Specific Data Processing Research Articles

Innovative molecular networking analysis of steroids and characterisation of the urinary steroidome

Steroids are cholesterol-derived biomolecules that play an essential role in biological processes. These substances used as growth promoters in animals are strictly regulated worldwide. Targeted assays are the conventional methods of monitoring steroid abuse, with limitations: only detect known metabolites. Metabolism leads to many potential compounds (isomers), which complicates the analysis. Thus, to overcome these limitations, non-targeted analysis offers new opportunities for a deeper understanding of metabolites related to steroid metabolism. Molecular networking (MN) appears to be an innovative strategy combining high-resolution mass spectrometry and specific data processing to study metabolic pathways. In the present study, two databases and networks of steroids were constructed to lay the foundations for the implementation of the GNPS-MN approach. Steroids of the same family were grouped together, nandrolone and testosterone were linked to other analogues. This network and associated database were then applied to a few urine samples in order to demonstrate the annotation capacity in steroidome study. The results show that MN strategy could be used to study steroid metabolism and highlight biomarkers.

Real-Time Monitoring of Odour Emissions at the Fenceline of a Waste Treatment Plant by Instrumental Odour Monitoring Systems: Focus on Training Methods.

Waste treatment plants (WTPs) often generate odours that may cause nuisance to citizens living nearby. In general, people are becoming more sensitive to environmental issues, and particularly to odour pollution. Instrumental Odour Monitoring Systems (IOMSs) represent an emerging tool for continuous odour measurement and real-time identification of odour peaks, which can provide useful information about the process operation and indicate the occurrence of anomalous conditions likely to cause odour events in the surrounding territories. This paper describes the implementation of two IOMSs at the fenceline of a WTP, focusing on the definition of a specific experimental protocol and data processing procedure for dealing with the interferences of humidity and temperature affecting sensors' responses. Different approaches for data processing were compared and the optimal one was selected based on field performance testing. The humidity compensation model developed proved to be effective, bringing the IOMS classification accuracy above 95%. Also, the adoption of a class-specific regression model compared to a global regression model resulted in an odour quantification capability comparable with those of the reference method (i.e., dynamic olfactometry). Lastly, the validated models were used to process the monitoring data over a period of about one year.

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

In recent years, wireless sensors have progressively supplanted conventional limited sensors owing to their attributes of small size, low cost, and high accuracy. Consequently, there has been a growing interest in leveraging wireless sensor networks for bridge structural health monitoring applications. By employing wireless sensor nodes to gather data from various segments of the bridge, information is relayed to a signal-receiving base station. Subsequently, the health status of the bridge is inferred through specific data processing and analysis, aiding monitoring personnel in making informed decisions. Nonetheless, there are limitations in this research, particularly pertaining to power consumption and efficiency issues in data acquisition and transmission, as well as in determining the appropriate wireless sensor types and deployment locations for different bridge configurations. This study aims to comprehensively examine research on the utilization of wireless sensor networks in the realm of bridge structural health monitoring. Employing a systematic evaluation methodology, more than one hundred relevant papers were assessed, leading to the identification of prevalent sensing techniques, data methodologies, and modal evaluation protocols in current use within the field. The findings indicate a heightened focus among contemporary scholars on challenges arising during the data acquisition and transmission processes, along with the development of optimal deployment strategies for wireless sensor networks. In continuing, the corresponding technical challenges are provided to address these concerns.

Data-centric species distribution modeling: Impacts of modeler decisions in a case study of invasive European frog-bit.

Species distribution models (SDMs) are widely utilized to guide conservation decisions. The complexity of available data and SDM methodologies necessitates considerations of how data are chosen and processed for modeling to enhance model accuracy and support biological interpretations and ecological applications. We built SDMs for the invasive aquatic plant European frog-bit using aggregated and field data that span multiple scales, data sources, and data types. We tested how model results were affected by five modeler decision points: the exclusion of (1) missing and (2) correlated data and the (3) scale (large-scale aggregated data or systematic field data), (4) source (specimens or observations), and (5) type (presence-background or presence-absence) of occurrence data. Decisions about the exclusion of missing and correlated data, as well as the scale and type of occurrence data, significantly affected metrics of model performance. The source and type of occurrence data led to differences in the importance of specific explanatory variables as drivers of species distribution and predicted probability of suitable habitat. Our findings relative to European frog-bit illustrate how specific data selection and processing decisions can influence the outcomes and interpretation of SDMs. Data-centric protocols that incorporate data exploration into model building can help ensure models are reproducible and can be accurately interpreted in light of biological questions.

