copy-move forgery directly  [45]. One challenge of copy-move forgery detection is the presence of Similar but Genuine Object (SGO). In the case of SGO, most algorithms tend to produce a high false positive rate  [46]. This becomes a strong issue in the case of remote ID onboarding as pictures of ID documents contain many SGO. This has been addressed by a few previous papers  [47, 48]  and has yet to be further developed with more work and more datasets. Object-removal is a more specific attack and is less studied in the state of the art. One reason is that most object-removal forgeries are performed either with direct copy move or with inpainting algorithms, because current professional software tends to use exemplar-based inpainting algorithms.
Images Datasets A list of common image tampering datasets is given in Table  19. 2. This table is far from being exhaustive and thus illustrates the wide availability of datasets. In particular since 2019, three extremely large datasets (PS-Battles  [53] , DEFACTO  [54]  and IMD2020  [55] ) have been publicly released containing all kinds of forgeries that should allow researchers to properly evaluate, train or test their forensic algorithms. On the other hand, there exist only a few datasets that are face specific. In 2017, the FaceSwap dataset was released containing only face swap manipulations. They used two different methods to develop the dataset. The Biometix datasets, released in 2017, contained 1082 Face Morphing based on the FERET face dataset. In the Columbia gray and colour  [56]  Splicing 1092 MICC F220 and F2000  [57]  Copy-move 810 Casia v1 and v2  [58]  Splicing, copy-move 6044 COVERAGE  [46]  Copy-move 100 Biometix  [59]  Face morphing 1082 FaceSwap  [60]  Face swapping 1,927 PS-Battles  [53]  All 102,028 DEFACTO  [54]  All 229,000 IMD2020  [55]  All 72000 OpenMFC2020  [61]  All 16,000 DEFACTO dataset, about eighty thousand face morphing are available.
Video Datasets In contrary to image forgery datasets, there exist only a few general video tampering datasets. Some common datasets are given in Table  19. 3. We believe that this is due to the extremely large degrees of freedom when compressing a video that make it difficult to produce a dataset that would suit everyone's needs. Often, researcher ends up crafting their own dataset depending on the feature they use. Thanks to the initiative of the NIST, researchers now have access to a dataset of about 1500 tampered video. If we lack general-purpose video forgery datasets, many video datasets dedicated to the detection of deepfake forgeries have been proposed. This imbalanced could be explained by the dangers they represent against biometric authentication systems but also by the fact that deepfake detection algorithms often operate blindly.
Thus, Copy-move 10 MTVFD  [63]  Copy-move, splicing, frame swapping 30 OpenMFC2020  [61]  Various 1,500 the large degree of freedom when creating the forgeries is considered as part of the problem and the detection method should be able to work in all scenarios. To the best of our knowledge, we count seven large datasets of deepfakes (Table  19. 4). The most important and recent database is the one attached to the Deepfake Detection Challenge (DFDC)  [64]. The Deepfake Detection Challenge is an international competition, launched in December 2019, to help the scientific community to develop new techniques to detect deepfakes. The competition closes at the end of March 2020, and the winning solution achieved an accuracy score of 82%.
Counter-Forensics We described many different tampering detection approaches. In a way, all those methods implicitly assume that the attacker will perform a naive tampering which in turn will leave many traces. Though it is not unreasonable to assume so, it is important to also consider cases where the attacker will try his/her best to hide traces of his forgery. This can go from simply correcting incorrect EXIF metadata to directly target detection methods described in Sect. 19. 4. 2. For example, in ref. [65] , the authors propose a method to suppress the PRNU of an image and to replace it with the one of another camera. In ref. [66] , the authors showed that it was possible to mislead CFA-based algorithms. Other approaches try to hide the forgery at the compression level such as  [67] , and some methods even target specific forgeries such as  [68].
It also imposes a double verification process involving parallel humans and machines. In Europe, the security Agency ENISA has published in February 2021 an analysis of the Remote ID Proofing [77], in which they describe remote verification in several cases with or without a human operator and in the context of a video acquisition to prevent manipulation. They based their work on the 2018 document of German BSI [78] which precise clearly the threats and attacks, but also the condition of success of attacks. In order to qualify the solutions proposed by vendors, some dedicated laboratories begin to appear to test the capacities of manipulation detectors, with private large sets of software or hybrid attacks. Comparable methodologies, tools and datasets are clearly required in the next years to insure a global level of security against face manipulations.
