dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, it was shown that these approaches cannot differentiate samples printed using a same engraved cylinder but with different presses. In this paper, we want to explore this problem, i. e. the identification of rotogravure press used. We propose to use the similarity metric learning  (Duffner et al. , 2021) , which has shown its efficiency in biometric applications (as face and signature identification) for rotogravure press identification. The contributions of this paper are the following: 1. We explored the efficiency of similarity metric learning for the rotogravure printing process forensics. 2. We enlarged the database introduced in  (Tkachenko et al. , 2022)  by adding samples printed on strips using electro-mechanically engraved cylinder 2. To the best of our knowledge, electro-mechanical samples have never be considered in other studies in the field of medicine blister authentication.
Database description In this paper, we worked with a regular test pattern E (see Fig. 4. a) printed using two presses (P 1 and P 2 ) and two types of engraving cylinders: 1) two cylinders engraved using chemical etching (C 1 and C 2 ); and 2) a cylinder engraved using electro-mechanical process (M 1 ). The samples printed using the cylinders engraved using chemical etching (Fig. 4. b-c) differ a lot from the samples printed using the electromechanically engraved cylinder (Fig. 4. d-e). Additionally, we studied the impact of the printing support used, using the regular test patterns printed on: 1) thin aluminium foils -called blisters (letter 'b' in the tables and figures); and 2) thick aluminium foils -called strips (letter 's' in the tables and figures). The details about the data set used are given in Table  1.
The subsets P 1 C 2 s, P 2 C 2 s, P 1 C 1 b, P 2 C 1 b were already introduced in  (Tkachenko et al. , 2022). In this new study, we enlarged this database by adding the subsets P 1 M 1 s and P 2 M 1 s for the purpose of the evaluation of identification scenarios discussed in Section 3. 1. All images of the regular test pattern were captured using an USB-microscope 3 with ×5 magnification. The size of images is 432 × 452 pixels. This database was divided in combinations of pairs: E 1 E 2 E 3 E 4 E 5 E 6 Chemical etching P 1 C 2 s P 1 C 2 s -P 2 C 2 s, P 1 C 1 b -P 2 C 1 b and P 1 M 1 s -P 2 M 1 s. Next, samples corresponding to each pair were split on training, validation, and test data set in the same way. We used 50% of images for the training set, 25% of images for the validation and test sets.
The triplets (anchor, positive, negative) were defined as follow: we created unique pairs (anchor, positive) from the first subset (for example, P 1 M 1 s), and we added a random image from the second subset (in our example, P 2 M 1 s) to each pair as a negative sample. We used two approaches for making triplets. For the first strategy of splitting, we did not consider the regular test pattern position (E i , i = 1,. , 6) while preparing the triplets. For the second strategy, the regular test pattern position was taken into consideration. All the involved networks were implemented using Python 3. 6. 2 with Keras 2. 3. 1 and TensorFlow 2. 0. 0. The following sections report the results of experiments done for the validation of the proposed identification approach. We repeated each experiment 10 times with random splitting of the data set on train, test, and validation sets.
Identification of the press used We evaluated the press identification task using three subsets of our database: • Press identification while cylinders engraved using chemical etching and samples printed on strip (subsets P 1 C 2 s and P 2 C 2 s). • Press identification while cylinders engraved using chemical etching and samples printed on blister (subsets P 1 C 1 b and P 2 C 1 b). • Press identification while cylinders engraved using electro-mechanical engraving and samples printed on strip (subsets P 1 M 1 s and P 2 M 1 s). The first series of experiments, illustrated by the results shown in Table  2  let us conclude that the use of ResNet50 network gives better results (in terms of accuracy) than VGG16 network. We can note that after fine-tuning, the ResNet50 model gives a little bit worse result in comparison with the model without 3 Bodelin Proscope Microscope fine-tuning, but if we consider the engraved position the results after fine-tuning are better.
