Reviewerly/exHarmony huggingface dataset:
Temporal variation of Qc and its implications in medium characterization

The heterogeneities in the medium play important role not only in earthquake genesis but also strong ground motion simulation due to its bearing on attenuation characteristics. Observational data is one of the prerequisites for such studies which are acquired through deployment of seismological networks in active seismic regions. The Himalaya is considered as one of the most seismically active region in the world. It is also source of many river valley projects, like Tehri dam and others. The Tehri dam with 260.5 meter height is highest dam in India, located in the Lesser Himalaya of the Garhwal Himalaya that lies in seismic zone IV as per the seismic zoning map of India. Besides this, a number of development activity such as road and railway infrastructures are in progress. Thus, there is need to understand the effect of physical state of media on propagation of seismic waves in the Himalayas. The medium/path characteristics of this &amp;#160;region has been measured by determining the seismic wave attenuation of high frequency waves of local earthquakes which is accomplished by estimating the quality factor of coda waves (Qc). In the present study, Qc, has been determined using local earthquakes recorded during last fourteen years from 2008 to 2021. The local earthquakes used in the study have been obtained through deployment of 12 to 18 stations local seismological network around Tehri dam reservoir. The results showed in the study that there is no significant change in Qc is observed in the region during this period after dam impoundment. These results found in agreement with the findings that no reservoir trigger seismicity is observed in the region associated with Tehri dam reservoir during last sixteen years.
meteostations-geopy: a Pythonic interface to access data from meteorological stations

Observational meteorological data is central to understanding atmospheric processes, and is thus a key requirement for the calibration and validation of atmospheric and numerical weather prediction models. While recent decades have seen the development of notorious platforms to make satellite data easily accessible, observational meteorological data mostly remains scattered through the sites of regional and national meteorological service, each potentially offering different magnitudes, temporal coverage and data formats.&amp;#160;In order to overcome these shortcomings, we propose meteostations-geopy, a Pythonic library to access data from meteorological stations. The central objective is to provide a common interface to retrieve observational meteorological data, therefore reducing the amount of time required to process and wrangle the data. The library interacts with APIs from different weather services, handling authentication if needed and transforming the requested information into geopandas data frames of geolocated and timestamped observations that are homogeneously structured independently of the provider.&amp;#160;The project is currently in an early development stage with support for two providers only. Current and future work is organized in three interrelated main axes, namely integration of further providers, implementation of native support of distributed data structures and organization of the library into the intake technical structure with drivers, catalogs, metadata sharing and plugin packages that are provider specific.
Improving the statistical correlations between low seismic events and CO2 variations subtracting the rain contribution

A correlation between low seismic activity and CO2 measurements variations was observed at the Gallicano thermomineral spring, Tuscany, Italy, where an automatic monitoring multiparametric geochemical station is operative since 2003 (Pierotti et al., 2015). The above-mentioned correlation reported a time delay of about 2 days of small earthquakes with respect to CO2 anomalies. Starting from this correlation a conditional probability of earthquake occurrence given the CO2 anomaly detection was calculated, with a probability gain near 4 (Pierotti et al., 2022).&amp;#160; A statistical correlation was also calculated between rain events and CO2 anomalies which was observed for rain vents ahead CO2 anomalies of one days. This permitted to distinguish CO2 anomalies due to meteorological versus tectonic activities.&amp;#160; Following this distinction, and subtracting the rain contribution to the CO2 variations, a new correlation was observed between small earthquakes and CO2 anomalies which confirmed the past results whit a better performance. The new correlation peak is better defined and concentrated in the time lag of 2 days. The p-values of both earthquake and rain to CO2 correlations were calculated. The correspondent probability gain in an earthquake forecasting experiment, taking into account the rain events, increased from less than 4 to 4.5.&amp;#160;&amp;#160;&amp;#160; &amp;#160;&amp;#160;&amp;#160;Fidani, C. (2021). West Pacific Earthquake Forecasting Using NOAA Electron Bursts With Independent L-Shells and Ground-Based Magnetic Correlations. Front. Earth Sci. 9:673105.Pierotti, L., Botti, F., D&amp;#8217;Intinosante, V., Facca, G., Gherardi, F. (2015). Anomalous CO2 content in the Gallicano thermo-mineral spring (Serchio Valley, Italy) before the 21 June 2013, Alpi Apuane earthquake (M= 5.2).&amp;#160;Physics and Chemistry of the Earth, Parts A/B/C,&amp;#160;85, 131-140.Pierotti, L., Fidani C., Facca, G., Gherardi, F. (2022). Local earthquake conditional probability based on long term CO2 measurements. In 40st GNGTS National Conference, Trieste, 27 - 29 June 2022.
Insights from CO2/SO2 gas molar ratio variations and distribution at Stromboli volcano

