dataesr/scientific-paragraphs-categorization huggingface dataset:
Data scoring Data were scored over a 6 day period for each site, for 1 h per day during working days, hours and weeks to ensure that data excluded movements generated by tourism, festivals, etc. This scoring duration is sufficiently ample to provide a large dataset  [8, 17, 31]. Video cameras were set up in order to score the light colour and were placed in locations ensuring that crossing pedestrians were visible at all times. Behavioural sampling was used to score the crossing of pedestrians in one direction only, i. e. that recorded by the camera. Pedestrians were not informed about the purpose of the study. As both cities are touristic, pedestrians are accustomed to seeing tourists taking pictures or videos. We did not observe any difference in the way pedestrians behaved when they saw the camera.
We purposely did not take any other equipment such as counters or pocket PC in order to avoid influencing pedestrian behaviour. When observation of road-crossing behaviour was hampered by a visual obstacle (i. e. a car or a truck in front of the video camera), we removed this behaviour and the behaviours occurring immediately before and after it from the analyses. We also removed data in which cyclists were among the pedestrians or where tourists were present. Tourists were easily differentiated from citizens, as they were in large groups accompanied by a guide, were dressed differently from citizens and carried specific equipment (guidebook, map, camera, etc. ).
Research ethics Our methodological approach solely involved anonymous observations and anonymous data scores. Our protocol followed the ethical guidelines of our institutions (IPHC, Strasbourg, France and PRI, Kyoto University, Japan) and we received ethical approval from these institutions to carry out our study. All data were anonymous, and individuals were given sequential numerical identities according to the time of the road crossing and the arrival/departure order of crossing. Pedestrians had the possibility to be informed about the study by an information medium in their language (Japanese or French). They were also provided with an email address and phone number to contact our institution at a later date if desired. Persons who refused to participate in the study were removed from the data (i.
Data analysing Data are available from Dryad repository (http://dx. doi. org/10. 5061/dryad. tc365). Two kinds of data were analysed on the videos: (i) crossing against the red light and (ii) collective road crossing at the red or green light. Of course, collective road-crossing can involve crossing at the red light-the two behaviours are not exclusive. In the first case, i. e. crossing at the red light, we wanted to analyse the individual rule-breaking behaviour independently of collective behaviours, whereas in the second part, i. e. 'collective roadcrossing', we aimed to analyse the collective processes and identify those influencing an individual's decision to follow others across the road at a green or red light. (1) Crossing against the red light: for each site, two 10 min periods were chosen at random for each scored day in order to score each crossing behaviour.
This led to the analysis of 5445 road crossings, of which 3814 were in France and 1631 in Japan. For each pedestrian crossing the road, we scored the site, country, number of lanes, light colour, and gender and age of the pedestrian. We also recorded whether the pedestrian was accompanied by one person or more. The age of pedestrians was estimated at 10 year intervals, from 0-9, 10-19 [. ] to 70-89. A pedestrian is considered to be accompanied when he/she is talking with another person or walking very closely and synchronously with her/him, arriving at the road and crossing it at the same time. (2) Collective road crossing at red or green light: all 6 h of data were analysed for each site. We scored the behaviours of pedestrians during collective crossings, which were defined as those involving two or more pedestrians at the green light (occurring without prior jaywalking by pedestrians) or at the red light, when the time between the departures of the two pedestrians was lower than the mean road-crossing time indicated in table  1  for each site.
Statistical analyses A generalized linear model (GzLM) was used to test the influence of the independent variables on our five following response variables. We first analysed road crossing with the dependent variable, i. e. the light colour (red or green), using a binomial law. In a second step, the collective road-crossing behaviours at the green light were analysed by testing the order of departure (Poisson law), the departure period of the first pedestrian to cross (Gaussian law), and the departure period and the departure latency of other pedestrians (Gaussian law). To assess whether pedestrians are influenced in their way to cross by other pedestrians crossing at the red or at the green light, we also tested the departure latency of pedestrians in combined datasets for pedestrians crossing at the red and at the green light.
