dataesr/scientific-paragraphs-categorization huggingface dataset:
The clustering techniques gather similar data values into clusters and merge some clusters to reduce the communication cost. In what follows, we present a brief discussion of some recent works that match this category. In  [36] , authors proposed an unsupervised approach for outlier detection and clustering (ODC) based on a modified K-means algorithm. They consider a data value as an outlier if the average distance is p times far from its centroid. Then they remove the outlier data values from the data set to enhance the clustering process. In  [37] , authors proposed an outlier detection algorithm for IoT based on big data processing and k-means algorithm. Their approach uses the Hadoop framework and MapReduce to deal with distributed big data and use the Mahout machine learning library. They also extend the middleware LinkSmart of IoT architecture.
They implement and integrate their algorithm with this middleware which is a part of the Hydra middleware project  [38]. In  [39] , authors proposed a new algorithm based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm. They implement their modified algorithm of DBSCAN using SCALA programming language over Apache Spark. Their algorithm also uses a Resilient Distributed Dataset, which is called RDD-DBSCAN. Their distributed algorithm can deal with two-dimensional data set. It overcomes the limitation of the normal DBSCAN and MapReduce model in dealing with massive data set. In  [40] , authors proposed two algorithms for outlier detection and removal using an outlier score. The first one is distance-based, which operates with the Euclidean distance to determine data value similarity.
The second one is cluster-based, which operates with the k-means and Euclidean distance. They use two outlier scores, F-score and likelihood ratio, to evaluate clusters quality and remove outliers. They utilize the health care dataset founded in the statistical computing project R. They prove that the second algorithm has better accuracy than the first one when choosing the correct outlier score. In  [41] , authors proposed an online approach for density-based outlier detection on big data. This approach includes two steps and is based on computing the Local Outlier Factor (LOF)  [42]. During the first step, the Grid-Based Partitioning (GBP) algorithm divides the data into grides and allocates them to the distributed nodes in the network. In the second phase, the Distributed LOF Computing algorithm (DLC) detects density outliers in parallel.
They use two modules; the first one for parameter computations such as the minimum radius of accepted cluster threshold (Eps), and the second for class identification in a spatial-temporal dataset. Their method gives better accuracy for detecting outliers with small FAR compared to  [31]. In  [47] , the authors proposed an algorithm for outlier detection based on clustering in real big data. Their algorithm decreases the compactness of clusters while increasing separation from each other based on the distances between their centroids. Their algorithm detects outliers with higher accuracy than the k-means algorithm for different multivariate real datasets. In  [48] , authors proposed an algorithm for outlier detection based on k-means clustering and outlier removal (KMOR). Their algorithm creates an additional cluster to group all the outliers inside naturally during the clustering phase.
The extended k-means algorithm can cluster the data and detect outliers simultaneously, with good performance better than the ODC algorithm in  [36]. In  [49] , authors proposed a solution for anomaly detection in IoT smart city applications. Their approach was based on fog computing and used the Low Power Wide Area Network (LPWAN) technologies. They use Antwerp's City of Things testbed to develop and evaluate their solution. They find a similar performance of Birch clustering and robust covariance (RC) techniques with fog computing. In  [50] , authors proposed a new model for outlier detection in IoT big data. Their model is based on DBSCAN and uses a multi-dimensional resilient distributed dataset (NRDD-DBSCAN). They proved that their model is suitable for IoT applications in comparison with the normal DBSCAN algorithm.
Using many real-life datasets, they study and evaluate the neighbourhood size and spatial-temporal correlation on their approach. They prove that their system improves outlier detection under the assumption of data correlation. In  [57] , authors proposed a novel Grid-based Approximate Average Outlier Detection (GAAOD) framework based on the KNN algorithm in IoT streaming data. Firstly, they used a self-adapted grid index file based on a sliding window to manage streaming data by filtering data values that are not outliers. After that, they used the min-heap algorithm, which is based on the index file, to calculate the approximate distances between data values and their KNN. Finally, they use the k-skyband method to maintain the data values which become outliers in the window. Their framework solves the problem of the proper selection of distance threshold, with low computational cost and better performance in comparison with the KNN-LEAP algorithm  [53].
