dataesr/scientific-paragraphs-categorization huggingface dataset:
Hsu (2017)  tried to detect NLOS satellites with a support vector machine (SVM). Jiang et al. (2021)  aimed at modeling the code discriminator with Gaussian fitting to detect NLOS. Sanromà  Sánchez et al. (2016)  used fisheye images and the carrier-to-noise density ratio (C/N 0 ). This is due to the fact that NLOS signals and multipath interference often affect the positioning solution very differently  (Groves et al. , 2013). Some account for  LOS and 10. 3389/frobt. 2023. 1106439 NLOS reflections in the same algorithm such as the study by  Matera et al. (2019) , where the multipath is isolated from reference base stations data fused with fisheye images, or the study by  Suzuki et al. (2020) , where a rotating antenna is used to mitigate both categories of multipath errors. Lately, machine learning (ML) has been applied to GNSS data processing especially for multipath detection/mitigation.
Quan et al. (2018)  also used CNNs for multipath detection/exclusion but used the signal-to-noise ratio and pseudoranges as inputs to the network. Xu et al. (2020)  and  Hsu (2017)  used SVMs to detect NLOS using shadow matching and GNSS measurements (pseudoranges and Doppler shifts), respectively. In this article, the goal is to detect LOS reflections and then exclude them in real signals (Galileo E1-B and GPS L1 C/A) using GNSS data only. LOS reflections happen very often in challenging environments and excluding all affected measurements would lead to a low positioning availability, and not all LOS reflections severely degrade the positioning solution  (Prochniewicz and Grzymala, 2021). The definition of multipath depends on the targeted positioning accuracy. The definition used in this paper will be defined in section 2.
The zero crossing that is the closest to the prompt index and with a negative slope is chosen as the stable lock point from which to compute the tracking bias-since the EMLP discriminator is used. On the presented dataset, when this occurs, the second zero crossing is much further in terms of delay than the one chosen as the stable point with respect to the prompt correlator. When the computed tracking delay in seconds is multiplied by the speed of light, the tracking bias is obtained in (7). Once this bias is passed through the low-pass filter of the tracking process, this filtered bias is propagated onto the pseudorange measurement as follows: ∃t 0 ∈ [-T obs , T obs ] such that D (t 0 ) = 0,.
However, it required several identical receivers to do so, which was not possible in this study. Thus, the proposed solution here is a modified version of that in the study by  Fan and Mats Brenner (2006)  and estimates the satellite and receiver biases as one bias altogether. This method assumes that the hardware and receiver biases are constant over the duration of the dataset. They are also assumed to be the same over other data collected, even though they will vary according to temperature and the incidence angle. These supposedly small variations are assumed to be accounted for in the standard deviation of the noise, so in the threshold of (13). The method is presented in Figure  3.
L1 and L2 both have their strengths and weaknesses, as explained in the study by  Ng (2004). Hence, a combination of both called the elastic net, presented in  Zou and Hastie (2005) , further reduces overfitting. The new loss function becomes: E NET (θ) = J (θ) + λ L1 p L1 ∑ j 1 =1 |β L1,j 1 | + λ L2 p L2 ∑ j 2 =1 β 2 L2,j 2 , ( 15 ) where • J(θ) is the existing cost function • θ is the parameter to minimize • j is the sum index (L1 or L2) • λ is the regularization factor (L1 or L2) • β j is the weighting penalty (L1 or L2) • p is the chosen degree of regularization (L1 or L2) • E NET (θ) is the new cost function with L1 and L2 regularization At last, comparing the loss function values of the training and validation dataset is a good metric to check overfitting.
Dataset The dataset was obtained from a dynamic scenario. The dynamic data were collected in all challenging conditions (suburban and deep urban). This was to obtain multipath errors to detect and exclude them. The dataset is on the L1 frequency band (1575. 42 MHz) on GPS and Galileo with a 200 MHz sampling frequency. This large sampling frequency was used because it enabled a better reconstruction of the ACF over the span of the collect. An IFEN  SX3 was used as the GNSS receiver  (IFEN, 2023). The IFEN SX3 is a GNSS receiver that is capable of tracking all constellations and frequencies. It is very modular and can record raw data to then replay with different configurations. Indeed, the number and placement of correlators are selectable along with the loop filter bandwidths, EL spacing, etc.
