dataesr/scientific-paragraphs-categorization huggingface dataset:
Data management The hunting data collected in Spain are compiled by the responsible administrations of each region and are available upon request at the hunting ground resolution for scientific purposes. This accessibility contrasts with other existing models in Europe, where data are usually collected and archived by hunting associations, and the availability of this information is restricted or in many cases inaccessible at a spatial resolution suitable for incorporation into models of good spatial resolution (see  ENETWILD-consortium et al 2021). Furthermore, in terms of accessibility, only 7 out Although the reporting of hunting data is mandatory in Spain in most cases, most regions are far from having complete data collection. The availability of data at good spatial and temporal resolutions relies on more than half (9 of 17 regions) of the regional governmental hunting agencies, having a mobile or web-based application software, or an online form for hunting statistics data collection (Table  3 ; Fig.
Recently however,  Joo & Jee (2023)  studied the IHL in ten 1 ≤ z ≤ 1. 8 clusters based on deep infrared continuum data, and found a mean IHL fraction of 17%. Detection of the IHL at these higher redshifts, and furthermore accurate characterisation of stellar properties (such as age), are crucial in order for us to understand both the formation epoch and mechanisms of the IHL. In this paper, we use JWST Near InfraRed Camera (NIRCam) observations, along with complementary data from HST, for example, to investigate the physical properties of the IHL and member galaxies in a galaxy group at z = 1. 85. In Sects. 2 and 3, we outline the datasets being used for this analysis, and describe our methodology for identifying the group and performing spectral energy distribution (SED) fitting on both the member galaxies and the IHL.
JWST/CEERS data The Cosmic Evolution Early Release Science (CEERS 1 ) survey is one of the 13 programmes chosen to be carried out near the beginning of JWST's  (Gardner et al. 2006 ) scientific operation, where the observations are made available to the public immediately, in order to demonstrate both data reduction techniques and achievable new scientific goals. CEERS will consist of 10 NIRCam pointings over ∼100 arcmin 2  in the Extended Groth Strip (EGS), with the Near InfraRed Spectrograph instrument (NIRSpec) covering six of these pointings, and Mid InfraRed Instrument (MIRI) coverage for a subset of eight pointings. Here, we use a single pointing, CEERS3, of the first JWST/NIRCam observations taken as part of CEERS on the 21 June 2022. Data were obtained in the short wavelength channel F115W, F150W, and F200W filters, and long-wavelength channel F277W, F356W, F410M, and F444W filters.
Hubble Space Telescope We use the publicly available HST data products version 1. 9, available through CEERS 2 , for the ACS/WFC F606W, F814W, and WFC3/IR F125W filters. These mosaics are derived from HST archival data, but with improved calibration compared to the default pipeline products, and have astrometry tied to Gaia-ERD3. As described in the accompanying data release, the mosaics are created from the combination of  HST programmes 10134, 12063, 12099, 12167, 12177, 12547, 13063, and 13792 , and the reduction and calibration follow a similar procedure to those described in  Koekemoer et al. (2011). Mosaics are at a pixel scale of 0. 03 pix -1.
Searching for overdensities in the EGS field In order to systematically search for overdensities of galaxies that may indicate galaxy groups or clusters in the EGS 2 https://ceers. github. io/releases. html#hdr1 field, we use the  Stefanon et al. (2017) catalogue. Le Bail et al. (in prep. )  have created a compilation of the positions, photometric redshifts and photometric data from  Stefanon et al. (2017 ), Geach et al. (2017)  and  Zavala et al. (2017) , and combined this with super-deblended far-infrared (FIR) fluxes, which we subsequently refer to as the 'FIR catalogue', (with associated CANDELS IDs from  Stefanon et al. 2017). We searched for overdensities of galaxies within the photometric redshift bins [1. 2, 1. 6], [1. 5, 2. 0], [1. 9, 2. 5],  [2. 4, 3. 1], and [2. 4, 3. 8 ], for galaxies with stellar mass M ≥ 10 7 M , in the  Stefanon et al.
