Reviewerly/exHarmony huggingface dataset:
Composition Analysis of an Apatite Crystal using a Space-Prototype Mass Spectrometric Instrument and Machine Learning for Unsupervised Mineralogical Phase Detection

We present the analysis of a 2.06 Ga apatite crystal obtained from an ultramafic phoscorite rock from the Phalaborwa Complex (Limpopo Province, South Africa) [1]. A space-prototype laser ablation ionisation mass spectrometer (LIMS) [2,3] was used to study the chemical composition of the sample. Mass spectra were recorded from a sample area of 0.6x0.6 mm2, with a spatial resolution of 30 &amp;#956;m and sub-micrometre depth resolution.Apatite is a calcium phosphate mineral expressed by the chemical stoichiometric formula [Ca5(PO4)3(F, Cl, OH)]. The halogen site, occupied by F, Cl, and OH, corresponds to an isomorpous series with fluor-, chlor- and hydroxyl-apatite end members, respectively. Apatite, being an accessory mineral in igneous and other rocks, commonly contains a range of other elements that do not fit well into the major rock forming minerals, such as rare earth elements (REE). These are suitable targets for investigating physical and chemical conditions in igneous rocks and the volatile evolution of magmas.The analysis of the spectra recorded with our LIMS system for the abundances of the elements of interest at each location were performed in two steps. First, the abundances of each element across the sampled area were compiled in element maps. And second, an unsupervised machine learning algorithm based on clustering and network analysis was applied to the data set of analysed mass spectra to separate it into groups of distinct chemical composition. Subsequently, a more detailed analysis was conducted on each of the recovered groups to assign the corresponding mineral. In addition to the group of spectra belonging to apatite, which was assigned to fluorapatite, other minerals were identified, amongst others olivine. This method yields an unsupervised approach to identify different mineralogical entities present within a sample. This network analysis method was previously applied to a 1.88 Ga Gunflint sample (Ontario, Canada) to separate spectra recorded from the host (chert) from spectra containing signatures of organic matter from fossilized microbes [4].Given that the data were recorded using a miniature mass spectrometer designed for space flight, this analysis demonstrates the analytical capabilities of our LIMS system that could be achieved in-situ on other planetary bodies in our Solar System, for example on the Moon or on Mars. The current performance of this miniature LIMS instrument to study the chemical composition of apatite is sufficiently high to measure volatiles (H, F, Cl) and nearly all relevant mineral and partially trace elements (Na, C, Mg, Si, S, K, Mn, Fe,&amp;#160;Sr, Ba), including REE (La, Ce, Pr, Sm) which allows for a systematic quantitative analysis of their distribution.[1] Tulej, M. et al., 2022, https://doi.org/10.3390/universe8080410.[2] Riedo, A. et al., 2012, https://doi.org/10.1002/jms.3104.[3] Tulej, M. et al., 2021, https://doi.org/10.3390/app11062562.[4] Lukmanov, R.A. et al., 2022, https://doi.org/10.3389/frspt.2022.718943
Development of low-cost material for the remediation of inorganic contaminants by using PRB

&amp;lt;p&amp;gt;Groundwater is a key source of water used in many essential activities across the world, including irrigation (42%), public water supply (36%), industrial uses (24%), and power production. Furthermore, the quality of groundwater is quickly degrading owing to uncontrolled operations. Most developing countries, including India, have polluted groundwater due to an oversupply of heavy metals, fluoride, rich nutrients, and hydrocarbons. Various technologies like the Pump &amp;amp; Treat method were used to treat it but they are very expensive and less effective, so there is a need to develop low cost and effective technology for its treatment. A Permeable Reactive Barrier (PRB) is an effective groundwater remediation method. The key advantage is that it does not require the running of pumps or treatment vessels, saving on operation and maintenance expenses and enabling the economic worth of property to be maintained during rehabilitation. The goal of this research is to find low-cost materials that can be employed as reactive materials in a PRB to remove inorganic pollutants from groundwater and the aquifer. To that end, numerous low-cost material samples were exposed to characterization tests, batch testing, and a washing process to analyze the material&amp;#039;s adsorption and desorption capabilities at varied doses, concentrations, and contact periods. Few of the low-cost materials functioned admirably, removing all&amp;amp;#160;contaminants (&amp;gt;98%), whereas others were deemed inappropriate for several reasons. The best-performing material is then adopted for two bench-scale column tests to imitate the PRB.&amp;lt;/p&amp;gt;
A new approach to a Semi-automatic discontinuity sets extraction from point clouds .