This dataset was obtained with a car. Data collection took place in the city center of Toulouse, France, in a mix of urban and suburban environments for a total duration of approximately 40 min. The neural network had more than 4,000 images to be trained with, for each signal. There were more than 4 million correlators used per signal, and the satellite geometry was diverse. Having more multipath scenarios would likely be beneficial for the networks but the number of images and correlators was deemed sufficient to validate the proof of the concept. The assumptions were made on the material types because most buildings are made out of either concrete or bricks with glass windows, and the road, of asphalt. The IFEN SX3 was connected to a Novatel GPS 704-X pinwheel antenna. The data were replayed in post-processing to retrieve the full correlators for each epoch as it cannot be done in real-time with this number of correlators.
Results This section is divided into subsections: presenting the metrics used to analyze the results and the different configurations, the results for Galileo E1-B, and the ones for GPS L1 C/A. In order to investigate the benefits of using correlator images to detect multipath, the number of correlators used in images varies. To obtain accurate data labeling, the pseudorange bias has been computed with the most amount of correlators-101 at 200 MHz. In total, there are seven correlator variations presented in Table  3. Figure  5  illustrates the 101@200 correlators' configuration along with the three configurations using 25 correlators by comparing the range of values obtained with the different variations vs. the full theoretical autocorrelation function for GPS L1 C/A. The results for these correlators will be presented for both signals.
The images that are input to the CNN are the correlator values as a function of the chip delay (on the y-axis) with respect to the prompt correlator and time (on the x-axis) to yield 2D images. The CNN architecture and the labeled data remain identical no matter the correlator configuration. However, each correlator configuration induces a training period with the corresponding images of the chosen correlator configuration. Figure  6  represents the tracking bias and its filtered version for a given satellite on Galileo E1-B (left), while an example, from the training dataset, of both not multipath and multipath is given in Figure  6  (right) for the 101@200 correlator variation (colorized).
Result analysis To train a binary classifier, the two classes-here, multipath and no multipath-need to be equally represented. Otherwise, this can lead to the network overpredicting the dominant class. This leads to a class imbalance in the datasets. There exist several countermeasures for this condition as given in the study by  Buda et al. (2017). The chosen method is to downsample the dominant class. Indeed, the no multipath class will be reduced in size to roughly match the multipath one. To still train all images, the not-multipath images will be divided into several datasets and the final results presented in the following sections will be the average of all the trained models. CNNs can also be overfit when the input data are too correlated. Hence, special care was taken to ensure that the input images were not correlated with one another.
Galileo E1-B In Figure  8 , the accuracy, precision, recall, and F score are given for all correlator combinations for both CNN and tracking biasbased multipath classification. The tracking bias results are in blue, while the CNN results are in orange. The CNN results presented here are from the test dataset. The data split was 80% for the training data and 20% for the test data. As there is just one model and the hyperparameters of the model are not varied, no validation data were used to generate the results presented.
GPS L1 C/A In Figure  9 , the accuracy, precision, recall, and F score are given for all correlator combinations for both CNN-and tracking biasbased multipath classification. The tracking bias results are in blue, while the CNN results are in orange. The data split used here was the same as that for Galileo E1-B: 80% for the training data, 20% for the test data, and no validation data were used.
Discussion In this paper, a theoretical classification of multipath has been proposed that uses GNSS tracking correlator outputs. This classification has been used to label data to train a convolutional neural network. Then, this CNN has been used to predict whether multipath was present or not on the correlator outputs from GPS L1 C/A and Galileo E1-B. This CNN has been developed to be low in complexity so that it can be trained with a low amount of data while avoiding under--or overfitting. The model uses tracking correlator values as a function of delay (with respect to the prompt correlator) and time in the form of images of 256 × 256 pixels. The inputs are lightweight, helping the predictions yielded by the CNN to be swiftly computed. This is illustrated by the fact that it is around 21 times faster to compute than the tracking bias for GPS L1 C/A and two to six times faster for Galileo E1-B, when using 25@50.
