Ground Segment Research Articles

The Segmentation of Mobile Application Users in The Hotel Booking Journey

Aim/Purpose: This study aims to create customer segmentation who use Online Travel Agent (OTA) mobile applications in Indonesia throughout their hotel booking journey. Background: In the context of mobile hotel booking applications, research analyzing the customer experience at each customer journey stage is scarce. However, literature increasingly acknowledges the significance of this stage in comprehending customer behavior and revenue streams. Methodology: This study employs a mixed-method and exploratory approach by doing in-depth interviews with 20 participants and questionnaires from 207 participants. Interview data are analyzed using thematic analysis, while the questionnaires are analyzed using descriptive statistics. Contribution: This study enriches knowledge in understanding customer behavior that considers the usage of mobile apps as a segmentation criterion in the hotel booking journey. Findings: We developed four user personas (no sweat player, spotless seeker, social squad, and bargain hunter) that show customer segmentation based on the purpose, motivation, and actions in each journey stage (inspiration, consideration, reservation, and experience). Recommendations for Practitioners: The resulting customer segmentation enables hospitality firms to improve their current services by adapting to the needs of various segments and avoiding unanticipated customer pain points, such as incomplete information, price changes, no social proof, and limited payment options. Recommendation for Researchers: The quality and robustness of the customer segment produced in this study can be further tested based on the criteria of homogeneity, size, potential benefits, segment stability, segment accessibility, segment compatibility, and segment actionability. Impact on Society: This study has enriched the existing literature by establishing a correlation between user characteristics and how they use smartphones for tourism planning, focusing on hotel booking in mobile applications. Future Research: For future research, each customer segment’s demographic and behavioral factors can be explored further.

Doenças crônicas e longevidade: desafios futuros

Abstract This text proposes the design of care models that are more effective and appropriate for the characteristics of the aged population. To this end, the article expounds on the need for emphasis on low-complexity care that focuses on coordination, prevention and client monitoring to reduce wastefulness, offer better quality care and lower costs. Furthermore, the epidemiological assessment instruments used and the detailed routine of all health professionals are presented. Thus, it is endeavoured to deliver the best care for the aged and, within an estimated timeframe of around 20 years, render this sustainable, transforming not only care for this segment of users, but for the health system as a whole.

Adaptive Patchwork: Real-time Ground Segmentation for 3D Point Cloud with Adaptive Partitioning and Spatial-Temporal Context

Adaptive Patchwork: Real-time Ground Segmentation for 3D Point Cloud with Adaptive Partitioning and Spatial-Temporal Context

Reliability and Reproducibility of the Cryogenic Sapphire Oscillator Technology

The cryogenic sapphire oscillator (CSO) is a highly specialized machine, which delivers a microwave reference signal exhibiting the lowest frequency fluctuations (Allan deviation σy(τ)) for the integration time τ between 1 s and 104 s, indeed 4 decades. In such interval, good units feature σy(τ) < 10-15, with < 10-14 drift in one day. The oscillator is based on a sapphire monocrystal resonating at 10 GHz in a whisperinggallery mode, cooled at ≈ 6 K for zero thermal coefficient and optimal quality factor Q.We report on the progress accomplished implementing eleven CSOs in about 10 years since the first sample delivered to the Malargüe station of the European Space Agency (ESA) in Argentina. Short-term stability is improved by a factor of 3.10, depending on τ, and the refrigerator’s electric power is reduced to 3 kW single-phase line. Frequency stability and overall performances are reproducible, with unattended operation between scheduled maintenance every two years. The CSO is now a semicommercial product suitable to scientific applications requiring extreme frequency stability with reliable unattended long-term operation, like the flywheel for primary frequency standards, the ground segment of Global Navigation Satellite System (GNSS), astrometry, Very Long Baseline Interferometry, and radio astronomy stations.

Complexity challenges for transition policy: lessons from coastal shipping in Norway

This policy briefing discusses decarbonization policies of “hard-to-abate” sectors, emphasizing the implications of these sectors’ complexity. Specifically, we discuss two sources of complexity: (a) heterogeneity in the form of variation across and within technologies and user segments and (b) interdependencies between technologies (within and between their value chains) and between user segments and adopter groups. Based on research on coastal shipping in Norway, a global frontrunner in decarbonization of this sector, we suggest three guiding principles for developing policy mixes for decarbonizing hard-to-abate sectors: (1) employ technology-specific policies but aim at broad sectoral or general policies when suitable, (2) consider value chain interdependency and user segment heterogeneity when prioritizing technologies and user segments, and (3) translate (rather than transfer) successful policies to other settings (e.g. user segments).

