Space Network Research Articles

Communication between Astronauts and Nasa Deep Space Network: A Conversation Analysis

In the field of conversation analysis, conversation is one way to approach social order. Conversations are not the seemingly disorganized and random objects they seem at first. This is a special locus of interaction where changes in speakers are repeated (occur), the parties speak at the same time. Gapless and non-overlapping transitions are common. The aim of this study is to analyze aspects of conversation analysis use in communication between Astronauts and NASA Deep Space Network (DSN) under the framework. This study use qualitative descriptive method is applied in this research. The data are collected by transcribe their utterances from Youtube and transcribing their utterances directly into written text. The result of this study shows that 6 types of adjacency pairs, from the data analysis found there are 47 utterances that consist of adjacency pairs. From the findings, there are 7 requesting – agreement, 11 assessment – agreement, 17 question – answer, 1 compliment – acceptance, 1 complaint – apology, 10 offer - acceptance for adjacency pairs.

Constructing a multi-functional small urban green space network for green space equity in urban built-up areas: A case study of Harbin, China

Ensuring equitable access to green spaces in urban built-up areas is not only vital for fostering environmental justice but also aligns with the United Nations Sustainable Development Goals (SDGs). However, there is a noticeable gap in the current body of research regarding the role of small urban green spaces, especially their multifunctionality from an ecosystem services perspective. Taking the urban built-up area of Harbin as an example, this study first applied the Analytic Hierarchy Process to classify the supply and demand of green space into three types. Then, the article further analyzes the potential functional positioning of the newly added green spaces, including ecological and social functions, using Minimum Cumulative Resistance and Point of Interest. Finally, multi-criteria decision models are used to explore the priority and functional positioning of green space and construct a multi-functional and highly-efficient small urban green space network. The results indicate a significant imbalance in green space supply and demand, with severe and medium mismatch areas accounting for 30.17 % and 48.50 %, respectively. By assessing the multifunctionality of small green spaces, we propose guidelines that include five types of areas: Concentrated Development (85.85 km2, 16.94 %), Backup Development (70.74 km2, 14.31 %), Maintenance (304.49 km2, 61.51 %), Protection (14.94 km2, 3.02 %), and Optimization (20.89 km2, 4.22 %). Finally, the article proposes a 277.60 km multifunctional small urban green space network. By examining small urban green spaces, this study crafts a pivotal framework for enhancing green space equity in urban built-up environments, providing valuable insights for policymakers and urban planners. The approach has significant implications for developing multifunctional green networks in varied urban contexts and offers a model for wider application, serving as a reference for achieving green space equity in developing countries globally.

Cross-Space Conduction Assessment Method of Network Attack Risk under the Strong Coupling Characteristics of Electric Power Cyber Physics

With the deep integration and wide application of advanced digital sensing, Internet of Things technology, and energy technology in power systems. Power information systems and physical systems are gradually being coupled and developed into power cyber–physical systems (CPS). A number of blackouts in recent years have shown that cyberspace cyber attacks on CPS can lead to the intensification and rapid spread of faults in the physical space of the power grid, and even system collapse. Aiming at the difficulty of analyzing the evolution of cyber–physical cross-space impacts of cyber-attacks, this paper proposes a cross-domain propagation impact assessment method that considers cyber–physical coupling risks caused by attacks. First, according to the multiple coupling relationship between the power system information space and physical space, the monitoring function model and the control function model are established. Second, under the effect of high-concealment attack, analyze the impact of the risk caused by its failure after it is transmitted to the physical space with different propagation probabilities. Finally, the experimental verification was carried out using the IEEE RTS79 standard test system. The simulation results show that the proposed method can comprehensively consider the cyber–physical energy supply coupling relationship, the risk propagation probability, and the operating characteristics of the information system, and effectively quantify and evaluate the impact of information space network attacks on the physical space entity power grid. It further reveals the objective law that information space risks can evolve and spread across domains under the condition of strong coupling of information physics.

