dataesr/scientific-paragraphs-categorization huggingface dataset:
We know each learner has their own motivations and goals for taking a MOOC, yet in aggregate we can also look for patterns to learn from as education researchers, especially when using a common dataset with millions of records distributed across platforms. More research is needed in macro learning analytics and regional MOOC providers to fully appreciate the influence these factors are making in the learners that register to these courses and the quality of their learning process. By understanding learners at the macro level, it may be possible to further increase learning outcomes and performance for MOOC providers at platform and individual levels too. This study used a set of common metrics between different platforms. To expand on this work we had to ensure we could understand, and accurately analyse the differences in how each platform collects key operational metrics.
An enrolment, for example, may be similar across all platforms -but other metrics such as completion, viewing, active learning and grades can be understood in different ways. The authors are working on a legend / key that will enable multi-platform MOOC analysis for our further research and publication for other researchers too. Additional future steps include linking these headline demographics datasets to a deeper exploration of in-course behaviours and processes. This will initially include alignment to the activation, progress and completion each individual learner makes when taking courses with the providers in this study. It is anticipated the further research will unearth local and global patterns in how learners learn and explore what factors may lead to higher levels of interaction and engagement.
FUNDING MH was supported by the  Czech Science Foundation  ( GA CR ), project  EXPRO  (nr. 20-24186X ). PL was supported by the  SELFCEPTION  project (www. selfception. eu)European  Union Horizon 2020 Programme  ( MSCA-IF-2016 ) under grant agreement no. 74194. LJ was partially supported by the  EPSRC UK  (projects MAN 3 ,  EP/S00453X/1 , and  NCNR ,  EP/R02572X/1 ). AP was supported by  EQUIPEX-ROBOTEX (CNRS), chaire dexcellence CNRS-UCP , and project  Labex MME-DII  ( ANR11-LBX-0023-01 ). ES gratefully acknowledges the support of  ERC  Grant  323674 FEEL  and  FET Open Grant   713010 GOAL-Robots.
The former rely on classification algorithms that require a sufficient amount of labeled data for a training process  [6, 7]. On the opposite, the latter methods are based on clustering procedures which do not need a labeled training dataset  [6]. Finally, NILM approaches can be categorized into non event-based and event-based ap-proaches  [8]. Non event-based methods disaggregate the power consumption using techniques such as the Hidden Markov models (HMM)  [9, 10]  which identify repetitive HEAs' patterns in the load curve based on HEAs previous states. The event-based approaches assume that each event in the total household power consumption is a response to a Home Electrical Appliance (HEA) state change  [8]. Fig. 1  shows an overview of event-based NILM approaches. In this research work, a specific interest is given to HEAs' event detection which is an elementary step in the NILM pipeline  [11].
In  [18, 19] , a χ 2 test statistic is used to assess if two neighboring windows share a common distribution. If this is not the case, an event is then detected. Researchers in  [20, 21]  apply a CUSUM algorithm for the detection of both the beginning and the end of a HEA transient-state active power signal. Z. Zhu et al. [21]  reached a probability of detection of 90 % by applying their approach on real data comprising 200 events of eight different HEAs switched on and off. The common thread in all these research works is that the detection is done in a univariate context by only considering the active power signal, whereas prior literature on NILM focuses on identifying an effective features' set that defines a unique HEA signature  [22, 23, 24]. Fig. 2  illustrates three power time series related to the same scenario of several HEAs switched on and off using our own acquisition system  [25] , and three power time series derived from BLUED dataset  [26].
Firstly, power signals are obtained from the BLUED dataset current and voltage measurements  [26] , and secondly, power signals are computed from current and voltage measurements using our own acquisition system. The subsequent discussion addresses the selection of the power features that are best suited for the detection task. Finally, the main conclusions and contributions of the current work are provided in Section 5. Figure  2 : Active power signal, its 4 th harmonic, reactive power signal and its 5 th harmonic related to the same scenario of HEAs switched on and off (using our own acquisition system at the left and an extract from the phase B of BLUED dataset at the right).
