1,586 research outputs found
Digging through the dirt: a general method for abstract discrete state estimation with limited prior knowledge
No full textAutonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system dynamics. An example of a typical ADS problem that is used in this thesis is that of a wheeled robot driving through puddles of mud that, when entered, alters the velocity of the robot. When the robot is in such a puddle, it is in an ADS 'mud', and when it is not, it is in an ADS 'free'. ADS can be indirectly inferred through the analysis of lower-level data such as the velocity of the robot. The goal of this thesis is to design a general abstract discrete state estimator (ADSE) operating with limited prior knowledge. An ADSE is a hierarchical system for detecting changes in ADS. The ADSE should be general; applicable to multiple ADSE problems. The ADSE should further operate under limited prior knowledge: only assuming that the amount of ADS and the ADS that describes the regular operation are known. The basis for the ADSE designed in this thesis is a Gaussian hidden Markov model (GHMM), a hidden Markov model enhanced with Gaussian emissions. Randomly generated experiments are done on a simple but general ADSE problem. Two unsupervised learning methods derived from Expectation Maximization are evaluated, namely Baum-Welch (BW) and forward extraction (FWE). FWE is introduced in this thesis and is a simpler implementation of Viterbi extraction, leveraging assumptions of ADSE to in theory gain computational efficiency. We found that both BW and FWE exhibit superior performance compared to a likelihood-based baseline estimator when the maximum score of the learning curve is considered. When the final score is considered, in some cases, FWE displays a deteriorating learning curve, resulting in worse final scores compared to the baseline. Furthermore, it was found that the lower the overlap coefficient (therefore the less similar the ADS), the higher the maximum reached score. It was further shown that BW exhibits better convergence than FWE to the true model parameters. Besides this, FWE obtained comparable or in some cases even superior scores compared to BW. In general, from the results, the diversity of the experiments conducted, and the assumptions made we can conclude that the GHMM can be a general method for an ADSE with limited prior knowledge. To quantify the suitability of the GHMM for ADSE, further research should include the evaluation of different ADSE methods on the same problem. There exists a tradeoff between the lower computational cost FWE and the more stable but more computationally intensive BW learning. Therefore, future research can include a combination of these methods. Other extensions include extending the GHMM to a Gaussian mixture hidden Markov model to allow for the modeling of more complex distributions, or the application to multiple states or a changing environment.https://github.com/Wouter-deBoer/adseMechanical Engineering | Vehicle Engineering | Cognitive Robotic
Assessing the potential of using a soil moisture climatology based on L-band passive microwave observations for the ESA CCI Soil Moisture
No full textESA CCI SM RZSM Long-term Climate Record of Root-Zone Soil Moisture from merged multi-satellite observations
No full text<h3><strong>Context and methodology</strong></h3>
<div>This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB" ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture"). </div>
<div>It contains information on the Root-Zone Soil Moisture (RZSM) content at different depth layers as derived from Surface SM satellite observations of the ESA CCI SM products<em>.</em></div>
<div><em> </em></div>
<div>The RZSM estimates and relative uncertainties are derived using the method of Pasik et al. (2023) forced with observations of the ESA CCI SM Combined product (Dorigo et al., 2017; Gruber et al., 2019; Preimesberger et al., 2021).</div>