Fatigue Testing and Modeling of Bonded Composite Specimens in Mode I, II, and Mixed-Mode I/II Loadings

Abstract Fatigue debond growth of bonded composite specimens in mode I, mode II, and mixed-mode I/II loadings were studied experimentally and numerically. For the mode I loading, test data sets were analyzed under the linear elasticity condition. For the mode II and mixed-mode loadings, specific test data processing was used to ensure that nonlinear effects that act outside of the cyclic loading range are excluded from the fatigue behavior assessment. An important finding under the mode II and mixed-mode loadings is that the actual loading ratios in the tests varied and were different from the applied displacement ratio. The experimental characterization of the fatigue debond behaviors was complemented with numerically determined correlations between the compliance and debond length under each individual loading. This correlation was used to determine an effective debond length from the compliance determined experimentally. According to the actual loading ratio variation, the fatigue growth behaviors were characterized using Paris’ law. Numerical fatigue analyses using two codes, Abaqus finite element (FE) and AFGROW, were carried out and validated using the developed test database. The details on an FE model setup and analysis strategy for a mixed-mode specimen are presented. Good agreement in the fatigue lives was obtained between the test and numerical analyses for the three types of loading. Also, good agreements in the load versus debond growth length relations were obtained between the test and FE analysis in the mode II and mixed-mode loadings. Reasons for the result discrepancy are discussed. The study shows that the presented analysis procedure is effective. The thorough description of both the used test data processing and numerical analysis procedure fills a knowledge gap in the available literature.

Biological-based techniques for real-time water-quality studies: Assessment of non-invasive (swimming consistency and respiration) and toxicity (antioxidants) biomarkers of zebrafish

Swimming consistency and respiration of fish are recognized as the non-invasive stress biomarkers. Their alterations could directly indicate the presence of pollutants in the water ecosystem. Since these biomarkers are a routine process for fish, it is difficult to monitor their activity manually. For this reason, experts employ engineering technologies to create sensors that can monitor the regular activities of fish. Knowing the importance of these non-invasive stress biomarkers, we developed online biological behavior monitoring system-OBBMS and online biological respiratory response monitoring system-OBRRMS to monitor real-time swimming consistency and respiratory response of fish, respectively. We continuously monitored the swimming consistency and respiration (OCR, CER and RQ) of zebrafish (control and atrazine-treatments) for 7 days using our homemade real-time biological response monitoring systems. Furthermore, we analyzed oxidative stress indicators (SOD, CAT and POD) within the vital tissues (gills, brain and muscle) of zebrafish during stipulated sampling periods. The differences in the swimming consistency and respiratory rate of zebrafish between the control and atrazine treatments could be precisely differentiated on the real-time datasets of OBBMS and OBRRMS. The zebrafish exposed to atrazine toxin showed a concentration-dependent effect (hypoactivity). The OCR and CER were increased in the atrazine treated zebrafish. Both Treatment I and II received a negative response for RQ. Atrazine toxicity let to a rise in the levels of SOD, CAT and POD in the vital tissues of zebrafish. The continuous acquisition of fish signals is achieved which is one of the main merits of our OBBMS and OBRRMS. Additionally, no special data processing was done, the real-time data sets were directly used on statistical tools and the differences between the factors (groups, photoperiods, exposure periods and their interactions) were identified precisely. Hence, our OBBMS and OBRRMS could be a promising tool for biological response-based real-time water quality monitoring studies.

