dataesr/scientific-paragraphs-categorization huggingface dataset:
In this paper, we explore the potential of latency reduction techniques for narrowband cellular networks. We first discuss the factors that contribute in the uplink latency of 4G LTE networks and then evaluate the latency by means of both theoretical calculations and simulations. The downlink latency is not considered in this work since the downlink latency in LTE networks is lower than 10 ms. We then explain latency reduction techniques -initially proposed by 3GPP  [18]  -and discuss their potential for decreasing latency in cellular networks. There is a lack of implementation and evaluation of these techniques in the known open-source simulators. To this end, we implement and evaluate latency reduction techniques for the narrowband UE category LTE-M in Medium Access Control (MAC) and Physical (PHY) layers of the LTE module  [20]  of the open-source Network Simulator (ns-3)  [21].
In order to evaluate these techniques, the overall setup of the simulation and our development is shown in Fig. 2. The developed techniques are part of LTE module and the simulation scenarios are defined as part of the simulation setup which includes the definition of node topology, network parameters and configurations. The setup is then used to run the simulation of LTE module. The simulation model generates the trace files for each layer of LTE protocol stack containing different Key Performance Indicators (KPI). The results are then extracted from the trace files generated from the simulations. Our main contributions in this paper are as follows: -Investigation of delay components in 4G LTE networks, -In-depth analysis of minimum achievable uplink latency of cellular networks for narrowband UE by theoretical calculations and evaluation through realistic simulations, -Conceptualization, implementation and evaluation of latency reduction techniques for narrowband cellular networks in the open source ns-3 simulator.
Control-plane latency: random access To get access to the network resources, a 'contentionbased' RA is performed by the UE on a dedicated physical channel called Physical Random Access Channel (PRACH). The PRACH consists of six Physical Resource Blocks (PRBs) and its location in the network resources is periodically transmitted by the eNB in a System Information Block 2 (SIB-2). A UE initiates the RA procedure whenever (a) it has new data to transmit but without uplink synchronization, (b) it recovers from a link failure, (c) it changes its state from Radio Resource Control idle (RRC_IDLE) to RRC_CONNECTED, or (d) it performs a handover. Contention-based RA consists of four messages presented in Fig. 4   [34]. The first message is called preamble transmission, where a UE selects a random sequence from a pool of pre-defined preambles/sequences and transmits it over PRACH.
Simulation setup The parameters for our simulations are shown in Table  3. In our previous work  [26] , we evaluated control-plane latency of LTE-M UE category with a realistic RA model. Further details about realistic ns-3 RA model can be found in  [34]. Especially, the authors developed a RA model for ns-3 LTE module and evaluated it for wideband UE category. Moreover, we used their model to evaluate narrowband UE category for MTC in  [26]  by investigating multiple parameters of random access. In this paper, we further extend the latency analysis by evaluating the userplane latency. The results presented in this paper are mean values calculated from ten independent simulation runs.
Evaluation of latency reduction techniques In this section, we present the results from simulations of sTTI and SPS techniques we implemented into the ns-3 LTE module. Indeed, the publicly available implementation of the LTE module does not support any of the latency reduction techniques. A sTTI feature for LTE module to include sTTI of 0. 5 ms (7-os) and 0. 14 ms (2-os) along with the legacy TTI of 1 ms (14-os) has been developed in this work. In our previous works  [24, 27] , latency reduction techniques with different numbers of UEs in a single network cell have been evaluated. Here, we extend our analysis for specific URLLC use cases taking account of realistic channel propagation models.
Evaluation for a single UE Theoretical calculations of uplink, downlink and end-toend latency with sTTI and SPS are given in Table  5. A comparison between theoretical and simulated uplink latencies for a single UE is shown in Fig. 9  for 2-os, 7-os and the legacy 14-os TTI with dynamic and semi-persistent scheduling. These results validate the features developed in ns-3 LTE module. The small difference in simulated and theoretical calculation is due to the waiting time for the resources to send either scheduling request or data. As presented in Table  2 , the average waiting time to resources considered for theoretical calculations is 0. 5 ms; however, it could be between 0 to 1 ms in simulations. It is worth noticing that with 14-os TTI and dynamic scheduling, the minimum uplink latency is theoretically limited to 11.
