Wide Area Coverage Research Articles

VEGAS-SSS: An intra-group component in the globular cluster system of the NGC 5018 galaxy group using VST data

Globular clusters (GCs) offer a valuable tool as a fossil tracer of the formation and evolution of galaxies and their environment. Studying the properties of these stellar systems provides crucial insights into the past formation and interaction events of the galaxies, especially in galaxy group and cluster environments. We study the properties of globular cluster (GC) candidates in an area of 1.25 times 1.03 square degrees centred on the NGC,5018 galaxy group. We use the deep, wide field, and multi-passband (ugr) observations obtained with the VLT Survey Telescope (VST) as part of the VST Elliptical GAlaxy Survey (VEGAS). With a focus on studying small stellar systems (SSS) associated with bright galaxies, this paper constitutes an extension of the VEGAS-SSS series investigating GCs in the NGC,5018 group. We derived photometric catalogues of compact and extended sources in the area and identified GC candidates using a set of photometric and morphometric selection parameters. A GC candidate catalogue has been provided and inspected using a statistical background decontamination technique, benefiting from the wide area coverage of the data. The 2D distribution map of GC candidates reveals an over-density of sources on the brightest member of the NGC,5018 group. No significant GC over-densities are observed in the other bright galaxies of the group. We report the discovery of a candidate local nucleated low-surface-brightness (LSB) dwarf galaxy that may possibly be engaged in a tidal interaction with NGC,5018. The 2D map also reveals an intra-group GC population aligning with the bright galaxies and along the intra-group light (IGL) component of the group. The radial density profile of GC candidates in NGC,5018 follows the galaxy surface brightness profile. The (g-r) colour profile of GC candidates centred on this galaxy shows no evidence of the well-known colour bimodality; however, it is observed instead in the intra-group population. From the GC luminosity function (GCLF) analysis, we find a low specific frequency N =0.59 ± 0.27 for NGC,5018, which is consistent with previous results based on less deep optical data over a smaller area. This relatively low S_! N value, coupled with the lack of colour bimodality, might be due to a combination of observational data limitations as well as the post-merger status of NGC,5018, which might host a population of relatively young GCs. For the intra-group GC population, we obtained a lower limit of N,gr ∼0.6. Using the GCLF as a distance indicator, we estimate that NGC 5018 is located $38.0 ± 7.9$ Mpc away, which is consistent with values reported in the literature.

SWOT Analysis Of The Mobile Banking Promotion Strategy At Bank Aceh Syariah

This research aims to find out how strategic the promotion of Bank Aceh Syariah's action is for the people of Aceh Tamiang and to find out the obstacles to promoting Bank Aceh Syariah's action for the people of Aceh Tamiang. The method used is a qualitative approach. Data collection techniques use observation, interviews and documentation. The information in this research is employees of Bank Aceh Syariah, customers of Bank Aceh Syariah Aceh Tamiang. The results of the SWOT analysis show that the strategy that can be used by Bank Aceh is the S-O (strength-opportunity) strategy. The research results show that Bank Aceh Syariah's promotional action strategy uses advertising and personal selling media. The obstacles experienced by Bank Aceh Syariah in promoting action among the people of Aceh Tamiang are the lack of personnel in the promotion department, wide area coverage and network difficulties in several areas in Aceh Tamiang. Keywords: SWOT Analysis; Bank Aceh Syariah; Mobile Banking

