Retrieval Process Research Articles

Correlation Aware Relevance-Based Semantic Index for Clinical Big Data Repository.

In this paper, we focus on indexing mechanisms for unstructured clinical big integrated data repository systems. Clinical data is unstructured and heterogeneous, which comes in different files and formats. Accessing data efficiently and effectively are critical challenges. Traditional indexing mechanisms are difficult to apply on unstructured data, especially by identifying correlation information between clinical data elements. In this research work, we developed a correlation-aware relevance-based index that retrieves clinical data by fetching most relevant cases efficiently. In our previous work, we designed a methodology that categorizes medical data based on the semantics of data elements and merges them into an integrated repository. We developed a data integration system for medical data sources that combines heterogeneous medical data and provides access to knowledge-based database repositories to different users. In this research work, we designed an indexing system using semantic tags extracted from clinical data sources and medical ontologies that retrieves relevant data from database repositories and speeds up the process of data retrieval. Our objective is to provide an integrated biomedical database repository that can be used by radiologists as a reference, or for patient care, or by researchers. In this paper, we focus on designing a technique that performs data processing for data integration, learn the semantic properties of data elements, and develop a correlation-aware topic index that facilitates efficient data retrieval. We generated semantic tags by identifying key elements from integrated clinical cases using topic modeling techniques. We investigated a technique that identifies tags for merged categories and provides an index to fetch data from an integrated database repository. We developed a topic coherence matrix that shows how well a topic is supported by a corpus from clinical cases and medical ontologies. We were able to find more relevant results using an annotation index from an integrated database repository, and there was a 61% increase in a recall. We evaluated results with the help of experts and compared them with naive index (index with all terms from the corpus). Our approach improved data retrieval quality by providing most relevant results and reduced data retrieval time as we applied correlation-aware index on an integrated data repository. Topic indexing approach proposed in this research work identifies tags based on a correlation between different data elements, improves data retrieval time, and provides most relevant cases as an outcome of this system.

GradHC: highly reliable gradual hash-based clustering for DNA storage systems.

As data storage challenges grow and existing technologies approach their limits, synthetic DNA emerges as a promising storage solution due to its remarkable density and durability advantages. While cost remains a concern, emerging sequencing and synthetic technologies aim to mitigate it, yet introduce challenges such as errors in the storage and retrieval process. One crucial task in a DNA storage system is clustering numerous DNA reads into groups that represent the original input strands. In this paper, we review different methods for evaluating clustering algorithms and introduce a novel clustering algorithm for DNA storage systems, named Gradual Hash-based clustering (GradHC). The primary strength of GradHC lies in its capability to cluster with excellent accuracy various types of designs, including varying strand lengths, cluster sizes (including extremely small clusters), and different error ranges. Benchmark analysis demonstrates that GradHC is significantly more stable and robust than other clustering algorithms previously proposed for DNA storage, while also producing highly reliable clustering results. https://github.com/bensdvir/GradHC.

Multi cascaded transformer network and hybrid heuristic-aided optimal bi-clustering mechanism for patent retrieval system using query expansion

To enhance the reliability of the document retrieval system, the most efficient techniques such as Query Expansion (QE) are utilized. It has offered more adequate queries for the user when assimilated over original or initial queries by adding up one or more expansion keywords. Moreover, these techniques are more effective to enhance the performance of document retrieval and return the unnecessary information. In recent times, searching the suitable documents in the huge datasets is tiresome work. Generally, the automatic QE is used to address the refining query. A typical technique for QE has included the extracted close expression and the related documents clustering by utilizing the clusters. However, classical clustering poses some issues to QE. Hence, a novel optimized bi-clustering mechanism is proposed in this paper for patent retrieval by QE. The ultimate aim of this implemented model is to retrieve the patent information by expanding the request query. Initially, the patent-related data is collected from standard data sources in terms of abstract and text. It is then given to the text pre-processing stage. Consequently, the pre-processed text or word is converted into vector formation by using the Multi-cascade Transformer Network (MTN). Finally, the retrieval process is done by proposing the Optimal Bi-Clustering (OptBi-C) process, in which the parameters are optimally determined by a hybrid algorithm of Reptile Search Algorithm (RSA) and Lion Algorithm (LA) termed as Iteration-based Reptile Search and Lion Algorithm (IRSLA). Thus, the performance of the model is examined with certain metrics and compared with traditional techniques. The precision of the implemented patent retrieval system using the QE model is maximized by 8.82% of DHOA-OptBi-C, 7.35% of HHO-OptBi-C, 10.29% of RSA-OptBi-C, and 7.35% of LA-OptBi-C respectively when the number of retrieved data is 10. Moreover, the recall of the designed patent retrieval system using the QE model is enhanced by 21.83% of KNN, 24.13% of CNN, 19.54% of FUZZY, and 11.49% of Bi-clustering respectively when the number of retrieved data is 6. Thus, the findings demonstrate that the system improves the retrieval performance.