For the first strategy when we formed triplets for the "anchor-positive" pairs we did not consider engraved position, but for the second strategy we formed "anchor-positive" pairs only from the elements in one engraved position. Therefore, the training datasets are different in these two experiments. The accuracy results in Table  3  prove that the consideration of engraved position can improve the identification accuracy (see Table  3 column 3 ). The worst results (identification accuracy equals to 0. 88) are obtained for subsets P 1 C 2 s and P 2 C 2 s. One possible explanation is the big variability of training samples due to the complex standardization of chemical etching  (Kipphan, 2001).
ACKNOWLEDGEMENTS We would like to thanks the  PAUSE Program :  Emergency Assistance  to  Ukrainian Researchers  to support the scientific stay of T. Yemelianenko in LIRIS laboratory. This work was done in the context of the  FakeNets  project funded by  Fédération Informatique de Lyon. All the printed samples were provided by  Sergusa Solutions Pvt Ltd  in the context of  PackMark  project ( IFCPAR-7127 ) supported by the  Indo-French Center for the Promotion of Advanced Research.
2 m to 6 m) and seepage velocity (from 0 m/day to 2 m/day). We expect to observe a beneficial effect of the water movement as it renews the soil energy around the pile. Also, we expect to observe a negative effect of the pile proximity, as the thermal anomalies will have a cumulative effect on the soil temperature drop. All the numerical modeling is realized with CESAR-LCPC software, which is a FEM software specialized for civil engineering problems  [8]. It has the possibility of treating diffusive problems such as thermal problems or hydraulic diffusion in porous media. Also, CESAR-LCPC could be used with a Python script interface, which is useful to describe the needed iterative process and conduct the parametric studies. The numerical modeling processes principally aim to solve the thermal problem in the soil and foundation systems.
The volume fraction, , can be deduced from the position of the top plate recorded by a sensor. We estimated the accuracy based on the fluctuations around the stationary values to be 7%, 3%, and ±0. 002 on the measurements of ⌧ , P , and , respectively. The rheometer could be run in a pressure-imposed or a volume-imposed mode using a feedback control loop involving the scale measurement or the position of the top data collected in volume-imposed rheome pensions having suspending fluids of varia ent. A Newtonian regime in which the mal viscosities, ⌘ s = ⌧ ⌘ f ˙ and ⌘ n = P ⌘ f are independent of St (i. e. independent of with increasing is observed for St < 10. A the rheology transitions smoothly from continuously shear thickening. For St > 10 regime in which ⌘ s and ⌘ n scale linearly w ˙ ) is reached.
Experimental results The practical goal of these experiments is to validate the proposed control architecture. For better illustrate the good performance of the optimal bounded robust controller  (11) , it is compared, in two scenarios, with respect to its nominal form  (7). It is worth to mention that both algorithms have a low computational cost, which permits to be implemented in low cost CPU. The experimental tests are performed in a quadcopter vehicle AR Drone 2. Its firmware was modified to work under the software Fl-AIR -Framework libre AIR which is open source and runs a Linux-based operating system, capable of implementing a wide range of control schemes, see  [28]. An OptiTrack motion capture system was used to estimate the vehicle's position, while its internal Inertial Measurement Unit (IMU) measures its orientation and angular rates.
An optical shutter was controlled by the ProScan III automation system (ProScan III, Prior Scientific Instruments GmbH, Jena, Germany) and used to regulate light exposure. A scientific complementary metal-oxide-semiconductor (sCMOS) camera (pco edge, PCO GmbH, Kelheim, Germany) in conjunction with a Plan Fluor 10X/0. 3 NA objective (Nikon, Egg, Switzerland) was used to image cells for periods between 24 and 48 hours. A flow EZ™ pressure-based flow controller (Fluigent Deutschland GmbH, Jena, Germany) was used to deliver cells and reagents into the microfluidic device. MH1 solenoid valves (Festo AG, Lupfig, Switzerland) were incorporated within the microfluidic device and used to manipulate cells and automate the whole experimental process via a custom-developed MATLAB 1 code.