Euclid: The rb−M* relation as a function of redshift

Core ellipticals, which are massive early-type galaxies with almost constant inner surface brightness profiles, are the result of dry mergers. During these events, a binary black hole (BBH) is formed, destroying the original cuspy central regions of the merging objects and scattering stars that are not on tangential orbits. The size of the emerging core correlates with the mass of the finally merged black hole (BH). Therefore, the determination of the size of the core of massive early-type galaxies provides key insights not only into the mass of the black hole, but also into the origin and evolution of these objects. In this work, we report the first Euclid-based dynamical mass determination of a supermassive black hole (SMBH). To this end, we study the center of NGC 1272, the second most luminous elliptical galaxy in the Perseus cluster, combining the Euclid Visible Camera (VIS) photometry coming from the Early Release Observations (EROs) of the Perseus cluster with the Visible Integral-field Replicable Unit Spectrograph (VIRUS) spectroscopic observations at the Hobby-Eberly Telescope (HET). The core of NGC 1272 is detected on the Euclid VIS image. Its size is 1.​​″29 ± 0.​​″07 or 0.45 kpc, which was determined by fitting PSF-convolved core-Sérsic and Nuker-law functions. We deproject the surface brightness profile of the galaxy, finding that the galaxy is axisymmetric and nearly spherical. The two-dimensional stellar kinematics of the galaxy is measured from the VIRUS spectra by deriving optimally regularized non-parametric line-of-sight velocity distributions. Dynamical models of the galaxy are constructed using our axisymmetric and triaxial Schwarzschild codes. We measure a BH mass of (5 ± 3)×109 M⊙, which is in line with the expectation from the MBH − rb correlation, but is eight times larger than predicted by the MBH − σ correlation (at 1.8σ significance). The core size, rather than the velocity dispersion, allows one to select galaxies harboring the most massive BHs. The spatial resolution, wide area coverage, and depth of the Euclid (Wide and Deep) surveys allow us to find cores of passive galaxies that are larger than 2 kpc at a redshift of up to 1.

Software Define Wide Area Network (SDWAN) network optimization analysis on radiolink-fiber optic access media migration

The development of technology in radiolink networks has grown rapidly and is suitable to be implemented on wide coverage area networks, but weather and physical environment factors greatly affect signal conditions. The background that has been stated, this study proposes migration to fiber optic access media because it is able to send data faster and is not affected by electromagnetic interference and is stable. Furthermore, this study conducted an optimization analysis on the SDWAN (Software Defined Wide Area Network) network, SDWAN functions as a firewall that can provide connectivity support and infrastructure network development in branch offices with system security in one centralized platform. The test method used in this study is a connectivity test from each remote fortigate located in the branch office. While the analysis method used is Quality of Service (QoS), which is a method to find out data delivery (Packet Loss, Delay and Jitter) using the FortiManager application, which is the analytical and experimental proof of concept of the SDWAN network model using the BGP method for auto failover testing.

FRR: A Fast Routing Recovery mechanism minimizing network formation time in smart grids

In recent years, Internet of Things (IoT) technologies have been applied to the most diverse types of applications, highlighting their use in Smart Grids (SGs), such as smart metering, demand response, fault detection and renewable energy integration, as well as in Smart Cities, such as smart lighting, smart parking and environmental monitoring. A technology that has been widely used in these contexts is the Wireless Smart Ubiquitous Networks Field Area Network (Wi-SUN FAN) standard, which defines the implementation and behavior of interoperable networks based on open standards such as IEEE 802.15.4 and the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). The adopted standards allow Wi-SUN FAN networks to take advantage of features such as security, frequency hopping, multi-hop packet routing, and IPv6 addressing, ensuring a wide coverage area and immunity against electromagnetic interference. Despite all the advantages, a shortcoming of Wi-SUN FAN networks is the long network formation time. In this article, we propose a mechanism called Fast Routing Recovery (FRR), which aims to accelerate the reconnection process in networks that use the RPL protocol by memorizing information about the nodes’ preferred parents. We evaluate the FRR mechanism by simulations and experiments. The results indicate that FRR can reduce the network formation time by up to 50% in certain network scenarios.

Model-free distributed state estimation with local measurements.

In this paper, the state estimation problem of physical plants with unknown system dynamic is revisited from the perspective of limited output information measurement, which corresponds to those with characteristics of high-dimensional, wide-area coverage and scatter. Given this fact, a network of sensors are used to carry out the measurement with each one accessing only partial outputs of the targeted systems and a novel model-free state estimation approach, named distributed stochastic variational inference state estimation, is proposed. The key idea of this method is to compensate for the impacts of local output measurements by adding nearest-neighbor rule-based information interaction among estimators to complete the state estimation. It finds from the numerical experiments that the proposed method has clear advantages in both estimation accuracy and speed, and it also provides guidance on how to improve the efficiency of state estimation under local measurements.

