dataesr/scientific-paragraphs-categorization huggingface dataset:
PLOS ONE Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks PLOS ONE | https://doi. org/10. 1371/journal. pone. 0248764 March 24, 2021 2 / 35 observations ("Rhone-Taxis dataset") have not been released. Concerning point  (1) , we highlight that removed data do not prevent to reproduce the results in the paper, as they have only been used as inputs to the KNR-interpolation mechanism that generates the larger-scale instances of the observed dynamic graph. Both kinds of dynamic weighed graphs datasets have been fully and publicly shared by the authors as supplemental material. Explicit consent to access the additional data features specified at point (1) can, if necessary, be sought by interested researchers by directly contacting the IGN provider via the contact form available on the website: https://www.
ign. fr, by specifying the research purposes motivating such specific need (which go beyond the scope of the paper). Concerning point  (2) , the individual traces represent sensitive information that we cannot share due to privacy concerns, and which is again unnecessary to reproduce the results included in our paper. The results of our manuscript can in fact be reproduced based on the aggregate speed information that have been publicly shared in the "Rhone-Obs" dynamic datasets and in its KNR interpolated version. In the remote eventuality detailed traces are required, researchers can contact our data officer Dr. /Eng. Bernard Schnetzler at the following address: bernard. schnetzler@univ-eiffel. fr. A confidential agreement will be stipulated with the interested researchers, in case of properly justified motivation.
dynamic graphs are still unsatisfactory, mainly due to the high computation time required. By highly dynamic graphs, we refer in this paper to graphs whose dynamic attributes, i. e. , edge weights, change frequently, as in the case of transportation networks. This paper proposes an approach to compute BC values on periodic snapshots of dynamic graphs representing transportation networks. The proposed approach can be thus leveraged to promptly detect anomalies or abrupt changes of network properties and traffic dynamics, by monitoring the sudden variations of the weighted shortest paths that traverse the links of the network. The contributions of the paper are the following: • the adoption of dynamic BC for ahead monitoring of transportation networks conditions. • the analysis of static and dynamic BC in different scenarios and with various graphs by exploiting a large dataset of traffic-related real observations (GPS traces of vehicles).
Conversely, when computing shortest paths in SSSPs, edge weights are assumed as in the notion of length (or travel time), i. e. , "higher is farther". We use a distributed variant of the Louvain algorithm for weighted and directed graphs  [57, 58] : all vertices select a new community simultaneously, updating the local view of the graph after each change. Even though some choices will not maximize modularity, after multiple iterations, communities will typically converge thus producing a final result relatively close to the sequential version of the algorithm. Given a graph Ĝ, we split it into a set of clusters (i. e. , C) by using the Louvain (Alg. 1, line 2) method for weighted graphs  [57, 58]. The implementation leverages a Scala parallel solution partially based on the Distributed Graph Analytics (DGA) by Sotera: https://github.
Dataset For our analysis, we consider a very-large directed graph, namely Rhone-ROADS, corresponding to the entire road network of the Rhone department, France. The graph includes the whole agglomeration of Lyon and its surroundings, with a geographical extent of approximately 3,300 Km 2. This dataset was created using digital maps supplied by the French National Institute of Geographic Information (IGN). The network consists of 117,605 nodes and 248,337 edges. By extracting the largest connected component of the resulting network, the final undirected and unweighted graph (see Fig  1a ) included 75,474 nodes and 96,406 edges (see Table  1  for the main characteristics of the datasets). By considering the graph only as undirected and unweighted, many relevant properties of the road network and its dynamics could be missed.
Therefore, in the first part of our evaluation, we consider multiple weighted, directed and static graphs that have been derived from the Rhone-ROADS network by selecting some of the available topological attributes (e. g. , road segment lengths, capacity, free-flow travel times, etc. ) as weight for the edges. Moreover, to evaluate our algorithm for efficient BC computation also in more realistic dynamic settings, we leverage an additional dataset for extracting reliable time-varying traffic information for a portion of the Rhone-ROADS network. Our second dataset, namely Rhone-TAXIS, contains anonymized GPS traces of taxi trips, observed over the Rhone department. The source dataset has been collected by the French operator Radio Taxi via a fleet of approximately 400 taxis, during 2011-2012.
