Ground Segment Research Articles

Research on e-commerce user segmentation and customized marketing strategy based on cluster analysis

Abstract E-commerce user segmentation is the basis for enterprises to accurately formulate marketing strategies and successfully manage their customer base. With the rapid development of e-commerce, this paper improves the traditional K-means clustering algorithm and proposes a SAPK-means algorithm, which effectively excludes the noisy data and isolated points in the dataset and obtains the high-quality initial clustering center. Company A’s e-commerce platform is used to apply the SAPK-means algorithm for customer segmentation, and the results are analyzed in detail before proposing targeted marketing strategies. The customized marketing strategy’s effect is evaluated through sales and platform user satisfaction. Through experimental testing, this paper concludes that the five types of segmented customer groups account for 9.46%, 18.43%, 36.95%, 21.91%, and 13.24% of the total number of samples, respectively, known as the “platinum customer group”, “gold customer group “Platinum customer group”, “Gold customer group”, “Silver customer group”, “Copper customer group” and “Iron customer group”, respectively.

Analysis of social media user segments as part of content marketing in sport

Analysis of social media user segments as part of content marketing in sport

Potential User Segmentation Based on Expectations of Social Robots Using Q-Methodology

Potential User Segmentation Based on Expectations of Social Robots Using Q-Methodology

Precision space telescope attitude determination and control system design principles

In order to preclude the impact of atmospheric factors on the quality of received data, state of the art astronomy research projects require spacecraft mounted telescopes. However, such designs entail the issue of sustaining the accuracy of telescope boresight attitude relative to the object of observation for the required period of time. This paper is a summary of design principles for an attitude determination and control system of a space telescope mounted rigidly onboard a spacecraft. The paper formulates requirements for attitude accuracy, and proposes a viable equipment configuration as well as a three- stage attitude control algorithm. The first stage of the algorithm ensures primary boresight pointing towards the target object prior to the object being captured in the telescope’s FoV based on star tracker and angular rate sensor data. The second stage of the algorithm ensures boresight positioning relative to the target object direction up to an error value allowing for the required image quality, based on telescope readings. The third stage of the algorithm ensures telescope boresight holding relative to the target object direction with a required accuracy for the duration of exposure. Interferences from operating satellite equipment are reviewed, and the principles of reducing their impact on sighting accuracy are formulated. Low inclination, low eccentricity geostationary orbit is recommended as the best operating environment for the space telescope’s scientific instrumentation, as well as for providing a continuous data interface between spacecraft and ground segment.

Enhancing Digital User Experiences through Personalized Interfaces and Predictive Modelling

This paper explores the development and implementation of a personalization strategy designed to enhance user experiences on digital platforms. By leveraging user behaviour data and machine learning algorithms, we propose a methodology for creating adaptive user interfaces, predictive actions, and personalized user journeys. The paper presents a real-time example application, complete with data analysis, user segmentation, feature engineering, predictive modelling, and a comprehensive A/B testing framework. We aim to demonstrate how personalization can significantly improve user engagement, satisfaction, and platform efficacy

The Influence of the COVID-19 Pandemic on Travel Behaviour in the Greater Copenhagen Area

The COVID-19 pandemic drastically changed the way of living for billions of people and severe restrictions were implemented by governments around the world, affecting the travel patterns of all citizens. This article investigates how travel patterns changed in the Greater Copenhagen area of Denmark during the full two-year period covering 2020 and 2021, thus allowing for an analysis of both the short-term and medium-term impacts as society gradually reopened and restrictions were lifted. The analysis covers large-scale travel survey data as well as a segmentation clustering analysis of public transport smart card data. The results showed that impacts were strongly linked to changes in trip purpose and were thus not uniformly distributed throughout the public transport system. User segmentation analysis revealed that most users changed to less intense travel use of public transport. The results highlight important policy implications in terms of how to adapt service provision within a public transport network more efficiently.

Construction of Objective Segmentation Model of Automobile Users Based on Unsupervised Learning

With the rapid development of new energy vehicles and intelligent connected vehicles, today's automotive consumption has shifted from product centric to user centric. Grouping automobile users and conducting in-depth research on their characteristics and needs can provide certain support and guidance for manufacturers in planning and developing vehicle models, market validation, etc. However, current research on car user segmentation models is relatively lagging behind, and existing models use subjective factors such as values/automotive views/consumption views as modeling indicators. In the actual business development process of car companies, there are limitations of inconsistent user subjective cognition in various business processes, resulting in unclear vehicle development direction and inaccurate user positioning. In order to solve the above problems, this research innovatively constructs a set of automobile user segmentation model which is easy to understand and use by using unsupervised clustering algorithm based on objective indicators, and provides certain references for the development of new models and user positioning of automobile enterprises.