Secure low-cost photovoltaic monitoring system based on LoRaWAN network and artificial intelligence

The advanced study will focus on developing a secure and cost-effective photovoltaic monitoring system using Long Range Wide Area Network (LoRaWAN) and Artificial Intelligence technologies for highly risky missions. With the world shifting to using renewable energy, the development of secure and efficient cost-effective monitoring systems for photovoltaic installations becomes very important. However, most of the solutions in use for PV installation monitoring are characterized by high costs, hence their limited use, especially in developing regions. In this regard, the present work presents country-specific protocols designed for the country of Turkey. The proposed system in this study is expected to offer real-time monitoring and data collection at a very meager cost by utilizing inexpensive microcontrollers and sensors. The accuracy rate demonstrated by the research is highly above board: 97.65%. It costs $210, with a very low power consumption of 0.5W and a range of 10 km. The major features underpin real-time monitoring with wide area coverage for effectiveness and efficiency in PVs monitoring. Test results in many places around Turkey have turned out reliable and accurate to justify a possible reduction in operational costs without compromising the quality of data received. This design is reinforced by a detailed mathematical model that assures one of its thorough analysis in performance. There is custom-built firmware that enhances communication efficiency and ensures data integrity in implementation. Comparative studies carried out with existing technologies indicate that the proposed system is cost-effective and operationally sound. This work gives a scalable, low-cost solution that probably might raise the uptake of PV systems in economically constrained areas.

Non-Terrestrial Network Concepts in 5G and Beyond Communication Technologies

Mobile communication systems used in 5G and beyond will provide high-capacity, reliable and low-latency services within wide coverage areas. Currently, terrestrial networks are widely used, but it is expected that these networks will be replaced by hybrid network solutions where terrestrial and non-terrestrial networks operate together. These hybrid ones can perform cutting-edge complimentary solutions when the traditional networks have weak signal levels and geographical difficulties. The main motivation of this study can be stated as the increasing use of hybrid network structures for the solution of digital divine and access problems. In this study, design optimizations of hybrid wireless networks according to different system architectures and performance analyzes are given and various non-terrestrial network technologies are compared.

Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN

Long-Range Wide-Area Network (LoRaWAN) technology offers efficient connectivity for numerous end devices over a wide coverage area in the Internet of Things (IoT) network, enabling the exchange of data over the Internet between even the most minor Internet-connected devices and systems. One of LoRaWAN’s hallmark features is the Adaptive Data Rate (ADR) algorithm. ADR is a resource allocation function which dynamically adjusts the network’s data rate, airtime, and energy dissipation to optimise its performance. The allocation of spreading factors plays a critical function in defining the throughput of the end device and its robustness to interference. However, in practical deployments, LoRaWAN networks experience considerable interference, severely affecting the packet delivery ratio, energy utilisation, and general network performance. To address this, we present a novel ADR framework, SSFIR-ADR, which utilises randomised spreading factor allocation to minimise energy consumption and packet collisions while maintaining optimal network performance. We implement a LoRa network composed of a single gateway that connects loads of end nodes to a network server. In terms of energy use, packet delivery rate, and interference rate (IR), our simulation implementation does better than LoRaWAN’s legacy ADR scheme for a range of application data intervals.

Analysis of Deformation and Flood Inundation Spatial Distribution Using Sentinel-1 SAR Data for The 2021 South Kalimantan Floods

Abstract Major flood disasters that have occurred in Indonesia, one of which occurred in South Kalimantan in early 2021. This flood disaster was caused by high rainfall intensity. There are several relationships between rainfall, flood, and deformation. The objective of this study is to monitor deformation in flood inundation that occurred during the flood disaster in South Kalimantan. Observations with direct measurements in the field provide high data accuracy, but in its implementation there are limitations. Therefore, we utilize the development of Synthetic Aperture Radar (SAR) technology in observing flood inundation spatial distribution and deformation in South Kalimantan. Using SAR cannot be as accurate as direct measurements in the field, but it is effective in saving time, can be applied to wide area coverage, and can be observed multitemporally. The SAR data used is Sentinel-1 imagery with C-band which can be freely accessed and has high spatial and temporal resolution. In flood mapping with backscatter using change detection, the flood inundation distribution was 2022.558 km2. The results show deformation variations in the form of land subsidence of up to 15.9 cm in the flood distribution area.