Morphology measurements using GALFIT In order to quantify the morphology of the group galaxies, we used GALFIT 3. 0. 5  (Peng et al. 2002; Peng 2010). As seen in Fig. 2 , the morphology of the galaxies in the bluer bands is particularly complex (e. g. clumpy or disturbed, galaxy seg. IDs 78, 92). We are interested in using GALFIT to broadly quantify whether the galaxies are disk-like or spheroid-like, by measuring their Sérsic indices  (Sérsic 1963). For this reason, we used the F444W NIRCam images for the morphological fitting. We fixed A3, page 7 of 17 Fig. 6. Spectral energy distributions. Left: SEDs for galaxies, except for the BGG, in the group (various colours), in comparison to that for the IHL peak (red). Data points show photometric measurements, and correspondingly coloured lines show best-fitting templates to those data from FAST++.
Spectral energy distributions and physical properties of group member galaxies We summarise the physical properties for group member galaxies in Tables  1  and 3. SFRs and stellar masses are shown in Table  1. S /N FIR is defined as the combined S/N of a galaxy in 'FIR' bands, taking into account Spitzer 24 µm to VLA 20 cm data. SFRs in the FIR catalogue were then derived from superdeblended SED fitting by Le Bail et al. (in prep. ), using IRradio data, and are only shown for those galaxies that have S /N FIR > 2. 5, with those at S /N FIR > 5 shown in bold. We  additionally show SFRs derived from our FAST++ SED fitting (see  Sect. 3. 3). We find no evidence for SFR at >3σ in our group galaxies using the HST+NIRCam data in FAST++. It is interesting to note the differences in the two SFR estimates for our galaxies that are IR-detected.
14 fluorite samples from different sites in the Bou-Izourane district and the Tamazeght complex were selected for microthermometric analysis of fluid inclusions. Homogenization (Th) and water ice melting temperatures (Tm) of the two-phase aqueous inclusions were acquired using a LINKAM MDS600 cooling and heating stage, mounted on a Leica microscope and equipped with a Lumenera Infinity 3 camera, at GEOPS. Each fluid inclusion was measured twice. Data that cannot be replicated was excluded. The few fluid inclusions belonging to the same plane having variable liquid-vapour ratios were excluded and considered as evidence of post-trapping deformation (stretching, leakage) or neckingdown  (Goldstein and Reynolds, 1994). The uncertainties associated with Th and Tm measurements are ± 1. 0 • C and ± 0.
, 2015; Gigoux, 2015). The 59 measurements performed on the NIST 612 glass reference material  (Pearce et al. , 1997)  during this study gives an accuracy ranging from 1. 4% to 7. 6% on elemental concentrations. 43Ca was used as internal standard to convert intensities into concentrations. For fluorite analysis, we used a CaO value set at 71. 83 wt% oxide (wt % CaO), corresponding to the value of stoichiometric fluorite. Similarly, a CaO value set at 56. 03 wt% was used for calcite. Data was reduced using Iolite 4 software  (Paton et al. , 2011). The precision and accuracy of the elemental concentrations measured in fluorite and calcite was evaluated using the matrix-match reference materials MACS-3 (calcite) and PP16 (fluorite). The results of Rare Earth Element (REE) quantifications in MACS-3 carbonate reference material and in-house fluorite material "PP16" are listed in Table  S5 , S6 (Appendix B).
The distributions obtained on "PP16" by LA-ICP-MS are compared to the distribution obtained by dissolution -ICP-MS  (Gigoux, 2015)   S4  (Appendix B)). Before dissolution, the samples were washed and rinsed with distilled water and then crushed. These analyses are complemented in this paper by data from the works of  Bouabdli et al. , 1988 (lamprophyre) ;  Kchit, 2007 and Bouabdellah et al. , 2010b (carbonatite)  and  Wang et al. , 2016 (syenite). Two hydrothermal calcite filling fractures in the Tabja and Bou-Izourane deposits were sampled to measure their O and C stable isotopic composition (Appendix A (Fig. A. 7A, D)). After acid digestion of calcite powders, CO 2 was analyzed using a dual-inlet Isoprime 100 spectrometer (Elementar) coupled to a multi-Carb system at the Laboratory of Sciences of Climate and Environment (LSCE; Paris-Saclay University).
