VISUALIZATION AND ANALYSIS OF VECTOR NETWORK ANALYZER DATA FOR THE EXPERIMENTAL MODEL OF THE ACTIVE ANTENNA SECTION OF THE GURT RADIO TELESCOPE

ABSTRACTClimate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (~10× speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328%). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.

As massive distribution automation terminals connect and data is acquired at high frequencies, the demand for low‐latency processing of distribution service data has increased dramatically. Edge clusters, integrating multiple edge servers, can effectively mitigate transmission delays. Cloud‐edge fusion leverages its data processing capabilities and the real‐time responsiveness of edge computing to meet the needs of efficient data processing and optimal resource allocation. However, existing access methods for distribution automation terminals in cloud‐edge fusion architectures exclusively depend on either cloud or edge computing for data processing. These conventional approaches fail to incorporate critical aspects such as: adaptive access mechanisms for edge clusters of distribution automation terminals, flexible strategies including data offloading, knowledge sharing among edge clusters, and load awareness capabilities. Consequently, they demonstrate significant limitations in achieving deep fusion between cloud and edge computing paradigms. Additionally, they lack consideration for the perception of global information and queue backlog, making it difficult to meet the low‐latency data transmission requirements of distribution automation services in dynamic environments. To address these issues, we propose an adaptive access method for edge clusters of distribution automation terminals based on cloud‐edge fusion. Firstly, a data processing architecture for adaptive access of distribution automation terminal edge clusters are designed to coordinate terminal access, data processing distribution, and decision optimization for computing resource allocation, enabling efficient data transmission and processing. Secondly, an optimization problem for adaptive access in edge clusters of distribution automation terminals is formulated, aiming to minimize the weighted sum of total queuing delay and load balancing degree. Finally, a federated twin delayed deep deterministic policy gradient (federated TD3)‐based edge cluster adaptive access method for distribution automation terminal is proposed. This approach integrates model parameters from edge servers at the cloud level and distributes them to the edge cluster level, learning strategies for terminal access, data processing allocation, and computing resource allocation based on queue backlog fluctuations. This enhances load balancing between the distribution terminal layer and edge layer, achieving collaborative optimization of load balancing and delay under massive distribution terminal access. Simulation results demonstrate that the proposed method significantly reduces system queuing delay, optimizes load balancing, and enhances overall operation efficiency.

Honoring a radio astronomy pioneer Fifty‐seven years after Karl Jansky inadvertently helped spawn the field of radio astronomy by becoming the first person to hear radio waves from outer space, the pioneer was honored by Lucent Technologies' Bell Laboratories at a June 8 ceremony that included the unveiling of a 4‐m long replica of his antenna at the exact site of the original 30.5‐m long instrument. This instrument resembled a box kite lying on its side, and was supported by Model T Ford tires.Jansky's discovery “was ahead of its time,” said Tony Tyson, an astrophysicist with Lucent Technologies' Bell Laboratories in New Jersey. Tyson, who worked with Robert Wilson, a senior scientist at the Harvard‐Smithsonian Center for Astrophysics in Cambridge, Mass., to determine the location of Jansky's original antenna, said that in the early 1930s “radio waves had nothing to do with astronomy, so it really fell between radio engineering and astronomy.”

The emerging era of big data in radio astronomy demands more efficient and higher-quality processing of observational data. While deep learning methods have been applied to tasks such as automatic radio frequency interference (RFI) detection, these methods often face limitations, including dependence on training data and poor generalisation, which are also common issues in other deep learning applications within astronomy. In this study, we investigate the use of the open-source image recognition and segmentation model, Segment Anything Model (SAM), and its optimised version, HQ-SAM, due to their impressive generalisation capabilities. We evaluate these models across various tasks, including RFI detection and solar radio burst (SRB) identification. For RFI detection, HQ-SAM (SAM) shows performance that is comparable to or even superior to the SumThreshold method, especially with large-area broadband RFI data. In the search for SRBs, HQ-SAM demonstrates strong recognition abilities for Type II and Type III bursts. Overall, with its impressive generalisation capability, SAM (HQ-SAM) can be a promising candidate for further optimisation and application in RFI and event detection tasks in radio astronomy.

To find out the development of the livestock sector, the Livestock Service Office of Kampar Regency collects data on livestock in Kampar Regency. The data is then processed in excel and then reported to the relevant agencies. Data collection with this system creates problems of difficulty in processing and searching data. The more data that is recorded, the more difficult the process of processing and searching for data. As a solution, a web-based Kampar Regency livestock data collection system was built using the codeigniter framework and made using the php programming language and mysql database as well as a design modeled with UML (Unified Modeling Language). The purpose of this system is to facilitate livestock data collection and more efficient data processing and searching. The results can facilitate data management by the Livestock Service Office of Kampar Regency, present information about livestock in Kampar Regency and a more efficient data collection and processing process.

