Design A Prototype of Temperature Logging Tools for Geothermal Prospecting Areas

<p>The SCIentific Qt application for Learning from Observations of Plasmas (SciQLop) project allows to easily discover, retrieve, plot and label in situ space physic measurements from remote servers such as Coordinated Data Analysis Web (CDAWeb) or Automated Multi-Dataset Analysis (AMDA).  Analyzing data from a single instrument on a given mission can rise some technical difficulties such as finding where to get them, how to get them and sometimes how to read them.  Thus building for example a machine-learning pipeline involving multiple instruments and even multiple spacecraft missions can be very challenging. Our goal here is to remove all these technical difficulties without sacrificing performances to allow scientist to focus on data analysis.<br>SciQLop development has started in 2015 as a C++ graphical application funded by the Paris-Saclay Center for Data Science (CDS) then by Paris-Saclay SPACEOBS and finally it joined the Plasma Physics Data Center (CDPP) in 2019. It has evolved from  a monolithic C++ graphical application to a collection of simple and reusable Python or C++ packages solving one problem at a time, increasing our chances to reach users and contributors.</p><p>The SciQLop project is composed of the following tools:</p><ul><li>Speasy: An easy to use Python package to retrieve data from remote servers with multi-layer cache support.</li> <li>Speasy_proxy: A self-hostable, chainable remote cache for Speasy written as a simple Python package.</li> <li>Broni: A Python package which finds intersections between spacecraft trajectories and simple shapes or physical models such as magnetosheath.</li> <li>Orbit-viewer: A Python graphical user interface (GUI) for Broni.</li> <li>TSCat: A Python package used as backend for catalogs of events storage.</li> <li>TSCat-GUI: A Python graphical user interface (GUI).</li> <li>SciQLop-GUI: An extensible and efficient user interface to visualize and label time-series with an embedded IPYthon terminal.</li> </ul><p>While some components are production ready and already used for science, SciQLop is still in development and the landscape is moving quite fast.<br>In this presentation we will give an overview of SciQLop, demonstrate its benefits using some specific cases studies and finally discuss the planned features development.</p>

The SCIentific Qt application for Learning from Observations of Plasmas (SciQLop) project allows to easily discover, retrieve, plot and label in situ space physic measurements stored on remote servers such as Coordinated Data Analysis Web (CDAWeb) or Automated Multi-Dataset Analysis (AMDA).  Analyzing data from a single instrument on a given mission can raise some technical difficulties such as finding where to get them, how to get them and sometimes how to read them.  Thus building for example a machine-learning pipeline involving multiple instruments and even multiple spacecraft missions can be very challenging. Our goal here is to remove all these technical difficulties without sacrificing performances to allow scientist to focus on data analysis.The SciQLop project is composed of the following tools:Speasy: An easy to use Python package to retrieve data from remote servers with multi-layer cache support. Speasy_proxy: A self-hostable, chainable remote cache for Speasy written as a simple Python package. Broni: A Python package which finds intersections between spacecraft trajectories and simple shapes or physical models such as magnetosheath. Orbit-viewer: A Python graphical user interface (GUI) for Broni. TSCat: A Python package used as backend for catalogs of events storage. TSCat-GUI: A Python graphical user interface (GUI). SciQLop-GUI: An extensible and efficient user interface to visualize and label time-series with an embedded IPYthon terminal. While some components are production ready and already used for science, SciQLop is still in development and the landscape is moving quite fast.In this poster we will demonstrate how the SciQLop project makes masive in-situ data analysis simple and fast and we will also take the oportunity to exchange ideas with our users.

