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This paper aims to present an innovative reconfigurable series-fed microstrip antenna using radiofrequency positive intrinsic negative (RF PIN) diodes for cognitive S-band and C-band satellite communications. The antenna can dynamically reconfigure its frequency, polarization and radiation pattern to meet diverse application needs.

The design involves a reconfigurable four-element microstrip antenna using FR4 substrate and copper patches. RF PIN diodes enable dynamic frequency, polarization and radiation pattern reconfiguration. Simulations and optimizations are performed using CST and HFSS, using techniques like the Nelder-Mead algorithm, particle swarm optimization, covariance matrix adaptation and trust region framework. An antenna prototype is also fabricated to validate the simulations.

The proposed antenna demonstrates significant reconfigurability: it switches between S-band (2.45 GHz, 2.52 GHz) and C-band (5.55 GHz, 5.59 GHz) with bandwidths of 120 MHz and 550 MHz, respectively. It transitions between circular and linear polarization in the S-band and modifies the radiation pattern by 45 degrees, providing an alternative radiation direction in the C-band. The antenna achieves a maximum gain of 5.95 dBi at 2.52 GHz and 93% efficiency at 5.55 GHz. Simulated results closely match those from the fabricated prototype, confirming the design’s validity.

The innovative use of RF PIN diodes enables comprehensive reconfigurability in frequency, polarization and radiation patterns within a single microstrip antenna, meeting the demands of S-band and C-band satellite communications. This study demonstrates superior performance, significant gains and efficiencies across various reconfiguration modes, validated by rigorous simulation and practical fabrication. The simple structural design further distinguishes this study from others in the field.

This study aims to compare the local climate characteristics of Angkor Wat, Borobudur and Prambanan parks and determine effective strategies for mitigating thermal conditions that could suit Borobudur and Angkor Wat.

The study employed local climate zone (LCZ) indicators and ten-year historical climate data to identify similarities and differences in local climate characteristics. Satellite imagery processing was used to create maps of LCZ indicators. Meanwhile, microclimate models were used to analyze sky view factors and wind permeability.

The study found that the three tropical large-scale archaeological parks have low albedo, a medium vegetation index and high impervious surface index. However, various morphological characteristics, aerodynamic properties and differences in temple stone area and altitude enlarge the air temperature range.

Based on the similarities and differences in local climate, the study formulated mitigation strategies to preserve the sustainability of ancient temples and reduce visitors' heat stress.

The local climate characterization of tropical archaeological parks adds to the number of LCZs. Knowledge of the local climate characteristics of tropical archaeological parks can be the basis for improving thermal conditions.

Close inspection of some of the more intricate details of the two most recent planning efforts, the award-winning Amman Plan 2025 and the strategic master plan known as the Amman Development Corridor Study (ADC), particularly in their most direct area of overlap, that is, the Metropolitan Growth Strategy.

Study and interpretation of published documents relevant to the plans in question.

The study reveals that the emerging objectives of accommodating migrant capital within the context of state-wide neo-liberal restructuring, particularly at the city’s eastern and south-eastern edge, have yet to benefit from recent scholarship on productive suburbanization.

Lack of data on Foreign Direct Investment in Amman.

The results have implications for the future urban growth scenario of Greater Amman.

Informal (illegal) building on the fringes of the city continues unabated. It is encouraged by permissive planning practice, a long-standing aspect of local practice dating to the 1970s. The longer that planning action lags, particularly at the eastern fringes, the more intense will be the informal building, and the higher the prospects for social conflict.

There has been only one, rather uncritical, published research on the Amman Plan, but none so far discussing the ADC study and its proposals.

This paper aimed to develop a participatory methodology to analyze the disaster risk creation in coastal cities, based on an approach that combines social, urban, environmental and disaster risk elements.

The methodology uses some aspects of three theoretical approaches in a complementary way: i) the Pressure and Release (PAR) framework for the identification of dynamic pressures that contribute to disaster risk creation; ii) the application of Drivers, Pressure, State, Impact, Response (DPSIR) framework to analyze environmental dimensions; and iii) urban analysis, applying the Strengths, Weaknesses, Opportunities and Threats (SWOT) tool to classify urban processes. The methodology combined the use of satellite remote sensing data to analyze the urban sprawl and citizen science methods to collect social and environmental data, using the case study of the watershed of the Juqueriquerê River in the coastal city of Caraguatatuba, Brazil. The pilot project was part of a local university extension project of the undergraduate course on Architecture and Urban Planning and also engaged residents and city hall representatives.

The satellite remote sense data analysis indicated a continuous urban sprawl between 1985 and 2020, especially in the south of the Juqueriquerê watershed, reducing urban drainage and increasing the extension and water depth of urban flooding and riverine floods. Using citizen science methods, undergraduates identified settlements with limited economic resources to elevate houses and a lack of infrastructure to promote a resilient coastal city. After identifying the dynamic pressures that contribute to disaster risk creation and the weaknesses and strengths of a resilient city, undergraduate students proposed urban planning interventions and gray and green infrastructure projects to mitigate disaster risks.

The paper identifies urban sprawl in disaster-prone areas as one of the risk factors contributing to disaster. It also comprehensively analyzes differences between different zones in the Juqueriqere River, which will be useful for policy-making.

The method presented an interdisciplinary approach that used satellite remote sensing data and citizen science techniques to analyze disaster risks in coastal cities. The multidimensional approach used to evaluate risks is useful and can be replicated in other similar studies to gain a more comprehensive understanding of disaster risks.

This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices.

The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation.

