dataesr/scientific-paragraphs-categorization huggingface dataset:
a. Meteomodem M10 radiosonde The M10 is one of the radiosonde commercialized by Meteomodem. The M10 was released in 2010, and it is fully compatible with the SR10 receiver system and the Meteomodem software (Fig. 1 ). Its dimensions are 95 mm 3 95 mm 3 88 mm for 150 g with batteries. The M10 is composed of a capacitive humidity sensor covered by an innovative metal coated shield that allows good ventilation while protecting it from direct radiation and from water droplet freezing on the sensor, a temperature sensor that measures the air temperature and positioned at the very end of the sensor boom, and a GPS sensor whose coordinates are measured and from which the pressure, the vertical velocity, the wind speed and direction are derived. The capacitive humidity sensor is composed of three primary components: a basic layer that acts as an electrode; a dielectric material, whose characteristics are a function of relative humidity; and a fast response porous electrode that acts as the For that study, only calibration-related corrections were applied to the M10 measurements, as the M10 radiosondes had been released shortly prior to this campaign.
The experience acquired with M10 afterward during other campaigns and the willingness to use M10 radiosondes at several measurement stations of the GRUAN network, led to the development of the corrections that are presented in the section 3. Seven Meteolabor Snow White (night models; 026 and 059 versions) soundings and 20 RS92 soundings were performed during the campaign (Table  1 ). In this study, we use these 20 soundings to apply the corrections to the M10 RH and perform comparisons with the RS92 RH profiles. For these soundings, the Vaisala DigiCORA III software, version 3. 64, was used. No additional correction to the default corrections were applied to the RS92 data  (Bock et al. 2013). According to the Vaisala data continuity website (https://www. vaisala. com/en/press-releases/ 2009-12/vaisala-launches-website-ensure-data-continuityradiosounding-observations), the time lag, the daytime correction for solar heating, and the nighttime correction for radiative cooling started to be applied in December 2010 for standard RH data products recorded with DigiCORA 3.
c. SIRTA field experiment The Instrumented Site for Atmospheric Remote Sensing Research (SIRTA) is a French national observatory dedicated to studying clouds, aerosols, dynamics and thermodynamics in the boundary layer and the free troposphere. The SIRTA observatory is a midlatitude site (48. 78N, 2. 28E) located in a semiurban area, on the Saclay plateau 25 km south of Paris  (Haeffelin et al. 2005). Three intensive observational periods (IOPs) dedicated to comparisons of vertical profiles of humidity were carried out between October 2016 and August 2018 with 29 M10-RS92 dual flights (see more details in Table  1 ). In this study, both IR2010 and Eoscan software were used to record the data measured and sent by the M10 radiosondes. IR2010 is the former software, which is still in used at several sites, whereas Eoscan is the new software from Meteomodem.
For this study, we used the Eoscan and IR2010 programs solely to produce the raw data before any posttreatment. DigiCORA III software was used for the monitoring of the RS92 radiosondes and we allowed the standard corrections to be applied for the data considered in our study. The different corrections presented in section 3 are applied to the raw M10 data. Standard ground checks were applied for M10 (ambient temperature and humidity chamber) and RS92 (temperature and 0% RH humidity chamber). For all of the figures, the altitude corresponds to the altitude above mean sea level.
To take into account these points and the ranges, a curve was derived using the following equation: t [T(i)] 5 a T 1(i) =b h i g , (10) where the values for the coefficients are a 5 90, g 5 4, and b 5 60. This method turns out to be a useful evaluation technique as an alternative to a retrieval of the RHS response time in the laboratory. It is important to stress that the temperature dependence expression (Fig. 6 , left panel) derived here is an evaluation of the RHS time-lag behavior with measured air temperature. Indeed, Meteomodem points at 2408 and 2208C are not perfectly superimposed to the curve. Neither are some of the experimental points. A compromise was then adopted to optimize the three parametrical coefficients to the complete dataset. Figure  6  (right panel) shows an example of the application of the time-lag correction on the RH measurements from 8 to 14 km of altitude for a M10 radiosounding.
Sensitivity tests and statistics a. Sensitivity tests We have presented in section 3 the method to optimize the M10 radiosonde profiling with three main corrections. The first correction, hereinafter ''slow-regime correction,'' depends on two parameters t and m. The second correction, hereinafter ''temperature-gradient correction,'' is related to the period during which the temperature offset is fitted (daily, climatological) or measured (instantaneous). The third correction, hereinafter ''sensor-response-time correction,'' is directly fitted with a previous dataset. We estimate the impact of each successive corrections of M10 relative humidity (U 1 , U 2 , U 3 , and U 4 ) in comparison with RS92 relative humidity. In Tables  3  and 4 , we report a global correction effect difference constructed as the absolute mean over all the flights and altitudes on an interpolated 50 m grid.