The dataset logs geo-referenced taxi trips, segmented according to a variable sampling interval (between 10 and 60 seconds), with a global average of 800,000 measurements per day. We deem elementary taxi trip each measured trip segment from the same taxi trace. An elementary taxi trip includes the time-stamped start and arrival GPS positions of the associated taxi. These measures permit to estimate the travelled distance, and therefore the instant speed, on the traversed road segment via a map-matching operation. Map-matching has been performed via the open-source Mapillary Python map-matching library https://github. com/mapillary/map_matching. As an indicator of traffic dynamics, we adopt in the following the median speed observed over each road segment (i. e. , an edge of the network) during a fixed-duration of observation (e.
g. , 1-hour time slots). In order to improve the quality of the Rhone-TAXIS dataset and properly compute our traffic indicator, we have filtered out elementary trips with unrealistic speeds (i. e. , higher than 130 Km/h). A preliminary analysis of our dataset has shown a relatively low number of observations especially during evening and night-time (i. e. , the daily average of elementary trips per-hour is approximately equal to 8748. 5, with very high spatio-temporal variance and several time slots with 0 observations during the period March-May 2011). Therefore, to build realistic traffic dynamics, we perform an aggregation along the temporal dimension by reconstructing a typical working day from the whole set of elementary trips. Thus, we extract hourly median speeds for all edges with observations and generate a 24-sized array of median-speeds (i.
e. , one per 1-hour time slot of the typical day) for some of the Rhone-ROADS network edges. We choose the median operator as it is traditionally more robust than the average one and therefore more appropriate to derive realistic traffic measures. Clearly, the number of edges with median speed changes according to the spatio-temporal distribution of elementary trips on the Rhone-ROADS graph. In , median of all the median speeds associated to the edges at a given hour). The network median speed profile appears to be realistic from a traffic perspective, with the highest spikes at night/early-morning time (e. g. , around 4:00) and the lowest ones at morning and afternoon rush hours (e. g. , 8:00 and 17:00). We underline the fact that this aggregation step only stems from the limited size of the taxi fleet associated to our dataset (approx.
400 vehicles) and the consequent low spatio-temporal resolution of the observations. A larger dataset would have allowed us to refine the analysis by increasing both the temporal (e. g. , 10-minutes slots) and the spatial (e. g. , a larger portion of the Rhone-ROADS graph) resolution. Edge attributes: road segment length, width, number of lanes, speed limit, importance of the road segment (from 1 = max to 5 = min), post-code of the road segment area (INSEE), link geometry.
Rhone-OBS Multi-attribute, directed sub-graph derived by joining the Rhone-TAXIS dataset and the Rhone-ROADS network: each edge of the subnetwork has at least one elementary trip associated to it. Regarding the spatial dimension, Fig 1b graphically presents the sub-network (i. e. , the purple framed area) of the Rhone-ROADS graph for which at least one elementary trip is available during the typical working day. We call this sub-network Rhone-OBS graph, as reported in Table  1. Rhone-OBS comprises 35,940 nodes and 59,132 edges. With respect to Fig 2b, it is worth noting that the maximum number of edges with median speed is observed at 9:00 and corresponds to approximately 31,000 edges. The 59,132 edges of the considered Rhone-OBS graph derive from the fact that, during the 24 hours, the set of edges with median speed may vary significantly.
The figures clearly highlight that congestion is less intense during early-morning and late-evening time slots. However, as also previously reported in Fig  2a , a lower number of taxi trips is observed during these moments of the day. Moreover, extremely low observations take place during deep-night and very-early morning time (i. e. , from 01:00 to 05:00) as a consequence of low taxi usages, which also reduces the statistical relevance of the estimated speeds. Fig  2a  and 2b  indicate that the morning peak hour period (6:00-11:00) contains the largest number of observations (and therefore the largest observed traffic flow) from our dataset, and that such observations are mostly spread over the network, with around 200,000 to 380,000 individual hourly trips, concerning approximately 25,000 to 31,000 edges of the network.
