dataesr/scientific-paragraphs-categorization huggingface dataset:
Land-Sea Coral Reefscape Mapping at the Class Scale As might be logically expected, the best multispectral combination relied on the full optical dataset, from Coastal to NIR2. Relatively to the BGR discrete prediction, the discrimination between grass, wood and road classes were significantly refined. These findings might easily be explained if the NIR enhancement can capture the higher reflectance of both the chlorophyll-laden and the tar/asphalt -made classes better than the VIS one  [30]. The DSM fusion with the optical dataset strongly improved the roof and the soil detection. This boosting was correlated with the decrease in misclassification with topographically lower road and topographically higher dry vegetation, respectively. The knowledge of the elevation component within the landscape therefore helped separate human-made, on the one hand, and natural features, on the other hand, that are spectrally similar in the optical range  [30].
The best superspectral combination relied on the merge of the optical dataset with the MIR8 (2295-2365 nm). The positive effect at the class scale was tangible with grass, wood, roof, road and soil classes. Compared to the optical dataset, the roof and soil classes were better isolated. The roof outcome might stem from the MIR8 spectral fitting with the higher reflectance of the Polynesian roof made of oxidized-galvanized steel metal (0. 34 reflectance) than the optical one (0. 19 reflectance)  [30]. The soil gain might also be due to MIR8 matching more closely with the higher reflectance (0. 44) than the optical one (0. 28) (see "brown loam" in  [28] ). These explanations were also supported by the decline in both roof and soil misclassifications with road, that displays a low MIR8 reflectance of 0.
08  [30]. The DSM influence showed a better OA with the combination of the optical dataset with the MIR1 (1195-1225 nm). This optimum simply reinforced its positive effect on the same previous classes, suggesting that the additional elevation information was relatively redundant to this coming from the MIR. Concerning the coral reefs, the successive integration of the optical bands and the DSM to the BGR dataset, slightly but consistently, strengthened their detection. The addition of the Coastal and yellow bands favoured the coral reefs' separability among other benthic features given the refinement in spectral signatures  [11]. The benthic terrain information was also profitable due to the robustness of the depth proxy for delineating benthos' ecophysiological belts  [23]. In view of the neat classification of the coral reefs along the lagoon width (Figures  5 6 7 8 ), further research should divide the coral reefs' current class according to their landscape position (fringing, barrier and outer reefs), and their morphology (encrusting, branching, massive, tabular, columnar, etc.
Conclusions The superspectral WorldView-3 providing 16 bands, from 400 to 2365 nm, pansharpened at 0. 3 m, was acquired in the form of stereo-imagery. Both topographic and bathymetric DSMs were built using a handful of ground and sea control points, enabling us to calibrate/validate the land-based photogrammetry and the sea-based radiative transfer model, provided with 1. 11 and 0. 89 m vertical accuracy, respectively. The best superspectral combination for enhancing the land-sea mapping of 13 habitats relied on the merging of the optical dataset (VIS+NIR) and the MIR8, which enhanced the basic BGR classification accuracy by 9. 58%, thus reaching an OA of 98. 73%. The classes that most benefited from this were the land use "roof" and land cover "soil" classes. The "coral reefs" consisted of the sea class that was the most favoured.
Spaceborne Dataset Launched on 13 August 2014, the commercial WorldView-3 spearheads the choice of sub-metre civilian satellite, given its hyperspatial and superspectral capabilities, namely one panchromatic band at 0. 3 m, five VIS + three NIR bands at 1. 2 m, and eight MIR bands at 3. 7 m. The pansharpening technique can successfully produce 16 spectral bands at 0. 3 m spatial resolution ([27], Figure 2).
2. 3. 3 Spaceborne Dataset Launched on 13 August 2014, the commercial WorldView-3 spearheads the choice of sub-metre civilian satellite, given its hyperspatial and superspectral capabilities, namely one panchromatic band at 0. 3 m, five VIS + three NIR bands at 1. 2 m, and eight MIR bands at 3. 7 m. The pansharpening technique can successfully produce 16 spectral bands at 0. 3 m spatial resolution ([27], Figure 2).
