dataesr/scientific-paragraphs-categorization huggingface dataset:
This is done using OSM2World  [40]  which we adjusted to use our ENU coordinate system. We then elevate the model using SRTM data and store all the vertices and faces which define it. Detecting occlusion between sample i and candidate j then becomes simple: if any triangle of the mesh intersects the vector between the two, the check fails. Note that special care must be taken for implementation to avoid checking faces that are clearly out of range. For the actual intersection test, we used the the popular Möller-Trumbore ray-triangle intersection algorithm  [32].
Parameters and instances Using our approach, we generated 32 instances using parts of 8 European cities, listed in Table  2  along with the symbols we will be using later on and their OSM IDs in the OpenStreetMap database. Tables  3  and 4  give the sampling configurations (in meters and radians) which we used to generate these instances, such that, for example, instance u2 refers to Mulhouse in configuration 2. Note that we used an image quality requirement of P = 25 pixels-per-meter, a camera resolution of 1920 × 1080 pixels and field-of-view angles of 65 • in both directions. Table  1  summarised the key statistics of our instance set.
As the reader will notice, both measures follow the same pattern, save for an offset which can be attributed to missing OSM data. Indeed, in continuous space, agents are allowed to wander into open areas which were never sampled as they correspond to undocumented private lots. In cities where OSM contributors are more numerous (e. g. London) or where most of the open space is public, the curves remain closer together. Should these gaps in the available data be filled, the simulations make it safe to assume that the OCP/MCP models are indeed suited to our application. The theoretical fronts, which remain easy to compute, faithfully mirror the data recovered from simulations in continuous space. They can therefore be used by decision makers to balance cost and coverage. Knee points can help identify the range in which to negotiate costs, and the effective MCP greedy algorithm can be used to yield appropriate solutions.
Several algorithms exist for the computation of the size of power systems. Optimal sizing techniques are divided into three classes  [107] : classical approaches, modern approaches and computational software. Currently, modern approaches are more suitable than classical approaches, due to their ability to solve and optimise complex models. In addition, these approaches are combined to formulate a hybrid algorithm to provide more promising proposals  [108, 109]. Calculation software such as HOGA, IHOGA and HOMER are the most widely used in the literature for MGs design. The simplest techniques are the deterministic approaches, citing: analytical, numerical, iterative, graphical construction methods. Heuristic and metaheuristic algorithms are also used for sizing, for example: the firefly algorithm, particle swarm sizing, the grey wolf technique, the Cuckoo search approach and genetic algorithms  [110] [111] [112] [113] [114] [115].
• Minimizing the operating costs of the micro-grid; • Maximising the output power of the generators at a given time; Figure  12  describes all the aspects related to energy management systems in MGs. In general, an EMS problem leads to a constrained non-linear mixed-integer optimization problem, which can be relaxed to linear optimization problem. It can be solved using linear or non-linear methods, which can be implemented using available software such as Matlab, HOMER, etc. Several cost functions can be considered such as investment cost, operating and maintenance costs, losses cost, emissions cost, etc. The objective is to find the optimal values for decision variables that allow minimizing the cost function considering actual measured data (weather forecast, energy market, load demand, etc.
Software for EMS Several software and programming languages are used for EMS implementation on MGs, including tools for simulating the operation of MG components, tools for sizing and siting, and other for solving optimization problems such as rule-based techniques or optimisation approaches. An energy management simulation tool called WindSim is used in [132] and used for computational fluid dynamics for wind prediction and wind turbine power. It optimises the placement of wind turbines in onshore and offshore wind farms. In  [133] , authors have used a software package called PVsyst to simulate the efficiency of PVs and solar installation. PVsyst is designed to be used by engineers, researchers and architects for the design of MGs. PSCAD/EMTDC is used in  [134]  to simulate a suggested control strategy for MG.
CPLEX is among the most efficient solvers of GAMS; CPLEX is useful for large-scale linear programming, integer programming and quadratic constraint problems. The outputs of GAMS and CPLEX provide optimal investment and operating solutions with cost reduction. Software called Versatile Energy Resource Allocation (VERA) is used in  [138]  by Khodaei to minimise the total cost of system planning. It also makes a prediction of demand coverage based on local weather conditions. MATLAB is widely used for MG simulations and optimal operation design. The HOMER software is widely used for MG modelling purposes. In  [139] , Nayar et al. presented an innovative hybrid wind-electric/vehicle/diesel system implemented on three remote islands in the Republic of Maldives. Authors used HOMER to undertake the planning of the renewable energy system and to analyse the different options.
