dataesr/scientific-paragraphs-categorization huggingface dataset:
GPWv4 is a raster data collection of globally integrated national population data from the 2010 round of Population and Housing Censuses, which occurred between 2005 and 2014. The input data are extrapolated to produce population estimates for the years 2000, 2005, 2010, 2015, and 2020. One major drawback of using these data to infer migration trends is that the population changes estimated in the GPWv4 data are also affected by changes in birth and death rates. In our main specification, we assume these rates to remain stable within cells (around a global trend) over the observation period and thus be captured by the cell fixed effects. This implies that immigration effects may be overestimated in locations where birth rates increase or mortality fall in a given period with respect to the global trend observed, with the opposite holding for emigration effects.
The nature of the data used also does not allow us to assess different types of migration (long-term vs. short-term mobility, or return migration patterns). Given the fact that census information is the main source of GWPv4, the dataset also presents limitations concerning the potential exclusion of persons in some groups (refugees, internally displaced people or nomadic populations) that may, in turn, be particularly vulnerable to climatic shocks and affected by immobility  26. The change in population at the grid-cell level used in our analysis for the period 2005-2015 is depicted in Fig. 2. Differences in population changes are large both across and within nations of the continent, and low levels of population change can be found in many areas. The statistical analysis carried out aims at understanding the heterogeneity of human mobility reactions to environmental shocks as mediated by income and natural (mineral) resource availability.
In economies in which agricultural productivity is so low as to leave rural populations liquidity constrained and constrained to the primary sector, a worsening (improving) climate and lower (higher) agricultural productivity may actually slow (increase) economic transformation and economic growth, thus contributing to poverty traps. In order to investigate the potential tempering effects that resource availability as an alternative source of income might have on negative climate shocks, grid-level data sourced from recent surveys are employed in our analysis. Table  1  displays the estimates based on the specification relating drought and land degradation to log-transformed grid-level population density, using data spanning the period 2000-2015 in 5-year intervals. Column 1 displays a naive correlation of our drought measure with log-population levels without controlling for any time or cell fixed effects.
Similarly, a more rigorous analysis of the spatial spillovers that environmental shocks can have appears as a promising avenue to expand the results presented here. The estimation of statistical models with an explicit spatial autoregressive structure to the dataset analyzed in this contribution may shed new light on the propagation of mobility phenomena across space after climatic shocks take place. Notwithstanding the difficulty of assessing empirically spatial linkages in the framework of the natural resource nexus, the quantification of spillovers across space can be particularly important for the design of effective regional policies to support climate change adaptation.
Furthermore, the SPEI allows comparison of drought severity through time and space, since it can be calculated over a wide range of climatic zones. For our grid-level analysis, we construct a measure of drought periods as the proportion of months with SPEI scores below -1. 5 out of the past 12 months  42. That is, for a year where the longest consecutive streak of months below -1. 5 is three, the cell will be given a value of 0. 25. When the longest streak starts in the previous year, it is counted and included in the year in which the streak ended. Theoretically, the proportion can therefore be above unity. We aggregate this measure for the past 2 years to capture longer drought periods, as grid-level population data are only available every 5 years. Land degradation data in the form of changes in soil carbon stocks comes from the Trends.
earth project  43. Specifically, we use soil carbon stock measures. In order to estimate the potential tempering effects of alternative sources of income (mineral resources), we make use of the Mineral Resources Data System (MRDS). The MRDS is sourced from a collection of reports describing metallic and nonmetallic mineral resources throughout the world. The information provided includes deposit name, location, commodity, deposit description, geologic characteristics, production, reserves, resources, and references. We aggregate deposit locations at the grid level to proxy resource and labor availability. All the datasets are matched to the PRIO-GRID structure, a standardized spatial grid structure with global coverage at a resolution of decimal degrees  44. The PRIO-GRID dataset is a grid structure that aids the compilation, management and analysis of spatial data within a time-consistent framework.
It consists of quadratic grid cells that jointly cover all terrestrial areas of the world. For each grid cell, we collect cell-specific information on armed conflicts, socioeconomic conditions, ethnic groups, geophysical attributes and climatic conditions. In our analysis, we use a number of those cell-specific attributes to investigate the potential heterogeneous effects of droughts and land degradation. These attributes include in particular information on natural resources (presence in a cell of oil, diamonds, gold or gems), distances between the centroid of the cell and international borders and to capital city, as well as topographical features of the cell (whether it is mountainous terrain and its particular land composition). Our final dataset comprises information on 10,667 cells across 4 years, yielding a total of 42,936 observations.