SEAIS: Secure and Efficient Agricultural Image Storage Combining Blockchain and Satellite Networks

The image integrity of real-time monitoring is crucial for monitoring crop growth, helping farmers and researchers improve production efficiency and crop yields. Unfortunately, existing schemes just focus on ground equipment and drone imaging, neglecting satellite networks in remote or extreme environments. Given that satellite internet features wide area coverage, we propose SEAIS, a secure and efficient agricultural image storage scheme combining blockchain and satellite networks. SEAIS presents the mathematical model of image processing and transmission based on satellite networks. Moreover, to ensure the integrity and authenticity of image data during pre-processing such as denoising and enhancement, SEAIS includes a secure agricultural image storage and verification method based on blockchain, homomorphic encryption, and zero-knowledge proof. Specifically, images are stored via IPFS, with hash values and metadata recorded on the blockchain, ensuring immutability and transparency. The simulation results show that SEAIS exhibits more stable and efficient processing times in extreme environments. Also, it maintains low on-chain storage overhead, enhancing scalability.

Development of a detailed bathymetry map of the Nhat Le estuary, Quang Binh province by remote sensing image treatment

Multispectral remote sensing images with the advantages of low cost, wide area coverage, and increased resolution have been widely used recently for determining the bathymetry of coastal waters. In this study, the correlation equation is developed based on the Landsat 8 OLI satellite images captured on September 22, 2022, and the survey data measured during the time period of September 12–22, 2022, was used for mapping bathymetry in the Nhat Le Estuary area, Quang Binh Province, a relatively clear area from sediment. The correlation between the image and the field survey data is quite good, with R2 = 0.88. This shows that Landsat 8 OLI data is suitable for mapping sea areas with depths up to 20 m.

Multi-Node Joint Jamming Scheme for Secure UAV-Aided NOMA-CDRT Systems: Performance Analysis and Optimization

Unmanned aerial vehicle (UAV) communication using non-orthogonal multiple access-based coordinated direct and relay transmission (NOMA-CDRT) supports both massive connectivity and wide-area coverage, becoming a key technology for future emergency rescue communications. However, relay forwarding and high-quality line-of-sight links may subject UAV-aided NOMA-CDRT to multiple eavesdropping attempts by saboteurs. Therefore, we propose a multi-node joint jamming scheme using artificial noise (AN) for the UAV-assisted NOMA-CDRT to improve the system’s physical layer security. In the proposed scheme, the base station directly serves a nearby user while using a UAV relay to serve a disaster-affected user, and both the users and the UAV relay utilize AN to jointly interfere with eavesdroppers around the users. To accurately characterize and maximize the ergodic secrecy sum rate (ESSR) of the proposed scheme, we derive the corresponding closed-form expressions and design a joint power allocation and interference control (JPAIC) algorithm using particle swarm optimization. Simulations verify the correctness of the theoretical analysis, the ESSR advantage of the proposed scheme compared with the conventional NOMA-CDRT, and the effectiveness of the proposed JPAIC.

Effect of raceway surface topography based on solid lubrication on temperature rise characteristics of HIPSN full ceramic ball bearings

PurposeThis study aims to understand the influence of raceway surface topography on the temperature rise characteristics of silicon nitride (Si3N4) full ceramic ball bearing and improve its service life.Design/methodology/approachThe arithmetic average height Sa, skewness Ssk and kurtosis Sku in the three-dimensional surface roughness parameters are used to quantitatively characterize the surface topography of the raceway after superfinishing. The bearing life testing machine is used to test the Si3N4 full ceramic ball bearing using polytetrafluoroethylene (PTFE) cage under dry friction conditions, and the self-lubricating full ceramic ball bearing heat generation model is established.FindingsWith the decrease of Sa and Ssk on the raceway surface and the increase of Sku, the average height of the raceway surface decreases, and the peaks and valleys tend to be symmetrically distributed on the average surface, and the surface texture becomes tighter. This kind of raceway surface topography is beneficial to form a thin and uniform filamentous PTFE transfer film with a wide coverage area on the raceway surface based on consuming less cage materials and improving the temperature rise characteristics of hot isostatic pressing silicon nitride full ceramic ball bearings.Originality/valueThe research results provide a theoretical basis for the reasonable selection of Si3N4 ring raceway processing technology and have important significance for improving the working characteristics and service life of Si3N4 full ceramic ball bearings under dry friction conditions.