<h3><strong>Technical details</strong></h3>
<div>The dataset provides global daily estimates for the 1978-2023 period at 0.25° (~25 km) horizontal resolution. The compressed downloadable rzsm_v09.1_1978_2023.tar.gz file is structured in sub-directories each including all files for a specific year.</div>
<div>Each netCDF file contains the data of a specific day (DD), month (MM), and year (YYYY) in a 2-dimensional (longitude, latitude) grid system. The file name has the following convention:</div>
<div>ESA_CCI_RZSM-YYYYMMDD000000-fv0.9.1.nc</div>
<div>The RZSM data reflects the estimates calibrated for 4 depth layers:</div>
<ul>
<li>rzsm1: 0-10 cm</li>
<li>rzsm2: 10-40 cm</li>
<li>rzsm3: 40-100 cm</li>
<li>rzsm4: 0-100 cm</li>
</ul>
<div>A package is available in python for reading the data as daily images and converting these images to time series and reading them. The source code for our python package and installation instructions are available here: <a href="https://github.com/TUW-GEO/esa_cci_sm" target="_blank" rel="noopener noreferrer">https://github.com/TUW-GEO/esa_cci_sm</a></div>
<ul>
<li>The package can be installed via pip using "pip install esa_cci_sm"</li>
<li>The documentation for this package is available here: <a href="https://esa-cci-sm.readthedocs.io/en/latest/" target="_blank" rel="noopener noreferrer">https://esa-cci-sm.readthedocs.io/en/latest/</a></li>
<li>The "parameter" argument (e.g., <a href="https://github.com/TUW-GEO/esa_cci_sm/blob/33a8a453bbccb55188804bce07a37315e9a3db43/src/esa_cci_sm/interface.py#L39" target="_blank" rel="noopener noreferrer">https://github.com/TUW-GEO/esa_cci_sm/blob/33a8a453bbccb55188804bce07a37315e9a3db43/src/esa_cci_sm/interface.py#L39</a>) can be specified to any of the layer variables (rzsm1, rzsm2, ...)</li>
</ul>
<div>Any software that can handle CF conform data should be able to import the raw netCDF files (e.g. <a href="https://code.mpimet.mpg.de/projects/cdo" target="_blank" rel="noopener noreferrer">CDO</a>, <a href="http://nco.sourceforge.net/" target="_blank" rel="noopener noreferrer">NCO</a>, <a href="https://www.qgis.org/" target="_blank" rel="noopener noreferrer">QGIS</a>, ArCGIS, Matlab, R, ...). You can also use the GUI software <a href="https://www.giss.nasa.gov/tools/panoply/" target="_blank" rel="noopener noreferrer">Panoply</a> to view each file.</div>
<h3>Reference</h3>
<p><strong>Pasik, A., Gruber, A., Preimesberger, W., De Santis, D., and Dorigo, W.: Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations, Geosci. Model Dev., 16, 4957–4976, </strong><a href="https://doi.org/10.5194/gmd-16-4957-2023,%202023"><strong>https://doi.org/10.5194/gmd-16-4957-2023, 2023</strong></a></p>
<h3>Additional citations</h3>
<p>Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, <a href="https://doi.org/10.1016/j.rse.2017.07.001">https://doi.org/10.1016/j.rse.2017.07.001</a>.</p>
<p>Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, <a href="https://doi.org/10.5194/essd-11-717-2019">https://doi.org/10.5194/essd-11-717-2019</a></p>
<p>Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W. (2021). Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record, in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.</p>
<h2>Related Records</h2>
<p>The following records are all part of the <a href="../communities/soilmoisture-climaterecords/records?q=&l=list&p=1&s=10&sort=newest">Soil Moisture Climate Data Records from satellites</a> community</p>
<table>
<tbody>
<tr>
<td>1</td>
<td>
<p>ESA CCI SM MODELFREE Surface Soil Moisture Record </p>
</td>
<td><a href="../doi/10.48436/rqfmp-jp420">10.48436/rqfmp-jp420</a></td>
</tr>
<tr>
<td>2</td>
<td>
<p>ESA CCI SM GAPFILLED Surface Soil Moisture Record </p>
</td>
<td><a href="../doi/10.48436/hcm6n-t4m35">10.48436/hcm6n-t4m35</a></td>
</tr>
</tbody>
</table>
<p> </p>
Automatic retrieval of crop characteristics: an example for hyperspectral AHS data from the AgriSAR campaign.
