dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, it registered a completeness value of 88. 7% which is nevertheless quite good considering the dimensions of the object (height of around 25 meters). An interesting visual observation in Table  1  may show, however, a discrepancy as regards to the density level of the point cloud in several areas. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVIII-M-2-2023 29th CIPA Symposium "Documenting, Understanding, Preserving Cultural Heritage: Humanities and Digital Technologies for Shaping the Future", 25-30 June 2023, Florence, Italy Thirdly and finally, in order to ascertain the quality of the point cloud density, Table  2  shows a density analysis as performed in the software CloudCompare. In this analysis, the number of neighbouring points were counted for each point in the point cloud within a spherical radius of 5 cm.
In both dataset A and B, Metashape showed a very homogeneous density throughout the resulting point clouds. While this may be due to point cloud post processing algorithms inside Metashape itself, it still presents a clean result throughout as far as point cloud density is concerned. With Nerfacto, a more heterogeneous density can be observed visually from Table  2. In dataset A, Nerfacto registered a very low average of only 86. 75 neighbours while Metashape gave an almost ten times denser value. The higher density area is concentrated on the left, central, and lower parts of the object while the upper parts registered lower densities. This finding is consistent with the previous completeness analysis. For dataset B, Nerfacto yielded an average number of neighbours of 34 points. Interestingly, Metashape gave a slightly lower value of 29.
40 points. Visual inspection indicate that Nerfacto generated very dense point clouds in several specific parts of the object, namely the central pillar. A cropped image showing more details of this central pillar can be seen in Figure  2. Note the higher density of the central part of the dataset, which may be explained by the fact that most of the images in the dataset is centred on this part, in line with keeping a convergent geometry commonly practised in close range photogrammetry. This induces more radiance fields on the said area, thereby generating a much denser part of the point cloud. In Figure  2 , several parts of the pillar gave a very high density value in Nerfacto's point cloud, averaging on 60 points. In some points, the number of neighbours even reach 204. The points with the highest density seem to be concentrated on object borders, while flat surfaces generally have reduced density.
CONCLUSIONS This paper presented a preliminary test on the use of the novel NeRF method specifically for use in cultural heritage objects. The method performed well in terms of processing time, which is a fraction of the time required by traditional MVS. It is also accurate as far as point fidelity is concerned, as evidenced by the values of M3C2 mean error. It also fared well in the completeness test, despite encountering complex cases such as the ornate dataset B, scoring 88. 7%. The simpler dataset A gave a slightly albeit even higher score of 92. 2%. Several limitations can however be identified within the context of heritage documentation from the authors' point of view. The current state of NeRF still requires image orientation, which in turn requires multiple overlapping images. Therefore, this does not reduce the field effort and time required to perform data acquisition.
The solid angle coverage is completed with forward copper/LAr and tungsten/LAr calorimeter modules optimised for electromagnetic and hadronic measurements, respectively. The ATLAS trigger and data acquisition system is responsible for the online processing and event selection for permanent storage and offline analysis. It employs a two-level trigger system  [16]  to select data at an average rate of about 3 kHz. Its first level (Level-1 or L1) is hardware based. It uses custom-made electronics to select events after processing signals coming from the calorimeters and muon chambers. The Level-1 system runs at a fixed latency of 2. 5 𝜇s and accepts events at a maximum rate of 100 kHz. The second level of the trigger system, the high-level trigger (HLT), uses a dedicated computing farm of approximately 60,000 physical processing cores or 2.
0M HS06  [17]  to run algorithms similar to those used in the offline reconstruction. The HLT software was redesigned for Run 3 to support multi-threaded execution, allowing for more efficient use of computing resources. The algorithms execution order is optimised to run fast algorithms first, providing early background rejection, and more precise and CPU-intensive algorithms later to make the final event selection. Both the L1 and HLT triggers, in addition to performing selection, can identify Regions-of-Interest (RoIs) in 𝜂, 𝜙, and 𝑧, which limit the regions of the detector on which subsequent algorithms are executed. The processing uses data from all detectors either in RoIs only or in more extended 𝜂-𝜙 and 𝑧 range (up to the full detector), depending on the algorithms. The HLT makes a decision within a few hundred milliseconds on average.
