dataesr/scientific-paragraphs-categorization huggingface dataset:
African city distribution and related issues The first aim is to identify the cities that have been the most frequent subject of research in connection with climate change over the past thirty years. The GeoNames database lists 514 African cities with more than 100,000 inhabitants and 53 names of countries on the continent. These names, as well as their derivatives (various names of a city, adjectival forms of countries, demonyms), are systematically searched in the titles, key expressions and abstracts of more than twenty-four thousand articles extracted from the WoS database. After various checks, automatic (e. g. , we assume that the co-occurrence of a city name and the name of the country in the same article certifies the occurrence of this city) or manual (checking the occurrences of homonymous city names without mention of country; the city of Man in Ivory Coast is never mentioned although the word "Man" appears about ten times for example in "UNESCO's Man and the Biosphere Programme"), the metadata of 1,101 articles are retained as mentioning at least one African city.
Some of the useful-to-know characteristics of the graph-such as the centralities-are estimated with the NetworkX library  (Hagberg et al. , 2020)  and the plotting uses the general-purpose diagramming program yEd  (yWorks GmbH, 2020). "Social stakes" category has the highest degree centrality when taking links' weights into account. Indeed, social stakes form a central issue for all kinds of impacts and risks related to climate change. The literature repeatedly notes that in large African cities this theme is determined by the levels of poverty, inequality, unemployment, access to basic amenities such as drinking water and sanitation (see the link with the "water" issue on Figure  3 ), food security and by various institutional factors. The analyzed corpus strongly links the social stakes with human health.
Climate services contribute directly to the eleventh ("sustainable cities and communities") and thirteenth ("climate action") Sustainable Development Goals through the collection of climate data, the generation and provision of information and the development of products to improve the understanding of regional to local climate change and its impacts on natural and human systems. However, most often African cities lack data necessary to climate studies. Even when such data exist, they are scattered among different administrations and difficult to access  (Mhedhbi, 2021). In such contexts, satellite remote sensing provides a mass of raw information useful for climate urban studies. In particular Landsat data are widely used due to the depth of the time series, the ease of access to the data, and the availability of processing tools.
Discussion and conclusion The analysis of a corpus of metadata from more than a thousand articles extracted from the Web of Science database gives a fairly precise picture of the work published from 1991 to 2020 on the theme of climate change in African cities. Text mining of the corpus shows that while cities of the Northern, Western coastal, Eastern and African sub-regions are the subject of research, cities with more than 100,000 inhabitants in the central Sahel strip and a north-south corridor (extending from Libya to Namibia through Chad) are omitted. Too numerous and intertwined, the explanatory factors of this distribution remain beyond the scope of the analysis of such corpus but are probably linked to one form or another of attractiveness (existence of renowned local universities, availability of funding, historical links between countries, linguistic affinities, etc.
Acknowledgments We thank the two reviewers whose critical comments enabled us to considerably improve the manuscript. Main results have been presented at the  International Conference on Urban Environmental Degradation and Urbanization , 18-19 November 2021,  Paris (France ). The LISST UMR5193 laboratory supported this study through the  URBASUDS  project (2021). Certain data included herein are derived from Clarivate Web of Science. © Copyright Clarivate 2021; all rights reserved.
Statistics Data were expressed as means ± SEM or estimated marginal means ± SEM when specified (i. e. , for results stemming from repeated measures ANOVA). Curcuminoid distribution in the digestate was first analyzed by two-way ANOVA, with CUR, dCUR, or bdCUR percentage as the dependent variable, using a full factorial design with formulation and meal as fixed between-subject factors. Since  2 values for the effect of formulation were close to 1, data were then analyzed by Welch's one-way ANOVA using Games-Howell test as a post hoc test. In a first approach, differences in curcuminoid bioaccessibilities were analyzed using repeated measures ANOVA, using a full factorial design with formulation (common turmeric extract, highly pure turmeric extract, and gamma-cyclodextrins) and meal (no food, meat-and potato-based meal, and wheatbased meal) as fixed between-subject factors and curcuminoid (CUR, dCUR, and bdCUR) and digestion phase (aqueous and micellar phase) as within-subject factor.