e. \30) in a cell. The major factors impacting the latency of narrowband LTE are number of UEs, packet size, and packet sending interval. The uplink latency of 50 ms is still achievable in this use case for 60 UEs in a single cell. We further extend our analysis of sTTI and SPS to discuss the trade-offs for satisfying URLLC latency requirements. This time, we evaluate factory automation (event-triggered data uploads) and process automation (periodic data uploads) with different number of UEs in the network cell. The rationale behind this evaluation is to discuss the limit on the number of UEs while fulfilling the URLLC latency requirements. Figure  11  represents the uplink latency for different number of UEs with periodic data uploads. The periodic data transmissions represent mainly the process automation use case, where sensors on the machines send data periodically.
The ns-3 code will be made public once our ongoing development activities are complete. Moreover, as a next step to this work, the control overhead incurred by reducing the transmission time interval will also be investigated. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Background General classes of univariate continuous distributions are involved in various facets of statistical modeling. They offer solutions to practitioners who wish to understand and best explain their subject of study from the data observed. In fact, most of these general classes are based on well-known distributions serving as generators. Basic works and recent developments on this topic are discussed in  [1] [2] [3]. Among the most interesting classes in terms of heterogeneous distributions, are the Weibull-generated-type classes based on the standard Weibull distribution. Formally, they are identified by a cumulative distribution function (cdf) of the following form: F H (x) = 1 -exp -αH(x) β , x ∈ (a, b), (1) F H (x) = 0 for x ≤ a, and F H (x) = 1 for x ≥ b, where α > 0 is a scale parameter, β > 0 is a shape parameter, a ∈ R ∪ {-∞}, b ∈ (a, +∞) ∪ {+∞}, and H(x) is a nonnegative monotonically increasing function over (a, b) satisfying lim x→a H(x) = 0 and lim x→b H(x) = +∞.
Most of these extensions consider an extended Weibull cdf as generator, or a mathematical transformations of the cdf of the Weibull-G class, with the use of one or more parameters. For the use of the Weibull-G class for bivariate modeling, we refer to  [14]. A discrete analogue of the the odd Weibull-G class was developed in  [15]. The motivation for our study is connected with the classical Weibull-X, Weibull-G, and modified odd Weibull-G classes. A retrospective on these classes is discussed below. When H(x) is determined by the following logarithmic function: H(x) = H 1 (x) = -ln(1 -G(x)), where G(x) denotes the cdf of an arbitrary or targeted reference continuous distribution with support on (a, b), then the cdf given as Equation (2) defines the Weibull-X class by  [4]. By considering the logistic distribution as reference, it is proved in  [16]  that the corresponding Weibull-X distribution is flexible enough to perfectly fit the glass fiber data set of  [17].
In particular, the fit of the proposed Weibull logistic model outperforms the adjustment criteria of the logistic, skewed logistic, and skewed logistic "with location" models. Further, when H(x) denotes the following "odd ratio" function: H(x) = H 2 (x) = G(x) 1 -G(x) , the cdf given as Equation (  2 ) characterizes the Weibull-G class by  [5]. The foundational work of  [5]  shows that the distributions of the Weibull-G class are applicable in various settings. In particular, the Weibull-G models defined with the exponential, log-logistic, and Burr XII models as references are appropriate for the adjustments of the glass fiber data set of  [17]  and the fatigue time data set of  [18]. As a valuable indicator of its usefulness, the Weibull-G class has been cited in more than 400 references.
On the other hand, when H(x) denotes the following modified odd ratio function: H(x) = H 3 (x) = G(x) 1 -G(x)(1 + G(x))/2 , the cdf given as Equation (  2 ) defines the MOW-G class by  [6]. The MOW-G class is illustrated with the reference distributions: gamma, Weibull, and Lindley distributions. The related models are proved to be efficient for fitting the guinea pig data set by  [19] , the fatigue time data set by  [18] , and the failure time data set by  [20]. Thanks to their flexible properties, these models are preferable to several competitors based on the same reference models. A deep relationship exists between the Weibull-X, Weibull-G, and MOW-G classes. In particular, they are complementary in the following stochastic dominance sense: For any x ∈ R, we have F H 1 (x) ≤ F H 3 (x) ≤ F H 2 (x).