In each window, the detector decision function is computed and compared to logarithmically spaced values of the threshold h ∈ H. If the decision function is larger than h, TP is incremented when an event truly occurs, whereas FP increases when no event occurs. The optimal threshold h min corresponding to the minimum value of the J 3 score is determined. The feature with the lowest minimum is then selected. At the subsequent iterations, each remaining feature is added to the set of previously selected features, and the same process is applied to find the lowest score J 3. The most relevant features are the ones that contribute to decrease the score J 3. FSADP can be applied to any dataset containing labeled events. It selects the most relevant features for which P FA is the lowest and P D is the highest.
Application to NILM In this section, the Hotelling T 2 detector, which showed the best performances, is combined to the proposed FSADP and applied to real data streams in order to find the most relevant features for the detection task. We first introduce the power features computed using current and voltage measurements. Then we present the publicly available BLUED dataset dedicated to NILM event-based approaches and a new proposed dataset built using our acquisition system. Both datasets contain several events of small or large amplitudes that are representative of a house.
Extracted features As shown in Fig. 1 , in a NILM context, current and voltage measurements are first acquired and then features are extracted. We are interested in the features related to the power characteristics including the active power, the reactive power and their respective harmonics. Indeed, these features meet an additive property  [22, 42]. Thus, when a HEA is connected (resp. disconnected) from the power system, an "additive" feature is added (resp. substracted) by an amount equal to that produced by this HEA working individually. This property is required for extracting HEAs' features from an aggregated signal and for comparing them with the dataset of HEAs' signatures used by supervised NILM classification methods. These features are computed using the mathematical formulations set out in  [24]  based on the latest IEEE 1459-2010 standard for the definition of the single phase physical components under non-sinusoidal conditions  [43] : • The k th harmonic components (k ∈  [1, 15] ) of the active and reactive powers P k , Q k and their respective sums P H , Q H : P k [n] = v ak [n]i ak [n] + v bk [n]i bk [n] (32) Q k [n] = v ak [n]i bk [n] -v bk [n]i ak [n] (33) P H [n] = 15 k=2 P k [n], Q H [n]= 15 k=2 Q k [n] (34) • The active and reactive powers P and Q: P = P 1 + P H , Q = Q 1 + Q H , (35) where v ak , v bk , i ak and i bk are the Fourier coefficients: v ak [n] = √ 2 M n m=n-M +1 v[m] cos 2π mk M (36) v bk [n] = √ 2 M n m=n-M +1 v[m] sin 2π mk M (37) i ak [n] = √ 2 M n m=n-M +1 i[m] cos 2π mk M (38) i bk [n] = √ 2 M n m=n-M +1 i[m] sin 2π mk M (39) with M = F s /F 0 is the number of samples per period and F 0 is the utility frequency.
BLUED data streams The BLUED  [26]  is one of the most commonly used research dataset for event detection benchmarking  [12, 19, 44, 45]  due to its fine granularity of event labeling. It contains time stamped current and voltage signals of two phases A and B with ground truth events  [2]  obtained from a house in the United States (with a utility frequency of F 0 = 60 Hz) during a whole week. The sensors used for measuring the electrical current in the mains have a cutoff frequency of approximately 300 Hz. This means that from the sampled current and voltage signals, although sampled at F s = 12 kHz, only the active and reactive powers up to the 5 th harmonic of the current and the voltage can be computed. Due to some data download issues, only the first segments of both phases A and B of the BLUED dataset are used, which corresponds to almost 13 hours of recording.
In the data segment of phase A, 461 events are involved. In phase B, there are 1132 events. Several types of HEAs in the dataset are monitored  [26] : lights, laptops, air conditionner, hair dryer, etc. leading to a large diversity of HEAs' transients. A total of p = 14 electrical power features are extracted from the current and the voltage measurements. They correspond to the active and reactive powers P and Q in addition to their harmonics P k and Q k up to the 5 th order and the sum of their harmonics P H and Q H.