Departures from normality were assessed using Q-Q plots of standardized residuals. Differences in the bioaccessibility of each curcuminoid for each meal type were further analyzed by oneway ANOVA. Departures from normality were assessed using Q-Q plots of standardized residuals. Homogeneity of variances was tested by Levene's test. Tukey's test was used as a post hoc test for pairwise comparisons while in case of heteroscedasticity, Welch's ANOVA was carried out with Games-Howell test as a post-hoc test. Values of p < 0. 05 were considered significant. Statistical analyses were performed using SPSS 28 (SPSS Inc. , Chicago, IL, USA).
Acknowledgements The authors are grateful to  Anderson Loundou  ( CEReSS, Aix-Marseille Université, France ) for his assistance in linear mixed models. The costs of this project were covered equally by the own institutional budget of P. B. and C. D. research team and by  ID4Feed. The sponsor was not involved in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
(HT): Heat treatment; (*): for all samples (Parall. and Perp. together); (**): data measured at oven dry state (0% MC), and for an oven dry ML of 11. 2% being the same than in the measured data of the present study; (***): correction of the stiffness values from 0% MC to respectively 12. 0% MC for the non-treated values and 9. 3% MC for the treated ones thanks to formulas provided in (Guitard 1987). The methodology is described in details in  Viguier et al. (2017).
Acknowledgements The authors acknowledge the company  Bois Durables de Bourgogne , and particularly  Jean Perche , for its contribution in the thermal modification processes. This study was performed thanks to the partnership of BOPLI (https://artsetmetiers. fr/fr/boplibourgogne-placage-innovation). The authors would also like to thank the  Xylomat Technical Platform  from the  Xylomat Scientific Network , funded by  ANR-10-EQPX-16 XYLOFOREST.
Then, participants were invited to identify the actors in the FS and brainstorm about the roles they play when dealing with challenges. This way of collecting data may imply direct relationships between some of the identified roles and the challenges addressed. For example, one of the farmers' roles identified to deal with the lack of succession in FSs is to actively participate in private/public interventions that enable succession, increasing the likelihood of finding new entrants. After that, a debate was conducted to classify the roles of the actors according to the resilience capacity they enhance (current resilience-enabling roles) or constrain (current resilience-constraining roles). For example, participants in the FS' focus group in Sweden identified that farmers' associations enhance robustness as they play the role of negotiating good emergency payments; adaptability because they provide advisory services; and transformability as they address longterm challenges and find strategies to deal with them.
Then, and based on the resilience-enabling roles and residence-constraining roles identified in the previous activities, participants were asked to score 1  (on a scale of -3 to +3) the current contribution of the actors to the resilience capacities. The positive values (i. e. , +1, +2, +3) meant that the actors enable resilience at different intensity levels, 0 meant that the actors' contribution to resilience is not clear, and negative values (i. e. , -1, -2, -3) meant that the actors constrain resilience capacities at different intensity levels. Finally, time for general reflection was devoted to draw conclusions about the main insights from the focus groups. As the data collection was conducted by different researchers in each FS, standardized guidelines were prepared to guarantee that all FS' focus groups pursued the same goal and followed the same approach (flexible to idiosyncrasy).
Digital drawings were done under the software Krita 5. 0. 2. The re-examination of  Thoral's (1935)  original type series and of over 30 additional specimens (Fig. 1A-E ) did not bring any significant additional morphological information to the very detailed description of Minervaecystis vidali provided by  Ubaghs (1970). However, this abundant material made it possible to elaborate the first detailed, specimen-based reconstruction of this solutan (Fig. 2 ). Tentative reconstructions of M. vidali were already provided by  Lefebvre et al. (2012, fig. 2. 1) and  Rozhnov (2022, fig. 4h ), but they were simply combining two original cameralucida drawings figured in  Ubaghs (1970, figs 39. 1 and 41. 1). Moreover, the observation of all available individuals of M. vidali indicates that, when it is preserved, their twisted dististele is not straight, but consistently curved towards the right (Fig.
Recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities for agricultural engineering and computer scientists to meet the demands of the manual. The need for diverse and representative sets of pod images is essential for developing and testing automatic cocoa pod maturity detection sys-tems. In this perspective, we collected images of cocoa pods to set up a database of cocoa pods of the Côte d'Ivoire named CocoaMFDB. We performed a pre-processing step using the CLAHE algorithm to improve the quality of the images since the effect of the light was not controlled on our data set. Co-coaMFDB allows the characterization of cocoa pods according to their maturity level and provides information on the pod family for each image.
Our dataset comprises three large families, namely Amelonado, Angoleta, and Guiana, grouped into two maturity categories: the ripe and unripe pods. It is, therefore, perfect for developing and evaluating image analysis algorithms for future research. © 2023 The Author(s The data was acquired by taking pictures with Nikon D500 and infinix cameras of cocoa pods from the plantation in the village of Yakassé 1. The images taken were stored in a computer Data format Analyzed Filtered Description of data collection The implementation of our cocoa pod dataset was done in three steps. First, the stage of photographing the pods was carried out on two different dates, namely May 26, 2022, and August 18, 2022. Then a classification of the images of cocoa pods was carried out according to the varieties of cocoa families and by categories of pods, namely ripe and unripe pods.
Objective Côte d'Ivoire is the first cocoa producer in the world, and cocoa farming represents 14% of its GDP. In order to have better quality cocoa beans, it is necessary to shape the best cocoa pods harvesting period with regard to the state of ripe. Thus, unripe pods do not have enough sugar for a proper beans fermentation. As for overripe cocoa pods, dryness, and germination generally lead beans to development of fungal infections and unitability. This dataset would allow the set-up of automatic systems for harvest optimization.
Data Description Cocoa pods were photographed with a Nikon D500 and Infinix cameras in a cocoa plantation located in Yakassé 1 village a few km from Grand Bassam in Côte d'Ivoire. The shots of these images were taken at different times of the day, in the morning between 9:30 a. m. and 11 a. m. , and in the evening between 2 p. m. and 4 p. m. , from different angles in an uncontrolled environment. The images of the pods were classified by category and family. The categories used are ripe cocoa pods and unripe cocoa pods. For each category, we obtained three main families which are: Amelonado, Angoleta, and Guiana. • Amelonado Amelonado comes from the Spanish word for melon-shaped. The fruits look like elongated melons, usually with a slight bottleneck. They have thick, usually smooth shells, rarely with some warts.
The first line displays images of ripe Guiana pods and the second line contains images of unripe Guiana pods. • Data directory structure The directory to the dataset is structured as follows: A parent directory named CocoaMFDB, then in the CocoaMFDB directory, two sub-directories named mature(ripe) and unmature(unripe) were set. Finally, each folder hosted the Amelonado, Angoleta, and Guiana directories. Fig. 4  shows the directory structure of the dataset • Data summary Table  1  presents a summary of the cocoa pods images according to their type and maturity status. Fig. 5  displays the histogram from to Table  1.
• Cocoa pods collection Images collection took place in Yakassé 1 Grand Bassam on the following dates: May 26, 2022, and August 18, 2022. The images were captured from different angles in an uncontrolled environment, including various obstacles such as varying weather conditions such as wind, rain, fog, sun, etc. , as well as changing lighting conditions such as the brightness, color, and direction of light, with the presence of shadows and reflections. The images contain occluded stems and leaves and cocoa pods. • Data pre-processing Contrast Limited Adaptive Histogram Equalization (CLAHE) is useful for enhancing digital images and is a variant of Adaptive Histogram Equalization (AHE) that supports contrast boosting. CLAHE operates on small regions of the image, called tiles, rather than on the entire image.