Analyzing the Impact of Digital Transformation in Islamic Philanthropy using Utaut Model

This study aims to assess individual habits in adopting digital payment technologies in Islamic philanthropic institutions. This research uses the UTAUT model, which explains user behavior in relation to information technology adoption. The population of this study was people who have used digital payments in philanthropic institutions, e.g., M-banking, QRIS, e-wallet, e-money, crowdfunding, marketplace, etc. A sample of 250 respondents was obtained using a random sampling technique. The UTAUT model was performed using structural equation modeling (SEM) via SmartPLS. The results showed that all constructs had a significant positive impact, except for the effect of facilitating conditions on usage behavior. This study shows that the digitization of payment transactions is still aimed at the segment of well-informed technology users. Policyholders, therefore, need to become more involved in the digital transformation campaign, which targets all potential segments, e.g., the middle-aged working class. Due to this segmentation, they have a high awareness of donations in addition to the income opportunities.

TESTING THE CONTRIBUTION, ACCURACY AND PERFORMANCE OF MGEX (GNSS (GPS+GLONASS+GALILEO+BEIDOU+QZSS)) POSITIONING IN THE STUDY REGION

The European Commission (EC) originally proposed ideas for a European Galileo satellite navigation system in 1999. A four-phase development is planned, involving investment from both the public and commercial sectors. Galileo is intended for both public and government use; the system is administered and operated by civil administration. Galileo will consist of a constellation of 30 satellites, a number of globally situated ground stations, and a ground control and monitoring system – all of which are quite similar to the structure, format, and layout of GPS. This document discusses an experiment at the project site that used the static approach to integrate GPS, GLONASS, GALILEO, Beidou, and QZSS signals. This research analyses the possible precision of GPS-only and GPS/GLONASS/GALILEO/Beidou/QZSS. These results suggest that combining a GPS system with GALILEO, GLONASS Beidou, and QZSS is preferable for surveying purposes. Integrating GPS/GLONASS/GALILEO/Beidou/QZSS static measurements in the study region with 0–120 millimetre accuracy looks to be possible in three days.

Model of operation of a high-speed satellite communication system for rational distribution of frequency-energy and time resources in the space and ground segment

Model of operation of a high-speed satellite communication system for rational distribution of frequency-energy and time resources in the space and ground segment

PENGGUNAAN KAEDAH PENGGANDA PENANDA ARAS BAGI MENENTUKAN POPULASI PENGGUNA DADAH DI MALAYSIA

The issue of drug abuse at the global level shows an increase in demand and supply. However, annual statistics released by the National Anti-Drug Agency (NADA) show a decrease in detected drug users compared to the previous year. The main factor that caused the decrease was the government's efforts to curb the spread of the COVID-19 pandemic in the year in question. Inaccurate estimates of the drug user population cause policies and strategies designed to deal with this issue to be less effective. Therefore, the main objective of this study is to estimate the population of youth drug users as the largest age segment of drug users in Malaysia using the benchmark multiplier method. Data from the NADA database were used as benchmarks and secondary data from the 2020 prevalence study were used as multipliers. The findings of the study showed that an estimated 654,000 individuals between the ages of 15 and 39 used drugs in 2021. A state-by-state comparison shows that Selangor has the highest number of drug users, with an estimated 133,000 individuals involved in drug use. Sabah is ranked second highest, with an estimated 91,000 drug users, while Johor is ranked third, with approximately 73,000 drug users in the state. Efforts to address the issue of substance abuse need to be increased based on the estimates shown from this study. This estimate is useful as a basis for future researchers in estimating the population of drug users or other hidden populations.

Use of a Residual Neural Network to Demonstrate Feasibility of Ship Detection Based on Synthetic Aperture Radar Raw Data

Synthetic Aperture Radar (SAR) is a well-established 2D imaging technique employed as a consolidated practice in several oil spill monitoring services. In this scenario, onboard detection undoubtedly represents an interesting solution to reduce the latency of these services, also enabling transmission to the ground segment of alert signals with a notable reduction in the required downlink bandwidth. However, the reduced computational capabilities available onboard require alternative approaches with respect to the standard processing flows. In this work, we propose a feasibility study of oil spill detection applied directly to raw data, which is a solution not sufficiently addressed in the literature that has the advantage of not requiring the execution of the focusing step. The study is concentrated only on the accuracy of detection, while computational cost analysis is not within the scope of this work. More specifically, we propose a complete framework based on the use of a Residual Neural Network (ResNet), including a simple and automatic simulation method for generating the training data set. The final tests with ERS real data demonstrate the feasibility of the proposed approach showing that the trained ResNet correctly detects ships with a Signal-to-Clutter Ratio (SCR) > 10.3 dB.

