Abstract: Non-invasive assessment of tissue water content is clinically relevant for edema detection, fluid management, and monitoring of local inflammation. In the short-wave infrared (SWIR), water exhibits strong absorption near 1450 nm with a secondary band near 1650 nm, enabling hydration-sensitive reflectance measurements. However, many SWIR systems rely on spectrometers or high-power broadband sources, limiting translation to compact or wearable platforms. We present a compact SWIR diffuse-reflectance probe built from small-form-factor components using four discrete LEDs (1450 nm and 1650 nm) and a single photodetector to acquire spatially resolved measurements at two source–detector separations (4.5 mm and 7 mm). Probe-geometry-matched Monte Carlo simulations were used to generate lookup tables relating reduced scattering to same-wavelength spatial ratios. 11 A diffusion-based forward model was then used to perform a calibration-anchored water-fraction consistency analysis. Eight gelatin–Intralipid phantoms spanning two scattering conditions and formulation-defined water fractions were evaluated. Spatial-ratio signatures were repeatable and monotonic with nominal water fraction, yielding a mean absolute percent error of 1.55% and a maximum absolute percent error of 3.33% under absorption-consistent conditions. These results demonstrate the feasibility of compact SWIR ratio sensing for controlled hydration changes in tissue-mimicking phantoms and provide a modeling framework for future extension to unknown or in vivo samples.

Abstract: The paper deals with comparative analysis of machined surfaces by classic electrical discharge machining (EDM) and hybrid ultrasonic EDM of CoCr alloys, using computer vision aimed at emphasizing the advantages of this hybrid technology. The analysis revealed generally the superior stability of EDM+US process against classic EDM explained by better evacuation of debris from the working gap due to ultrasonically induced cavitation. This key phenomenon also contributed to the enhancement of machining rate by removing the material in liquid state and also the in solid state from the microgeometry peaks but also reducing the surface roughness if the power on the ultrasonic chain is optimzed.

Abstract: Determining which sensor modalities carry genuine discriminative signal for CNC monitoring—and how many can be removed before performance degrades—is a practical question that prior work rarely answers quantitatively under leakage-resistant evaluation. We address this through a systematic cross-validated ablation study on a 9-class CNC toolpath and condition classification task, combining three toolpath strategies (adaptive, face, pocket) with three conditions (air-cutting, active-cutting, and damaged-spindle). Using 120 operation files from a desktop CNC mill with six consistently active sensors (17 channels per sensor) plus 8 machine-level electrical features, we evaluate six model families across 690 cross-validated runs spanning five cumulative feature-ablation levels (110–56 features) and ten temporal resolutions. To handle the fusion challenge, we introduce MM-DTAE-LSTM, a multi-modal denoising temporal attention encoder with unidirectional LSTM-based classification that combines learned modality gates, cross-modal attention, and a self-supervised denoising objective. Key findings on this single-machine, single-material dataset: (1) MM-DTAE-LSTM reaches 96.3% ± 4.7% accuracy at a 98-feature configuration (excluding proximity and pressure), leading all baselines by 3.1–5.2 points, though differences are not statistically significant at n=5 folds; (2) reducing the feature set by 49% (to accelerometer, gyroscope, temperature, RMS audio, and electrical channels) retains 92.5% ± 8.3% for the encoder while XGBoost drops to 84.4%, a loss of 10.7 points from its full-feature peak; (3) at full features, baselines are competitive (Random Forest: 95.6%, XGBoost: 95.1%); and (4) one-way ANOVA reveals that pressure channels encode session-level barometric confounds (F > 2,000) rather than machining dynamics, explaining baseline degradation when confound-prone channels are removed. These results suggest that core inertial, acoustic, and machine-level modalities may be sufficient for effective CNC operation classification on similar platforms, providing sensor-selection and temporal-configuration guidance for cost-effective monitoring deployments. Generalization to industrial machines, diverse materials, and production environments requires further validation.