Data availability The original source of the international migration data used in the analysis is ref. 27. The gridded population data are from the Gridded Population of the World (GPW) version 4 (https://sedac. ciesin. columbia. edu/data/collection/gpw-v4). The SPEI data are sourced from https://spei. csic. es/database. html and land degradation data in the form of changes in soil carbon stocks come from the Trends. earth project. Data on mineral resources are from the Mineral Resources Data System (MRDS, https://mrdata. usgs. gov/mrds/). All datasets are matched to the PRIO-GRID structure. All data used for the analysis can be found at the Harvard Dataverse under https://doi. org/10. 7910/DVN/ QXP0TY.
For the second and third strategies, we plot r •u1 and F 1 in Fig. 9. These two terms correspond to the buoyancy forcing term R and they are not equal simply because we did not run the model for a su ciently long time, and because the divergence of the eddy buoyancy flux is slower to converge than F 1 (see Fig. 10). The plots in Fig. 9 illustrate the warming and cooling tendency (if we interpret buoyancy as temperature) due to the eddies on the large-scale solution. We observe alternating cooling and warming patterns in the western boundary and in the gyre -24-manuscript submitted to Journal of Advances in Modeling Earth Systems (JAMES) Acknowledgments This work is supported by  NSF  grants  1434780 ,  1537304. TU and WKD acknowledge the support from the  NSF  grant  OCE-2023585 ,  OCE-2123632 , and the  French 'Make   Our Planet Great Again' (MOPGA) program  managed by the  Agence Nationale de la Recherche  under the  Programme d'Investissement d'Avenir , reference  ANR-18-MPGA-0002.
It allows for restructuring the data into homogeneous groups strongly related to each other using a simple architecture of the LSTM model. It is advantageous in terms of learning time and the possibility of using limited computational capabilities. Then, we focus on the interpretability of deep learning prognosis using Explainable AI to achieve interpretable RUL prediction. The proposed approach offers model improvement and enhanced interpretability, enabling a better understanding of feature contributions. Experimental results on the available NASA C-MAPSS dataset show the performance of the proposed model compared to other common methods.
• We discussed and analyzed the settings and effects of some critical parameters as well as the comparison with other methods. Therefore, we open the code of this experiment, in order to contribute to future developments. To present our work, this paper is organized as follows: Section 2 explains Explainable Artificial Intelligence's (XAI's) need in PHM and reviews related work in this field. Section 3 provides an overview of previous related work to our study. Section 4 outlines the techniques used in this study and the proposed framework. An overview of the dataset and experiments setting are illustrated in Section 5. Results are presented and discussed in Section 6. Section 7 concludes the article.
Related Work The C-MAPSS dataset is one of the most used datasets for the goal of improving RUL estimations. Whether using machine learning models or deep learning models, many approaches and methods have been developed and proposed throughout the years. Wang et al. [23]  built a fusion model that extracts features from the data based on a broad learning system (BLS) and integrates LSTM to process time series information. Heimes  [24]  was the first to implement a recurrent neural network (RNN) for RUL prediction. de Oliveira da Costa et al. [25]  proposed a domain adversarial neural network (DANN) approach to learning domain-invariant features using LSTM. Jiang et al. [26]  used a fusion network combined with a bidirectional LSTM network to estimate the RUL with the use of sequenced data.
Stability of Feature Partitions To assess the stability of all partitions into 2 to p -1 classes resulting from the hierarchical clustering, a bootstrap resampling procedure is used to determine the appropriate number of feature classes that can be retained for analysis. The right choice for this number of classes is the one that retains the most stable partition. B bootstrap replications of the original data are generated and the associated B dendrograms are obtained. The partitions of these B dendrograms are compared to the partitions of the original hierarchy through the adjusted Rand index  [39]. The stability of the partition is evaluated based on the average of the values of the adjusted B Rand indices. The closer the Rand index value is to 1, the more similar the partitions are.
Experiment Settings In this section, we explain the proposed model to predict the RUL. The experimental environment was DELL-Intel Core vPRO i9, 64 GB RAM, 16 GB GPU NVIDIA RTX A5000, and Microsoft 10 operating system. The models and algorithms were implemented in Python language, with an exception when using the R language for the ClustOfVar package to compute the feature clustering. The frameworks used were Tensor-Flow and Keras on Jupyter-Notebooks IDE. The experiment's code is available on GitHub (https://github. com/adam-aalah/Feature-clustering-and-XAI-for-RUL-estimation, (accessed on 17 May 2023)).
Data Overview NASA has developed a generic military turbofan engine simulation  [44] , the Modular Aero-Propulsion System Simulation (C-MAPSS), which has been released for public use to rapidly implement and test control algorithms on a validated engine model. C-MAPSS simulates an engine model of the 90,000 lb thrust class and the package includes an atmospheric model capable of simulating operations at an altitude ranging from sea level to 40K ft, Mach number from 0 to 0. 90, and sea-level temperatures from -60 to 100 °F. The C-MAPSS engine contains five rotating components, Figure  3. These components can be divided into two subcategories: a low-pressure shaft that contains fan, low-pressure compressor (LPC), and a low-pressure turbine (LPT); and a high-pressure shaft that contains a high-pressure compressor (HPC) and a high-pressure turbine (HPT).