Enhancing post-hurricane regional loss assessment with crowdsourcing: A probabilistic inference approach

With the emergence of efficient, widespread social networking infrastructure, along with advances in sensing technology, data retrieval, and data processing techniques, crowdsourcing and participatory sensing have garnered attention as promising approaches for leveraging the general public’s decision-making capacity for more effective and scalable disaster management practices. Although these approaches have recently been acknowledged for their potential in technical and domain-specific tasks, such as post-disaster damage assessment, there is still room for exploration and enhancement. This paper represents a significant stride in this direction by introducing a probabilistic approach designed to improve the reliability and quality of data gathered through participatory disaster damage assessments. By rigorously addressing crucial challenges related to task design and implementing sophisticated truth-inference mechanisms, this study aims to minimize uncertainty and subjectivity in the assessment of building damage states from imagery data conducted by non-expert crowd participants. The proposed methodology is demonstrated using a post-disaster damage database from Hurricane Laura, with Amazon Mechanical Turk (M-Turk) serving as the online crowdsourcing platform. With an achieved accuracy of approximately 50% and a ±1-class accuracy of around 95%, the proposed methodology significantly outperforms other inference methods derived from crowd-assigned labels in similar applications. Furthermore, damage assessment is extended to loss estimation, providing a comprehensive view of end-to-end damage and loss assessment. The findings of this study can significantly contribute to the development of efficient and reliable strategies for post-disaster assessments, ultimately facilitating expedited response and recovery efforts in areas affected by disasters.

Proposed Improvements to Reduce the Risk Work Accidents on CV HB with FMEA and HAZOP Method

A company called CV HB makes socks based on what its customers want. Bandung is home to CV HB. Several machines, including Santoni, Dakong, Obrass, Stock Steam, and others, are employed in CV HB's sock production process. Numerous personnel who are in charge of running the machinery and performing other production tasks are also a part of the production process. The execution of health and safety protocols has yet to be well-executed in the production sector. The organization saw 24 workplace accidents between 2019 and 2023, including injuries from needles and blades, hands stuck in machinery, brain injuries from machines, slipping down stairs, and many more incidents. In order to address health and safety concerns at CV HB, the research attempts to pinpoint the root causes of workplace mishaps and offer suggestions for enhancement. Hazard and Operability Review (HAZOP) and Failure Mode and Effects Analysis (FMEA) were the methodologies used in the investigation. A few suggestions for improvement for the chosen departments are using material handling tools during the material retrieval process, posting warnings about machines with sharp parts, requiring protective gear like masks and gloves, designing foam padding for sharp machine parts, and installing fans.

Blockchain-based Scalable IoT Cloud for Electronic Health Record using Horizontal and Vertical Mining

The twin technology of Blockchain with IoT(BIoT) deployed in healthcare systems has fetched out the mutative potentiality for accessing and managing Electronic Health Records(EHRs). However, this divergence also has considerable challenges, primarily in terms of the protection of data, scalability, and interoperability. To overcome these challenges, this research paper presents a new approach, leveraging Blockchain technology and mining mechanisms, to launch a secure, scalable, and proficient IoT cloud ecosystem for the administration of Electronic Health Records. This research study includes Blockchain scalable IoT cloud architecture intended to address the scalability problem of storing and managing large amounts of EHR records produced through IoT sensors. The proposed mechanism implements both horizontal and vertical mining concepts for the optimization of data storage as well as the retrieval processes. Horizontal mining concentrates on allocating and distributing Electronic Health Records data to more than one node, which can ultimately sink the overload on each node in the network and increase the scalability. Vertical mining, on the other flip of the coin, prioritizes the transactions, and optimizing the data with each transaction can drastically improve the acceleration speed of retrieving the data access competency. Blockchain technology constructs the core technology of BEHRHVM, assuring data integrity, privacy, and transparency. All the EHR record transactions are authorized as mutable blocks on the Blockchain framework. This mechanism improved patient privacy while accessing the secure distribution of EHRs among authorized health providers. In addition, the proposed system is built to integrate with current healthcare systems and the Internet of Things.