Abstract: Microplastics (MPs) are emerging pollutants of global concern, posing significant ecological and human health risks. They are frequently detected in stormwater systems, with urban runoff serving as a major transport pathway into the environment. Green storm-water infrastructure, particularly bioretention systems (BRS), offers a promising approach to mitigate these risks by filtering and retaining various contaminants. However, the occurrence of MPs in BRS and their capacity in retaining these pollutants remain largely unexplored in the literature, despite being critical for stormwater management and water quality protection. Therefore, this study examines the occurrence, vertical distribution, and retention of MPs within field-installed BRS, emphasizing their role in reducing MPs transport. Field samples were collected at depths of 2, 12, and 24 inches below the surface and processed in the laboratory for MP detection and quantification. The results revealed an average concentration of 1,095 particles per kg of dried sediment, with fragments (microplastics shape) accounting for 78.54% of total MPs. Although no clear vertical distribution pattern was observed, MPs accumulated predominantly at 24 inches, indicating their transport through the media and the retention capacity of BRS (surface and mid layer) in capturing microplastics from stormwater environments. Integrating BRS into urban stormwater infrastructure provides dual benefits: improved stormwater management and reduced plastic pollution. This study highlights the importance of optimizing bioretention design and media composition to improve the removal efficiency of MPs.

Abstract: This study extends the application of the 3M&I (human, Material/Machine, Money, and Information resources) body science framework to explore the wisdom law of body wealth and energy in additive-multiplicative systems, integrating both artificial and natural bodies. The research formalizes the mechanisms underlying body intelligence and survival through look-ahead and nano-transforming schemes grounded in a static-stable system perspective, modeled across two- and three-dimensional spaces. We propose refinements to Matsui’s original motion equations by introducing an ad-renewable formulation (ME) and a generalized version, MW = ZL, to capture both cumulative and progressive dynamics in body schemes. A ternary/pair-map framework is developed to address three-body systems and dualism-based challenges, extending beyond traditional two-body models. The framework also incorporates a wealth-additive scheme, reinterpreting the flow retention concept to prioritize energy/wealth maximization over cost minimization. Finally, we introduce Chameleon’s objective function—defined as (revenue × lead time) / departure—as a metric for optimizing marginal diversity within sustainable development contexts. The proposed model offers a theoretical foundation for predictivity versus sustainability processing and integrates body system modeling with broader eco-entropy and sustainability goals.

Abstract: Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in New Mexico, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (k_e) and wind speed. The model performed strongly across 12 lakes (R² = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with k_e variability, supporting the operational use of a constant k_e = 0.95 for daily upscaling.

Abstract:

This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. The main objective was to experimentally measure orthogonal cutting force components (F_x, F_y, F_z) and electrical power consumption under varying spindle speeds (14 000, 16 000 and 18 000 rpm), feed speed (6, 8 and 10 m/min), and milling strategies (conventional and climb), and to evaluate the suitability of the obtained data for predictive modelling. Cutting forces were measured using a Kistler 9257B piezoelectric dynamometer, and power consumption was recorded by a three-phase power quality analyser. Statistical analysis confirmed significant effects of machining parameters on force components, total cutting force, and power consumption. Spindle speed showed the strongest influence on total cutting force and power consumption, while milling strategy predominantly affected F_x and F_y components. Power consumption increased with increasing spindle speed. Based on the measured data, several machine learning models were developed to predict the total cutting force. After model comparison using RMSE, R², training time, and model size, a Fine Tree model was identified as the most suitable, achieving high prediction accuracy without signs of overfitting. The results confirm that experimentally obtained force and energy data are suitable for reliable predictive modelling in CNC milling of MDF.