Gas-sensor-based monitoring stations (aka MultiGAS) near degassing volcanic vents sensibly increased the sampling rate of gas composition measurements. In particular, the CO2/SO2 gas molar ratio was demonstrated to be a good indicator of magma depth thanks to the CO2 and SO2 contrasting solubilities in magma. Numerous high CO2/SO2 gas transients recorded days before an effusive/explosive eruption have been reported in the literature (e.g. Etna, Villarrica, Masaya, Poas). The successful detection of a precursor gas signal in these volcanoes has been favoured by the presence of open or highly permeable closed-conduit (as in the case of Poas) and the instrument&amp;#8217;s vicinity to a high-flux degassing vent (high signal/noise ratio). Volcanic gas monitoring in Stromboli represents a particular case. Stromboli is characterised by degassing from multiple vents, which exhibit simultaneous different molar gas ratios during quiescent degassing. The gas emissions during transient strombolian explosions are also different, with higher CO2 contents. Stromboli occasionally exhibits major explosions and paroxysms of greater energy which have often shown a substantial variation of the gas bulk composition weeks before their onset. Finally, the safest sites for a monitoring station in Stromboli are located hundreds of meters from the crater terrace, implying that the detected gas concentrations depend on the wind direction and speed. All these features make the analysis and interpretation of the volcanic CO2/SO2 challenging. This work presents a 2-year-long CO2/SO2 time series recorded at the Stromboli&amp;#8217;s summit. We discuss different types of analysis that can be performed to enhance the variations before major and paroxysmal eruptions. We apply an iterative algorithm to estimate the time, number and magnitude of abrupt changes within the CO2/SO2 time series and discuss the origin of such variations. We use an algorithm for finite mixture models on the whole dataset to characterise the source of different gas phases. Finally, we compare the measured CO2 and SO2 concentrations with the wind parameters obtained for the area of Stromboli from the ERA5 reanalysis dataset. Hence, we determine the best conditions for gas ratio measurements and how meteorological conditions may affect the measurements' quality.
Semi-supervised seismic event detection using Siamese Networks

Detecting seismic events and their precursors is vital to understand and assess risks in areas of seismic instability. Most recent detection methods are based on supervised learning, where machine learning models are first trained using a labelled dataset, before being deployed. However, seismic sensors are often difficult to install and maintain, and large-scale events are few and far between. Furthermore, labelling collected data requires a great deal of time and effort from seismologists. Noise can vastly increase the difficulty of this task and labels can be highly subjective. Labelled data used for training machine learning models depends on the monitoring setup and geological characteristics of the terrain where the sensors are installed. For example, a dataset of events recorded in the Alps will likely not be representative of events that could be seen in less mountainous regions, meaning that transferability of proposed networks is vital. The Rest and Be Thankful in Scotland is a remote hillside prone to weather-induced seismic events which can cause disruption to the road infrastructure in the valley below, after rockfalls and landslides due to quakes. In this paper we propose a semi-supervised method of clustering these different types of events. Grouping data into categories of both known and unknown event types can reduce the time needed by experts to create labelled datasets via the use of Siamese networks and further understand the dynamics of the slope. We validate results against the BGS earthquake database from within a 50km radius, as well as human induced rockfalls. Grouping across around 100 days of data has detected a possible 10 earthquakes, 82 rockfalls, and 137 micro-quakes.
IESDB – A comprehensive database of evaporite structures in Iberia

Evaporite rocks are one of the most mined mineral commodities and act as a seals or shaping traps in some of the major hydrocarbon provinces worldwide. In addition, they are also considered as suitable sites for the storage of energy and nuclear waste, being a key asset for the energy transition. Due their historic, present and future value, vast amounts of surface and subsurface information about evaporite structures have been generated by earth scientists, mining and exploration companies or geological surveys in the last century. However, this information is often scarcely useful due to access issues, segregation and scarce dissemination. Here we present the Iberian Evaporite Structure DataBase (IESDB), the first overall assessment focused on evaporite structures developed in any region of the world. The IESDB includes information and figures of 150 outcropping and buried evaporite structures and their surrounding rocks inventoried in Iberia. The Iberian Evaporite Structure Database (IESDB) includes information about the stratigraphy, structure, evolution, geophysical and petrophysical data availability, and mining activity, including a complete set of geological maps, sketches and geological cross-sections. The database targets different evaporite structures, such as undeformed successions, diapirs, evaporite-cored anticlines, evaporite-detached thrusts or allochtonous evaporite bodies. The IESDB is sourced from six different databases and more than 1,500 published and unpublished references, and includes information and figures for each of the 150 evaporite structures inventoried. The IESDB follows the FAIR principles of data management (Findable, Accessible, Interoperable, Reusable) and aims to be a resource for earth science teaching, academic research and resource exploration and appraisal. The IESDB is freely available at https://iesdb.euThis research was performed within the framework of DGICYT Spanish Project PID2020-118999GB-I00 funded by the Ministerio de Ciencia, Innovaci&amp;#243;n y Universidades/Agencia Estatal de Investigaci&amp;#243;n/Fondo Europeo de Desarrollo Regional. Grants RYC2021-033872-I (Juan Alcalde) and RyC-2018-026335-I (Enrique Gomez-Rivas) funded by MCIN/AEI/10.13039/501100011033 and ESF &amp;#8220;Investing in your future&amp;#8221;.
Dynamic forecasting of the next largest earthquake during hydraulic fracturing stimulations