Inner detector tracking A previous paper describes the design and performance of the HLT inner detector tracking on ATLAS during the LHC Run 2  [19]. The algorithms are improved for Run 3 but the Run 2 description is sufficient for the purposes of this paper. The tracking algorithms operate in two steps: a fast tracking stage typically followed by a precision tracking stage. Before any tracking algorithm is run, the data in the silicon modules of the inner detector are reconstructed into clusters. This can be run on the full detector or within RoIs; the later approach saves both processing time and data transfer bandwidth. The TRT data within RoIs is only processed for the precision tracking stage. In events characterised by large jet multiplicities or large number of simultaneous 𝑝 𝑝 interactions per bunch crossing (pile-up), multiple individual RoI constituents can be merged into a super-RoI, such that the tracking algorithm does not run multiple times in RoIs with large overlapping regions, which would lead to wasted CPU resources and biases due to track double-counting.
The network was trained on ten million jets from simulated 𝑡 t interactions that include all jet flavours, with hyperparameters similar to those selected for the original DIPS tagger  [28]. To prevent the discriminant from preferentially selecting jets based on kinematics, the jets were resampled to have the same twodimensional 𝑝 T and 𝜂 distributions for each flavour. The discriminant was trained in the K interface to T  [29]  and ported to the ATLAS trigger software with  [30, 31].
The effect of multiple 𝑝 𝑝 interactions in the same event was modelled by overlaying the hard-scatter interactions with events from the P 8. 160 generator, using the NNPDF2. 3 PDF set and the A3 parameter tune  [41]. Particle interactions with the detector are simulated with ATLAS software  [42]  based on G 4  [43]. Events generated with √ 𝑠 = 13 TeV were used for training the fast 𝑏-tagging algorithm, while events generated with √ 𝑠 = 13. 6 TeV were used for performance evaluation. Since the background trigger rate is driven by light flavour jets, the results presented in this paper focus on light jet rejection and omit 𝑐-jet rejection, which is lower but otherwise similar as documented in other ATLAS results described above. Besides ROC curves and efficiency calculations, the CPU consumption was also evaluated and taken into account for deciding the optimal operational points of the algorithm.
In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT, Argentina ;  YerPhI, Armenia ;  ARC, Australia ;  BMWFW  and  FWF ,  Austria ;  ANAS ,  Azerbaĳan ;  CNPq  and  FAPESP ,  Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ;  CERN ;  ANID ,  Chile ; CAS,  MOST  and  NSFC, China ;  Minciencias , Colombia;  MEYS  CR,  Czech Republic ; DNRF and  DNSRC ,  Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU, France ;  SRNSFG ,  Georgia ;  BMBF ,  HGF  and  MPG, Germany ;  GSRI ,  Greece ;  RGC  and  Hong Kong SAR, China ;  ISF  and  Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ;  CNRST, Morocco ;  NWO, Netherlands ;  RCN ,  Norway ;  MEiN, Poland ;  FCT, Portugal ;  MNE/IFA, Romania ;  MESTD, Serbia ;  MSSR ,  Slovakia ;  ARRS  and  MIZŠ, Slovenia ;  DSI/NRF, South Africa ;  MICINN, Spain ;  SRC  and  Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of.
EVALUATION This section evaluates EIDA, in particular the termination and correctness of the algorithm, and compares it with two other assignments using several metrics. As real experiments are not possible with such a dense nanonetwork, we evaluate our assignment mechanism through simulations. Several nanonetwork simulators exist, but only one, BitSimulator  [3] , is scalable and allows to simulate dense networks  [11]. BitSimulator can simulate tens of thousands of nodes, especially at routing and transport levels. It comes with a visualization program that graphically displays the simulation event. It is free software and has been used to validate the results of several papers 1. We use it to evaluate our EIDA scheme. We implemented in BitSimulator EIDA and zone density computation described in Sec.
