As part of a fundamental study on undersea communication, we explore a system that enables data transmission between undersea drones and land stations or satellites. It is extremely difficult to maintain offshore wind turbines, especially those located underwater. Even when multiple divers or underwater drones are used for inspection, there are problems, such as communication tethers getting tangled. The proposed system utilizes the foundation of an offshore wind turbine as a transmission line to facilitate signal propagation. Consequently, this report examines the propagation characteristics of an annular concrete pillar across various frequencies. By analyzing its transmission properties, we aim to assess its potential effectiveness as a communication pathway in marine environments.

This study introduces a Rotationally Symmetric Square R-Shaped Metamaterial Absorber designed for high-frequency applications in the X, Ku, and K bands. The absorber achieves peak absorptivity of up to 99.80% across multiple resonant frequencies, leveraging single-negative (SNG) properties that enable near-zero permittivity and negative permeability. Constructed using a compact 9 mm by 9 mm FR-4 lossy substrate with copper patch and ground layers, the design ensures high efficiency and broad applicability. Notably, the proposed absorber achieves remarkable peak absorption rates of 95.61%, 97.89%, 99.45%, 99.80%, and 99.34%, at frequencies of 11.2 GHz, 12.21 GHz, 14.32 GHz, 16.59 GHz, and 18.5 GHz, respectively, demonstrating stable performance across incident wave angles up to 180 degrees. The innovative inductive-tailed metamaterial structure eliminates the need for lumped elements, enhancing reliability and simplifying fabrication. Simulation results confirm its broadband absorption capabilities, demonstrating superior electromagnetic wave absorption. This absorber presents a promising solution for High-Frequency 5G and EMI shielding applications.

This paper presents a novel multi-band metamaterial absorber operating in the C-band (4-8 GHz) and Ku-band (12-18 GHz), with resonances at 4.088 GHz, 4.736 GHz, 5.942 GHz, and 15.866 GHz. It achieves over 90% absorption, peaking at 99.99% at 15.866 GHz. The compact 20 mm by 20 mm symmetric design enables multi-resonance behavior, which is ideal for RF energy harvesting in satellite systems. S-parameter analysis confirms strong absorption with stability across varying incident angles, demonstrating polarization and angle insensitivity. Material analysis reveals negative permittivity, permeability, and refractive index at resonances, ensuring efficient impedance matching. These properties make the absorber highly efficient, frequency-selective, and robust for applications in EMI mitigation, stealth technology, and wireless communications. Its compactness and high absorption capabilities enhance its suitability for modern satellite and RF energy harvesting systems, ensuring consistent performance in dynamic environments.

This paper proposes a dual-band circular metamaterial absorber for X and Ku band applications with a symmetric split-ring structure. We found that the excellent absorption rates for the TEM mode at normal incidence up to 180 degree are 99.95%, 92.44%, 93.74%, and 92.75%, respectively, for various frequencies (8.37 GHz, 10.63 GHz, 15.23 GHz, and 17.63 GHz). The proposed MMA's unit cell is subwavelength in size and shows single negative (SNG) characteristics, such as negative permittivity/permeability and near-zero refractive index. Its substrate comprises a lossy FR-4 layer with a thickness of 1.6 mm and patch and ground layers constructed of annealed copper with a thickness of 0.035 mm. The unit cell has a unique patch structure and measures 0.223λ by 0.223λ. The proposed absorber is flexible and lightweight, and because of its excellent absorption capabilities and small patch size, it performs a distinctive function in satellite communications, VSAT applications, and military and weather radar applications.

This research introduces a compact, broadband metamaterial absorber (MMA) with a tri-layer Metal-Dielectric-Metal (MIM) structure. The absorber achieves near-unity absorption efficiencies of 93.17%, 95.35%, 98.61%, and 95.07% at 3.32 GHz, 5.02 GHz, 5.99 GHz, and 11.61 GHz, respectively. It demonstrates polarization insensitivity (180 degrees) and angular stability (up to 80 degrees) using single-negative (SNG) materials and near-zero refractive index behavior. With dimensions of 9mm by 9mm (0.1λ by 0.1λ), the structure effectively matches impedance with free space, minimizing reflection and transmission. Compared to existing MMAs, it offers superior size, bandwidth (2-12 GHz), and angular tolerance, making it ideal for electromagnetic shielding, radar stealth, and 5G applications. This design advances metamaterial development for next-gen RF systems.

This paper presents the design and analysis of a highly efficient, compact met-amaterial absorber (MMA) with integrated sensing capabilities, operating pri-marily within the C-band frequency range (5-8 GHz). The proposed MMA uti-lizes an innovative octagonal nut-shaped resonator patterned on an FR-4 substrate in a sandwich-like configuration, optimizing electromagnetic absorption and sen-sor responsiveness. Through simulation in CST Studio Suite 2024, the structure achieved peak absorption of 99.80% at 5.67 GHz with a reflection coefficient as low as -37 dB, demonstrating excellent impedance matching. The design was further adapted for sensing applications by incorporating a dielectric-sensitive layer, enabling accurate detection of various oils and fuels based on their dielec-tric constants. The sensor displayed a maximum sensitivity of 0.65, successfully distinguishing substances such as jet fuel, kerosene, and petroleum. This work confirms the suitability of the MMA design for multifunctional applications in-cluding biosensing, environmental monitoring, and food quality assessment.

The spherical reflector antenna is a promising antenna type for 5G and satellite communications due to its characteristics for good multibeam applications. To improve antenna efficiency, a shaped sub-reflector is designed. In order to evalu-ate shaped sub-reflector effects, a constant phase surface is proposed. On this surface, all reflected wave phase becomes constant by sub-reflector shaping. At the same time, reflected wave is blocked by a feed horn. The minimum blocking feed horn position is derived. By electromagnetic simulations by FEKO software, phase distribution on the constant phase surface, the radiation pattern, and aper-ture efficiency are obtained. The FEKO EM shows that the blocking results is 120mm when Ds/Dm=0.35, compared to 2B=160mm when Ds/Dm=0.25.

Space weather, driven by solar activity such as solar flares and coronal mass ejections (CMEs), can cause significant disturbances to the Earth's magnetosphere, impacting technological systems including satellites, communication networks, and power grids. This study investigates the geomagnetic impacts of Active Region 3664 (AR3664), a highly active sunspot group that emerged in early May 2024. Using ground-based in-struments operated in Malaysia-specifically, the Langkawi National Observatory (LNO), CALLISTO solar radio spectrometer at Selangor, and Magnetic Data Acquisition System (MAGDAS) from several location, the research documents the evolution of AR3664 and the subsequent geomagnetic storm that occurred on 10-11 May 2024. The storm, triggered by intense X-class flares and CME-driven shocks, was classified as a G5-class event and was associated with Dst index values reaching -412 nT and AE in-dex exceeding 1000 nT. MAGDAS data across equatorial latitudes revealed substantial variations in the Earth's magnetic field components, particularly the Z and H components. Notably, auroras were observed unusually far south, including across central Ja-pan. Although the strongest geomagnetic perturbations in Malaysia reached up to -845.09 nT, no major operational disruptions were recorded. These findings highlight the critical importance of continuous solar monitoring and regional preparedness, especially as Solar Cycle 25 approaches its peak. The study emphasizes the role of Malaysia's space weather infrastructure in contributing to international observation networks and enhancing national resilience against future space weather threats.

Geomagnetically induced currents (GICs) are a significant concern for low-latitude power grids, particularly during extreme space weather events driven by solar wind fluctuations. These currents, induced by fluctuating geomagnetic fields, can saturate transformers, distort voltages, and accelerate the aging of electrical equipment, often leading to blackouts and infrastructure damage. While most studies have focused on high-latitude regions due to their proximity to auroral currents, low-latitude systems also face considerable risk, especially during severe solar storms. This study presents an optimised deep learning Recurrent Neural Network (RNN) for predicting one-minute-ahead GICs at the geomagnetic observatory Huancayo station (HUA) in Peru, integrating data imputation using the Known Subsequence Algorithm (KSSA) to handle missing values in the solar wind and geomagnetic datasets. The study evaluates Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Units (GRU), highlighting their performance in continuous predictions using Root Mean Square Error (RMSE) and Rare-event Detection (ReD). The results indicate that while GRU offers robust continuous prediction performance, BiLSTM excels in detecting extreme geomagnetic events, making it a valuable tool for rare-event forecasting. The findings propose a complementary deployment strategy using GRU for baseline forecasting and BiLSTM for extreme-event alarms. This dual approach enhances the resilience and reliability of low-latitude power grids under extreme space weather conditions.

