dataesr/scientific-paragraphs-categorization huggingface dataset:
Co-occurrence In the field of bibliometrics, an examination of co-occurrence was performed by utilizing a matrix of keywords that appear together. The objective of this analysis was to reveal potential relationships between two elements found within bibliographic works that are present in the same work  [236]. The methods for carrying out co-occurrence analysis in bibliometrics have matured over the past two decades  [237, 238]. Consequently, some researchers have introduced strategies aimed at creating novel approaches for analyzing Medical Subject Heading (MeSH) terms sourced from the PubMed literature database  [239]. This approach is commonly referred to as MeSH term co-occurrence analysis. The typical workflow for MeSH term co-occurrence analysis involves: (i) extracting MeSH term data from the PubMed database; (ii) constructing a co-occurrence matrix; and (iii) performing downstream analysis on the matrix  [236, [240] [241] [242].
These approaches for keyword extraction and visualization have already been automated and standardized in software tools such as Ucinet,  2  Pajek,  3  or VOSviewer. In this study, VOSviewer software was employed because of its extensive adoption for conducting keyword co-occurrence analysis in diverse areas of expertise  [245] [246] [247] [248] [249] [250] [251] [252]. Co-occurrence analysis was conducted for all keywords using the same tools mentioned earlier (VOSviewer), specifically utilizing a full counting method. Table  11  displays the initial findings of this analysis, highlighting the connection between occurrence count and the number of keywords in photovoltaic systems-related fields. As shown in Table  11 , there are a total of 1922 distinct keywords that appear in the comprehensive data corpus consisting of 620 peer-reviewed articles.
This is likely due to the trend toward the high complexity of training systems designed for fault classification in photovoltaic systems. Such systems require previous work to build a robust database and subsequently train artificial intelligence systems under optimal conditions. The effectiveness of the outcomes and their capacity to detect every potential fault in a photovoltaic system is greatly influenced by the quality and amount of accessible data. For these reasons, the main objectives of the authors are aimed at improving the capacity to evaluate the healthy behavior of the photovoltaic power plant and determining when the behavior deviates from the healthy baseline, which is referred to as abnormal or anomaly behavior. Additionally, an important trend has emerged recently, with a shift in research interest from fault detection based only on electrical signals to hybrid detection methods that incorporate image analysis (thermal, infrared, etc.
Cluster 1 -symbolic approach This cluster groups algorithms whose master algorithm is identified as inverse deduction, also known as induction. The approaches in this cluster aim to reach specific conclusions based on preexisting knowledge that has been previously learned  [254]. Due to the computational expense of inverse deduction, the application of these algorithms on massive datasets has been challenging to generalize. Some well-known algorithms in this cluster include Decision Trees, Random Forests, and Fuzzy Logic  [256]. Several works propose the use of these algorithms, also referred to as symbolist algorithms, for fault detection in photovoltaic systems. The most cited article by  Chen et al. (2018)   [232] , with 110 citations, addresses the issues of overfitting and limited performance in supervised fault classification algorithms trained on fault data samples.
This algorithm achieved a 100% precision rate in both the training and testing phases. On the other hand, 𝑅𝐾 -𝑅𝐹 𝐾𝑚𝑒𝑎𝑛𝑠 reduces the training data volume by applying K-means clustering and PCA. This method also demonstrated a precision rate of 100% in both training and testing phases. The primary focus of these approaches is to reduce the dimensionality of databases while preserving relevant information. These classifiers were assessed using data from an emulated grid-connected photovoltaic system, with the results showcasing high classification accuracy and efficient computation. Finally, a study by  Madani et al. (2012)   [258] , cited 19 times, introduces an intelligent-based islanding detection algorithm for photovoltaic and Doubly Fed Induction Generator (DFIG) units. Initially, the Decision Tree algorithm is employed for islanding detection, thanks to its low computational cost and interpretability.