Bigdata is breaking new grounds

Abstract Health services research (HSR) must now integrate the technological and methodological developments of ‘Big Data’ that are transforming health research as a whole. E-health devices, social networks, and electronic medical records (EHR) are all sources that can enrich HSR. For example, social networks have become tools for hospital evaluation (ranking) and patient experience, as well as strategies for the dissemination of prevention messages by health authorities. Electronic medical records make it possible to carry out ‘real-life’ longitudinal studies on the entire population of a territory, offering a vision of the health care pathways in health care services. This cross-sectional view of the use of healthcare services renders possible the description and analysis of the evolution of healthcare networks in space and time. These sources also make offer a better understanding of the disparities in access to and use of healthcare services on various geographical scales. Finally, E-health devices can be used to help organize and optimize care. However, we must remain cautious in the face of this acceleration of information flows and the mass of data that are only of interest if they bring value to our research and do not endanger the users of healthcare services: respect for ethical rules, respect for the rights of individuals, or rights, duties and responsibilities in the use of artificial intelligence tools that can be used in big data.

Phase space reconstruction and self-attention convolutional neural network for sound source ranging with trial data

To improve the feature extraction ability of multipath signals and reduce the dependence on environmental information, this paper studies a method that combines multipath signal feature information mining and neural network in high-dimensional phase space instead of using the received complex acoustic pressure as the input of the neural network directly. Based on the phase space reconstruction (PSR) technology, we propose a hybrid model of the self-attention-based Transformer and convolutional neural network (CNN), named PSR-TransCNN. The covariance matrix of the reconstructed data is constructed as the input of the sparse auto-encoder (SAE). A transfer learning strategy is employed to obtain the optimal weight and offset value of SAE, which serve as the initial values of the PSR-TransCNN. Real-world experimental results demonstrate that the PSR-TransCNN significantly reduces the relative root mean square error (RMSE) by 0.006 compared with the matched field processing (MFP) method and exhibits outstanding generalization and practical application potential compared to other state-of-the-art (SOTA) methods. The PSR-TransCNN achieves an RMSE of 0.046 and an accuracy of 88.75%, successfully classifying sound sources with unknown labels. The proposed method effectively reduces the multipath effects and addresses the mismatch issue in sound source ranging (SSR) within an unexplored ocean environment.

Detecting Technosignatures from Earth-scale Civilizations

Assuming civilizations have similar interplanetary communications and radar capability to NASA’s Deep Space Network, what is the feasibility of intercepting their communications? Interplanetary conjunctions between Earth-like exoplanets, their stars, and other planets in their systems provide one of the most unique and pragmatic opportunities for detecting technosignatures. While eavesdropping on terrestrial communications becomes limited by a planet’s rotation, the beams of satellite communications and interplanetary radar transmissions are tracked, providing the most persistent and powerful opportunity for signal interception. In this study, we present a framework for assessing exoplanet habitability and establishing quantitative bounds for detecting Earth-scale technosignatures from Earth-like planets. These constraints for time, frequency, sky positions, and observatory sensitivity provide recommended observational guidelines for using state-of-the-art and future ground-based radio observatories toward technosignature detection. Applying this framework, 16 exoplanet targets are proposed for radio observation.

Phase space visibility graph

We introduce a topological approach for quantifying the dynamical complexity of time series. A novel complex network of visibility graph family is proposed based on defining visibility algorithm in phase space. The statistical properties of the constructed network show powerful potentiality for distinguishing stochastic and chaotic systems. And for some remarkable chaotic systems, it allows for quantitative correspondence with Lyapunov exponents. The potential practical application of this approach is demonstrated on the multiphase flow system and bearing fault identification. This work paves a natural way constructing complex network in phase space, and provides a complexity quantitative estimate for chaotic time series.