The neighboring tiles are combined using bilinear interpolation to remove artificial boundaries  [4]. Fig. 6  represents the general methodology of the quality improvement process of the dataset. The process of enhancing our images is as follows: -Step 1 : Process of images reading in RGB format. -Step 2 : Process of RGB images convertion into Lab images. In this step, the image has initially used the conversion of RGB color images to Lab color images. The Lab color space uses the concept of coloring based on luminance using the brightness (L) of white-black and chromatic components (a. red-green and b. yellow-blue). The Lab color space is a development of the CIEXYZ color space, with values L that has a value between 0 and 100 that represent respectively black to white, as for the chromatic representation of red-green and yellow-blue colors, each is between -128 and 128  [5].
These tags form a representation of the class of objects to which the data belongs and help a machine learning model learn to identify that particular class of objects when encountered in data without tags. The Labeling tool is used to annotate the positional information of cocoa pods in the image. It is annotated according to the PASCAL VOC 2007 dataset format which represents a dataset containing several labeled images and annotated files. It automatically generates corresponding XML annotation files  [6 , 7]. Fig. 12  shows an image and the file generated.
B. COVERAGE PATH PLANNING PROBLEM FOR PA To solve the issues of precision farming, it is possible to specify the task of coverage path planning (CPP) in two modifications: • CPP1 -for monitoring the condition of the field and crops (monitoring task), when the impact on the analyzed surface is not expected. • CPP2 -for the application of fertilizers, herbicides, phytocides, etc. (application task), which involves additional restrictions related to the impact on the cultivated fields. The standard solution for the problem of coverage is to use the back-and-forth algorithm, also called Zamboni  [26]. It was found that for the CPP2 task other coverage options (spiral, waves) are not effective, since many turns consume battery power faster and negatively affect the quality of spraying  [27]. The market offers both commercial UAV control products and open source programs; the following commercial products are offered in the market: senseFly(eMotion)  [28] , Pix4D  [29] , DroneDeploy  [30]  and DJI  (Terra)   [31] ; the open source software are represented by OpenDroneMap  [32] , microkopter  [33] , LibrePilot  [34] , and Dronecode  [35] , which carry out (in different degrees of readiness) allocation of the territory within which surface monitoring is carried out; calculation of the optimal route for covering the territory with one UAV; simulation of flight along the route (pre-launch check); receiving flight data (UAV status) in real time with the possibility of emergency termination of the flight if necessary.
The study  [48]  reveals the route optimized for the application of fertilizers and pesticides, taking into account the areas exposed to stress and identified at the preliminary stage. Optimal routes are based on the Traveling Salesman algorithm and Voronoi diagram. The article  [49]  discusses an approach to managing a variety of UAVs for optimal coverage in order to monitor the area of interest. The main objective is to minimize the time of mission completion by optimizing the distribution of waypoints. The simulation was carried out using MATLAB  [50]  and GAZEBO  [51]. Perhaps, for the first time the issue of collisions in the problem of planning the path of a set of UAVs using GA was considered in  [52] , where the cost function takes into account the length of the path, deviation from the original trajectory, distance from the trajectory to obstacles, the angle of rotation and collision.
The experimental study has been implemented based on a genetic algorithm from the Python DEAP library. The population size is set at 250 individuals, 150 generations. According to the preliminary study 50 generations are sufficient for algorithm convergence, but this number has been increasing to have guaranteed convergence of the algorithm. A two-point crossover and a standard tournament selection with a group size of 3, implemented in DEEP, were used. The mutation function was implemented independently due to non-standard optimization parameters, such as an ordered subset of drones and an ordered set of points on the road for take-off sites. These parameters are defined for the fitness function  (1). The custom mutation function deals with each of the 4 optimizable parameters independently:  with mean (mathematical expectation) equal to 0 and standard deviation equal to 45.