In  [51] , Chen et al. proposed to use time-series and neural feedback network techniques for cost-benefit analysis for the optimal sizing of an energy storage system in a MG. The aim is to predict solar radiation and wind speed. The main problem has the form of a MILP, which is solved in AMPL (A Modelling Language for Mathematical Programming). Authors also introduced a specific artificial neural network algorithm for the prediction of the production of DERs. Finally, they used a simple method for optimization. In  [189] , Nnamdi et al. developed a game-theoretic demand response program to manage a MG. They were able to minimise the operating cost of the MG by increasing the output of DERs when fuel prices are low. In  [190] , Abir and Ali used neural networks to estimate the optimal tilt angle of PV at a given location to optimise the amount of energy produced by the PVs and thus reduce operating costs.
To achieve this optimization objective, they mixed a genetic algorithm with a Mamdani fuzzy logic algorithm. In  [199] , the authors presented a highly efficient EMS for the management of a connected network containing controllable and uncontrollable batteries and loads, with the objectives of reducing the power demand to the main grid while increasing the local generation of the RERs, based on a hybrid approach that combines wavelet functions and extended Kalman filtering to predict the consumption and production of RERs. In  [200] , Teo et al. proposed a methodology for managing ESSs tested against a rule-based control strategy. The simulation is done on MATLAB/Simulink and shows that this study increases the resilience of the MG.
Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences.
Data sources In this study, we analysed the government plans of the 2016 and 2021 presidential elections in Peru. These plans are open access and in the public domain, so that all citizens can read these and become informed of their proposals  22, 23. For this work, which is health-oriented, we only selected the health chapters. For the 2016 presidential elections, there were 19 government plans presented, although we analysed only 18 in this study as one was only available as a scanned image and could not be analysed as text. For the 2021 presidential elections, there were 20 government plans and we analysed just 19 as one was only available as a scanned image and could not be analysed as text. As we aimed to analyse the government plans, and not the political party, we analysed all government plans regardless of whether the party or the candidate was disqualified during the campaign period.
Within the health chapter of each government plan, we copied and pasted onto a spreadsheet each sentence in a row; in other words, for each government plan, we had as many rows as sentences. Furthermore, the dataset we generated had three columns: the name of the political party, a sentence indicator (e. g. , political_party_A_sentence_1), and the sentences we extracted. All sentences were copied as they were in the original government plan. The government plans were translated into English using Google translate.
Amendments from Version 1 We are uploading a new version to address the comments by reviewer #1. In particular 1. We expanded the limitations section to discuss about the fact that the documents had different length or number of words. 2. We updated the methodology and results (including Figure  1 ) following the recommendation to exclude a few extra filler words. 3. We expanded the discussion about the KeyBERT results. 4. We expanded the limitation section to discuss that not only algorithms in Spanish lack, but datasets in Spanish lack as well. 5. We added two references suggested by the reviewer. Any further responses from the reviewers can be found at the end of the article meaning to the text. To begin with NLP, we need to put a text into a two-dimensional matrix. In the model Bag of Words 24 , each text or document is represented by the group of words in it.
As a previous step to the construction of the data matrix, a vocabulary is elaborated with the union of all the terms that appear in a document. From this, a matrix is built in which each row represents a document, and each column (each characteristic) corresponds to a term. For this analysis we conducted a simple pre-processing, which included deleting HTML labels and non alphabetic characters for all datasets. Moreover, an advanced preprocessing of the datasets depends on the algorithm used. Therefore, in the description of the algorithms, the data pre-processing that has been used will be described for each algorithm. Algorithms TF-IDF: This is the most basic NLP algorithm. Each position (d, t) in the matrix has a value for the metric Term Frequency which is denoted as t f d,t , and reflects the number of times the term t appears in the document d.
There are variants to the Term Frequency metric, so that the matrix can include: • t f d,t ∈{0,1} -if the term appears or not in the document. • log (1 + t f d,t ) -logarithmic scale. • t f d,t /max j t f d,t -scale in relation to the most frequently used term in the document. There are terms which appear several times in a document, but which not carry much information; these could include 'the', 'a', 'an', and 'of', among others. The metric Inverse Document Frequency penalises the terms in a magnitude equal to the frequency in which they appear in the text. The inverse frequency of a term is computed as: # # t documents id f log documents that include word t   =     Because both metrics (Term Frequency and Inverse Document Frequency) provide information to the NLP algorithm to dissect the text, it is common practice to combine them as the TF-IDF metric, which can be expressed as: , , t d t d t t f id f t f id f - = × For TF-IDF, we used the dataset partitioned by phrases to analyse globally because a wide corpus of words is needed, so here the frequency of words in general is analysed (for all political parties).