Churn Prediction for High-Value Players in Freemium Mobile Games: Using Random Under-Sampling

Many game development companies use game data analysis for mining insights about users' behaviour and possible product growth. One of the most important analysis tasks for game development is user churn prediction. Effective churn prediction can help hold users in the game by initiating additional actions for their engagement. We focused on high-value user churn prediction as it is of particular interest for any business to keep paying customers satisfied and engaged. We consider the churn prediction problem as a classification problem and conduct the random undersampling approach to address imbalanced class distribution between churners and active users. Based on our real-life data from a freemium casual mobile game, although the best model was chosen as the final classification algorithm for extracted data, we can definitely say there is no general solution to the stated problem. Model performance highly depends on the churn definition, user segmentation and feature engineering, it is therefore necessary to have a custom approach to churn analysis in each specific case.

Integrated framework of knowledge-based decision support system for user-centered residential design

A user-oriented residential environment is considered essential to providing occupants with a comfortable living experience while meeting their demands. However, design assessment in the early stage of residential design is a complex problem due to multifactor coupling, the diversity in user needs, and the incompleteness of design knowledge. This study proposes an integrated framework of knowledge-based decision support system (KBDSS) to mitigate such problems. In this framework, the user segmentation is performed in accordance with people’s particular requirements priority and, thus, enables design decisions that create better satisfaction among a wider audience. Meanwhile, the quality function deployment approach, decision support system, and fuzzy set theory are used to translate user requirements into quantifiable design specifications, thereby making the design a multicriteria decision-making problem. To illustrate and validate the proposed framework, a case study of a KBDSS for a multiunit residential building kitchen design is demonstrated to explore a user-centered residential design with such decision support. This proposed framework provides naive design practitioners with specific knowledge about decision problems and assists them in consistently making user-centered design decisions. The research results represent a further step toward a systematic methodology of user-centered built environment design.

Initial Tracking, Fast Identification in a Swarm and Combined SLR and GNSS Orbit Determination of the TUBIN Small Satellite

Flight dynamics is a topic often overlooked by operators of small satellites without propulsion systems, as two-line elements (TLE) are easily accessible and accurate enough for most ground segment needs. However, the advent of cheap and miniaturized global navigation satellite system (GNSS) receivers and laser retroreflectors as well as modern, easy-to-use, open-source software tools have made it easier to accurately determine an orbit or to identify a spacecraft in a swarm, which helps with improving the space situational awareness in orbits that are more and more crowded. In this paper, we present tools for small satellite missions to generate orbit predictions for the launch and early orbit phase (LEOP), identify spacecraft in a swarm after a rideshare launch, and carry out routine orbit determination from multiple sources of tracking data. The TUBIN mission’s LEOP phase set a new standard at Technische Universität Berlin: the first global positioning system (GPS) data were downloaded less than four hours after separation, orbit predictions allowed successful tracking by the ground stations, and the spacecraft could be identified in the swarm as soon as the TLE were released by Space-Track. Routine orbit determination from GPS and satellite laser ranging (SLR) tracking data was carried out over several months, and the quality of the orbit predictions was analyzed. The range residuals and prediction errors were found to be larger than those of most SLR missions, which was due to the difficulty of modeling the atmospheric drag of a tumbling, non-spherical spacecraft at low orbital altitudes.

Development and Automation of Fixed Wing UAV for Reconnaissance Mission with FPV Capability

Today's military missions require pre-emptive knowledge of hostile activities in and around the conflict zone. Using a multi rotor UAV for Reconnaissance has many disadvantages, such as cost, range, reduced maneuverability and much more. The main objective is to make the UAV completely autonomous, with the transmitter on standby. By entering the flight plan into the Ground Control System, the UAV will complete the assigned mission autonomously and will be guided by an operational flight plan wherein active waypoints are plotted in the GCS. The GCS can be linked to the Telemetry and the Global Positioning System to have a complete knowledge of the UAV location.

