dataesr/scientific-paragraphs-categorization huggingface dataset:
It is obvious that there were more PTs detected by the CSK time series analysis in the subsidence bowl zone, than the S-1A analysis. This implies that fewer PSs were determined due to the lower resolution of the S-1A data. By increasing the spatial extent between each PS and the reference point, an unsuccessful derivation of the absolute phase can result in second estimates of the unknown parameters. This causes unreliable estimates and low temporal coherence for points, which would be lost in the final deformation map. In general, although CSK data benefit from a high spatial resolution and are capable of revealing more displacement detail, free S-1A data are more accessible than this commercial dataset. They lead to an estimate of an almost dense deformation map, making the S-1A data useful for conducting a general assessment of PTs.
The estimates obtained with the CSK and S-1A data were evaluated against the vertical displacements measured by GPS. The root mean square error (RMSE) statistics were calculated based on 18 and 30 paired observations of CSK and S-1A vs. GPS, respectively. RMSE values of 9. 11 mm for S-1A and 3. 8 mm for CSK analyses were obtained. The larger RMSE of the S-1A analysis is probably due to its lower spatial resolution as well as longer wavelength. The scatter plots of the vertical displacement of CSK vs. GPS estimates of the total vertical displacement (from April 2014 to February 2016) and S-1 vs. GPS (from October 2014 to December 2016) are shown in Figure  9. Only InSAR and GPS measurements on the same dates are considered. According to Figure  9 , a high correlation between the two PSI time series results and the corresponding GPS observations is observable.
Uncertainity Assesment Apart from the error propagation of the velocities according to Equation (  3 ), the spatial statistical uncertainty of the velocities was also calculated to assess the error of the estimates (Equation (  5 )). Assuming the subsidence rate of a stable target is zero, the error of the velocity estimates in an arbitrary non-subsided area is  [38] : Finally, we compared the InSAR estimates with the valid GPS observations for both the Sentinel-1 and CSK analyses separately. The estimates obtained with the CSK and S-1A data were evaluated against the vertical displacements measured by GPS. The root mean square error (RMSE) statistics were calculated based on 18 and 30 paired observations of CSK and S-1A vs. GPS, respectively. RMSE values of 9. 11 mm for S-1A and 3. 8 mm for CSK analyses were obtained.
The larger RMSE of the S-1A analysis is probably due to its lower spatial resolution as well as longer wavelength. The scatter plots of the vertical displacement of CSK vs. GPS estimates of the total vertical displacement (from April 2014 to February 2016) and S-1 vs. GPS (from October 2014 to December 2016) are shown in Figure  9. Only InSAR and GPS measurements on the same dates are considered. According to Figure  9 , a high correlation between the two PSI time series results and the corresponding GPS observations is observable. Finally, we compared the InSAR estimates with the valid GPS observations for both the Sentinel-1 and CSK analyses separately. The estimates obtained with the CSK and S-1A data were evaluated against the vertical displacements measured by GPS. The root mean square error (RMSE) statistics were calculated based on 18 and 30 paired observations of CSK and S-1A vs.
Uncertainity Assesment Apart from the error propagation of the velocities according to Equation (3), the spatial statistical uncertainty of the velocities was also calculated to assess the error of the estimates (Equation (  5 )). Assuming the subsidence rate of a stable target is zero, the error of the velocity estimates in an arbitrary non-subsided area is  [38] : error = std √ n 2 + MEAN 2 ( 5 ) where MEAN is the mean velocity value in the selected area. std represents the standard deviation of the velocities, and n is the number of PS points in that region. The estimates of both the total vertical displacements over time and of the subsidence rates obtained from the three data sources are rather similar (see Table  1 ). According to Table  1 , the subsidence rates were calculated at the GPS location, and the GPS rates were estimated for both the InSAR datasets' time intervals.