During more than 1 hour, each of these HEAs is randomly activated and shut down every 3 seconds. The current i(t) and voltage v(t) signals are measured using our own acquisition system that was specially designed for this research work, based on an Arduino MKR Zero microcontroller (32 bit processor SAMD21 Cortex-M0 microcontroller, 48 MHz) with a sampling frequency F s = 6. 25 kHz  [25]. A list of events was then generated by visually inspecting and hand-labeling transitions in the power signals as done for the BLUED dataset generation  [26]. Timestamps of the labeled events were adjusted to match the transitions. Almost 1200 events were labeled in the dataset allowing an accurate evaluation of an arbitrary event detection method. with the selected features subset using the FSADP are compared to the detection performances using the active power signal only, as commonly considered for detection in NILM event-based methods  [18, 21].
They are also compared to the multivariate detection performances with the P,Q feature subset and the whole features subset. Tables 1, 2 and 3 show the obtained results. Several observations can be addressed:   1. When using only the active power, the proposed tuning of the Hotelling T 2 detector (minimizing the J 3 criterion) allowed to outperform the performance results obtained in  [26] , where authors used the same detector and found P D = 69. 2 % for phase B and P D = 94. 5 % for phase A. In addition, the results that we obtained for phase A of BLUED dataset in 460 an univariate context are better than those in  [44] , for which a Probability of Detection of 90 % is reached when using an excerpt of the active power signal containing 115 events. 2. The multivariate version of the Hotelling T 2 detector with appropriate features improves the detection performances compared to the univariate version considering only the commonly used active power signal for phase B of BLUED dataset and our own dataset.
3. For BLUED data streams (phase B), the FSADP allows the selection of d = 4 power features {P 1 , P, Q H , Q 3 } that lead to a minimization of the criterion J 3. For phase A, the first order harmonic of active power P 1 is the only feature that is selected by the FSADP. For both phases, the whole set of p = 14 features makes the detection performances worse. This is an argument in favor of performing feature selection for detection tasks. 5. For our own experimental data streams, the FSADP allows to identify a small subset composed of d = 4 power features {P, P H , Q 3 , Q 9 } that minimizes the criterion J 3 and allows a better detection performance than the whole set of p = 34 features. 6. In our own experimental data streams as well as BLUED data streams, the subset of features selected by the FSADP allows to reach a P FA smaller than 5%  [16]  and a larger P D when compared to the other subsets.
7. There are harmonics of odd order only in the set of selected features. This is due to the fact that even order Fourier coefficients are negligible. Indeed, HEAs current and voltage waveforms have half-wave symmetry, thus even harmonics have very small magnitude compared to odd harmonics, and their step heights when a change occurs are hence scarcely noticeable (see active power 4 th harmonic order signals in Fig. 2 ). 8. The selected features clearly depend on the HEAs types that are turned on/off (resistive, electronics, with motors, etc. ). In phase A of BLUED dataset, air compressor, fridge, lights and kitchen aid chopper were monitored. In phase B of BLUED dataset, a much larger number of HEAs are monitored: printer, laptop, dryer, hair dryer, iron, microwave, washing machine, lights, TV, etc.
[26]. In phase B of BLUED dataset, as well as in our own experimental dataset, some of the monitored HEAs' step heights, when turned on or off, might be lost in signal noise. This explains the need of several features for the detection task improvement. Moreover, according to Fig. 9 -11: 1. The use of some power features does not improve the detection performances since for some of them, the criterion J 3 remains constant (see Fig. 11  from feature Q H to feature P 2 and in Fig. 10  from feature P 1 to Q). This means that these features do not bring relevant information detection purposes. The same observation was made for the features selected for classification purposes in  [24]. 2. The use of some features increases the criterion J 3. This means that these features are not discriminant.