The authors then introduce a boosting technique known as AdaBoost to enhance the performance of the Decision Tree classifier. AdaBoost iteratively trains weak or base classifiers, giving more importance to previously misclassified data to create a more robust classifier. This method was tested on a distribution system that includes photovoltaic, DFIG, and synchronous generators. For islanding detection features, a set of 16 first-and second-order derivatives of current and voltage signals is used. The results show a precision rate of 100% for AdaBoost and 92. 5% for the Decision Tree algorithms. In Fig. 15 , the algorithms detected within this cluster 1 are displayed along with their respective occurrence counts. As depicted in Fig. 15 , the Cluster 1 primarily employs decision tree and random forest algorithms, which are the most commonly utilized methods in this context.
The study assesses the proposed method using both actual and simulated photovoltaic systems, taking into account parameters such as current, voltage, and power. The authors demonstrate the efficacy of their approach through an analysis of computation time, Missed Detection Rate (MDR = 7. 14%), and False Alarm Rate (FAR = 6. 83%)  [259]. The second article, authored by Haiji et al. in 2021  [260] , with 21 references, introduces a two-stage fault detection process that begins with feature extraction and selection as the initial step. They employ the PCA algorithm to reduce the dimensionality of the dataset and then input the resulting PCA data into six supervised machine learning classifiers, including Support Vector Machines (SVM), Naive Bayes (NB), Decision Tree, Discriminant Analysis (DA), K-Nearest Neighbors (kNN), and Random Forest (RF).
The results obtained facilitate the identification of the category with the highest probability as the predicted category for the new sample. After the model is calibrated, a set of fault data is employed to train the system, and fault signatures are then extracted from the maximum power point signals of photovoltaic inverters and meteorological data. However, the results are solely validated using simulated data. The author attests to a 99. 2% accuracy for Hotspot fault. In Fig. 17 , the algorithms detected within this cluster are displayed along with their respective occurrence counts. In Fig. 17 , it is evident that there are more articles employing graph-based algorithms. This is because graph-based algorithms enable addressing semi-supervised problems, meaning they partially solve the issue of fault labeling in photovoltaic systems.
Cluster 4 -analogy-based approach This cluster comprises algorithms known as kernel machines, which are called by a master algorithm. These algorithms assess resemblances between previous and new data by employing nearest-neighbor kernel machine algorithms to scrutinize their surroundings and create generalizations founded on similarity. This group of algorithms produces results like neural networks  [254]. However, a major drawback of these algorithms is their high dependence on the size of the dataset, calculation time, and the complexity of programming, which can quickly become significant. Among the most popular algorithms in this group are K-Nearest Neighbor and support vector machines algorithms  [256]. Several studies in this category suggest utilizing analytical-based strategies for detecting faults in photovoltaic systems.
The most referenced article in this regard, authored by Baghae et al. in 2020 [265] (with 46 citations), addresses the problem of islanding detection. The proposed technique involves the application of support vector machines (SVM) and acquires input data through simulations in MATLAB/Simulink. These simulations are based on an electrical analogy model of the system, calibrated using experimentally measured parameters from a photovoltaic (PV) plant. The approach assesses nondetection zones (NDZ) and detection time (DT) along two dimensions: the active power generated by the PV plant (ranging from 0% to 110% of the installed power capacity, 𝑃 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 ), and the voltage levels of the distribution network (±10% of Nominal distribution network voltage 𝑈 𝑛 ). The author demonstrates a 100% accuracy in detecting islanding and grid fault events.
In another article presented by Harrou et al. in 2019  [266]  (with 42 citations), a distinct fault classification approach is introduced, with a focus on anomaly detection. The authors propose a model-based anomaly detection method for monitoring the direct current (DC) side of photovoltaic systems. This method combines the k-nearest neighbors (kNN) technique with univariate monitoring methods. The study introduces kNN-based and exponentially weighted moving average (EWMA) Shewhart schemes with both parametric and nonparametric thresholds to effectively identify faults in photovoltaic systems. The kNN method is particularly highlighted for its strong performance in handling non-linear datasets, such as those found in photovoltaic systems. The EWMA approach is employed due to its sensitivity in detecting minor changes.