The estimates of the subsidence rate are generally consistent, although the differences between the InSAR retrievals and the GPS observations are larger than the estimated error, i. e. , they are significant. On the other hand, the InSAR retrievals and the GPS observations relate to different locations; thus, such differences may simply be explained by spatial variability. It should be noted that the CSK estimation has a better agreement with the field observations due to its high spatial resolution and capability of determining more PS points, even those around the GPS station, which cannot be detected with the S-1A data. As we discussed, the CSK data are also able to allow the detection of small deformation rates since they have a shorter wavelength compared to the S-1A data. Thus, the CSK estimates are closer to the real-world ground deformation.
Conclusions The main purpose of this research was simply to apply two different datasets, C and X bands, in a unique application of the PSI technique in determining the effects of the land subsidence phenomenon on PTs. Great performance of the PS technique for monitoring the power transmission line was shown, which had not been done before in the area of interest. Four data stacks from the S-1A and CSK satellites collected between 2014 and 2016 over the Tehran basin area (affected by subsidence due to groundwater withdrawal) and a linear model helped us to investigate the displacement of PTs around the subsidence bowl. The results indicate that the maximum subsidence velocity reached -86 and -79 mm/y using the CSK and S-1A data during the common time interval, respectively. Compared to the GPS data, RMSE values of 0.
indicates the configuration of the interferometric image datasets, where each point shows an image and each line between two points represents an interferogram. Figure 2a,b show a selected single master image on 13 December 2015, and 15 October 2015, in ascending and descending paths for the S-1A datasets, respectively. Additionally, Figure 2c,d show a selected single master image on 27 May 2015, and 17 February 2015, in ascending and descending paths, respectively, for the CSK datasets.
) and temporally matched with the dataset time intervals. Well 1 is located at 35 • 39 14. 62 N latitude and 51 • 15 50. 87 E longitude. In addition, Well 2, with the most withdrawal, is located at 35 • 38 24. 55 N latitude and 51 • 13 17. 26 E longitude. The time series observation of the wells is illustrated in Figure4. Figure4ashows the observation of the water level depth in each well in the first and last observation timeslots. The yellow part of each column shows how much the water level depth has decreased in meters. The maximum decrease in the depth of the water level was for Well 2, which was more than 10 m from April 2014 to December 2016. There was also the highest deformation rate in that location, induced by groundwater over-extraction. Remote Sens. 2021, 13, 407 8 of.
16 - 16 ) and temporally matched with the dataset time intervals. Well 1 is located at 35 • 39 14. 62 N latitude and 51 • 15 50. 87 E longitude. In addition, Well 2, with the most withdrawal, is located at 35 • 38 24. 55 N latitude and 51 • 13 17. 26 E longitude. The time series observation of the wells is illustrated in Figure4. Figure4ashows the observation of the water level depth in each well in the first and last observation timeslots. The yellow part of each column shows how much the water level depth has decreased in meters. The maximum decrease in the depth of the water level was for Well 2, which was more than 10 m from April 2014 to December 2016. There was also the highest deformation rate in that location, induced by groundwater over-extraction. Remote Sens. 2021, 13, 407 8 of.
Overview of the SDK4ED Platform The high-level overview of the SDK4ED Platform is presented in Fig. 1. As shown in Fig. 1 , the platform comprises five core toolboxes, which are: (i) the Technical Debt (TD) Management Toolbox, (ii) the Energy Optimisation Toolbox, (iii) the Dependability Optimisation Toolbox, (iv) the Forecaster Toolbox, and (v) the Decision Support Toolbox. The first three toolboxes are responsible for monitoring and optimising the Maintainability, Energy Consumption, and Dependability of embedded software products, respectively. The other two are meant to further support decision-making during software development, through the provision of quality forecasts and analysis of potential trade-offs. The SDK4ED Platform provides solutions for monitoring and improving the quality of software applications written in Java, C, and C++ programming languages, covering, but no limited to, a wide range of embedded software applications.