Coronavirus disease-2019 and orthopedics: A bibliometric analysis of the literature.

The coronavirus disease-2019 (COVID-19) pandemic has had a dramatic impact on global health, with orthopedics among the most affected specialties. An increasing number of COVID-19-related orthopedic studies have been published. The purpose of this study was to analyze the orthopedic literature published during the COVID-19 pandemic to guide future research. The Scopus database was searched for relevant literature published between 2020 and 2022. The keywords used in the retrieval process were ("COVID-19" OR "Coronavirus" OR "2019-nCoV" OR "SARS-CoV-2" OR "Betacoronavirus" OR "novel coronavirus 2019" OR "novel coronavirus" OR "coronavirus-19" OR "COVID 19" OR "nCOV" OR "COVID-2019" OR "COVID 2019") and ("orthopedic" OR "orthopedics" OR "orthopedic" OR "orthopedical" OR "orthopedical" OR "orthopedics"). Spreadsheet software (Excel, Microsoft Corp., Redmond, WA) was used to analyze the top 10 cited authors, countries, journals, and articles. The top 5 publication types were also analyzed. VOSviewer (Center for Science and Technology Studies, Leiden, Netherlands) was used to network and visualize the literature. A total of 1619 publications relevant to COVID-19 and orthopedics were reviewed. Among these publications, the most active country, author, and publication type included the United States, Vaishya R, and original articles, respectively. The most frequently used keywords were human, coronavirus disease-2019, pandemic, and orthopedics. The Journal of Bone and Joint Surgery American Volume was the most cited journal, whereas the greatest number of articles was published in the Journal of Clinical Orthopedics and Trauma. This study provides a perspective on the development of orthopedic publications during the COVID-19 pandemic and evidence for researchers worldwide to strengthen global cooperation in fighting the epidemic.

Efficient Image Retrieval Using Hierarchical K-Means Clustering.

The objective of content-based image retrieval (CBIR) is to locate samples from a database that are akin to a query, relying on the content embedded within the images. A contemporary strategy involves calculating the similarity between compact vectors by encoding both the query and the database images as global descriptors. In this work, we propose an image retrieval method by using hierarchical K-means clustering to efficiently organize the image descriptors within the database, which aims to optimize the subsequent retrieval process. Then, we compute the similarity between the descriptor set within the leaf nodes and the query descriptor to rank them accordingly. Three tree search algorithms are presented to enable a trade-off between search accuracy and speed that allows for substantial gains at the expense of a slightly reduced retrieval accuracy. Our proposed method demonstrates enhancement in image retrieval speed when applied to the CLIP-based model, UNICOM, designed for category-level retrieval, as well as the CNN-based R-GeM model, tailored for particular object retrieval by validating its effectiveness across various domains and backbones. We achieve an 18-times speed improvement while preserving over 99% accuracy when applied to the In-Shop dataset, the largest dataset in the experiments.

O conceito de relevância na recuperação de informação

This work aims to present a bibliographical survey on the concept of relevance, its properties and characteristics, as well as systematizing its manifestations and its use in Information Science, particularly in the process of information retrieval. To achieve this, the research was based on a bibliographical survey of several authors who have relevance as an object of study. It found that the concept of relevance has several formal definitions and can be interpreted in different ways. Relevance is also strongly linked to information retrieval systems. In this context, it can be classified under two approaches: user relevance and system relevance. From the results, we conclude that relevance plays a fundamental role in Information Science, with emphasis on the information retrieval process, which often uses this concept as a metric to evaluate the effectiveness of algorithms and systems.