The air sucked into the turbofan engine is split into two parts; the first part moves to the combustor chamber, passing through the compressor, and the second part moves through the bypass. The role of the bypass is to increase thrust without increasing fuel consumption, as well as to provide cooling air. We choose to work with the C-MAPSS subset FD004, as it is the most complex dataset out of four subsets (see Table  1 ), containing two fault modes and six operational conditions which are detailed in Table  2. The data was collected using 21 sensors located in different parts of the engine presented in Figure  3. The different sensors are summerized in Table  3  as well as their description. The experiments can be conducted through three stages: preprocessing, training and RUL estimation, and then explaining the degradation.
Feature Clustering Feature clustering is illustrated on the combined preprocessed train and test sets, and then we carried out a hierarchical feature clustering, reducing the inputs of the LSTM neural network. The stability criterion is employed based on a bootstrap approach to choose the number of clusters using the "ClustOfVar" R-package  [38]. The stability in Figure  4  is achieved up to seven clusters and then restabilizes from nine clusters. Based on the stability, the choice of three clusters gives a gain in cohesion equal to 66. 98%, while the choice of seven clusters gives a gain in cohesion equal to 90. 09%. Therefore, it seems reasonable to keep seven clusters, as it allows us to give a deeper insight into how the engine's degradation occurs. The synthetic feature scores for each observation are extracted.
Parameters Study We first adjusted the data smoothing parameter α to 0. 2, 0. 3, 0. 7, and 0. 9, consistent with previous studies. After 30 independent trials, the results are shown in Figure  6. The 95% confidence interval of the RMSE is displayed for each α value. The red dot indicates the average RMSE. The best value of the smoothing parameter α is equal to 0. 3, which leads to the smallest root mean square error (RMSE). Regarding the size of the time window, most studies using the C-MAPSS FD004 database set it at 30. Nevertheless, we believe that this parameter should be investigated. Setting α to 0. 3, we adjusted the time window to 10, 20, 30, 40, and 50. Each configuration was repeated 30 times independently. The results in Figure  7  show that the RMSE first decreases as the window size increases and exceeds the value of 30, indicating that a window size that is too large does not help improve performance.
Comparison with Other Work A comparison of the proposed method with state-of-art studies is presented in Table  6. It illustrates a comparison of works using the FD004 C-MAPSS dataset and summarizes the researcher with the publication date, network algorithm, predicted RMSE, and S-score results from previous studies. The value in brackets denotes the standard deviation of the metrics for the experiments in the study. Accuracy has been improved by using advanced deep learning algorithms, with a deeper network layer or by using fusion algorithms. Compared with work carried out in recent years, the RUL predictions of turbofan engines show the effectiveness of our approach. As some methods tend to have a lower S-score or RMSE, the proposed method achieves better results in terms of the trade-off between performance and explainability, while using a simple architecture with only a single-layer LSTM.
Conclusions In this paper, we proposed a deep-learning-based feature clustering with an XAI approach to predictive maintenance. The proposed model applied feature clustering on sensors during the preprocessing phase and built one single hidden LSTM layer algorithm. Our model was implemented and evaluated to predict the RUL of engines on the NASA turbofan engine dataset FD004. An ablation study was performed and compared to those obtained without any dimension reduction using LSTM with different numbers of hidden layers and those using a PCA dimension reduction algorithm. The experimental results indicate the effectiveness and suitability of the proposed method. SHAP values were used to explain decisions based on the predictive model outputs, help understand the decision of the LSTM model, and highlight the most important features contributing to the degradation of turbofan engines.
In our baseline reduction using FIREFLy Fast InfraRed Exoplanet Fitting for Lightcurves  24  , we perform calibrations on the raw data using the jwst Python pipeline  12  , and then identify and correct for bad pixels and cosmic rays. We mitigate the 1/f noise  9  at the group level rather than the integration level to ensure accurate slope fitting, which we find to be a crucial step for NIRSpec PRISM observations with few groups per integration. We bin the resulting spectrophotometry in wavelength to create 207 variable-width spectral channels with roughly equal counts in each. Fig. 1  shows the FIREFLy white and spectrophotometric light curves at this step in the top panel. Several absorption features are visible by-eye as darker horizontal stripes within the transit region in the 2D light curve (Fig.