Method comparison The works cited in Sec. 2. 3 either assign sparse ids, like the random assignment mechanism (leading to nonequity), or consecutive ids, like the ideal assignment (leading to a high number of packet exchanges). Thus, this section compares the id assignment results of EIDA, random, and ideal assignment mechanisms. The methods are applied on the same zone, which contains 31 nodes. Table  3  shows the assignment results. The ideal assignment assigns ids sequentially to nodes and incrementally (0, 1,. , 5, 0, 1,. , 5,. ), hence, ids are equitably assigned (5 or 6 nodes per group). EIDA results are taken from Fig. 2. Random assignment results are taken from a simple C++ program using the classical Mersenne Twister RNG with seed=10 3. Table  4  compares the three methods in terms of minimum guarantee, number of exchanged packets, and equitability.
These new measures are first tested on synthetic images and further applied to a biomedical dataset. Results: The results suggest that ensemble techniques are able to detect different levels of image dynamics and their degrees of randomness. These methods lead to more stable entropy values (lower coefficients of variations) for the synthetic data. The results also show that these new measures can obtain up to 92. 7% accuracy and 88. 4% sensitivity when classifying patients with pulmonary emphysema through a k-nearest neighbors algorithm. Conclusions: This is a further step towards the potential clinical deployment of bidimensional ensemble approaches to detect different levels of image dynamics and their successful performance on emphysema lung computerized tomography scans. These bidimensional ensemble entropy algorithms have potential to be used in various imaging applications thanks to their ability to distinguish more stable and less biased image patterns compared to their original counterparts.
For instance, if the elements of an image are sorted, its DistEn 2𝐷 value is not changed considerably. However, as expected theoretically and intuitively, sorting leads to a lower entropy value (less irregularity). To alleviate this shortcoming, two-dimensional dispersion entropy (DispEn 2𝐷 )  [7]  was proposed based on mapping pixels within an image into different 𝑐 classes, leading to a reliable, fast, and stable method  [7]. DispEn 2𝐷 was applied to synthetic and real medical datasets and was found to be more stable, faster, and without undefined values compared to SampEn 2𝐷  [7]. Recently, to address the sensitivity of one-dimensional entropy to their parameters, the concept of ensemble entropy was introduced based on generating stable and low bias data patterns for entropy estimation  [16].
This condition is derived from chronic obstructive pulmonary disease (COPD) where the patient's airflow is limited and is not fully reversible. Based on the lobular anatomy, pulmonary emphysema can be classified as centrilobular emphysema (CLE), panlobular emphysema (PLE), and paraseptal emphysema (PSE)  [21]. In this study, three types of pulmonary tissue were analyzed: normal, CLE, and PSE. Computed tomography (CT) is established as an in-vivo method for emphysema analysis as it can be useful for both diagnosing and quantifying pulmonary emphysema, providing morphological information  [22, 23]. When using high-resolution CT, emphysema is identified by the presence of low attenuation areas contrasting with the surrounding parenchyma of normal attenuation values  [22]. The remaining paper is divided as follows: section 2 describes the original and ensemble entropy methods, and details the synthetic data and biomedical dataset used; section 3 shows and discusses the results obtained for the ensemble and original entropy techniques.
Methods and materials Based on the ensemble approaches recently published  [16] , we propose the two-dimensional extensions of ensemble dispersion entropy, ensemble permutation entropy, and ensemble sample entropy for twodimensional data (respectively named EnsDispEn 2𝐷 , EnsPerEn 2𝐷 , and EnsSampEn 2𝐷 ). Moreover, we also introduce the use of two-dimensional ensemble distribution entropy (EnsDistEn 2𝐷 ), and two forms of twodimensional ensemble fuzzy entropy (EnsFuzEn 2𝐷 ). First, we describe the ensemble techniques and summarize the corresponding original entropy metrics. Later, we describe the synthetic data for validation tests and the biomedical dataset used herein. Finally, we present the parameter settings used for both the synthetic and biomedical datasets. The MATLAB ® code used in this paper is available at https://github.