A User Segmentation Method in Heterogeneous Open Innovation Communities Based on Multilayer Information Fusion and Attention Mechanisms

The heterogeneity and diversity of users and external knowledge resources is a hallmark of open innovation communities (OICs). Although user segmentation in heterogeneous OICs is a prominent and recurring issue, it has received limited attention in open innovation research and practice. Most existing user segmentation methods ignore the heterogeneity and embedded relationships that link users to communities through various items, resulting in limited accuracy of user segmentation. In this study, we propose a user segmentation method in heterogeneous OICs based on multilayer information fusion and attention mechanisms. Our method stratifies the OIC and creates user node embeddings based on different relationship types. Node embeddings from different layers are then merged to form a global representation of user fusion embeddings based on a semantic attention mechanism. The embedding learning of nodes is optimized using a multi-objective optimized node representation based on the Deep Graph Infomax (DGI) algorithm. Finally, the k-means algorithm is used to form clusters of users and partition them into distinct segments based on shared features. Experiments conducted on datasets collected from four OICs of business intelligence and analytics software show that our method outperforms multiple baseline methods based on unsupervised and supervised graph embeddings. This study provides methodological guidance for user segmentation based on structured community data and semantic social relations and provides insights for its practice in heterogeneous OICs.

A Feasibility Study for Signal-in-Space Design for LEO-PNT Solutions With Miniaturized Satellites

The global navigation satellite systems (GNSSs) are increasingly suffering from interferences, such as coming from jammers and spoofers, and their performance is still modest in challenging urban and indoor scenarios. Therefore, there are efforts worldwide to develop complementary positioning, navigation, and timing (PNT) solutions. One such complementary method under current research is the so-called LEO-PNT, namely, PNT solutions based on low-Earth orbit (LEO) satellites, and in particular on small-sized or miniaturized satellites. Such satellites have low-to-moderate costs of building, launching, and maintenance. Several challenges are to be overcome when designing a new LEO-PNT solution, concerning all three satellite segments: 1) the signal-in-space (SIS) or space segment; 2) the ground segment; and 3) the user/receiver segment. This article presents a survey of the SIS design challenges under the inherent constraints of wireless-channel propagation impairments as well as some design recommendations for SIS features. We address different constellation types, achievable coverage limits, and geometric dilution of precision (GDOP) bounds, as well as achievable carrier-to-noise ratios (CNRs) under a realistic wireless channel model, based on a MATLAB Quadriga simulator. We also discuss several optimization criteria regarding LEO-PNT SIS design, by taking into account the tradeoff between a low cost/low number of satellites in orbit on the one hand, and a sufficient coverage and good CNR for PNT purposes on the other hand.

Blockchain-Based Trusted Traffic Offloading in Space-Air-Ground Integrated Networks (SAGIN): A Federated Reinforcement Learning Approach

In the future era of intelligent networks, communication technology and network architecture need to be further developed to provide users with high-quality services. The Space-Air-Ground Integrated Networks (SAGIN) is seen as a potential architecture to provide ubiquitous communication and drive the era of the intelligent global network. The space and air segments in SAGIN can assist in offloading traffic from the ground segment. However, in a highly dynamic and heterogeneous network like SAGIN, offloading decisions are easily affected by the incorporated/malicious nodes. How to ensure security and improve network performance becomes a critical problem. In this paper, we address the above problem by jointly using blockchain and federated reinforcement learning (FRL). Firstly, we propose a blockchain-based secure federated learning framework that combines topology information chain and model chain to assist traffic offloading. Then, we propose a node security evaluation and an enhanced practical byzantine fault tolerance (EPBFT) algorithm to secure the traffic offloading process. Furthermore, we describe the traffic offloading problem as a Markov decision problem (MDP) and employ the Blockchain-based Federated Asynchronous Advantage Actor-Critic (BFA3C) algorithm to solve this problem. Finally, the simulation results show that the BFA3C-based algorithm used in SAGIN with/without malicious nodes achieves superior performance in terms of latency and security.

Flight Software Development for a CubeSat Application

This article presents a development of a CubeSat mission software running on an STM32-based on-board computer (OBC). This was conducted under the Canadian CubeSat Project, initiated by the Canadian Space Agency in 2018 to support the development of 15 CubeSats across Canada. The proposed mission software has a multilayered architecture and is divided into five layers from a low layer dedicated to the peripherals to the top layer dedicated to the Mission Applications. The CubeSat protocol (CSP) is used at the communication layer for easing connectivity between subsystems and to communicate with the ground segment. The mission software running on the OBC is built to meet many requirements defined for this satellite, such as version control, classifications, margins, etc. The CubeSat will be able to accomplish two scientific missions related to the study of space weather once the satellite is put into orbit from the International Space Station. An overview of the software running on the OBC is presented, written in C Language, and includes the implementation of the CSP. FreeRTOS used as an operating system for the OBC is also presented. A Command Line Interface was designed for testing purposes to ensure software efficiency and some results are discussed in this article. The flight software consists of three main tasks and subtasks. Of the 1024 kB of flash memory, only 240 kB was used which represents less than 20% of the total memory. The CPU load is 34% for normal, manual, and maintenance modes and 16% for failure modes.