Sensors are being used in thousands of applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control. As these applications collect zettabytes of data everyday sensors play an integral role into big data. However, most of these data are redundant, and useless. Thus, efficient data aggregation and processing are significantly important in reducing redundant and useless data in sensor-based big data frameworks. Current studies on big data analytics do not focus on aggregating and filtering data at multiple layers of big data frameworks especially at the lower level at data collecting nodes (sensors) that reduce the processing overhead at the upper layer, i.e., big data server. Thus, this paper introduces a multi-tier data aggregation technique for sensor-based big data frameworks. While this work focuses more on data aggregation at sensor networks. To achieve energy efficiency it also demonstrates that efficient data processing at lower layers (sensor) significantly reduces overall energy consumption of the network and data transmission latency.

The main current trends in theory and practice of operation of radio engineering devices for flight support in civil aviation is paying attention to the analysis possibility of the operation systems in terms of system and process approaches. Process approach involves structuring operation systems into separate processes with inputs, outputs, resources, and control actions, and these processes are somehow interconnected. It is known that all processes must occur under conditions suitable for monitoring and control. This paper considers actual scientific and technical problem of sequential engineering data processing procedures synthesis and analysis during the monitoring and conformity assessment of process constituent elements in case of radio engineering devices of flight support operation. The proposed sequential procedure for conformity assessment of operation processes can significantly reduce the duration of audits (in 1,3 – 1,6 times, on the average), and therefore, reduce material and time resources.

Institute of Astronomy (University of Latvia) with Ventspils International Radio Astronomy Centre (Ventspils University of Applied Sciences) participation is implementing the scientific project “Complex investigations of the small bodies in the Solar system” related to the research of the small bodies in the Solar system (mainly, focusing on asteroids and comets) using methods of radio astronomy and signal processing. One of the research activities is hydroxyl radical (OH) observation in the radio range - single antenna observations and VLBI (Very Long Baseline Interferometry)  observation. To detect weak (0.1 Jy) OH masers of astronomical objects using radio methods, a research group in Ventspils adapted the Irbene RT-32 radio telescope working at 1665.402 and 1667.359 MHz frequencies. Novel data processing methods were used to acquire weak signals. Spectral analysis using Fourier transform and continuous wavelet transform were applied to radio astronomical data from multiple observations related to weak OH maser detection. Multiple comets (Comet C/2017 T2 (PANSTARRS), Comet C/2019 Y4 (ATLAS), Comet C/2020 F8 (SWAN)) observations were carried out in 2019-2020.IntroductionThere are four known (1612.231,  1665.402, 1667.359 and 1720.530 MHz) hyperfine transitions of OH at 18 cm wavelength which have been used for 40 years, historically to observe comets. In 1973, the molecule OH in comet Kahoutek [1] was observed from the Nancay 30 meter telescope.  The 18 cm line is the result of an excitation from resonance fluorescence, whereby molecules absorb solar radiation and then reradiate the energy. The OH molecule absorbs the UV solar photons and cascades back to the ground state Lambda doublet, where the relative populations of the upper and lower levels strongly depend upon the heliocentric radial velocity (the “Swings effect”) [2]. The result of comets observations in 1.6GHz frequency band made by other astronomy groups [3],[4],[5],[6] and others - show that the typical peak source flux densities of the comet are in the range of 4 to 40 mJy. Weakness of the radio signal is the main challenging factor. Assuming that the detection threshold is 3*σ, at least 1.3 to 13 mJy noise floor  is required. Significant work was invested to prepare the instrumentation of Irbene 32-meter antenna for spectral line observation at L band. This includes improvement of receiver system sensitivity at 1.665 and 1.667 GHz, by building and installing new secondary focus front-end [7].Observations and data processingTo detect OH masers of the comets, multiple observation sessions were performed using Irbene radio telescope RT32 at 1665.402 and 1667.359 MHz frequencies. Comet Atlas C/2019 Y4 was observed 133 hours, Panstarrs C/2017 - 149 hours, Swan C/2020 F8  - 110 hours. Data calibration and processing methods were necessary to filter out weak OH maser signals from radio astronomical data sets. A programmed USRP X300/310+TwinRX spectrometer is used to record data using 16bit+16bit (real + imag part) per sample. For spectral data calibration, the frequency switching method [8] was integrated in the observation process and data processing was implemented to collect data using long integration time, consequently to perform the compensation of the Doppler shift. For data filtering Fourier transforms, Blackman-Harris window function, Butterworth Low Pass, Locally Weighted Scatterplot Smoothing functions and wavelet transforms were used. Observations of small bodies are possible with the best available accuracy when optical (using the optical Schmidt telescope of Institute of Astronomy) and radio methods are combined [9]. Data processing from two independent simultaneous measurements (using specific Kalman filters) allows one to reduce human errors in sporadic sources. Summary and ConclusionsObservations of OH masers of comets can be a very challenging task. The upgrade of the L frequency band receiver was performed in Irbene, Latvia to observe OH masers of comets. Multiple data processing methods were developed to acquire a weak signal. OH masers of the comets (Comet C/2017 T2 (PANSTARRS), Comet C/2019 Y4 (ATLAS), Comet C/2020 F8 (SWAN)) were observed, and the observation process of Comet C/2019 U6, Comet 2P/Encke and Comet C/2020 F3 (NEOWISE) are ongoing in summer 2020.AcknowledgementsThis research is funded by the Latvian Council of Science, project„Complex investigations of the small bodies in the Solar system”, project No. lzp-2018/1-0401.