For each topic, this model estimates the probability of the terms, while for the document, this model estimates the relevance of each topic. Therefore we used LDA to define and describe, through the most relevant terms, the categories in which each data entry can be assigned to. We trained the LDA model with a dataset in which each sentence was an instance, and the categorisation was then conducted for the government plan. We used LatentDirichletAllocation class from the Scikit-Learn library 27 with the parameter n_components=2; this parameter reads the number of topics to be created. For this work, we defined two groups (i. e. , we specified that the algorithm should create two groups following an unsupervised approach from the data). Finally, for the data pre-processing, we used stop_words and WordNetLemmatizer classes from nltk library 26.
TextRank: This algorithm is a graph-based algorithm and it is based on PageRank 18 developed by Google, where PageRank is used to define the position of websites in the search engine through the extraction of key terms. TextRank informs about the semantic sequence used in the text with unique keywords. We used the library Gensim 28. For this analysis, we used the full government plans to extract the key words in each plan. Following the recommendations in the documentation for TextRank, we did not pre-proccess the data and thus used the original text as it was. KeyBERT:  KeyBERT 13, 29  is an adapted keyword extraction tool based on BERT  19. In the last couple of years, algorithms based on BERT are pushing the boundaries of the state of the art technology in social and technical multiple aspects of the natural language processing tasks 30 ?].
BERT is a pre-trained model on the Book-Corpus and English Wikipedia datasets 31. It uses a multi-head attention mechanism, so it can create better representations for entities considering the context, and leveraging the classic word-based approach. For this analysis we used the full government plans to extract the keywords in each one. Also following the documentation for the KeyBERT algorithm, the texts were not pre-processed and used as they were. Rake: Rake 15 is used to extract keywords and key phrases. To use the Rake algorithm, we followed these steps: 1. In the text we identified stop words and punctuation. 2. We removed stop words and punctuation, and generated a list of the phrases that were separated by them. 3. We calculated the number of times each word appeared in all the phrases (i.
Data characteristics The original dataset with the 2016 government plans had 559 rows (i. e. , sentences) in total, which represented 18 government plans; the shortest government plan contributed with four sentences, while the longest contributed with 96 sentences (See Table  1 ). The original dataset with the 2021 government plans had 1,586 rows (i. e. , sentences) in total, which represented 19 government plans; the shortest government plan had 10 sentences, while the longest had 215 sentences (See Table  1 ).
LDA The LDA algorithm was trained in a dataset with as many rows as sentences per government plan, to define two groups of ten words each (see Table  2 ); we did not define more groups, or more terms per group, because the dataset was small. For the prediction phase, we used the full text of each government plan (i. e. , a dataset with as many rows as sentences per government plans), and analysed which group each government plan would belong. We did this analysis for the years 2016 and 2021 separately. Overall, in both 2016 and 2021, Group 0 appeared to cluster terms signaling things the population would receive (see Table  2 ). For example in 2016 and in 2021, Group 0 included 'service' and 'insurance'. Conversely, in both 2016 and 2021, Group 1 appeared to cluster terms that are related with the structure of the health system like 'program', 'hospital', and 'capacity' (see Table  2 ).
Interpretation and potential explanations The first finding was that the dataset with the 2021 government plans was larger than the 2016 dataset. Similarly, the TF-IDF analysis also showed that the number of terms with a frequency of ≥0. 10 was 4. 8-fold higher in 2021 than in 2016. This could imply that health has gained more relevance over the last five years, particularly since 2020 when the COVID-19 pandemic revealed the limitations and deficiencies of the health systems in Peru. The TF-IDF analysis suggested different underlying approaches between the 2016 and 2021 government plans. Our hypothesis is that the 2016 plans were more general, because frequent terms were 'agreed', 'agreement', 'political' and 'plan'; overall, these terms would suggest a non-specific approach. Conversely, in the 2021 government plans, words like 'bonus', 'gerontology', and 'migration', were often found and would suggest specific proposals or subjects.
In addition to more algorithms in Spanish, the scientific community should also secure datasets in Spanish which currently lack to train available and new NLP models. Where possible, these datasets of text in Spanish (and other languages) should be tagged and untagged, as well as annotated or annotated. Third, the analyses and interpretations were mostly data-driven, and should be interpreted in that context. This work was not designed to be a comprehensive political (though NLP is also useful for studying political speech)  34  , anthropological, or linguistic scrutiny of the 2016 and 2021 government plans in Peru, rather the application of novel artificial intelligence techniques to further expand the understanding of the health chapters in these government plans. Fourth, we did not compare the government plan of the same political party in 2016 and 2021.