Data-Driven Calibration Algorithm and Pre-Launch Performance Simulations for the SWOT Mission

The Surface Water and Ocean Topography (SWOT) mission will be affected by various sources of systematic errors, which are correlated in space and in time. Their amplitude before calibration might be as large as tens of centimeters, i.e., able to dominate the mission error budget. To reduce their magnitude, we developed so-called data-driven (or empirical) calibration algorithms. This paper provided a summary of the overall problem, and then presented the calibration framework used for SWOT, as well as the pre-launch performance simulations. We presented two complete algorithm sequences that use ocean measurements to calibrate KaRIN globally. The simple and robust Level-2 algorithm was implemented in the ground segment to control the main source of error of SWOT’s hydrology products. In contrast, the more sophisticated Level-3 (multi-mission) algorithm was developed to improve the accuracy of ocean products, as well as the one-day orbit of the SWOT mission. The Level-2 algorithm yielded a mean inland error of 3–6 cm, i.e., a margin of 25–80% (of the signal variance) with respect to the error budget requirements. The Level-3 algorithm yielded ocean residuals of 1 cm, i.e., a variance reduction of 60–80% with respect to the Level-2 algorithm.

Who are those fast cyclists? An analysis of speed pedelec users in the Netherlands

In many regions, conventional bicycles and electrically assisted bicycles (e-bikes), are gaining popularity and becoming a commonly used sustainable mode in the urban transportation system. Speed pedelecs are a type of high-speed e-bike with motorized pedal assistance up to a maximum speed of 45 km/h. This relatively new mode is most often used for trips that range from 10 to 40 kilometers, making it a promising mode for promoting drivers to switch to using sustainable and active transportation. The goal of this study is to assess speed pedelec usage in the Netherlands. We set out to understand who the users are, what motivates them, and whether they experience any barriers to speed pedelec usage. Using a factor-cluster analysis we group users based on their attitudes and preferences regarding the physical environment they cycle in, their sociodemographic characteristics, personal attitudes, and social environments. The analysis revealed four primary user groups: Enthusiastic riders, Efficient riders, Concerned riders and Relaxed riders. Across the groups, 85% of the respondents reported using a speed pedelec at least once a week for a trip they would have previously made by car. However, each group has specific travel habits, motivations, and safety concerns. In contrast to previous literature about the use of conventional bicycles, attitudes about competition play an important role for speed pedelec users. Stress reduction and being outside in nature also play an important role for certain segments of speed pedelec users. In addition, believing that other road users have a negative image of speed pedelecs also determines how speed pedelec users use their bicycles. While infrastructure preferences are often cited as being a distinctive factor for users of conventional bicycles, this did not clearly apply to speed pedelec users. However, all user groups reported that on average the cycling infrastructure on their regular routes was insufficiently developed for speed pedelecs. To motivate continued speed pedelec usage, each user groups requires specific policy interventions that are aligned with their personal values related to preference, safety, and image. By understanding the specific needs and desires of different segments it becomes possible to develop effective policy interventions aimed at improving the experiences of all speed pedelec users.

A User Portrait of Express Software Based on Full Life Cycle Data

With the rapid development of traditional express industry, it is of great significance to study the portrait of express users. The express industry can realize the industrial transformation and upgrading by means of the express customer portrait. However, the research on the user portrait of express delivery is still scarce, at present. In this paper, a user portrait model based on life cycle data is proposed, which comprehensively describes the behavior preferences of users through multi-dimensional feature vectors. The model takes into account the basic attributes, preference attributes and feedback attributes of users, and uses the idea of classification before clustering to realize the segmentation of express users. In the classification stage, this paper fuses the K-means and the Support vector machine algorithm, adds the preference coefficient, and designs a new objective function to complete the user classification. In the process of clustering, we follow the idea of density-based peak clustering, combine the characteristic attributes of the multidimensional data of express users, add the characteristic coefficient, modify the index calculation formula in the model, and complete the clustering of express users. The simulation data show that the user portrait model proposed in this paper can achieve better segmentation of courier users, with better accuracy and timeliness.