Sensor such as light detection and ranging (LiDAR) or SfM (Structure from Motion), point clouds (PC) are nowadays an essential tool for the rock instabilities analysis. PCs currently allow us to images complex 3-dimensional discontinuities. Thanks to the high density of data and the high accuracy of the LiDAR have the potential of a semi-automatic fault identification. Previous work has already tackled the question by using methods such as least square analysis or the normal vector orientation calculation and other more complex method. Those methods where successful but the accuracy on fault detection were not sufficiently high enough. In order to overcome those encountered issue, the development of a new kind of fault detection algorithm were needed. During our work we have developed a new semi-automatic method of fault identification using the variation of point density. The developed method has successfully detected discontinuity as well as their orientation and their number. The 3-dimentional scanning of the PC by the algorithm allow us to have a good redundancy even on complex fault shape. Results on simple synthetic data are convincing enough to test our algorithm to more complex synthetic data with more randomized structures. Some test can be also be done on simple LiDAR dataset(simple shape (cube)) to consider instrumental noise or potential artefact before experimenting the algorithm on more real data. In the future this work could lead to data analysis of the output of the algorithm in to determine the frequency of similar discontinuity that can lead to the estimation of the potential volume of material that could be in movement.
Unsupervised clustering to jointly interpret geophysical datasets across the Alhama de Murcia active fault, Spain.

Joint interpretation of multidisciplinary geophysical data is the best way to reduce ambiguity in subsurface exploration. The combination of seismic velocity and electricalresistivity has proven to be an excellent geological characterization strategy, however, the integration of these geophysical parameters is a complex process. In this work, we use unsupervised clustering to jointly interpret three geophysical datasets (P wave velocity, S wave velocity, and electrical resistivity). The target is a cross-section across the Alhama de Murcia Fault (FAM), which is one of the main active faults in the Iberian Peninsula. In our approach, we first join the three datasets into a common multiparametric grid. Then, in order to find data clusters that can be correlated with known lithologies in the area, we investigated the performance of three unsupervised machine learning algorithms: one hierarchical, one centroid-based, and one model-based. The latter proved to be the most efficient for clustering our highly mixed data and providing geological meaning. The three classes obtained correlated well with the lithological units present in the area and, from their relationship, it was possible to deduce structural elements not yet well understood, providing new perspectives in the characterization of the Alhama de Murcia fault zone. Research supported by grants: VECTOR EU project ID 101058483, and SIT4ME -EITRawMaterials.
Using cGAN for cloud classification from RGB pictures

Clouds of all kinds play a large role in many atmospheric processes including, e.g. radiation and moisture transport, and their type allows an insight into the dynamics going on in the atmosphere. Hence, the observation of clouds from Earth's surface has always been important to analyse the current weather and its evolution during the day. However, cloud observations by human observers are labour-intensive and hence also costy. In addition to this, cloud classifications done by human observers are always subjective to some extent. Finding an efficient method for automated observations would solve both problems. Although clouds have already been operationally observed using satellites for decades, observations from the surface shed a light on a different set of characteristics. Moreover, the WMO also defined their cloud classification standards according to visual cloud properties when observations are done at the Earth&amp;#8217;s surface. Thus, in this work a utilization of machine learning methods to classify clouds from RGB pictures taken at the surface is proposed. Explicitly, a conditional Generative Adversarial Network (cGAN) is trained to discriminate between 30 different categories, 10 for each cloud level - low, medium and high; Besides showing robust results in different image classification problems, an additional advantage of using a GAN instead of a classical convolutional neural network is that its output can also artificially enhance the size of the training data set. This is especially useful if the number of available pictures is unevenly distributed among the different classes. Additional background observations like cloud cover and cloud base height can also be used to further improve the performance of the cGAN. Together with a cloud camera, a properly trained cGAN can observe and classify clouds with a high temporal resolution of the order of seconds, which can be used, e.g. for model verification or to efficiently monitor the current status of the weather as well as its short-time evolution. First results will also be presented.
Downscaling climate change impacts on socio-economic parameters in a storyline-based investigation