This flexibility is also observed in diverse moment measures, such as the mean, variance, moment skewness, and moment kurtosis. This new survival distribution is thus adapted for the adjustment of versatile data sets. We illustrate this claim by considering engineering and environmental data, estimating the model parameters using the maximum likelihood (ML) method. By following the standards, we use established statistical tools to prove that the fit of the proposed model surpasses those of several extended exponential models. Statistical comparisons with those derived from the Weibull-X, Weibull-G, and MOW-G classes are discussed. Numerical tables and graphics support the findings.
Moments We now work with a random variable X whose distribution belongs to the WOW-G class. Let us denote by E the standard expectation. Then, if the r th order moment about the origin of X converges in the mathematical sense, it is given by m r = E(X r ) = ∞ -∞ x r f (x)dx = αβ 2 ∞ -∞ x r g(x) 1 + 1 (1 -G(x)) 2 G(x) 1 -G(x) β-1 w(x) β-1 exp -α G(x) 1 -G(x) β w(x) β dx. After some changes of variable based on the involved qfs, it can be re-expressed as m r = αβ 2 1 0 [G -1 (u)] r 1 + 1 (1 -u) 2 u(1 -u/2) 1 -u β-1 exp -α u(1 -u/2) 1 -u β du, or, based on Proposition 3, m r = 1 0 [Q(u)] r du = 1 0    G -1   - 1 α ln(1 -u) 1/β + 1 - - 1 α ln(1 -u) 2/β + 1      r du. In all cases, these integrals are not simply developable; if the reference distribution is known, along with the parameters α and β, numerical treatments of them are possible via mathematical software such as Matlab, Mathematica, R, Python, etc.
Simulation Here, based on the WOWE model, a simulation study is performed in order to investigate the behavior of the ML estimation. We start by generating N = 10,000 samples from the model, each of sample size n ∈ {35, 50, 100, 200, 300, 400, 500}, for some selected parameter values. The assessment considers the average value of the ML estimates (AE) and the root-mean-square error (RMSE) defined by AE(ψ) = 1 N N ∑ i=1 ψi , RMSE(ψ) = 1 N n ∑ i=1 ( ψi -ψ) 2 , respectively, where ψ = α, β, or φ. We employ the package named AdequacyModel and developed in  [27]  of the R software (see  [28] ). The results are given in Table  3. It can be seen from Table  3  that the performance of the ML estimation shows consistency and is quite satisfactory; the AEs converge to the corresponding actual values and the RMSEs decrease as the sample size increases.
Conclusions We have presented a new motivated Weibull-generated-type class of univariate continuous distribution, called the weighted odd Weibull-generated class. Thanks to the use of a coherent weighted version of the odd transformation, it offers a solid alternative to the Weibull-X, Weibull-G, and MOW-G classes. We have explored its main theoretical and practical facets, with a focus on a special distribution based on the exponential distribution. The faculties of the proposed class make this special distribution particularly flexible in the modeling sense, with pdf and hrf demonstrating heterogeneous skewness and tail weights. We exploit this advantage in a data fitting scenario. By estimating the model parameters with the maximum likelihood method, we show that our model is efficient enough to fit two data sets of importance: one with engineering data and the other with environmental data.
Optical water types (OWTs) were identified from an in situ dataset of concomitant biogeochemical and optical parameters acquired in the Amazon River and its tributaries, in the Lower Amazon region, at different hydrological conditions from 2014 to 2017. A seasonal bio-optical characterization was performed. The k-means classification was applied to the in situ normalized reflectance spectra (r n (λ)), allowing the identification of four OWTs. An optical index method was also applied to the r n (λ) defining the thresholds of the OWTs. Next, level-3 Sentinel-3 Ocean and Land Color Instrument images representative of the seasonal discharge conditions were classified using the identified in situ OWTs as reference. The differences between Amazon River and clearwater tributary OWTs were dependent on the hydrological dynamics of the Amazon River, also showing a strong seasonal variability.