A new ground control measure for double-use entries in underground coal mines

Retreat longwall mining with a double-use entry system has many advantages over other multiple-entry systems including high coal recovery, extra entry excavation, and low risk of coal bursts. The essential of the double-use entry system is to construct a competent solid sidewall to ensure the stability, shape, and profile of the entry during retreating the first longwall face. In this study, a new comprehensive ground control measure is proposed for the construction of the sidewall and ground control of double-use entries. The system consists of strong support components to support the entry roof, in conjunction with destressing measures to diminish the influence of excessive mining-induced pressures. Laboratory tests and numerical modeling are performed to evaluate the effect of the competent support element and destressing measures, to optimize the proposed ground control system. Field-monitored data and on-site examinations showed that the proposed system successfully controlled the deformation of the double-use entry and maintained its profile during the coal mining of the first and second longwalls. The proposed system provides a new alternative for underground coal mines with restricted requirements of high coal recovery and coal burst prevention through optimizing longwall entry system.

E-Scooter usage and mobility behavior during the Covid-19 crisis – Evidence from a large scale survey in Munich and implications for leisure and tourism

Abstract This paper presents the actual situation and the background on micromobility and e-scoooter research and their operations in cities especially during the pandemic. To better understand the reasons for use and the current implications a large scale representative survey has been conducted in Munich. As micromobility cannot be considered isolated, the results focus on urban mobility and the integration of micromobility into the overall context especially looking into usage before and after the pandemic, user preferences, safety and specific user segments. Although tourism is not the focus of this survey, it is argued that the use of specific modes at home and being accustomed to, will positively influence mode use abroad as a tourist as well. Consequently, the article discusses and interprets e-scooter findings and derives recommendations for sustainable city planning as well as for tourism and leisure.

Identifying unobserved heterogeneity in mobile wallet adoption – A FIMIX-PLS approach for user segmentation

PurposeThe purpose of this study is to segment mobile wallet users using a finite mixture partial least squares (FIMIX-PLS) approach and evaluate the unobserved heterogeneity across segments.Design/methodology/approachPartial least square structural equation modeling (PLS-SEM) using a convenience sample of 744 responses was used to analyze the measurement, structural model and hypotheses testing. To examine unobserved heterogeneity and identify user segments, FIMIX-PLS technique was employed. To generate more precise recommendations, importance-performance map analysis (IPMA) was performed with attitude as the target variable.FindingsA structural equation model revealed that except perceived ease of use (PEOU) all other dimensions, namely perceived usefulness (PU), lifestyle compatibility (LC), facilitating conditions (FC), trust and security significantly influences attitude which, in turn, determines intention. The FIMIX-PLS technique resulted in four segments – The Rationalist, Early Adopters, Late Adopters and The Innovators.Practical implicationsThe paper provides segment specific and between segment differences to derive implications. Identification of relevant predictors and segments will help academicians, marketing researchers and practitioners in gaining further understanding of the mobile wallet adoption. The findings of the paper can guide mobile wallet providers to frame appropriate strategies and offerings pertaining to the obtained segments.Originality/valueThe paper builds upon Technology Acceptance Model (TAM) to propose an integrated model to explain adoption behaviors associated with mobile wallet. To the best of the authors' knowledge, this is one of the first empirical attempts using FIMIX-PLS technique to assess precursors of adoption and substantiates the perceived value-attitude-intention linkage to identify heterogeneity among mobile wallet users.

Intelligent Reflecting Surfaces Enhanced Mobile Edge Computing: Minimizing the Maximum Computational Time.

Intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) have recently attracted significant attention in academia and industry. Without consuming any external energy, IRS can extend wireless coverage by smartly reconfiguring the phase shift of a signal towards the receiver with the help of passive elements. On the other hand, MEC has the ability to reduce latency by providing extensive computational facilities to users. This paper proposes a new optimization scheme for IRS-enhanced mobile edge computing to minimize the maximum computational time of the end users' tasks. The optimization problem is formulated to simultaneously optimize the task segmentation and transmission power of users, phase shift design of IRS, and computational resource of mobile edge. The optimization problem is non-convex and coupled on multiple variables which make it very complex. Therefore, we transform it to convex by decoupling it into sub-problems and then obtain an efficient solution. In particular, the closed-form solutions for task segmentation and edge computational resources are achieved through the monotonical relation of time and Karush-Kuhn-Tucker conditions, while the transmission power of users and phase shift design of IRS are computed using the convex optimization technique. The proposed IRS-enhanced optimization scheme is compared with edge computing nave offloading, binary offloading, and edge computing, respectively. Numerical results demonstrate the benefits of the proposed scheme compared to other benchmark schemes.