Engineering pH responsive carboxyethyl chitosan and oxidized pectin -based hydrogels with self-healing, biodegradable and antibacterial properties for wound healing

As an important organ of the human body, effective protection of the skin during trauma is crucial. An ideal wound dressing should have adhesion, adsorption of wound secretions, and good antibacterial properties. Two kinds of natural polysaccharide-based hydrogels, carboxyethyl chitosan/oxidized pectin hydrogel (CEC/OP) and carboxyethyl chitosan/oxidized pectin/polyethyleneimine hydrogel (CEC/OP/PEI), were reported by using carboxyethyl chitosan as the matrix, and oxidized pectin and branched polyethyleneimine as the crosslinking agents. Both hydrogels could be formed in a short time and exhibited the pH responsively due to the presence of imine bond. Compared with carboxyethyl chitosan/oxidized pectin hydrogel, polyethyleneimine containing hydrogel can form gel quickly, a more compact and stable three-dimensional space network structure was formed, which exhibited better swelling performance, the swelling ration, rheology property, self-repair ability, and antibacterial performance. When the mass fractions of carboxyethyl chitosan and oxidized pectin solutions are 4 wt% and 9 wt%, respectively, the hydrogel exhibited an antibacterial efficiency of >96 % against both Staphylococcus aureus and Escherichia coli. After adding 0.75 wt% polyethyleneimine, the antibacterial efficiency of hydrogel could reach up to >98 %. More importantly, the polyethyleneimine containing hydrogel has a significant effect in the treatment of bacterially infected wounds.

Information Wars and Mythology of Social: Ideological, Mental and Media aspects

In the modern epoch, there is a transformation of social space, forms of interaction between people. “Thinking models”, the so-called mental attitudes, are changing, that stipulates research of the processes of transformation of the mental aspects in a modern culture. The ideological and mental aspects are the dimensions where these modifications are reflected. One can measure social and cultural transformations, existing problems and development in the future through the mental aspect in retrospect and at the present time. A person becomes a “political being”, functioning as a subject of public life. One of the features of “post-modernization” of mass culture noted today is a some special network of the global information space with its own laws, which draws attention to the most acute or minor problems. In political science, a network approach to public administration is increasingly observed. Today, big data plays an important role in public administration. They are used to improve the policy in the field of interethnic relations, develop a strategy for the state's foreign information policy. Big data is used in the process of forming a particular policy. In the context of the use of big data, one can note the analysis of “digital citizenship” culture of network users, actors, formation technologies, socio-political effects. Determining the direction of information flows in a particular segment of social media, risks and technologies to overcome negative effects in the online environment is becoming increasingly important and relevant. In particular, this applies to information flows representing the problems of relations in international conflicts. In this context, concepts such as consciousness, including political consciousness, political culture, mentality and ideology are becoming increasingly important and relevant. The mental transformations observed today must also be taken into account in defining modern methods of teaching and education.

Dispersed urban growth in the intermediate city and its impact on public space. A case study in Loja- Ecuador

INTRODUCTION Urban growth in intermediate Latin American cities has given way to low-density, discontinuous peripheries. This uneven development negatively impacts the urban environment and quality of life. In this context, public spaces do not fulfill their structural function, they fail to generate connections between soil uses, recreation areas, and social exchange. OBJECTIVE This study proposes to fill a gap in existing research by focusing on the incidence of dispersed urban growth specifically in public space through the study case analysis. METHODOLOGY Using a descriptive and qualitative approach, dispersed urban growth is analyzed multidimensionally and the networks of public spaces are evacuated in terms of form, accessibility, uses, sociability, and identification. RESULTS The study area presents low and very low densities in about 70% of its area, soil and 30% of the area presents a continuity of the fabric and 76% of the area is intended for land use in this way it is evident that urban growth dispersion focuses on problems of connectivity, fragmentation, segregation, the marginalization that limit access to public space. DISCUSIÓN AND CONCLUSIONS The low density and limited diversity of users, erode social and economic dynamics, in this sense, the public space of the periphery limits its function to a minimum which deteriorates it further instead of contributing to urban quality.