The different toolboxes of the SDK4ED Platform have been implemented in the form of Microservices. The main features of the five core toolboxes that constitute the SDK4ED Platform are described below, in an attempt to help the reader gain a deeper understanding of the SDK4ED Platform, as well as of the novel functionalities that it provides. Technical Debt Management Toolbox The main responsibility of the TD Management Toolbox is to monitor and optimise the Maintainability of software products through the notion of TD  [6]. In particular, its main goal is to provide efficient support for TD measurement, prioritisation, and handling in Embedded Software applications. The TD Toolbox provides solutions in the form of web services for: (i) TD Analysis, which is responsible for analysing and evaluating the TD aspects of a software application including TD Principal, TD Interest, and TD Interest Probability using the SonarQube 1.
platform and FITTED framework  [2] , (ii) TD Evaluation of New Code, which is responsible for assessing the TD of the newly added code and prevent the new code from being committed to the software repository if its value is high, and (iii) TD Refactorings Identification, which is responsible for suggesting code-, architecture-, and designlevel refactorings that are expected to improve the TD of the analysed application. A multitude of novel features have been developed within the course of the SDK4ED project with respect to the aforementioned solutions (i. e. , services)  [1, 2, 4, 5, 8, 9, 16, 23, 35, 37, 39]. Energy Optimisation Toolbox The purpose of the Energy Optimisation Toolbox is to monitor and optimise the Energy Consumption of a given software program. More specifically, this module provides solutions for (i) estimating the energy consumption of a software program and identifying energy-hungry parts, and (ii) providing recommendations for potential energy optimisations.
These solutions are implemented in the form of web services, which are the Energy Consumption Analysis web service and the Energy Estimation and Optimisation web service of the SDK4ED ecosystem. More specifically, the former is responsible for analysing a given software program with respect to its energy consumption using popular dynamic analysis tools including Linux Perf 2 and Valgrid 3. with the purpose to detect energyhungry parts (i. e. , energy hotspots). The latter is responsible for suggesting code-and design-level optimisations that are Fig. 1  The overall architecture of the SDK4ED Platform 1 https ://www. sonar qube. org/ 2 https ://perf. wiki. kerne l. org/index. php/Main_Page. 3 http://www. valgr ind. org/ expected to reduce the energy consumption of the identified hotspots, and, in turn, of the broader software program.
With respect to software reliability, novel mathematical models for suggesting the optimum checkpoint interval that optimises important Quality-of-Service (QoS) parameters  [10-12, 14, 25-27] , with emphasis on energy consumption and performance were developed and used as part of the Optimum Checkpoint Interval Recommendation web service of the Dependability Toolbox. Forecaster Toolbox The purpose of the Forecaster Toolbox is to predict the evolution of the three quality objectives of choice, which are the Maintainability, the Energy Consumption, and the Dependability of software products. This is achieved through the provision of advanced time series and machine-learning (ML) models, which are built based on the results of the three aforementioned modules (see Fig. 1 ). Hence, the Forecaster Toolbox provides three individual web services, namely TD Forecaster, Energy Forecaster, and Dependability Forecaser, which provide ML models for predicting the evolution of TD, Energy Consumption, and Dependability, respectively.
Novel models have been developed for this purpose, especially for the case of TD forecasting  [40, 41, 43]. Decision Support Toolbox The main objective of the Decision Support Toolbox is to facilitate decision-making during the development and maintenance of embedded software products. In particular, the purpose of this toolbox is to provide information about the impact that refactorings for improving a specific quality attribute (e. g. , Dependability) will have on the other two attributes that the platform focuses on (i. e. , Energy Consumption and Maintainability). In fact, trade-off analysis is employed by the Decision Support Toolbox (based on the outputs of the other modules, as shown in Fig. 1 ) to compute these impacts and provide useful recommendations. Throughout the SDK4ED project, several empirical studies have been conducted for reaching conclusions with respect to potential trade-offs among the three quality attributes of choice  [22, [30] [31] [32] 34].
Acknowledgements The authors would like to thank  Dr. Magdalena Sajdak ,  Prof. Tadek Czàchorski , and  Dr Krzysztof Grochla , and all Scientific Programme Committee members, for their contributions to the organization of the EQSEM Conference. This work was funded by the  European Union 's  Horizon 2020 Research and Innovation Programme  through  SDK4ED  project under Grant Agreement No. 780572.