Data pre-processing Fastq files were pre-processed using an in-house bio-informatics pipeline on the Nextflow platform (Seqera Labs, Barcleona, Spain)  [35] , as described elsewhere  [34]. Briefly, the first step removed Illumina adaptors sequences. The second step de-multiplexed the sequences according to their plate barcodes. Next, all reads containing at least 4 T bases following the cell barcode and UMI sequences were kept. Using the UMItools whitelist, the cell barcodes and UMI were extracted from the reads. The sequences were then mapped on the reference transcriptome (Gallus GallusGRCG6A. 95 from Ensembl) and UMIs were counted. Finally, a count matrix was generated.
Quality control and data filtering All analyses were carried out using R software (version 4. 2. 1  [36] ). Cells were filtered based on several criteria: read number, gene number, count number and ERCC content. For each criterion the cut off values were determined based on the SCONE  [37]  pipeline and calculated as follows: Mean (criterion value) -3*sd (criterion value). After the cell filtering step there remained 7 chip-sorted cells and 82 FACS sorted control cells. Among the chip-sorted cells, 4 of these were sister cells (two couples of cells arising from the mitosis of the same mother cells), and 3 were orphan cells, meaning cells for which the other sister cell was eliminated from the dataset due to poor quality, either from lack of recovery or insufficient lysis. Based on work by Breda et al.
Following cell isolation, within each sample, ERCC spikes (External RNA Controls Consortium  [42] ) were added. ERCCs consist of 92 different synthetic RNAs species and are used as experimental controls, since they are inserted in a known concentration and will undergo all the steps of library construction, as do the cellular mRNAs. Each cell's mRNA and associated ERCC were barcoded with a unique cell barcode and UMI, by reverse transcription using RT primers for which the cell barcode sequence was known (see Methods section). The scRNA-seq library, consisting of the 10 microfluidically-sorted single-cells and the 86 FACS-sorted single-cells, was then generated using a protocol detailed elsewhere  [34]  and sequenced as described previously. As noted, raw sequencing data were processed on using an in-house bio-informatics pipeline, filtered and normalized.
Acknowledgments We thank the computational center of  IN2P3 (Villeurbanne/France ) and  Pole Scientifique de Modelisation Numerique (PSMN, Ecole Normale Superieure de Lyon)  where computations were performed. We acknowledge the contribution of the  AniRA-Cytometrie core facility of SFR BioSciences  ( UAR3444/US8 ). We thank the  BioSyL Federation  and the  LabEx Ecofect  ( ANR-11-LABX-0048 ) of the  University of Lyon  for inspiring scientific events.
The goodness of the model can be evaluated by various metrics, such as the root mean square error (RMSE) which is a classical metric to provide the average distance between the predicted values from the kriging meta-models and the reference values in the dataset (for a predefined number of comparison points). Classically, a convergence study by increasing the number of points used for the construction of the Kriging meta-model must be carried out to judge the relevance of the latter. More sophisticated indicators  [149]  such as the leave-one-out (LOO) strategy or the classical normal quantile-quantile plot graph (normal QQ-plot) can also be used as diagnostics to validate the relevance of kriging meta-models. One of the main advantages of these indicators is that they give also how well the meta-model predicts the values at unknown locations without requiring the use of additional computations.
Acknowledgments This research was sponsored by  Shanghai Sailing Program  (Grant No. 22YF1452000 ), the  Natural Science Foundation of Shaanxi Province  (Grant No. 2022JQ-011 ), the  Fundamental Research Funds for the Central Universities  (Grant No. G2021KY0601 ) and the  National Natural Science Foundation of China  (Grant No. 11972295 ). We appreciate the valuable comments from the reviewers and the fruitful discussions with  Dr. Xiaoxiao Liu  at  Xi 'an  Jiaotong University.
Enhanced weathering module: ORCHIDEE emulator The biogeochemical module is an emulator of the response of net primary productivity (NPP) to phosphorus fertilisation induced by the dissolution of phosphorus-rich basalt dust, as simulated by the land surface model ORCHIDEE-CNP model. Tailored simulations in which basalt dust is applied on all icefree non-agricultural land in the year 2018 were used for the calibration. Once applied to soils, basalt is assumed to have a constant dissolution rate, referred to in this study as the weathering rate. This simplistic approach re ects current understanding and data availability to parameterize weathering rates, and does not account for certain phenomena, such as the reduction of reactive surface over time, as well as the soils, plants and hydrological processes that can potentially in uence weathering.