No full textThis paper presents the results of automated extraction of crop characteristics from hyperspectral earth observation data. The data was acquired with an airborne AHS imaging spectrometer in the framework of the joint European AgriSAR 2006 campaign. The AgriSAR campaign was directed by the ESA and took place at the DEMMIN test site in northeast Germany, an agricultural area dominated by large monocultures. An important objective of this campaign was to establish to what degree novel radar and optical technologies are able to provide accurate agro-meteorological parameters for precision farming purposes.
Parameter retrieval in this study was performed with the CRASh approach, a software module based on the inversion of radiative transfer models. CRASh was developed at DLR as part of an automated operative processing chain for future hyperspectral missions. Validation of the model inversion results was performed with field measurements of leaf area index and leaf chlorophyll content which were carried out for winter wheat, winter barley, winter rape, maize, and sugar beet at two time steps during the 2006 growing season. Although spatial patterns of the model results generally coincide with the trends observed in the field, absolute accuracy of the fully automatically extracted variables appeared insufficient for precision agriculture purposes. The unsatisfying results are ascribed to a combination of causes, including angular anisotropy across the swath-width of the flight lines, the configuration of the applied bands, and the large number of model inversion solutions inherent to an automated environment in which little additional information on the observed canopy is present. Employing the airborne version of CRASh and incorporating a priori information on land cover and variable distributions is expected to drastically increase the retrieval performance
embalming and reperfusion of porcine kidneys
No full text<p>These are the data of the following article:</p>
<p>Understanding Thiel embalming in pig kidneys to develop a new circulation model</p>
<p>First author: Wouter Willaert</p
Does Indonesia have a"low-pay"civil service?
Get PDFGovernment officials and polcy analysts maintain that Indonesia's civil servants are poorly paid and have been for decades. This conclusion is supported by anecdotal evidence and casual empiricism. The authors systematically analyze the realtionship between government and private compensation levels using data from two large household surveys carried out by Indonesia's Central Bureau of Statistics: the 1998 Sakernas and 1999 Susenas. The results suggest that government workers with a high school education or less, representing three-quarters of the civil service, earn a pay premium over their private sector counterparts. Civil servants with more than a high school education earn less than they would in the private sector but, on average, the premium is far smaller than commonly is alleged and is in keeping with public/private differentials in other countries. These results prove robust to varying econometric specifications and cast doubt on low pay as an explanation for government corruption.Decentralization,Public Health Promotion,Health Monitoring&Evaluation,National Governance,Knowledge Economy,Health Monitoring&Evaluation,NationalGovernance,Knowledge Economy,Education for the Knowledge Economy,Parliamentary Government
Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology
No full text© 2019 Author(s). The European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) merging algorithm generates consistent quality-controlled long-term (1978-2018) climate data records for soil moisture, which serves thousands of scientists and data users worldwide. It harmonises and merges soil moisture retrievals from multiple satellites into (i) an active-microwave-based-only product, (ii) a passive-microwave-based-only product and (iii) a combined active-passive product, which are sampled to daily global images on a 0.25 regular grid. Since its first release in 2012 the algorithm has undergone substantial improvements which have so far not been thoroughly reported in the scientific literature. This paper fills this gap by reviewing and discussing the science behind the three major ESA CCI SM merging algorithms, versions 2 (https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c; Wagner et al., 2018), 3 (https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c; Dorigo et al., 2018) and 4 (https://doi.org/10.5285/dce27a397eaf47e797050c220972ca0e; Dorigo et al., 2019), and provides an outlook on the expected improvements planned for the next algorithm, version 5.sponsorship: This research has been supported by the eartH2Observe project of the European Union's Seventh Framework Programme (grant no. 603608), the ESA's Climate Change Initiative (CCI) for soil moisture (grant no. 4000104814/11/I-NB), and the KU Leuven C1 internal fund (grant no. C14/16/045). (eartH2Observe project of the European Union's Seventh Framework Programme|603608, ESA's Climate Change Initiative (CCI) for soil moisture|4000104814/11/I-NB, KU Leuven C1 internal fund|C14/16/045)status: Published onlin