For each GzLM, we ran multi-model inferences to compare and rank candidate models according to (i) their respective Akaike information criterion after correction for small sample sizes (AICc) and (ii) normalized Akaike weights (AICw)  [33]. AICc is the difference in AICc between one given model and the model with the lowest AIC. The AIC weight indicates the probability of a given model being the best among candidate models. Models with a AICc < 4 were considered equally possible candidates and then their statistics averaged. The null model was included as a possible candidate but was never among the models with lowest AICc. Averaged model coefficients were obtained for models with a AICc < 4. Model inference and averaging were carried out with the R package 'MuMIn'  [34]. We checked for multicollinearity of the predictor variables by calculating the variance inflation factor (vif).
This design was developed to avoid potentially interfering with some of GRIPS' sensitive analog electronics. Special care was also given to ensure that the electronics box could operate in the stratosphere. The low atmospheric pressure meant that, despite the low air temperature, heat could easily build up in electronic components. At-risk components were identified via thermal imaging (see, e. g. , Fig. 5 ). Custom copper heat sinks were fabricated and installed to increase the surface area for thermal radiation and to provide a path to conduct heat from these components to the box itself. The effectiveness of this thermal management was confirmed prior to flight via testing in a vacuum chamber. The level of background radiation also significantly increases with altitude and posed a risk for data storage.
Nevertheless, between 02:09 and 02:18 UTC and between 02:36 and 02:50 UTC (from 8. 5 to 9. 5 km and from 11. 6 to 13. 2 km in altitude, respectively), a large fraction of the measurements returned by the TILDAE anemometer had nonphysical values. The spikes during portions of these periods become so common that they came to dominate the data. For this reason, the points in Fig. 6  that correspond to these periods have been highlighted and should be treated as suspect. These points are some of the coldest in that plot; while this Figure  6. Plot of sounds speed versus altitude as measured by TILDAE's sonic anemometer. The right-hand axis shows equivalent air temperature (per Eq. 3). The data shown in the plot have been averaged down to a 1 min cadence. The red data points (i. e. , those between 02:09 and 02:18 UTC and between 02:36 and 02:50 UTC) are suspect because, as described in Sect.
Preliminary scientific analysis TILDAE, during its ascent, recorded science-quality measurements in the troposphere and the lower stratosphere (i. e. , below an altitude of about 18 km). The scientific analysis of these measurements is ongoing and will be presented in full detail in one or more forthcoming articles. While the focus of this article is on the technical aspects of TILDAE's system and flight, a preview of this analysis is warranted. Figure  7  shows plots (versus time) of TILDAE data from a 90 s exemplar period beginning at 03:04:00 UTC (14. 7-14. 9 km). The anemometer measurements (upper two panels) are plotted at their native 200 Hz cadence and are shown both in their raw form and after a three-point median filter has been applied. At the beginning and the end of this period, the air flow was relatively smooth and steady: the value of each velocity component only changed gradually and on the timescale of seconds.
Discussion The exemplar period explored in Figs. 7-9 is just one of several bursts of fluctuations that have been identified in TILDAE's measurements from the stratosphere. Many of these appear to be well-developed turbulence, and efforts are underway to quantify them in terms of strength and isotropy. In particular, the following scientific investigations of the TILDAE dataset are currently underway: -The dissipation rate of kinetic energy provides an important diagnostic for characterizing any turbulent phenomenon. Using techniques such as those developed by  Theuerkauf et al. (2011)  for single-point balloon measurements, dissipation rates will be inferred for various periods in the TILDAE dataset. -Intermittency is considered a signature of well developed turbulence. As stratification increases, flows are expected to become more stable and less intermittent.