The RS92 dataset has been used to develop the three main corrections on M10 radiosonde, and this dataset validates the new M10 dataset. Table  3  corresponds to the data collected at the SIRTA observatory with 25 radiosondes during the daytime periods. The initial difference in relative humidity between M10 and RS92 is around 5. 4% RH with a standard deviation of 1. 8% RH. The impact of t and m appears directly on U 2 , but the final impact on U 4 is very significant (U 4 ranges from 3. 9% to 5. 1% RH on average). The standard deviation is also divided by 2 between U 1 and U 4. The best configuration corresponds to t 5 500 s and m 5 0. 1 for a measured temperature gradient with a final mean difference of 3. 9% 6 0. 8% RH. The daytime climatological model dedicated to fit with the temperature correction leads to a decrease in the standard deviation to 1.
Conclusions A set of corrections for M10 RH has been developed and tested for two field experiments, one with daytime launches at the SIRTA observatory and one with nighttime launches at the OHP. The corrections are based on observed physical phenomena and postprocessing verification. The corrections applied on the dataset are the following: 1) a correction for calibration, applied to the raw data of the OHP campaign (necessary because of a calibration bias in the early series of M10 radiosondes), 2) a slow-regime correction, based on the observed behavior of the sensor when responding to a step change in RH, 3) a temperature-gradient correction, which is based on the estimation of the difference between the RH sensor temperature and the actual air temperature, and 4) a time-lag correction, which improves the RH sensor response at very low temperatures.
The corrections have been applied to the whole M10 dataset and the impact of each correction on the M10 relative humidity profile has been quantified. As mentioned by  Bock et al. (2013) , the M10 used during the OHP campaign was a very recent product at the time. These radiosondes were actually the very first series that had been produced. The calibration issues encountered then, because of the early production stage, have a priori no link with the physical nature of the M10 relative humidity sensor. The three remaining corrections are based on physical phenomena. The time-lag correction and the slowregime correction have comparable amplitude for day and night periods, but the time-lag impact is much more significant at high altitude (above 10 km) whereas the slow regime affects the relative humidity in the lower to middle atmosphere (below 7 km).
We present the concept and propose a correction method. With regard to relative humidity bias due to spurious temperature of the sensor, often referred to ''radiation dry bias,'' several authors (e. g. ,  Vömel et al. 2007; Miloshevich et al. 2009; Dirksen et al. 2014)  propose correction methods that depend on temperature and relative humidity or pressure. We test and implement such methods. The average RH profiles measured by M10 and RS92 have been compared after each correction has been applied to the M10 observations. The remaining difference between the two sensors is lower than 1. 7% RH and 3. 9% RH for the OHP and SIRTA field experiments, respectively. In a future work, we will use these three main corrections to evaluate the Meteomodem M10 final products with 1) the GRUAN-corrected RS92 dataset, 2) a Snow White radiosonde, and 3) a cryogenic frostpoint hygrometer (CFH) radiosonde for more than 10 dual flights conducted at Réunion Island between 2017 and 2019.
In this article, an autonomous robotic fish is designed for underwater operations like object detection and tracking along with collision avoidance. The computer-aided design model for prototype robotic fish is designed using the Solid Works ® software to export an STL[AQ3] file to MakerBot, a 3D printer, to manufacture the parts of robotic fish using polylactic acid thermoplastic polymer. The precise maneuverability of the robotic fish is achieved by the propulsion of a caudal fin. The oscillation of the caudal fin is controlled by a servomotor. A combination of visual and ultrasonic sensors is used to track the position and distance of the desired object with respect to the fish and also to avoid the obstacles. The robotic fish has the ability to detect an object up to a distance of 90 cm at normal exposure conditions.
Moreover, Pixy's filtering parameter is dominant in changing the lighting and exposure and is significantly better than other vision sensors. 27, 28 n this article, we have designed and developed a lowcost embedded vision-based, autonomous robotic fish to detect and follow an underwater target. The developed robotic fish is capable of recognizing the object of interest and drive toward it using a Pixy vision sensor. The contributions of this work include a prototype design of robotic fish that has a rigid cylindrical shaped hull from the middle while the head has a conical shape with a round nose to cope with the problem of drag force. In addition, a low aspect ratio (AR) caudal fin is designed to achieve better locomotion while diving. Moreover, ANSYS Fluent software[AQ7] is used to investigate the hydrodynamics of the hull design of the robotic fish.
Mechanical design The mechanical design of the robotic fish is divided into two parts: (i) hull and (ii) caudal or tail fin. The fundamental structure of the robotic fish body is a compact, waterproof hull. First, the 3D model of the fish is designed using computer-aided design (CAD) software SolidWorks 2016. The hull is cylindrical from the middle while the head has a conical shape with a round nose to reduce the drag force. The assembled CAD model is shown in Figure  1. The mass property analysis has been carried out to find the physical properties of the designed structure. The volume, mass, and length of the robotic fish are 32. 53 in 3 , 0. 56 kg, and 415 mm, respectively. The choice of material for the fabrication of the robotic fish is a critical part of this research work as the material should exhibit various properties for the effective underwater operation.