Statistical analysis All analyses will be performed with the Stata software (V. 15, StataCorp, College Station, USA). Continuous variables will be presented as mean and SD or median and IQR. The assumption of normality will be assessed by using the Shapiro-Wilk test. Patients will be described and compared between three steps for the following inclusion variables: eligibility criteria, epidemiological and clinical characteristics. A difference will be determined based on clinical and statistical considerations. The type 1 error will be 5% two sided. A description of the protocol deviations and the causes of lost to follow-up will be carried out. The primary analysis will be conducted in intentionto-treat sample. A per-protocol analysis will then be conducted.
A sensitivity analysis will be conducted to determine the statistical nature of missing data and then to propose the most appropriate method of data imputation (maximum bias or multiple imputation). Regarding the data from HR monitoring, all devices will be uploaded with Bioharness Zephyr software. Analysis from Zephyr will involve Kubios software. We will delete incorrect data due to artefacts using very low filter in the Kubios software. [20] [21] [22] [23] [24]  ETHICS AND DISSEMINATION Ethics were approved by the Comité de Protection des Personnes Nord-Ouest in January 2022. All participants will receive oral and written information on the aim of the study and the protocol. Written informed consent will be obtained before their inclusion in the study and before performing any specific procedure.
All data collected will be anonymised. Our study will be continued by the investigator and a second article with the results will be published. Dissemination of the results of this study will be through peer-reviewed publications, and national and international conferences. Patient and public involvement Patients were not involved in the design and planning of the study. The information will be provided during the previous consultation (at least 1 month before the injection). Patients will be given sufficient time to think about inclusion before giving consent.
Fourth, some sensors collect physiological condition or response data (e. g. , heartrate, body temperature, blood oxygen, blood glucose, and perspiration). Fifth, psychological and emotional status can be not only inferred by physiological sensors (e. g. , electrodermal activity) but also observed by behavioral sensors (e. g. , eye movement, facial muscle reactivity, voice variations). Sixth, behavioral sensors can capture health-related behaviors (e. g. , smoking, eating). Finally, many portable environment sensors enable assessing person-centered environmental and social exposure including air quality, micro-atmosphere, noise, radio frequencies, light, visual stimuli, human voice, and floating population  (Poom et al. , 2021). In active sensing, many features of personal health, behaviors, and environments, that are observed by passive sensors, can also be selfreported by a participant through reporting instruments.
Fourth, new multi-channel and multi-sourced sensing data collection and processing methods have given birth to novel digital biomarkers, defined as digital physiological and behavioral measures (e. g. , step count, sleep duration) that explain and predict health-related outcomes  (Sim, 2019) , and improved exposure measures. Sixth, integrating data through common attributes of timestamps and geographic coordinates across datasets have generated multi-layered individual behavior and health data and enhanced the understanding of cognitive and decision-making processes and situational/contextual backgrounds that lead to particular types of space-time behaviors and health outcomes  (Kestens et al. , 2016). Finally, beyond understanding, more personalized and situation-adaptive intervention has become possible with real-time mobile sensing  (Nahum-Shani et al.
g. ,  Giannouli et al. , 2022)  and develop digital biomarkers directly associated with clinical outcomes  (Sim, 2019). Applying data interoperability standards (e. g. , Fast Healthcare Interoperability Resources (FHIR) -ONC, 2022) is essential to integrate existing electronic health record systems  (Sim, 2019). Data infrastructure. Study design and data collection further entail pragmatic plans for data transfer, data curation, and compatibility. Some studies request participants to revisit a lab to transfer data to the data storage while others send data to the data server in real time. Sensor data could be large and need large databases to store them (e. g. , video, IMU). For heterogeneous sensing data, it is useful to build linked data possibly using knowledge graphs that can help extract knowledge from semantic networks constructed across different datasets (e.