Conclusion This paper explores and extends to the multivariate case four methods for detecting HEAs state changes in a NILM context. Furthermore, a new labeled dataset is introduced and made available for the sake of reproducible research. We also propose a new method for properly selecting power features that improve detection performances over the existing univariate methods based on active power signal only. Our study further supports prior NILM studies that also emphasized the need of an effective set of electrical features for classification purposes. For this, the performances of four detectors in the univariate and multivariate cases are first assessed through numerical simulations and specific detection performance indicators on both synthetic and real-world data. According to our results, the Hotelling T 2 detector in its multivariate version provides the best performances.
Then, the proposed FSADP method that aims at selecting the best features for detection purposes is combined with the multivariate Hotelling T 2 detector and applied to a 13 hours long excerpt of the BLUED dataset that comprises current and voltage measurements of two phases A and B. For phase A, only the feature P 1 was retained, whereas for phase B, four features were selected: {P 1 , P, Q H , Q 3 }. Most of them are related to odd order harmonics. The proposed FSADP method is also applied to other realworld experimental power signals containing 1203 hand-labeled events coming from several HEAs switched on and off, and extracted from current and voltage measurements acquired using our own system. Among the 34 features related to power characteristics, only a subset of four features corresponding to The idea would be to observe if changes in the signals have high-frequency components that can be used for event detection.
Since the segments of signals from phase A and phase B of the BLUED dataset are extracted from the same house, only one of the subset of features selected by the FSADP should be used. It appears that the feature P 1 , which is the most relevant one for detection in phase A, is the suited one. Indeed the detection performances brought by feature P 1 applied to phase B are very close to the performances obtained by the subset of features {P 1 , P, Q H , Q 3 }. On the opposite the subset of features selected by the FSADP for phase B sharply decreases the detection performances when applied to phase A.
4. 4 Since the segments of signals from phase A and phase B of the BLUED dataset are extracted from the same house, only one of the subset of features selected by the FSADP should be used. It appears that the feature P 1 , which is the most relevant one for detection in phase A, is the suited one. Indeed the detection performances brought by feature P 1 applied to phase B are very close to the performances obtained by the subset of features {P 1 , P, Q H , Q 3 }. On the opposite the subset of features selected by the FSADP for phase B sharply decreases the detection performances when applied to phase A.
An EKF is designed in Section 3, where the gradient equation is added, to tackle measurement noises and to estimate not only the velocity but also the magnetic field and its gradient. The EKF is fed with a determined quaternion, given by an attitude estimation block. In Section 4, the ZUPT-aided position estimation is detailed, based on the previous velocity estimation, in the context of foot-mounted inertial navigation. Section 5 presents a scenario test based on an open source database  [22]  representing a foot-mounted navigation scheme. The obtained results in this case are displayed. While in Section 6, some conclusions and potential future works are stated.
Methods Establishment of DTB observation datasets. A total of 6,382 DTB observations mainly sampled from the Chinese National Important Geological Borehole Database (NIGBD: http://zkinfo. cgsi. cn) were used in our study. These observations also contain a small part (about 2%) of pseudo-observations of DTB generated from expert knowledge. Observations sampled from the NIGBD. The DTB of every borehole must be interpreted manually and interpreting more than 80 million boreholes logs therefore demands an immense amount of work and has high costs. However, many boreholes that are located close to each other have similar DTB and environmental factors. Therefore, we developed a sampling scheme (named stratified-additive sampling) to take a fraction of the borehole drillings from the NIGBD as the observation data set in this study.
Data processing Fig. 1  A brief overview of this study. The first part is a sampling scheme similar to stratified sampling. Mapping methods, regardless of methods based on spatial autocorrelation or soil environmental correlation, have requirements based on the number, distribution, and typicality of the samples, which ensure global representation of the samples  19. To obtain representative samples from these boreholes, we used a sampling scheme similar to stratified sampling to acquire our training points from the NIGBD. The stratified sampling scheme includes designation of grid shape (such as a square grid, triangular grid, or hexagonal grid) and grid size. A square grid is the easiest and most effective, and is most widely used in sampling  19. In general, smaller grid size leads to more accurate predictions, but with greater sampling costs.