INTRODUCTION The mechanical contact between two bodies is one of the difficult problems in solid mechanics, indeed the contact area is unknown. There exist several algorithms to solve the contact problems  [8, 9] , most of them involve the concept of master/slave, which prevents the penetration of the slave body into the master one, and therefore causes the non-symmetry of the algorithm. Otherwise the interior point method was not widely used in contact problems, we can cite for example  [10, 11, 12]. In this paper, we restrict ourselves to the contact between two hyperelastic bodies (where the linear elastic body is a particular case). The frictional case is not considered here. One of the goals of this work is to propose a method that uses the FreeFEM  [1]  software and its tools to solve the contact problem.
As it is formulated as an optimization problem, the interior point method is used and the Interior Point OPTimizer algorithm (IPOPT)  [5] , which is already interfaced with FreeFEM, is used to solve the optimization problem and to reach the solution. The second goal of this work is to obtain a symmetric formulation, in order to allow the user to no longer distinguish between slave and master bodies. An algorithm to solve Houssam Houssein, Simon Garnotel and Frédéric Hecht Signorini's problem using FreeFEM and IPOPT software can be found in  [2]. This paper is organized as follows: in section 2, we present briefly the contact problem and the symmetric formulation. In section 3, we present the numerical methods, used to solve the constrained minimization problems and we show that it will not generate numerical difficulties, in addition to the convergence of the method.
For each integration point, its projection point xi depends linearly on the actual displacement 3. Reverse the role of the master and the slave bodies 4. Form the Energy and the symmetric linear constraints 5. Use one of the previous methods in order to solve the minimization problem with linear constraints, and to obtain the actual displacement U n 6. error= U n -U n-1 ∞ U n-1 ∞ end while 6 Numerical examples The algorithm was developed using FreeFEM  [1]  and IPOPT  [5]  software, and validated against several test cases.
We port the mbedTLS library to OpenWSN, a 6TiSCH reference implementation, and deploy the code on the state-of-the-art OpenMote platform. We show that, when the peers use an ideal channel, the DTLS handshake uses up to 800 B less and completes 0. 6 s faster. Nonetheless, using an unreliable communication link, the DTLS handshake duration suffers a performance penalty of roughly 45%, while TLS' handshake duration degrades by merely 15%. Similarly, the number of exchanged bytes doubles for DTLS while for TLS the increase is limited to 15%. The results indicate that IoT product developers should account for network characteristics when selecting a security protocol. Neglecting to do so can negatively impact the battery lifetime of the entire constrained network.
We run our code on the OpenMote platform, an open hardware initiative designed for low-power IEEE802. 15. 4 networks. We show that the reliability of the underlying communication link significantly impacts the performance of the TLS and DTLS handshake. In scenarios with 100% end-to-end reliability, the performance of the DTLS handshake is slightly better. It transmits between 500 B and 800 B less and completes approximately 0. 6 s faster. However, when the end-to-end reliability deteriorates, the performance of DTLS degrades rapidly. If a transport layer packet has a 5% probability of getting lost, the median handshake duration for DTLS more than doubles, and it uses from ±37% to ±45% more bytes. TLS performance remains more stable. The handshake duration grows between ±10% and ±15%, and the number of exchanged bytes grows between ±2% and ±15%.
Setup We consider the setting where a constrained 6TiSCH device, acting as a (D)TLS client, connects to a powerful (D)TLS server, see Figure  4. The constrained device uses the OpenWSN stack  [10] ; the powerful host uses the out-of-the-box Linux networking stack. The TCP/IP network stack of the server is unmodified and is unaware it is communicating with a constrained device. The (D)TLS handshake always uses mutual authentication through the exchange of certificates. The certificate chains contain only one X. 509 certificate. The received certificate matches with a stored root certificate, acting as a trust anchor for authentication. To obtain experimental results on the performance of the (D)TLS handshake on top of 6TiSCH, we port mbedTLS to the OpenWSN project. We implemented 6LoWPAN fragmentation and the TCP protocol for the OpenWSN stack.