A second article, presented by Garoudja et al. in 2017  [269] , with 78 citations, introduces a method to detect and diagnose faults on the direct current (DC) side using the probabilistic neural network (PNN) classifier. This approach involves four stages: (i) parameter extraction of the photovoltaic module, (ii) simulation and experimental validation of the assembly, (iii) creation of a database of healthy and defective operations, and (iv) network construction, training, and testing. The unknown electrical parameters of the one-diode model (ODM) are precisely identified in the first stage using the best-so-far ABC algorithm based on the Artificial Bee Colony algorithm. These parameters facilitate system simulation using PSIM/Matlab. The detection and diagnosis of faults are then carried out using the PNN network.
In  [276] , a CNN and a fine-tuned VGG-16 model are used to diagnose faults with infrared thermographic images of photovoltaic modules. They achieved a fault detection accuracy of 99. 91% and a fault diagnosis accuracy of 99. 80% using the VGG-16 model. The  faults identified in that study are bypass diode malfunction, partially covered photovoltaic module, shading effect, short circuit, and dust deposition on the photovoltaic panel. In  [277] , a hybrid model combining CNN and machine learning is presented for various photovoltaic module issues. The hybrid model, implemented on a Raspberry Pi 4, achieved an accuracy level of 94% using a database collected from three different climatic regions. This study focuses on various photovoltaic module faults, including accumulated sand faults in photovoltaic modules, covered photovoltaic modules, cracked photovoltaic modules, degradation, dirty photovoltaic modules, short-circuited photovoltaic modules, and overheated bypass diodes.
In  [278] , authors introduce a deep learning approach for identifying cracks in photovoltaic cells. They evaluate four deep learning models and apply an ensemble learning technique to achieve a precision of 97. 93%. Their methodology involves training, evaluation, and comparison of four deep learning models: U-net, LinkNet, FPN, and attention U-net. Model performance is measured using different metrics, including Intersection over Union (IoU) and F1-Score. In  [279] , machine learning and deep learning techniques are assessed for detecting and diagnosing faults in photovoltaic modules. Deep learning-based methods exhibited a precision of 98. 71% for both binary and multiclass detection and classification tasks. The authors compiled a dataset of infrared thermography images from normal and faulty photovoltaic modules such as bypass diode fault, shading effects, photovoltaic module short-circuiting, and accumulated dirt on the photovoltaic module.
In  [280] , a hotspot detection system is proposed using thermographic images and a CNN. The conventional neural network achieved an accuracy of 95. 05%, outperforming a short-term memory-based method. In  [281] , authors introduce a Probabilistic Neural Network (PNN) with a 100% accuracy rate for analyzing historical datasets related to normal and faulty states of photovoltaic systems during the winter season. In  [282] , weightless neural networks (WNN) are used for detecting visual faults in photovoltaic modules. WNN uses random access memory (RAM) devices to simulate neuronal functionality. This approach achieved a 100% classification accuracy within 1. 44 seconds of testing. This approach uses a pre-trained deep learning network (DenseNet-201) and to select the most significant features for classification, the authors used a decision tree algorithm (J48).
Prediction-based fault detection involves comparing predicted values to actual values and ascertaining fault presence by evaluating predefined limit parameters, which may include system damage levels or fault types. In Subsection 6. 3, the results obtained from these algorithms, particularly the methods based on semi-supervised learning based on graphs, are very interesting. Researchers are encouraged to further research and apply these algorithms in combination with the data normalizations proposed in the referenced articles. Data normalization can expand the size of databases and contribute to the generalization of fault detection models applied to photovoltaic systems. Analyzing the results of Subsection 6. 4, it can be concluded that both kNN and SVM are effectively applied. It is recommended to continue working with these algorithms due to their favorable results.