We must anticipate the use of those powerful technologies against any operational system based on face and behaviour recognition software. Thus, the detection of those tampered media is becoming increasingly important. In this chapter, we introduced the four common Face Manipulation categories. We described how each of these methods could be used against an operational system. We focussed our attention on the example of a remote system for identity document verification. It is an interesting use case as it gives many opportunities for an attacker to fool the system. We also gave a brief overview of many detectors specific to faces, general image manipulation detection methods but also some datasets for image and video tampering detection. Then we introduced a few common counter-forensics methods, but also countermeasures to increase the overall system reliability whether the acquisition device is controlled or not.
This would allow the development of more specific datasets, detection methods and interpretability of such methods. This in turn would be extremely beneficial as it would help to close the gap between theoretical research and operational applications. We believe that Face Manipulation Detection must be included in the pipeline of required processing for any official document. It could be organised in a way similar to the ICAO Picture Compliance test for instance. We also recommend the standardisation of the tests and the methodologies to measure the efficiency of the Face Manipulation Detection algorithms in order to obtain fair and efficient comparison between vendors. This standardisation effort needs to take into account the various strategies, contexts and goals described here, in order to define meaningful metrics.
Now all recent density estimates, including  Viikinkoski et al. (2018) ,  Drummond et al. (2018) ,  Baer and Chesley (2017) , and others, falls within the range given in  Elkins-Tanton et al. (2020)  of 3,400 and 4,100 kg m -3 (Fig. 1 ). Furthermore,  Becker et al. (2020)  obtained UV spectra of the asteroid Psyche using the Hubble Space Telescope, showing that the surface could be interpreted as pure metal. They also cautioned that when modeling the observed spectra using intimate mixture modeling, the model spectra are highly sensitive to the assumed grain sizes of the iron and silicate particles: spectra similar to the observations could be obtained with iron volume fractions as low as 10%. These density and compositional data provide constraints on asteroid Psyche's possible formation.
Alternatively, the asteroid could be primarily composed of primitive (unmelted) highmetal material, perhaps a chondrite with silicates already low in iron-oxide, maybe even from a parent body already sampled in the terrestrial meteorite collection. We have hypothesized that Psyche could be formed from highly reduced material that formed near the Sun  (Weidenschilling 1978 ), but we argue below that an origin beyond Jupiter might, in fact, be more likely. When the Psyche orbiter reaches the asteroid, all its science instruments will contribute to answering this science objective. The total iron content (in metal and silicates) will be determined from element data acquired by the Gamma Ray and Neutron Spectrometer (GRNS;  Lawrence et al. this journal)  and from filter data from the Psyche Multispectral Imager  (Bell et al.
2020) is comparable to three other M-types larger than 100 km: (21) Lutetia, (22) Kalliope, and possibly (216) Kleopatra. Vernazza et al. (2021)  used high-angular-resolution imaging from VLT/SPHERE/ZIMPOL to estimate the 3D shapes for these bodies and calculated the densities of Psyche, Lutetia, Kalliope, and Kleopatra as 3,890 ± 530, 3,450 ± 210, 4,360 ± 500, and 3,450 ± 410 kg m -3 , respectively). Given these similarities, our working assumption here is that Psyche is a representative member of its taxonomic class. To more closely examine M-type asteroids and compare them to other asteroids, we compiled the diameters and albedos of all asteroids in the AKARI and WISE datasets  (Masiero et al. 2011; Alí-Lagoa et al. 2018 ). These data were then linked to (i) their taxonomic types as defined by  DeMeo et al.
How We Will Use Our Data to Determine Psyche's Origin Discriminating whether Psyche is core material from a differentiated planetesimal, or an unusually metal-rich and FeO-poor chondritic body, will require the combination of measurements and interpretations from all the instruments. Few individual measurements would be definitive on their own; one definitive measurement might be a strong remanent magnetic field, indicating the presence in the deep past of a core dynamo and requiring a differentiated parent.