It is not possible to easily change the initial TCP RTO from user space. Updating this value in the code would require the recompilation of the Linux kernel. To solve the problem in a more elegantly, we use a TCP Performance Enhancing Proxy (PEP)  [11]. PEPs are a commonly used technique to accelerate TCP connections over satellite links without tampering with the TCP implementation details. It operates as follows: 1. The PEP intercepts a TCP connection before the segments are sent to the satellite, and it terminates the connection as if the interceptor is the intended destination. It immediately sends an acknowledgment back to the original sender.
We report on an inter-annotator agreement experiment involving instances of text reuse focusing on the well-known case of biblical intertextuality in medieval literature. We target the application use case of literary scholars whose aim is to document instances of biblical references in the 'apparatus fontium' of a prospective digital edition. We develop a Bayesian implementation of Cohen's  for multiple annotators that allows us to assess the influence of various contextual effects on the inter-annotator agreement, producing both more robust estimates of the agreement indices as well as insights into the annotation process that leads to the estimated indices. As a result, we are able to produce a novel and nuanced estimation of inter-annotator agreement in the context of intertextuality, exploring the challenges that arise from manually annotating a dataset of biblical references in the writings of Bernard of Clairvaux.
At the beginning, a focus on precision can help editors dealing with the bulk of more obvious cases. Towards the end of the process, the majority of the references have been spotted, and high-recall algorithms can help suggesting potential candidates. In this context, the curation of benchmark datasets on which retrieval algorithms can be put to the test is an important milestone of applied research, since benchmark datasets are necessary not only for assessing the relative advantage of particular approaches but also in order to reliably measure the level of precision and recall that editors can expect from automated systems. However, the curation of benchmark datasets depends on reliable annotation processes. Two aspects of text reuse studies in literary contexts turn the process of benchmark corpus compilation into a problematic enterprise.
Dataset Underlying Collections The target collection consists in the 85 Sermons on the Song of Songs by Bernard of Clairvaux-made available at the Sources Chrétiennes Institute as part of the Bib-lIndex project  (Mellerin, 2013 (Mellerin, , 2014 )-which we segmented into "documents" using a sliding window of 20 tokens with an overlap of 10 tokens. The processed collection comprises 19,987 such documents. The source collection is the Vulgate Bible, available in digital form from the Perseus repository  (Crane, 1996). For the Vulgate, we follow the traditional segmentation into verses, which amounts to 36,663 documents. Both collections were lemmatized using the neural Latin lemmatizer provided by the software library pie  (Manjavacas et al. , 2019a).
Focus Dataset The focus dataset underlying the inter-annotator agreement experiment contains candidate matches between target documents from Bernard and source documents from the Bible. The matches represent optimized guesses about potential misses by the editors in a late-stage retrieval phase during the curation of the digital edition of Bernard's sermons. The current version of this digital edition contains already a total of 6,689 manually identified biblical references. On the basis of the available annotations, we fine-tuned two text reuse retrieval algorithms: one using the local alignment algorithm Smith-Waterman  (Smith and Waterman, 1981) , with a bias towards verbatim cases, and another one based on the Soft-Cosine similarity measure  (Sidorov et al. , 2014) , which takes into account lexical similarity using word embeddings and has been used in a previous study on Bernard  (Manjavacas et al.
, 2019b). 3 These algorithms were then applied to the remaining dataset in order to find references potentially overlooked by the editors. From the candidate set of each algorithm we took 300 candidate matches for a total of 600 items. An example candidate match, retrieved with the Smith-Waterman algorithm is shown in Figure  1. Some of the suggested matches involve target documents that are already annotated with a reference to the Bible, whereby the labeled source diverges from the suggested one. In principle, referencing passages in Bernard need not be restricted to a single biblical source. For this reason, we did not exclude such cases from the final target dataset. Table  1  displays dataset statistics about the focus dataset.