The commissioning (i. e. , calibration and configuration) of this system required more time than had been anticipated; Sun tracking was not fully established until the gondola was at float altitude. Thus, the change in horizontal-flow direction seen in Fig. 7  may indicate a rotation of the gondola. The high-frequency noise that is evident in Fig. 7  (and at various times throughout the TILDAE dataset) is not expected to deleteriously impact the analysis of turbulence. As is evident from Fig. 9 , this noise has a relatively narrow bandwidth and occurs at frequencies for which the spectrum is already saturated. One hypothesis for the cause of this noise is electrical and/or vibrational interference from the motors in GRIPS' pointing-control system. This would account for the noise's narrow bandwidth and the variability in its frequencies.
The scientific analysis of the TILDAE dataset is ongoing. TILDAE was launched from one of the polar regions, where the height of the tropopause is notably low. It bears mention, though, that the maximum operating altitude that was established for the sonic anemometer, 18 km, is above the typical height of the tropopause over any region of the Earth at any time of year  (Hoinka, 1999). Thus, in principle, the TILDAE sonic anemometer, a commercial device with only relatively modest modifications, would be capable of observing any part of the tropopause (at least in terms of pressure). The results of TILDAE's flight strongly motivate the continued use of sonic anemometers in the exploration of the tropopause and lower stratosphere. While the sonic anemometer itself requires further refinement and ground testing (to optimize performance at these altitudes), a balloon mission dedicated to sonic anemometry would allow the most dramatic improvements over TILDAE.
In the meantime, requests for a preliminary version of the science-quality dataset may be directed to Raffaele Marino (raffaele. marino@ec-lyon. fr), who is leading the TILDAE scientific effort. Competing interests. Herb Zimmerman's principal contribution to the TILDAE mission was leading the calibration of the sonic anemometer for use at high altitudes. He also provided general guidance on the anemometer's design and operation. Though Applied Technologies covered the calibration costs (e. g. , facility usage fees), they provided no direct financial support to the TILDAE project, which purchased the anemometer from them.
Acknowledgements. We gratefully acknowledge  P. Saint-Hilaire  and  A. Y. Shih  for accommodating TILDAE as an add-on instrument to the GRIPS mission. We extend our thanks to the entire GRIPS team for their help and support in the development of TILDAE. We also thank  Ball Aerospace  for use of their vacuumchamber facilities and  NASA's Columbia Scientific Balloon Facility (CSBF)  for the successful launch of GRIPS. We extend our gratitude to the  Antarctic Meteorological Center (AMRC) , whose radiosonde program provided the comparison data for Fig. 6. We thank the anonymous referees of this article for proposing our use of these data, our addition of Fig. 8 and Table 1, and various improvements to the text. The preparation of this article made use of the  SAO/NASA Astrophysics Data System (ADS).
BAM thanks  L. Marcus ,  C. Trew , and the entire staff of the  Technical Reports and Standards (TRS)  unit of the  United States Library of Congress  for their assistance with background research. Bennett A. Maruca 's contribution to TILDAE was partially funded by the  Charles Hard Townes postdoctoral fellowship. Vincenzo Carbone  and  Raffaele Marino  were partially supported by the  Italian Ministry for Education and Research MIUR PRIN  grant no. 2012P2HRCR  on "The active  Sun  and its effects on Space and Earth climate. " Vincenzo Carbone ,  Raffaele Marino , and  Bennett A. Maruca  acknowledge support from the  Turboplasmas project  ( Marie Curie   FP7 PIRSES-2010-269297 ). Raffaele Marino  acknowledges support from the  PRESTIGE program  coordinated by  Campus France  ( co-financed under Marie Curie   FP7 PCOFUND-GA-2013-609102 ) and the  PALSE program  at the  University of Lyon  (through the  PALSE postdoctoral fellowship  and the action IMPULSION).
A critical concern of autonomous vehicles is safety. Different approaches have tried to enhance driving safety to reduce the number of fatal crashes and severe injuries. As an example, Intelligent Speed Adaptation (ISA) systems warn the driver when the vehicle exceeds the recommended speed limit. However, these systems only take into account fixed speed limits without considering factors like road geometry. In this paper, we consider road curvature with speed limits to automatically adjust vehicle's speed with the ideal one through our proposed Dynamic Speed Adaptation (DSA) method. Furthermore, 'curve analysis extraction' and 'speed limits database creation' are also part of our contribution. An algorithm that analyzes GPS information off-line identifies high curvature segments and estimates the speed for each curve.