Data was standardized to Pee Dee Belemnite (PDB) based on repeated measurements of international reference materials NBS19 and NBS18. The 1SD external reproducibility of the in-house laboratory carbonate standard (MARGO) is 0. 05 ‰ for δ 18 O and 0. 03 ‰ for δ 13 C. A sufficient amount of calcite powder was extracted from each sample to perform two measurements. The difference in the values between the two measurements is less than 0. 1 ‰ for both δ 18 O and δ 13 C. As a result, the mean value of the two measurements was considered. The fractionation equation of  Zheng (1999)  was used to recalculate the O isotopic composition of parental fluids. At Takkat, optical and cathodoluminescence microscopy shows euhedral fluorite crystals associated with calcite and micro-quartz (Fig. 3E, G ). In cathodoluminescence, calcite precipitated in fractures intersecting the fluorite appears as a homogeneous orange colour and/or a succession of dark orange and luminescent zones (Fig.
Acknowledgments This work is part of the results of the collaborative project  PHC  Toubkal  N • TBK/20/110  - Campus France Maroc N • 43705TG "Origine des minéralisations à fluorine de Bou-Izourane  ( Tamazeght, Haut Atlas Central Marocain )" between the  University Mohammed I, Morocco  and the  University Paris-Saclay, France. The authors would like to express their gratitude to the members of "Global Mines", for facilitating the field data collection process. C. Boukari ,  S. Courba , and  S. Boudrif  deserve special mention for their assistance with field support, photography, and sample preparation. We are grateful to  S. Miska  and  J. Nouet  for assistance with SEM,  F. Haurine  for assistance during LA-ICM-PS analyses, and  M. -C. Caumon  for Raman spectroscopy analyses. We also express our gratitude to  Y.
Systematic reviews are essential in reducing the likelihood of bias and ensuring that a comprehensive body of knowledge on the chosen subject is accurately identified  [13]. By following a systematic approach, this study can evaluate all available research related to a set of research questions. This study outlines a systematic process to define the research questions, identify a suitable database, determine the search terms, select analytical software, extract relevant data, and analyze the findings. These steps are illustrated in Figure  1  and elaborated further in the subsequent paragraphs. by comparing them with those of other studies in this field. Finally, Section 5 provides our concluding remarks.
Data Visualization and Analysis The Bibliometrix R-package is utilized for conducting descriptive statistical analysis of the extracted data. Charts are drawn using MS Excel. Furthermore, scientific literature visualization networks are constructed using CiteSpace. These networks consist of researchers, journals, and research institutions, as well as keywords, titles, and abstracts as network nodes. These nodes are linked through co-authorship, co-citation, and co-occurrence analysis. Co-authorship analysis determines the similarity relationships between items based on the number of co-authored documents, while co-citation and co-occurrence analyses illustrate the relationship between items based on the number of times they are cited together and the number of studies where they appear together, respectively  [19].
Results This section presents the findings obtained from the data visualization of the total sample of 505 publications selected based on the search strategy explained in Section 2. 3. The primary results of this study include bibliometric maps of co-authorship among authors and institutions, co-citation among authors, journals, and references, and co-occurrence of keyword and terms. Additionally, a dual-map overlay of journals is created to provide a comprehensive view of the interconnections among the identified journals  [20]. To better understand the evolution of this field over time, descriptive statistics are presented in tabular and graphical formats. The forthcoming sections will present the result of a bibliometric analysis conducted on a dataset of 505 publications, comprising of 335 journal articles (66.
As shown in Table  5 , the first five keywords have burst among researchers at some point in the past years, making them research hotspots during the corresponding periods. Federated learning and collaborative work began to burst in 2022 and continue to be research hotspots currently. Given its relatively high centrality (0. 11) and moderate co-occurrence with other keywords (degree of 11), we believe that federated learning has a greater potential to emerge as a research trend in integration with blockchain-related topics in the field of smart cities. 11), we believe that federated learning has a greater potential to emerge as a research trend in integration with blockchain-related topics in the field of smart cities. The WoS dataset provides four text fields for each bibliographic record: title, abstract, author keywords, and keywords plus.
The WoS dataset provides four text fields for each bibliographic record: title, abstract, author keywords, and keywords plus. To better define the concept  [45] , we analyze the first two unstructured text fields (i. e. , title and abstract) in addition to the keywords studied in Section 3. 4. 1 since they contain a higher frequency of relevant terms than keywords  [46]. CiteSpace is configured with parameters k = 50, LRF = -1, LBY = -1, and e = 1, as well as a pathfinder algorithm that prunes the co-occurrence merged network map. The resulting network includes 557 terms in 15 clusters connected through 2016 links, with a density of 0. 013, modularity Q of 0. 844, and the weighted mean silhouette of 0. 918. Figure  11  displays this network and identifies clusters, where node size indicates frequency and color spectrum represents repetition in different years.
