dataesr/scientific-paragraphs-categorization huggingface dataset:
015 and NFPP < 0. 001 for a planet to be validated). This result represents a notable improvement with respect to that obtained by  Giacalone et al. (2021). Giacalone et al. (2021)  ran this analysis only on the TESS data, obtaining an FPP ∼ 0. 3, according to which the candidate is not validated, but must be classified as "likely a planet". In the case of the 1. 04 d transit signal, the code provides an FPP ∼ 0. 09 and an NFPP ∼ 0. 006. Based on the TRICERATOPS criteria this test tells us that this object is "likely a planet" but there is a low probability of false positives due to binaries. Since planet c is already confirmed, folding in the FPP of planet c to the validation analysis of planet b would likely decrease the FPP of planet b. Furthermore, based on multiplicity statistics determined from Kepler discoveries  (Lissauer et al.
Especially for HD 93963 A, the measurement of the planetary masses and eccentricities will allow us to perform stability analysis and understand whether the low-eccentricity migration process described in Sect. 10 could explain its current architecture. Acknowledgements. CHEOPS is an ESA mission in partnership with Switzerland with important contributions to the payload and the ground segment from Austria, Belgium, France, Germany, Hungary, Italy, Portugal, Spain, Sweden, and the United Kingdom. The CHEOPS Consortium would like to gratefully acknowledge the support received by all the agencies, offices, universities, and industries involved. Their flexibility and willingness to explore new approaches were essential to the success of this mission. We acknowledge the use of public TESS data from pipelines at the TESS Science Office and at the TESS Science Processing Operations Center.
Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center for the production of the SPOC data products. This research has made use of the NASA Exoplanet Archive, of the Exoplanet Follow-up Observation Program website, which are operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. L. M. S. and D. G. gratefully acknowledges financial support from the CRT foundation under Grant No. 2018. 2323 "Gaseous or rocky? Unveiling the.
A1, page 21 of 25 A&A 667,  A1 (2022 ) nature of small worlds". S. H. gratefully acknowledges CNES funding through the grant  837319. ABr was supported by the  SNSA. S. G. S. acknowledge support from  FCT  through  FCT  contract nr. CEECIND/00826/2018  and  POPH/FSE (EC). Some of the observations in the paper made use of the  High-Resolution Imaging instrument 'Alopeke obtained under Gemini LLP Proposal Number :  GN/S-2021A-LP-105. 'Alopeke was funded by the  NASA   Exoplanet Exploration Program  and built at the  NASA Ames Research Center by Steve B. Howell, Nic Scott ,  Elliott P. Horch , and  Emmett Quigley. Alopeke  was mounted on the Gemini North (and/or South) telescope of the international Gemini Observatory, a programme of  NSF's OIR Lab , which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation.
LCOGT telescope time was granted by  NOIRLab through the Mid-Scale Innovations Program (MSIP). MSIP is funded by  NSF. Y. A. and M. J. H. acknowledge the support of the  Swiss National Fund  under grant  200020_172746. We acknowledge support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  through grants  ESP2016 MDM-2017-0737  Unidad de Excelencia Maria de Maeztu-Centro de Astrobiología (INTA-CSIC) , as well as the support of the  Generalitat de Catalunya/CERCA  programme. The MOC activities have been supported by the  ESA  contract no. 4000124370. S. C. C. B. acknowledges support from FCT through FCT contracts nr. IF/01312/2014/CP1215/CT0004. X. B. , S. C. , D. G. , M. F. and J. L. acknowledge their role as ESA-appointed CHEOPS science team members.