Most traffic-light systems (TLS) related hydraulic fracturing stimulations still adopt the maximum observed magnitude as the decisive metric to aid decision making by stakeholders. However, waiting for the red-light magnitude to be observed is not a proactive stance, especially given that jumps of up to two magnitude units are evidently common enough between events. Clearly there is a need to actively forecast rather than to passively record the size of the next largest earthquake (NLE). In this study, we demonstrate that we can do just that using an ensemble of 6 existing models from the literature designed with similar purposes in mind (Shapiro et al. 2013; McGarr 2014; Mendecki 2016; van der Elst et al. 2016; Galis et al. 2017; Cao et al. 2020). Following a logic-tree approach, these 6 models are calibrated and dynamically weighted in near real-time using as sole inputs the initial parts of the earthquake catalogs and the reported injection rates. The proposed forecasting tool is tested against 18 past stimulations from 9 different Enhanced Geothermal Systems around the world (Helsinki, Basel, Soultz, Cooper Basin, Basel, Pohang, FORGE, Paralana, Newberry). Overall, the results indicate a consistent (across sites and time) and accurate estimation of the next largest magnitude with a tight uncertainty range (1&amp;#963;) of less than 0.5 magnitude units. Our proposed framework underestimated the next largest magnitude only in one occasion (out of the 18 stimulations), while reliably maintaining a tight safety margin of less than 1 magnitude unit. We recommend that the forecasted NLE replaces the largest observed magnitude as the default metric adopted by future TLS governing any type of fluid-injection operation.&amp;#160;
Melt Detection and Estimation of the Current Magma Intrusion Rate beneath the East Eifel Volcanic Field, Germany

Deep low-frequency seismic events are detected in the East Eifel Volcanic Field (EEVF) since 2013. To well detect and locate such events the Deep Eifel Earthquakes Project - Tiefe Eifel Erdbeben (DEEP-TEE) started in July 2014 which now is composed of ca. 10 permanent and 15 mobile recording stations. Up to now, the DEEP-TEE seismic dataset contains eight years of continuous seismic records and the network has been reconfigured and continuously developed to achieve an optimum configuration regarding detection and location of seismic events.In order to detect the weak deep low-frequency (DLF) events we developed a seismic event detector and found ca. 330 localizable DLF events in 2014-2021. The DLF hypocenter distribution outlines a near-vertical structure close to the Laacher See Volcano (LSV) which erupted about 13,079 years ago. The hypocenters are as deep as ca. 45 km, close to the assumed lithosphere-asthenosphere boundary, and reach to about 5-8 km depth. Most events occur close to the Moho and in the lower crust what is interpreted as magmatic underplating and deep crustal intrusion. In the same depth range but further to the west, we find seismic reflections with a negative polarity. These are also interpreted as magmatic pockets in the lower crust and the Moho region.We try to estimate the mass flux (magma and volatiles) which is related with the seismicity. For this we apply Aki et al.'s model (JVGR, 1977) for describing the magma movement (a so-called chain of cracks connected by narrow channels) and estimate the related magma intrusion volume rate in the EEVF lithosphere. We assume an initial set of model parameters and evaluate the sensitivity and stability of the modelling results by allowing a reasonable range of each individual input parameter. Our results give an estimate of about 2,000-16,000 cubic meters of melt per year which is transported in the lithosphere.
Soil quality assessment with imaging spectroscopy under land use changes

