Global Earth Monitor (GEM, Horizon 2020) takes advantage of the large volumes of available Earth Observation (EO), weather, climate and other non-EO data to establish economically viable continuous ...monitoring of the Earth. Within the GEM framework, the development of scalable and cost-effective solutions is being tested on several use-cases, with crop identification being one of them.Crop identification uses a combination of EO and weather data to enable automatic identification of crops. The use case supports operational decisions when managing crops and the monitoring of actual vs. planned or reported agricultural land use (e.g., Common Agricultural Policy monitoring). Satellite data and weather data come at very different temporal and spatial resolutions: Sentinel-2 constellation nominally provides an observation of a field every 5 days at 10 m spatial resolution, while weather data has continuous hourly time series at multi-km spatial resolution. We have designed ad-hoc routines to spatially aggregate satellite data at field level and to systematically compose layers of different time discretization series, so that each EO is associated with a complete time series (of opportune length) of weather variables at daily resolution. For each field, we extract the time series of the median over field pixels of Sentinel-2 L1C bands, cloud mask and cloud probability. For doing this we take advantage of Sentinel Hub's Statistical API (Sinergise, 2020), that enables the retrieval of statistics of band values and derived indices over a specified geographic area and time range. Using meteoblue dataset API (meteoblue, 2017), complete time series of daily weather data (NEMS4 model, meteoblue, 2008) are then associated to each field observation, following the systematic layer composition approach mentioned above. An opportune time series length is defined for each of the 17 weather variables we considered. To handle this kind of multi-dimensional layered data, we use a flexible encoding-decoding framework (FlexMod, designed by TUM as part of GEM project): multiple encoders are designed for features of different time length (namely EO data and weather variables) and are then passed to the decoder via a mediator. Thanks to the flexible design of FlexMod framework, different models and architectures can be easily tested by simply defining new encoders and/or decoders. We present results obtained on a dataset in Slovenia, where crop fields are labelled according to a Hierarchical Crop and Agriculture Taxonomy (HCAT). This taxonomy, based on the EAGLE-Matrix and EU regulations, is the one adopted in the EuroCrops project (Schneider et al. 2021). The classification of field crops takes advantage of Sentinel-2 satellite data and Numerical Weather Prediction model output data. We exploit the potential of FlexMod to test different feature extractors, temporal encoding frameworks and decoders and we present a comparison between results obtained training a long-short term memory (LSTM) implementation (Breizhcrops, Rußwurm et al. 2020) and a Self-attention transformer model (Vaswani et al. 2017), the latter showing the best performances with accuracy 0.904 and Cohen’s kappa 0.824. We moreover investigate the role of weather data by benchmarking results against those obtained with just satellite imagery. To better appraise the influence of the weather data we analyse how perturbing weather data in the testing dataset affects the final results. So far, we obtain in both cases very similar accuracies and Cohen’s kappa. A deeper analysis of crop-specific scores (precision, recall, F1) suggests that the training and testing datasets are too limited in terms of size and crop variability to draw any general conclusion over the role of weather. As future developments, once the EuroCrops datasets are ready, we plan to expand the training and testing dataset to cover a higher variability of climatological areas and increase the numerosity of the so far under-represented crops, in the attempt to draw more general conclusions around the influence of weather and the predictability of specific crop classes. Moreover, given the encouraging scores, we aim to perform crop type mapping at least at European scale, thanks to the availability of the EuroCrops data and the cost-effective big data solutions developed during GEM project.
A comprehensive analysis of the effects of Geant4 algorithms for condensed transport in detectors is in progress. The first phase of the project focuses on electron multiple scattering, and studies ...two related observables: the longitudinal pattern of energy deposition in various materials, and the fraction of backscattered particles. The quality of the simulation is evaluated through comparison with high precision experimental measurements; several versions of Geant4 are analyzed to provide an extensive overview of the evolution of Geant4 multiple scattering algorithms and of their contribution to simulation accuracy.