A Study on the Extension of Chinese Traditional Culture in College Art Education Combined with Gray Correlation Analysis Methods

Abstract This study incorporates gray correlation analysis from gray statistical theory to assess the quality of traditional culture and art education. It establishes an evaluation index system and a gray correlation analysis model, examining the relationship between contemporary art education and traditional culture. The model is applied to identify the correlation, key characteristics, and principal influencing factors of traditional Chinese culture and art education in higher education institutions. Additionally, it examines the advantages of random sequences of factors through specific data processing techniques. The structural model tests the influencing factors on the quality of traditional culture and art education, with an analysis of path coefficients. The results showed that 97.4% of teachers chose traditional culture and art teaching resources, mainly by referring to the network aspect. The results of the weight distribution indicate that the teaching process U3 (0.3613) is the most important in traditional culture and art teaching, and the innovative activities in teaching content (0.8113) hold the largest weight. The top three gray correlation values of the indicators of the second-level factor layer are U43>U22>U32. The direct influence effect of excellent traditional culture permeating the teaching process is 0.232, which has the greatest influence on the quality of teaching. The direct impact of excellent conventional culture permeating teaching content is 0.184, which has the second largest influence on teaching quality. This paper successfully constructs a gray correlation model to analyze the relationship between traditional culture and contemporary art, which has high scientific validity.

Drone-Based Vertical Atmospheric Temperature Profiling in Urban Environments

The accurate and detailed measurement of the vertical temperature, humidity, pressure, and wind profiles of the atmosphere is pivotal for high-resolution numerical weather prediction, the determination of atmospheric stability, as well as investigation of small-scale phenomena such as urban heat islands. Traditional approaches, such as weather balloons, have been indispensable but are constrained by cost, environmental impact, and data sparsity. In this article, we investigate uncrewed aerial systems (UASs) as an innovative platform for in situ atmospheric probing. By comparing data from a drone-mounted semiconductor temperature sensor (TMP117) with traditional radiosonde measurements, we spotlight the UAS-collected atmospheric data’s accuracy and such system suitability for atmospheric surface layer measurement. Our research encountered challenges linked with the inherent delays in achieving ambient temperature readings. However, by applying specific data processing techniques, including smoothing methodologies like the Savitzky–Golay filter, iterative smoothing, time shift, and Newton’s law of cooling, we have improved the data accuracy and consistency. In this article, 28 flights were examined and certain patterns between different methodologies and sensors were observed. Temperature differentials were assessed over a range of 100 m. The article highlights a notable accuracy achievement of 0.16 ± 0.014 °C with 95% confidence when applying Newton’s law of cooling in comparison to a radiosonde RS41’s data. Our findings demonstrate the potential of UASs in capturing accurate high-resolution vertical temperature profiles. This work posits that UASs, with further refinements, could revolutionize atmospheric data collection.

Exploring Acute Pancreatitis Clinical Pathways Using a Novel Process Mining Method.

Mining process models of medical behavior from electronic medical records is an effective way to optimize clinical pathways. However, clinical medical behavior is an extremely complex field with high nonlinearity and variability, and thus we need to adopt a more effective method. In this study, we developed a fuzzy process mining method for complex clinical pathways. Firstly, we designed a multi-level expert classification system with fuzzy values to preserve finer details. Secondly, we categorized medical events into long-term and temporary events for more specific data processing. Subsequently, we utilized electronic medical record (EMR) data of acute pancreatitis spanning 9 years, collected from a large general hospital in China, to evaluate the effectiveness of our method. The results demonstrated that our modeling process was simple and understandable, allowing for a more comprehensive representation of medical intricacies. Moreover, our method exhibited high patient coverage (>0.94) and discrimination (>0.838). These findings were corroborated by clinicians, affirming the accuracy and effectiveness of our approach.

Pigment Identification and Depth Profile in Pictorial Artworks by Non-Invasive Hybrid XRD-XRF Portable System

A non-invasive XRD-XRF portable system for Archaeometry based on Angle and Energy Dispersive XRD (Hybrid XRD) plus XRF has been previously proposed by the authors. It gathers an attractive compromise between analytical performance and measurement time, as well as a higher energy penetration with respect to conventional Angle Dispersive XRD (ADXRD). Penetration to inside layers, usually perceived as an inconvenient for non-invasive portable XRF or XRD analyzer, can instead be informative for the proposed hybrid XRD-XRF system to perform depth profiling analysis. This hybrid configuration harness from the redundancy of the data, in the sense that a single XRF or XRD specimen, taken at the same sample point, appears in multiple spectrograms to account for data counting statistics. On this work, the performance of the system on standard reference material, as well as the identification of pigments in the surface layer of paintings is evaluated. Specific Hybrid data processing was conceived to examine the layer structure of the painting, by distinguishing signal patterns of the exposed and the under layers. The system is ventured for the first time to the study of pictorial artworks in coffin and cartonnage samples from ancient Egyptian sarcophagi. Prussian blue pigment in a modeled painting is also discussed.