Third, random errors refer to unpredictable errors that shift a measurement from its true value by a random amount (e. g. , GPS signal noise). Data preprocessing and integration. Systematic and random measurement errors can partially be resolved by (automated) data preprocessing, cleaning, and integration but potentially propagated in data processing and analysis. Noise removal and interpolation techniques can alleviate the impact of signal noise (e. g. , Butterworth, moving average, median, low-pass, high-pass, or non-linear filters;  Allahbakhshi et al. , 2019; Incel et al. , 2013). Imputation methods for data gaps or missing data points can potentially prevent error propagation (e. g. , GPS data imputation -  Yoo et al. , 2020). Integrated heterogeneous datasets through multi-sensor fusion or linked databases may contribute to filling data gaps of one another.
Mobile sensing and accompanied analytical techniques have been the key solution to tackle the behavioral/situational uncertainties in exposure, as discussed about benefits of multi-sensor mobile sensing in Section 4. Some studies addressed this type of uncertainties by distinguishing exposure in different mobility behaviors (e. g. , stationary time, walking time, and in-vehicle time -Jankowska et al. , 2021). As another example, a smoking behavior detection sensor can help distinguish respiratory disease risk mainly contributed by smoking from the risk increased by exposure to air pollution, which can inform us where to put smaller weights of air pollution exposure. Exposure to social interactions can be inferred based on trip destination characteristics (e. g. , activity type, sociability) via linking GPS to POI data and trip purpose inference as well as human voice detection via audio sensing and momentary self-reports on accompanied persons.
Funding Eun-Kyeong Kim  was funded by the  Velux Stiftung, Switzerland  (Project No. 1155 ; Project acronym: MOBITEC-GP; Principal investigator: PD  Dr. Timo Hinrichs ). Funders had no role in organizing the special issue, executing the review research, and writing the manuscript. Camille Perchoux  was funded by the  European Union , under the  Horizon European Research Council (ERC)   Starting grant program  (Grant Agreement No. 101040492 ; Project acronym: FragMent). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the  European Research Council ,  European Union. Neither the  European Union  nor the granting authority can be held responsible for them.
Theoretical and practical concerns with gradience Despite some previous research problematizing categorical distinctions in linguistics (e. g. ,  Wälchli 2009) , non-gradient approaches to word order have been traditionally dominant in cross-linguistic studies (e. g. ,  Dryer 1992 Dryer , 2013a Dryer , 2013b;; Greenberg 1963; Lehmann 1973; Vennemann 1974). In contemporary quantitative typology and Bayesian phylogenetic models of language evolution, word order is still usually treated categorically  (Cathcart et al. 2018; Dunn et al. 2011; Greenhill et al. 2017; Hammarström 2015; Jäger and Wahle 2021; Maurits and Griffiths 2014). Popular large-scale crosslinguistic databases, such as WALS  (Dryer and Haspelmath 2013) , AUTOTYP  (Bickel et al. 2022) , Grambank 2 and DiACL (Carling 2017), provide categorical word-order data.
Consider an illustration. Using corpora of online news in the Leipzig Corpora Collection  (Goldhahn et al. 2012)  in 31 languages, annotated with the Universal Dependencies  (Zeman et al. 2020) , we computed the proportions of Subject -Object order in the total number of clauses in which both arguments were expressed by common nouns. The sample sizes were very large, with a median number of about 138,000 clauses. The data are available online as Dataset1. txt in an OSF directory. 9 he result is shown in Figure  1. In all 31 languages, Subject usually comes before Object. The distribution of the scores represents a continuum from flexible languages (Lithuanian, Hungarian, Tamil, Latvian, and Czech) to well-known rigid languages (e. g. , Indonesian, French, English, Norwegian, Danish, and Swedish).