P. C. thanks the  LSSTC Data Science Fellowship Program , which is funded by  LSSTC, NSF  Cybertraining Grant Number  1829740 , the  Brinson Foundation , and the  Moore Foundation. Her participation in the programme has benefited this work. A. De. acknowledges support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  FOUR ACES , grant agreement no. 724427 ), and from the  National Centre for Competence in Research "PlanetS " supported by the  Swiss National Science Foundation (SNSF). The Belgian participation to CHEOPS has been supported by the  Belgian Federal Science Policy Office (BELSPO)  in the framework of the  PRODEX Program , and by the  University of Liège  through an  ARC grant for Concerted Research Actions  financed by the  Wallonia-Brussels Federation.
L. D. is an F. R. S. -FNRS Postdoctoral Researcher. This work was supported by  FCT -Fundação para a Ciência e a Tecnologia  through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional  Competitividade e Internacionalizacão by these grants:  UID/FIS/04434/2019 ,  UIDB/04434/2020 ,  UIDP/04434/2020 ,  PTDC/FIS-AST/32113/2017 & POCI-01-0145-FEDER-032113 ,  PTDC/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953 ,  PTDC/FIS-AST/28987/2017 & POCI-01-0145-FEDER-028987 ,  O. D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through FCT. B. -O. D. acknowledges support from the  Swiss National Science Foundation  ( PP00P2-190080 ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  FOUR ACES  grant agreement No  724427 ).
ZG was supported by the  VEGA  grant of the  Slovak Academy of Sciences  number  2/0031/22 , and by the  Slovak Research and Development Agency  -the contract no. APVV-20-0148. M. G. is an F. R. S. - FNRS Senior Research Associate. C. X. H. 's work was funded by the  Australian Government  through the  Australian Research Council. K. G. I. is the  ESA   CHEOPS  Project Scientist and is responsible for the  ESA CHEOPS Guest Observers Programme. She does not participate in, or contribute to, the definition of the  Guaranteed Time Programme  of the CHEOPS mission through which observations described in this paper have been taken, nor to any aspect of target selection for the programme. This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche.
M. L. acknowledges support of the  Swiss National Science Foundation  under grant number  PCEFP2_194576. P. M. acknowledges support from  STFC  research grant number  ST/M001040/1. This work is partly supported by  JSPS KAKENHI  Grant Number  JP17H04574 ,  JP18H05439 ,  JST CREST  Grant Number  JPMJCR1761 , the  Astrobiology Center of National Institutes of Natural Sciences (NINS)  (Grant Number  AB031010 ). This article is based on observations made with the MuSCAT2 instrument, developed by  ABC , at  Telescopio Carlos Sánchez  operated on the island of Tenerife by the  IAC in the Spanish Observatorio del Teide. This work was also partially supported by a grant from the  Simons Foundation (PI Queloz , grant number  327127 ). Acknowledges support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  through grant  PGC2018-098153-B-C33 , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
WiFi Access Points (APs) can be used to offload data or computation tasks while users are commuting. However, due to APs' limited coverage, offloading performance is heavily impacted by the users' mobility. This work proposes to leverage human mobility to inform offloading tasks, taking a data based approach leveraging granular mobility datasets from two cities: Porto and Beijing. We define Offloading Regions (ORs) as areas where a commuter's mobility would enable offloading, and propose an unsupervised learning methodology to extract ORs from mobility traces. Then, we characterise and analyse ORs according to offloading opportunity metrics such as type, availability, total time to offload, and offloading delay. Results show that in 50% of the trips, users spend more than 48% of the travel time inside ORs extracted according to the proposed methodology.
We use mobility data to show that such a strategy is necessary and feasible to accommodate the differentiated user connectivity profiles during commute. This sort of strategies will run on the user's device and, after learning their habits of movement, will adapt and extract the best regions to perform offload. This brings a perceptive feature to the offloading strategy and the proposed learning methods are general to any type of dataset. Our paper explores how human mobility impacts mobile offloading systems during commuting trips bringing evidence that individual profiles should be considered. We seek answers to questions like: 1) How can individual commuter offloading mobility profiles be established? 2) Does human mobility in urban areas allow for offloading to limited coverage technologies? 3) What are the limits of the predictability of per user offloading connectivity and which factors impact that predictability? 4) Which trade-offs should be considered in the system design? To answer this questions we develop and implement the methodology pipeline presented in Figure  1.