Methods This paper builds on a systematic review of island case studies included in the Global Adaptation Mapping Initiative (GAMI) database, which includes in total more than 1,600 articles published between 2013 and 2019 that document evidence for observed and implemented climaterelated responses  (Berrang-Ford et al. , 2021b). Systematic reviews serve as an evidence-based method to analyse a large body of literature by assessing the current state of knowledge on a specific topic in a transparent, reproducible, and comprehensive manner  (Berrang-Ford et al. , 2015 , 2020). They are gaining increasing attention in adaptation research due to the exponentially growing literature base. The GAMI review is the first global systematic mapping exercise of peerreviewed literature on climate change adaptation.
It draws mainly on English-language literature included in three large scientific databases [Web of Science, Scopus, and Medline; see Supplementary material  1 and Berrang-Ford et al. (2021a)  for more details on the GAMI database and methodology]. This bias of "Anglophone squint" has been described as narrowing perspectives, weaking arguments, and delegitimising approaches not expressed well in English  (Aalbers, 2004; Chowdhury et al. , 2022). For this paper, we use the online platform sysrev  (Bozada et al. , 2021)  to screen the GAMI database for island case studies. For the initial screening, we include any papers dealing with an inhabited island case study. These include, for example, articles dealing with SIDS or islands which are part of SIDS, island case studies in island states that are not SIDS (e.
g. , Indonesia, Japan, Philippines, Sri Lanka, and the United Kingdom), case studies of islands that are part of a continental state (e. g. , Bangladesh, Germany, and the United States), and case studies of islands within coastal cities (e. g. , Mombasa Island). We include only papers with an explicit island case study and information about "on-the-ground" adaptation. We exclude all such papers without case studies of inhabited islands or articles that do not deal with empirical evidence on climate change adaptation (including articles reviewing planning documents or national communications), literature reviews, conference papers, and even entire books. Articles potentially dealing with island case See https://globaladaptation. github. io/ for the full documentation of the GAMI project. Any area of land smaller than a continent and entirely surrounded by water  (Encyclopedia Britannica, n.
d. ). studies but lacking sufficient information to identify the island are excluded. All included articles are coded according to the study type, political status and island type of the case study (see Table  1 ). For the category of political status, we build on  Petzold and Magnan's (2019)  classification. The category of island type builds on  Grydehøj et al. (2015)  and  Nunn and Kumar (2018). While features such as geomorphology and elevation are certainly relevant for influencing risk levels in terms of impacts and exposure  (Nunn et al. , 2016) , in this review, we focus on the above-mentioned island-related features that may affect the vulnerability and adaptive capacity of diverse islands. The coded dataset was exported from sysrev into an Excel spreadsheet and merged with the GAMI dataset for descriptive analysis (see Supplementary material 2 for the full review database).
Data cleaning and analysis were performed in R (R Core Team, 2021). After merging the GAMI database with our additional coding, we selected various variables of interest and performed chi-square tests of independence and calculated residuals. This analysis covered the main variables defined in the GAMI approach  (Berrang-Ford et al. , 2021b; Lesnikowski et al. , 2021) , which are of relevance to our research questions concerning geographic distribution and potentially differing patterns of responses across island types to weather-related hazards influenced by climate change, including actors involved in adaptation, the inclusion of indigenous/traditional and local knowledge, how far limits to adaptation are challenged, and the depth of responses (see Supplementary material 1 for the list of variables and labels).
In a second analytical step, we extracted the island case studies that were marked according to indicators of high transformational adaptation in the GAMI database, i. e. , those including high "depth" and defined as "adaptations reflect[ing] entirely new practices that involve deep structural reform, complete change in mindset, major shifts in perceptions or values and changing institutional or behavioral norms"  (Berrang-Ford et al. , 2021b, p. 993). Finally, we used an explorative qualitative approach to review the case studies and identify the island-specific aspects of transformational adaptation by carefully reading through each extracted article to understand what transformational adaptation means in the particular contexts. The additional screening of the GAMI database for island case studies was done through title and abstract screening, and, in case of doubt, a full text screening.