The main limitation of vision approaches is visibility, since shadows, highlights, occlusions or weather conditions affect the accuracy of data analysis. Moreover, IMU sensors are able to estimate the current position of the vehicle. Fusing GPS and IMU with the typically Kalman filter  [15]  provides absolute position and orientation, even if GPS data are not available all of the time  [16]. Road map databases (GIS maps) together with GPS data provide accurate road geometry and localization  [6, 17]. An approach to analyze road geometry identifying critical curves where accidents occur frequently was proposed in  [6]. Wang et al. [17]  calculated the road ahead of the vehicle through a flexible road model. Nevertheless, GIS information is not always available, and GPS is starting to be accessible or will be soon for the general public.
For this reason, the choice of GPS data for tracking approaches is convenient. Our work is based on Real-Time Kinematics GPS (RTK-GPS), which has the highest absolute position accuracy (up to a few centimeters), to implement an automatic approach that goes beyond warnings for adjusting the vehicle speed depending on the upcoming road characteristics, e. g. , curves or speed limit zones. This paper proposes a Dynamic Speed Adaptation (DSA) method for control systems, which together with speed limits database creation and curve extraction, adjusts automatically the vehicle's speed (see Figure  1 ). These two approaches, speed limits database creation and curve extraction, are performed off-line including the preprocessing step. Their output provides the main parameters to be analyzed by the speed negotiation algorithm of DSA in order to compute the ideal speed.
The identification of sharp curves is performed using GPS positions to define segments that belong to a curve or a tangent line. This is in order to estimate the convenient driving speed for the detected high curvature segments. The speed limit database is created for the traveled paths identifying the positions where speed limits change. As the tracking performance is evaluated through lateral errors, our DSA is tested in simulations with different steering control algorithms. On the modeling level, our proposed work would:  The rest of this paper is organized as follows. Section 2 presents a review of related works. Section 3 provides an overview of the steering control algorithms that we used to test our approach. Section 4 explains our DSA method. In Section 5, we discuss and analyze the results obtained with and without considering automatic speed adaptation in the different steering control algorithms.
It is also known that in order to take advantage of fully-controlled ISA systems, the speed limit information and the positioning system must be accurate  [20]. For this reason, speed limit databases up to date with possible variations (time of the day, weather conditions, type of vehicle) are necessary. An approach to develop and use a speed limit database in the ISA warning system was implemented as a mobile application software for Australian roads  [23]. Even though Google Maps' application provides speed limits, it does not contain information regarding temporal situations (i. e. , weather conditions, timing, construction areas) since it is an exhaustive work to maintain an updated database worldwide. Our speed limit database is based on static speed limits defined by the traffic speed regulations in France  [24].
GPS Preprocessing Real-Time Kinematic (RTK) refers to a technique able to correct positioning information (GPS) with centimeter-level accuracy. This correction is carried out by a base station that reduces or removes positioning errors  [40]  caused by environmental surroundings or weather conditions. RTK-GPS was used during data collections in order to have reliable GPS paths. Nevertheless, due to different circumstances like the loss of connection between the base and receiver, noise appeared in the GPS data. This noise was removed manually in our datasets in order to have reliable learning routes. We identified the noisy points and interpolated their new positions based on the previous and following points. Once GPS noisy points are removed, we computed the Euclidean distance between each pair of points to keep GPS positions at an approximately 3.
Speed Limit Database Speed limits vary between countries, types of roads, areas, vehicles and inclusive timing  [23]. They may also be affected by certain circumstances like construction or special events around a zone (i. e. , fairs, carnivals, street markets, etc. ). Even though it is important to consider all possible scenarios for the speed limit database, it is very hard to maintain updated zones where temporary situations occur. Digital maps contain speed limit information, but most of them are not free. OpenStreetMaps  [41] , on the other hand, provide open data with fixed speed limits (without considering temporary data), but for our traveled paths, the information is not included. We created our own speed limits database, based on the typical manual driving situations and considering the code regulations established by the French government  [24].