Geomagnetic storms can significantly impact technological infrastructure, making accurate and timely classification critical for operational space weather forecasting. This study evaluates the performance of three classical machine learning algorithms which are Logistic Regression, Support Vector Machine (SVM), and Artificial Neural Network (ANN) for classifying geomagnetic storm severity using the SYM-H index. A small, imbalanced dataset comprising 12 labeled storm events was used to assess the models in terms of accuracy, precision, recall, F1-score, and computational efficiency. Logistic Regression and SVM achieved perfect classification results, demonstrating strong performance and robustness under limited data conditions. In contrast, the ANN model failed to detect any severe storm events, highlighting its limitations in scenarios with class imbalance. Additionally, Logistic Regression and SVM required significantly less training time, making them more suitable for real-time forecasting applications. These findings suggest that classical algorithms not only outperform more complex models in this context but also offer reliable and computationally efficient solutions for operational deployment in space weather monitoring systems.

Planar magnetic structures (PMSs) are a frequent occurrence within the sheath regions of interplanetary coronal mass ejections (ICMEs). A defining characteristic of these structures is the confinement of rapid magnetic field variations to a consistent plane over extended periods, often lasting several hours. This study investigates the potential for a preferential orientation in the normal direction of PMSs. Three ICME events observed by the Advanced Composition Explorer (ACE) spacecraft are examined. The PMS normal vectors are estimated from minimum variance analysis of the magnetic field vectors, with the constraint of = 0. Here, is the average of the normal component of the magnetic field. Analysis of latitude-longitude diagram of the magnetic field confirms the presence of PMS within the ICME sheath. For the three ICMEs studied, the angles between the PMS normal and the ICME axis are found to be 96o, 98o, and 93o. These preliminary results suggest a strong tendency for the PMS normal to align nearly perpendicular to the ICME axis.

Meteorological equipment has evolved massively, which is reflected in its accessibility and range. In Malaysia, meteorological variables such as temperature and cloud cover are monitored and managed by the Malaysian Meteorological Department (MET). However, the department not only has meteorological data from principal stations, but also manages automatic weather stations (AWS) at Malaysian airports. This study intends to examine the consistency of the weather readings of temperature and cloud cover at four locations with different climate profiles. The analysis uses data from the extreme rainy and dry seasons to minimize random fluctuations. These data are processed and filtered before being analysed using time series analysis and key statistical values such as mean, mean bias and standard deviation of difference, to identify differences in the weather variables from the two station types. The study found that the temperature data is consistent for both station types, except Ranau, due to geographical differences between the two station types. However, the analysis of cloud cover shows differences in the values between the principal station and AWS, which might be due to different measuring methods. This study highlights the differences between weather data, identifies the contributing factors, and recognizes the need to compare them with other available meteorological data from satellite measurements to ensure the validity of the principal station and AWS data.

This study examines the significant impact of geomagnetic storms on Earth's ionosphere during the events of May 10-11, 2024. Data were collected from four stations in Malaysia: Langkawi, Ranau, Terengganu, and Johor. The ionospheric disturbance dynamo (Ddyn) was analyzed using the H component of Earth's magnetic field. The intensity of the geomagnetic storms was determined using the Disturbance Storm Time (Dst) index, derived from ground-based magnetometer data. The main phase of the storm began on May 10, 2024, with a Dst index of -351 nT, followed by a second peak on May 11, reaching a minimum Dst value of -412 nT. The recovery phase lasted approximately 11 days. This geomagnetic activity disrupted the electric field in the polar ionosphere, affecting thermospheric circulation. Significant perturbations were observed through ground-based magnetometer data. Among the four stations, Langkawi (LKW), being closest to the geomagnetic equator, exhibited the most pronounced Ddyn effects compared to the others. These findings provide valuable insights into ionospheric responses to geomagnetic storms, contributing to space weather research.

This study investigates the reliability of polarization ratio analysis in detecting ultralow frequency (ULF) geomagnetic anomalies as potential earthquake precursors. Using geomagnetic field data from INTERMAGNET stations and seismic records from the USGS catalog (2012-2022), anomalies were identified in 64.4% of seismic events and 61.0% of nonseismic events after applying strict filtering and selection criteria. Statistical analysis revealed a significant distinction between seismic and nonseismic median polarization ratio values, while the temporal distribution of anomalies remained uniform across the observation window. These results suggest that polarization ratio analysis, when rigorously constrained, can partially differentiate lithosphericorigin anomalies but lacks clear short-term predictive clustering. Future improvements, such as incorporating additional geophysical parameters and advanced analytical methods, are recommended to enhance the robustness of earthquake precursor identification frameworks

As the 25th solar cycle peak approaches, the increasingly active ionosphere leads to frequent GNSS signal ionospheric scintillations, posing a significant threat to GNSS satellite stability and high-precision positioning services. Accurate monitoring and modeling are crucial for mitigating GNSS scintillation interference. However, the existing specialized equipment for scintillation (ionospheric scintillation monitoring receivers) is sparsely distributed and has limited coverage, making it challenging to meet the demand for wide-area scintillation monitoring. In contrast, geodetic receivers are widely distributed and serve as a valuable complement to ionospheric scintillation monitoring research. However, the current GNSS geodetic reference station network has inconsistent receiver sampling intervals, and low sampling rates can interfere with the accuracy of extracting ionospheric scintillation factors. Therefore, this paper proposes a GNSS ionospheric scintillation factor extraction method applicable to multi-sampling interval geodetic receiver data. The aim is to utilize the widely and relatively densely distributed GNSS geodetic reference station network for ionospheric scintillation monitoring research, enabling high-precision monitoring of ionospheric scintillations on a global scale.

Monitoring reservoir water levels is vital for managing water resources in monsoon-affected regions. However, monsoonal variations in water surface elevation (WSE) in small Malaysian reservoirs remain underexplored due to limited in-situ data. This study assesses Sentinel-3 synthetic aperture radar (SAR) altimetry (2019-2022) for tracking WSE in the Kenyir, Temengor, and Chenderoh reservoirs. Satellite-derived WSEs demonstrating strong agreement with gauge data, with correlation coefficients between 0.84 and 0.99 and root mean square errors of 20 to 42 cm. Kenyir exhibited notable WSE increases during the Northeast Monsoon (NEM), reaching up to 2.4 m, while Temengor and Chenderoh remained more stable due to regulation. The relationship between rainfall and WSE was also examined. Overall, results show that Sentinel-3 altimetry is reliable for monitoring monsoonal dynamics in data-scarce regions, supporting hydrological modelling and climate-resilient water management.

Accurate and timely nowcasting of cloud-to-ground (CG) lightning events is essential for mitigating weather-related hazards, particularly in regions like Malaysia where severe thunderstorms are prevalent. This study presents a deep learning-based framework that integrates Constant Altitude Plan Position Indicator (CAPPI) radar imagery and fast antenna electric field measurements to enhance lightning type classification. The proposed dual-branch Convolutional Neural Network (CNN) processes spatial features from radar data and temporal patterns from electric field waveforms, facilitating the classification of lightning into eight distinct categories, including +CG, -CG, and Intra-Cloud (IC) events. Utilizing a dataset of approximately 5,600 paired samples collected in May 2024, the model demonstrates that fusing radar and electric field data improves CG lightning classification accuracy from 98.58% (using only electric field data) to 99.24% with the combined approach. These findings underscore the effectiveness of multimodal data fusion in capturing complementary features, leading to more robust and ac-curate lightning nowcasting. Future research will explore advanced network architectures and temporal sequencing to further enhance model performance and interpretability.