Biomedical application The biomedical dataset used in this study is publicly available at https://lauge -soerensen. github. io /emphysema -database  [26]. This public dataset has 168 patches of high-resolution computed tomography scans from 39 subjects. These subjects were either never-smokers individuals  (9) , smokers  (10) , or smokers diagnosed with chronic obstructive pulmonary disease (COPD). The patches were manually segmented by an experienced radiologist and experienced pulmonologist. These patches were divided into 3 categories regarding their leading pattern: normal tissue (NT), centrilobular emphysema (CLE), and paraseptal emphysema (PSE). CLE is usually distributed in the upper lobe of the lung or in the superior segment of the lower lobe. When CLE is present, an enlargement of the centriacinar airspace is verified mainly in proximal respiratory bronchioles.
Parameters settings First, the effects of parameter settings and the performance of ensemble entropy approaches was compared to the original entropy methods in the validation tests. For this purpose the methods were tested with the following parameter settings: The ensemble entropy algorithms were also tested, on the biomedical dataset, with the parameters above. For the original entropy algorithms, the parameters were fixed to 𝑚 = 2 for DistEn 2𝐷 , FuzEn 2𝐷 , PerEn 2𝐷 , and SampEn 2𝐷 , 𝑟 = 0. 2×SD(X) for FuzEn 2𝐷 , and SampEn 2𝐷 , 𝑛 𝑐 = 6 and NCDF mapping for DispEn 2𝐷 , 𝐵 = 1024 for DistEn 2𝐷 , and 𝑛 = 2 for FuzEn 2𝐷. For the statistical tests, we used Matlab's ® kruskalwallis function for the Kruskal Wallis test, and the multcompare function for Tukey's honestly test. In addition, we also used the function fitcknn for the k-nearest neighbor (KNN) classifier with automatic hyperparameters optimization option.
Moreover, in terms of accuracy, all of these ensemble methods (except for EnsFuzEn 𝑅 2𝐷 ) achieve higher accuracy values than the corresponding original entropy algorithms (see Table  6 ). In Figs. 7a-7b, we showed four examples of patches that were correctly classified as normal tissue (NT) and CLE, respectively, using the best entropy method for classification, EnsFuzEn 𝑀 2𝐷 , with a KNN classifier. On the other hand, Conversely, in Figs. 7c-7d , we can observe two instances of misclassified patches, where NT was incorrectly classified as CLE and PSE, respectively. In Ref. [28] , one of the latest works using the same database, the authors proposed two neural networks with data augmentation obtaining a total of 5520 CT images (512 × 512 pixels) to classify emphysema. When classifying each class, their best network, the enhanced multiscale residual network with data augmentation (EMS-ResNet-DA), obtained an accuracy over 95% for PSE.
The remaining classes obtained accuracy values between 90 and 95%. Overall, the average accuracy was 94. 6%. The sensitivity and precision values of each class were all around 95%. The F1-score (macro) was approximately 93% for CLE, around 95% for PSE, and 94% for NT  [28]. However, it is important to mention that these results were obtained for a substantially higher number of data. Our results for our best model, EnsFuzEn 𝑀 2𝐷 , are only slightly inferior using only 168 patches. Furthermore, our results are obtained based on a single ensemble-based entropy feature to classify emphysema patches retrieved from CT scans. Furthermore, the pre-trained Visual Geometry Group with 16 layers depth (VGG16) model was also used to classify the 512 × 512 pixelssized CT images  [29]  using the dataset of  [26].