Consequently, some grids still have no DTB observations although these grids had more boreholes other than the borehole in the first sampling. In the second sampling, we tried to sample another borehole for each of these grids without DTB interpretation. Then, we aimed interpret all the new borehole profiles in the second sampling to obtain the DTB values, but we were sometimes still unable to interpret the DTB. A third sampling was then taken and so on. After a finite number of additive samplings, the borehole logs of the NIGBD were considered efficiently used, and samples from all the samplings were used in our study. The spatial distribution and statistics of DTB observations interpreted from boreholes is introduced in Data Records section. Interpretation of borehole records. Interpreting DTB from borehole profiles sampled from the NIGBD was one of the crucial aspects of this study.
These differences result in inconsistencies or uncertainties in the borehole database, which were propagated into our DTB observations. To reduce the error of DTB in the process of interpreting borehole profiles as much as possible, we did not use borehole profiles whose lithological descriptions were too vague (such as a layer that is composed of bedrock mixed with weathered rocks) to accurately determine the DTB. To interpret the DTB from a borehole profile in the form of a scanned picture, we manually determined the boundary between the regolith and fresh bedrock based on lithological descriptions and the dip angle of the borehole. There are four kinds of cases in the interpretation of DTB. In the first case, DTB is reached for most boreholes and the dip angle is 90°. Then, the DTB is taken as the boundary depth.
Therefore, we used the following data sources to generate pseudo-observations to add to the training points: We generated a certain number of points in random positions within deserts based on the distribution map of China's deserts. The DTB values of these points were obtained from existing material and previous studies of the sand thickness of the deserts. We obtained some information about the thickness of desert and sand dunes from websites such as Baidu Encyclopedia (https://baike. baidu. com). Some publications provide information on the profiles of China's deserts, from which we obtained about 40 pseudo-observations. Boreholes logs from Pishan, Moyu, and Yutian in the Taklimakan Desert show that the subsurface of this desert is mainly medium to fine-grained sand and silt with a thickness less than 200 meters  20.
The 147 covariates classified as "scorgan" factors included: (1) Harmonized soil database images: percent coverage of Andosols, Histosols, and dozens of other soil types. (2) Climatic images: images indicating the values of 8-day MODIS day-time and night-time local standard time (LST), long-term and monthly precipitation data, etc. (3) Land use and land cover images: including vegetation maps, land cover and land use classifications, biomass and yield maps, etc. (4) Relief data, mainly derived from digital elevation models: slope maps, the topographic wetness index, the topographic openness index, physiographic landform units, elevation and secondary terrain attributes, etc. These covariates were calculated by R and SAGA-GIS. (5) Geological and parent material maps: rock type based on the global lithology and geological ages based on surface geology.
Analysis and evaluation of uncertainty help data users to understand its existence and can also help to improve decision quality  36. In this study, we used quantile regression forests (QRF) to estimate the uncertainty of estimations. QRF is a tree-based ensemble algorithm for estimation of conditional quantiles. This method is particularly suitable for high-dimensional data. QRF were implemented via the R environment in the "quantregForest" package  37. We used the same 53 covariates of the final RF model for quantile predictions of the QRF model. To estimate the uncertainty of predictions at every location, we generated the uncertainty map of predictions by: = -. where qp 0. 9 is the 0. 9 quantile prediction of DTB, qp 0. 1 is the 0. 1 quantile prediction of DTB, and qp 0. 5 is the 0. 5 quantile prediction of DTB.
xlsx" format, named "covariates_list_for_ generating_DTB100_of_China. xlsx")  27. The data sources of these covariates are provided in this file. Topographic covariates are obtained based on digital elevation model via R and SAGA (see file "DEM_functions. R" on Github: https://github. com/yanfp/DTB100China). The essential information about every covariate are provided in columns. The last three columns show the choice of covariates for the RF, GBT and QRF models, where a value of one indicates that the corresponding covariate was used in the final model. The final RF, GBT and QRF model used 53, 44 and 53 covariates, respectively, which are marked in the covariate list file. prediction results. The resulting maps are available on Figshare  27  and at http://globalchange. bnu. edu. cn/ research/cdtb.