Forest fires pose severe threats to ecosystems, human settlements, and biodiversity, especially in tropical regions like Malaysia. Traditional fire risk assessment methods, which rely heavily on ground data and meteorological models, often fall short in providing scalable and timely insights. This research proposes a deep learning-based approach for the classification of forest fire risk zones using satellite imagery. By leveraging remote sensing data from platforms such as Sentinel-2, and Planetscope, combined with historical fire incidents and meteorological variables, the study employs Convolutional Neural Networks (CNNs) to detect spatial patterns and predict fire-prone areas. Advanced architectures such as ResNet, EfficientNet, and DenseNet are explored for accurate classification and spatial localization of fire risk. The system is trained using transfer learning to enhance model performance even with limited labeled datasets. EfficientNet-B0 surpassed both ResNet50 and DenseNet121 in detecting forest fires using multisource satellite imagery, achieving the highest accuracy of 94% and a ROC-AUC of 0.9854, which indicates its superior training efficiency and generalization capabilities. In contrast, ResNet50 attained an accuracy of 90.2% with a ROC-AUC of 0.9617, while DenseNet121 achieved an accuracy of 87.6% and a ROC-AUC of 0.9381. Despite challenges like data quality and ecosystem complexity, the proposed system significantly enhances the accuracy and efficiency of forest fire monitoring, supporting environmental agencies and policymakers in informed decision-making.

Mangrove ecosystems play a crucial role in coastal resilience, biodiversity support, and carbon sequestration, particularly in climate-vulnerable countries like the Philippines. This study assessed the carbon stock, species diversity, and community structure of the mangrove forest in Sitio Quitay, Barangay Laurel, Tagkawayan, Quezon, Philippines. Using a non-destructive quadrat sampling method across three ecological zones-Seaward, Midward, and Landward-data were collected from nine 10×10m plots. A total of 678 individuals comprising nine mangrove species were recorded. Carbon stock estimations revealed Sonneratia alba and Rhizophora mucronata as the highest contributors to biomass and carbon sequestration. Diversity indices indicated the Landward zone had the greatest species richness and evenness, while the Seaward zone was more species-dominant but less diverse. Community structure analysis showed Rhizophora mucronata, Avicennia marina, and Sonneratia alba as ecologically dominant, based on their Importance Value Index (IVI). These findings emphasize the ecological significance of mangrove diversity and species-specific contributions to carbon sequestration. Conservation efforts should prioritize preserving both high-biomass species and diverse habitat zones to enhance climate resilience and maintain ecosystem functionality.

Seismic hazards in tectonically active regions such as Indonesia demand advanced analytical methods for accurate earthquake characterization, particularly in complex geological set-tings. This study presents an integrated approach to improve earthquake epicenter mapping, motivated by the 5.0-magnitude earthquake that occurred in Bandung on September 18, 2024. The methodology combines digital seismic accelerometer data processing, deep learn-ing-based phase picking, high-precision hypocenter relocation, and gravity anomaly analysis within a Geographic Information System (GIS) framework. A convolutional neural network was trained on 80% of the seismic data to automatically detect P- and S-wave arrivals, achieving high accuracy with minimal error. Hypocenter relocation was performed using a double-difference algorithm to refine the epicenter positions, particularly enhancing the resolution of aftershock sequences. Additionally, gravity data from the Global Gravity Mod-el Plus (GGMPlus) was used to derive Complete Bouguer Anomaly (CBA) and First Hori-zontal Derivative (FHD) maps, which facilitated fault structure identification. The integra-tion of these components via GIS enabled the generation of high-resolution seismic hazard maps. Results demonstrate the effectiveness of this multi-disciplinary framework in enhanc-ing epicenter localization and fault delineation. This study underscores the value of combin-ing deep learning, seismic relocation techniques, and gravity anomaly modeling to support resilient disaster mitigation and sustainable geophysical planning in Indonesia.

Light pollution, caused by increased urbanization and artificial light, brings severe challenges to environmental integrity, human well-being, and astrotourism. This research uses satellite-based VIIRS-DNB nighttime imagery for the years 2020-2021 and 2023-2024 to evaluate light pollution trends in Johor, Malaysia. The research used an unsupervised K-Means classification algorithm to classify light pollution into seven level categories. The findings show that artificial light continues to rise in both level and spatial extent, particularly in urban and coastal areas. While areas formerly classed as "Low" or "Very Low" pollution have shifted to "Medium" or "High", a slight decrease in high-pollution areas in 2024 indicates the potential benefit to mitigation actions. These outcomes underline the critical need for sustainable lighting techniques and strategic planning to protect dark sky, maintain biological neutrality, and promote astrotourism as a long-term economic opportunity.

In this article, a new metamaterial absorber (MMA) based non-gear-ring loaded cross-shaped resonator has been discussed. This MMA shows a 90% absorption bandwidth ex-tending from 11 to 23 GHz. Rogers RT5880LZ substrate is used for this design, with the electrical dimensions of the unit cell being 0.38λ × 0.38λ × 0.24λ, where λ is the wavelength at 23 GHz. The performance of the proposed MMA structure is studied with parametric studies considering variation of substrate thickness, effect of lumped resistance, and surface current analysis through numerical simulation performed in CST Microwave Studio. The reflection characteristics, along with absorption, are further verified using HFSS software, and nearly identical result is obtained. Moreover, this MMA is also tested under different incident angles and polarizations to check its performance, and it shows incident and polari-zation angle-insensitive behavior in all tested conditions. The absorption covers Ku-band (12-18 GHz) and part of K-band (18-27 GHz), so it can be useful in satellite communica-tion applications such as minimizing reflections and protecting critical satellite systems from self-interference and external electromagnetic noise.

This paper presents a high-performance 4-element patch array antenna designed for 5G Vehicle-to-Everything (V2X) communication operating at 5.9 GHz. The proposed antenna features inset-fed rectangular truncated patch elements arranged in a 2×2 array configuration. U-line parasitic elements are positioned around each patch, and a U-slot Defected Ground Structure (DGS) is incorporated beneath the parasitic radiating layer. This cooperative technique significantly improves the antenna's bandwidth, gain, and radiation efficiency. The design was developed by comparing all three experiment stages, and the results highlight the impact of combining parasitic elements with the DGS. The design aims to develop a high-performance antenna on a compact Rogers RT5880 substrate (2.13λ × 2.13λ × 0.047λ) that delivers a simulated gain of 12.9 dBi, a wide bandwidth of 530 MHz, and radiation efficiency exceeding 95%. The design is highly suitable for reliable, high-data-rate vehicular communication in the 5G spectrum.

This paper presents a compact MIMO antenna for multiband satellite communication applications. The antenna features a fork-shaped slotted radiating element and a partial ground structure to enhance bandwidth and isolation. The antenna with an area of 31×31 mm2 achieves a wide operating band from 5.65 to 24.63 GHz, covering the C, X, Ku, and K-bands while maintaining strong performance metrics, including an ECC below 0.005 and a DG exceeding 9.956 dB. The antenna also attains high isolation (>20 dB), stable radiation patterns, and an average gain of ≥4.25 dBi. The antenna's compact size, multiband capability, and robust diversity performance make it suitable for satellite communications, radar systems, and terrestrial broadband applications.

The transformer is a crucial element in the power system, and its evaluation depends on essential factors such as load, current, oil temperature, and ambient temperature. The oil temperature is the most crucial characteristic among these variables due to its strong interconnection with all other factors. Surpassing the recommended threshold for oil temperature might lead to transformer rupture or mechanical faults. This study aims to use GSM technology to monitor and control the oil temperature of distribution transformers in a specific scenario. The investigative technique thoroughly examines field data about oil temperature from various businesses. Afterward, a prototype is created to monitor and regulate the oil temperature. This prototype enables sending Short Message Service (SMS) notifications to authorized workers, providing them with real-time oil temperature data for monitoring. In addition, the system allows for remote execution of appropriate interventions to manage temperature by SMS commands. The effectiveness of the prototype is supported by its operating efficiency, as demonstrated by a strong link between oil temperature and the overall condition of the transformer. Integrating GSM technology in this monitoring and control system improves operational supervision and allows for prompt intervention, reducing the likelihood of transformer malfunction caused by high oil temperatures.

This paper presents a single layer Compact Square Spiral Resonator (CSSR) Based frequency selective surface (FSS) for K band application. The FSS structures are widely applicable in various wireless technologies to control desired frequency spectrums. The proposed FSS exhibits a wide stopband at K-band frequency region. The FSS with band stops characteristic is designed by using Rogers RT-duroid 5880 substrate with thickness of 0.254 mm and overall unit cell size of 0.54λ × 0.54λ. The FSS exhibits stopband resonances frequency at 21.68 GHz and bandwidth is 11.76% (20.25-22.78 GHz). Therefore, the proposed FSS can be unitized for K-band EMI shielding applications.