The SCIentific Qt application for Learning from Observations of Plasmas (SciQLop) project allows to easily discover, retrieve, plot and label in situ space physic measurements stored on remote servers such as Coordinated Data Analysis Web (CDAWeb) or Automated Multi-Dataset Analysis (AMDA).  Analyzing data from a single instrument on a given mission can raise some technical difficulties such as finding where to get them, how to get them and sometimes how to read them.  Thus building for example a machine-learning pipeline involving multiple instruments and even multiple spacecraft missions can be very challenging. Our goal here is to remove all these technical difficulties without sacrificing performances to allow scientist to focus on data analysis.The SciQLop project is composed of the following tools:Speasy: An easy to use Python package to retrieve data from remote servers with multi-layer cache support. Speasy_proxy: A self-hostable, chainable remote cache for Speasy written as a simple Python package. Broni: A Python package which finds intersections between spacecraft trajectories and simple shapes or physical models such as magnetosheath. Orbit-viewer: A Python graphical user interface (GUI) for Broni. TSCat: A Python package used as backend for catalogs of events storage. TSCat-GUI: A Python graphical user interface (GUI). SciQLop-GUI: An extensible and efficient user interface to visualize and label time-series with an embedded IPYthon terminal. While some components are production ready and already used for science, SciQLop is still being developped and the landscape is moving quite fast.In this poster we will demonstrate how the SciQLop project makes masive in-situ data analysis simple and fast and we will also take the oportunity to exchange ideas with our users.

CandiUmbul is one of the manifestation locations in Telomoyo geothermal prospect area. This manifestation is warm spring which assumed as outflow. A magnetic study had been conducted in a geothermal prospect area in CandiUmbul, Telomoyo, Central Java. Magnetic method is one of the geophysical methods used to geothermal exploration. Magnetic method measures in the Earth’s magnetic field. The purpose of this research has to map the susceptibility distribution in the subsurface due to the geothermal system. Proton Precession Magnetometer (PPM) was used to measure the magnetic field. Data processing started from correcting magnetic data with diurnal and IGRF correction, to achieve the total anomaly magnetic field. Then, the total anomaly magnetic field was reduced to pole. For the quantitative interpretation, we used 2D forward modeling. The 2D forward modeling was conducted to produce the magnetic anomaly modeling with using mag2DC for windows. The result of the quantitative interpretation is the horizontal position of the body due to the anomaly which is located close to the CandiUmbul warm spring. This warm spring was controlled by fault which is the weak zone in this area, so the hot water can easily passed from Telomoyo geothermal reservoir. The research is still ongoing to support the conclusion of qualitative interpretation.

Geothermal activities in the Main Ethiopian Rift: Hydrogeochemical characterization of geothermal waters and geothermometry applications (Dofan-Fantale, Gergede-Sodere, Aluto-Langano)

This study was carried out to identify the temporal changes of the magnetic anomaly values and geochemical data at Mt. Pandan geothermal prospect area. The results from magnetic and geochemical analysis are used to determine the geothermal energy potential in study area. The residual magnetic anomaly in 2012 and 2018 was used to compare the temporal changes of geological structures in Mt. Pandan, while geochemical data was obtained in 2 locations (Jari and Banyukuning) of hot spring. The results of magnetic observation indicate the decreasing of residual magnetic anomaly values from 2012 to 2018 due to the tectonic activities which generate the secondary structures in study area. Geochemical analysis shows the decreasing of reservoir temperature from 139°C in 2012 and 130°C in 2018. The pH of water in 2 locations also changes from neutral to acid due to the presence of new fractures, lead the water to flow and pass through the surrounding rocks that can release silica, thus providing high concentrations in water and decrease the pH to acidic water. The electrical potential of Mt. Pandan geothermal energy prospect area is 0,2378 Mwe per second.

Preliminary assessment of MT Pancar was carried out using remote sensing and geochemical studies. Remote sensing can be applied as the first guidance in the exploration stage of developing geothermal prospect area. It is usually used to provide an overview of geothermal system before conducting geological survey. In this research, remote sensing using DEM ASTER was performed to give information about lithology and structure of Mt. Pancar geothermal prospect area. Delineation of several lithological unit is based on texture and pattern of the remote sensing image. After field checking, four lithological units have been confirmed covering Mt Pancar area, such as Pancar andesite, Anak Pancar andesite, Kencana-Limo volcanic breccia and Jatiluhur shale. Furthermore, indication of geological structures in NE-SW and NW-SE trend caused by sinistral movement of Baribis thrust is also successfully imaged by remote sensing analysis. The structures probably control the existence of Kawah Merah and Kawah Hitam hotsprings in the Mt Pancar area. Moreover, geochemical studies were also carried out by taking water samples and measuring the temperature and pH. The results show that the temperature of Kawah Merah, Kawah Hitam and Kawah Putih hotsprings are 68.4°C, 58.1°C and 47°C, respectively. All of the hotsprings are classified as neutral hotsprings with pH range of 6.4 to 7.7. It indicates that the whole hotsprings are an outflow zone of Mt. Pancar geothermal system. In addition, based on morphological feature, the heat source is inferred beneath the center of Mt. Pancar. Based on Na/K geothermometry, the subsurface temperature in Mt Pancar geothermal prospect area is estimated in the range of 180 to 190°C. Therefore, Mt Pancar geothermal area can be classified as a low to moderate geothermal system.