Inter-annotator Agreement Our experiment involved a total of three expert editors of Bernard, 4  who were shown candidate matches retrieved by the algorithms. The guideline provided to the participants was limited to indicate whether the candidate match would be considered for inclusion in the 'apparatus fontium' of the prospective edition. In order to quantify agreement, we chose a chance-corrected inter-annotator agreement index for multiple raters based on Cohen's . As shown in  (Artstein and Poesio, 2008) , a series of chancecorrected indices can be formulated as follows: S, ⇡,  = A o A e 1 A e (1) Here, A e expresses the expected agreement due to chance as the probability of agreement based on a theoretical annotator casting judgments on a random fashion. In contrast, A o expresses the observed agreement as the probability-commonly computed as the proportion of instances of agreement in the dataset-that agreement actually occurs-i.
A Probabilistic Model for Cohen's  As already mentioned, the common approach to obtaining A o consists in computing the proportion of pairs of judgements that are in agreement over the total number of pairs of judgments implied by the dataset. For each candidate match i, the corresponding agreement agr(i) is given by: agr(i) = 1 c 2 k X j=1 ✓ n ij 2 ◆ (2) where c and k refer respectively to the number of raters and labels, and n ij refers to the number of times label j was assigned to candidate match i. The dataset-level A o is given by the average agr(i) on the entire dataset: A o = 1 N P i agr(i), where N indicates the total number of candidate matches. Our approach differs in that we base the computation of A o on a statistical model of the possible outcomes of a pairwise judgement comparison, as we will show below.
Letting ✓ xy 00 be the probability of the 00 outcome for annotators x and y, A o can be computed by Equation  7. A o = k X i=1 1 c 2 X x,y2C ✓ xy ii (7) Finally, we compute  from Equation 1 using A o from Equation  7 and the dataset-level computation of A e given by Equation  8. A e = k X i=1 1 c 2 X x,y2C P (i|x)P (i|y) (8) where P (i|x) corresponds to the probability that annotator x assigns label i, which we obtain as the relative proportion of i-judgements casted by annotator x. 7 Bayesian Modeling Moreover, in this study we turn to Bayesian inference methods in order to fit the multi-level model. Bayesian inference has a number of advantages in this context, as it has superior modeling capacity in multi-level modeling scenarios with reduced number of cases  (Gelman and Hill, 2006) , and it produces a posterior distribution over model parameters, upon which further computation can be run in order to propagate parameter uncertainty to the agreement coefficients.
Finally, we model (iv) the biblical source book (Book) as random intercepts with 50 levels-one for each of the biblical books attested in the dataset-in order to test whether references to particular biblical books tend to be more or less controversial. First, we test the explanatory power of these multi-rater agreement index, commonly known as Fleiss , which was introduced by Fleiss in  (Fleiss, 1971 ) but that, as  (Artstein and Poesio, 2007)  argue, actually corresponds to a generalization of Scott's ⇡. Following  (Artstein and Poesio, 2007) , we will refer to our index as multi-, in order to avoid confusion. 8 We compute lexical overlap using the weighted Jaccard similarity shown in the following Equation: J(Di, Dj) = X w2D i [D j min[c(w, Di), c(w, Dj)] max[c(w, Di), c(w, Dj)] where Di refers to the i th document, and c(w, Di) refers to the count of word w in document Di.
As it is customary, we rescale the predictor variable to be centered around a zeromean and unit standard deviation. effects using a statistical model comparison approach based on the expected log pointwise predictive density (ELPD) as measure. This quantity provides an estimate of the predictive accuracy of a model on out-of-sample datasets. These estimates can be efficiently obtained-i. e. without having to refit the model on the different splits-using an approximatation to leave-one-out (LOO). 9.
This is a strong indication of the importance of the target reference book for annotator behavior and supplements the evidence from the ELPD comparisons in Section 3. 1, where including book-level varying intercepts resulted in a large ELPD increase of 342. 29 points-model m. MB vs. model m. M-, corresponding to a 58. 92% ELPD increase with respect to the total increase between worst and best models-i. e. model m vs model m. klMB. Effects of lexical overlap Figure  3  shows the effect of lexical overlap on agreement under different combinations of underlying retrieval method and familiarity of the target passage, using counter-factual plots. These plots visualize the statistical dependency relying on the posterior predictions for the entire range of the lexical overlap variable-i. e. including values for which no observation is attested in the original dataset  (McElreath, 2018).