This paper introduces a symmetric Hilbert-shaped Frequency Selective Sur-face (FSS) for electromagnetic interference (EMI) shielding. The structure is designed on a single-layer Rogers RT5880 substrate (εr = 2.2, thickness = 0.81 mm) and ensures low loss and high stability. Copper is used as the con-ductive material to minimize resistive losses. The proposed FSS exhibits dual resonances at 28.24 GHz and 37.95 GHz, targeting 5G millimeter-wave N257 and N260 bands. Its rotational symmetry ensures polarization insensi-tivity across varying incident angles. The Hilbert-shaped design aids minia-turization, supporting compact integration into modern communication sys-tems. With high transmission efficiency and selective frequency response, the metasurface is well-suited for EMI shielding, radar cross-section (RCS) reduction, stealth technology, and next-generation wireless systems.

This paper discusses the errors in assembly quality checks of CoTM (Communication on The Move) platform associated with various parameters such as positional accuracy, component security, orientation, alignment and their effect on its RF (Radio Frequency) performance by carrying out mechanical measurements such as perpendicularity, eccentricity, flatness, angular, position and radial and axial play. Calibration of mechanical parameters was carried out to ensure alignment of the feed along z-axis and its perpendicularity to the azimuth plane. Similarly, alignment of elevation and azimuth axes and effect of radial and axial play was investigated. It was concluded that bearing misalignment has negligible effect over the system as compared to alignment and balancing. Weight (gravity) and orientation of the components is more important. The maximum misalignment in azimuth, elevation and polarization calculated as about 0.3108°, 0.011° and 0.0744° respectively. Misalignment in the system is calculated as 0. 396°.

Gerhana matahari merupakan fenomena Astronomi yang berlaku apabila Bulan berada di antara Matahari dan Bumi, lalu menyekat sinaran suria daripada mencapai atmosfera atas Bumi. Keadaan ini menyebabkan pengurangan mendadak sinaran ultraungu dan sinar-X, sekali gus mempengaruhi tahap pengionan di lapisan ionosfera. Kajian ini meneliti kesan peristiwa gerhana matahari penuh terhadap pengionan di lapisan ionosfera Bumi, dengan memberi tumpuan kepada analisis variasi medan geomagnet dan jumlah kandungan elektron (Total Electron Content, TEC) berdasarkan beberapa peristiwa yang berlaku antara tahun 2000 hingga 2024. Maklumat berkaitan tarikh, tempoh, dan laluan gerhana diperoleh daripada sumber astronomi rasmi. Data medan geomagnet dikumpulkan daripada stesen magnetometer dasar melalui platform SuperMAG dan INTERMAGNET, manakala data TEC diperoleh daripada stesen GPS melalui rangkaian International GNSS Service (IGS). Analisis dijalankan dengan membandingkan variasi medan geomagnet dan TEC pada hari gerhana dengan data pada hari senyap, iaitu hari tanpa gangguan ribut geomagnet. Hasil kajian menunjukkan penurunan yang konsisten dalam kedua-dua set data semasa gerhana matahari penuh, dengan nilai TEC mengalami penurunan yang lebih ketara berbanding data medan geomagnet. Sementara itu, perubahan kecil dalam variasi medan geomagnet yang diperhatikan adalah dipercayai berkait dengan perubahan arus ionosfera akibat pengurangan tahap pengionan. Akhir sekali, didapati tiga faktor utama boleh menjejaskan keupayaan data medan geomagnet dan TEC untuk mengesan sebarang variasi akibat gerhana, iaitu kehadiran ribut geomagnet, jarak stesen daripada laluan gerhana penuh, dan altitud Matahari sewaktu gerhana matahari berlaku. Kajian ini memberi gambaran tentang kesan fenomena astronomi seperti gerhana matahari terhadap persekitaran ruang angkasa berhampiran Bumi.

Gunung Berapi Hunga Tonga-Hunga Ha'apai yang terletak di Kepulauan Pasifik telah meletus pada 15 Januari 2022. Letusan ini menyebabkan berlakunya tsunami dan juga meteotsunami yang memberi kesan kepada kawasan sekitar Lautan Pasifik. Gelombang Lamb daripada letusan ini, yang bergerak pada kelajuan ~ 310 m/s, tiba di bahagian timur Indonesia (~ 5000 km dari Tonga) dalam masa 4 jam dan tiba di Malaysia (kawasan Borneo) sekitar 6 jam kemudian. Kesan letusan in telah mencetuskan kejadian gelembung plasma khatulistiwa di luar musim. Kajian ini mengkaji kesan letusan ini terhadap ionosfera di Malaysia menggunakan stesen penerima GNSS di seluruh Malaysia. Data dalam format RINEX diproses bagi mendapatkan nilai ROTI, satu indeks yang digunakan untuk mengenalpasti kejadian gelembung plasma khatulistiwa. Hasil kajian menunjukkan kejadian gelembung plasma khatulistiwa direkodkan mula muncul di kawasan timur Malaysia pada jam 10.50 UT dan berlarutan sehingga 14:40 UT, 10 jam selepas berlakunya letusan tersebut.

The application of deep learning for identifying specific geophysical phenomena from image-based data remains challenging. This study presents a hybrid approach combining deep learning and classical computer vision to identify targeted structures of Equatorial Plasma Bubble (EPB) based on Global Positioning System (GPS) data observed. The proposed method employs a state-of-the-art deep learning model to detect features in image data and generating bounding boxes around identified regions. A subsequent filtering technique is then applied to refine detections and isolate primary structures, ensuring accurate characterization. Post-detection analysis revealed delicate characteristics within individual detections, including varying spatial relationships and temporal dynamics. Building upon these initial detection and characterization results obtained from a substantial training set, future work will focus on extensive analysis to quantitatively assess these geophysical characteristics using the presented deep learning model.

This paper presents the design of a tuneable hexagonal split-ring resonator (SRR) metamaterial unit exhibiting negative permittivity and permeability characteristics for 5G applications. The proposed unit features a simple structure and employs a multilayer hexagonal SRR with coupled gaps, surrounded by a coupling frame for tuning purposes. The copper structure is embedded on a Rogers 5870 dielectric substrate, measuring 6 x 6 cm2 with a thickness of 1.575 mm. The simulation result shows that the proposed design exhibits a -10 dB bandwidth spanning from 12-14 GHz and 20-30 GHz. The unit demonstrates negative permittivity at 12.496 GHz, 21 GHz, and 24 GHz, and negative permeability within the ranges of 5-11 GHz and 13-24 GHz. The key contribution of this research is the proposed metamaterial unit can be integrated with antenna to achieve high performances, making it suitable for high-frequency communication supplication such as 5G and radar systems. This work highlights the potential of tuneable metamaterial structures in enhancing the functionality of advanced electromagnetic systems.

Auxetic metamaterials, characterized by a negative Poisson's ratio, have attracted significant interest due to their exceptional deformation behavior, which offers enhanced energy absorption, impact resistance, and flexibility. However, conventional auxetic structures often face limitations in scalability and manufacturing. Origami-inspired designs provide a promising alternative by enabling tunable mechanical properties through foldable and repeatable geometries. This study presents the design and mechanical characterization of an auxetic structure based on an origami four-leaf clover pattern. The origami concept was selected for its capacity to generate controllable mechanical responses through geometric transformations. The proposed structure was evaluated using both finite element simulations. Finite element analysis was conducted using COMSOL Multiphysics to assess deformation patterns and stress distribution under applied loads. Observations indicate that as the applied force increases, the displacement of the structure also increases proportionally. This behaviour is consistent with the characteristics of auxetic metamaterials, confirming that the proposed structure exhibits auxetic properties. These findings validate the effectiveness of the origami-inspired design in facilitating auxetic behaviour.

Space missions necessitate robust and unobtrusive systems for physiological and health monitoring, particularly for cardiovascular assessment, due to the adverse effects of microgravity on cardiac function and rhythm regulation. This study presents the development and validation of a smart in-ear photoplethysmography (PPG)-based system designed for continuous heart rhythm monitoring and arrhythmia detection. The system integrates in-ear PPG sensors with machine learning algorithms to classify heart rhythm patterns in real time. A total of 2,926 PPG signal segments were acquired, comprising 1,275 arrhythmic, 1,355 normal, and 296 unreadable segments. Sixty-three features were extracted from each segment and evaluated using multiple classification models, including Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning approaches. The best-performing model yielded an area under the receiver operating characteristic curve (AUC) of 0.96, with high classification accuracy across all categories. Data acquisition and validation were conducted in a clinical setting, with participant recruitment from cardiology and neurology clinics. The proposed system demonstrates substantial potential for integration into wearable technologies designed for real-time cardiovascular monitoring. The integration of in-ear sensing into smart wearable devices may offer an effective, user-friendly solution for real-time cardiovascular monitoring in extreme environments, including space, where conventional electrocardiographic monitoring may be operationally constrained.