Sustainable Cross-Regional Transmission Control for the Industrial Augmented Intelligence of Things

Sustainable connectivity between an Augmented Intelligence of Things (AIoT) system and terminal devices can provide a strong guarantee for an enterprise's continuity of production. To ensure sustainable data interaction in emergency situations such as damage to communication infrastructure facilities in a postdisaster period, a pyramidal air–space–ground network model is proposed in this paper, and its transmission performance is investigated. The space network, constructed of geostationary (GEO) satellites and medium and low Earth orbit (MEO and LEO) satellites, is heterogeneously fused with the terrestrial AIoT network to ensure around-the-clock network service for enterprise production. Then, bandwidth–delay product (BDP) analysis is performed for this heterogeneous network. The proposed scheme improves the data transmission efficiency of the network model by mitigating the impact of a long round-trip time (RTT) and improving the congestion window optimization in the case of data loss. The proposed scheme increases the transmission rate in the network model by a factor of 3.72 compared to the traditional transmission scheme in the case of a 5-hop terrestrial AIoT network and has a 3.94-fold advantage in download time.

TranNas-NirCR: A method for improving the diagnosis of asymptomatic wheat scab with transfer learning and neural architecture search

Wheat scab is one of the most important diseases endangering the health of wheat which severely affects the yield and quality of wheat. Thus, the diagnosis of wheat scab is very important. However, it is difficult to distinguish between asymptomatic wheats without visible spots on the surface and healthy wheats by traditional visual methods under natural conditions, which has greatly hindered the diagnosis of wheat scab. To address the challenge of poor model classification caused by the difficulty in distinguishing asymptomatic wheats from healthy wheats, we use near-infrared spectral data with healthy, symptomatic and indistinguishable asymptomatic wheats and propose a new approach Transfer Learning and Neural Architecture Search for Near-infrared with Convolutional Networks and Recurrent Networks (TranNas-NirCR). This approach integrates neural architecture search with transfer learning and employs a combination of convolutional neural networks and recurrent neural networks in the search space. Compared to other methods, the TranNas-NirCR method achieved better classification results with an accuracy of 90.42%, which is 2.68% higher than support vector machines (SVM), 5.36% higher than neural architecture search (NAS), and 4.21% higher than Transfer Learning with Neural Architecture Search (Tran_NAS). This method shows strong generalization performance in the case of only a small amount of near-infrared spectral data, which is of referential significance for diagnosing early wheat scab in real conditions.

A Social Recommendation Model Based on Basic Spatial Mapping and Bilateral Generative Adversarial Networks.

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. Therefore, how to effectively fuse interaction information and social information becomes a hot research topic in social recommendation, and how to mine and exploit the heterogeneous information in the interaction and social space becomes the key to improving recommendation performance. In this paper, we propose a social recommendation model based on basic spatial mapping and bilateral generative adversarial networks (MBSGAN). First, we propose to map the base space to the interaction and social space, respectively, in order to overcome the issue of heterogeneous information fusion in two spaces. Then, we construct bilateral generative adversarial networks in both interaction space and social space. Specifically, two generators are used to select candidate samples that are most similar to user feature vectors, and two discriminators are adopted to distinguish candidate samples from high-quality positive and negative examples obtained from popularity sampling, so as to learn complex information in the two spaces. Finally, the effectiveness of the proposed MBSGAN model is verified by comparing it with both eight social recommendation models and six models based on generative adversarial networks on four public datasets, Douban, FilmTrust, Ciao, and Epinions.

Association of urban green spaces with urban ecological zones

Today urban development lacks ecological foundations in many cities of Turkey. The purpose of this study is to reveal the relationship between urban green spaces and ecological zones in the sample of Aksaray/Turkey. In this study, a study design has been created to improve the urban ecological infrastructure and to associate the green space network with the ecological zones. This design is divided into four parts as data processing, landscape pattern of urban green spaces, analysis of the spatial boundaries of urban natural ecological zones, and determination of the importance of spatial regions by overlaying two different stratified analyses. This study proposes a methodological framework that can be integrated into efforts to identify ecological zones to increase the sustainability of urban ecology and green space quality. One potential limitation of the proposed methodology can be the lack of consensus and enthusiasm among the administrative actors regarding the issue. Therefore, it is recommended that the administrative bodies should be correctly informed by the relevant scholars and practitioners who are working on the subject.