Abstract: The objective of this study was to produce, characterize, and optimize modified potato starch derived from locally sourced potatoes, and to evaluate the physicochemical properties of native, cross-linked, acetylated, and dual cross-linked–acetylated potato starches as disintegrants for tablet formulation. Starch modification was performed through cross-linking and acetylation using sodium hexametaphosphate (SHMP) and acetic anhydride (AA) as modifying agents, respectively. Native and modified potato starches were characterized using Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), rapid visco analysis (RVA), and X-ray diffraction (XRD). The key modification parameters investigated included reaction temperature, reaction time, pH, concentration of the modifying agent (AA), and concentration of the NaOH catalyst. Based on preliminary experiments, reaction temperature (40, 60, and 80 °C), modifying agent concentration (10, 20, and 30%), and reaction time (40, 55, and 70 min) were selected as the primary variables. Process optimization for dual crosslinked-acetylated potato starch was carried out using response surface methodology based on a Box-Behnken experimental design, with acetyl content as the response variable. The optimized modification conditions were a reaction temperature of 40.22 °C, a reaction time of 69.85 min, and an acetic anhydride concentration of 21.92% (w/w). Under these optimized conditions, an acetyl content of 1.32 ± 0.077% was obtained. Tablets formulated using the dual crosslinked-acetylated potato starch as a disintegrant exhibited a disintegration time of 29.2 ± 0.29 min, a disintegration efficiency ratio of 500 ± 0.99 N min⁻¹, a crushing strength of 92.35 ± 0.86 N, and friability of 0.63 ± 0.08% (w/w). The modified starch was employed as a disintegrant in tablet formulations containing 10% paracetamol as the active pharmaceutical ingredient, magnesium stearate (10%) as a lubricant, and suitable fillers.

Abstract: The city of Baghdad is witnessing a continuous increase in traffic and urbanization, which has led to frequent traffic jams and deterioration of the urban environment and quality of life due to pollution and the waste of time and energy. Hence, it has become necessary to adopt integrated planning concepts that regulate land uses, promote transport efficiency, and support sustainable urban development. This research aims to investigate the concept of transport-oriented development (TOD) and explore its applicability in the city of Baghdad, focusing on identifying obstacles and challenges that may face the implementation of this concept in the local context, whether related to transport infrastructure, urban planning, or community participation, to provide an analytical framework that can be relied upon in the development of effective strategies to promote sustainable transport and integrated urban development. The BRT bus rapid transit system is an essential part of the Comprehensive Development Plan for Baghdad 2030, aiming to improve mass transit and reduce congestion on major streets such as Palestine Street by providing fast, efficient transportation that connects the city's neighborhoods and encourages walking and the use of sustainable transport. The project supports sustainable urban development by integrating the principles of TOD, increasing residential and commercial density around the stations, and adopting an integrative methodology that analyzes the relationships among transport, land uses, and urban density to provide a scientific framework to support planning and future decision-making.

Abstract: The Colorado River and its tributaries housed in the Colorado River Basin (CRB) are the primary source of water to the western United States and the Republic of Mexico. The river system is under intense stress due to prolonged drought and anthropogenic activities which have worsened its water quality. Total dissolved solids (TDS) and total suspended solids (TSS) are two water quality parameters (WQPs) that are crucial to the sustainability of the river system. These parameters are noted to have caused varied severity to the sustenance of the basin’s water. Monitoring of these WQPs has been conventionally conducted using field and laboratory analysis which are cost and labor-intensive. This study utilized a novel method to effectively develop machine learning (ML) models to estimate TDS and TSS concentrations in the CRB by utilizing the potential of optically sensitive multispectral Sentinel 2 A/B Multispectral Scanners (MSI) and Landsat 8 Operational Land Imager (OLI) remote sensing (RS) data retrieved from the Google Earth Engine (GEE) and in situ measurements collected from 2013-2022. Several standalone models such as linear regressions (LR), ridge regressions (Ridge), lasso regressions (Lasso), and k-nearest neighbor (KNN), and ensemble methods including the gradient boosting machines (GBM), random forest (RF), adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), and bagging were applied for the accurate estimation of TDS and TSS. Results found ensemble models like the XGBoost as the most optimal model estimating TDS using images from both Sentinel-2 MSI and Landsat 8 OLI with performance on the external validation dataset derived as 0.99, 26.52 mg/L, and 19.19 mg/L, respectively for R², RMSE, and MAE for Sentinel-2 images. The XGBoost yielded R², RMSE, and MAE of 0.97, 35.82 mg/L, and 27.90 mg/L, respectively. The AdaBoost was found to be best model for TSS estimations with values of 0.92, 29.48 mg/L, and 24.64 mg/L, respectively, for R², RMSE, and MAE for the Sentinel-2 image on the external validation dataset. The RF model was found to be the optimal model for TSS estimations with the Landsat 8 OLI with reported R², RMSE, and MAE of 0.90, 32.80 mg/L, and 22.91 mg/L, respectively on the external validation dataset. These findings show great potential of utilizing RS data to produce cost-efficient spatiotemporal changes on the WQPs which has an important implication for the continuous management of the limited water resources. Further study should consider the effect of land use land cover, runoff, and other climatic data to understand the complexity and dynamics of these parameters on TDS and TSS in the river.