As cities face increasing exposure to hydrometeorological hazards, the need for accurate spatial models becomes critical. Urban flood simulations, climate impact assessments, and infrastructure planning all rely on precise representations of terrain and built environments. Remote sensing and GIS technologies offer powerful tools to construct such models, but their full potential remains underutilized in many urban applications. However, many existing approaches treat terrain and building geometry as separate entities, resulting in inconsistencies that limit the reliability of spatial analysis resulting in inconsistencies such as floating or submerged buildings. These distortions compromise the accuracy of simulations and undermine the effectiveness of decision-support tools in disaster risk management. This presentation aims to present a generalizable, multidimensional framework for integrating terrain and building data using remote sensing and GIS. The goal is to produce structurally coherent urban models that improve simulation accuracy and analytical reliability in flood and hydrological risk modelling. The proposed framework combines airborne LiDAR, orthophotos, and 3D building models within a GIS environment. Key methods include morphometric terrain analysis, overlay interpolation, and node-based geometry correction along building perimeters. These techniques harmonize surface discontinuities and enhance elevation realism. Validation processes involve both statistical error analysis and visual cross-sectional comparisons. Integrated terrain-building models demonstrate significant improvements in accuracy and spatial coherence. By adjusting building base geometries and refining interpolation techniques, terrain surfaces show reduced elevation errors and enhanced alignment with physical structures. These improvements directly benefit hydrological modelling by reducing artificial elevation artifacts, particularly in flood-prone areas. This multidimensional integration framework contributes to a more reliable foundation for urban disaster modelling. By bridging the gap between terrain and structure, it supports more realistic simulations, informs better planning decisions, and strengthens the spatial backbone of smart city and digital twin initiatives. The approach is scalable, adaptable to various urban contexts, and suitable for future integration with AI and IoT-driven systems.

The linear regression model produced different levels of accuracy across the stations. The coefficients of determination R^2 ere 0.02 at Silpakorn, 0.61 at Ubon Ratchathani, and 0.77 at Chiang Mai, respectively. The combined R^2 from these three sites was 0.59. In comparison, the polynomial model achieved slightly higher accuracy than the linear model. The R^2 values were 0.24 (RMSE = 14.14) at Silpakorn, 0.63 (RMSE = 17.34) at Ubon Ratchathani, and 0.80 (RMSE = 13.81) at Chiang Mai. The overall R^2 from the combined data was 0.65, with the RMSE of 14.84. These findings suggest that incorporating multiple satellite-derived variables offers modest performance improvements, particularly in regions where the linear model showed lower accuracy.

Highland ecosystems such as Cameron Highlands in Malaysia are increas-ingly threatened by rapid land cover change driven by agriculture and urban development. Yet, consistent monitoring is challenged by persistent tropi-cal cloud cover and limited up-to-date datasets. This study asks: How has land use and land cover changed in Cameron Highlands from 2020 to 2024, and what are the dominant transition pathways? The objective is to generate annual LULC maps and identify directional trends using remote sensing. Using Sentinel-2A and Landsat-8 imagery, we applied a Random Forest classifier within Google Earth Engine (GEE), with cloud-masking, median composites, and stratified sampling for accuracy assessment (500 valida-tion points). The dataset includes LULC classifications and change matri-ces across six classes: trees, crops, shrubland, built area, bare ground, and water. Results show significant forest-to-crop (1,000 ha) and forest-to-urban (500 ha) conversions, especially in western slopes. Built-up areas expanded steadily, while shrubland declined. Spatial transitions followed road networks and valley edges. This study demonstrates that GEE enables effective, cloud-resilient LULC monitoring in tropical highlands and sup-ports data-driven planning for zoning, erosion control, and ecological pro-tection.

This study leverages GEE's capability to process multi-temporal satellite imagery, including Landsat, and MODIS, for automated LULC(Landuse/Landcover) classification, LST (Land Surface Temperature) extraction, and spectral analysis NDWI (Normalized Difference Water Index), NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Builtup Index) from 2000-2020 in Jhelum and Chenab River basins of Pakistan. Findings reveal an increasing trend in water, built-up, and barren land, whereas vegetation, cropland, and snow cover are declining. LST analysis indicates an extended temperature range from 2000 to 2020, signifying intensified seasonal variations. A strong positive correlation exists between LST and NDBI (R² > 0.77, peaking at 0.82 in 2020), affirming urban expansion's role in temperature rise. LST and NDWI exhibit a stronger negative correlation than LST and NDVI, while NDVI and NDWI show the most pronounced inverse correlation (R² > 0.8), highlighting vegetation-water trade-offs. NDWI and NDBI demonstrates a moderate negative correlation (R² = 0.27), suggesting urbanization impacts water variability. The weak correlation between NDBI and NDVI indicates the presence of vegetation within urban areas. These insights contribute to sustainable land water and water resource management, guiding future development strategies for the Jhelum and Chenab River basins using GEE and remote sensing data.

Saltmarshes are a critical but overlooked component of blue carbon ecosystems in the Gulf Cooperation Council (GCC) region, including the United Arab Emirates (UAE). While mangroves and seagrasses have received significant conservation attention, saltmarshes remain underrepresented in national environmental policies despite their role in carbon sequestration, biodiversity conservation, fisheries support and coastal resilience. Our study provides a comprehensive mapping of saltmarshes in the UAE for 2024 using Sentinel-2 multispectral imagery and random forest classification, thus establishing a baseline data for future conservation. The total saltmarsh area was 115.35 km². Fragmentation analysis suggests that although there are intact saltmarshes in the form of interior areas (32.11%), there is a significant amount of fragmentation, especially in the form of patches (26.78%), edge (20.66%) and transitional zones (16.23%). These suggest that a substantial portion of the saltmarsh is fragmented into small, isolated patches or occurs at the periphery of larger bodies. Additionally, we discuss their ecosystem services, including contributions to carbon sequestration, habitat provisioning to fish, shellfish and other species, and coastal protection. Despite ongoing threats from urban expansion, industrial activities and oil spills, saltmarshes have not been integrated into blue carbon accounting frameworks. We therefore propose policy recommendations and technological solutions including remote sensing applications for improved monitoring and integration into climate adaptation strategies. This could help UAE align conservation efforts with the Paris Agreement and Nationally Determined Contributions (NDCs).

The utilization of antennas in telecommunication technologies, particularly in Wireless Local Area Networks (WLAN), has become essential in the modern era, with Wi-Fi being one of its most prominent applications. This paper presents the design and analysis of a flexible dual-band antenna for Wi-Fi and satellite communication applications. The antenna operates at 2.4 GHz and 5.8 GHz for Wi-Fi, and also supports S-band satellite communication within the 2-4 GHz range. The proposed antenna employs a flexible RT Duroid 5880 substrate with a relative permittivity of 2.2, a loss tangent of 0.0009, and a thickness of 0.254 mm. The overall physical dimensions are 29 × 33 mm², making it suitable for compact and wearable devices. Full-wave electromagnetic simulations were carried out using CST Microwave Studio (CST MWS), demonstrating excellent impedance matching with return loss values of |S₁₁| = -55 dB at 2.4 GHz and |S₁₁| = -15 dB at 5.8 GHz. The antenna also exhibits wide impedance bandwidths of 168.53%, ensuring effective performance across its operating frequencies. These characteristics make the proposed design a promising candidate for integration in modern portable and wireless electronic systems.

The field of high-performance photodetectors has seen a significant increase in research activity lately, owing to their vital applications in environmental monitor-ing and communication systems. This paper explores the potential of a bilayer graphene (BLG)/zinc oxide (ZnO)/silicon (Si) dual heterojunction as an advanced platform for ultraviolet (UV) to near-infrared (NIR) photodetection. The unique combination of BLG, ZnO, and Si creates a synergistic heterojunction, utilizing graphene's exceptional properties, ZnO's wide-bandgap characteristics, and the scalability of Si as a substrate. The photodetector exhibits excellent performance, as demonstrated by Silvaco TCAD simulations, with a low dark current density (Jdark) of 2.68×10-15 A/cm², a significant photocurrent density (Jlight) of 0.26 µA/cm², and a high Jlight/Jdark ratio of 9.77×107 at -1.0 V. Additional perfor-mance metrics include a 3-dB cut-off frequency of 7.36 THz, quantum efficiency of 69.3%, a photocurrent responsivity of 0.26 A/W, a detectivity of 8.12×10¹5 Jones, and a rapid rise (fall) time of 0.47 (0.88) ns at -1.0 V. The device achieves broad UV-to-NIR detection with high sensitivity, low power operation, and rapid response, making it ideal for IoT-enhanced sensing and communication systems.