The waypoint is always located on the road (shape of available starting positions) • Remove random waypoint from the list • Sorting the waypoints list. Sorting rather than shuffling is justified by the hypothesis, that sequential points on the road are more likely to be optimal, than chaotic moving back and forth across the road (the hypothesis is supported by the results of preliminary experiments) The algorithms were implemented as part of a Python project using the Django framework for working with data (fields, drones, missions) and visualization. The genetic algorithm is launched through the distributed computing module SCOOP (Scalable Concurrent Operations in Python). The full source code is available in the GitHub repository  [74].
This work was supported in part by the  Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan  through the  Development and Adaptation of Computer Vision and Machine Learning Methods for Solving Precision Agriculture Problems Using Unmanned Aerial Systems  under Grant  AP14869972 ; in part by the  Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan  through the  Development of Intelligent Data Processing and Flight Planning Models for Precision Farming Tasks Using UAVs  under Grant  AP08856412 ; in part by the  Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan  through the  Development of a Data Mining System for Monitoring Dams and Other Engineering Structures under the Conditions of Man-Made and Natural Impacts  under Grant  BR18574144 ; in part by the  Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan  through the  Space Monitoring and GIS for Quantitative Assessment of Soil Salinity and Degradation of Agricultural Lands in South Kazakhstan  under Grant  BR10965172 ; in part by the  Slovak Research and Development Agency  through the  New Methods Development for Reliability Analysis of Complex System  under Grant  APVV-18-0027 ; and in part by the  Ministry of Education, Science, Research, and Sport of the Slovak Republic  through the  New Approaches of Reliability Analysis of Non-Coherent Systems  under Grant  VEGA 1/0165/21.
AU : Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly: Tactical warfare is considered a driver of the evolution of human cognition. One such tactic, considered unique to humans, is collective use of high elevation in territorial conflicts. This enables early detection of rivals and low-risk maneuvers, based on information gathered. Whether other animals use such tactics is unknown. With a unique dataset of 3 years of simultaneous behavioral and ranging data on 2 neighboring groups of western chimpanzees, from the Taï National Park, Cote d'Ivoire, we tested whether chimpanzees make decisions consistent with tactical use of topography to gain an advantage over rivals. We show that chimpanzees are more likely to use high hills when traveling to, rather than away from, the border where conflict typically takes place.
Feeding and traveling activities involve a particular focus on a goal and usually produce noise and often require stopping to focus on distant vocalizations (S1 Video). Resting offers the best conditions for information gathering from the surrounding auditory environment, especially to detect distant chimpanzee sounds. Third, we hypothesized that early detection of rivals enables tactical use of information obtained in elevated border areas to make low-risk travel decisions. Riskier situations to the in-group occur when rivals are in high numbers while own numbers are low and when rivals are located close by (<2,000 m). We used simultaneous data on 2 neighboring chimpanzee groups to assess if potential information gathering about neighboring rivals during high elevation use within the overlap border area would lead to risk-reduction tactical movement decisions to retreat from or advance toward rivals.
We simultaneously collected behavioral and GPS ranging data between December 2013 and October 2016 on 2 neighboring chimpanzee communities who share a territory overlap area, the South and East groups  [41]. Human observers conducted daily focal follows of 58 habituated individual chimpanzees for 8 to 12 h/day (10,480 and 10,706 observation hours across 1,287 and 1,350 follow-days for South and East, respectively). We continuously recorded the individuals visible within the focal party, allowing us to assess adult party sizes. We reconstructed the topographical landscape across the groups' territories using the GPS points accrued during each minute of focal sampling, recording location and elevation (range of elevation: 115 to 287 m above sea level, Figs 2, S1 and S2). Simultaneous focal follows of individuals from both neighboring communities allowed us to assess intercommunity GPS distances.