Climate change has drastic impact on our lives and many socio-economic parameters. The exact consequences are hard to quantify and in general not obvious and not clear to the general public. In this work we use the storyline scenarios from within the SCENIC project for the prediction of various socio-economic parameters in warmer climates. We use machine learning algorithms to investigate how parameters that have direct impacts on society and the population develop in different warmer world scenarios. For this purpose we train echo state networks. Input data are temperature and humidity fields over Europe. Output data are time series of, e.g., mortality rates, forest fires, crop yield, and consumer price indices from Germany. The output data are predicted under the investigated climate scenarios of +2K and +4K with respect to pre-industrial time.
A Python tool to monitor noise characteristics in large seismic data bases

Continuous monitoring of data quality is a major issue in seismology because the achievement of robust scientific results depends on the reliability of the underlying data resources. We present a Python package which provides means to perform a systematic analysis of noise data in the time and frequency domain. The tool is designed to process large amounts of channels and years of data. In a first step, average amplitude levels and power spectral densities are computed for large parts - preferably the whole available time range - of the data of a station. Depending on the size of the data set, this processing takes minutes to hours. Therefore, the results are stored in rapidly accessible HDF5-files. Subsequently, they are visualized using color-coded matrix displays (spectrograms) and interactive 3D-figures. The resulting figures give insight to characteristic noise patterns at the station and possible noise sources, like various forms of anthropogenic noise or wind generated noise. Furthermore, changes in noise levels or noise patterns are easily detectable. Such changes either indicate changes in the environmental conditions at the recording site or changes in the recording hardware improperly reflected by the station metadata, often signaling a problem with the metadata. Furthermore, the processed data can easily be restricted to selected times, e.g. to investigate the influence of day/night cycles or to obtain wind-speed dependent spectrograms. In this manner, a comprehensive picture of relevant characteristics at a station site may be acquired.The package is build from established Python libraries like obspy, scipy and h5py. Matplotlib and plotly are used for data visualization. The core functionalities are accessible via command line interface while the underlying API allows for more customized workflows.
We invent pictograms

A pictogram is an image on a label that contains a warning symbol and special colors to provide information about the harmfulness of substances or mixtures, or how these substances can affect us or the environment. The CLP Regulation introduced a new system for the classification and labeling of hazardous chemicals in the European Union, and it has also been adopted in Serbia. The pictograms have changed and are now in line with the Globally Harmonized System of the United Nations&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; In the seventh grade of elementary school, when students are introduced to chemistry, the new subject they are studying, it is planned to learn basic pictograms in the part of curriculum called&amp;#160; &amp;#8203;&amp;#8203;the Chemical Laboratory.&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; In class, students are introduced to the concept of pictograms. Then the students are presented with basic pictograms that they may encounter in their everyday life. After that, students should find pictograms on the packaging of chemical products used in the household and food packaging, recognize them and determine their meaning. After that, students are given the task of designing a pictogram for a topic of their choice.&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160;&amp;#160; This activity can be carried out with different age groups of students with adjustments to the teaching content.
The Alteration Chemistry of a Multi-Stage Volcanic Hosted Epithermal Gold Mineralization; Kısacık Case (Biga Metallogenic Province of NW, Turkey)