According to Friis' transmission equation, terahertz waves are considered difficult to propagate over long distances due to their short wavelength. However, if the physical area of the antenna remains constant, higher frequencies result in a larger aperture area relative to the wavelength, increasing the gain and thus the received power. Reflector antennas and lens antennas can achieve high-efficiency, high-gain antennas even at high frequencies due to minimal feed losses. Multi-beam antennas using reflector antennas are employed in space applications for covering wireless service areas. We are developing multi-beam phased lens array antennas in the terahertz band to independently control the direction of multiple beams for future high-capacity wireless communication. This report presents the results of our development.

Tele-health presents a promising platform for remote patient mobility monitoring and assessment as the focus on digital healthcare increases. Continuous gait analysis outside of traditional clinical settings is made possible by the integration of wearable sensors with mobile technology. In this human gait analysis, inertial measurement units (IMUs) serve as portable and cost-effective tools for capturing human motion or gait data and a developed application is necessary to collect the data from the IMUs. So, this study proposes a mobile application developed on the Android platform as a data acquisition terminal. The developed application can collect gait related raw IMU data by pairing the IMU with the application installed in mobile phone via low-power Bluetooth (BLE). In this analysis, a single IMU sensor was securely attached to the anterior tibia of the participant's left leg, and the participant was instructed to walk at a natural pace along a 6-meter straight walkway. Key gait parameters such as stride time, stance time, swing time and cadence were computed using the collected data from the developed app. The mean absolute errors were 0.07s and 3.29 steps/min considering all sample data and 0.04s and 1.86 steps/min using average data for stride time and cadence respectively. The percentage errors are also less than 5% for both parameters. The outcome of this analysis ensures the validity of the developed mobile application and measurement of gait parameters using the remotely collected patient data.

A metamaterial absorber is designed with a Multiple Split Ring structure that has symmetrical circular edge splits. The absorber is polarization-insensitive and shows a peak of absorption in the quad-band. The absorber is constructed on a FR-4(lossy) substrate with annealed copper. The dimension of the absorber is 0.128λ by 0.128λ. Simulations using numerical methods are conducted in Com-puter Simulation Technology (CST) across a frequency range of 2 to 15 GHz. Results at frequencies of 2.75 GHz, 5.04 GHz, and 13.92 GHz show near-unity absorption, with absorption of more than 99%. The absorption rate is 96.7% at a frequency of 8.54 GHz. The structure exhibits the same absorption in both trans-verse electric (TE) and transverse magnetic (TM) modes. The absorber polariza-tion insensitivity occurs up to 180 degrees and incident angle up to 60 degrees. Analysis of surface current, electric fields, and magnetic fields confirms strong resonance at each band. An examination of various geometries, gap sizes, and material properties reveals their effects on resonance frequencies and transmis-sion efficiency. The proposed absorber is suitable for use in S, C, X, and Ku-band systems.

This paper presents a Geometrical shape MIMO antenna for 5G millimeter wave communication. The purpose of this paper is to design an antenna that can operate at the millimeter wave frequency band & which can be useful to future wireless communication. The single antenna unit cell is designed with a microstrip patch antenna structure. The proposed MIMO antenna is a 2 x 2 array antenna that is arranged in an orthogonal placement. The antenna is de-signed using Rogers RT 5880 substrate material & copper (annealed) ground material. The results indicate that return loss is measured at -54 dB, a peak gain of 9 dBi, directivity of 9.6 dB & an impressive efficiency of 87%. The proposed antenna meets the parameters such as ECC which is less than 0.01, diversity gain which is almost close to 10 dB, and MEG of 3, which indicates low mutual coupling. The MIMO antenna also has an isolation of 23 dB & CCL of 0.25 bps/Hz. The proposed antenna has excellent performance for future 5G wireless communication.

To address the issue of significant changes in the beam tilt angle due to fre-quency variations in CRLH transmission line-type leaky-wave antennas, we propose a transmit array using Huygens metasurfaces. The array was de-signed with the aim of achieving no beam tilt at 3.5 GHz and approximately 30deg beam tilt at 3.8 GHz. Transmit Array consists of two I-shaped elements printed on a dielectric substrate with a dielectric constant of 3 and a thick-ness of 1.6 mm, with a spacing of 15.3 mm between the elements. In addi-tion, this transmitter array forms a supercell consisting of four elements with a phase difference of 90deg. The side length of a unit cell was set to 47.9 mm to ensure sidelobes in the zenith direction remain below -20dB. Using electromagnetic field analysis with Femtet, the transmission loss for each element was kept within 1dB. At 3.5 GHz, phase variation was minimized, while at 3.8 GHz, a phase difference of approximately 90deg between adjacent elements was achieved. As a result, although slight wavefront distortion was observed, the proposed transmit array structure successfully achieved no beam tilt at 3.5 GHz and approximately 30deg tilt at 3.8 GHz, as intended.

The COVID-19 pandemic has significantly impacted the efficacy of conventional face recognition systems, primarily due to the frequent use of facial masks, which obscure key facial features. This research proposes a lightweight and robust deep learning framework for real-time face recognition that performs effectively with both masked and unmasked faces. The model leverages MobileNetV2, a computationally efficient convolutional neural network, combined with OpenCV for real-time detection and preprocessing. A comprehensive dataset comprising 700 facial images per individual-including both masked and unmasked variants-was constructed to train and evaluate the system. The proposed architecture achieves a remarkable recognition accuracy of 99.97%, outperforming traditional models such as VGG16, ResNet-50, and FaceLite in both precision and computational efficiency. The system also includes a mask-aware detection pipeline and a matching mechanism that differentiates between genuine and impostor identities. This framework holds strong potential for deployment in real-world applications including access control, surveillance, and attendance systems.

In mobile communications, frequency-shared base station antennas that operate across multiple frequency bands are employed to reduce the number of installations. However, in lower frequency bands, the beamwidth becomes wider, leading to interference with adjacent areas. To address this issue, a frequency-dispersive phase shifter using a CRLH (Composite Right/Left-Handed) transmission line with nonlinear phase characteristics has been proposed. This approach enables deeper downtilt of the low-frequency beam, thereby mitigating interference. In a previous study, the simulation results could not be reproduced in the prototype due to insufficient compression between the movable dielectric and the interdigital capacitor. In this paper, a new method is explored in which a jig is used to press down from above to ensure proper contact. However, in the conventional structure, the presence of an air layer between the dielectric substrate and the ground plane can lead to instability due to deformation under pressure. To resolve this, the air layer was replaced with a dielectric substrate, and the unit cell structure was redesigned to compensate for changes in transmission characteristics. The Bloch impedance was de-signed in the range of 30-45 Ω, and the microstrip line width was widened from 5.8 mm to 8.2 mm to adjust the overall structure. Furthermore, a ran-dom search method was employed to derive an equivalent matching circuit structure that achieves VSWR less than 2 within the target frequency band.

With its proximity to the geomagnetic equator, the Philippines experiences dynamic ionospheric conditions that can disrupt satellite communication, navigation, and timing systems. The observations presented here provide a comprehensive view of ionospheric behavior over the past decade, particularly under the influence of geomagnetic storms and solar activity. Using ground-based GNSS receivers distributed across the country, variations in Total Electron Content (TEC) have been tracked and analyzed, capturing key phenomena such as plasma depletions, equatorial plasma bubbles (EPBs), and ionospheric scintillation. Significant space weather events-including the St. Patrick's Day geomagnetic storm in March 2015 and the May 2024 disturbance-highlighted the region's vulnerability to large-scale ionospheric fluctuations. To visualize these disturbances more effectively, two-dimensional TEC maps were generated using Kriging interpolation from GNSS ionospheric pierce points. These maps reveal the structure and spatial distribution of ionospheric irregularities, enhancing our ability to detect localized features that are often invisible through single-station observations. The presentation also covers the effects of intense solar phenomena, such as the X7.13-class solar flare of February 2014, which caused low-latitude geomagnetic disturbances and TEC enhancements. Seasonal and diurnal patterns of TEC variation are shown to align with equinoctial conditions and solar cycle phases, while the rate of TEC change (ROTI) serves as a reliable indicator for identifying scintillation events. Together, these results demonstrate how far the Philippine space weather community has come in understanding and monitoring ionospheric behavior. The progress reflected in this work contributes to regional space weather awareness, supports the protection of space-based infrastructure, and strengthens the foundation for future space environment applications. As reliance on satellite services continues to grow, sustained efforts to monitor and respond to space weather impacts remain a national and regional priority.