In  [59] , authors proposed two approaches: the first one is Centered Hyperellipsoidal Support Vector Machine (CESVM). The second one is a distributed hyperellipsoidal one class Quarter-Sphere Support Vector Machine (QS-SVM) for WSN. The two approaches have high DR and accuracy rates for different real-life and synthetic datasets. The QS-SVM extends the SVM to be an unsupervised learning algorithm. It has less communication overhead and a comparable accuracy compared with the CESVM centralized approach. In  [60] , authors proposed an approach for outlier detection in WSN based on Spatial-Temporal-Attribute one class Quarter-Sphere Support Vector Machine (STA-QS-SVM). They did not only consider the spatial-temporal correlation between sensor nodes, but they also used the attributes correlation of sensor nodes to detect errors and events.
This mechanism is suitable for networks with constrained resources such as WSN and IoT. Their approach has better performance than the centralized mechanism. In  [63] , authors proposed an Adaptive Distributed Outlier Detection (ADOD) approach for WSN. Their proposal is based on probabilistic inference on Bayesian networks. It enhances the classification accuracy, execution time, and communication cost. In  [64] , authors proposed a Distributed Bayesian Algorithm (DBA) for fault detection in WSN. They use the Bayesian networks to calculate the fault probability by exchanging the data values between the neighbouring nodes. After that, this probability will be adjusted. If the fault probability of the sensor node exceeds a certain level of probability threshold, they consider this node a faulty node.
Their approach has better performance than the originally distributed fault detection (DFD) discussed in  [65]  with low FAR and high DR. In  [66] , authors proposed an online outlier detection algorithm based on Least Squares One-Class Support Vector Machine (LS-OC-SVM) classifiers. These classifiers detect outliers in large power grid streaming sensor data. They implement their algorithm using the IEEE 14 bus test system and prove that it has low computation cost and memory usage. In  [67] , authors proposed a new threshold-free error detection (TED) approach for industrial wireless sensor networks (IWSN). They get rid of the selection of the correct predefined threshold. In addition, they use the temporal correlation between the data values of the sensor node and the spatial correlation between the neighbouring nodes.
Artificial Intelligence Techniques Artificial intelligence techniques are the recent approaches for the identification of outliers in IoTs. Neural networks (NN) and fuzzy logic are examples of these techniques. They are based on decision-making theories. The NN is a logical model that provides a global overview and makes decisions using the entire data set. The fuzzy logic is a technique used to distinguish between the normal data values using distinction values such as (yes/no, right/wrong), which can be helpful in outlier detection issues. In what follows, we present a brief discussion of some recent works that match this category. In  [72] , authors proposed an algorithm for Fault Detection and Isolation (FDI) in WSN using the Fuzzy Knowledge-based Control (FKBC). Their algorithm detects faulty communications between nodes using a clustering framework.
In  [20] , authors propose an algorithm that used three input fuzzy inference systems (FIS) with 27 developed fuzzy rules which help in decision making. Their algorithm detects sensor hardware faults such as battery, transmitter, and receiver circuit errors in IoT. The conditions of these hardware elements are measured and used as input to the FIS system. Their approach gives high accuracy and low FAR compared to the Faulty Detection WSN (FDWSN) approach  [77]. In  [78] , authors proposed a Distributed Fuzzy Logic-Based Faulty Node Detection (DFLFND) algorithm for heterogeneous WSNs. Their scheme used the spatial-temporal correlation and can detect and isolate faulty nodes and discover interesting events. Each node has a fuzzy logic controller (FLC) to weight its sensed data values with the data values of its neighbours.