Fig. 2  highlights representative transit light curves spanning the entire wavelength range. These data are binned into wider wavelength channels than those used for the final transmission spectrum for ease of presentation. Light curve systematics have not been removed from these data, demonstrating the unprecedented stability and precision of the PRISM observing mode. We also compared the results from the FIREFLy reduction to three other independent reductions that use different treatments for the saturated region of the detector, limb darkening, and various detector systematics (see Methods). All four reductions obtain consistent results. Fig. 3  shows a comparison of the four reductions. The consistency provides confidence in the accuracy of derived atmospheric parameters, demonstrating that any residual systematics are minimal and do not strongly bias results for NIRSpec PRISM observations.
Data Reduction One transit of WASP-39 b was observed with the NIRSpec PRISM mode, with the 8. 23-hour observation roughly centred around the transit event. We used NIRSpec's Bright Object Time Series (BOTS) mode with the NRSRAPID readout pattern, the S1600A1 slit (1. 6"×1. 6"), and the SUB512 subarray. Throughout the exposure, we recorded 21,500 integrations, each with 5 0. 28-second groups up the ramp. We achieved a duty cycle of 82%. We extracted transmission spectra of WASP-39b using four different reductions with the FIREFLy, tshirt, Eureka!+ExoTEP, and Tiberius pipelines. The results from all reductions are broadly consistent (see Fig. 3  and Fig. 5 ). We used the FIREFLy reduction as our baseline for comparison to models throughout this paper, however equivalent overall results can be deduced from the other reductions.
Some key attributes of the reductions are compared in Table  2. All reductions correct for 1/f noise: correlated frequency-dependent read noise in the images caused by detector readout and current biases in the electronics  31. We note that since the GAINSCALE step of the JWST pipeline applies a gain correction to the raw count rate files, the counts and count rates quoted herein are in units of electrons and electrons per second, respectively. We find that recovery of the saturated region was possible by applying several custom steps described here. Without these steps, the heavily saturated region showed a large and unexpected point-to-point scatter on the order of several thousand ppm in the transmission spectra. We note that there was limited on-sky NIRSpec calibration data available when the data were obtained and reduced, including an incomplete detector bias image whose values were all set to zero.
We performed tests to search for zero-point offsets in the calibration versus the planetary and stellar spectra and did not find any at the level of half a pixel width or greater. JWST detectors integrate using a non-destructive up-the-ramp sampling technique, where the flux is measured in counts-per-second from fitting the ramp from the groups contained within each integration. Fig. 6  shows the regions of the spectrum impacted by saturation. Within a column where a pixel was marked as saturated by the pipeline in any given group, we used only the data from the preceding groups for ramp fitting, and manually set an entire column of the detector as saturated if a pixel in that column was saturated. Because a small portion of the spectrum reaches our saturation threshold in the second group, this region of the spectrum only uses one group to derive a "ramp.
" While we were able to recover the spectra in this wavelength range by flagging and ignoring saturated pixels at the group level, we note that the data quality is lower in the saturated region than in the rest of the spectrum given the counts-per-second ramp was measured from fewer than the total 5 groups. We measured the positional shift of the spectral trace across the detector throughout the time series using cross correlation and used them to shift-stabilize the images with flux-conserving interpolation. This procedure reduced the amplitude of position-dependent trends in the light curves. We optimized the width of our flux extraction aperture at each wavelength pixel and extracted the spectrophotometry. For each wavelength we tested a wide range of aperture widths and determined the width that minimized the scatter of the photometry of the first 350 datapoints.
tshirt We use the tshirt pipeline e. g. 41 to extract an independent set of light curves and spectrum. We begin with the uncalibrated "uncal" data product and apply a custom set of processing steps on stage 1 that build on the existing jwst stage 1 pipeline software version 1. 6. 0 with reference files CRDS jwst 0930. pmap. We use a custom bias file shared by the instrument team (Stephan Birkmann, private communication), which is the same file that was delivered to the JWST Calibration Reference Data System (CRDS). We attempt to minimize the biasing effects of count rate non-linearity by modifying the quality flags of pixels surpassing 90% of full-well depth at the group stage. To ensure that there are no systematic differences between pixels within the spectral trace and in the background region, we adjust the quality flags uniformly along the entire pixel column at each group for all integrations.
We skip the "jump" and "dark" steps of stage 1. The tshirt code includes a Row-by-row, Odd-Even By Amplifier (ROEBA) correction to reduce 1/f noise. We first identify source pixels by choosing pixels with more than 5 Data Numbers per second (DN/s) in the rate file, and expanding this region out by 8 pixels. We then identify background pixels for 1/f corrections by choosing all non-source pixels and pipeline flagged non-'DO NOT USE' pixels. We loop through every group and subtract the median of odd (even) row background pixels from all odd (even) rows. We next find a column-by-column median of all background pixels to calculate a 1/f stripe correction and subtract this from each column. After calculating rate files in DN/s, we use tshirt to perform covariance-weighted extraction of the spectrum 31.