Pi2 pulsations are critical indicators of geomagnetic storm activity and require accurate detection for effective space weather monitoring. Traditional Pi2 detection methods often face limitations due to sparse or low-resolution geomagnetic data. This study presents a deep learning approach for classifying Pi2 pulsations using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model applied to raw geomagnetic data sampled at 1-second resolution. The dataset was constructed by identifying Pi2 events across more than 50 global observatories, with minimal preprocessing steps including spline interpolation and Z-score normalization. The CNN component extracts spatial features, while the LSTM captures temporal dependencies within the time-series data. Experimental results demonstrate that the proposed model achieved an accuracy of 91.36%, with a precision of 0.90, recall of 0.93, F1-score of 0.92, and an AUC of 0.96. These results highlight the model's effectiveness in distinguishing Pi2 from non-Pi2 events and its potential for real-time, automated space weather monitoring.

The SID Monitor project by the Stanford Solar Centre is a project to spread knowledge and awareness of space weather to school children. Over the years, it had gained popularity and even produced significant results in the scientific literature. However, a lack of standardisation of the antenna resulted in varying yield and difficulty reproducing results. This study investigates the relationship between the antenna loop count and the signal reception of the NWC transmitter for a SID-type VLF receiver. Antennas with varying numbers of loops were constructed with AWG30 wires. Then for each configuration of the antenna, the signal strength of NWC is recorded for 7 consecutive days using DHYL4F's Spectrum Lab software, while its ohmic resistance is measured and recorded to infer the material of the wire. The result is then fitted with a curve to determine the optimal loop count for the system. A lognormal relationship is observed between the loop count and the signal strength, with the peak at 128.06 loops. The resistance data matched aluminium's resistivity, which confirmed the wire composition. A drop in signal strength is observed with increasing loop count, likely due to increased resistance and other physical effects such as impedance mismatch. These results help guide the practical design of the antenna for SID monitors, which allows for further standardisation and improved data quality without complex modifications to the system, while providing a starting point for new adopter of the system.

This study investigates ionospheric equatorial plasma bubbles (EPBs) over the Philippine region using the Rate of Total Electron Content (TEC) change (ROT) and the Rate of TEC Index (ROTI) derived using Beidou geostationary satellite observations received from GNSS stations and 630-nm nighttime airglow observations from an all-sky imager located above the region. Fluctuations in both ROT and ROTI are observed starting from 8:00 PM Philip-pine Local Time (UTC+8) with amplitudes that reached up to ±2 TECU/min. This indicates the presence of such ionospheric irregularities commonly as-sociated with EPBs. The drift velocity of the EPBs was estimated by tracking the apparent motion of ROT and ROTI disturbance peaks across fixed IPPs, yielding an average speed of approximately 120 m/s. This is consistent with previously reported values under geomagnetically quiet, post-sunset conditions. Time-lapse all-sky airglow imagery from Lulin Observatory in Taiwan revealed localized depletions propagating across the sky, corroborating the EPB activity observed in the GNSS-based measurements.

Geomagnetic Induced Currents (GIC) are a well-documented threat to underground pipeline infrastructure, primarily in high-latitude regions, where geomagnetic storms frequently induce significant current variations. These currents disrupt Impressed Current Cathodic protection (ICCP) systems, accelerate corrosion, and pose long-term risks to pipeline integrity. While extensive studies have focused on the effects of GIC in polar and mid-latitude regions, there is a notable lack of research on their impact in equatorial regions, despite increasing reports of geomagnetic disturbances at low latitudes. This study examines research on GIC effects on pipelines near the Equatorial region based on the Equatorial Electrojet index, i.e., EUEL towards pipeline ICCP efficiency parameter, i.e., pipe-to-soil potential and the rectifier current output. The analysis results suggest that the pipeline is affected by the EEJ current, identified by correlation measure and spectral analysis of these signals with a mutual-energy peak centered at 10⁻⁵ Hz, and magnitude-squared coherence exceeded 0.90 at this frequency for the pipe-to-soil potential data.

Train detection and train movement (train localization) is the most important el-ement for a safe railway operation. The safety of the railway signaling system is very crucial and need stringent requirement to ensure the safest oper-ation of the railway system. As the world is shifting itself for digital system ad-vancements, it becomes a need for the railway system's safety element to be digitalized. In this work, a proposal on to use the Global Navigation Satellite System (GNSS) such as Global Positioning System (GPS) Technology to detect the movement of the train is being introduced as it seems to be more cost-effective solution, compare to the existing Global System for Mobile Communications- Railway (GSMR) system - which is considered as older technology and will be absolute by 2030.

This paper presents the preliminary implementation and test of low-cost Software Defined Radio using computer, the SDR dongle radio receiver and python soft-ware as software for data loggers. The main key aspects prior installation and configuration of python software and SDR drivers on computer are addressed. The resulting device can detect and log HF, VHF and UHF signals in the frequency range from 5 MHz to 1.7GHz. This system offers a cost-effective solution for radio frequency monitoring and analysis. The setup provides reliable performance for educational research and professional applications in telecommunications. The implementation shows good results for signal detection and data collection purposes.

This paper presents the design and analysis of a compact metamaterial ab-sorber (MMA) operating from 4 to 18 GHz, featuring five distinct resonance peaks with absorption rates of up to 99.6%. The unit cell, measuring 8 mm × 8 mm, is built on an FR4 substrate with a thickness of 1.6 mm and incorpo-rates a novel copper pattern of interconnected rings. The compact structure (0.21λ × 0.21λ × 0.04λ at 7.86 GHz) enables efficient subwavelength absorp-tion. Notably, the absorber demonstrates broadband resonance in the Ku band (15.13-15.91 GHz) and achieves a low radar cross-section (as low as 2.90 × 10⁻⁶ m²), making it suitable for stealth applications. A high Q-factor of 919.33 at 10.98 GHz ensures strong frequency selectivity, while near-zero refractive indices across multiple bands enhance its performance in radar and microwave systems. The proposed MMA offers improved absorption ef-ficiency, miniaturization, and multifunctionality, making it a strong candidate for advanced electromagnetic sensing and stealth technologies.

Previous research has established that in geospace, the total electron content (TEC) in the ionosphere can be modulated efficiently by ultralow frequency (ULF) waves in high-latitude regions. However, the correlations between TEC variations and ULF waves in middle-latitude and low-latitude regions remain inadequately explored. In this study, using ground-based magnetometer data from the Chinese Meridian Project, we identified UL wave events within Pc4 frequency bands in the midlatitude region. During the period from July 1 and December 30, 2023, we identified 438 distinct ULF wave events in the Pc4 band, thereby creating a comprehensive ULF wave database. Statistical analysis indicated that Pc4-band ULF wave events predominantly occurred on the dusk side in midlatitude regions. Notably, on August 24, 2023, simultaneous observations of geomagnetic disturbances and TEC disturbances at the similar frequency were recorded, suggesting a potential correlation between Pc4 ULF waves and TEC variations at midlatitudes. Through quantitative analysis, we determined that a high coherence was observed between TEC and magnetic field pulsations in the Pc4 band during this event. Thus, we infer that ionospheric TEC variations were possibly triggered by Pc4 ULF waves. This result provides direct observational evidence of the modulation of the TEC by Pc4 ULF waves in the midlatitude region. These findings broaden our understanding of the coupling between the magnetosphere and ionosphere in midlatitude regions.