Global population growth has resulted in land-use (LU) changes in many natural ecosystems, causing deteriorated environmental conditions that impact soil quality. This rapid growth in the global population caused many natural ecosystems to be transformed into human-dominated ones. Such LU dynamics require greater resource exploitation, commonly resulting in degraded environmental conditions that are acknowledged in the soil quality. The effects on the soil are even more acute in water-scarce and limited resources environments such as drylands. Therefore, developing appropriate approaches for soil quality assessment and function evaluation is necessary since the soils in those areas are usually undeveloped and retain lower organic matter capacity. The research aim was to apply, measure, and evaluate soil properties based on the imaging spectroscopy (IS) differences between natural and human-dominated LU practices in the dryland environment of the Negev Desert, Israel. &amp;#160;A flight campaign of the AisaFENIX hyperspectral airborne sensor was used to develop an IS prediction model for the SQI on a regional scale. The spectral signatures extracted from the hyperspectral image were well separable among the four LUs using the partial least squares-discriminant analysis (PLS-DA) classification method (OA = 95.31%, Kc = 0.90). The correlation was performed using multivariate support vector machine regression (SVM-R) models between the spectral data, the measured soil indicators, and the overall SQI. The SVM-R models were significantly correlated for several soil properties, including the overall SQI (R2adjVal = 0.87), with the successful prediction of the regional SQI mapping (R2adjPred = 0.78). Seven individual soil properties, including fractional sand and clay, SOM, pH, EC, SAR, and P, were successfully used for developing prediction maps. Applying IS, and statistically integrative methods for comprehensive soil quality assessments enhances the accuracy of predicting soil health and evaluating degradation processes in arid environments. This study establishes a precise tool for sustainable and efficient land management and could be an example for future potential IS earth-observing space missions for soil quality assessment studies and applications.
The Effects of Text Length on the Readability of Model Essays

Providing self-access libraries and the texts therein (anthologies of model essays) has long been cited as an essential part of university writing center services, as the reading of model essays has been found to positively affect the reading-writing relationship. When choosing such texts, readability is typically measured via quantitative readability formulae (e.g., the Lexile Readability Formula). However, this practice only measures two (i.e., semantic, syntactic) of the many features that influence readability, leaving others (e.g., text length) unexplored. To address this, this article reports the findings of an exploratory mixed-methods study conducted in an Asian university writing center setting which showed that the informants' ranking of ease and difficulty was significantly different than the Lexile Formula and that text length had a significant positive association with this ranking. It was further found that length was viewed as (a) a primary (i.e., an isolated feature), (b) a conjoined feature (i.e., comprising two or more associated entities where the second impacts the first: interest, vocabulary), and (c) a feature which impacts the influences of other features (interest, vocabulary, and vocabulary in context). The study also offers suggestions for writing studies professionals (teachers, writing center staff) and the publishing industry that text length be included in a hybrid (quantitative-qualitative) procedure when considering the difficulty of model essays found in anthologies.
Pragmatics Study on Deixis Analysis in Expository Text by Students EFL Secondary School

This study attempts to identify the varieties of deixis (according to Levinson's deixis theory) employed and the major deixis types used by EFL secondary school students in expository texts. This study used qualitative research as a method and used a documentary technique for the analysis. The analysis's conclusion demonstrates that Students in EFL secondary schools use every sort of deixis in writing expository text. The deixis that is mostly used is discourse deixis, which is 158 times, the second is followed by person deixis with 150 times, time deixis with 14 times, place deixis with 10 times, and the lowest is social deixis which is only 6 times. According to the findings of this study, discourse deixis becomes the predominant type used in the expository texts. This result implies that the dominant deixis employed varies depending on the type of text.
Research on the Living Lab Operation Plan According to the Survey of Exercise Equipments and Programs for 'Rehabilitation Exercise and Physical Education'

The purpose of this study was to analyze 13 tasks of the National Rehabilitation Center and investigate domestic rehabilitation exercise-related institutions to investigate exercise equipment and programs that can provide "Rehabilitation exercise and physical education" service for the disabled to return to society and it also can use as basic data for building the "Rehabilitation exercise and physical education" living lab.As a result of analyzing the 13 tasks of the National Rehabilitation Center, it was analyze exercise programs and exercise equipments for sensory impairments (visual and hearing), physical disabilities (cerebral palsy, spinal cord disability), developmental disabilities, and all disabilities.As a result of investigation and analysis of 1427 rehabilitation exercise centers and exercise facilities, it was found that the expansion of exercise programs for the physically diabiled was necessary.Finally, the living lab operation plan for rehabilitation exercise and physical education should be user-led and operated mainly by disabled users, and a model consisting of the public and the private sector was developed.This model is expected to maximize its effectiveness through a cooperative system with the public and private sectors(4P Rehablitaion Exercise and Physical Education Living LAB Model(4P-REPELLM)).
Analysis of ICT Factors and Gender Differences Predicting Reading Literacy and Mathematics Achievement Using Random Forest: Focusing on PISA 2018 Data

Based on PISA2018 data, this study analyzed the diffence between 11 items of ICT literacy ability as a predictor of reading literacy and math achievement.At this time, gender differences in ICT education were also studied by studing how gender aggects the expression of differences as a preditor vaiable.For the analysis, partial dependence diagram was used to find out the difference in feature importance and positive/negative effects through random forest analysis using a Python program as a machine learing technique.Data visualiazation data was provided so that the diffecences could be seen in graths so that each result could be easily analyzed visually.