Investigating the Molecular Rearrangement of Polar Liquid Mixtures Using Terahertz Frequency Domain Reflection Spectroscopy.

A special data processing method for the terahertz frequency domain reflection spectroscopy system has been proposed to analyze the characteristics of a polar mixed liquid. This novel and practical measurement system is characterized by a simpler optical structure and a tunable output frequency range of 0.1-1 THz. By Hilbert transform, stationary wavelet transform, and time domain zero setting method, the reflection coefficient that has been eliminated by the noise and the Fabry-Pérot effect can be obtained by the self-reference calibration method. Then, the dielectric function of the ethanol/n-hexane and propanol/n-hexane mixtures with different mixture ratios can be extracted by this method. In addition, a significant deviation can be observed between the imaginary part of the measured dielectric function and the ideal calculated value. These results indicate that during the mixing process of polar and nonpolar liquids, the hydroxyl functional groups of alcohols significantly change the molecular arrangement pattern of the mixture. The arrangement pattern will result in the formation of the new permanent dipole moment. This study lays a solid foundation for future research on the microscopic mechanism of intermolecular interaction using terahertz frequency domain reflection spectroscopy.

Increasing digital capabilities of agricultural projects to reduce costs

This paper provides an overview of the extent of digital transformation in the Republic of Iraq. It contains a discussion of the opportunities and challenges that the economy faces due to digitization and its potential to promote a sustainable economic transformation to achieve the 2030 Agenda for Sustainable Development. By analyzing the experience of implementing these projects, it is possible to identify the economic and accounting actions that can be taken to reduce the costs of digitizing crop production enterprises, as well as identify problems in industrial relations that impede effective digitization and the establishment of a geographic information system for regional agriculture in the Middle Euphrates, Iraq. Based on an assessment of the state of digitization in the Middle Euphrates region of Iraq, key areas for digitization efforts in the agricultural sector were identified. The study revealed the fundamental trends of modern and innovative digital technologies presented by the “Digital Agriculture” management project with special data processing methods and the use of geographic information systems that make it possible to obtain a significant economic impact to reduce and rationalize costs. According to the results obtained, the use of elements of digital technologies in crop production in the Middle Euphrates, Iraq, has a significant economic effect, the replication of which can greatly accelerate the development of the industry. These efforts to unlock Iraq's digital potential will accelerate the achievement of the Sustainable Development Goals.

Transformation of Water Consumption and Sanitation in the Russian Part of the Transboundary Ural River Basin

Aim. Study of the transformation of water consumption and wastewater disposal in the Russian part of the Ural River basin. Ural in 1995-2020 due to changing socio-economic and technological conditions.Materials and Methods. The data of state statistical reporting (form 2-TP (vodkhoz)) for the period 1995-2020 were used. Materials of the AllRussian Agricultural Census of 2016, collections of the Ministry of Agriculture of the Russian Federation, "Agro-industrial Complex of Russia", on the commissioning of irrigated lands, the regional Department of Land Reclamation and reports on the state and environmental protection for the constituent entities of the Russian Federation were consulted. The study was carried out on the basis of a systematic approach using the following methods: (1) analytical (collection and systematization of primary information); (2) special (tables compiled, charts constructed; (3) statistical data processing and comparative analysis.Results. It was revealed that from 1995 to 2020 water intake in the Russian part of the Ural River basin to meet the needs of the population and the economy was reduced by 2.4 times. The average daily specific water consumption per 1 inhabitant has decreased in all regions. The water cycle coefficient in the basin increased from 73 to 88%. In recent years, an increase in the area of irrigated land has begun.Conclusion. There has been a noticeable reduction in water consumption and wastewater disposal in the Russian part of the Ural River basin. This is mainly due to the decline in industrial and agricultural production, as well as the development of circulating water supply in industry and the introduction of water-saving equipment in housing and communal services.