This study explores the integration of Islamic eco-theology and nature-based expressive arts counseling as an innovative intervention to address climate anxiety among students in faith-based schools. As ecological crises increas-ingly disrupt psychological well-being, young people in Islamic educational settings are particularly vulnerable to emotional distress, including fear, help-lessness, and eco-guilt. Drawing on core Qur'anic principles, such as ama-nah (trust), tawazun (balance), and rahmah (compassion), a three-session Eco-Art Counseling program was designed to regulate emotions and cultivate spiritually grounded ecological awareness. A total of 279 students from Madrasah Aliyah completed the Climate Anxiety Scale (CAS), with ten students showing high anxiety levels selected for qualitative exploration. These participants engaged in natural-material-based art-making, reflective journaling, and semi-structured interviews. Thematic analysis revealed three primary domains: emotional articulation, symbolic healing through nature, and the emergence of eco-spiritual respon-sibility. Visual metaphors, such as withered leaves, dark stones, and circular compositions, reflected students' inner experiences and theological respons-es to environmental disruption. Findings suggest that embedding spiritual narratives and nature-based ar-tistic expression within environmental education fosters emotional resilience, meaning-making, and a sense of planetary humility. This model demon-strates strong potential for integration into Islamic counseling services and climate psychological education, providing a culturally responsive pathway to support youth in the era of ecological uncertainty

The faint young Sun (FYS) paradox is a fundamental problem in paleoclimatology and astrobiology, first proposed by Carl Sagan and George Mullen in 1972. Based on stellar evolution model, it is estimated that the solar luminosity of the young Sun during Hadean-Eoarchean (4.5 - 3.5 Ga) was ~30% lower than present Sun, resulting a completely frozen Earth. This didn't happen because evidence show that the early Earth have had liquid water and life had occurred, suggesting that these effects must have been mitigated. Various models by previous researchers have mitigated the FYS paradox, by setting the concentration of greenhouse gases (carbon dioxide, CO2 and methane, CH4), temperature and other physical properties of the early Earth, to explain the occurrence of liquid water and life on early earth. However, the effects of solar radiation passing through the early Earth's atmosphere remain unknown, and this information is essential for understanding how prebiotic chemistry, using solar radiation as a source of chemical energy, could have occurred on the planet's surface. To address this gap, we use the Virtual Planetary Laboratory (VPL) simulator to model the young Sun and early Earth system. We present modelling data on solar spectral output and atmospheric net flux, which are crucial for prebiotic chemists conducting prebiotic chemistry experiments that account for the influence of the FYS.

Astronomy education in the Philippines has undergone significant evolution, yet it remains underrepresented in national education priorities and lacks a cohesive research framework. This study presents a strategic, theory-driven research agenda for astronomy education, grounded in Bronfenbrenner's Ecological Systems Theory. Utilizing a sequential exploratory mixed methods approach, the study engaged astronomy educators, scientists, and policymakers to identify thematic research areas and validate their relevance using OECD coherence criteria. Thirteen research priorities emerged across five domains: learner engagement, pedagogical innovation, socioeconomic integration, institutional collaboration, and cultural-legal foundations. The proposed agenda emphasizes inclusivity, sustainability, and policy alignment, offering a roadmap for advancing astronomy education and research in the country. The findings aim to inform curriculum reform, foster international collaboration, and position the Philippines as a regional leader in astronomy education.

The emergence of the young crescent moon, which has both religious and calendrical significance, determines the Islamic lunar months. Low visibility, brief observation windows, and positional uncertainty frequently make reliable observation difficult. This study compares the Celestron NexStar Evolution 8 telescope, against traditional theodolite methods for young crescent moon observations, focusing on coordinate precision and visibility. The telescope's precision, usability, and capacity to detect weak crescents in marginally atmospheric circumstances were evaluated. It was outfitted with GPS, Wi-Fi control, and DSLR integration. Because predicted altitude and azimuth celestial coordinates were computed beforehand, accurate automatic tracking and effective alignment to the crescent's anticipated position were made possible. Field observations were carried out at the helipad site of Universiti Teknologi Malaysia throughout the three major lunar sighting years of 2016, 2019, and 2022. High dependability and repeatability were con-firmed by comparing the telescope's azimuth and altitude measurements with those made with a Topcon DT200 theodolite. The average azimuth deviations were between 1′30″ and 3′30″, while the altitude variances were between 0′40″ and 1′30″. Coordinate-based tracking decreased search time and increased observer confidence, while enhanced optical magnification and light-gathering capabilities allowed for faster identification of the crescent than with conventional approaches. For increased consistency and credibility in Islamic calendar determinations, this study recommends the integration of small-aperture telescopes into official and community-based lunar sighting initiatives. The results show that combining positional accuracy with improved optical visibility can strengthen the reliability of young crescent moon sighting practices.

Spaceflight has diverse impacts on the human body. But due to the complexity of biological data, these changes are sometimes difficult to detect. Thus, in this study, network analysis is used to uncover important hub genes affected by short duration spaceflight to civilians. Differentially expressed genes from the transcriptome profiles of the Inspiration4 mission crew members were mapped in a network. And through degrees and centrality measures, hub genes were identified. Functional enrichment analysis (FEA) was done hereafter to understand their biological functions and were validated with other datasets in SOMA. Results uncovered eight hub genes namely UBA52, FAU, RACK1, RPS2, RPS11, RPS14, RPS15 and RPL35. FEA showed that these hub genes were all linked to ribosomal function. SOMA validation revealed that these ribosomal genes were consistently downregulated during spaceflight, suggesting links to interference in ribosome biogenesis. These findings can help future studies in understanding the effect of spaceflight on humans.

Hypergravity, defined as a gravitational force greater than Earth's 1×g, has been shown to trigger a range of phenotypic, physiological, and structural responses in plants. In this study, we investigated whether hypergravity, influences the salinity tolerance of mung beans (Vigna radiata (L.)) during early germination. Results from a two-way ANOVA indicated that salinity had a significant inhibitory effect on sprout length (p < 0.05), while gravity alone did not exhibit a statistically significant main effect. However, a significant interaction between salinity and gravity (p < 0.05) revealed that hypergravity exposure alleviated the negative impact of high salinity on seedling elongation. These results suggest that hypergravity can partially mitigate the negative effects of salinity on sprout growth. Understanding how short-term hypergravity, encountered during launch and transit, influences plant stress responses can support the development of resilient crop systems for sustained food production in space.

The growing interest in plant space biology is motivated by the need to create sustainable life support systems for long-duration human spaceflight. Plants are essential elements of bio-regenerative systems due to their ability to produce food, generate oxygen, recycle carbon dioxide, and help in water purification. One of the significant issues, however, is the stimulation of plant growth under altered gravitational conditions, such as in microgravity environments (~10⁻³ G). While space-based experiments have yielded valuable results, they are often limited by high costs, limited access, and complicated logistical demands. Ground-based simulators like the random positioning machine (RPM) or 3D clinostat offer accessible alternatives. By rotating samples continuously around two orthogonal axes, the RPM temporally averages the gravity vector, thus simulating microgravity conditions. We present an economically viable, table-top random positioning machine made from easily sourced parts in the market. The device consists of two independent extruded aluminum frames which rotate and are powered by stepper motors, allowing continuous three-dimensional randomization. Rotational speed, orientation, and time are controlled by a microcontroller, and utilizing sensor feedback to enhance control precision. The sample holder placed in the center of the device can hold four standard petri dishes, thus allowing simultaneous biological experiments. Built for modularity and accessibility, this RPM provides a practical platform for microgravity research, particularly in resource-constrained research and academic environments. It is a cost-effective entry to space biology and facilitates local capacity building in preparation for future space science missions.

Radio telescope is used in radio astronomy to measure the intensity of the radiation received from various parts of the sky. This paper presents the development of a microstrip array antenna which is designed to be the feeder of a parabolic radio telescope system. The telescope is developed at astronomical observatory (OAIL) which is part of Institut Teknologi Sumatera (ITERA) Lampung. The patch of antenna is similar to ITERA emblem. The ground plane of the antenna is a triangular shape. The operating frequency of the antenna is 1.42 GHz. The antenna is designed in elements of a single and array element. In single element, the VSWR, return loss, gain, and beamwidth of final design at 1.42 GHz obtained from simulation are 1.22, -19.96 dB, 1.799 dBi, and 122°, respectively. The VSWR and return loss of final design at 1.42 GHz obtained from measurement are 1.31 and -23.71 dB. In array, the VSWR, return loss, gain, and beamwidth of final design at 1.42 GHz obtained from simulation are 1.18, -21.85 dB, 2.153 dBi, and 111.1°. The VSWR and return loss of final design at 1.42 GHz obtained from measurement are 1.83 and -10.61 dB.