In France, urban areas have speed limits of 50 km/h varying to 30 or even 20 km/h in agglomeration areas, residential areas or school zones  [24]. According to the recorded RTK-GPS information, we: 1. Define start and end points. 2. Identify positions where speed limits change. 3. Compute the distance (d l ) from the speed limit change point to the reference starting point in the path. 4. Save the distance (d l ) for every speed limit change position identified together with its respective speed limit value (v l ). We identified positions where speed limits change from 50 km/h to 30 km/h and vice versa. By default, the speed limit at the starting point is set to 50 km/h if no other speed limit is identified. An illustration of the procedure is presented in Figure  3. The creation of the speed limits database is performed off-line and only after GPS information is preprocessed.
In this work, a threshold value of 1. 25 • was chosen as proposed in  [6]  after evaluating curvature identification with different values. It is worth mentioning that values smaller than 1. 25 gave us false positives, as almost every point was considered as being part of a curve, and values bigger than 1. 25 discarded long and smooth curves. Based on Li et al. 's work  [6] , curves were classified as simple or compound. A compound curve is a curve formed with at least two consecutive curves separated by a certain distance between each other. In our datasets, this distance was set to 10. 5 m, which is the approximation of considering three consecutive points in our preprocessed GPS path. An example of the two types of curves is illustrated in Figure  5. After each curve is defined by its start and end points (PT and PC), its geometric information, consisting of its radius (R), length (L) and central angle (θ), is computed through Equations (  6 ) to  (8).
Results In this section, we perform simulations with different lateral control algorithms on a simulated path and real datasets. This is with the aim to show how tracking accuracy is improved if correct speed definitions are taken into account for each segment in the traveled path. Consequently, a sense of comfort and safety will be provided to the passenger, reducing traffic accidents and severe injuries  [1]. Three paths (one created and two real) were considered for the simulations. Each path has different length, curve characteristics and speed limits. The real datasets (traveled paths) were acquired by Qiao et al. [45]  with the experimental platform shown in Figure  10  in Belfort, France. The data of the paths are based only on GPS information captured by GPS and RTK-GPS receivers. The simulated path was created with the aim to compare how noise affects the tracking performance of lateral control algorithms.
A visual representation of the considered paths can be seen in Figure  11. They are briefly presented below. Its total distance is about 374 m with four sharp curves identified. Since it is a virtual path, we set the speed limit for the entire track to 50 km/h, as this is the default speed limit in urban areas in France  [24]. 2. UTBM-2 dataset: The traveled path contains 541 points after the preprocessing step performed as described in Section 4. 1. The distance of this track is about 2. 2 km with detected speed limits of 30 km/h in school zones. Six sharp curves were identified with different geometric characteristics. 3. UTBM-3 dataset: This path is the longest one with a distance of about 4. 5 km. It is represented by 1136 points after the preprocessing step. The number of sharp curves (dangerous curves) detected was 17.
The speed limits for this path are 30 km/h in residential areas and 50 km/h otherwise. Different path tracking simulations were performed through the tool developed by Lombard et al. [37]. These simulations include the analysis of different lateral control algorithms with and without considering the ideal speeds obtained by our DSA method. In order to compare the performance of our proposed DSA method between the different steering control algorithms, (1) Pure Pursuit (PP), (2) Stanley, (3) Alice and (4) Lombard, we compute the root mean square error (e rms ) per each dataset for each of the four algorithms as follows: e rms = ∑(p d (t) -p c (t)) 2 m ( 17 ) where p d and p c represent the desired and current vehicle position, respectively. The difference between p d and p c is the lateral error, while m is the total number of sampling results every 400 ms (latency of the system).