AI for fault diagnosis in photovoltaic systems To adequately address a problem of fault diagnosis in photovoltaic systems using artificial intelligence, it is necessary to first build relevant and robust databases. In other words, these databases should include at least the following eight key elements. First, it is essential to determine the data collection level. This involves specifying whether the algorithm will be applied at the level of individual photovoltaic panels, photovoltaic string aggregates, protection systems, and so on. Second, you need to define the nature of the input data for the algorithm. This entails deciding whether the algorithm will work with data types like characteristic curves, electrical signals including voltage, current, power, time-dependent meteorological data, images, etc.
Third, you must establish the sampling rate. The sampling rate is crucial and depends on the type of fault to be detected. Some faults occur rapidly, within a minute or less (such as arc faults), while others might take as long as 15 minutes or even weeks to manifest (like degradation faults). Thus, a balance between sampling frequency and data storage and processing capacity is essential. Furthermore, a fourth consideration is generalizing photovoltaic models. The accuracy of machine learning diagnostic results is heavily influenced by the quality of the training data. In other words, if the algorithm is trained on a specific dataset A and then used to diagnose data from a completely different photovoltaic plant, even if the data types are similar, the diagnostic results are likely to be inaccurate.
Third, the current challenge is not only the construction of more robust photovoltaic systems but also ensuring their optimal production. In this regard, this study recommends diligent research on the construction of robust databases and specialized signal processing for early fault diagnosis. Additionally, this study, as demonstrated in Section 5. 1, identifies highly interesting countries or target countries for establishing collaborative networks. For example, China, India, and the United States are countries that dominate most of the research in the field of study. Interestingly, this methodology also highlights France as a country of interest. With approximately 15% publications compared to China, India, or the United States, France surpasses India in terms of citations and achieves roughly half the citations of China or the United States.
IV. IMPLEMENTATION AND VALIDATION Our force-directed layout algorithm is implemented in JavaScript, building upon the d3force library  [38]. There, force equations are simulated using a velocity Verlet integrator  [39, 40]. A ready-to-use implementation, which we call Leipzig Layout, is available here: https://github. com/pournaki/leipzig-layout. 10  It builds upon the force-graph library  [41]  to interactively display the graph and the evolution of node positions along the numerical force simulation. Note that, at the current state, this layout tool works reasonably fast for networks below 10,000 links. For larger networks, approximations such as Barnes-Hut are usually employed to ensure fast convergence. In the case of the presented forces and parameters, Barnes-Hut is not directly applicable.
Moreover, the Greens tend to use Twitter quite homogeneously (see Appendix), which leads to their placement close to  Reingold can also be found in the Appendix. This layout also illustrates the difference between ForceAtlas2 and the layout algorithm at hand here: ForceAtlas2 incorporates a rejecting force between node pairs proportional to d -1 ij , which leads to a stronger separation of nodes within clusters. Hence, while the overall arrangement of parties is similar to the Leipzig Layout, parties themselves are more strongly spaced out. The layout of the retweet network shows that the debate was divided into two camps: On the left side of Fig. 5 , the account of Harper's magazine as well as prominent signees such as Gary Kasparov and Joanne K. Rowling are visible, while the right pole includes critics of the letter and its signees, such as Judd Legum and Julia Serano (who occupies a position close to the margin of the pole).
, 2020)  to machine learningbased  (Tourille et al. , 2016b; Chikka, 2016; Tourille et al. , 2017c; Viani et al. , 2019b)  and deep learningbased models  (Tourille et al. , 2017a; Liu et al. , 2019; Lin et al. , 2020; Alfattni et al. , 2021; Chokwijitkul et al. , 2018). However, the performance of the proposed models is still insufficient for practical applications  (Gumiel et al. , 2021). Indeed, event modeling is task-specific and heavily dependent on the nature of the text, and annotating temporal relations in clinical texts is more challenging since it requires medical expertise, which is costly and time-consuming. As a result, inter-annotator agreement (IAA) is poor in available datasets  (Ning et al. , 2018) , and annotated corpora are limited for non-English languages. Only a few studies, in particular, use French corpora  (Tourille et al.
