Occurrence rate of ultra-low frequency waves in the foreshock of Mercury increases with heliocentric distance

Studies of Mercury’s foreshock have analyzed in detail the properties of ultra-low frequency waves. However, an open question remains in regards to understanding favorable conditions for these planetary foreshocks waves. Here, we report that 0.05–0.41 Hz quasi-monochromatic waves are mostly present under quasi-radial and relatively low intensity Interplanetary Magnetic Field, based on 17 Mercury years of MESSENGER Magnetometer data. These conditions are consistent with larger foreshock size and reflection of solar wind protons, their most likely source. Consequently, we find that the wave occurrence rate increases with Mercury’s heliocentric distance. Detection of these waves throughout Mercury’s highly eccentric orbit suggests the conditions for backstreaming protons are potentially present for all of Mercury’s heliocentric distances, despite the relatively low solar wind Alfvén Mach number regime. These results are relevant for planetary magnetospheres throughout the solar system, and the magnetospheres of exoplanets, and provide knowledge of particle acceleration mechanisms occurring inside foreshocks.

Pemetaan batuan penyusun candi di bawah permukaan berdasarkan metode geomagnetik studi kasus: cagar budaya candi Songgiriti desa Songgokerto kecamatan Batu kota Batu

The geomagnetic method is often used in petroleum, geothermal and mineral exploration and can be applied to searching prospect archaeological objects (Siahaan, 2009). Several studies has been used the geomagnetic method to seek for archaeological objects by Ariani (2012) in Losari Temples and Sismanto et al (1997) in Kedulan Temple. In Batu City there is one of the cultural heritage that have not been intact called Songgoriti Temple. So, in this research we expected the existence of temple’s rocks using geomagnetic method. The de sign of the research using geomagnetic methods begins with the study of litera ture. This research was conducted with two methods there is calculating the val ue of the magnetic susceptibility of samples Temple’s rock using Bartington Susceptibility Meter MS2B and retrieving data field using the Proton magneto meter type ENVI SCINTREX. In the end of this research, we accepted the re sults of the two methods were compared to find out the site rock of temple. In magnetometer data analysis, data is processed using Magpick software, surfer 9.0 software and Mag2dc software. The results of the research show that a local magnetic field patterns of rocks in the area of cultural heritage Songgoriti Tem ple is divided into three parts namely high local magnetic field (yellow to red), the moderate local magnetic field (green to yellow) and low local magnetic field (blue). Furthermore based on five cross-sectional modeling using Mag2dc, we obtained the prediction the site of temple Songgoriti that is the sample number 1 found on the 2 position i.e at coordinates 49S 664547.5m 9130115m and coor dinate 49S 664585m 9130105m; sample number 3 found on the 2 position i.e at coordinates 49S 664583m 9130100m and coordinate 49S 664585m 9130100m; sample number 4 found on the 2 position i.e. at coordinates 49S 664577.5m 9130115m and coordinate 49S 664577.5m 9130110m; sample number 5 found on the 3 position i.e. at coordinates 49S 664566m 9130095m, coordinates 49S 664573m 9130095m and coordinate 49S 664582.5 m 9130095m; sample num ber 6 is found in 5 position i.e. at coordinates 49S 664545.5 m 9130110m, coordinates 49S 664550m 9130110m, coordinates 49S 664546m 9130105m, coordinates 49S 664565m 9130105m and coordinates 49S 664562.5 m 9130100m. For sample number 2 that is not found at all in the five models cross section. This may be caused by the value of the magnetic susceptibility sample number 2 is minor when compared to another samples that is 0.86 x 10-6 m3/kg

Prediction of Labor Activity Recognition in Construction with Machine Learning Algorithms

It is essential that the control and management of the work of labors in construction project management is effective. In this study, it is aimed to building artificial intelligence models to recognition on activities in a construction work to effectively utilization project management and control. In accordance with this purpose, 3-axis accelerometer, gyroscope, and magnetometer data were obtained from the labors through the sensor to predict the activities determined for a construction work. These raw data were made compliance for the model by going through a series of preprocessing applications. These data are trained and modeled with basic machine learning algorithms logistic regression, SVC, DT and KNN algorithms. According to the results of the analysis, the best prediction was obtained with the SVC algorithm with an accuracy of 90%. In other algorithms, respectively, 87% accuracy was contrived in the KNN algorithm, and approximately 80% accuracy in the logistic regression and DT algorithms. According to these values, it has been observed that the activities performed in a construction work can be estimated at a high rate. In this way, at the construction sites, it can be automatically determined which work the laborer do at a certain accuracy rate.

Identifikasi mineral kalsit di kawasan sungai desa Sumbermiri kecamatan Lengkong kabupaten Nganjuk dengan metode geomagnet

This research aims to identify the mineral rocks that are found in the river village districts of Sumbermiri village, Lengkong district, Nganjuk regency. XRF tests have been conducted on a rock an the result was 57 percent of the rocks is a calcite minerals. He method used is the geomagnetics, is by measurig the value of the total magnetic field in the area of research using the magnetometer. Data collection is done three times at each point, then retrieved the data with the smallest noise value to be processed. Corection calculation of the daily (diurnal) and IGRF corection. IGRF value of research location is 33281,4, with the degree of inclination is 25,9993 and declination 8,1201. Then the data has be performed using a contour in the Surfer 10 and Magpick transform and see the contours of the surface magnetics field. Interpretation of results indicates calcite mineral content in the study area. Mag2DC modeling obtained using 2D contour transversely. On the first line, calcite rock has been indentified susceptibility of -2,955 - -0,651 with the misfit 747,79. Second line has been susceptibility of -0,897 - -0,249 with the misfit 592,35. Third line has been susceptibility of -0,509 - -0,005 with the misfit 867,85.

