dataesr/scientific-paragraphs-categorization huggingface dataset:
09%) (Table  1 , Figure  3b ). The Pearson correlation for real (R = 0. 6109) and mixture (R = 0. 5838) datasets is similar, but PLS has very different error performances between the two datasets (MAPE of 43. 08% and 29. 66%, respectively) (Table  1 ). A PLS model was obtained using six LVs. The high number of LVs has implications in the interpretation of the PLS coefficients (b pls ). By adding new dimensions, more interferences are accounted for WBC quantification, resulting in a weighted oblique projection of all existing covariance modes  [14, 31]. As the eigenstructure of X is similar to Y, the PLS algorithm is able to converge into an acceptable correlation value. As there are many types of spectral gradients due to interference, PLS is not able to take into account the details of each CovM.
6619) and mixture datasets (R = 0. 6110) but significant differences in terms of MAPE values (40. 37% and 28. 51%) (Table  1 ). LocPLS breaks down the complexity of the global covariance (X t Y) into an ensemble of PLS models along the spectral variance space T, considering that a subset of similar samples may hold stable covariance. We also expected a significant reduction in the dimensionality of the PLS models, but results show a modest decrease to five LVs (see Table  1 ) and no significant gains in correlation (R) or prediction errors (MAPE) compared to PLS. LocPLS does not perform a systematic search for stable covariance, but it uses similarity metrics (Euclidean distance) to group samples. These may or may not belong to the same CovM, resulting in a non-systematic dimension reduction and model performance.
LocPLS efficiency is higher in blood constituents that have dominant information in the spectra (e. g. , RBC, Hgb, and HTC)  [31] , not being effective with nondominant constituents, e. g. , WBC. SIM, PLS and LocPLS cannot model extreme high values of WBC, which have outlier characteristics to the rest of the datasets. Two extreme groups with WBC in the range of 40 to 70 × 10 9 cells/L are outliers to the main model (Figure  3a-c ). The high-dimensionality of PLS and LocPLS (6 and 5 LVs, respectively) does not capture the CovM to which these samples should be associated to predict WBC accurately. ANN (PCA-ANN and PLS-ANN) exhibit low performance when modeling WBC spectral information compared to SIM, PLS, and LocPLS. The Pearson correlation (R) is 0. 4214 and 0. 5412 for PCA-ANN and PLS-ANN.
Prediction errors are high, with an MAPE of 37. 33% and 32. 63% for PCA-ANN and PLS-ANN (Figure  3d ). Both ANN models were optimized with three LVs and architecture of three hidden layers (Table  1 ). The performance of ANN models is consistent between the real and mixture datasets, showing that the information is similar between datasets, obtaining the same level of performance. PLS-ANN has a better performance than PCA-ANN, because PLS scores are obtained maximizing the covariance, whereas PCA maximizes the variance of the spectral datasets. ANN has high difficulty in finding consistent covariance, especially with low levels of spectral variance of WBC. As ANN is designed using piecewise linear and activation functions, they have better performance mapping non-linear phenomena to which there are clear decision boundaries between classes.
PCA-ANN and PLS-ANN showed satisfactory performances only when modeling dominant spectral information, e. g. , RGB, Hgb and HTC  [31]. ANN approaches struggle to cope with multi-scale interference of non-dominant blood constituents, e. g. , WBC. LS-SVM exhibits poor correlations (R = 0. 4372) and high prediction errors (MAPE = 41. 35%) (Figure  3f ). It also has high discrepancies between the real and mixture datasets, with R values of 0. 5976 and 0. 4207; and MAPE of 53. 04% and 32. 83%. LS-SVM has to use a significant number of samples as support vectors, providing more representation of the real than the mixture dataset. Figure  3f  shows that LS-SVM has a significant number of outliers at both high and low WBC levels, not modeling extreme WBC values. The Gaussian RBF is used to convert the PLS scores space U into the SVM kernel matrix.