These techniques are for IoT cloud-based indoor climate control vertical plant walls systems. They prove that the autoencoder neural network is better than other methods in detecting point outliers. They also confirm that the long short-term memory encoder-decoder (LSTM-ED) is better at detecting contextual outliers. In conclusion, we can say that techniques based on artificial intelligence could work with fragmented, noisy, or limited data, and they are capable of generalizing with such data. They hardly build the model because of the need for more fine-tuning before working in real applications. When adding new data or rules, there is no need for further training for the system. They are rule-based techniques, so for example, when dealing with the spatial-temporal correlation of data, this will increase the number of rules needed.
In  [82] [83] [84] , the authors proposed a new outlier detection technique based on kernel principal component analysis (KPCA). They use the MD for outlier detection in WSN and compare it with a predefined threshold to separate the outlier values from the normal ones instead of comparing with the reconstruction error (RE). The outlier value is a measured data value that deviates from the principal subspace of the normal data. If the MD is more significant than their specific threshold, they consider the data value as an outlier; elsewhere, it is a normal data value. They prove that this approach (KPCA-MD) is better than the KPCA with a training set (KPCA-RE) and OCSVM algorithms regarding the accuracy rate and FAR. In conclusion, we can say that PCA-based techniques can be used with a vast amount of data with multiple attributes.
Their experimental results have higher accuracy and DR for identifying outliers and interesting events than  [71] ; which is an event detection method based on a distributed collaboration among neighbour nodes. In  [87] , authors propose a new algorithm for anomaly prediction in data streams based on PCA, information entropy theory, and support vector regression. The data feature and the entropy feature matrixes are generated from a sliding window, then the PCA analyses and treats the matrixes. Finally, the support vector regression model will predict anomalies by comparing components and newly coming data values. In  [88] , authors proposed a Recursive PCA (R-PCA) algorithm based on k-means clustering for outlier detection in IoT systems. The clustered data is gathered and sent to the cluster heads where the R-PCA will be applied considering the spatial correlation and the dynamic changes in IoT data.
Finally, the SVM will be trained from the normal data to classify and separate the outlier data values from the normal data values. In  [90] , authors proposed an algorithm for fault detection in WSN. It uses SVM to classify sensor data values and detect faults based on statistical learning. The SVM is executed at the cluster head or the sink node based on a kernel decision function to detect faulty nodes. So, their technique, like the cloud technique, is suitable for resourceconstrained sensors. It differs from other techniques such as (Bayes, HMM and SODSEN), where the algorithm runs on both cluster head and sensors. In  [91] , authors proposed an approach for detecting abnormal data values in smart homes IoT devices. It is based on statistics and machine learning techniques. In their process, they sniff and collect the sensed traffic data, which is exchanged between sensor IoT devices and the gateways.
Then, they try to classify these data into different behavioural templates according to a set of features predefined statistically. These templates will be used later to detect the anomalies that deviate from the expected behaviour according to a specific threshold. The selection of a bad threshold can affect the rate of FAR. In  [92] , authors propose a scalable outlier detection approach for IoT time-series data. Their method works on passing the time series data into a noise filter, then performing two steps. The first step is offline and starts by using the PCA to reduce the recorded time series data dimensions. Then, it uses hierarchical clustering to find the clusters and the correlated sensors data values. The second step starts offline by segmenting the time series data into segments to minimize the data distribution effect.
Then, it uses the ARIMA model for time series data analysis to build the Long Short-Term Memory (LSTM) neural network trained from known data set. Also, they use the statistical analysis M-estimator, and the result is entered in the outlier detector module to detect outliers in an online manner. In  [93] , the authors proposed a new anomaly detection algorithm based on deep learning techniques. They perform their approach on their proposed Industrial IoT (IIoT) framework called Stack4Things (S4T). Firstly, they used the data gathered from the sensors by the S4T for training the algorithm by using two neural networks autoencoders. The two autoencoders are responsible of first dimension reduction, they attempt to learn an efficient encoding of the input data, and the resulting data will be stored in a specific layer, called code layer.