Determination of magnetopause and bow shock shape based on convolutional neural network modelling of MESSENGER data

<p><span id="E87">The magnetosphere of Mercury is relatively small and highly dynamic, mostly due to the weak planetary magnetic field. Varying solar wind conditions principally determine the location of both the </span><span id="E89">Hermean</span><span id="E91"> bow shock and magnetopause. In 2011 – 2015 MESSENGER spacecraft completed over 4000 orbits around Mercury, thus giving a data of more than 8000 crossings of bow shock and magnetopause of the planet, this makes it possible to study in detail the bow shock, the magnetopause and the </span><span id="E93">magnetosheath</span><span id="E95"> structures.</span></p> <p>In this work we determine crossings of the bow shock and the magnetopause of Mercury by applying machine learning methods to the MESSENGER magnetometer data. We attempt to identify the crossings during the whole duration of the orbital mission and model the average three-dimensional shapes of these boundaries. The results are compared with those previously obtained in other works.</p> <p><span id="E101">This work may be of interest for future Mercury research related to the </span><span id="E103">BepiColombo</span><span id="E105"> spacecraft mission, which will enter the orbit around the planet in December 2025.</span></p>

Penentuan Orientasi dan Translasi Gerakan UAV menggunakan Data Fusion berbasis Kalman Filter

In an unmanned aircraft vehicle, a navigation system is needed to calculate its orientation and translation. The navigation system can utilize data from the accelerometer, gyroscope, magnetometer, and GPS. The orientation can be precisely calculated from the accelerometer and magnetometer data when the sensor is in a static state. Meanwhile, under dynamic conditions, the orientation can be more precisely calculated from the gyroscope data. In order to obtain the robust navigation system, a data fusion based on Kalman filter is built to calculate the orientation from the accelerometer, gyroscope, and magnetometer. The Kalman filter trusts more in the data from the accelerometer and magnetometer when the UAV is static and trusts more in to the gyroscope data when the UAV is in dynamic conditions. Meanwhile, the UAV translation is obtained by performing data fusion of the accelerometer data with location data from the GPS sensor. The Kalman filter combines data from the accelerometer and GPS when available, otherwise trusts in data from the accelerometer only. The trust level shifting is done by changing the measurement noise covariance. The data fusion based on Kalman filter provides more accurately the orientation and translation data. The orientation as a result of the calculation from the gyroscope has an average error of 18.12%, while the orientation as a result of the accelerometer and magnetometer has an error of 1.3%. By using Kalman filter-based data fusion, the error of the orientation decreases to 0.87%

Multi-sensor biologgers and innovative training allow data collection with high conservation and welfare value in zoos

Zoos are valuable resources for research, providing scientists with access to rare and elusive species in an easy to observe environment. Animal-attached loggers (aka biologgers) offer profound insight into animal behaviour. Their use in zoos has high yet largely untapped potential to collect data relevant for wild animal research and conservation but also welfare and enrichment monitoring of the zoo animals themselves. However, affixing biologgers to study animals can be problematic in captive settings, limiting the accessibility of this technology for use on zoo species which ordinarily need to be sedated for the fitting of such devices, including large carnivores. Here we show that biologging collars and crate-training allow collection of novel datasets on captive animals with high welfare and conservation value, using endangered African wild dogs (Lycaon pictus) tagged with tri-axial accelerometer and tri-axial magnetometer loggers, as a case study. Two yearling female wild dogs were fitted with biologging collars while sedated in preparation for translocation from London to Whipsnade Zoo, with data collected for 10–26 hours until collar detachment. Two adult male wild dogs at London Zoo were trained to accept collars in a modified crate in exchange for a food reward, which allowed fitting and detaching the collars without sedation, with data collected for 28 days. First, we show how accelerometer and magnetometer data allow detection of fine-scale individual differences in the recovery from sedation as well as within- and between-individual variation in activity patterns in relation to the type of food received (tong vs. rabbit and pony carcass). Using the vectorial dynamic body acceleration metric (VeDBA), a proxy for movement-related energy expenditure, further shows that daily energy expenditure was higher on days with partial pony carcass feeds compared to rabbit feeds but varied considerably between days where flesh pieces were fed with tongs. Using the dead-reckoning method allowed reconstruction of fine-scale (1 Hz locations) movement paths within enclosures, indoors and outdoors, allowing visualisation and quantification of fine-scale movement and space use differences between individuals and over time, for example in response to different enrichment methods. Using multi-sensor biologgers, combined with training captive animals to accept collars without the use of anaesthetic, can enable flexible, experimental approaches to data collection with minimal impact on study animals, providing novel understanding of relevance for both zoo and wild animals.