The Bluetooth connection provides a wireless network throughout the operation between the fish and the central platform. PixyMon software displays the image when it is detected by the sensor. The range of the x-and y-positions of the center of the object in the image is from 0 to 319 and 0 to 199, respectively. 26 If the x-position of the object is between 120 and 190, it is considered that the object is in the center of the image. In other words, the desired object is in the heading direction of the fish. Similarly, if the x-coordinates of the desired object are between 10 and 119, the object is at the left side of the fish while for the right side position, the range is considered from 191 to 319. Figure  9  shows various movements and corresponding x-and y-coordinates of the desired object (golf-sized red ball) to the robotic fish heading position.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the  National Key Research and Development Program  (Grant Number  2016YFC-0300801 ), the  Natural Science Foundation of China  (Grant Number  51679213 ), the earmarked fund for China Agriculture Research System ( CARS-45 ), and the  U. K. Royal Society International Exchanges 2017 Cost Share  (Grant Number  IECnNSFCn170405 ).
Hardware and software To fill in all the prerequisites mentioned before, the development platform "Unity" was chosen. A collection of scripts for VR applications from the VRTK (Virtual Reality Toolkit) was used within Unity. Some predefined tools, like a pointer or a teleport, can be found in VRTK. SteamVR was used to communicate with the VR headset. Finally, all the scripts were written in C# using Visual Studio, a development software. As for the hardware, a HTC Vive headset has been used. It has a refresh rate of 90 Hz, a 110° view angle with a screen of 2160 by 1200 pixels. This technology enabled us to create a more immersive environment and a more realistic experience of the loading operations.
We are interested in the suburb of Paris, whose population was recently modeled in  [6]. In this paper the authors had access to the regional household travel survey, which is not available publicly. This survey called EGT-Global Transport Census, a Household Travel Survey specific to the Greater Paris-gathered accurate individual information about mobility patterns of a representative set of people of Great Paris area (Île-de-France). Individual travel plans are grouped into a set of a ten possible activity plans. However, the individual information of the census is not publicly available. Only aggregated information is granted public access about trip purposes and their mode, average number of trips. These data are presented for the height departments of the Greater Paris metropolis. This information is coarse if one consider a smaller region of interest, since a department can contain about a hundred Traffic Analysis Zone.
Let us consider department 92 called Hauts-de-Seine illustrated in yellow in Figure  1. From the EGT survey one can learn that for Home-Work trips, 37% of people use their car while 43% use public transportation. However, this information is not available at a smaller scale such as the grey sub-region which is defined as the catchment area of a district of interest in this study. Specifically, we are focusing on the mobility patterns of a population over this 7 km × 8 km area surrounding an urban district of interest. This catchment area was determined as the region of attraction of the district mostly because it can be reached in less than 15 min using a private vehicle, public transportation, walking or cycling. In this catchment area, illustrated in Figure  1  (right), it is not possible to access accurate and direct fine-grained information on activity plans of people, unless we have access to unpublished data or produce our own public census.
Analysis and Results The analysis aims at identifying clusters of relatively homogenous vulnerability structures among the geons dataset inherited from  [14]  (Analysis Part 1). Once the clusters are identified, they are mapped and analyzed based on their descriptive statistics. In Analysis Part 2, their inner structures-in terms of significant correlations between vulnerability indicators-are displayed as Causal Loop Diagrams (CLDs). Both the establishment of homogenous clusters (Analysis Part 1) and the disclosure of their inner structures (Analysis Part 2) utilize correlation: as a distance measure in the cluster analysis and as a filter for possible causal relationships between indicators in the CLDs. Significant correlations between indicators per cluster are calculated and handed over to a function, which automatically generates CLDs on the basis of said correlations.
By calculating correlation alone, however, neither the existence of causal relations nor their direction can be determined with certainty. To clearly draw knowledge from the CLDs, it is therefore indispensable to interpret them qualitatively in terms of causality and direction. However, since this article is focused on the data analysis that lays the foundation for a qualitative analysis, the in-depth interpretation is not part of this exercise. Figure  1  shows the analysis steps that are performed in the following sections. To make the single steps of the analysis transparent and reproducible, a script was developed using the programming language R, which is widely used for statistical computing. The essential packages are factoextra  [39]  and causalloop  [40]. A list of abbreviations can be found in Appendix A at the end of this document (Table  A1 ).