Island evidence The review of the GAMI database for evidence on island case studies resulted in 186 articles dealing with islands. Among these articles, we identified 261 case studies on 134 islands (see Figure  1 ). Most of the articles that include islands in the GAMI database deal with single island case studies (59%), followed by articles that include or compare multiple islands (either within the same archipelago or across different regions) (17%), and those that include island and mainland case studies within the same article (17%). Seven percent of the articles deal with archipelagos as a whole, not targeting precise islands (Figure  2 ). Most island case studies involve islands of a non-SIDS island state or archipelago country (40%), followed by SIDS (25%), dependent islands of a continental state (21%), and finally, subnational island jurisdictions (SNIJs), which effectively equate to non-sovereign territories (14%; Figure  3 ).
Global climate change adaptation patterns in islands The documented hazards in the island climate change literature show similar patterns with slight differences between islands and the globally available evidence in the whole GAMI database (Figure  6 ). After general climate change impacts, the most often reported hazard in GAMI is drought (19%) while on islands, it is extreme precipitation and inland flooding (20%). This is followed on islands by sea level rise (15%), which is reported in only 5% of the articles in the whole GAMI database. In the GAMI database, in turn, increased frequency and intensity of extreme heat is reported in 16% of the articles, while on islands only 7% of studies report this hazard. A noteworthy difference between island types is that drought is often documented in studies on islands of non-SIDS island states (17%), while sea level rise is most commonly reported on dependent islands (22%).
SNIJs are most associated with loss of Arctic sea ice, compared to other island types (see also Supplementary Figure  SM1. 1). The "other" category, which is more often used in island case studies compared to the whole GAMI dataset refers, for example, to tsunamis and earthquakes. The response types documented in the literature show overall very similar patterns (Figure  7  and Supplementary Figure  SM1. 5). The most commonly documented response type is behavioral/cultural (overall 33%, on islands 38%), followed by technological/infrastructural, ecosystem-based, and finally, institutional responses. Also between different island types there are hardly any differences in the order of reported response types, apart from institutional responses being the second most reported in SNIJs instead of technological/infrastructural.
In many cases, studies in GAMI were coded as reporting many of the four response types, which explains the lack in statistically significant differences and a seemingly homogeneous picture. Whereas there are only negligible differences between the types of actors involved in adaptation on islands overall and the GAMI database, distinguishing between islands of different political status reveals different patterns (Figure  8 ). Individuals or households, the overall most common actors, are associated mostly with studies in SNIJs. Civil society actors (e. g. , formal community associations or non-governmental organizations) are reported less often in the studies in SNIJs compared to other island types, and more often in SIDS. The roles of different government actors also vary. While national governments are reported frequently in SIDS, sub-national governments are reported relatively more often especially in dependent islands.
International or multinational governance institutions (e. g. , organizations under the United Nations) are reported most often in SIDS case studies compared to other island types or the whole GAMI dataset. Overall, private sector actors (e. g. , small and medium enterprises or larger corporations) are relatively less common compared to other actors yet most common on islands of non-SIDS island states and SNIJs. While in the GAMI dataset, only a quarter of articles includes aspects related to indigenous/traditional and local knowledge, almost a third of the studies on islands include such knowledge. This ratio differs between island types. The majority of studies include indigenous/traditional and local knowledge in the analysis of climate change adaptation only on SIDS, while around a third of the studies in SNIJs, a quarter of studies on dependent islands, and only 17% of those on islands of non-SIDS islands states include such knowledge (Figure  9 ).
Across island types, between 38 and 59% of studies report low evidence of known adaptation limits being overcome, and between 3 and 15% report medium evidence (only in SNIJs do more than half of the studies report low evidence; Figure  10 ). In almost half of the island case studies (47%), there was no/uncertain evidence of whether adaptation limits had been exceeded. In the whole GAMI database there is only one island article with high evidence of adaptation limits being overcome, which is in a SIDS case study [see Otoara  Ha'apio et al. (2018) , described below].