Similarly, some specific arterials traversing the city center also appear as associated with higher TTBC. It is worth noting that such critical roads change frequently, depending on the specific time slot, thus unveiling high traffic dynamics at morning peak hours. In order to dig deeper into the interactions between TTBC and traffics dynamics, we performed a more specific analysis of the temporal correlation between dynamic BC (TTCB) and traffic flows. The latter are derived from an additional dataset provided by the Lyon municipality. Flow data are collected via vehicle counting sensors (loop detectors) installed along multiple road segments of the city. Such sensors reports the hourly flow values observed along a subset of the edges of our Rhone-OBS graph. Hence, we calculate the linear correlation score (which can be either positive or negative) for each edge l of the network.
This correlation is computed by considering, for a given edge l of the graph, the vector corresponding to the per-edge TTBC values over the considered time slots and the vector of the corresponding observed vehicle flows (measured as veh/hour). Due to the sparsity of the available flow data (loop detectors are only available for a limited number of edges of our Rhone-OBS network), the reported correlation analysis refers to edge TTBC and not to node TTBC, which is the metric computed by our W2C-Fast-BC algorithm. However, the main insights from such correlation analysis can be safely generalized to node's TTBC as well  [28, 34]. In fact, it is possible to compute per-node aggregate traffic flows by summing up the flow values available on incoming/outgoing edges and observe similar behaviors as those reported in Fig 7c.
As a preliminary step to evaluate the performance of W2C-Fast-BC, we have compared our approach to both Brandes  [15]  and BADIOS  [42, 43] , which represent the two most relevant approaches for fast computation of exact betweenness centrality from the state-of-the-art. To perform a fair comparison and remove any bias due to the adopted programming language and the associated runtime environment, we have compared our W2C-Fast-BC implementation to a custom Scala implementation of Brandes. Concerning BADIOS, we have considered the open-source C++ implementation provided by its authors and compared it to the C++ implementation of Brandes, included within the BADIOS framework for benchmark purposes. The reported analysis has been performed in sequential mode for two reasons: first of all, a sequential execution permits to clearly quantify the benefit of our technique with respect to Brandes only as a consequence of the reduced number of SSSP explorations, whose number corresponds to the cardinality of the identified set of pivot nodes.
Solutions such as the well-known METIS algorithm  [61]  and its more recent parallel improvements  [62]  appears as a valid lead to pursue in order to achieve the aforementioned objective; ii) remove the error associated to classes of equivalence and the pivot mechanism  [59]. Concerning data availability, in our empirical evaluation we use consecutive GPS position observations from taxis on-board GPS sensors and the derived instantaneous speed measurements, which have been post-processed and associated to specific road segments of the underlying network via a map-matching solution. It is important to remind that we derive hourly typical travel-time graph weights by exploiting road length information and multiple speed observations related to a multitude of vehicles traversing each road segments, collected over different days.
Conclusion In this paper, we have proven, through an in-depth analysis performed on a large real network, that betweenness centrality is a useful indicator of both structural bottlenecks (in static, unweighted and weighted graphs) and traffic conditions (in dynamic, weighted graphs). At the same time, we have pointed out that in a dynamic context the estimation of traffic flows requires fast computation of BC. A requirement that we satisfy with our algorithm able to compute good approximation of BC values in short times. For the study, we have used two datasets: one related to the whole road network of Lyon, whose weights have been computed using free-flow travel times and road lengths, and another one containing GPS traces of taxi trips, with a partial coverage of the whole road network, for estimating dynamic weights from average travel times.
In the future, we intend to extend our analysis by exploiting a dataset with a larger coverage for dynamic weights as, due to a limited coverage of GPS data, we have been forced to apply an interpolation technique based on non-parametric regression to realistically scale the dynamic analysis on a larger network. Moreover, we plan to evaluate and eventually integrate BC with other information related to a preventive or statistical knowledge of traffic distribution that could contribute to the definition of a more effective traffic predictor based on betweenness centrality.