A Model Fitting In the present study, we deploy Bayesian linear models as implemented by the brms library (Bürkner, 2018), an R (3. 6. 3) package  (Ihaka and Gentleman, 1996)  providing a user-friendly interface to Rstan, which utilizes a powerful Hamiltonian Monte Carlo sampler. In order to ensure the validity of the resulting posterior distributions, we make sure that the following diagnostics check: first, effective sample sizes are large enough; secondly, the samples are homogeneous across chains (i. e. R values should be close to 1), and, finally, divergent transitions are kept to a minimum. This was accomplished using 4 chains for 2000 iterations with the first 1000 used as warmup iterations. Some models required fine-tuning the "adapt delta" and the "maximum tree depth" parameters. Moreover, we choose weakly informative priors to avoid exploring highly unlikely regions of the parameter space.
Luke, 10:27, however, is a more closely related match in terms of lexical overlap, which may lead annotators with more superficial knowledge of Bernard's oeuvre to select it. In the last example in   4 : Examples from the dataset, showcasing different types of agreement problems. The first one highlights segmentation issues, and the second one and the last two relate respectively to diverging degrees of familiarity with the Bible and Bernard. The Bernardine chunk on the left is accompanied by the retrieved candidate in the center and a better verse proposed by the annotator during the post-experimental report in the right. Words in bold correspond to lexical overlap with the biblical references, while words in italics indicate a relevant fragment left out by the applied segmentation. Biblical references contain hyper-links re-directing to the BiblIndex online version that includes English translations.
This paper extends the results recently proposed in Part I of this research work focused on the stabilization of power electronic converters. This second part is devoted to cases in which the underlying control problems can be translated into tracking control problems. This is the case for DC-AC converters whose output must track a sinusoidal reference signal. The idea is to tackle the problem in a unified manner in order to avoid as much as possible the use of approximations and to exploit all the mathematical properties of the corresponding switched models. The case in which measurable or non-measurable perturbations are present is considered. The proposed techniques are illustrated for two particular DC-AC converters simulated using the PSIM software.
Then, it is possible to compute the value of λ(t) for a given value of y re f (t). All the values of λ(t) for a given function y re f (t) can be determined off-line. Integrating the differential system (28), we deduce the approximated reference x a (t) for an output reference given by y re f (t) = 220 sin(2π50t). The resulting waveforms were approximated as Fourier series to implement the simulation in PSIM software as follows: To determine the control, we solve the optimization problem  (30). The solution is given by S =     0. 0054 0. 0000 0. 0000 0. 0000 0. 0000 0. 0001 0. 0000 -0. 0000 0. 0000 0. 0000 0. 0054 0. 0000 0. 0000 -0. 0000 0. 0000 0. 0001 Figure  7  shows the currents and the voltages i L i , v C i , i = 1, 2, the output voltage and the duty cycle obtained by filtering the control u 1.
The interest in and potential of the proposed techniques are illustrated in terms of the very popular full-bridge inverter and a fourth-order boost inverter simulated with the PSIM software. In particular, it is discussed in terms of the delicate determination of the reference signals. The method based on the classical harmonic balance method can be used, but for the cases in which this method is not so easy to apply, an alternate technique is proposed. This technique is efficient when the dynamic of the output reference signal is slow compared to the dynamic of the converter. From the results of simulations, the proposed methods appear very promising and a crucial step is now to apply them to power converter prototypes. This will be carried out in the near future. Several other points could be investigated to improve the method.
Microfluidic device fabrication The microfluidic device used to construct the microvessel-ona-chip experiments was adapted from our previous design  22  and is schematically shown in Fig. 1. Briefly, the microfluidic device was designed using AutoCAD software. The design contains two side channels that have a width of 180 μm and a height of 100 μm. It also includes three channels in the middle, where two channels have a width of 400 μm and a height of 100 μm, and one channel that has a width of 120 μm and a height of 100 μm. A glass slide was used as a bottom part of the device to provide better image quality. The microfluidic master was fabricated using the soft lithography process. SU-8 negative photoresist (MicroChem) was coated onto a 6″ silicon wafer via spinning coating. Mask was then patterned using standard ultraviolet exposure.