Bulk screen-printed electrodes (bSPEs) modified with zirconium phosphate (ZrP) and Meldola blue (MB) and by electrochemical deposition of a Reineckate film (bMBZrPRs-SPEs) have been constructed and ...used as NADH sensors. Cyclic voltammetric investigation of these bulk electrochemically modified screen-printed electrodes revealed stable catalytic activity in oxidation of the reduced form of the coenzyme nicotinamide adenine dinucleotide (NADH). Flow-injection analysis (FIA) coupled with amperometric detection confirmed the improved stability of the bMBZrPRs-SPEs (10-⁴ mol L-¹ NADH, %RSD = 4.2, n = 90, pH 7.0). Other conditions, for example applied working potential (+50 mV relative to Ag|AgCl), flow rate (0.30 mL min-¹) and pH-dependence (range 4.0-10.0) were evaluated and optimized. A glycerol biosensor, prepared by immobilizing glycerol dehydrogenase (GDH) on the working electrode area of a bMBZrPRs-SPE, was also assembled. The biosensor was most stable at pH 8.5 (%RSD = 5.6, n = 70, 0.25 mmol L-¹ glycerol). The detection and quantification limits were 2.8 x 10-⁶ and 9.4 x 10-⁶ mol L-¹, respectively, and the linear working range was between 1.0 x 10-⁵ and 1.0 x 10-⁴ mol L-¹. To assess the effect of interferences, and recovery by the probe we analyzed samples taken during fermentation of chemically defined grape juice medium and compared the results with those obtained by HPLC.
Annealing studies of effective trapping times in silicon detectors Kramberger, G.; Batič, M.; Cindro, V. ...
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment,
02/2007, Letnik:
571, Številka:
3
Journal Article
Recenzirano
Annealing of effective trapping times of electrons and holes in neutron irradiated silicon detectors was measured at different temperatures 40, 60,
80
°
C
. The evolution of effective trapping times ...seems to be governed by the first-order process. The effective trapping probability of holes was found to increase by 40% and of electrons to decrease by 20% during annealing. The time constants are of order 600
min at
60
°
C
. The scaling to different temperatures can be obtained from Arrhenius relation with activation energies of around 1
eV.
Recent efforts for the improvement of the accuracy of physics data libraries used in particle transport are summarized. Results are reported about a large scale validation analysis of atomic ...parameters used by major Monte Carlo systems (Geant4, EGS, MCNP, Penelope etc.); their contribution to the accuracy of simulation observables is documented. The results of this study motivated the development of a new atomic data management software package, which optimizes the provision of state-of-the-art atomic parameters to physics models. The effect of atomic parameters on the simulation of radioactive decay is illustrated. Ideas and methods to deal with physics models applicable to different energy ranges in the production of data libraries, rather than at runtime, are discussed.
A system for in vivo tracking of 1
Ci
192Ir source during brachytherapy treatment has been built using high resistivity silicon pad detectors as image sensors and knife-edge lead pinholes as ...collimators. The sensors consist of 256
pads arranged in 32 ×8
grid with pad size
1.4
×
1.4
mm
2
and 1
mm thickness. The sensors have two metal layers, enabling connection of readout electronics (VATAGP3_1 chips) at the edge of the detector. With source self-images obtained from a dual-pinhole system, location of the source can be reconstructed in three dimensions in real time, allowing on-line detection of deviations from planned treatment. The system was tested with 1 Ci
192Ir clinical source in air and plexi-glass phantom. The movements of the source could be tracked in a field of view of approximately
20
×
20
×
20
cm
3
with absolute precision of about 5
mm. Positions of the source, relative to the first measured source position, could be mapped with precision of around 3
mm.