Acknowledgments The authors would like to thank  Lorenzo Goglia  for his support in the comparative evaluation of W2C-Fast-BC with the BADIOS framework, as well as  Loïc Bonnetain  and  Elise Henry  for their support in the analysis of the correlation between TTBC and traffics dynamics. The authors also acknowledge the  French National Agency of Research, the Italian CINI "Smart Cities and Communities" National Lab  and the  SAI  project for supporting this work.
This paper explores the potential of all-sky cameras to retrieve aerosol properties with the GRASP code (Generalized Retrieval of Atmosphere and Surface Properties). To this end, normalized sky radiances (NSRs) extracted from an all-sky camera at three effective wavelengths (467, 536 and 605 nm) are used in this study. NSR observations are a set of relative (uncalibrated) sky radiances in arbitrary units. NSR observations have been simulated for different aerosol loads and types with the forward radiative transfer module of GRASP, indicating that NSR observations contain information about the aerosol type, as well as about the aerosol optical depth (AOD), at least for low and moderate aerosol loads. An additional sensitivity study with synthetic data has been carried out to quantify the theoretical accuracy and precision of the aerosol properties (AOD, size distribution parameters, etc.
Inversion strategy The normalized sky radiances obtained at the three effective wavelengths from the sky camera have been used as input in the GRASP inversion code to retrieve aerosol properties. Additional information about the surface reflectance is introduced in GRASP through bidirectional reflectance distribution function (BRDF) data. These BRDF values are obtained from a 8 d climatology table created for Valladolid station using satellite data (MCD43C1 product from MODIS V005 collection;  Schaaf et al. , 2011)  from the 2000-2014 period; more details about these climatology values are provided in  Román et al. (2018). The inversion of camera radiances with GRASP has been carried out considering some assumptions: (1) the aerosol size distribution is bimodal, with one fine and one coarse mode, and each mode equal to a log-normal distribution; (2) there is no dependence of the real and imaginary parts of refractive index on the wavelength since we assume the differences between the three camera wavelengths are short to produce high variation in the complex refractive index; and (3) the imaginary part of refractive index (IRI) cannot be retrieved (NSR measurements are not sensitive to aerosol light absorption), and it is assumed equal to 0.
Other aerosol properties like AOD are derived from the retrieved aerosol products. This configuration of GRASP and the products derived by it are labelled in this paper as "GRASP-CAM" in order to make reference to the use of camera measurements in GRASP. The GRASP-CAM retrieval is run for each camera measurement (5 min sampling interval) only if at least six cloudfree NSR measurement points are available for each wavelength in order to ensure enough information about the aerosol properties in the measurements (at least double the number of input measurements, 18, than the number of retrieved parameters, 8). In addition, every GRASP retrieval provides, for each wavelength, the residual differences between the NSR measured (input) and generated by the retrieval (modelled). This residual is calculated for each wavelength in a relative way (%) as the root mean square of all relative differences (%) between measured NSR values and modelled (forward module) ones, as Eq.
Single-pixel approach In order to study the performance of the GRASP-CAM products using real measurements, the GRASP-CAM method has been applied to NSR measurements obtained with the allsky camera at Valladolid (see Sect. 2. 1). The data span from 11 July 2018 to 15 September 2020. This has provided a total of 42 105 retrievals (satisfying at least six cloud-free NSR data points per wavelength), but only 34 536 pass the convergence criteria. An additional quality-control criterion has been added: the need of at least one NSR measurement, in any wavelength, with a low scattering angle (≤ 14  (> 80 • ). This new criterion is based on the results of Sect. 3, which revealed the need of low scattering angles for an accurate retrieval, but also based on the AERONET criteria  (Holben et al. , 2006) , which demand a minimum number of sky radiance measurements in various scattering angle ranges, including high angles, for quality-assured retrievals.
