Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing Transfer Learning with Artificial Intelligence for Scaling-Up Lichen Coverage Maps

Lichen mapping is essential for sustainable caribou and lichen conservation. Previous studies have used artificial intelligence to create lichen coverage (%) maps using a scaling-up methodology. This paper proposes a transfer learning approach to lichen mapping, where the model weights from a lichen coverage (%) neural network trained on Quebec and Labrador data were used for training a northern Ontario model. The training and evaluation dataset consisted of 22 10-m lichen maps from Geraldton, Martin Falls, and Peawanuck Ontario, aligned to Sentinel-2 imagery. The model with transfer learning outperformed a similar neural network with randomized initial weights and a random forest model, despite predicting lichen in different ground conditions than Quebec and Labrador. A northern Ontario lichen map was created using a Sentinel-2 mosaic, displaying high amounts of lichen surrounding Peawanuck, Ontario. Whenever feasible, transfer learning approaches should be considered when organizing regional vegetation coverage (%) maps.
A Novel Method of Beam Collimation Factor Estimation for EOS-04 Active Phased Array Antenna

EOS-04, a C-band active array antenna based Synthetic Aperture RADAR (SAR) launched by ISRO, provides continuity of the services to its predecessor RISAT-1. It is equipped with advanced features such as full polarimetric mode and computation based onboard antenna beam forming. In this paper, we present a novel method to compute complex beam collimation factor of large-sized active phased array antenna (6m x 2m) of EOS-04 payload using far-field information of sequentially excited Transmit-Receive Integrated modules (TRiM), which consist of Transmit Receive pairs for H and V chain with controller. We utilize ISRO’s Compact Antenna Test Facility (CATF) for this purpose. Estimated beam collimation factor is compared with factors generated from Near-Field measurement and close match is observed. Finally, antenna patterns are generated using far-field estimated beam collimation factors and near-field estimated beam collimation factors.
Performance Comparison of Multispectral Channels for Land Use Classification

Land cover classification using satellite imagery plays a crucial role in monitoring changes on the earth’s surface. This paper presents an analysis of the EuroSAT dataset using state-of-the-art deep learning models to benchmark the impact of additional bands on classification accuracy. The dataset consists of 27,000 images across 10 classes captured by the Sentinel-2 satellite, including RGB and multispectral bands. Performance evaluation was conducted using popular convolutional neural network models based on Resnet variants and Vision Transformer (ViT). The results show that the combination of all bands achieved the highest accuracy, with ResNet-152 achieving a validation accuracy of 96.63% on the multispectral dataset. Precision, recall, and F1 scores were also utilized to assess the models’ performance. The findings highlight the significance of incorporating additional bands for improved classification accuracy in satellite image analysis.
Onboard Processing Capabilities of an Earth Observation Compressive Sensing Payload

In this paper, we explore the onboard processing capabilities of an optical Earth observation instrument operating under the principles of compressed sensing, currently under preliminary study. In particular, we focus on two main aspects for onboard operations: i) how to process measurements in a computationally-efficient way to obtain previews of the reconstructed image that can be easily used by downstream inference algorithms; ii) the possibility of having simultaneous compression and encryption by proper management of the pseudorandom patterns used for the sensing matrix and measurements
An Optimization Model of an Intelligent Monitoring Network Security System

Nowadays, the Internet has been greatly popularized and penetrated into all aspects of people's lives. In the campus, the level of network construction has also been continuously improved. However, the issue of campus network security has become an important issue that the whole society is concerned about. The research of this paper focuses on this hot spot, and based on the actual situation and characteristics of the campus network in the specific research process, using the relevant network security technology to develop an intelligent monitoring campus network security system optimization model, through the module test and performance test of the system optimization model, the test results verify that the system in this paper can effectively prevent network attacks, monitor campus network security, and ensure the practicability and scientificity of the system.
A NOVEL SENTIMENT CLASSIFICATION MODEL USING GRASSHOPPER OPTIMIZATION ALGORITHM WITH BIDIRECTIONAL LONG SHORT TERM MEMORY