4 - Supported programming languages

Drainage network extraction from a high-resolution DEM using parallel programming in the .NET Framework

In the context of the technological revolution and the flourishing digital economy, data analysis technology is increasingly applied across various industries. Specifically, in the fields of finance and auditing, data analysis technology not only enhances the efficiency of data processing and decision-making but also promotes the transformation of undergraduate teaching models. This study aims to explore the application of data analysis technology in undergraduate finance and auditing education by analyzing its theoretical foundation, current development, practical applications, and future directions. The research finds that while data analysis technology can significantly improve teaching outcomes, it also faces challenges such as data acquisition and processing, teaching resources and equipment, and faculty training.

The use of technology has a big role, especially to support data processing activities in organizations. Data processing is a series of actions or operations that transform data into quality information, namely relevant, accurate and timely information. BPS as a statistical organization is responsible for providing quality statistical data and information. This can be realized by standardized, effective and efficient data processing. In statistical organizations, data processing is one of the business functions of statistical activities. The technology that can be used in integration and standardization is the platform. The data processing platform in this study is software that provides the foundation for services or related functions in the data processing process. The consideration of developing data processing platforms is the need for integration and standardization of statistical activity data processing. In this study, the platform will be designed to serve the SCSE system which can guarantee the alignment of services and IT service needs. Furthermore, the design will be evaluated based on SOA principles. Based on the results of the design evaluation that has been produced, the value of the coupling factor is 0.0051 and the cohesion factor is 0.923. This value indicates that the design of data processing platforms is in accordance with SOA principles, namely loosely coupling and high cohesion.

Internet of things (IoT) and big data technologies are increasingly gaining significance in the implementation of various services and applications. Consequently, much of the research focused on energy efficiency and building management revolves around integrating IoT and big data technologies for data collection and processing. Occupancy detection, comfort, and energy management are the most important services for optimizing building energy consumption in smart buildings, and environmental data play a key role in improving these services. Furthermore, the integration of advanced and recent techniques, such as IoT, big data, and machine learning, is progressively becoming more vital for both researchers and industries. This paper presents and discusses various emerging technologies that will contribute to designing novel IoT-based architectures to improve smart building services. These technologies offer innovative solutions to address the challenges of interoperability, scalability, and real-time data processing within intelligent environments, paving the way for more efficient, adaptive, and user-centric smart building systems. The main aim of this research is to help researchers define an optimal architecture that presents all layers, from sensing to big data stream processing. We established comparative criteria between the most popular data processing techniques to select the appropriate framework for developing intelligent platforms for managing building services, such as occupancy detection systems and occupants’ comfort management, and further, to enhance the deployment of digital twins for critical environment monitoring and anomaly detection. The proposed architecture uses Apache Kafka, Apache Storm, and Apache SAMOA as its core components, creating a comprehensive platform for efficient data collection, monitoring, and processing with high performance in terms of fault tolerance and low latency.

Automated systems capable of real-time operation with minimal energy consumption are increasingly important in modern radio telescopes. Spiking Neural Networks (SNNs) promise efficient and dynamic spatio-temporal data processing. This paper reformulates a significant challenge in radio astronomy, Radio Frequency Interference (RFI) detection, as a time-series segmentation task suited for SNN execution. We explore several spectrogram encoding methods and network parameters, applying first and second-order leaky integrate and fire SNNs to tackle RFI detection. We introduce a divisive normalisation-inspired pre-processing step, improving detection performance across multiple encodings strategies. Our approach achieves competitive performance on a synthetic dataset and compelling initial results on real data from the Low-Frequency Array (LOFAR) establishing a baseline for future work. We position SNNs as a viable path towards real-time RFI detection, with many possibilities for follow-up studies. These findings highlight the potential for SNNs performing complex time-series tasks, paving the way towards efficient, real-time processing in radio astronomy and other data-intensive fields.

In the field of civil engineering, the adoption and use of Falling Weight Deflectometers (FWDs) is seen as a response to the ever changing and technology-driven world. Specifically, FWDs refer to devices that aid in evaluating the physical properties of a pavement. This paper has assessed the concepts of data processing, storage, and analysis via FWDs. The device has been found to play an important role in enabling the operators and field practitioners to understand vertical deflection responses upon subjecting pavements to impulse loads. In turn, the resultant data and its analysis outcomes lead to the backcalculation of the state of stiffness, with initial analyses of the deflection bowl occurring in conjunction with the measured or assumed layer thicknesses. In turn, outcomes from the backcalculation processes lead to the understanding of the nature of the strains, stresses, and moduli in the individual layers; besides layer thickness sensitivity, the determination of isotropic layer moduli, and establishing estimates in the subgrade CBR. Overall, impositions of elastic and low strain conditions foster the determination of resilient modulus and the analysis of unbound granular materials. Hence, FWD data processing, analysis, and storage gain significance in civil engineering because it informs the nature of designing new pavements and other rehabilitation design options.