User Segmentation Based on Purchasing Habits and Preferences on the Amazon Platform Using K-Means Clustering

As a large company, Amazon operates an online marketplace with a diverse user base exhibiting varied purchasing habits. This diversity challenges Amazon to provide tailored services and marketing strategies for each user with distinct characteristics. Therefore, this research aims to assist Amazon in segmenting its users based on their characteristics, enabling the implementation of targeted marketing strategies and service provision for each user. The study employs the K-Means Clustering method to segment Amazon platform users based on their purchasing behavior, site feature interactions, and preferences. The research process involves Knowledge Data Discovery (KDD) stages, including data processing, attribute selection, and applying the K-Means Clustering algorithm. The analysis results reveal five distinct user clusters, each with unique characteristics reflecting user behavior and preferences. These clusters depict variations in purchasing frequency, interactions with site features, and responses to product recommendations. This user segmentation provides valuable insights for Amazon to develop more focused marketing strategies, enhance personalized services, and improve overall customer satisfaction.

Consumer Segments in Blockchain Technology Adoption

Abstract Recent research has highlighted the adoption of blockchain technology (BCT) and the increasing recognition of its multiple capabilities for users. The main objective of this paper is to delineate different consumer segments based on their BCT beliefs and attitudes. Using a survey method, 934 questionnaires were collected and subjected to hierarchical and K-Means cluster analysis and contingency analysis. The results reveal four identifiable user segments with respect to BCT adoption: Innovators (enthusiastic about BCT), Cautious (careful about BCT), Sceptics (doubtful about BCT), and Suspicious (very distrustful about BCT). These results provide valuable insights into understanding behavioral intentions and serve as a basis for future research.

What drives researcher preferences for chemical compounds? Evidence from conjoint analysis.

We investigated the attributes and attribute levels that affect researcher preferences for chemical compounds. We conducted a conjoint analysis on survey data of Korean researchers using chemical compounds from the Korean Chemical Bank (KCB). The analysis estimated the part-worth utility for each attribute's level, calculated relative importance of attributes, and classified user segmentation with different patterns. The results show that the structure database offers the highest part-worth utility to researchers, followed by high new functionality, price, screening service, and drug action data provided only by the KCB. Notably, researchers view the offer of a structured database and high new functionality as more important than other attributes in decision-making about research and development of chemical compounds. Furthermore, the results of segmentation analysis demonstrated that researchers have distinct usage patterns of chemical compounds: researchers consider structure database and high new functionality in cluster 1; and high new functionality and price in cluster 2, to be the most appealing. We discussed some policy and strategic implications based on the findings of this study and proposed some limitations.

Clustering-based Categorization of Music Users through Unsupervised Learning

The process of categorizing music users without the need for explicit guidance, known as unsupervised learning, has been explored through a technique called clustering. This innovative approach involves the use of algorithms to group music users based on their preferences, behaviors, or other relevant characteristics, thereby uncovering patterns and structures within the music consumption landscape. By identifying distinct clusters of music users, this method facilitates the creation of personalized recommendations, targeted marketing strategies, and tailored music experiences, ultimately enhancing user satisfaction and engagement. Through unsupervised learning, the clustering-based categorization of music users has the potential to revolutionize the music industry by enabling precise segmentation and understanding of diverse user segments. By leveraging this approach, music streaming platforms and other industry stakeholders can gain valuable insights into user behavior, preferences, and trends, empowering them to develop more effective content curation techniques, user interfaces, and promotional campaigns. Additionally, the application of unsupervised learning in music user categorization opens up opportunities for the development of more sophisticated recommendation systems, capable of delivering highly personalized and relevant music suggestions to individual users.

N-version programming for nanosatellite telemetry processing

Software is a key element that ensures the functioning of any modern complex technical system. One such system is the constellation of spacecraft and associated ground control complexes that provide reception, transmission, and processing of collected telemetry. The process of data acquisition and its subsequent processing is critical to the flight control of the spacecraft and its onboard scientific equipment. Furthermore, telemetry data processed by ground control systems involves large volumes of raw data, the processing of which is a complicated and time-consuming task. In order to solve this problem, various methods of automatic data processing are used. Improving them is a key factor in ensuring the fault tolerance of onboard software and hardware, improving its reliability. Of all the existing widely-used methods of data processing, we shall focus on N-version programming (NVP) approach. N-version programming has firmly established itself as an effective method for increasing software reliability and designing fault-tolerant systems. Since its inception in the 1970s, this approach has been deeply connected with the development of aerospace software systems, including, among others, satellite ground control stations. In light of the aforementioned, this paper discusses the application of NVP for processing telemetry data gathered from nanosatellites (CubeSats). Due to the fact that there exists a skeptical view on the NVP approach in terms of its efficiency, the author covers this issue in existing literature in terms of the approach’s applicability for processing satellite telemetry.