Acknowledgements We would like to thank the scientific and ethical council of the  AP-HP  health data warehouse and the  Georges Pompidou European Hospital , who gave us access to the corpora used in this work. Nesrine Bannour  received funding from the  ITMO Cancer Aviesan. Bastien Rance  is supported by the  SIRIC CARPEM program. The authors also acknowledge the support of  ANR  under grant  CODEINE   ANR-20-CE23-0026-01.
A Transdimentional Bayesian Estimation of Interplate Locking We used data from a total of 70 GNSS stations situated between 23°S and 32°S. This data set includes 28 newly deployed stations, which were added to the existing backbone network of the National Seismological Center of Chile (Figure  1 , Table  S1  in Supporting Information S1). We processed the GNSS data using Bernese software to produce daily positional time series for the period between January 2018 and February 2023  (Dach et al. , 2015; Teunissen & Montenbruck, 2017; VMF Data Server, 2021). Then, we clean the time series and adjust a trajectory model to isolate the secular velocity for each station in the ITRF2014 system  (Báez et al. , 2018; Bevis & Brown, 2014; Huang et al. , 2012; Köhne et al. , 2023). We refer the reader to Text S1 in Supporting Information S1 for further details.
Data Availability Statement The seismic waveforms we used in this paper to compile the earthquake catalog was retrieved from the GEOFON data centre of the GFZ German Research Centre for Geosciences and IRIS Web, and come from the networks Y6  (Tilmann et al. , 2021) , XZ  (Socquet et al. , 2025) , CX (GFZ & CNRS-INSU, 2006), C1 (Universidad de Chile, 2013), C (https://www. fdsn. org/networks/detail/C/) and IU (Albuquerque Seismological Laboratory/ USGS (ASL), 2014). XZ data are archived at the EPOS-FRANCE RESIF data center (https://seismology. resif. fr/fr/reseaux/#/XZ__2020) and will be opened at the end of the project (2026). Moment tensors used in Figure  4.
Traditional magnetic-field positioning methods collect magnetic-field information from each spatial point to construct a magnetic-field fingerprint database. During the positioning phase, realtime magnetic-field measurements are matched to a magnetic-field map to predict the user's location. However, this approach requires a significant amount of time to traverse the entire magnetic-field fingerprint database and does not effectively leverage the magnetic-field sequence's unique patterns to improve the accuracy and robustness of the positioning system. In recent years, the application of deep learning for the indoor positioning of magnetic fields has grown rapidly, especially by using the magnetic-field sequence as a time series and a trained long short-term memory (LSTM) model to predict the position, directly avoiding the time-consuming matching process.
There are also some disadvantages, such as low discernibility (i. e. , identical magnetic-field measurements can be found elsewhere), the heterogeneity of devices (i. e. , heterogeneous smartphones have different magnetic-field measurements at the same location), and the susceptibility to interference from the presence of ferromagnetic materials in the surrounding environment  [12]. The contributions of this study are summarized as follows. • A magnetic-field-based indoor positioning system was designed. Six heterogenous smartphones, namely, iPhone 12 Mini, iPhone Xs Max, Redmi Note 7, Samsung Galaxy S20, Samsung Galaxy S9, and Oneplus 7T Pro, were used to collect magnetic-field trajectories to construct an extensive database of magnetic-field trajectories.
Related Work Due to its simplicity and effectiveness, the fingerprinting method is widely used for indoor positioning on the basis of magnetic fields. The fingerprinting approach consists of two phases, online and offline, starting with an online phase when data from ground-truth locations are collected to create a database, followed by an offline phase when the data from the smartphone are used for positioning. Magnetic fields are mainly used in narrow one-dimensional spaces such as corridors  [14, 15]. There are difficulties in using magnetic fields in wide environments  [16] , and it is challenging to achieve positioning using magnetic fields in old buildings with almost no metal structures  [17]. The fingerprinting positioning problem can often be seen as a classification problem, and many machine-learning methods, such as k-nearest neighbors (k-NN)  [18] , support vector machines (SVMs)  [19, 20] , decision trees  [21] , and neural networks  [22, 23] , can be used to predict the location by classifying the fingerprinting signal.