In recent times, social media has received great attention among the research communities towards the domain of sentiment analysis (SA). The proficient design of SA is needed to improve the service and product qualities for the marketing and financial schemes for increasing the company’s profit and user satisfaction. Although several SA techniques are available in the literature, it is needed to further enhance the classification results of the user review which helps to comprehend the user reviews, thereby quality of the products can be improved. This study devises an effective SA and classification technique using grasshopper optimization algorithm (GOA) with bidirectional long short term memory (Bi-LSTM), named GOA-BiLSTM. The GOA-BiLSTM model involves word2vec based feature extraction process to derive a useful set of features. In addition, Bi-LSTM based classifier is applied to determine the optimal class label of the extracted features. Moreover, GOA is utilized for the hyperparameter optimization of the Bi-LSTM model. To ensure the better outcome of the GOA-BiLSTM model, an extensive set of simulations were carried out on four datasets. The simulation outcome verified the superiority of the GOA-BiLSTM model by accomplishing a higher accuracy of 99.57%, 99. 71%, 99. 06%, and 98.98% on the applied Canon, Nokia DVD, and iPod dataset respectively.
AUTOMATED CHANGE IMPACT ANALYSIS TOOL FOR SOFTWARE MAINTENANCE PHASE

Often, software projects are enhanced based on clients' requests or companies' policies. This process happens in the maintenance phase of the project. The maintenance phase of the Software Development Life Cycle (SDLC) occurs after the product is in production. Maintenance of software can include software upgrades, repairs, and fixes. The required changes are mentioned in the form of change request (CR) which is given to the developers who calculate the impact of changes on the software application. The effect must be assessed without modifying the software application. The change impact analysis is one such way that helps in assessing the impact. It takes time for the developer to analyze the change request and the software application to identify the required changes. In addition, impacts of the changes need to be identified, which requires more time for the developer. And also there is a high chance of missing the impacts due to manual effort. Rather, we can use an automated tool that does the change impact analysis within less time and give more accurate results. The objective of this paper is to develop and test the automated CIA Tool (StaticPy) that performs the change impact analysis. With the help of the tool developer will have an idea of the changes to be made. This will reduce the cost and time. So, this paper proposes a Change Impact Analysis Tool (CIAT) - StaticPy that helps to identify the impacted files, methods, fields, and elements that are affected because of the proposed changes. CIAT takes a change request and project repository GitHub link as input. Then the tool does static analysis on the given repository and forms a data structure. This data structure contains all the details of the impacted elements. From the data structure, we display the information of affected files, methods, fields, and impacted line numbers in the files. StaticPy has been developed and tested using four different software applications. The average accuracy of the tool is 97.8%.
Rejection Sensitivity and Psychological Well-being: Moderating Role of Self-esteem and Socio-demographics

The present study aimed to examine the moderating effects of self-esteem and socio-demographics of gender, age, family system, and the number of friends on the relationship between rejection sensitivity and psychological well-being. Participants were adolescents (n = 112; M = 16 years) and emerging adults (n = 189; M = 23 years) who belonged to nuclear (n = 181) or combined family system (n = 120) and had limited friends (n = 185) or numerous friends (n = 116). Almost an equal number of boys (n = 150) and girls (n = 151) responded to the Rejection Sensitivity Questionnaire, Rosenberg Self-esteem Scale, and Ryff’s Psychological Well-being Scale. The hypothesis stated that rejection sensitivity will predict poor psychological well-being, and high self-esteem will decrease the negative effects of rejection sensitivity on psychological well-being. Findings indicated that rejection sensitivity was a signi�cant negative predictor of psychological well-being and accounted for 14% of the variance in the outcome measure. Rejection sensitivity and high self-esteem had a signi�cant negative correlation and buffered its negative effects on psychological well-being. Participants, i.e., boys, emerging adults, belonging to the nuclear family system, and with more friends, reported having more psychological well-being than their counterparts. The study has intrapersonal and interpersonal behavioral implications at individual, social, and community levels to safeguard behavior from rejection sensitivity, low self-esteem, and poor psychological health. Parents, teachers, policy-makers, psychologists, sociologists, and mental health practitioners can practically bene�t from the �ndings to foster positive behavior and implement prevention-intervention support.
QOS AWARE WEB SERVICES COMPOSITION PROBLEM IN MULTI-CLOUD ENVIRONMENT USING HYBRID OPTIMIZATION ALGORITHM