Quatro++: Robust global registration exploiting ground segmentation for loop closing in LiDAR SLAM

Global registration is a fundamental task that estimates the relative pose between two viewpoints of 3D point clouds. However, there are two issues that degrade the performance of global registration in LiDAR SLAM: one is the sparsity issue and the other is degeneracy. The sparsity issue is caused by the sparse characteristics of the 3D point cloud measurements in a mechanically spinning LiDAR sensor. The degeneracy issue sometimes occurs because the outlier-rejection methods reject too many correspondences, leaving less than three inliers. These two issues have become more severe as the pose discrepancy between the two viewpoints of 3D point clouds becomes greater. To tackle these problems, we propose a robust global registration framework, called Quatro++. Extending our previous work that solely focused on the global registration itself, we address the robust global registration in terms of the loop closing in LiDAR SLAM. To this end, ground segmentation is exploited to achieve robust global registration. Through the experiments, we demonstrate that our proposed method shows a higher success rate than the state-of-the-art global registration methods, overcoming the sparsity and degeneracy issues. In addition, we show that ground segmentation significantly helps to increase the success rate for the ground vehicles. Finally, we apply our proposed method to the loop closing module in LiDAR SLAM and confirm that the quality of the loop constraints is improved, showing more precise mapping results. Therefore, the experimental evidence corroborated the suitability of our method as an initial alignment in the loop closing. Our code is available at https://quatro-plusplus.github.io.

Services of Regional Universal Scientific Libraries: Problems and Approaches to Their Presentation on Websites

Scientific libraries are active social institutions that promptly respond to the challenges of the external environment, changing preferences of readers and users, their needs for different library, information, socio-cultural demands in online and offline spaces, for self-realization of creative and educational potential. Among scientific libraries a special place is occupied by regional universal scientific libraries (RUSL), which take an active part in the formation of educational, cultural, scientific and economic potential of their territories; ensure the creation of a unified regional information and library space; provide services to the population taking into account regional needs and specifics of the region’s development. The projects reflected in the “IFLA Bank of Ideas”, “Library Creativity Bank”, on the Library Map of the World, in the final materials of the All-Russian contests of library innovations, international contests of marketing projects of IFLA libraries, and in the professional press testify to the innovative practice of the RUSL. The results of the analysis of the websites of 50 regional universal scientific libraries of Russia (the main innovators in library services in the regions) are presented, the purpose of which was to identify the problems of reflection of library and information and other services in the web space. The study analyzed free and paid services offered in online and offline modes and ranked their most common types. The results showed gaps in the presentation of library and information and other services on websites (lack of a unified approach to the presentation of services by type and a complete list of all library and information and other services, difficult search for paid services, lack of a section “Services” and visual support for the provision of additional services, etc.), as well as showed a panorama of opportunities to better reflect them for different categories and segments of users. It is concluded that the solution of the above problems will allow public libraries to raise the level of awareness of users and demonstrate the fullest range of stationary, non-stationary and remote services provided on a paid and free basis.

The Idea of Digital Currency for India based on Blockchain Technology: A Conceptual Study

This paper aims to explain the idea of digital currency, its prospects in the Indian context, its role in digital transformation, and how it would be beneficial to our country, businesses, and different user segments. As in the phase of digital transformation, a constant improvement in technology is happening day by day globally. In today's virtual world, where everything is on its way forward, why should we not develop our economy accordingly so that we will be able to strengthen ourselves globally? And we had to begin the work of transformation by converting our currency to digital mode, i.e., digital currency, which is accessible in virtual or electronic form rather than physical.
 The paper opted for an exploratory study using existing literature, case studies, books, electronic resources, speeches of various officials of the central bank, and experts on national as well as international platforms, which gave clarity on some of the core elements involved in digital currency, like the concept of blockchain with its kinds and segments like wholesale & retail and also with use case data in different counties across the globe, strengths, and weaknesses; initial coin offerings (ICO); and central bank digital currency (CBDC).
 In this paper, data were complemented by documentary analysis, in which literature like the concept note of the Reserve Bank of India, the discussion paper of the Bank of England, and existing literature regarding centrally backed currency were studied for the clarity of the above work through figures like the money flower issued by the BIS (Bank for International Settlements). The paper concludes with discussion, suggestions, and future directions, with the conclusion that the user segments for the virtual currency backed by a central bank are classified.