PERANCANGAN APLIKASI PENGAJUAN PENERIMA BANTUAN KESEJATERAAN SOSIAL BERBASIS WEB PADA DINAS SOSIAL KOTA METRO

The Social Service is a government institution that is given the mandate to regulate community welfare in accordance with Law no. 11 of 2009 concerning Social Welfare. The field of filing beneficiaries is one of the problems handled by the Social Service. Types of community assistance include Staple Food, Family Hope Program (PKH), Direct Cash Assistance (BLT) with certain conditions. With the number of beneficiaries currently totaling 9,205 Family Cards, processing of data on receiving assistance still utilizes data processing using Microsoft Excel. Where data processing using Microsoft Excel has drawbacks, including application users who must really understand the function, formula or logic, then if the user enters the formula incorrectly, the resulting data will be affected, besides that there will be many documents produced that are less stable for each version and for files with the latest version of Microsoft Excel requires a computer with medium to high specifications. Based on these problems, a special data processing application is needed for data on recipients of web-based social welfare assistance. The application aims to facilitate the work of service staff in the social welfare submission section in preparing recapitulation reports and reports on the list of people who will receive assistance at the Metro City Social Service.

Quantitative assessment of rock lithology from gamma-ray and mud logging data

The data on vertical variations of the rock lithological content along the geological profiles of boreholes are essential for interwell correlation, geological or hydrodynamic modeling, optimization of drilling process, etc. There are direct (investigation of core samples) and indirect (processing and interpretation of well-logging data) methods applied in petroleum engineering to determine rock lithology. Both approaches have several limitations (such as low quality of logging data in case of washouts, low frequency of sampling of drill cuttings, limited number of wells drilled with coring, etc.) calling for alternative approaches to determine rock lithological content along the borehole. One of the alternative solutions could be application of mud logging (drilling and mud gas data) due to the strong physical background of interrelations between drilling and mud gas data on one side and rock lithology on the other. Literature review shows that many of existing machine learning-based solutions assume application of mud logging data mainly for rock typing (qualitative assessment of rock lithology). Concurrently, many applied problems require quantitative data on rock lithology (volumetric concentrations of rock lithological components). To fill this gap, we assessed the capabilities of mud logging data for quantitative characterization of rock lithology. We proposed a new algorithm for the task at hand relying on special data processing and machine learning algorithms. As an input data we used drilling, mud gas, and gamma-ray data. The results of the developed algorithm application for three industrial wells confirmed the effectiveness of the developed algorithm. During our research we compared the performance of XGBoost algorithm and 1-D convolutional neural network. Obtained results allow concluding that the obtained regression models and developed algorithm exhibit good generalization ability providing the average total prediction uncertainty on unseen well of less than 0.15 c.u.

Magnonic active ring co-processor

In this work, we consider the possibility of building a magnonic co-processor for special task data processing. Its principle of operation is based on the natural property of an active ring circuit to self-adjust to the resonant frequency. The co-processor comprises a multi-path active ring circuit where the magnetic part is a mesh of magnonic waveguides. Each waveguide acts as a phase shifter and a frequency filter at the same time. Being connected to the external electric part, the system naturally searches for the path which matches the phase of the electric part. This property can be utilized for solving a variety of mathematical problems including prime factorization, bridges of the Konigsberg problem, traveling salesman, etc. We also present experimental data on the proof-of-the-concept experiment demonstrating the spin wave signal re-routing inside a magnonic matrix depending on the position of the electric phase shifter. The magnetic part is a 3 × 3 matrix of waveguides made of single-crystal yttrium iron garnet Y3Fe2(FeO4)3 films. The results demonstrate a prominent change in the output power at different ports depending on the position of the electric phase shifter. The described magnonic co-processor is robust, deterministic, and operates at room temperature. The ability to exploit the unique physical properties inherent in spin waves and classical wave superposition may be translated into a huge functional throughput that may exceed 1060 operations per meter squared per second for 50×50 magnetic mesh. Physical limits and constraints are also discussed.

Microwave Reflectometry Sensing System for Low-Cost in-vivo Skin Cancer Diagnostics

Skin cancer is one of the most commonly diffused cancers in the world and its incidence rates have constantly increased in recent years. At the current state of the art, there is a lack of objective, quick and non-invasive methods for diagnosing this condition; this, combined with hospital crowding, may lead to late diagnosis. Starting from these considerations, this paper addresses the implementation of a microwave reflectometry based-system that can be used as a non-invasive method for the in-vivo diagnosis and early detection of biological abnormalities, such as skin cancer. This system relies on the dielectric contrasts existing between normal and anomalous skin tissues at microwave frequencies (in a frequency range up to 3 GHz). In particular, a truncated open-ended coaxial probe was designed, manufactured and tested to sense (in combination with a miniaturized Vector Network Analyzer) the variations of skin dielectric properties in a group of volunteer patients. The specific data processing demonstrated the suitability of the system for discriminating malignant and benign lesions from healthy skin, ensuring simultaneously effectiveness, low cost, compactness, comfortability and high sensitivity.