This methodology encompasses a method to establish individual offloading mobility profiles that would be part of the offloading strategies we propose -system components; and the steps that provide quantitative answers on the feasibility of such a systemsystem evaluation. We use a data based approach relying on two granular datasets from Porto and Beijing to support the generality of the results. Therefore, we bring answer to the first question in our contributions (i) and (ii). We answer the second and the third question in the contributions (iii) and (iv) respectively, and finally, we answer the last question in the contribution (v). In short, we define our contributions as follows: (i) We propose an unsupervised learning methodology to identify individual offloading regions (ORs), reflecting user habits and routines, considering different time windows to offload.
Details are given in Section IV. (ii) In Section V we categorize ORs in terms of their relevance and spatial characteristics. A comprehensive evaluation shows that although collected in different cities and time periods, the laws driving users' mobility reflect similar relevance and spacial characteristics of the ORs for both datasets. (iii) We evaluate the offloading opportunities offered to users at the OR while travelling in terms of type, availability, time to offload and offloading delay. Our results show the small offloading delays along with the high-temporal coverage of ORs confirm opportunities to offload to limited coverage cells while the users are in transit. Details are given in Section VI. (iv) We assess the mobility predictability in an offloading scenario using theoretical and algorithmic evaluation of several mobility predictors.
Thus, offloading systems should rely on a combination of opportunistic and 1 Exploration phase is the time to visit each OR for the first time. deterministic strategies. Attending to the characteristics of offloading locations observed in both datasets, we show that in the majority of the cases, APs already deployed in urban environments can provide full coverage to users while offloading, decreasing the need for handovers. Details are given in Section-VIII. The remainder of this paper is organized as follows. The related work on human mobility and mobile offloading is discussed in Section II. The datasets used in this work are described in Section III while the methodology to extract ORs from trajectory traces is introduced in Section IV. The characteristics of ORs are presented in Section V and the analysis of the offloading opportunities offered to users in transit is detailed in Section VI.
In  [19] , rather that focusing on PoI, we first introduce a methodology to identify and extract offloading zones from individual GPS trajectories, when small offloading time windows are considered. Despite the methodology proposed, the mobility patterns between offloading zones are not explored and the study was conducted on a single dataset. In the present work, we use the same methodology to extract offloading zones from two mobility datasets collected in different countries at different times and analyse the users' mobility in an offloading scenario. To the best of our knowledge, this is the first work to study and characterize human mobility for the purpose of offloading when the users are in transit.
III. MOBILITY DATASETS Several data sources have been used to study human mobility but high sampled GPS trajectories remain the most reliable way to track outdoor movements. The main dataset used in this work was collected in the city of Porto, Portugal (Figure  2a ) from 2016 to 2018 through several crowdsensing campaigns, and involved a total of 408 users. During these campaigns, GPS data, i. e, user's speed, estimated user location (latitude, longitude), and the accuracy of this estimation, were collected at a high temporal resolution (see Table  I ). Moreover, automatic start/stop strategies were being used by the application to detect the beginning and the end of a trip. At the end of each trip, a pop-up with a survey was automatically presented to the users asking for movement confirmation.
This data collection process is detailed in  [20]  and provides two unique features to the dataset: (i) high temporal resolution of positioning data of 1Hz; and (ii) annotated mobility traces concerning the beginning and the end of a trip. In order to ensure that the results presented in this work are not biased by the usage of a single dataset, a second dataset, namely Geolife  [21] , is used. This dataset provides time-stamped GPS locations of 182 individuals collected from 2007 to 2012 and its choice was due to two main factors: (i) it is widely used in human mobility studies as it is one of the first publicly released datasets containing mobility; and (ii) the trajectories are logged in a dense representation -91. 5% of the trajectories are logged every 1-5 seconds or every 5-10 meters per point.