The Blawan-Ijen volcanic complex is located in Bondowoso regencies, East Java province. The complex is expected to have geothermal system which is indicated by the occurrence of Blawan hotspring, acid lake on Ijen Crater and alterations. In 2017, measurements of gravity and magnetic methods have been conducted for the first time through the PITTA 2017 program. In 2018, further measurements are carried out to infill the previous data in order to strengthen the interpretation results. There are 151 stations obtained from each method until 2018. In this study, gravity method is used to detect the contrast density of an anomalous body while magnetic method is applied to discover the location of demagnetization zone. This paper presents the integration of both methods in geothermal exploration to determine the geothermal prospect area. The result of CBA and residual gravity indicated the existence of high gravity anomaly in the center to the southwest of the study area. Moreover, after processing RTP on magnetic data, there is the presence of low magnetic anomaly usually associated with demagnetization zone. Generally, the overall results supported one each other and pointed out the occurrence of the geothermal prospect possibly around the center of the study area.

A separate section is presented for each of the six prospect areas studied. Each section includes a compilation and discussion of environmental baseline data derived from existing sources. The data are arranged as follows: geology and geohydrology, air quality, water resources and flood hazards, ecological systems, and land use. When data specific to the prospect were not available, regional data are reported. (MHR)

Summary Ranau Geothermal Prospect Area is located at borderline South Sumatera and Lampung Province on the Southwest Sumatera Island, bordered by three clusters of young volcanic, namely Mt. Pugung on Southwest, Mt. Seminung on South, and Mt. Raja on Northeast. With this condition, based on geological map, Ranau Area is dominated by volcanic breccia, tuff, and andesite-basaltic lava (Qhv) from Seminung Volcanoes, Andesite-Basaltic (Qv) with thickness 300 m from Pugung Volcanoes, contained by sulphide minerals and quartz veins (Tomh), and Rhyolitic tuff, pumiceous tuff, welded tuff with carbonaceous claystones intercalations (QTr). On surface mapping and based on reference found three geothermal manifestation such as Kota Batu hotspring on northeast of Seminung Mountain, Ujung hotspring on the west seminung, and wahid hotspring on northwest area. Based on Land Surface Temperature Map, we obtain the high temperature indicated by dark-red color with value is 35-50oC. In this research, we would to correlate the result of landsat-8 satellite imagery (processed into land surface temperature map) with geothermal manifestation (three hotspring) based on reference to know the hotspot zone as a preliminary study of geothermal and geo-tourism prospect area.

Current remote sensing technologies shows that surface manifestation of geothermal system could be detected with optical and SAR remote sensing, but to assess target beneath near the surface layer with the surficial method needs a further study. This study conducts a preliminary result using Optic and SAR remote sensing imagery to detect near surface geothermal manifestation at and around Mt. Papandayan, West Java, Indonesia. The data used in this study were Landsat-8 OLI/TIRS for delineating geothermal manifestation prospect area and an Advanced Land Observing Satellite(ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) level 1.1 for extracting lineaments and their density. An assumption was raised that the lineaments correlated with near surface structures due to long L-band wavelength about 23.6 cm. Near surface manifestation prospect area are delineated using visual comparison between Landsat 8 RGB True Colour Composite band 4,3,2 (TCC), False Colour Composite band 5,6,7 (FCC), and lineament density map of ALOS PALSAR. Visual properties of ground object were distinguished from interaction of the electromagnetic radiation and object whether it reflect, scatter, absorb, or and emit electromagnetic radiation based on characteristic of their molecular composition and their macroscopic scale and geometry. TCC and FCC composite bands produced 6 and 7 surface manifestation zones according to its visual classification, respectively. Classified images were then compared to a Normalized Different Vegetation Index (NDVI) to obtain the influence of vegetation at the ground surface to the image. Geothermal area were classified based on vegetation index from NDVI. TCC image is more sensitive to the vegetation than FCC image. The later composite produced a better result for identifying visually geothermal manifestation showed by detail-detected zones. According to lineament density analysis high density area located on the peak of Papandayan overlaid with zone 1 and 2 of FCC. Comparing to the extracted lineament density, we interpreted that the near surface manifestation is located at zone 1 and 2 of FCC image.