Magnetic Field Magnetometer data may enable discrimination between the competing hypotheses that Psyche formed as the metallic core of a mantle-stripped planetesimal or as an unmelted, ironrich chondritic aggregate. If Psyche has a strong magnetic moment, this would strongly favor its origin as a mantle-stripped planetesimal. This is because a molten core formed after mantle stripping could generate a long-lived magnetic field by the dynamo process  (Bryson et al. 2017; Maurel et al. 2020). The essence of a dynamo is that large-scale advective motions of a conductive fluid can inductively generate currents that amplify an arbitrarily weak initial magnetic field  (Moffatt and Dormy 2019). This magnetic field could then be recorded as remanent magnetization by ferromagnetic minerals (e. g. , the iron-nickel minerals kamacite and tetrataenite) as they crystallize or cool through their magnetic ordering temperatures on the outside of the cooling core.
A dynamo-generating core would produce nonuniform magnetization due to the curved geometry of the field  (Runcorn 1975 ) and its likely temporal variation during the gradual solidification of the body  (Neufeld et al. 2019). In contrast, a nebular field may impart more uniform magnetization, as the field source is external to the body  (Runcorn 1975). These two types of magnetization configurations could be distinguished using a combination of data analysis and modeling tools developed by the Magnetometry Investigation  (Weiss et al. this journal). Magnetometer data can also be used to constrain the bulk electrical conductivity of Psyche by detecting induction fields due to variations of the solar wind that Psyche is exposed to. If the body has very low bulk conductivity the induced fields outside the body would be weak (a Moon-like interaction); if highly conducting, there would be a region of enhanced magnetic field in the upwind side of the body (a Venus-like interaction).
"Silicates" are then defined as other major rock-forming elements  (Si, O, Al, Mg, Ca, etc. ). The highest sensitivity GRNS metal-to-silicate measurements are accomplished using neutron data from the Neutron Spectrometer (NS). The NS measures neutrons in three energy bands; thermal, low-energy epithermal, and high-energy epithermal neutrons. The Gamma-Ray Spectrometer (GRS) anti-coincidence (AC) shield provides a measure of fast neutrons  (Lawrence et al. 2019 (Lawrence et al. , 2016)). Thermal neutrons and fast neutrons are independently sensitive to the concentration of Fe and Ni via their thermal-neutron absorption properties (e. g. ,  Feldman et al. 1998; Peplowski et al. 2016 ) and average atomic mass (e. g. ,  Gasnault et al. 2001; Lawrence et al. 2017) , respectively. High metal fractions result in relatively low thermal neutrons and high fast neutrons; the converse is true for high silicate fractions.
Unlike GRNS or laboratory elemental techniques, however, multispectral observations in the visible to short-wave near-IR cannot typically be used to infer absolute elemental or mineralogic abundances. This is because of a variety of fundamental ambiguities in the interpretation and modeling of such data caused by the competing effects of particle size, particle shape, degree of compaction, viewing geometry, and other factors. Thus, multispectral observations like those to be made with the Psyche Multispectral Imager can be used to detect metallic iron-and nickel-bearing materials as well as to detect the presence of some common rock-forming iron-bearing silicates, but abundances of these phases can usually only be inferred or modeled in a relative sense. Still, the limitations on deriving absolute abundances can be offset to some degree by the ability to map relative variations in the inferred metal-to-silicate ratio at high spatial resolution across a planetary surface.
Silicate Compositions GRNS measurements of the elemental composition of silicates are primarily measured using gamma-ray data, and supplemented using neutron data. For GRNS data it is important to note that the gamma-rays and neutrons are sensitive to Psyche's bulk composition and therefore do not and cannot distinguish between the surface's mineralogic form or mixture. The primary elements used to characterize Psyche's silicate compositions are Fe, Si, Al, K, and Ca. As described in  Elkins-Tanton et al. (2020) , various combinations of these elements can discriminate the type of material (chondritic versus achondritic) that is present on Psyche. The specific detection sensitivities for these elements are given in  Lawrence et al. (this journal). The Imager's study of silicates will be primarily based on the detection, characterization, and mapping of the so-called "1-micron" absorption band in iron-bearing silicates like pyroxene and olivine (see, e.