As most data from Geolife was collected in Beijing, only mobility traces collected in the metropolitan area of this city (area in Figure  2b ) are considered in this work, comprising data from a total of 133 users, Table  I. Both Porto and Beijing datasets include a similar amount of location samples (≈ 10 ), however, as shown in Figure  2 , due to the different cites' area, the dataset from Porto presents a higher density of location samples per cell (25x25m). Although there are much more users in Porto's dataset when inspecting the number of weekdays with data per user, Figure  3  shows that users from Beijing present almost six times more days with data. While the median number of weekdays for the Porto's users is 5 weekdays, in Beijing, is 28 weekdays. However, when focusing on weekends, both datasets present a median number of 2.
C. Mobility Traces Selection In the second stage of Figure  1 , a filter is also applied to the mobility traces in order to select users that provide enough historical data for studying their mobility patterns. As there is a reduced number of weekends in both datasets (see Figure  3 ), only mobility traces collected from weekdays are considered. Due to the extensive data collection period in both datasets, some users exhibit mobility traces that were collected several months or even years apart. Traces with such a large time offset may lead to wrong assumptions regarding the user's mobility behaviors. Therefore, a time sliding window of 4 months is defined and applied to each user in order to select consecutive months where the user presents the highest number of days with data in the dataset.
Users are then selected based on two filters: (i) minimum number of days, and (ii) minimum number of hours with, at least, one GPS sample. Figure  5  represents the number of users after intersecting these two filters and shows that both datasets present a similar number of users with a large number of hours per day with GPS (top left corner of the heatmaps). However, as the minimum number of days threshold increases, Beijing dataset provides more users with a high number of GPS hours per day (top right corner of the heatmaps). This reveals a higher degree of engagement of users in Geolife towards the data collection process when compared to users in Porto. To avoid favoring a single dataset, we only consider traces from users having at least 5 weekdays with 8h of GPS data, resulting in mobility traces from a total of 55 and 57 users in Porto and Beijing datasets, respectively.
D. Offloading Regions Extraction Finally, in order to extract ORs from the users' trajectory traces the DBSCAN  [22]  clustering algorithm is used for the identification and aggregation of OLCs. The use of a densitybased clustering algorithm to extract ORs allows to obtain clusters with arbitrary shapes, focusing only on locations/areas with high concentration of points. There are two parameters that need to be set in DBSCAN before the clustering process: MinPts and. A data point is a core point if it has at least MinPts in its neighborhood. This notion of core point is adopted to define an OLC. As in our dataset the location samples are produced at a fixed rate of 1 Hz, MinPts is defined as ℎ ℎ and as ℎ ℎ , once more reflecting the WiFi connectivity into spatial constraints. Then, ORs are the clusters comprising OLCs that are density connected.
A. Categories of Relevance In order to better characterize ORs, we classify them into categories of relevance. Relevance of OR is defined based on the frequency of user u visiting , i. e. , = , where is the total number of days that was visited by the user u and is the total number of days in the dataset for the user u within the day period DP (Table  II ). The number of relevance categories that best suits all users is estimated using unsupervised learning. In this way, kmeans algorithm is applied to each user relevance values for a different number of clusters (k), where k represents the relevance categories in use. The optimal k is then determined using the elbow method along with the total WSS (Within Sum of Squares)  [23]. For the vast majority of users, k=3 gives the best number of clusters, which determines three categories of relevance: low, medium, and high.