The meta-data are related to the overall time spent by the model observing every symbol for a long time span, irrespective of the state from which this symbol is observed. Classification experiments involving four classification tasks have been carried out on the recently constructed UniMiB SHAR database which contains 17 classes, including 9 types of activities of daily living and 8 types of falls. As a result, the proposed approach has shown best accuracies between 92% and 98. 85% for all the classification tasks. This performance is more than 10% better than prior work for 2 out of 4 classification tasks.
These parameters are then iteratively adjusted at each iteration of the Baum-Welch algorithm to obtain the final HMM λ w. Thereafter, meta-data derived from λ w are saved as the components of the descriptor vector -→ w associated with w. The performances of the proposed approach are evaluated through flat classification experiments on UniMiB SHAR  [23]  which is a database containing acceleration patterns captured by smartphones and constructed in 2017 for the objective evaluation of ADLs recognition and fall detection techniques. The rest of this paper is organized as follows: The state of the art is presented in Section II, followed by a summarized presentation of HMMs in Section III. A detailed description of the approach proposed in this paper is given in Section IV. Experimental results are presented in Section V and the last section is devoted to the conclusion.
3) PERFORMANCES OF EXISTING WORK The implementation of a typical HAR system requires the design of a suitable dataset containing the raw data from which the signal windows will be derived. The datasets utilized in existing papers contain data related to human activities performed by various participants whose characteristics (gender, age, weight, height, etc. ) vary from one dataset to the other. Some authors prefer private custom datasets  [13] -  [16] ,  [26] ,  [27] ,  [29] -  [32] ,  [34] ,  [37] ,  [39] -  [42] ,  [45] -  [48] ,  [50] ,  [53] ,  [58] ,  [60] ,  [65] ,  [66] ,  [68] -  [71] ,  [77] ,  [82] ,  [84] -  [86] , but it is difficult to perform comparisons with these work in such conditions. Other authors adopt the use of publicly available datasets to enable further comparisons  [17] -  [25] ,  [28] ,  [33] ,  [35] ,  [36] ,  [43] ,  [49] ,  [51] ,  [52] ,  [54] -  [59] ,  [61] ,  [62] ,  [64] ,  [67] ,  [72] ,  [74] -  [81] ,  [83] ,  [86] -  [88].
A deep survey of the evolution of modern datasets for HAR is available in  [93]. The performances of a given HAR systems depend on several parameters including: the type of sensors, the number of participants, the protocol of data collection, the selected features and the selected models. Tables  1  and 2  present the performances of relevant existing work with experiments on publicly available and custom datasets. In these tables, the column entitled '#HA' contains the number of human activities involved in the considered work and the last column contains the accuracy of each reviewed work (except for  [36] ,  [49] ,  [61] ,  [79] ,  [88]  where the F1-measure is rather provided). Among the datasets experimented by existing deep learning techniques listed in Table  1 , OPPORTUNITY, PAMAP2 and UCI-HAD are the most experimented datasets.
However these datasets have not been considered in the current work for several reasons. OPPORTUNITY and PAMAP2 were respectively designed with 4 and 9 participants, which are low values. Additionally, the subset of human activities selected by the authors for these two datasets varies from one work to the other. The dataset UCI-HAD was design with enough participants (30 participants) but it only enables us to identify 6 human activities. The advantages of the dataset UniMiB SHAR (selected here) compared to the other publicly available datasets listed in Table  1  are thoroughly analyzed in  [23]. 8 A summarized description of the UniMiB SHAR datatset is given in Section V-A.
IV. THE PROPOSED APPROACH A. MAIN IDEA Given a human activity, we assume that the experimental data are recorded by a triaxial smartphone accelerometer which generates 3 vectors x = (x 1 ,. , x T ), y = (y 1 ,. , y T ) and z = (z 1 ,. , z T ) for each signal window w. These vectors are respectively obtained after sampling the signal on each Cartesian axis at a unique sampling frequency. Figure  5  depicts this process for fall detection where the smartphone is located inside subject's waist pocket. In that figure, a signal window extracted from the raw data generated by the smartphone triaxial accelerometer is sampled on each Cartesian axis to derive the 3 data vectors x, y and z. Existing techniques for HAR generally concatenate these 3 raw data vectors to obtain a unique vector w = (x 1 ,.