| Spatial-temporal climate distributions of T. aestivum We defined a climate envelope for European T. aestivum using georeferenced occurrence records from the Global Biodiversity Information Facility (GBIF. org) supplemented with 33 geospatial T. uncinatum records from  Wedén et al. (2005) , and our own 20 monitoring sites (Figure  2a ). All statistical analyses were conducted in R Studio (Version 2022. 2. 0. 443)  (RStudio Team, 2022). GBIF occurrences were checked against common spatial mis-placement using the R package "CoordinateCleaner"  (Zizka et al. , 2019).
| Climate means and time series We tracked the historical annual climate for each monitoring site by extracting monthly mean temperature at 2 m for the years 1980-2018 from the ERA-5 reanalysis dataset  (Hersbach et al. , 2020). For each year, we calculated the mean temperature for the months June, July, and August. We then compared these temporal climate records to the spatial mean climate values experienced by European T. aestivum. We extracted 30-year mean temperature of the hottest quarter for all T. aestivum occurrence coordinates from WorldClim 2. 0 (inclusive of the monitoring sites, rasters at 1 km resolution; Fick & Hijmans, 2017). To track time series of air temperature at 2 m and total precipitation during the monitoring period, we downloaded ERA-5 re-analysis hourly weather data for the period of 2011-2018, covering the extent of the map inset in Figure  2a  (5-10°E, 45-50°N) (Copernicus Climate Change Service, 2019, p.
| Random forest model selection and validation The machine learning algorithm random forest was used in R (package "randomForest";  Liaw & Wiener, 2002)  to predict variability in productivity among sites and among years. Relative to other multiple regression models, random forests have been shown to be less prone to over-fitting, more robust to co-linearity among predictors  (Matsuki et al. , 2016 ) and more capable of identifying complex interactions among predictor variables  (Strobl et al. , 2009). Model selection was performed as follows: first, we included all soil physicochemical, meta-community, dendrological and climatic variables in our models; second, variables were sorted according to importance metrics (inc node purity and % mean squared error); third, we compared model performance (% variability explained) as predictors were removed from the model, starting with the least important.
Our final model included only those variables beyond the inflection point of diminishing returns on performance (Figure  6a ; Table  S1 ). To visualize the effects of individual variables on annual truffle productivity, we extracted the partial feature contributions using the "forestFloor" package in R  (Welling et al. , 2016). We cross validated our models using k-fold cross-fold evaluation. Data were separated into k = 10 partitions and model performance was gauged by summarizing the coefficient of determination for 1000 random folds.
Metagenomic sequencing and NRPS gene annotation Differences in potential functions between maize and faba bean microbiomes were compared using rhizosphere DNA samples of monocultured maize and faba bean grown in the fields in the zero P fertilizer treatment. DNA was fragmented to an average size of ~300 bp using a Covaris M220 focused ultrasonicator (Woburn, MA). Paired-end libraries were constructed using TruSeq DNA Sample Prep Kits (Illumina, San Diego, CA). Sequencing was conducted using the Illumina HiSeq 4,000 platform in 2 × 150 bp paired-end mode, resulting in a total dataset of 95. 7 Gb. The raw sequence data were deposited in the Genome Sequence Archive under accession number CRA005329. The low-quality reads were filtered by Fastp and the high-quality reads were assembled with  MEGAHIT (mink = 27, maxk = 141, and step = 20; Li et al.
, 2015). Contigs ≥300 bp were selected as the final assembly result for further gene prediction and annotation. The open reading frames (ORF) were predicted using MetaGene  (Lv et al. , 2014). The predicted ORFs with length ≥ 100 bp were retrieved and translated to amino acid sequences using the NCBI translation table. All sequences from gene sets with a 95% sequence identity (90% coverage) were clustered as the non-redundant gene catalog by CD-HIT  (Fu et al. , 2012). Finally, high-quality reads were compared with non-redundant gene sets (95% identity) using SOAAPaligner, and the abundance of genes in the corresponding samples was determined  (Li et al. , 2008). Representative sequences of the non-redundant gene catalog were compared (e-value threshold, 10 -5 ) against databases COG (The Clusters of Orthologous Groups) and KEGG (the Kyoto Encyclopedia of Genes and Genomes) using BLASTP (Version 2.