Effect of Inlet Pressure on the Polyurethane Spray Nozzle for Soil Cracking Improvement: Simulations using CFD Method

Rigid spray polyurethane (RSPU) was commercially used as an injection in crack walls or soil surfaces to enhance material performance, increase lifetime, and save operating costs. The limitation of the RSPU nozzle was reported as easily clogging when sprayed out to the insulation and crack surface area and the finished product was less aesthetically pleasing. In this study, the RSPU nozzle of flat fan nozzle, 180° angle (Design A), Hollow cone nozzle, 60° angle (Design B), and Full cone nozzle, 90° angle (Design C) were prepared by using the SOLIDWORKS software. The effect of different pressures for RSPU nozzle design at the ranges of 4Mpa, 5MPa, and 6MPa was examined by ANSYS FLUENT software. The velocity of the outer spray nozzle shows a significant increase with increasing inlet pressure of the RSPU nozzle. The results reveal that the highest velocity of RSPU was obtained at the Hollow cone nozzle (Design B) as compared to (Flat fan nozzle) Design A and (Full cone nozzle) Design C at 394.249 m/s at 4MPa, 442.327 m/s at 5MPa and 485.37 m/s at 6MPa, respectively. The distribution droplet area shows the Design B spray formation had wider area coverage at 60° and exhibited that the injection nozzle spray was scatted and uniform at 6MPa. RSPU shows the velocity increased, distribution droplet increased, and output volume spray was decreased at the increasing of injection pressure. Hence, in this study was suggested that the size of RSPU nozzle design must be made at the ranges of 60° to 90° of outlet nozzle to obtain a good RSPU area. In conclusion, design B is the most effective for RSPU nozzle and is useful in injection cracking and insulation materials applications.

Small Object Detection in Medium–Low-Resolution Remote Sensing Images Based on Degradation Reconstruction

With the continuous development of space remote sensing technology, the spatial resolution of visible remote sensing images has been continuously improved, which has promoted the progress of remote sensing target detection. However, due to the limitation of sensor lattice size, it is still challenging to obtain a large range of high-resolution (HR) remote sensing images in practical applications, which makes it difficult to carry out target monitoring in a large range of areas. At present, many object detection methods focus on the detection and positioning technology of HR remote sensing images, but there are relatively few studies on object detection methods using medium- and low-resolution (M-LR) remote sensing images. Because of its wide coverage area and short observation period, M-LR remote sensing imagery is of great significance for obtaining information quickly in space applications. However, the small amount of fine-texture information on objects in M-LR images brings great challenges to detection and recognition tasks. Therefore, we propose a small target detection method based on degradation reconstruction, named DRADNet. Different from the previous methods that use super resolution as a pre-processing step and then directly input the image into the detector, we have designed an additional degenerate reconstruction-assisted framework to effectively improve the detector’s performance in detection tasks with M-LR remote sensing images. In addition, we introduce a hybrid parallel-attention feature fusion module in the detector to achieve focused attention on target features and suppress redundant complex backgrounds, thus improving the accuracy of the model in small target localization. The experimental results are based on the widely used VEDAI dataset and Airbus-Ships dataset, and verify the effectiveness of our method in the detection of small- and medium-sized targets in M-LR remote sensing images.

Classifying Ground Rippability and Weathering Grades in a Sedimentary Rock Geological Environment Using Seismic Refraction Survey

Introduction An in-depth understanding of the ground subsurface is crucial for foundation design and excavation works and for avoiding potential hazards during land development.In this regard, the ground rippability and weathering grades are some of the ground information needed. While geotechnical works are preferred, their limited horizontal coverage and high cost are often constraints that limit their use. Aims To counter this, a geophysical survey is employed for its wider area coverage and cost-efficiency. Therefore, this study used the seismic refraction method to assess the rippability and weathering grades in a sedimentary rock geological setting (interbedded sandstone, siltstone, and shale) as a preliminary ground assessment. Methods A seismic refraction survey was carried out using Aktiebolaget Elektrisk Malmletning (ABEM) Terraloc Pro 2, where the survey line was 115m long. Rippability was obtained by correlating seismic values with the Caterpillar D10R rippability table. Meanwhile, the weathering grades of the ground were determined by correlating the study area with another study area of a similar geological setting. Results Within the 39m penetration depth, three layers can be classified from the ground’s P-wave velocity values and D10R Caterpillar rippability chart, which include rippable, marginal, and non-rippable layers. A break in the continuous ground layers could be seen, causing lower velocity values to be sandwiched between high velocities, which signified the presence of fracture. The weathering grades were also successfully classified from the seismic velocity values. Conclusion Using seismic refraction method, this study successfully employed seismic velocity values in determining the rippability and weathering grades of interbedded sedimentary rock without borehole record.