Comparative Study of Outlier Detection Techniques in IoT: In what follows, we provide a summarizing comparative table for each category. In each table, we compare works that present techniques belonging to the same category. For each work, we mention the year of the publication, the keywords contribution, the nature of data used by the algorithm, the adopted approach as discussed in Section 2. 2, and finally, which application type the work is suitable for (IoT, WSN, streaming data, etc. ). In all tables, we note by '-' the fact that the used approach is not well mentioned or defined in the concerned work. We can see from Table  2  that all listed works share some same approaches like being online (except the last one), dealing with univariate data and focusing on outlier detection. We can see from Table  3  that all listed works share some same approaches, like being unsupervised and focusing on outlier detection.
We can see from Table  4  that all listed works share some of the same approaches, like being unsupervised and focusing on outlier detection. We can see from Table  5  that all listed works share some same approaches like focusing on outlier detection. Furthermore, all of them are suitable for WSNs or IoTs applications. From Table  6 , we can see that all listed works share some same approaches like dealing with univariate data (except the third one) and focusing on outlier detection. Moreover, all of them are suitable for WSNs or IoTs applications. Both works listed in Table  7  share some of the same approaches, like being unsupervised and focusing on outlier detection. Moreover, both of them are suitable for WSNs applications. We can see from Table  8  that all listed works share some same approaches like being focusing on outlier detection.
Conclusions Outlier detection is a significant issue in IoT. With the actual growth in IoT use in different applications, traditional data analysis methods are inefficient. Therefore, IoT needs creative energy-efficient solutions for outlier detection that overcome specific limitations and constraints. Many outlier detection strategies for IoT have been proposed in recent years. In this paper, we highlight the outlier detection fundamentals while discussing the different sources of an outlier, the existing approaches, how we can evaluate an outlier detection technique, and the challenges facing designing such techniques. After that, we provide a comprehensive literature review of the existing outlier detection techniques used in IoTs and their classification. We adopt a classification based on seven main categories that cover all previous works in the literature while highlighting the advantages and disadvantages of each of them.
Female flowering date was considered as the GDD until 50% of plants in a plot were flowering with 2 cm of silk outside of husk leaves. Plant height was measured as the distance from the base of the plant to the top of the tassel. Grain was collected using a combine harvest. Grain yield and TKW were both adjusted to 15% humidity. TKW was estimated from a 100 kernel sample. Data was also collected from an additional environment, Szeged, Hungary in 2017. We did not use this data in the analyses presented here as flowering date was not collected on the same schedule as in the other environments and this caused issues with the GÂE analyses. Data from Szeged are available in the data repository associated with this paper. Between 292 and 309 of the MAGIC F1 lines were grown in each environment. There were a total of 325 lines that had both genotype data and phenotype data from at least 1 environment.
Estimating kinship Kinship matrices for the DH lines were estimated from the genotyped SNPs using the VanRaden method as implemented in the R package sommer (Covarrubias-Pazaran 2016; VanRaden 2008; R Core Team 2020). SNPs were first filtered for linkage disequilibrium using Plink with a window size of 50 kb, a step size of 5, and an r 2 threshold of 0. 2  (Purcell et al. 2007). In order to perform genome-wide association analyses, we used the leave 1 chromosome out method  (Lippert et al. 2011).
Genotype 3 environment interactions Variance components for each trait were estimated using the R package sommer. We used the formula: y ¼ Z G u G þ Z E u E þ Z E:G u E:G þ f E ðx; yÞ þ e; where y is a vector of n observations from individual plots of a single trait including both plots of all lines in all environments, y ¼ Z G u G þ Z E u E þ Z E:G u E:G þ f E ðx; yÞ þ e is a n Â r design matrix for the genotypic main effects of the r lines, Z E is a n Â 5 design matrix for the environmental main effect, Z E:G is a n Â 5r design matrix for genotype-specific effects in each environment, u G is a length r vector of random genotypic effects, u E is a length 5 vector of environmental random effects, u E:G is a length 5r vector of random GÂE effects with same variance and covariance among environments, f E ðx; yÞ is a 2-dimensional spline for the effect of the x/ y position in the field nested within environment modeled as a single random effect fit from an incidence matrix containing the tensor products of the x and y coordinates in the field, and e is the error.