Reiterating our two main arguments, presuming within-language variability and cross-linguistic gradience when it comes to typological categorization, we posit that gradience itself might be a good candidate for an inherent property of language. In some cases, conventional categories can be misleading. For example, the plot in Figure  2  displays the distribution of proportions of head-final phrases in 123 corpora annotated with Surface-syntactic Universal Dependencies  (Osborne and Gerdes 2019). 10 The dataset is available as Dataset2. txt in the OSF directory, which also contains the Python script that was used for the data extraction. The plot shows that the corpora do not follow a distribution with two peaks at each end, confirming the results obtained by  Liu (2010)  based on a small sample of languages.
Consider an illustration. It is well known that there is a negative correlation between word order rigidity and case marking  (Sapir 1921; Sinnemäki 2008 ). This categorical information can be represented by continuous variables, as shown in Figure  3 , which is based on data from 30 online news corpora. Word order variability is measured as entropy of Subject and Object order (see Section 4. 1. 1), while case marking is represented as Mutual Information between case markers and the syntactic roles of Subject and Object. It shows how much information about the syntactic role of a noun we have if we know the case form, or, in other words, how systematically cases can actually help one guess if the noun is Subject or Object in the corpus (see Levshina 2021b for more details). The dataset is available in the OSF directory as Dataset3.
That is to say, we first sample 20 clauses, then 40, 60, and so on until we reach a size of 2,000 clauses. For each sample, we measure the proportion of the S&V and V&O orders. The data are 17 It is worth pointing out that different studies may use variants to calculate the entropy, and these variants may be affected in different ways by corpus size. For instance,  Futrell et al. (2015b)  have a more complex definition of word order entropy (as opposed to the coarse-grained entropy we use in our examples), which is conditional on several factors and may be more unstable in small corpora. 18 See www. shannonentropy. netmark. pl for a live calculator of the entropy. available as Dataset4. txt in the OSF directory. Next, we calculate the entropy as described above and also the difference as compared to the entropy measured with the corpus size set as 2,000 clauses.
19  Our comparison focuses on the Shannon entropy of the relative order of nominal heads and modifiers in eleven languages from five different genera of the Indo-19 UD v. 2. 5, except for Welsh: v. 2. 6. European family. Furthermore, we limit our analyses to the following syntactic relations/UD relations: adpositions (case), determiners (det), 20 adjectival modification (amod) and relative clauses (acl:relcl). 21 Results are plotted in Figure  6. The dataset is available as Dataset5. txt in the OSF directory, which also contains the code for the 20 Italian, Polish and Portuguese have specific UD relations for possessive pronouns and/or quantifiers; for data consistency, we have added the entropy of these relations to the entropy of the det relation. 21 The UD German parser (model: GSD) has a very limited support of the acl:relcl; accordingly, we do not have data for CIEP+ relative clauses in German.
The data are available as Dataset6. txt in the OSF directory. There was an obvious difference between the conversations and the written texts: the conversations contained 61 examples of OV, and only 39 examples of VO, whereas both the fiction and news contained 17 examples of OV and 83 examples of VO each. In order to control for potential accessibility effects, associated with the length and animacy of the nominal objects, a multiple logistic regression was fitted, with the word order as the response variable (OV or VO), and text type, animacy, and logtransformed length of the object as the predictors. As was shown in Section 2. 1, it is advantageous for language users to produce more accessible (short and animate) constituents first. This is why we could not exclude that the preferences for OV in the conversations could be explained by a preference for short and animate objects.
g. ,  Dąbrowska 2013). Figure  8  shows flexibility scores for several languages using this method. The data are available as Dataset7. txt in the OSF directory. Of these, Avar, Malayalam, Hindi, and Korean would all be labeled as "SOV flexible" languages, were we to take a categorical approach. However, taking a gradient approach allows us to see meaningful heterogeneity within that group, as well as similarity across languages which might be categorized differentlywe see continuity between the "SOV flexible" languages and English, for example. Of course, more languages are required to make stronger cross-linguistic claims, but this nonetheless shows that asking gradient questions can yield patterns which we might not otherwise have seen. These experiments are quite portable, robust to environmental noise (especially compared to other psycholinguistic measures), and relatively straightforward to explain.