This fails to capture groups of systematic covariance because the RBF can also be considered as a similarity metric. SLAI presents significant correlations (R = 0. 8478) and low prediction errors (MAPE = 20. 94) (Figure  3g ). Furthermore, it also has results between real and mixture datasets: (i) R: 0. 8789 and 0. 8432; and (ii) MAPE: 25. 37% and 20. 57%, respectively. SLAI reduces the dimensionality to 1 LV, being able to determine 100 CovM among the two datasets. The capacity to model extreme values of WBC is significantly improved, where WBC levels between 40 and 70 ×10 9 cells/L are predicted with significantly less error, allowing to correctly diagnose high levels of WBC. All presented models CV and hold-out samples results have been shown to have statistically similar R, SE and MAPE (p < 0.
Bias-Variance Analysis POC WBC quantification linearity (Pearson correlation-R) and total error (MAPE) benchmarks are presented in Figure  4a, b. Only SLAI, PLS-ANN, and LocPLS have correlations above 0. 60, where SLAI excels with an R = 0. 8789 (Figure  3a , Table  1 ). SLAI also has the lowest total error, with an MAPE of 25. 37%, whereas SIM (MAPE = 31. 45%), PLS-ANN (MAPE = 34. 67%) and LocPLS (MAPE = 40. 37%). SLAI has the highest consistency of results metrics (R and MAPE) between the real and mixture datasets. Other consistent methods are PLS and LocPLS, whereas all other methods are not. Quantitative information about WBC are present in the specific interference gradient of ROIs. As the mixture dataset is obtained by mixing real data, the WBC and spectral gradients are expected to be reasonably preserved, and the information of the mixture dataset represents the real data.
Both SLAI, LocPLS, and PLS algorithms maximize covariance, being able to take advantage of the spectral gradients information expansion given by the mixture dataset. Data augmentation by a random mixture of real data can preserve the spectral information structure, allowing one to complete the necessary information in the knowledge base for complex biological samples, e. g. , dog blood. This is particularly relevant for dataset gaps, such as the lack of information on WBC between 40 and 70 ×10 9 cells/L in the real dataset. The mixture of extreme values with lower WBC samples is not free of distortions, affecting model performance. Data augmentation is a trade-off-without filling the information gap, it would be difficult to derive local relationships between extreme WBC values and other samples.
The presented data augmentation method is a first proof-of-concept approach that in our opinion should be expanded as a way to complete complex biological datasets, where anomalous samples are rare, and therefore, difficult to be covered by restricted datasets. Bias analysis was performed by determining the percentage of results that comply with the American Society for Veterinary Clinical Pathology (ASVCP) allowable total error (ATE) quality criteria for WBC: (i) Optimal (Opt): 7. 16%; (ii) Desired (Des): 14. 29%; and (iii) Acceptable (Accep): 21. 45% (see Table  2 ). These criteria are based on veterinary doctor's expectations in analytical limits for hemogram and other clinical analysis, where: (i) optimal is the best ATE limit for diagnosis, from where there is no clinical advantage in improving the method detection limits; (ii) desired-the limit value of ATE where the clinical decision is still comfortable; and (iii) acceptable-the limit from which the ATE is still valuable for clinical decisions, complementing other sources of clinical information.
State-of-the-art methods-SIM, PLS, PCA-ANN, PLS-ANN, and LS-SVM-present very low levels of acceptable results inside and outside the RI. These methods also show significant inconsistencies between real and mixture datasets (Table  2 ). Only PLS was able to obtain the best results for the mixture dataset inside (48. 13%) and outside (55. 10%) RI. LocPLS attained the highest percentage of acceptable results from state-of-the-art methods, with 47. 17% and 48. 68% (real and mixture) inside the RI; and 42. 85% and 52. 34% (real and mixture) outside the RI. These results are yet outside the ATE criteria of ASVCP  [20]. SLAI has the highest values of acceptable errors ATE for real and mixture datasets (Table  2 ). In the (i) real dataset, it attains a percentage of 58. 49% and 42. 87%; and (ii) in the mixture dataset, 66.
92%, and 72. 45% acceptable results are obtained inside and outside the RI, respectively. Within the desired category, 52. 04% of results are outside the RI from the mixture dataset. SLAI does not yet satisfy the ATE of 21. 45%. Only 58. 49% of results can pass this criterion (Table  2 ). The limits imposed for WBC ATE are considered optimistic for the existing technology capabilities, being difficult to achieve even by the ground truth method-microscopy manual counts. It has been widely recognized that both WBC and their differentiated cell counts have high imprecision  [20, 49, 50] , and consequently, a wider decision interval should be considered  [51]. Figure  4d  presents how the number of correct high WBC diagnoses evolves with the WBC predicted by the spectroscopy POC. At the ASCVP ATE limit, the POC is unable to provide enough diagnosis confidence.