Optimization of the capacity of a rose sorting system using discrete event simulation
No full textIn Rose cultivation companies in the Netherlands, there is a demand for a higher sorting capacity on the existing sorting systems. The objective for this research is to advice which part of the sorting system needs to be adjusted to gain a higher sorting capacity. For the current sorting systems, five bottlenecks are defined, the bottlenecks limit the sorting capacity. To be able to forecast the effect of machine adjustments, a discrete event simulation model has been constructed, using Simulink and Matlab. This simulation model is verified, matched and validated using data of on an existing rose sorting system. Results of a single day validation showed that the time to process the roses can be simulated with a 97% accuracy. Subsequently, five different simulations are executed. In each simulation, one of the five bottlenecks is removed or reduced. With the results of these simulations the capacity limitation due to each bottleneck is quantified. However entirely removing a bottleneck is not feasible in reality for all bottlenecks. A last situation is simulated where all feasible bottleneck reductions are combined. This showed that the time to sort all roses is reduced by 35%.Marine Technology | Transport Engineering and Logistic
From exemplar to copy: the scribal appropriation of a Hadewijch manuscript computationally explored
Get PDFThis study is devoted to two of the oldest known manuscripts in which the
oeuvre of the medieval mystical author Hadewijch has been preserved: Brussels,
KBR, 2879-2880 (ms. A) and Brussels, KBR, 2877-2878 (ms. B). On the basis of
codicological and contextual arguments, it is assumed that the scribe who
produced B used A as an exemplar. While the similarities in both layout and
content between the two manuscripts are striking, the present article seeks to
identify the differences. After all, regardless of the intention to produce a
copy that closely follows the exemplar, subtle linguistic variation is
apparent. Divergences relate to spelling conventions, but also to the way in
which words are abbreviated (and the extent to which abbreviations occur). The
present study investigates the spelling profiles of the scribes who produced
mss. A and B in a computational way. In the first part of this study, we will
present both manuscripts in more detail, after which we will consider prior
research carried out on scribal profiling. The current study both builds and
expands on Kestemont (2015). Next, we outline the methodology used to analyse
and measure the degree of scribal appropriation that took place when ms. B was
copied off the exemplar ms. A. After this, we will discuss the results
obtained, focusing on the scribal variation that can be found both at the level
of individual words and n-grams. To this end, we use machine learning to
identify the most distinctive features that separate manuscript A from B.
Finally, we look at possible diachronic trends in the appropriation by B's
scribe of his exemplar. We argue that scribal takeovers in the exemplar impacts
the practice of the copying scribe, while transitions to a different content
matter cause little to no effect
Advancing forest mapping: Pretraining strategies and deep-ensemble based uncertainty for predicting evergreen broad-leaved cover from Sentinel-2 time series
Get PDFThe distribution changes of evergreen broad-leaved tree and shrub species (EVE) at the border of Mediterranean and temperate forests due to climate change and land-use changes necessitates accurate mapping techniques to support biodiversity monitoring and climate adaptation strategies. Remote sensing time series provide valuable data for vegetation analysis, yet functional-level mapping in mixed forests remains challenging due to limited observation data. To tackle this limitation, the present study investigates pretraining strategies for mapping EVE cover in selected Italian forest areas using Sentinel-2 time series and a probabilistic Convolutional Neural Network. Additionally deep ensemble-based uncertainty estimation is used to further enhance the interpretability of the output. We compare three model training strategies: (i) direct training on up-to-date but limited field data, (ii) supervised pretraining on a larger, diverse forest vegetation database before fine-tuning, and (iii) self-supervised pretraining on large-scale unlabeled time series before fine-tuning. Our results demonstrate that pretraining on contextually similar datasets in combination with a spatial split of training and validation data significantly enhances predictive performance and generalization to unseen regions in a cross-validation experiment. Additionally, we assess epistemic and aleatoric uncertainty to improve interpretability and identification of out-of-distribution predictions. This study highlights the benefits of pretraining and uncertainty quantification for large-scale remote sensing applications with limited availability of labeled data
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