Lessons for science and technology policy? Probing the Linkedin network of an RDI organisation

In this paper, we seek to examine the network of the Greek National Documentation Centre (EKT) as formed by its LinkedIn followers. By applying specific data collection and processing techniques, we explore the network of all the individuals that follow EKT’s LinkedIn page. Significant manual and automatic approaches have been implemented with regard to data extraction, data curation and data homogenization. The aim is to identify the network’s advancement over time, the institutions involved and the countries. The timeframe of the study spans from when the relevant LinkedIn page was constructed in 2015 to 2020. Findings indicate that there is a steady increase in the number of new followers, peaking in 2020. On an international scale, the evolution of the network of followers is imprinted and distributed in worldwide maps. In total, 68 countries have followed EKT over the examined time period. Also, in terms of followers’ institutional sector the Business Sector (BES) stands out (46.5%). Higher Education (HES) and Government Sector (GOV) are associated with 26.4 and 22.2% of the followers, respectively. Lastly, this paper provides a first institutional and country-level mapping of who constitutes the organisation’s interlocutors in the national and global RDI ecosystem.

Influence of Single Deuterium Replacement on Frequency of Hydrogen Bond Dissociation in IFNA17 under the Highest Critical Energy Range.

The effect of single substitutions of protium for deuterium in hydrogen bonds between pairs of nitrogenous bases on the open states occurrence probability at high critical breaking energies of these bonds has been studied. The study was carried out using numerical methods based on the angular mathematical model of DNA. The IFNA17 gene was divided into three approximately equal parts. A comparison of the open states occurrence probability in these parts of the gene was done. To improve the accuracy of the results, a special data processing algorithm was developed. The developed methods have shown their suitability for taking into account the occurrence of open states in the entire range of high critical energies. It has been established that single 2H/1H substitutions in certain nitrogenous bases can be a mechanism for maintaining the vital activity of IFNA17 under critical conditions. In general, the developed method of the mathematical modeling provide unprecedented insight into the DNA behavior under the highest critical energy range, which greatly expands scientific understanding of nucleobases interaction.

On the relation of GNSS phase center offsets and the terrestrial reference frame scale: a semi-analytical analysis

Phase center offsets (PCOs) of global navigation satellites systems (GNSS) transmit antennas along the boresight axis introduce line-of-sight-dependent range changes in the modeling of GNSS observations that are strongly correlated with the estimated station heights. As a consequence, changes in the adopted PCOs impact the scale of GNSS-based realizations of the terrestrial reference frame (TRF). Vice versa, changes in the adopted TRF scale require corrections to the GNSS transmit antenna PCOs for consistent observation modeling. Early studies have determined an approximate value of alpha =-0.050 for the ratio of station height changes and satellite PCO changes in GPS orbit determination and phase center adjustment. However, this is mainly an empirical value and limited information is available on the actual PCO-scale relation and how it is influenced by other factors. In view of the recurring need to adjust the IGS antenna models to new ITRF scales, a semi-analytical model is developed to determine values of alpha for the four current GNSSs from first principles without a need for actual network data processing. Given the close coupling of satellite boresight angle and station zenith angle, satellite PCO changes are essentially compensated by a combination of station height, zenith troposphere delay, and receiver clock offset. As such, the value of alpha depends not only on the orbital altitude of the considered GNSS but also on the elevation-dependent distribution of GNSS observations and their weighting, as well as the elevation mask angle and the tropospheric mapping function. Based on the model, representative values of alpha _text {GPS}=-0.051, alpha _text {GLO}=-0.055, alpha _text {GAL}=-0.041, and alpha _text {BDS-3}=-0.046 are derived for GPS, GLONASS, Galileo, and BeiDou-3 at a 10^circ elevation cutoff angle. These values may vary by varDelta alpha approx 0.003 depending on the specific model assumptions and data processing parameters in a precise orbit determination or precise point positioning. Likewise changes of about pm 0.003 can be observed when varying the cutoff angle between 5^circ and 15^circ .