Abstract: High gain and low sidelobe level remain challenges for 5G millimeter-wave antenna systems. This paper presents a low-sidelobe, high-gain microstrip array antenna based on non-uniformly slotted identical-sized radiating patch, designed to simultaneously enhance gain and suppress sidelobe levels for 5G millimeter-wave (mmWave) communication systems. The key innovation lies in the use of an intermediate-deep, edge-shallow non-uniform slotting technique to precisely control the surface current distribution of the radiating elements. thereby achieving significant sidelobe level (SLL) suppression and antenna isolation enhancement without increasing the physical footprint of each element. The final design operates at a center frequency of 78.5 GHz, achieving a maximum gain of 15 dB and suppressing the first sidelobe below −20 dB, outperforming conventional linear arrays. Notably, the patch width is reduced to only 1 mm—compared to Chebyshev-distributed arrays—resulting in a compact array layout with over 40% unit width size reduction while simultaneously improving inter-element isolation by more than 18 dB. This current-distribution engineering approach offers a novel, structure-efficient pathway for designing high-performance, densely packed mmWave antenna arrays, circumventing the need for additional decoupling structures or enlarg the antenna spacing，simulation results show that the average isolation has increased by more than 5 dB from 76 GHz to 79 GHz.

Abstract: The rapid evolution of Embodied AI and Large Language Models presents significant opportunities for home robotics, yet challenges persist in enabling robots to execute long-term, high-level natural language instructions. Current LLM-driven embodied agents often suffer from sub-optimal task planning, limited memory systems struggling with multi-hop queries, and inflexible agent routing mechanisms. To address these limitations, we propose the Context-Rich Adaptive Embodied Agent (CRAEA) framework, designed to significantly enhance task planning and memory-augmented question answering in household robots. CRAEA integrates core components: Semantic-Enhanced Task Planning (SETP), which enriches LLM-driven planning with object relationship graphs, hierarchical strategies, and implicit physical constraints; Multi-Modal Contextual Memory (MMCM), which stores comprehensive contextual memory units in a relational graph for sophisticated multi-hop reasoning and employs an advanced retrieval mechanism with temporal decay; and Adaptive Agent Routing and Coordination (AARC), featuring intent recognition with confidence evaluation, proactive clarification, and a planning feedback loop. Evaluated in an artificial home environment across complex tidying scenarios, CRAEA consistently demonstrates superior performance. Empirical results show that CRAEA achieves notable improvements in Task Planning Accuracy, Knowledge Base Response Total Validity, and Agent Routing Success Rate compared to baseline methods. A human evaluation further confirms enhanced coherence, naturalness, and user satisfaction, while an ablation study validates the critical contribution of each proposed module. CRAEA represents a significant step towards more intelligent, robust, and user-adaptive home robots.

Abstract: Collaborative robots (cobots) are designed to improve productivity and safety in industrial settings. However, to be effective Human-Robot Collaboration (HRC) relies heavily on the human operator’s trust in the robotic partner. This study posits that trust is significantly enhanced by the robot's ability to adapt to human behavior, particularly when the human teammate has a behavior unpredictable and outside the box. To achieve this adaptability, we propose an Adversarial Reinforcement Learning (ARL) framework to the activity planning of the robot. The assembly process is modeled as a Markov Decision Process (MDP) on a Directed Acyclic Graph (DAG). The robot learns an assembly policy using an on-policy algorithm, while a simulated human agent acts as an adversary trained with the same algorithm to introduce disturbances and delays. The proposed approach was applied to a simple industrial case study and evaluated on complex assembly sequences generated synthetically. While the ARL-trained robot did not outperform conventional assembly optimization algorithms in terms of task completion time, it guaranteed robustness against human variability, ensuring task completion within a bounded timeframe regardless of human actions. By demonstrating consistent performance and adaptability (Ability) in the face of uncertainty, the robot exhibits characteristics that align with the Ability and Benevolence components of the ABI model of trust, thereby fostering a more resilient and trustworthy collaborative environment.