After pre-processing the images, they divided the 115 CT images into 2 classes: normal and emphysema subjects. In that work, the accuracy, precision, sensitivity, and F1-score were verified to be 88%  [29]. In our case, when using only a single feature EnsFuzEn 𝑀 2𝐷 , the sensitivity value of the classifier was slightly lower (86. 5%). Isaac et al. [30]  also proposed a model to classify the same emphysema dataset based on a competitive co-evolution model with feature selection, reporting an accuracy of 93. 7%, precision of 90. 61%, recall of 90. 61%, and specificity of 95. 3%  [30]. On the other hand, Barnacles Mating-based Butterfly Optimization Algorithm hybrid classifier (BM-BOA-HC)  [31]  was also proposed to study this dataset  [26]. In this case, the accuracy obtained was 93. 8%. Moreover, the precision was 92.
Acknowledgements This work was supported by  FCT (Foundation for Science and Technology)  under the projects  UIDP/04559/2020  and  UIDB/04559/2020  to fund human resources and activities of  Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics. This work was also supported by both  FCT  and the  ESF (European Social Fund)  under the scholarship  UI/BD/152802/2022. The authors would also like to thank to the  Laboratory of Advanced Computing (LCA) at the University of Coimbra  for providing computing resources that have contributed to the research results reported within this paper (https://www. uc. pt /lca).
In addition, the cathode in the SinplexPro plasma torch is significantly different from the tapered cathodes usually studied in the context of cathode-plasma coupling. Thus, there is no ground to neglect the effect of the plasma and cathode on the cathode sheath. In the current context, the approach of Cayla et al. (Ref 21)  was optimal in terms of complexity and accuracy; it can be easily employed for the present application. This approach can be implemented as a relatively simple script in the programming language Python with only open-source tools as it does not require any external sources of large amounts of data or models. In addition, the use of such Python script with a polynomial Fig. 1  Internal geometry of the plasma torch SinplexPro TM. The tungsten parts of electrodes are colored in dark gray and the copper neutrodes and copper parts of electrodes in orange.
Model Assumptions The model of the plasma torch was three-dimensional and unsteady. The time step used for the solution of the model equations was 10 -7 s. All the conservation equations of the model in plasma and electrodes were solved with the finite volume method in the open-source CFD (computational fluid dynamics) software Code_Saturne (Ref 32). The specific assumptions for some certain parts of the model are given below. Solid cathode and anode: 1. The cathode and anode were made of tungsten doped with lanthanum oxide. 2. The electrode material was assumed to be homogeneous. The cathode thermal and electrical conductivity, enthalpy and specific heat were that of pure tungsten. They were all functions of the temperature and taken from Ref 33. The tungsten properties used in this study covered the temperature range of the solid and liquid states with an enthalpy increment during the melting of the material (latent heat of fusion).
The internal iterations of the cathode sheath model started from R ¼ 0 mm and ended at R ¼ 4 mm. The required number of external iterations between the two-temperature torch model and cathode sheath model varied from 7 to 15 depending on the initial estimate of the cathode sheath parameters. The sequence of the cathode sheath model iterations is shown in Fig. 3. The solution of Eq 1 and 2 is done by a Python script using the scipy. optimize. fsolve. For the solution, a continuous function n e T e T h ; T e was created using scipy. interpolate. interp2d based on the lookup tables of the argon plasma properties. Once calculated, the radial profiles of the properties on the cathode surface were approximated by polynomials up to the power of 8 using scipy. optimize. curve_fit and fed into the two-temperature torch model in Code_Saturne.
These environments incorporate games, multiple-choice questions, and text to enable learners to fully understand basic concepts. The level of engagement and immersion of learners shows that the Labster is integrated with several learning management systems  (Blackboard, Canvas, Moodle, Sakai, etc. ). (El Kharki et al. 2021 ) designed and developed a web-based virtual laboratory for physics teaching in Moroccan universities. This virtual laboratory consists of 12 virtual assignments of the physics curriculum modules, deployed in 12 sciences faculties in Morocco. The laboratory is a computer simulation implemented in JavaScript and integrated into an interactive learning environment based on Moodle platform. (Salame et al. 2021). The PhET aimed to teach physics, mathematics, chemistry, biology, and earth sciences worldwide using free interactive simulations translated into 65 languages.