Genome-wide association studies Genome-wide association analyses for loci contributing to GÂE interactions were performed with the R package GridLMM  (Runcie and Crawford 2019). Imputed founder probabilities at each locus were used as markers, meaning that at each marker we asked if the identity of the founder which contributed that genomic region at a given locus was a significant predictor of differences in plasticity among the hybrids. We set GridLMM to obtain maximum likelihood estimates of the effect of each marker. GÂE models can be parameterized in multiple ways which could potentially capture different aspects of GÂE. We chose to model GÂE in 3 different ways in our GWAS analyses, which we describe below.
As the G-matrices differed in their environment but not population, the genetic variances and covariances that contribute the most to the eigentensors are those which are most influenced by the environment. Eigentensor analysis was performed on the posterior median Gmatrices. Uncertainty in the eigentensors was estimated by performing eigentensor analysis on the MCMC samples of the G-matrices. Finally, to determine whether an eigentensor explained more of the variation among G-matrices than would be expected by chance, we shuffled the real phenotypic data among environments, estimated G-matrices, and asked whether the eigentensors of the randomized G-matrices explained as much of the variation as the MCMC samples from the real data. If an eigentensor of the estimated G-matrices explain more of the variation, this indicates that this eigentensor is explaining biological variation and not only variation due to random sampling.
The threedimensional distribution of gas around galaxies in the denser dynamic volume (with offset velocities in the range [-2. 500; +1. 500] km s -1 ) is suggestive of connecting filamentary structures on scales up to ≳100 kpc (see white and black scales). between approximately ±500 km s -1. We perform a kinematic model of W0410-0913 using the 3D tilted ring model fitting code 3D Barolo (see details on the procedure in Methods) and obtain models of the rotational velocity (v rot ) and velocity dispersion (σ) radial profiles across the whole CO(6-5) emitting region (Fig. 4a ). The v rot and σ radial profiles are shown in Fig. 4b. We find that, after a declining trend in the inner 1-2 kpc, v rot (r) follows a flat curve (within the uncertainties) reaching a value of about 500 km s -1 at a radius of ~6 kpc.
MUSE observations MUSE observations of W0410-0913 were carried out in February 2019 and January 2021, during the ESO Observing Periods P102 (Program 0102. A-0729) and P106 (Program 106. 2184. 001), respectively (see Data Availability Section). The collection of data in P102 was organized in one observing block (OB) composed of four exposures of 680 s each, for a total exposure time on target of 45 m (1 h of telescope time including overheads). Seeing conditions ranged between FWHM = 0. 8″-1. 1″, with airmass values of 1. 1-1. 3. In P106, the data acquisition was split into three exposures of 900 s each, with time on target (45 m) and telescope time (1 h) similar to the P102 program. Observations were carried out under good seeing conditions, FWHM = 0. 8″, and airmass ≲ 1. 1. In both programs the exposures were slightly dithered by ±0.
3″, and rotated with respect to each other by 90 degrees, to reduce systematic noise. We have processed the individual exposures from both datasets with standard data reduction techniques using the ESO-MUSE Pipeline Version 2. 8. 4  49. In detail, for each exposure, we used the MUSE_BIAS, MUSE_FLAT, and MUSE_WAVECAL pipeline recipes  49  to perform bias subtraction, flat fielding, twilight and illumination correction, and wavelength calibration. In order to ensure an accurate relative astronomy, we registered the individual exposures using the position of point sources in the field. This step is needed since the spatial shifts introduced by the derotator wobble  50  can cause offsets of a few arcsecs. We performed the next stages of data reduction using the tools provided by the CubExtractor software package 37 (Version 1.