The response of Schizosaccharomyces pombe towards the oxyanions selenate Se(VI) and dichromate Cr(VI) was investigated in order to establish the involvement of the yeast ATP sulfurylase in their ...reduction. An ATP sulfurylase-defective/selenate-resistant mutant of S. pombe (B-579 Se(R) -2) and an ATP sulfurylase-active/selenate-sensitive strain of S. pombe (B-579 Se(S)) were included in this study. The inhibitory effect of Se(VI) and Cr(VI) oxyanions on growth and bioaccumulation was measured. The sensitive strain showed natural sensitivity to selenate while the resistant mutant tolerated a 100-fold higher concentration of selenate. These results indicate that selenate toxicity to microorganisms is connected with the reduction of selenate to selenite. Both strains showed similar sensitivity to Cr(VI) and in this study there was no evidence that ATP sulfurylase participates in the reduction process of Cr(VI).
Field boundaries are at the core of many agricultural applications and are a key enabler for the operational monitoring of agricultural production to support food security. Recent scientific progress ...in deep learning methods has highlighted the capacity to extract field boundaries from satellite and aerial images with a clear improvement from object-based image analysis (e.g. multiresolution segmentation) or conventional filters (e.g. Sobel filters). However, these methods need labels to be trained on. So far, no standard data set exists to easily and robustly benchmark models and progress the state of the art. The absence of such benchmark data further impedes proper comparison against existing methods. Besides, there is no consensus on which evaluation metrics should be reported (both at the pixel and field levels). As a result, it is currently impossible to compare and benchmark new and existing methods. To fill these gaps, we introduce AI4Boundaries, a data set of images and labels readily usable to train and compare models on field boundary detection. AI4Boundaries includes two specific data sets: (i) a 10 m Sentinel-2 monthly composites for large-scale analyses in retrospect and (ii) a 1 m orthophoto data set for regional-scale analyses, such as the automatic extraction of Geospatial Aid Application (GSAA). All labels have been sourced from GSAA data that have been made openly available (Austria, Catalonia, France, Luxembourg, the Netherlands, Slovenia, and Sweden) for 2019, representing 14.8 M parcels covering 376 K km2. Data were selected following a stratified random sampling drawn based on two landscape fragmentation metrics, the perimeter/area ratio and the area covered by parcels, thus considering the diversity of the agricultural landscapes. The resulting “AI4Boundaries” dataset consists of 7831 samples of 256 by 256 pixels for the 10 m Sentinel-2 dataset and of 512 by 512 pixels for the 1 m aerial orthophoto. Both datasets are provided with the corresponding vector ground-truth parcel delineation (2.5 M parcels covering 47 105 km2), and with a raster version already pre-processed and ready to use.
Besides providing this open dataset to foster computer vision developments of parcel delineation methods, we discuss the perspectives and limitations of the dataset for various types of applications in the agriculture domain and consider possible further improvements. The data are available on the JRC Open Data Catalogue: http://data.europa.eu/89h/0e79ce5d-e4c8-4721-8773-59a4acf2c9c9 (European Commission, Joint Research Centre, 2022).
One of the biggest problems related to mandatory tanks of oil and oil derivatives are evaporative losses in the tanks. It is well known that the storage, manipulation, and transport of oil and oil ...derivatives results in the evaporation of the liquid. In the case of tanks where commodity reserves are stored for a long period of time, the most pronounced problems are ?breathing? losses and degradation of the quality of petroleum products. Many of these volatile organic compounds also have a strong negative impact that is harmful to human health and the environment. The aim of this research is to improve system in order to reduce evaporative losses in the tanks which are used for mandatory reserves of oil and oil derivative in warehouse in location near Pozega in Serbia as well as to reduce the harmful impact on the environment with the proposed improvement measures.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Ongoing investigations for the improvement of Geant4 accuracy and computational performance resulting by refactoring and reengineering parts of the code are discussed. Issues in refactoring that are ...specific to the domain of physics simulation are identified and their impact is elucidated. Preliminary quantitative results are reported.
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