Abstract: The suspension system plays a significant role in ride comfort, car weight support, and road handling, which is crucial for the safety of the ride. This paper illustrates a derivation of a mathematical model and proportional-integral-derivative (PID) controller design for an active suspension system for a quarter car model of a passenger car. The performance of an active suspension system in terms of the vertical acceleration of the car body, suspension deflection, and tyre deflection is compared with that of a passive suspension system when subjected to road disturbance. The results show that the active suspension system with PID controller provides better performance compared to that of the passive suspension system.

Abstract: We present a parametrised charging infrastructure model developed to support the design of a hybrid-electric zero-emission vessel with corresponding charging infrastructure for operation along the Norwegian Coastal Express route. The charging model includes functionalities to analyse the required battery storage capacity and power ratings and locations of charging facilities for achieving battery-electric operation. We demonstrate the use of the charging model to analyse different zero-emission scenarios for the Norwegian Coastal Express route. In the presented example scenarios, the model takes as input the estimated energy demand for a new zero-emission vessel design for the Coastal Express in different weather conditions, and includes functionality to consider realistic port stays based on existing timetables and historical data of delays. The analyses show minimal required battery and illustrate a trade-off between charging power and battery capacity, as well as exemplifying the impact of different timetables as well as historic deviations on charging and energy delivered from the battery. The charging model presented is general and can be used for other routes than the Norwegian Coastal Express, as a tool for decision-makers to optimize for battery-electric operation whilst keeping the need for onboard storage capacity and charging infrastructure installations at a minimum.

Abstract: This article examines the impact of intermodal wagon technical specifica-tions and railway infrastructure parameters on electricity consumption in rail freight transport. To conduct this investigation, a three-stage analytical model was developed. The first stage establishes core assumptions, encompassing train lengths, rolling stock types, container configurations, infrastructure constraints, and the characteristics of the energy-consumption model. The second stage identifies technical constraints of specific wagons, determines representative train compositions, and executes loading simulations. The third stage focuses on evaluating energy efficiency across diverse loading scenarios. The case study demonstrates that specific energy consumption varies significantly with wagon type, train mass, and route characteristics, challenging the use of static energy-consumption values prevalent in current literature. Results indicate that 40-foot wagons incur high energy penalties due to their tare weight and axle count, despite maximizing loading capacity. While 60-foot wagons consume less energy, they result in a high frequency of empty slots under a 20 t/axle limit. Conversely, 80-foot wagons emerge as the most energy-efficient, particularly at a 22.5 t/axle limit. Mixed consists offer a balance of operational flexibility and competitive performance. Inter-estingly, extending train length from 600 m to 730 m increases volume but does not inherently reduce unit energy consumption. These findings underscore the necessity of aligning wagon fleet selection with infrastructure capabilities and cargo characteris-tics. Ultimately, this study provides actionable recommendations for planning ener-gy-efficient intermodal operations.

Abstract: Automatic modulation classification (AMC) is a core capability for spectrum monitoring, adaptive receivers, and electronic support. Most radio-frequency machine learning (RFML) studies train multi-class classifiers on benchmark datasets that contain a single modulation per recording at baseband. In operational settings, however, the objective is often to detect only a small set of signals of interest, making large multi-class models unnecessarily expensive to train and deploy. This paper investigates an alternative workflow based on targeted binary transformer detectors and evaluates their robustness under practical RF complications. Using the RadioML 2018.01A dataset, we construct binary detection tasks with BPSK as the signal of interest and introduce three increasingly realistic conditions: (i) center-frequency shifts away from baseband, (ii) sampling-rate mismatches via decimation and interpolation, and (iii) multi-signal mixtures where modulations co-occur either in frequency (simultaneous transmissions) or in time (temporal concatenation). The results show that baseband-trained detectors do not generalize to center-frequency-shifted signals, and multi-signal interference can cause complete detection failure unless explicitly modeled during training. We investigate early-exit transformer inference to reduce computation on high-confidence examples, showing it maintains (and occasionally improves) detection performance. We also evaluate inter-modulation transfer learning and intra-modulation adaptation from baseband to mixed- and multi-signal scenarios.