A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT

The increasing trend toward using the Internet of Things (IoT) increased the number of intrusions and intruders annually. Hence, the integration, confidentiality, and access to digital resources would be threatened continually. The significance of security implementation in digital platforms and the need to design defensive systems to discover different intrusions made the researchers study updated and effective methods, such as Botnet Detection for IoT systems. Many problem space features and network behavior unpredictability made the Intrusion Detection System (IDS) the main problem in maintaining computer networks' security. Furthermore, many insignificant features have turned the feature selection (FS) problem into a vast IDS aspect. This paper introduces a novel binary multi-objective dynamic Harris Hawks Optimization (HHO) enhanced with mutation operator (MODHHO) and applies it to Botnet Detection in IoT. Afterward, the Feature Selection (FS) is undertaken, and the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Decision Tree (DT) classifiers are used to estimate the potential of the selected features in the precise detection of intrusions. The simulation results illustrated that the MODHHO algorithm performs well in Botnet Detection in IoT and is preferred to other approaches in its performance metrics. Besides, the computational complexity analysis results suggest that the MODHHO algorithm's overhead is more optimal than similar approaches. The MODHHO algorithm has performed better in comparison with other compared algorithms in all 5 data sets. In contrast with the machine learning methods of the proposed model in all five data sets, it has had a better error rate according to the AUC, G-mean, and TPR criteria. And according to the comparison made with filter-based methods, it has performed almost better in three datasets.

INTERNATIONAL SECURITY AND OUTER SPACE – TODAY’S LAW CHALLENGES

Space security means safe and permanent access to space and limiting threats coming from there. This definition also includes the security aspects of man-made devices sent into space and of ground stations. Space infrastructure can be described as a network of space and ground systems connected by communication channels and allowing access to space. Today, the largest space powers have begun to consider space as an operational domain of warfare. Space more and more often appears to be a field for competition, which might become an arena of conflict. The aim of this article is to present today’s many law challenges to the security of space infrastructure, such as unintentional threats (space debris, geomagnetic and solar storms, and other random disturbances), intentional threats (ASAT anti-satellite weapons, malicious interference, and cyber-attacks), the growing problems of Earth orbit congestion, and the increasing amount of space debris from devices launched into space. The article also presents the role of international organizations (such as the UN Committee on the Peaceful Uses of Outer space) in making laws that are intended to observe and react to all changes necessary in the outer space environment and to be proactive to help outer space to be safe and secure for all mankind. The conclusion is, however, not optimistic. Space security is a sensitive issue, mainly during conflicts or wars. States are not inclined to bind themselves by international law in this matter. Thus, due to the absence of hard international law (treaties), bilateral and multilateral agreements as well as the best practices from countries that organize space flights must apply. Space monitoring systems, such as the Situational Awareness System (SSA), the code of conduct in space, the UN Long Term Sustainability, or the space Traffic Management rules are legal tools to manage the above challenges in space today.

Transforming the Network of Co-working Spaces for Start-ups into an Open Knowledge Ecosystem

The paper focuses on the issues of transforming the geographically spread co-working spaces of start-up firms into the open knowledge ecosystems, i.e. hubs of growth-focused organizations characterised by sharing and generating new industrial knowledge. The presented insights are based on the combination of theoretical review and empirical survey of 92 managers of start-ups and SMEs in Lithuania. This is an emergent community of regional start-up firms with rudimentary linkages and elements of knowledge sharing. The paper looks into the key issues that could contribute to strengthening the knowledge-based collaborations and synergies in such community, while assessing both the current situation and the evolutionary potential of such collaborations. We present the critical discussion in the light of ecosystem-as-structure vs. ecosystem-as-affiliation (Adner, 2017) approaches. Ecosystem-as-affiliation approach stresses the affiliation of ecosystem actors (to the network or focal actor), while ecosystem-as-structure views ecosystem through the lense of value proposition that network actors are co-creating. The first view has a more open-ended network perspective where spontaneous collaborations may lead to different outcomes and the network welcome different members with no prior objective in sight. The second view is more focused on aligning the resources and activities of multilateral set of partners for materialising specific value propositions, and members are invited to join based on these specific needs. The research has shown that, on one hand, the emergent start-up community relies on institutional coordination while retaining the element of openness. On the other hand, there is an explicit need for leadership in building the shared vision and designing the knowledge partnerships for delivering the shared value propositions. One should stress the importance of focal organizations and boundary spanners - both in terms of the organizational and knowledge boundaries. The shared business interests, network leadership and trust have been identified as the key cornerstones for further development of network into an open knowledge ecosystem, yet they are also associated with the greatest challenges to sustained knowledge sharing in the network.