AGNES vs. DIANA: Testing Different Parametrizations for the Most Homogenous Results To test whether AGNES or DIANA and which parametrization yields the most homogenous clusters, the data were clustered using the eclust function from the package factoextra in the combinations AGNES vs. DIANA, with three vs. four clusters and Spearman's ρ vs. Kendall's τ as the within-cluster distance measure. Usually, Scree Plots are used to determine the optimal number of clusters by calculating and visualizing the decline of within-cluster sum of squares per each additional cluster. While the used R package factoextra offers the option the generate Scree Plots in general, the function is not implemented to handle correlation-based distance measures. As a substitute, Scree Plots for the Kmeans algorithm in combination with the Euclidean distance as the distance measure were tested, which showed a discernible decrease in the within-cluster sum of squares decline at about three or four clusters.
AGNES vs. DIANA: Testing Different Parametrizations for the Most Homogenous Results To test whether AGNES or DIANA and which parametrization yields the most homogenous clusters, the data were clustered using the eclust function from the package factoextra in the combinations AGNES vs. DIANA, with three vs. four clusters and Spearman's ρ vs. Kendall's τ as The parameter between-cluster distance, or linkage criterion, was set to Ward. D (Ward's method), a widely used method  [47, 48] , which, however, had no impact on the cluster generation. The method groups the geons based on the reduction of the sum of squared distances of each geon from the average geon in a cluster  [49]. Figures  3  and 4  show Silhouette Plots, plots designed to help validate cluster quality  [26, 50]. The longer the bars, the higher the cluster score-a measure to describe how well a geon fits into its cluster (cohesion) and how strongly it differs from the other clusters (separation).
However, the relationship between malaria and malnutrition is not fully understood, ranging from malnutrition as increasing susceptibility  [62, 63]  to decreasing  [64, 65]  susceptibility to no clear link  [66]. Another factor that has potentially impaired the results is the limited reliability of the data. The assessed nations lack reliable census data. Therefore, all the used datasets were either modeled, interpolated or otherwise modified in order to be able to work with what is available. While this approach is valid in this case of a general lack of "ground-truth" data, the results should be considered to show a general tendency rather than an exact reflection of reality. Finally, the ecological determinants of malaria persistence were not covered in this exercise. The EIR indicator and the immunity indicator, accordingly, are the only indicators that indirectly reflect the ecological and climatic dimensions, clearly making the 13 vulnerability indicators the dominant factors in the clustering process.
Validation According to  [26] , an intrinsic shortcoming of vulnerability assessments is that vulnerability cannot be validated, since it is a potential state. They therefore highlight the importance of the validity of the process. Process validity can be established by keeping the choices and sources regarding the conceptual framework, data, indicators, sub-indices and aggregation functions as transparent as possible  [68]. By keeping the processes transparent, criticisms of the distortion of complex realities through indicators to reflect preconceived ideas can be countered. The R script that was programmed for this exercise was therefore made publicly available on Github  [69]  to ensure process transparency and reproducibility, as well as the opportunity for reuse and improvement by the community.
Conclusions In this work, a spatial geon dataset (n = 108) covering a total of five East African Nations, where each spatial region is composed of an expression pattern of 14 variables-13 vulnerability indicators and one hazard indicator-has been analyzed and clustered into groups of geons, which share a similar systemic structure. The clusters were mapped, and descriptive statistics in tabular form gave an insight into the distribution of values. The clustered data were then handed over to a function that calculated Spearman's correlations between the 14 respective variables. Those correlations that were significant (p < 0. 05) were in turn handed over to another function that generated CLDs from them. The differing structures of the CLDs indicate that the assumption that one CLD alone cannot sufficiently capture the diverse relationships among the variables throughout the study region is justified.
From the observational side, many results were obtained from spectrointerferometry, and van  Belle (2012)  give a review of some of the different attempts made on several stars. Although important advances were achieved thanks to these and other theoretical and observational studies, they are based on simplified models, not taking into account the 2D internal structure of the rotating stars. Recent progress in programming techniques and computer power has enabled the creation of fully two dimensional stellar models by the ESTER code  (Rieutord et al. 2016). ESTER models indeed predict the differential rotation profile and the associated meridional circulation of an early-type star at a given stage of its MS evolution. The solution given by the code is presently the steady state solution of an isolated rotating star.
Interestingly, the new models enable an interpretation of the δ-Scuti type oscillations of Altair detected by  Buzasi et al. (2005). The paper is organised as follows: in Sect. 2, we list the observational data that we use. In Sect. 3, the ESTER code is briefly described. We present the preliminary models that match the previous determinations of Altair  's parameters (Sect. 4 ). We then focus on the combination of atmospheric models with interior models (ESTER and ω-model) and compute interferometric observables and spectra from the resultant intensity maps, to be compared with the observational data (Sect. 5). The modelfitting method we used and the results obtained are presented in sections 6 and 7. Finally, we discuss several points of interest (Sect. 8) and give some prospects for the future that this work offers.