Information technology is building communications networks for a company, safeguarding data and information, and creating and administering databases. Currently, cloud computing provides a single set of physical resources for providing multiple information technology (IT) services to a large user base with varying needs. With the advent of multi-cloud computing, abundant web services are published by several providers to their worldwide users. Web service composition technology attracted a lot of attention for the sake of reduction in software development costs. In a multi-cloud environment (MCE), each atomic web service published by any cloud provider with the same functionality has a different price and quality of service (QoS). Service discovery and composition are the key challenges for web services development. The challenges in the composition of services distributed in multi-cloud environments include increased cost and a reduction in its speed due to the increasing number of services, providers, and clouds. Consequently, to overcome these challenges, the QoS-aware multi-cloud web service composition is presented in this work. The research work initially proposed a Deep Neural Network (DNN) aim of the study is to recommend the web service composition. The proposed method recommends the matched services to the user based on the user's need. Subsequently, a Hybrid Firefly and Bee Colony Optimization Algorithm is introduced for the Multi-cloud service composition problem of NP-Hard in a multi-cloud environment. The proposed optimization algorithm reduces the number of cloud providers to provide the best services. Additionally, the security of multi-cloud web service composition is important, to provide this security, a Fuzzy Generalized Rough Set Theory is presented in this paper. Accordingly, this fuzzy rough set theory eliminates insecure services. Subsequently, the proposed work is implemented using Python software. The performance metrics are throughput, response time, availability, feasibility, efficiency, etc. The proposed method is compared with the existing BOTV-PSO, MLTS-MCSC, IWD, and GNN-QSC. Subsequently, compared to these existing methods, the proposed method is 2% more than the existing methods for execution time, for response time, the proposed method is approximately 3% higher than the existing methods. The proposed method is 5% higher than the existing methods for availability and 4% higher for throughput. The proposed work performs the best service composition to the user and in future determines the minimum and maximum allowable used clouds to minimize the communication costs in a multi-cloud environment respectively.
Scientific Research and Applications Development Based on Exploitation of PRISMA Data in the Framework of ASI – ISRO Earth Observation Working Group Hyperspectral Activity

The Italian Space Agency (ASI) and the Indian Space Research Organisation (ISRO) established the joint Earth Observation Working Group (EOWG) that is currently focusing on hyperspectral (HYP) activity. Eleven projects are investigating the use of ASI’s PRISMA data for agriculture, land cover classification, mineral and soil mapping, Martian analogues, urban lakes, biodiversity and calibration. The paper provides an overview and first results after one year and half since the EOWG HYP was launched.
Evaluating the Contribution of Cx to Leaf Nitrogen Quantification using Fluspect and Airborne Imaging Spectroscopy in Almond Orchards

Among all essential nutrients, nitrogen (N) is required by plants in large quantities throughout the entire developmental process. This is due to its importance for plant growth and development and as a primary source of energy for photosynthesis. Previous research has demonstrated that solar-induced chlorophyll fluorescence (SIF) coupled with chlorophyll a+b content (Cab) improved the estimation of leaf N, outperforming standard vegetation indices. The present study investigates the contribution of leaf Cx, a measure of the de-epoxidation state of the xanthophyll cycle, for explaining leaf N variability, concluding that it ranks third after Cab and SIF consistently over two growing seasons. Among the rest of the biochemical constituents estimated by model inversion, Cx contributed more than anthocyanins (Anth), the total carotenoid content (Ccar), and crown-level structural traits.
A Method Based on Multi-Scale Consistency Regularization and Color-Spatial Constraints for Road Segmentation with Noisy Labels

Road segmentation methods based on (Convolutional Neural Networks, CNN) commonly require accurate pixel-level labels. However, accurate labels are hard to obtain in some cases. To address this issue, a method based on multi-scale consistency regularization and color-spatial constraints is proposed for road segmentation with noisy labels. First, a multi-scale consistency regularization is devised to improve the multi-scale aggregation capability and noise immunity of model. In addition, we utilize the color and spatial information of input images to constrain the model predictions, thereby giving the model a more reliable learning objective. In the experimental section, the accuracy and visualization results obtained from two road datasets provide compelling evidence of the efficacy of our method in addressing the challenges posed by noisy labels in road segmentation tasks.
Tracking Dust Storms and Identifying Source Areas Using MSG Seviri

Traditionally, studies on dust rely on polar-orbiting satellites whose limited temporal coverage does not offer a detailed picture of how each dust storm evolves and changes over time. To address this, our study develops a method to identify and track individual dust storms via hourly images from the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on the Eumetsat geostationary orbit satellites. The process involves using the SE-VIRI Dust RGB false color composite to highlight airborne dust in images. We then use the DBSCAN machine learning model to cluster pixels into storms based on their spatial and temporal connectivity. Post-processing enables us to analyze properties such as the storm’s source area and source activity. Through these methods, we gain insights into dust storm sources, emission factors, and seasonal effects, which are key for understanding their impacts on air quality, health, and the environment.
Incidence Angle Normalization of Spaceborne GNSS-R Surface Reflectivity for Soil Moisture Retrieval