CovM Interpretation Spectra ROIs allow the interpretation of information used in each CovM WBC model. In addition to statistical validation, these allow the causal relationship by associating ROIs to absorbance and scattering characteristics of the group of samples belonging to a CovM. Figure  5  presents two CovMs at high and low WBC levels. Figure  5a  shows the spectra PCA scores space (T) with all datasets, where the low level of WBC CovM samples are in green, and the high-level are in blue. The presented arrows represent the covariance eigenvector (1 LV), correlating WBC to the spectral features. Both high and low WBC CovM eigenvectors point toward increasing values of PC2 and PC3, which is in agreement with the presented PCA scores and loading analysis presented in Figure  2b, c. The directions of each eigenvector are significantly different and represent the ROIs used to quantify WBC.
The quantification of low levels of WBC is limited by its small-scale variance in the spectroscopy signal. WBC detection limits are possible to be decreased by: (i) increasing the dataset knowledgbase, to find groups of stable values of RBC, Hgb and HTC, isolating WBC variance; and (ii) reference methods with higher accuracy-proving high statistical robustness to the spectra-WBC relationship. Current state-of-the-art hemogram instruments or manual counting still cannot decrease the detection limits. It may only be feasible to decrease the spectral POC detection limits when the accuracy of reference methods improves.
Conclusions UV-Vis spectroscopy is a viable POC technology for WBC dog blood hemogram analysis, providing real-time results with a single drop of blood. WBC spectral information is highly interferent with other blood constituents and, therefore, difficult to be unscrambled by the current state-of-the-art chemometrics and machine learning methods. CovMs have the highest result consistency between real and mixture datasets, further proving the relevance of using data augmentation with extreme WBC in restricted data, as a way to complete and expand the knowledgebase information. We also showed the challenges of lowering the POC WBC detection limits. At low WBC levels, more restricted ROIs with smaller scales of spectral variance are expected, implying that RBC/Hgb levels are necessary to isolate WBC spectral information to obtain accurate results.
The second scheme accelerates the sampling of the random field using Chromatic samplers. This method makes long sequential simulation boil down to group-parallelized or group-vectorized sampling. The attractive possibility to parallelize NNGP density can therefore be carried over to field sampling. A R implementation of the two methods for Gaussian fields is presented in the public repository https://github. com/SebastienCoube/Improving_NNGP_full_augmentation. An extensive vignette is provided. The presented implementation is run on two synthetic toy examples along with the state of the art package spNNGP. Finally, the methods are applied on a real data set of lead contamination in the United States of America mainland.
Here is an outline of the article. Section 2 focuses on the seemingly trivial fixed effects of the hierarchical model. In 2. 1 we propose a mild but efficient centering of the latent field on the least squares regression intercept. In 2. 2, we extend centering to other fixed effects, and we use interweaving from  [26]  to propose a robust, tuning-less application. Section 3 targets the simulation of the random field. In 3. 1, we propose to use the chromatic samplers developed by  Gonzalez and al. in   [12]  in order to carry the attractive parallelizability of NNGP density over to field sampling. In 3. 2, we analyze the sensitivity of NNGP graph coloring and we benchmark coloring algorithms. We apply our methods in section 4. We present our implementation (available at https://github. com/SebastienCoube/Improving_NNGP_full_augmentation) in 4.
About our implementation We tested our implementation along with the state of the art package spN-NGP presented in  [6] , that uses the Gibbs sampler architecture given in  [3]. spNNGP uses Rcpp  [4]  and parallelizes the computation of NNGP density. In order to monitor convergence using the diagnostics from  [11, 2] , various chains need to be run one after the other. Our implementation is available at https: //github. com/SebastienCoube/Improving_NNGP_full_augmentation. The code is done in R (see  [22] ), with the AS-IS Gibbs sampler architecture of  [26]. Chromatic sampling is implemented for individual locations. We used the package GpGp (  [15] ) for Vecchia's approximation factor computation. Our implementation runs several chains in parallel thanks to the package parallel (see  [23] ), but GpGp does not implement parallel Vecchia's approximation factor computation within each chain like spNNGP.