, x T , y 1 ,. , y T , z 1 ,. , z T ). All the (3T )-dimensional raw data vectors resulting from the application of this principle on all the signal windows of the database are then used for handcrafted feature extraction or for deep feature extraction. The main idea of this work is related to the fact that the former raw data vectors x, y and z can also be viewed as one sequence w = w 1 w 2. w T composed of 3-dimensional data vectors as depicted in Figure  7  where each w i = (x i , y i , z i ) with (1 ≤ i ≤ T ). Hence, the sequential variations of the spatial locations within the T vectors composing w can be captured into a MC δ w which can later be used to initialize and train a dedicated HMM λ w. Thereafter, one single feature vector -→ w derived from the model's meta-data can finally be associated with w for classification purposes.
C. TRANSFORMATION INTO MARKOV CHAIN As shown in Figure  4 , a MC is composed of symbols and states, both belonging to finite sets. In order to transform a signal window into a MC, these two finite sets must be defined. To determine the set of symbols, we are initially going to cluster the data vectors derived from all the signal windows found in the training database. More formally, let H = {H 1 ,. , H n } be the set of activities found in the training database, each activity being represented by |H j | signal windows, with (1 ≤ j ≤ n). Given that each signal window w is now considered as a sequence w 1. w T of 3-dimensional data vectors, the experimental database becomes a collection composed of T × n j=1 |H j | data vectors. The k-means clustering algorithm  [96]  is then executed off-line for organizing this collection of training data vectors into K clusters, where K is a positive user-defined integer.
A. EXPERIMENTAL DATASET Among the publicly available databases recorded with smartphones listed in Table  1 , the dataset UniMiB SHAR has been selected in this work  [23]. It is database of acceleration patterns measured by smartphones to be used as a common benchmark for the objective evaluation of both, ADLs recognition and fall detection techniques. This dataset contains 17 human activities including 9 different types of ADLs and 8 different types of falls. Table  3  presents the description of each ADL/fall. During the construction of the selected database, human activities where performed by 30 subjects (including 24 females) between 18 and 60 years of age. Each ADL/fall type was performed twice by each subject: the first time with the smartphone in the right pocket and the second time with the smartphone in the left pocket.
Signal windows of 3s each were saved during every experimental trial of a given ADL/fall performed by each subject. For each signal window w, the accelerometer recorded three data vectors (samples) x, y and z, each having T = 151 components. The database contains a total of 11. 771 samples not equally distributed across activity types: 7. 759 samples describing ADLs and 4. 192 samples describing falls. Further details about the data acquisition, the experimental protocols, the characteristics of the subjects, the signal segmentation and the signal processing are available in  [23].
Hence, the number of states of the HMMs designed in the current work is 51. To analyze the impact of the user-defined number K of clusters discovered by the k-means clustering algorithm on the performances of the proposed approach, we experimented with the 5 following values of K : 20, 40, 60, 80 and 100. Consequently, the number of symbols of the HMMs designed in this work also varies accordingly. The first step of the machine learning process presented in Figure  6  and corresponding to the transformation of every signal w into the MC δ w was entirely developed in Matlab. This choice was dictated by the fact that the database files were available as Matlab tables. Therefore, we executed a Matlab version of the k-means clustering algorithm during this step. Depending on the classification task and on the selected number of clusters, the off-line clustering step could sometimes take over an hour.
The two remaining steps of the proposed machine learning process were both developed in C language. Given a signal window w and its associated HMM λ w = (A w , B w , π w ), the stationary distribution ϕ w of λ w is obtained in this work by extracting the first line of the matrix (A w ) r with r = 100. After discovering the K clusters for each classification task, the computation of each feature vector -→ w associated with w took between 50 and 3500 ms, depending on the content of the signal window. The overall time taken for the computation of the feature vectors associated with all the signal windows varied from one classification task to another depending on the considered number of signal windows. For each classification task in {AF-2, F-8, A-9, AF-17} and for each number of clusters in {20, 40, 60, 80, 100}, the resulting descriptor vectors have been saved into one online available 'arff' files  10 which are taken as inputs by WEKA.