Leveraging Large Language Models for Tradespace Exploration

This paper proposes a method for leveraging large language models (LLMs) to improve the question-answering capabilities of artificial intelligence (AI) assistants for tradespace exploration. The method operates by querying an information space composed of fused data sources encompassing the tradespace exploration process and responding based on the gathered information. The information retrieval process is modeled as an internal dialog where an LLM-based dialog agent converses with a subquery answering agent. A case study is conducted on a next-generation soil moisture mission (SM-NG), and a generative AI assistant (named Daphne-G) is configured on it. The effect of the dialog agent and the choice of LLM are assessed by comparing the performance of three different system configurations on a validation question set. A second validation effort is conducted, comparing Daphne-G’s responses to those of a baseline template-based AI assistant, Daphne-VA. Results show that the dialog-based system is necessary for answering complex questions requiring multiple documents. Furthermore, results show that Daphne-G can correctly answer all the questions Daphne-VA can answer, while simultaneously being able to answer a greater number of questions than Daphne-VA. The results suggest that LLMs could significantly improve the outcomes of the tradespace exploration process, which may result in better and more cost-effective mission concepts being implemented.

Plant Decellularization by Chemical and Physical Methods for Regenerative Medicine: A Review Article.

Fabricating three-dimensional (3D) scaffolds is attractive due to various advantages for tissue engineering, such as cell migration, proliferation, and adhesion. Since cell growth depends on transmitting nutrients and cell residues, naturally vascularized scaffolds are superior for tissue engineering. Vascular passages help the inflow and outflow of liquids, nutrients, and waste disposal from the scaffold and cell growth. Porous scaffolds can be prepared by plant tissue decellularization which allows for the cultivation of various cell lines depending on the intended application. To this end, researchers decellularize plant tissues by specific chemical and physical methods. Researchers use plant parts depending on their needs, for example, decellularizing the leaves, stems, and fruits. Plant tissue scaffolds are advantageous for regenerative medicine, wound healing, and bioprinting. Studies have examined various plants such as vegetables and fruits such as orchid, parsley, spinach, celery, carrot, and apple using various materials and techniques such as sodium dodecyl sulfate, Triton X-100, peracetic acid, deoxyribonuclease, and ribonuclease with varying percentages, as well as mechanical and physical techniques like freeze-thaw cycles. The process of data selection, retrieval, and extraction in this review relied on scholarly journal publications and other relevant papers related to the subject of decellularization, with a specific emphasis on plant-based research. The obtained results indicate that, owing to the cellulosic structure and vascular nature of the decellularized plants and their favorable hydrophilic and biological properties, they have the potential to serve as biological materials and natural scaffolds for the development of 3D-printing inks and scaffolds for tissue engineering.

A novel fuzzy logic-based approach for textual documents indexing

In the evolving landscape of information retrieval and natural language processing, the quest for more effective automatic keyword extraction (AKE) techniques from textual documents has become a pivotal research focus. Existing methodologies, while offering valuable insights, often grapple with the challenges posed by the imprecision and variability inherent in human language. This has led to a growing recognition of the need for innovative approaches to navigating textual content’s nuances more adeptly. In response to this imperative, this paper proposes a novel fuzzy indexing approach designed specifically for the indexing of textual documents. Fuzzy indexing, grounded in the principles of fuzzy logic, provides solutions for handling the inherent uncertainty and imprecision in natural language, especially when confronted with the intricacies of linguistic ambiguity and variability. By leveraging the power of fuzzy logic, we aim to enhance the precision of keyword extraction. This paper unfolds the intricacies of our fuzzy indexing approach, detailing the theoretical methodology through empirical evaluation and comparative analysis; we seek to demonstrate the efficacy of our approach in outperforming traditional methods in the context of fuzzy indexing for textual documents.