A synthetic representation of water optical characteristics and variability can be performed through the definition of OWTs which has been a recurrent research topic in water color remote sensing since the pioneering work by Jerlov in marine waters  [19]. The increasingly large in situ radiometric, bio-optical, and biogeochemical database over the years, especially in oceanic and coastal regions, provides robust classifications  [12, [20] [21] [22] [23]. In inland waters, the same data collection is more substantial in the northern hemisphere supporting more reliable classifications  [20, 21]. In tropical regions, especially in the southern hemisphere, efforts have been made in the Amazon region  [13, 20, 24] , but much remains to be done in terms of classifying tropical water types. In a global analysis, Spyrakos et al.
The seasonality of river discharge also moderates the influence of the mainstem upon the tributaries. The dataset used in this study consists of in situ measurements of optically significant constituents and inherent optical properties acquired from 2014-2017 during different discharge seasons. The sampling period of 2014-2017 was marked by exceptional climate events. The year of 2014 was characterized by a record flood in the Amazon Basin that started with a La Niña event in 2012 increasing the rainfall regime and was followed by a positive sea surface temperature anomaly in the Tropical Atlantic south of the equator, and in the Western Tropical Pacific  [39] [40] [41] [42]. During the rising water season of 2014, the rainfall within the south-western Amazon basin was 80-100% above usual records  [41] , with a consequently higher river discharge (Figure  1C ).
The residual glint or white offset correction was not performed for the spectra dataset. According to Ruddick et al. [46] , this correction is usually based on NIR spectrum (e. g. , R rs (780) and R rs (720)) assuming that its shape is largely determined by pure water absorp- tion. In very turbid waters, like in the Amazon River, the NIR assumption is not verified, and the NIR spectrum still has a high water signal.
k-means technique (Equation (  9 )) requires a user-specified number of clusters (k), which are represented by their centroids  [60]. The clusters are defined by measuring the square Euclidian distance of the centroids. For this, an initial mean spectra ("seed") is arbitrarily specified for each of the k-clusters. Then, each spectra of the dataset are assigned to the class whose mean spectra value is closest to the seed, forming the first set of decision boundaries. A new set of cluster seed is then calculated from this classification, and the spectra is reassigned accordingly, until there is no significant change in the spectra assignments from one iteration (i) to the next  [59]. The Silhouette width analysis  [61]  was used to determine the number of clusters, by measuring how similar an individual point is to its own cluster.
∆µ(i) = K ∑ k=1 µ i k -µ i-1 k. ( 9 ) Another way to assess water color approached in this study was proposed by Vandermeulen et al. [62] , by using the weighted harmonic mean of the R rs (λ) that create an index called Apparent Visible Wavelength (AVW), in units of nanometers (Equation (  10 )): AVW = ∑ λ n i=λ 1 R rs (λ i ) ∑ λ n i=λ 1 R rs (λ i ) λ i = ∑ λ n i=λ 1 λ i -1 R rs (λ i ) ∑ λ n i=λ 1 R rs (λ i ) -1. ( 10 ) The dynamical spectral index proposed by Vandermeulen et al. [62]  does not require a training dataset and/or defining the number of classes a priori. Instead, the result is given as a gradient of wavelengths values that represent a quantitative descriptor of weighted mean color reflected from the water surface. Both classification k-means and the AVW index were applied on the normalized R rs (λ) spectra and, therefore, focus on the spectral shape.
Satellite Data A set of 48 level 1 OLCI images from Sentinel-3 satellite (S3A) were downloaded from the Copernicus Online Data Access (https://coda. eumetsat. int/, accessed on 9 March 2021) and processed to level 2 and 3 using SeNtinel Applications Platform (SNAP) software, version 8. 0. 2. This imagery has a nominal spatial resolution of 300 m with a 14-bit radiometric resolution  [26] , and was processed to derive R rs data. Initially, four images with low cloud coverage (<10%) and representative of each discharge season (rising water-3 January 2018, high water-27 June 2020, falling water-25 July 2017, low water-7 December 2017) were selected. From those four starting images, other ±10 days images were also downloaded and processed, with a total of 48 images. The level 2 images were derived using the Case 2 Regional Coast Color processor (C2RCC), and the flagged bright areas were removed from the scenes.