11), we believe that federated learning has a greater potential to emerge as a research trend in integration with blockchain-related topics in the field of smart cities. The WoS dataset provides four text fields for each bibliographic record: title, abstract, author keywords, and keywords plus. To better define the concept  [45] , we analyze the first two unstructured text fields (i. e. , title and abstract) in addition to the keywords studied in Section 3. 4. 1 since they contain a higher frequency of relevant terms than keywords  [46]. CiteSpace is configured with parameters k = 50, LRF = -1, LBY = -1, and e = 1, as well as a pathfinder algorithm that prunes the co-occurrence merged network map. The resulting network includes 557 terms in 15 clusters connected through 2016 links, with a density of 0.
013, modularity Q of 0. 844, and the weighted mean silhouette of 0. 918. Figure  11    11), we believe that federated learning has a greater potential to emerge as a research trend in integration with blockchain-related topics in the field of smart cities. The WoS dataset provides four text fields for each bibliographic record: title, abstract, author keywords, and keywords plus. To better define the concept  [45] , we analyze the first two unstructured text fields (i. e. , title and abstract) in addition to the keywords studied in Section 3. 4. 1 since they contain a higher frequency of relevant terms than keywords  [46]. CiteSpace is configured with parameters k = 50, LRF = -1, LBY = -1, and e = 1, as well as a pathfinder algorithm that prunes the co-occurrence merged network map. The resulting network includes 557 terms in 15 clusters connected through 2016 links, with a density of 0.
013, modularity Q of 0. 844, and the weighted mean silhouette of 0. 918. Figure  11  displays this network and identifies clusters, where node size indicates frequency and color spectrum represents repetition in different years. 11), we believe that federated learning has a greater potential to emerge as a research trend in integration with blockchain-related topics in the field of smart cities. The WoS dataset provides four text fields for each bibliographic record: title, abstract, author keywords, and keywords plus. To better define the concept  [45] , we analyze the first two unstructured text fields (i. e. , title and abstract) in addition to the keywords studied in Section 3. 4. 1 since they contain a higher frequency of relevant terms than keywords  [46]. CiteSpace is configured with parameters k = 50, LRF = -1, LBY = -1, and e = 1, as well as a pathfinder algorithm that prunes the co-occurrence merged network map.
The resulting network includes 557 terms in 15 clusters connected through 2016 links, with a density of 0. 013, modularity Q of 0. 844, and the weighted mean silhouette of 0. 918. Figure  11  displays this network and identifies clusters, where node size indicates frequency and color spectrum represents repetition in different years. 11), we believe that federated learning has a greater potential to emerge as a research trend in integration with blockchain-related topics in the field of smart cities. The WoS dataset provides four text fields for each bibliographic record: title, abstract, author keywords, and keywords plus. To better define the concept  [45] , we analyze the first two unstructured text fields (i. e. , title and abstract) in addition to the keywords studied in Section 3. 4.
1 since they contain a higher frequency of relevant terms than keywords  [46]. CiteSpace is configured with parameters k = 50, LRF = -1, LBY = -1, and e = 1, as well as a pathfinder algorithm that prunes the co-occurrence merged network map. The resulting network includes 557 terms in 15 clusters connected through 2016 links, with a density of 0. 013, modularity Q of 0. 844, and the weighted mean silhouette of 0. 918. Figure  11  displays this network and identifies clusters, where node size indicates frequency and color spectrum represents repetition in different years. 11), we believe that federated learning has a greater potential to emerge as a research trend in integration with blockchain-related topics in the field of smart cities. The WoS dataset provides four text fields for each bibliographic record: title, abstract, author keywords, and keywords plus.