Application on to lead contamination analysis We used our implementation to study a heavy metal contamination data set proposed by Hengl in  [17]   3. The dataset gathers measurements made by the United States Geological Survey of  [13]  and several covariates, including geophysical and environmental information about the sampling site, and potential contamination sources nearby. We added the predominant subsoil rock type given by the USGS study presented in  [18]   4. We scaled the quantitative regressors. After removing missing data, there was 64274 observations. We assumed the model log(z(s)) = w(s) + X(s)β T + (s), s being the sampling location, X(•) being the aforementioned covariates, w(•) being a latent Gaussian field with exponential covariance on the sphere, and being a white noise.
The model converged in 4000 iterations, and 1 hour and 38 minutes were needed. We tried to analyze the real data set with spNNGP in order to compare the results and the running time. Surprisingly, spNNGP had a pathological behavior in spite of its good performances on simulated data. The scale parameter kept straying towards values several orders of magnitude above the variance of the observed field, even with starting points corresponding to our estimates. This behavior was observed with both latent and response model, and various orderings of the locations. We present our implementation's estimates of the covariance parameters and some of the fixed effects in table 4. We left out some regressors such as the geological classification, indications about nearby mineral observations, the geophysical characteristics of the sampling site.
4  ) is introduced. The objective is to firstly analyse the related STL files in terms of part orientation, then to construct their path plans and build times so as to allocate jobs to two RepRap machines (named Machine 1 and Machine 2) with a maximum build volume of 20x20x20cm 3. By using Tweaker application and CuraEngine software, the oriented parts are subject to build time estimation (see Figure  5 ). Table  1  sums up the resulting build time per part per machine. With the build time pert part determined from the previous step, the average build time can be calculated. Once the average processing time is determined for each part, it is then possible to sort them by applying the LPT rule. It consists in sorting the parts from the longest to process to the fastest to process as shown on Table  2.
The part with the longest build time is at the left of the table (i. e. part D) and the fastest part to be processed is at the right side of the table (i. e. part C). The original set of parts, ranging from A to F is now ordered as follows: D, B, E, A, F and C. At this step, each part needs to be assigned to a FFF 3D printer in order to be produce. To assign a part, an iteration is made involving three parameters. First it is needed to determine the total processing time for each FFF 3D printer. This can be calculated by computing the sum of the build times of the parts that have been allocated to the FFF 3D printers. These parameters are shown with the columns (i) in Table  2. Secondly, it is required to know the build time of a part to a given FFF 3D printer. This has been previously determined by the CuraEngine software and is shown with the columns (ii) in Table  2.
Discussion and conclusion In this paper, a multi-part production planning framework for AM in the context of unrelated parallel FFF 3D printers in FabLabs/hubs has been proposed and implemented through a set of applications and illustrated via a case study. It includes, as main pillars, part orientation, path planning with build time estimation and part-to-printer components in a way to analyse heterogeneous part geometric models (STL files) towards the assignment of machine instructions (G-code files) to the available FFF 3D printers. The implementation strategy has been to consider and gather existing independent functional applications (such as Tweaker and CuraEngine) and also to develop a new one (greedy algorithmbased application) to fully cover the main components of the framework in a seamless manner.
| METHODS FOR ASSESSING COMPLIANCE OF WIND ENERGY CONTRIBUTORS WITH THE UNEP/ EUROBATS GUIDELINE To evaluate the application of the UNEP/EUROBATS guideline of siting wind turbines at least 200 m away from woody edges at a broad spatial scale, we computed the distance of wind turbines to the nearest woody edge in the UK, Germany, and France. We adopted different methodological approaches between countries. In the UK and Germany, the lack of available national-scale high-resolution land-cover or hedgerow data led us to manually measure distances between wind turbines and woody edges using satellite imagery. Thus, we created a random subset of wind turbines ($1000 wind turbines per country) using QGIS v. 3. 18. 3 coupled with Google Satellite Imagery (see Supporting Information 1 for more details).