The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker.

This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

The purpose of the study is to analyze changes in land use, specifically residential area expansion, in South Tangerang City and identify the factors that influence land use change.

The study used remote sensing methods in ArcGIS 10.8 for data analysis and processing, including spatial analysis and identification of land use changes. The study analyzed satellite images from 2010 and 2020 to identify changes in land use in South Tangerang City over the ten-year period.

The study found that the most significant land use changes in South Tangerang City between 2010 and 2020 were the reduction of mixed plantation area and the expansion of residential areas. The study identified the development of small townships by private developers as the main factor that influenced land use change in South Tangerang City.

The study has several limitations, including a focus on only one aspect of land use change (i.e. residential area expansion), limited scope of the study area (South Tangerang City) and a reliance on remote sensing methods for data analysis.

The findings of the study can be used by policymakers and city planners to develop sustainable land use planning strategies that balance the need for urban development with environmental and social concerns. By understanding the factors that drive land use changes in South Tangerang City, policymakers can develop policies that encourage sustainable urban growth and development while preserving natural resources and protecting the environment.

The study has social implications as the expansion of residential areas in South Tangerang City indicates a growing demand for housing in the area. The study highlights the importance of developing affordable and sustainable housing solutions to meet the needs of the growing population in South Tangerang City. Additionally, the study emphasizes the importance of understanding the social and economic factors that drive land use change and their implications for the well-being of local communities.

The residential area development in South Tangerang City is driven by private developers who make small independent cities that have all facilities in one area. These small cities attract people to reside and also drive high population growth in South Tangerang City, considering it is a buffer city of Jakarta that has good infrastructure development.

This study aims to investigate the influence of commercially available resins in water-based magenta pigment inkjet ink formulations on the properties of ink printability and the characteristics of ink application in food packaging. The impact of the resin on the jettability of the existing printability phase diagrams was also assessed.

Inks with different resin loadings were tested for selected properties, such as viscosity, particle size and surface tension. Stability was determined using a Turbiscan AGS turbidimeter and LumiFuge photocentrifuge analyzer. The ink layer fastness against abrasion and foodstuffs was evaluated using an Ugra device and according to PN-EN 646, respectively. JetXpert was used to assess Ricoh printhead jetting performance.

Printability diagrams successfully characterized the jettability of polyurethane inkjet inks on a multi-nozzle printhead and the binder improved droplet formation and printing precision.

Magenta water-based inkjet inks with commercial resins have been developed for printing on paper substrates. To the best of the authors’ knowledge, for the first time, inkjet ink stability was evaluated using the Turbiscan AGS and LumiFuge analyzers, and jettability models were verified using an industrial multi-nozzle printhead.

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).

This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.

The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.

Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.

Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.

The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.

Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

The cyber-physical system (CPS) is a well proven technology for improving system performance, resilience, and interconnectivity. In emerging nations like India, the logistics industry lacks practices connecting logistical equipment with cyberspace. This paper aims to bridge this gap by identifying and evaluating the performance metrics of connectivity solutions. Its goal is to establish an appropriate infrastructure that enables seamless connectivity within the CPS-enabled logistics ecosystem.

A novel integrated decision method is employed to classify the optimal connectivity solution for CPS. It integrates Regret Theory (RT) and Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE-1) method in a Hesitant Fuzzy (HF) environment. This method considers the psychological traits of decision-makers and effectively incorporates their hesitancy for the classification.

The findings highlight security (c10) as the foremost critical performance metric, followed by cost (c6), scalability (c9), traceability (c2) and trustworthiness (c1) to build connective infrastructure for CPS. For extensive coverage scenarios, like freight transportation, cellular connectivity (a2) emerges as the most suitable connectivity solution.

This study provides a roadmap to logistics managers for selecting a suitable connectivity infrastructure to enhance seamless connectivity in logistics operations and processes. Technology providers can utilize the findings to develop the CPS infrastructure for effective freight logistics management.

This research introduces a novel decision-making tool for making choices related to advanced technology assessment. It holds significant value in facilitating well-informed decisions in the digital transformation era.

This study aims to present a new rapid enhancement digital elevation model (DEM) framework using Google Earth Engine (GEE), machine learning, weighted interpolation and spatial interpolation techniques with ground control points (GCPs), where high-resolution DEMs are crucial spatial data that find extensive use in many analyses and applications.

First, rapid-DEM imports Shuttle Radar Topography Mission (SRTM) data and Sentinel-2 multispectral imagery from a user-defined time and area of interest into GEE. Second, SRTM with the feature attributes from Sentinel-2 multispectral imagery is generated and used as input data in support vector machine classification algorithm. Third, the inverse probability weighted interpolation (IPWI) approach uses 12 fixed GCPs as additional input data to assign the probability to each pixel of the image and generate corrected SRTM elevations. Fourth, gridding the enhanced DEM consists of regular points (E, N and H), and the contour interval is 5 m. Finally, densification of enhanced DEM data with GCPs is obtained using global positioning system technique through spatial interpolations such as Kriging, inverse distance weighted, modified Shepard’s method and triangulation with linear interpolation techniques.

The results were compared to a 1-m vertically accurate reference DEM (RD) obtained by image matching with Worldview-1 stereo satellite images. The results of this study demonstrated that the root mean square error (RMSE) of the original SRTM DEM was 5.95 m. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 2.01 m, and the generated DEM by Kriging technique was 1.85 m, with a reduction of 68.91%.

A comparison with the RD demonstrates significant SRTM improvements. The suggested method clearly reduces the elevation error of the original SRTM DEM.