This new criterion reduces the available retrievals to 23 368, removing all retrievals with SZA values between 47. 2 and 64. 2 • and with SZA values higher than 82. 5 •. The rejection of retrievals with the highest SZA values is also motivated by the GRASP assumption of plane-parallel geometry for forward modelling; this assumption could still affect the remaining retrievals with high SZA angles. These criteria are required for quality assurance of the retrieval. However, some data contaminated by clouds could pass them. Hence, additional cloud-screening criteria, based on the AERONET cloud-screening version 3  (Giles et al. , 2019) , have been applied. For this purpose, the time series of the GRASP-CAM-retrieved AOD has been used. A GRASP-CAM retrieval has been assumed as cloud contaminated, and hence removed, if (1) the time variation of the remaining cloud-free AOD at 536 nm is higher than 0.
Aerosol optical depth To study the goodness of the AOD retrieved by GRASP-CAM, it needs to be compared with alternative and independent measurements and in our case with the AOD measured by a collocated AERONET sun-sky photometer. To this end, the AOD from AERONET has been interpolated to the effective camera wavelengths following the Ångström law  (Angström, 1930 (Angström, , 1961)). Figure  8  shows both GRASP-CAM and AERONET AOD time series for a 12 d period in summer 2020 at Valladolid. AOD from GRASP-CAM looks a bit noisier than AERONET; however, both data series are well correlated for the three wavelengths, showing similar AOD values. For example, a decrease from moderate to low AOD load can be observed in both series on 1 August. Sometimes AOD from GRASP-CAM is available when the AERONET one is not, which points out that GRASP-CAM could be useful to complement AOD data series.
The full time series of retrieved AOD can be observed in Fig. S31. A more quantitative analysis has been done by a matchup of GRASP-CAM and AERONET AOD data. Each available GRASP-CAM AOD data point has been paired up with the closest AOD AERONET data point within ±2. 5 min. The ±2. 5 min interval has been used because GRASP-CAM data are available each 5 min, and this interval avoids overlapping one AERONET value for two or more GRASP-CAM retrievals. After this match-up, a total of 16 935 AOD data pairs (GRASP-CAM versus AERONET) are available for each wavelength. The upper panels of Fig. 9  show these data pairs through density scatter plots of AOD from GRASP-CAM as a function of AERONET AOD for each wavelength. The least squares linear fit, its equation and the determination coefficient (r 2 ) of these data pairs are also included.
AOD from GRASP-CAM correlates well with AERONET measurements, with r 2 of about 0. 87 at the three wavelengths. The differences between both AOD sources increase for high AOD values, but the availability of data pairs under these conditions is scarce. The scatter plots and the linear fit equations show a slight overestimation of GRASP-CAM to AERONET for low AOD values, while GRASP-CAM tends to underestimate the highest AOD values. The bottom panels of Fig. 9  present the frequency distribution of the differences between the AOD from GRASP-CAM and AERONET, as well as the mean (M), median and standard deviation of these differences. The peak of the three distributions are slightly biased to positive values, which could be caused by the mentioned overestimation of GRASP-CAM to AERONET for low AOD values (the most frequent in Valladolid).
Financial support. This research has been supported by the Ministerio de Ciencia, Innovación y Universidades (grant no. RTI2018-097864-B-I00), the Junta de Castilla y León (grant no. VA227P20), and the Marie Skłodowska-Curie Research Innovation and Staff Exchange (RISE) (GRASP-ACE project (grant no. 778349)). Review statement. This paper was edited by Edward Nowottnick and reviewed by Reed Espinosa and one anonymous referee.
That is, BVLOS operations in highly complex environments require high levels of autonomy to guarantee task success. However, the opposite is yet to be seen, i. e. , high levels of autonomy can tackle highly complex environments and missions, mostly because the extent in which an autonomous aircraft can tackle this scenario depends on advances in robust perception and AI. For example, high precision data acquisition is an example application in which the requirement of capturing data with the highest accuracy possible imposes additional demands on onboard autonomy. Complexity of environment and mission is generally low or medium in applications such as precision agriculture, marine monitoring or linear infrastructure inspection. Capturing data during these applications requires advanced precise guidance, path planning and trajectory tracking in order to ensure accurate waypoint navigation, data synchronisation, data overlap and correct resolution, amongst others.