The Biga peninsula (NW, Turkey) hosts numerous polymetallic mineral deposits including skarns, porphyry Cu-Mo, vein type Pb-Zn, and epithermal Au&amp;#8211;Ag. These mineralizations are mainly associated with widespread high-K magmatism in the Oligo-Miocene period. Of these the epithermal veins which have economic grade Au-Ag concentrations are generally found in volcanic rocks such as dacites, andesites, and pyroclastic rocks. The hydrothermal aureoles' interaction with highly permeable volcanic rocks caused voluminous alteration zones with potential precious metal enrichments. The K&amp;#305;sac&amp;#305;k (Biga Metallogenic Province of NW, Turkey) area can be considered as a promising precious metal mineralization zone due to the high-intensity alterations observed in Miocene volcanic rocks (dacites and pyroclastic rocks). In the study area, the hydrothermal activity can also be seen in basement metamorphic (calcschists) and ophiolitic (basalts - dolerites) rocks. The identified hydrothermal alterations in the field are sericitic and argillic alterations with intense silicifications. The silicifications are mostly observed in cracks and joint fractures of volcanic rocks in the form of veinlets and cavity fills. Major oxide-Trace Element and ICP-MS Ag-Au-PGE&amp;#8217;s analysis was performed on the samples taken from the field. The calculated alteration indexes (AI) are ranging between %82-99.2 and Chlorite-Carbonate-Pyrite Indexes (CCPI) values are between %73-97 according to major oxide values of altered volcanics. The Au content varies between 0.03 &amp;#8211; 21.34 ppm. When the hydrothermal trend is examined in the AI-CCPI diagram, the altered high grade volcanics shows a chlorite-sericite-pyrite alteration trend. These implications are parallel with field observations. The less altered volcanics are located within the andesite/basalt field. Highly altered volcanics are located in the "ore center" area of the diagram. The calculated alteration index (AI&amp;CCPI) values of the volcanic rocks are high, mainly caused by the removal of mobile cations such and a significant enrichment in immobile cations such as Al and Fe. In addition, a silica-carbonate alteration zone around ophiolitic rocks was observed and gold content of up to 0.16 ppm was detected in samples from this area which indicates a listwanite-type Au deposit.Keywords: Alteration indexes, Miocene Volcanics, Hydrothermal Deposits, Biga Peninsula, Turkey
Total Solar Irradiance and Terrestrial Outgoing Longwave Radiation Measurements obtained with CLARA onboard NorSat-

The Earth Radiation Budget at the Top of the Atmosphere (ToA) governs the status of climate change on our planet. The ERB is the balance between the incoming Total Solar Irradiance (TSI) and total outgoing radiation at the ToA. If more energy is stored in the system the Earth Energy Imbalance is positive and the temperature in the system rises. The Compact Lightweight Absolute RAdiometer (CLARA) experiment onboard the Norwegian micro satellite NorSat-1 is an SI traceable radiometer with the primary science goal to measure TSI from space. Besides TSI, CLARA also measures the terrestrial Outgoing Longwave Radiation (OLR) at the ToA on the night side of Earth. We present the latest status of the data and degradation correction obtained with this SI-traceable radiometer and compare the CLARA TSI and OLR time series with other available observations and reanalysis data. The validation of these measurements is key to advance our capability to determine the Earth Energy Imbalance from space.&amp;#160;
Advances in development of OSI geophysical techniques for mountainous environments

On-site inspection (OSI) is the final verification measure of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). According to paragraphs 69(e), 69(f) and 69(g) of Part II of the Protocol to the CTBT, an OSI may involve the following seismic and non-seismic geophysical techniques to search for, locate and characterize underground anomalies associated with a nuclear explosion: passive seismological monitoring for aftershocks; resonance seismometry and active seismic surveys; magnetic and gravitational field mapping; ground penetrating radar; and electrical conductivity measurements. In this presentation we review recent advances in development of OSI geophysical techniques, with a focus on the application of techniques in challenging mountainous environments. Previously, techniques were primarily tested in relatively flat or gently undulating terrain conditions.Most recent advances in passive seismological monitoring include the upgrade of the telemetry system for data transmission and development of the data processing software to accommodate topographically challenging environments. To assess current OSI geophysical imaging capabilities for the other geophysical techniques and for deep site characterization applications in an integrated manner, an extensive OSI field test was conducted in the Austrian Ybbstaler Alps in September 2022. This was the first OSI field test in a challenging mountainous environment. Therefore, a number of operational, logistical and technical challenges had to be addressed. The implemented OSI geophysical techniques included resonance seismometry (passive seismic imaging) and active seismic surveys, magnetic and gravitational field mapping, as well as electrical conductivity measurements along three profiles over a cave system at 40-350 m depths mimicking underground cavities produced by an underground nuclear explosion. A newly acquired active seismic data recording system was tested for the first time, with the aim to mature OSI methods for active seismic surveys. Prior to this field test active seismic surveys have only been applied in a limited capacity. A recently developed concept of operations for resonance seismometry was also tested. Furthermore, based on the results of the field test, a new OSI workflow for gravitational field mapping is being developed. During the field test, full OSI data workflow for geophysical techniques was implemented using current functionalities within the Geospatial Information Management system for OSI (GIMO).Overall, these recent advances in development of OSI geophysical techniques demonstrate the applicability of the full range of OSI geophysical techniques in mountainous terrain, and results of these projects will be used to further develop the geophysical techniques for challenging environments.