Gunung Endut, located in Banten Province, has geothermal pr ospect area in its mountain slope. Some of the area of Gunung Endut has limestone facies which commonly deposited in marine environment. Based on the regional geological map of Leuwidamar, limestone facies are the member of Bojongmanik Formation within the Bogor Physiographic Zone. In Gunung Endut, limestone was developed during the pre-volcanic period, meanwhile most of limestone in Java are found as outcrop in Southern Mountains Zone formed during the post-volcanic period. This study focused on macroscopic and microscopic identification of limestone in order to determine the distribution of facies and the history of limestone sedimentation in the area of Gunung Endut. The macroscopic and microscopic feature of the rock samples are analyzed and classified using modified Dunham classification by Embry-Klovan. The result of the analyses will provide information about the limestone facies distribution and geological history with better detail in the study area than previously known.

In this work we present a method to perform pituitary cell segmentation in image stacks acquired by fluorescence microscopy from pituitary slice preparations. Although there exist many procedures developed to achieve cell segmentation tasks, they are generally based on the edge detection and require high resolution images. However in the biological preparations that we worked on, the cells are not well defined as experts identify their intracellular calcium activity due to fluorescence intensity changes in different regions over time. This intensity changes were associated with time series over regions, and because they present a particular behavior they were used into a classification procedure in order to perform cell segmentation. Two logistic regression classifiers were implemented for the time series classification task using as features the area under the curve and skewness in the first classifier and skewness and kurtosis in the second classifier. Once we have found both decision boundaries in two different feature spaces by training using 120 time series, the decision boundaries were tested over 12 image stacks through a python graphical user interface (GUI), generating binary images where white pixels correspond to cells and the black ones to background. Results show that area-skewness classifier reduces the time an expert dedicates in locating cells by up to 75p in some stacks versus a 92p for the kurtosis-skewness classifier, this evaluated on the number of regions the method found. Due to the promising results, we expect that this method will be improved adding more relevant features to the classifier.

- Desktop applications continue to play a vital role in various operational and administrative environments, particularly where lightweight deployment and offline accessibility are essential. This paper introduces the design and implementation of a robust desktop-based Graphical User Interface (GUI) system developed using Python’s Tkinter library. The system focuses on simplifying the process of desktop application development by offering a modular, scalable, and user-oriented framework. It integrates multiple functional modules into a unified interface, promoting ease of navigation and operational efficiency. The development methodology emphasizes clear structure, maintainability, and enhanced interaction through intuitive design principles. Furthermore, the application ensures compatibility across major operating systems, thus supporting broader usability. Detailed testing and performance analysis reveal the system’s responsiveness and reliability under various use-case scenarios. This work demonstrates the practical applicability of Tkinter as a tool for creating efficient and user-friendly desktop solutions, and it provides a reference framework for future developments in similar domains. Key Words: Tkinter, Python GUI, Desktop Application, User Interface Design, Software Architecture, Modular Design.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique used to probe the local environment of fluorophores. The fit-free phasor approach to FLIM data is increasingly being used due to its ease of interpretation. To date, no open-source graphical user interface (GUI) for phasor analysis of FLIM data is available in Python, thus limiting the widespread use of phasor analysis in biomedical research. Here, we present Fluorescence Lifetime Ultimate Explorer (FLUTE), a Python GUI that is designed to fill this gap. FLUTE simplifies and automates many aspects of the analysis of FLIM data acquired in the time domain, such as calibrating the FLIM data, performing interactive exploration of the phasor plot, displaying phasor plots and FLIM images with different lifetime contrasts simultaneously, and calculating the distance from known molecular species. After applying desired filters and thresholds, the final edited datasets can be exported for further user-specific analysis. FLUTE has been tested using several FLIM datasets including autofluorescence of zebrafish embryos and in vitro cells. In summary, our user-friendly GUI extends the advantages of phasor plotting by making the data visualization and analysis easy and interactive, allows for analysis of large FLIM datasets, and accelerates FLIM analysis for non-specialized labs.