2. 28+). The annotation results were extracted with KOBAS (v3. 0. 3) software (-t blastout:tab, -s ko;  Xie et al. , 2011). Nonribosomal peptide synthetase (NRPS) involved in the biosynthesis of hydroxamate-, catecholate-, and nitrogen heterocycle-based siderophores and peptide antibiotics were annotated as described previously  (Kadi and Challis, 2009). Here, the genes involved in the biosynthesis of siderophore group nonribosomal peptides  (KEGG pathway: ko01053, e. g. , pchD, mbtA, irp1, entB, mxcE, dhbB, cibB)  were selected  (Satinsky et al. , 2014).
, 1997)  The antagonistic activities of bacterial isolates against FOF were tested using the dual culture assays on PDA  (Yin et al. , 2021). Sole inoculation of FOF was used as the control. All culture plates were placed in the dark and incubated at 28°C for 5 days until the PDA medium in the controls was completely covered with FOF mycelia. The colony diameter was measured radially with a ruler. The inhibition rate was calculated as follows: ( ) % - = Control Treatment Inhibition rate 100 Control × Four bacterial isolates (B004, B005, B021 and B208) showing relatively strong inhibition rates (30%) of radial fungal growth were selected for further experiments. A neighbor joining (NJ) phylogenetic tree was constructed by the sequences of four strongly antagonistic isolates and their reference sequences from GenBank: Bacillus subtilis (MH666097.
Statistical analysis Crop yields and F. oxysporum gene copies in the field experiment were analyzed using two-way analysis of variance (ANOVA) and mean values were compared by Student's t-test or Duncan's multiple range test using R version 4. 1. 0 1. Spearman'scorrelation coefficients were calculated in R software between F. oxysporum gene copies and maize and faba bean yields. The inhibitory effects of Bacillus and Pseudomonas strains were tested by calculating the correlations between F. oxysporum gene copies and the relative abundance of Bacillus and Pseudomonas. Bacterial richness and Shannon index were calculated using "vegan" package. Two-way ANOVA was used to analyze alpha diversity differences between planting pattern and fertilization in each compartment of maize and faba bean. The microbial community structure was analyzed through principal coordinate analysis (PCoA) based on a Bray-Curtis distance matrix, and the coordinates were used to draw 2D graphical outputs  (Chen et al.
, 2019). Permutational multivariate analysis of variance (PERMANOVA) was conducted to test the significance of planting pattern and fertilization using the adonis function in the "vegan" package with 999 permutations  (Oksanen et al. , 2007). In the pot experiment, Student's t-test was used to assess the differences in the number of siderophore-producing rhizobacteria and the abundance of the pathogen F. oxysporum between planting patterns, as well as in the number of siderophore-producing rhizobacteria and the functional categories or genes of NRPS and iron metabolism between crop species in the metagenomic analysis. Duncan's multiple range test was used in the inoculation experiment to test the antagonistic effects of the four isolates against F. oxysporum.
Coenzyme transport and metabolism, energy production and conversion, and secondary metabolite biosynthesis, transport and catabolism were significantly enriched in faba bean RS (Figure  4E ). Of all COGs likely related to Yields of maize and faba in monoculture and intercropping (A). Partial land equivalent ratio (pLER) of maize and faba bean in intercropping systems (B). Fusarium oxysporum gene copies in bulk soil, rhizosphere and root endosphere of maize and faba bean (C). Spearman correlations between yields of faba bean and maize with the abundance of Fusarium oxysporum in bulk soil, rhizosphere and root endosphere at zero P fertilization and 40 kg P ha -1 yr. Frontiers in Microbiology 08 frontiersin. org COG1199 (rad3-related DNA helicase) for replication recombination and repair and COG1032 (radical SAM superfamily enzyme YgiQ, UPF0313 family, 0.