B. Spatial Characteristics As ORs can be used as offloading sites, their spatial characteristics represent the areas where offloading can be performed. Figure  7  shows the distribution of the number of cells (11x11m grid squares) in the ORs for each category of relevance. As illustrated, the size of ORs increases with the relevance category for both datasets. The median number of cells when considering a ℎ ℎ = {40, 20, 10, 5}sec is 8, 6, 6 and 10 for the low-relevance category; 12, 11, 14 and 26 for the medium-relevance category; and 39, 64, 89 and 168 for the high-relevance category, respectively. Interestingly, similar ORs' dimensions are observed when considering a different city, with the ORs from Beijing's dataset presenting a median number of cells per OR of 7 for the low-relevance category; 13, 15, 19 and 47 for the medium-relevance category; and 29, 59, 81 and 238 for the high-relevance category, when considering a ℎ ℎ = {40, 20, 10, 5}sec, respectively.
Note that the ORs extracted for ℎ ℎ = 5sec are the largest in both datasets. Defining small time windows for the OR extraction increases the number of OLCs that can be aggregated, forming larger ORs in the clustering step. Additionally, as high relevance ORs are often personal PoI and indoor, their larger size is due to large indoor GPS errors. These results show that, although collected in different cities and time periods, the offloading sites present very similar characteristics evidencing that the laws driving users' mobility of both datasets are similar.
1) Inspecting MC Predictor: A matrix P built using a short history (length of the sequence of transitions between ORs) may not capture the diversity of users routines (movement between ORs). To test this hypothesis, the Pearson correlation coefficient ( ) between the accuracy of the predictions and the sequences length was computed, and no correlation was found indicating that small sequences may not be responsible for the predictor's poor performance. Instead, a negative correlation was found between the predictor's accuracy and the number of unique ORs of the sequence, = {-0. 38, -0. 29, -0. 30, -0. 29} for ℎ ℎ = {40, 20, 10, 5}sec, respectively. Similar ( ) values were found using Beijing's dataset, = {-0. 46, -0. 33, -0. 31, -0. 27} for ℎ ℎ = {40, 20, 10, 5}sec respectively. This evinces that novelty, which consists on finding new ORs may have more impact on the predictors' performance than the sequence length.
It is clear that in both datasets the number of predictions is evenly distributed among the prediction types, showing that the majority of predictions are not regular-based. In addition, unknown-based predictions, which are nearly onethird of predictions, are bound to fail. To further inspect the second scenario mentioned above, we compute the probability of the next OR being a new OR for each sequence. For that, an OR is considered a "new OR" if it appears in the sequence for the first time, or a "repeated OR" otherwise. Note that the predictor is an online predictor, consequently, even after training, P is updated after each prediction. To capture this behavior, the probability of finding a new OR is given by the fraction of "new ORs" in the test sequence over the test sequence length, see results in Figure  17.
For the sequences from Porto's dataset in Figure  17a , the median probability of the next OR being a "new OR" is 27% for ℎ ℎ = 40sec. This means that an online MC predictor will fail, on average, 27% of its regular-based predictions. This value increases to ≈40% for a ℎ ℎ = 5sec values. Similar results can be observed in Beijing's dataset in Figure  17b. Smaller ℎ ℎ values increase the probability of finding new ORs as the spatial-temporal constraint (see Section IV-A) for a location to be considered an OR is relaxed, increasing the number of unique ORs extracted from a mobility trace. 2) Improving MC Predictor: Since most of the prediction types are draw-based and unknown-based, we combine different strategies to improve the MC predictor's performance in these cases, evaluating also their impact.
0, avgNN, bayesglm and ranger, respectively, and the default parameter settings. Figure  19  shows the accuracy of ML predictors using a cross-validation of = 3. With the exception of the Bayesian predictor, the ML predictors perform better than the MC predictor, with RF presenting the best results for both datasets with median accuracy between ≈ 30% and ≈ 37% for all ℎ ℎ values. However, it is important to note that considering higher ℎ ℎ improves the prediction accuracy, being this more evident in the Beijing dataset, see Figure  19b. Note that the models considered could be further fine-tuned or even replaced by others which could lead to better results. However, our goal is not to produce a state-ofthe-art prediction model, but instead to inspect the limitations and sources of predictability in an offloading scenario.