In France, we used high-resolution land-cover data (CES OSO, http://osrcesbio. ups-tlse. fr/oso/, 10 m resolution) and data from the French National Bocage Monitoring System (IGN, BD Haie, https://geoservices. ign. fr/) to map forest cover and hedgerows at the national scale, respectively. We then automatically computed the Euclidean distance between each of the 8066 wind turbines installed until 2020 and the nearest forest patch or hedgerow using automated distance computations (see Supporting Information 1 for more details). Furthermore, as the computation of distances between wind turbines and woody edges was done manually for the UK and Germany, and automatically for France, we could only consider the period 2009-2020 (i. e. , period starting 1 year following the publication of the UNEP/EUROBATS guidelines) for the formers; whereas, we could focus on the whole period (1991-2020) for the latter.
Then, based on these computations we assessed at the national scale the cumulated number of wind turbines in relation with the distance to the nearest woody edge in the UK, Germany, and France, for the period after the publication of the recommendations (i. e. , 2009-2020). Since we had access to a more detailed dataset in France, we also assessed this compliance for each administrative region and for the periods prior to  (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008)  and after (2009-2020) the initial UNEP/EUROBATS guidelines publication in 2008. Finally, thanks to the exhaustive dataset in France, we assessed whether changes in wind turbines siting occurred in France after the publication of the European guidelines in 2008.
AUTHOR CONTRIBUTIONS Conceived the idea: Kévin Barré, Jérémy S. P. Froidevaux, and Charlotte Roemer. Collected data for France and the UK: Kévin Barré, Jérémy S. P. Froidevaux, and Charlotte Roemer. Performed analyses: Kévin Barré, Jérémy S. P. Froidevaux, and Charlotte Roemer. Led writing of the manuscript: Kévin Barré, Jérémy S. P. Froidevaux, and Charlotte Roemer. Computed distances between wind turbine and woody edges: Kévin Barré, Jérémy S. P. Froidevaux, Charlotte Roemer, Camille Leroux, and Léa Mariton. Collected shapefile data for Germany: Marcus Fritze. All authors contributed to the preparation of the manuscript, and approved the final version for submission.
ACKNOWLEDGMENTS Kévin Barré  is funded by the  Agence de la transition écologique (ADEME) ,  Jérémy S. P. Froidevaux  by the  SAD-Région Bretagne  and the  Leverhulme Trust  through an  early career fellowship  (Award Reference: ECF-2020-571),  Charlotte Roemer  by the  Office Français de la Biodiversité (OFB) ,  Camille Leroux  by the  Association Nationale de la Recherche et de la Technologie (ANRT ; Grant No. 2019/1566 ) and Auddicé biodiversité, and  Léa Mariton  by the  Institut de la transition environnementale de l'alliance Sorbonne Université (SU-ITE ). The authors thank  Jan Collins  ( Bat Conservation Trust ) for providing constructive feedback and proofreading the manuscript.
Existing thermal comfort prediction approaches by machine learning models have been achieving great success based on large datasets in sustainable Industry 4. 0 environment. However, the industrial Internet of Things (IoT) environment generates small-scale datasets where each dataset may contain lots of worker's private data. The latter is challenging the current prediction approaches as small datasets running a large number of iterations can result in overfitting. Moreover, worker's privacy has been a public concern throughout recent years. Therefore, there must be a trade-off between developing accurate thermal comfort prediction models and worker's privacy-preserving. To tackle this challenge, we present a privacy-preserving machine learning technique, federated learning (FL), where an FL-based neural network algorithm (Fed-NN) is proposed for thermal comfort prediction.
Privacy issues for the industrial IoT systems The smart meters equipped in the Industry have unintended consequences for worker privacy. Video camera information stored serves as an information-rich side channel, exposing worker habits and clothing style. Certain habits, such as repetitive clothing brands wear, have detectable style signatures. History has proved that if political or financial incentives coincide, data mining mechanisms will rapidly progress to suit the cravings of those who aim at exploiting that information. In industrial IoT systems, different methods and models rely on training datasets from users, occupants, workers, factories, buildings, and offices. Unfortunately, direct data exchange among different branches is prohibited by law with the increasing privacy awareness.