Attention-transfer-based path loss prediction in asymmetric massive MIMO IoT systems

The asymmetric massive multiple-input–multiple-output (MIMO) array improves system capacity and provides wide-area coverage for the Internet of Things (IoT). In this paper, we propose a novel attention-based model for path loss (PL) prediction in asymmetric massive MIMO IoT systems. To represent the propagation characteristics, the propagation image that considers the detailed environment, beamwidth pattern, and propagation-statistics feature is designed. Benefiting from the shuffle attention computation, the proposed model, termed a shuffle-attention-based convolutional neural network (SAN), can effectively extract the detailed features of the propagation scenario from the image. Besides, we design the beamwidth-scenario transfer learning (BWSTL) algorithm to assist the SAN model in predicting PL in the new asymmetric massive MIMO IoT systems, where the beamwidth configuration and propagation scenario are different. It is shown that the proposed model outperforms the empirical model and other state-of-the-art artificial intelligence-based models. Aided by the BWSTL algorithm, the SAN model can be transferred to new propagation conditions with limited samples, which is beneficial to the fast deployment in the new asymmetric massive MIMO IoT systems.

Implementation of Operational Management at the Manufacturing Company (Pt Nippon Indosari Corpindo, Tbk) Sari Roti

Operational management is very important for all types of industries, one of which is manufacturing. Manufacturing companies are concerned with production processes on a large scale that use equipment and machinery to manage raw materials into finished goods. PT Nippon Indosari Corpindo Tbk “SARI ROTI "is one of the leading bread manufacturing companies in Indonesia. This Journal discusses how SARI ROTI applies operations management concepts to achieve competitive advantage and meet growing market demand. Based on these objectives, researchers use qualitative research with qualitative analysis methods in which research is conducted by analyzing data using theoretical foundations as the basis of analysis. The data analyzed is secondary data, which is data that comes from existing sources and is not self-processed data The results showed that this company is a company that successfully runs the overall operations management concept, so that the company is able to produce quality products and ensure the cleanliness of all its products, as well as efficient in terms of the production process and distribution of products produced with a wide marketing area coverage.

Mega-NeRF++: An Improved Scalable NeRFs for High-resolution Photogrammetric Images

Abstract. Over the last few years, implicit 3D representation has attracted more and more research endeavors, typified by the so-called Neural Radiance Fields (NeRF). The original NeRF and some relevant variants mostly address on small-scale scene (such as, indoor or tiny toys), which already show good novel views rendering results. It still remains challenging when dealing with wide coverage area that is captured by large number of high-resolution images, the time efficiency and rendering quality is generally limited. To cope with large-scale scenario, recently, Mega-NeRF was proposed to divide the area into several overlapping sub-area and train corresponding sub-NeRFs, respectively. Mega-NeRF adopts the method of parallel training of multiple sub-modules, which means sub-modules are absolutely independent of each other, which might in principle not be an optimal solution, as two sub-NeRFs of adjacent sub-models obtained by parallel training are likely to get different rendering results for the overlapping area, and the final rendering result is supposed to be negative affected. Therefore, we present Mega-NeRF++, and our goal is to improve Mega-NeRF by implementing extra sub-models optimization that alleviate the rendering discrepancy of overlapping sub-NeRFs. More specifically, we further fine tune the original Mega-NeRFs by considering the consistency of adjacent overlapping area, which means the training data used in the optimization are only from the overlapping region, and we also proposed a novel loss, so that it not only takes into account the difference between the prediction of each sub-model and the true value, but also considers the consistency of the predicted results between various adjacent sub-modules in the overlapping region. The experimental results show that, for the overlapping area, our Mega-NeRF++ can qualitatively render better images with higher fidelity and quantitively have higher PNSR and SSIM compare to original Mega-NeRF.