Large-scale near-surface soil moisture can be retrieved from Global Navigation Satellite System Reflectometry (GNSS-R) surface reflectivity observations, which are dependent on the signal incidence angle and therefore need to be normalized. Using 4 years of Cyclone GNSS (CYGNSS) data, in this study we propose a new method for this normalization, accounting for the spatially varying effects of coherent and incoherent scattering. The method is based on a linear regression between the gridded incidence angle and surface reflectivity. We applied the normalized surface reflectivity observations in our soil moisture retrieval algorithm and found a median unbiased root-mean-square error (ubRMSE) of 0.0504 cm3 cm-3 using the Soil Moisture Active Passive (SMAP) as the reference, an improved result compared to other incidence angle correction methods described in the literature.
Why Did You Go There? Semantic Knowledge Extraction from Trajectory Traces

Exploring human mobility dynamics is a challenging semantic data analysis task as conventional information retrieval techniques fail to detect "why people travel". We propose a mobility analytics framework to discover such trip-purposes from individual’s GPS traces using a mobility knowledge graph (MKG) and a deep-learning architecture that automatically annotates the GPS log. Further, a novel transfer learning technique is proposed to analyze the movement dynamics in another geographically dispersed region with the help of the knowledge gained from a region of similar type (say, academic campus).
Functional anatomy and water transport strategies of rainforest plants

Understanding how tropical rainforest trees may respond to the precipitation extremes predicted in future climate change scenarios is paramount for their conservation and management. Tree species clearly differ in drought susceptibility, suggesting that variable water transport strategies exist. Using a multi-disciplinary approach, we examined the hydraulic variability in trees in a lowland tropical rainforest in north-eastern Australia. We studied eight tree species representing broad plant functional groups (one palm and seven eudicot mature-phase, and early-successional trees). We characterised the species’ hydraulic system through maximum rates of volumetric sap flow and velocities using the heat ratio method, and measured rates of tree growth and several stem, vessel, and leaf traits. Sap flow measures exhibited limited variability across species, although early-successional species and palms had high mean sap velocities relative to most mature-phase species. Stem, vessel, and leaf traits were poor predictors of sap flow measures. However, these traits exhibited different associations in multivariate analysis, revealing gradients in some traits across species and alternative hydraulic strategies in others. Trait differences across and within tree functional groups reflect variation in water transport and drought resistance strategies. These varying strategies will help in our understanding of changing species distributions under predicted drought scenarios.
Integration of a Geometric Optical Reflectance Model with an Evidential Reasoning Image Classifier for Improved Forest Information Extraction

RESUMEUn algorithme de classification d'image base sur la theorie de l'evidence a ete modifie et couple a un modele optique geometrique de reflectance d'un couvert forestier dans le contexte de la classification du couvert forestier et d'analyses biophysiques. Le modele GOMS (Geometric Optical Mutual Shadowing) de reflectance de la foret de Li-Strahler fournit une base physique pour definir les intrants des classes forestieres a un algorithme de classification dirigee et peut servir aussi de donnees d'entrainement pour un classificateur conventionnel. Le modele calcule aussi les fractions des composantes du melange a l'echelle intra-pixel (% de canopee, % de sol, % d'ombre), qui ont ete demontrees dans d'autres etudes comme pouvant fournir des estimations ameliorees des variables biophysiques telles que le LAI et la biomasse. Le modele a ete couple a un algorithme de classification base sur la theorie de l'evidence choisi pour sa capacite de traiter une plus grande diversite de types de donnees (i.e., ima.
A Survey of Deep Learning Methods for Vision-Based Fire Detection and Localization

Undetected fires have caused significant damage globally, highlighting the importance of a reliable fire detection system. However, current smoke and fire detectors have proven to be inadequate due to system inefficiencies. To address this issue, a surveillance camera-based vision-based fire detection system with a high detection rate and low fault warning rate has been proposed. This system utilizes live video data analysis for real-time fire detection and incorporates edge detection and thresholding approaches to recognize fire attributes. The model identifies harmful flames based on their color, velocity, form, and texture, and employs color models such as HSV to increase detection accuracy. Deep learning methods like YOLO and transfer learning, as well as IoT technologies, can be incorporated to improve fire detection both indoors and outdoors. This paper presents a comprehensive literature review on various approaches for the detection and localization of fires in indoor and outdoor environments. The paper highlights the strengths and limitations of each approach. It also discusses the challenges and future research directions in this field to identify opportunities for advancing the state-of-the-art in vision-based fire detection and localization.