Abstract: Over the last two decades, there has been a paradigm shift in geotechnical engineering driven by advances in sensing, communication, and data-driven techniques. These advancements enhanced the safety and reliability of geotechnical infrastructure through real-time monitoring and automated decision-making. In recent times, Large Language Models (LLMs) have emerged as advanced data-driven techniques contributing to automated risk assessment of geotechnical infrastructure. LLMs are advanced deep learning models widely used to solve complex numerical problems, analyze large volumes of data, and generate human language. This paper presents a comprehensive review of the application of LLM in geotechnical engineering. The integration of LLMs into geotechnical engineering has demonstrated significant advances in slope stability analysis, bearing capacity computation, numerical analysis, soil-structure interaction, and underground infrastructure. By summarizing the latest research findings and practical applications, this research paper underscores the potential of LLMs to advance and automate various processes in geotechnical engineering. The findings presented in this paper not only provide insights into the current LLM-based geotechnical practices but also emphasize the instrumental role LLM can play in advancing geotechnical engineering, ultimately ensuring a safer and more sustainable future.

Abstract: Maintaining the integrity of waterproof strata (WPS) between mine workings and overlying aquifers is critical, because water-conducting cracks (WCC) may cause mine flooding and surface subsidence. In the Upper-Kama potash deposit, the WPS is a 50-140 m thick stratified sequence of evaporites and clays overlying mined-out cham-bers. Under long-term loading, salt rocks tend to creep, soften, and localize damage, which can cause WPS failure. In this paper the Concrete damage-plasticity model, supplemented by the N2PC-MCT viscoplastic creep model, is applied to simulate WCC initiation and evolution in the Upper-Kama WPS. Model parameters are obtained from published laboratory tests, in-cluding uniaxial and triaxial compression and tension, and then validated using ob-served ground-surface subsidence. A plane-strain finite-element model incorporates stratified lithology, interface elements between layers, and stepwise excavation. Long-term simulations up to 50 years investigate two operational scenarios: with and without backfilling. The calibrated model reproduces the main stages of surface subsidence and chamber closure. Without backfilling, simulations indicate that tensile damage localizes mainly in a stiff central salt layer of the WPS. Most cracks appear approximately between 33 and 37 years after the beginning of mining. With backfill, tensile crack propagation stops and damage remains stable. A hypothetical homogeneous WPS case confirms that the observed central-layer cracking is associated with stiffness contrasts and composite bending in the stratified system. An approximate analytical multilayer beam solution, based on energy minimization, predicts bending stress concentration in stiff intermediate layers and is consistent with the numerical stress distribution. The combined numerical and analytical results clarify the mechanisms of long-term WCC initiation in stratified WPS and may be used for hazard assessment and planning of mitigation measures, including backfilling and focused monitoring of stiff central layers.

Abstract: Monitoring the progression of large wildfires in near-real-time is essential for active-fire situational awareness and emergency response management. Current satellite-based wildfire monitoring systems face a trade-off between temporal and spatial resolution: geostationary satellites such as GOES offer frequent (~5 minutes) but coarse observations (~2 km), while low earth orbit (LEO) instruments such as VIIRS provide fine spatial detail (∼375 m) with limited temporal coverage (twice per day). To bridge this gap, this study introduces a deep learning (DL) approach that enables near real-time, high-resolution wildfire monitoring using GOES data. The proposed approach consists of two main steps: a segmentation step to distinguish active fire regions from background areas and a regression step to estimate the active fire pixels brightness temperature (BT) across a region of interest. The output of these steps is combined to generate a high-resolution fire location and BT maps. To train the DL model, multi-spectral GOES inputs are paired with VIIRS-derived fire observations from several wildfires across the United States. Spatial consistency between GOES and VIIRS data is achieved through parallax correction, reprojection, resampling, and per-image normalization. Ablation studies are performed to demonstrate the impact of different assumptions (e.g., background values in the VIIRS ground truth) and strategies (e.g., loss functions) throughout the development process. The results show that the proposed DL approach effectively enhances GOES imagery, improving both BT estimation and fire boundary localization. Overall, the proposed method offers a practical and scalable solution for wildfire boundary detection and thermal mapping using existing satellite systems.