A prospective easement of the political parties was beyond the scope of this work, and such exercise would not be possible for all parties. Fifth, the text of the health chapters varied in length. The LDA analysis was trained in a dataset with all the government plans together. Government plans with longer text, hence with more words and information, could have driven the algorithm results; conversely, shorter government plans could have provided less information to the LDA algorithm. For transparency, we reported the length of each government plan and the results should be interpreted considering this potential limitation.
Data availability Underlying data Figshare: Data -Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters. https://doi. org/10. 6084/ m9. figshare. 14466699. v1. The project contains the following underlying data: • planesSalud 2016. csv (dataset where each row is a sentence of the health chapter in each government plan. For example, if the government plan 'ABC' has a health chapter with 15 sentences, there will be 15 rows (each containing a sentence)) • planesSalud 2021. csv (dataset where each row is a sentence of the health chapter in each government plan. For example, if the government plan 'ABC' has a health chapter with 15 sentences, there will be 15 rows (each containing a sentence)) • planesSalud Comp 2016.
csv (dataset where each row is a health chapter in each government plan. There will be as many rows as government plans, each row containing the health chapter in full) • planesSalud Comp 2021. csv (dataset where each row is a health chapter in each government plan. There will be as many rows as government plans, each row containing the health chapter in full) Data are available under the terms of the Creative Commons Attribution 4. 0 International license (CC-BY 4. 0).
Regular evaluation of integrated surveillance for antimicrobial use (AMU) and resistance (AMR) in animals, humans, and the environment is needed to ensure system effectiveness, but the question is how. In this study, six different evaluation tools were assessed after being applied to AMU and AMR surveillance in eight countries: (1) ATLASS: the Assessment Tool for Laboratories and AMR Surveillance Systems developed by the Food and Agriculture Organization (FAO) of the United Nations, (2) ECoSur: Evaluation of Collaboration for Surveillance tool, (3) ISSEP: Integrated Surveillance System Evaluation Project, (4) NEOH: developed by the EU COST Action "Network for Evaluation of One Health," (5) PMP-AMR: The Progressive Management Pathway tool on AMR developed by the FAO, and (6) SURVTOOLS: developed in the FP7-EU project "RISKSUR.
Assessment Tool for Laboratories and AMR Surveillance Systems The Assessment Tool for Laboratories and AMR Surveillance Systems (ATLASS) is a tool designed by the FAO for assessing and defining targets to improve national AMR surveillance systems in the food and agriculture sectors  (18). It is composed of two modules: a surveillance module and a laboratory module. Each module includes two standardized questionnaires, which are to be completed by the assessors. The assessments generate a baseline and classify a "stage" for AMR laboratory capacity detection, AMR surveillance, and dissemination of information.
The SWOT-Like Approach The results of the SWOT-like approach applied to assess the tools are shown in Table  5. The variation in answers to the four SWOT-like questions was low among the assessors of each tool, indicating consistency regarding the general impression of the tools. The PMP-AMR and ATLASS were liked for the semi-quantitative scorings that could be made directly and that the tools were particularly made for evaluation of AMR Workability in terms of required data (1: very complex and 4: simple) 2 3 1 4 2 3 Workability in terms of people to include (1: many and 4: few) 2 3 4 3 2 4 Workability in terms of analysis to be done surveillance systems. What is not covered in these two tools is the environmental, plant, and human parts of surveillance. The ECoSur was liked because it allowed evaluation of collaboration in detail; however, the level of abstraction in the language in the existing version of the tool was a struggle.
However, the evaluator should be aware that applying this tool requires time investment and training, including specific training in "systems thinking. " ISSEP, ECoSur, and SURVTOOLS also allow for an indepth analysis requiring collection of more complex data and information. For SURVTOOLS, a specific training in design of epidemiological studies and a wide spectrum of analytical methods are needed before a full exploitation of the tool can be expected. Many of the tools could also be used to guide the design of AMU and AMR surveillance systems in addition to evaluation of existing systems. Many of the tools, especially ATLASS, produce intermediate outputs of how well the different parts of the program are integrated and how well the partners collaborate. In contrast, the interpretation of evaluation results of ECoSur supports the identification of strengths and weaknesses of collaboration *Governance was included in this study by 9 of the 20 of the assessors (however, not a separate theme on the https://guidance.
e proposed simulation-based optimization framework (see Figure  1 ) is implemented on a desktop computer using MATLAB. e LTM toolbox  [62, 63]  is used to solve the LTM numerically. For the procedure simulation(y), we use the stopping criterion ε q � 0. 001 w∈W Q w , which represents a total deviation between two consecutive iterations of less than 0. 1% of the total demand. MATLAB's Pattern Search procedure is used as the global optimization heuristic to iteratively optimize the subsidy vector y with a threshold of ε y � 0. 001, i. e. , less than 1 cent.