The distance of the resulting input x to a given class ik was estimated by the squared Mahalanobis distance ∆ ik 2 (Equation (  11 )): ∆ 2 ik (x) = (x -µ ik ) T -1 ∑ ik (x -µ ik ) (11) where T indicates the matrix transpose. The probability (p) that an unknown spectrum (x) belongs to a class ik was estimated as (Equation (  12 )): p(ik, x) = 1 - γ(n/2, ∆ 2 ik (x)/2) Γ(n/2) ( 12 ) where Γ is the gamma function, γ is the lower incomplete gamma function, and n is the number of the bands (3) used in the classification. The normalized belonging p * was calculated as (Equation (  13 )): p * (ik) = p(ik) ∑ NC ic=1 p(ik) (13) More details of how p * (ik) and p(ik, x) were calculated can be found in Vantrepotte et al. [22]. The algorithm developed to calculate the Mahalanobis distance, as well as p * (ik) and p(ik, x), was implemented in MATLAB programming language.
CDOM Absorption The Amazon River's a CDOM (443) values (2. 12 ± 0. 53 m -1 ; see Table  2 ) are in the same range of values presented by Martinez et al. [13]  (a CDOM (440) = 3. 3 ± 1. 0 m -1 ), and relatively lower when compared to values presented by Jorge et al. [67] (a CDOM (420) ∼ = 3. 25 ± 0. 46 m -1 ) for Amazon white waters. CW rivers showed a similar average a CDOM (443) value (2. 01 ± 0. 88 m -1 ; see Table  2 ) in comparison to values reported by Martinez et al. [13]  (a CDOM (440) = 1. 8 ± 0. 8 m -1 ). The discrepancies between a CDOM values from different studies can be attributed to differences in each dataset size and representativeness, alternative filtering methods, chosen wavelengths, and protocol measurements (no baseline correction in Martinez et al. [13] , for instance), as well as to different sampling locations and seasonality (e.
Acknowledgments: The authors thank  Salvador A. Gaeta ,  Frederico Brandini ,  Mayza Pompeu , and collaborators from the  Primary Production Laboratory  from the  Oceanographic Institute of São Paulo University (IOUSP)  for the use of a fluorimeter. We also thank the collaborators from the  Center for Nuclear Energy in Agriculture (CENA-USP) laboratory  for the SPM analysis. We thank the collaborators from the  Monitoring Oceans from Space Laboratory  from the  National Institute for Space Research (MOceanS-INPE ). The authors thank Cica and the crew of the B/ M Mirage  for contributions made during the river cruises. We appreciate the value of the  Copernicus program  for providing full and free-of-charge access to OLCI imagery. We also recognize the value of the developers of free-of-charge SNAP software for providing the image process tools.
Identification of joint stiffness and damping The identification of the joint stiffness and damping can be done from the determination of system's natural frequencies by modal analysis (Fig. 7 ). The modal analysis is performed using LMS-TAB software. Three configurations have been chosen to measure natural frequencies of the robot: extended arm (q 2 = 0°), arm bent at 45°(q 2 = 45°) and arm bent at 90°(q 2 = 90°). Once the accelerometers are in place, the measurement protocol is done in two steps: Step 1: we apply an excitation force at the end effector using an impact hammer. Step 2: After choosing the bandwidth for signal acquisition, the frequencies are then selected manually by using PolyMax identification procedure. The software proposes unstable and stable modes representative of numerical or structural modes for the results.
Adams simulink co-simulation unit The numeric model of a robot with 2 degrees of freedom was developed in Adams software. The idea is to use its mechanism solver to simulate robot motion generated by discretised setpoint computed by the control law. The dimensions and dynamic characteristics correspond to the one identified on the Scara S600 robot of SIGMA Clermont's technological platform. The control laws are implemented in Simulink. The numeric model defined in Adams is set in motion via a co-simulation. The flexibility of the connections is represented under Adams by a linear torsion spring modelled by stiffness and damping. Two segments of negligible mass compared to that of the two robot axes (m 1 = m 2 = 0. 15 kg) are added to perform the simulation with flexible connections. The movement of these two segments is controlled.