Text Processing of Terms The WoS dataset provides four text fields for each bibliographic record: title, abstract, author keywords, and keywords plus. To better define the concept  [45] , we analyze the first two unstructured text fields (i. e. , title and abstract) in addition to the keywords studied in Section 3. 4. 1 since they contain a higher frequency of relevant terms than keywords  [46]. CiteSpace is configured with parameters k = 50, LRF = -1, LBY = -1, and e = 1, as well as a pathfinder algorithm that prunes the co-occurrence merged network map. The resulting network includes 557 terms in 15 clusters connected through 2016 links, with a density of 0. 013, modularity Q of 0. 844, and the weighted mean silhouette of 0. 918. Cluster #0 on digital agriculture is the largest with 105 nodes and a silhouette score of 0.
Sharma et al. [62]  propose a secure distributed architecture for IoT (called DistBlockNet) using software-defined networking and blockchain to securely verify versions, validate, and download the latest flow rule table for IoT forwarding devices. Aitzhan and Svetinovic  [61]  implement a proof-of-concept for decentralized energy trading that enables anonymous negotiation of energy prices and secure transactions without trusted third parties. Li et al. [58]  employ consortium blockchain technology to implement a secure energy trading system for P2P trading scenarios and propose a credit-based payment scheme to reduce transaction confirmation delays. Gai et al. [30]  present a consortium blockchain-based approach to protect the privacy of energy trading users in the smart grid without restricting trading functions.
Dual-Map Overlay Analysis Our study utilizes a dual-map overlay technique  [20]  to analyze citation patterns at a disciplinary level in smart cities' AI-blockchain research. The technique groups over 10,000 journals (according to JCR) into regions that represent publications and citation activities at a domain-level, providing a comprehensive view of how the field references intellectual sources. The resulting map, displayed in Figure  16 , shows clusters of citing (left side) and cited (right side) journals, with trajectories indicating the frequency and strength of interconnections between them (the parameter of snap to centroids set as radius > 500). Using this map, we identify patterns of how published articles in the field reference other intellectual sources. Our analysis reveals a single dominant citation path in the dataset, with citing region #1 (mathematics, systems, mathematical) citing primarily to cited regions #1 (systems, computing, computer), #12 (economics, economic, political), and #18 (history, philosophy, records).
Discussion The integration of AI and blockchain has gained significant attention from both practitioners and academics  [6]. In the context of smart cities, more stakeholders are recognizing the potential of this fusion to achieve sustainable development goals in urban digital ecosystems. Lotka's law analysis on our dataset shows a strong correlation between the number of authors and the number of publications. Bradford's law assessment identifies 13 core journals in this field. Our research results demonstrate the higher productivity of developing countries in this area. This could be attributed to the fact that smart city development is seen as an effective solution to alleviate the population pressure in developing countries and address their increasing demand for services and infrastructure  [37].
The top three productive journals are IEEE Access, IEEE Internet Things J, and IEEE Trans Ind Inform, while the top three influential sources include the Commun ACM, IEEE T Knowl Data En, and Appl Energ. The trend in AI-blockchain integration has shifted toward encrypted IoT and urban planning, leading to new research fronts in this area. Overall, this study provides valuable insights into the current state and future directions of the field. According to previous research by Hajizadeh et al. [6] , potential future directions for AI-blockchain-IoT integration include: (i) collaborative machine learning models for secure and decentralized information sharing between intrusion detection system participants, (ii) collaborative attack mitigation models that utilize resources from other nodes to share the burden and mitigate attacks, and (iii) a trusted signature database that uses blockchain to create a trusted database of attack signatures, where a group of IoT nodes are connected to the blockchain  [69].
67, 0. 005); industry 5. 0 (9. 67, 0. 005); blockchain (8. 35, 0. 005) distributed databases (0. 45); computation offloading edge computing; cloud software-defined (8. 85, 0. 005); recommender 5 0. 897 2017 computing; industrial networking (0. 45); deep systems (8. 85, 0. 005); internet reinforcement learning distributed databases (0. 45) (4. 42, 0. 05) deep learning; Multi-access edge cyber physical systems 6 0. 939 2019 iot-oriented infrastructure; computing (0. 12); artificial intelligence (0. 12); video (13. 05, 0. 001); iot-oriented infrastructure (13. 05, 0. 001); vehicular ad analytics (0. 12) smart city (9. 31, 0. 005) 7 0. 946 2013 blockchain technology; smart agriculture; bibliometric analysis wireless networks (0. 2); temperature measurement (0. 2); security and privacy (0. 2) key agreement (11.