An integrated model with a solution algorithm for improving an existing public bicycle sharing system

This study uses a time–space network flow technique combined with a mathematical programming method to construct a holistic model for improving an existing bicycle sharing system. The model considers bicycle rental station site selection, expansion of station capacity, expansion of bicycle fleet size, and bicycle deployment and relocation issues in the same integrated framework. The model can be formulated as an integer network flow problem with side constraints. Since the problem is NP-hard, we design a heuristic algorithm to solve the problem efficiently and effectively. Finally, numerical tests are conducted in order to perform a preliminary evaluation of the model and heuristic algorithm with data from an existing public bicycle sharing system. The results indicate that the model and heuristic algorithm could be helpful to planners to improve their bike-sharing system operations.

Harnessing the Potential of the Metaverse and Artificial Intelligence for the Internet of City Things: Cost-Effective XReality and Synergistic AIoT Technologies

The Metaverse represents an always-on 3D network of virtual spaces, designed to facilitate social interaction, learning, collaboration, and a wide range of activities. This emerging computing platform originates from the dynamic convergence of Extended Reality (XR), Artificial Intelligence of Things (AIoT), and platform-mediated everyday life experiences in smart cities. However, the research community faces a pressing challenge in addressing the limitations posed by the resource constraints associated with XR-enabled IoT applications within the Internet of City Things (IoCT). Additionally, there is a limited understanding of the synergies between XR and AIoT technologies in the Metaverse and their implications for IoT applications within this framework. Therefore, this study provides a detailed overview of the literature on the potential applications, opportunities, and challenges pertaining to the deployment of XR technologies in IoT applications within the broader framework of IoCT. The primary focus is on navigating the challenges pertaining to the IoT applications powered by VR and AR as key components of MR in the Metaverse. This study also explores the emerging computing paradigm of AIoT and its synergistic interplay with XR technologies in the Metaverse and in relation to future IoT applications in the realm of IoCT. This study’s contributions encompass a comprehensive literature overview of XR technologies in IoT and IoCT, providing a valuable resource for researchers and practitioners. It identifies challenges and resource constraints, identifying areas that require further investigation. It fosters interdisciplinary insights into XR, IoT, AIoT, smart cities, and IoCT, bridging the gap between them. Lastly, it offers innovation pathways for effective XR deployment in future IoT/AIoT applications within IoCT. These contributions collectively advance our understanding of synergistic opportunities and complementary strengths of cutting-edge technologies for advancing the emerging paradigms of urban development.

Air cargo network planning and scheduling problem with minimum stay time: A matrix-based ALNS heuristic

In air cargo delivery, cargo usually needs to stay in hubs after unloading operations until the designated outbound flight is ready to leave. The stay time may account for a significant part of the overall delivery time and is closely related to the service level of the cargo delivery. However, the prior studies on the air cargo network planning and scheduling problem mainly focused on the cost and revenue but paid little attention to the stay time. To bridge this gap, we introduce a new facet to the air cargo network planning and scheduling literature. The air cargo network planning and scheduling problem with minimum stay time involves the optimization of the hub location, flight deployment, flight timetabling, and flight-sequence-based cargo distribution to minimize the total stay time of freighter cargoes while guaranteeing a rational cost level. We develop a hierarchical modeling framework to handle this problem, where a time–space network is built to model the cargo stay time and the flight departure time. Next, to efficiently address the framework, the concept of the capacity matrix is introduced for model reformulation, and a matrix-based ALNS heuristic is proposed for model solving. The heuristic designs eight destroy operators from the column, row, and element perspectives, and develops a repair model for the repair operation. Finally, the proposed framework and algorithm are tested with a real-life instance from a leading express company in China.