Training semantic long-term memory retrieval transfers to executive function and reading fluency

The retrieval of information from long-term memory is a fundamental cognitive ability, crucial for most aspects of successful human functioning. Whether and how long-term memory retrieval (LTMR) can be improved with training has clear societal importance but also theoretical value for furthering our understanding of underlying mechanisms. Here, we provide electrophysiological evidence for the plasticity of semantic LTMR. Thirty-five university students were randomly assigned to adaptive semantic LTMR training (using a Posner task) or to a non-adaptive version of the training. Before and after training they were assessed on measures of semantic LTMR, working memory, central executive function (interference control, switching), reading fluency, and fluid intelligence. Adaptive LTMR training (relative to non-adaptive training) led to significant improvements in semantic LTMR. The intervention group (in contrast to the control group) also showed a significant reduction in the mean amplitude of the N400 ERP component and 700–1000 ms measured during a semantic LTMR task, suggesting that changes in retrieval occurred at an early/automatic point and retrieval processing in semantic processing. Moreover, transfer effects were observed for switching, working memory and reading fluency, but not for interference control or fluid intelligence. These results point to the plasticity of semantic LTMR, and suggest that improvement in this ability can transfer to other domains for which LTMR is key.

Combined Aspiration and Stent Retriever Thrombectomy for Distal Carotid Artery Occlusion Using Balloon Guide versus Non-Balloon Guide Catheter.

Background: The introduction of endovascular thrombectomy dramatically changed acute stroke management and became the standard treatment. Balloon guide catheters provide flow arrest during the clot retrieval process and have several advantages.This study aimed to compare balloon guide catheters (BGCs) versus non-balloon guide catheters (NBGCs) as a part of a combined treatment modality in patients presenting with acute ischemic stroke. Methods: This retrospective study included n = 65 patients who underwent a combined endovascular stroke treatment for distal internal carotid artery (ICA) occlusion. Patients underwent aspiration and stent retriever thrombectomy with the use of BGCs (Group 1, n = 27) or NBGCs (Group 2, n = 38). Results: The groups were compared for outcomes: the National Institutes of Health Stroke Scale (NIHSSS) score change, successful recanalization, good functional outcome at three months, and in-hospital mortality. Conclusion: The two groups didn't differ in terms of the NIHSS score change compared to baseline (p > 0.05). Moreover, there were no significant differences between the two groups in terms of the successful recanalization rate, three-month favorable functional outcome rate, and in-hospital mortality (p = 0.292, p = 0.952, p = 0.178), respectively. Further prospective studies with a larger number of patients and better methodology are warranted.

Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching R2 = 0.92 (LAI), R2 = 0.72 (CCC) and 0.68 (CWC), R2 = 0.94 (FAPAR), and R2 = 0.95 (FVC). To demonstrate the models’ applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between 4% and 16% (R2 between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level.

Rational satisficing heuristics as determinants of online search behavior

We design a set of satisficing heuristic algorithms that mimic the online information retrieval behavior of rational decision makers (DMs) as reflected in their click through rates (CTRs). We illustrate how basic heuristic algorithms formalizing binary decision trees composed by 21 nodes and requiring DMs to observe one satisficing alternative lack the structural capacity to characterize the retrieval process. The algorithm requiring DMs to observe two satisficing alternatives –formalizing binary decision trees composed by 111 nodes – provides a sufficient approximation to their CTRs. Adding a third alternative – accounting for 351 nodes – delivers an almost identical set of CTRs to those displayed by DMs. The mimicking quality of the heuristic algorithms prevails as alternatives are added up to include the ten ranked within the first page of search results, incorporating 2,047 nodes to formalize the corresponding retrieval process. The set of algorithms bridges the gap between purely rational decision theory and heuristic behavior, illustrating how the CTRs observed can be generated by rational DMs performing a sequential search process while aiming to observe two or three satisficing alternatives. The decision-tree algorithmic structures presented are sufficiently malleable to introduce any potential modification to the beliefs and preferences of DMs and study its consequences in terms of CTRs.

A spectral approach to Hebbian-like neural networks

We consider the Hopfield neural network as a model of associative memory and we define its neuronal interaction matrix J as a function of a set of K×M binary vectors {ξμ,A}μ=1,...,KA=1,...,M representing a sample of the reality that we want to retrieve. In particular, any item ξμ,A is meant as a corrupted version of an unknown ground pattern ζμ, that is the target of our retrieval process. We consider and compare two definitions for J, referred to as supervised and unsupervised, according to whether the class μ, each example belongs to, is unveiled or not, also, these definitions recover the paradigmatic Hebb's rule under suitable limits. The spectral properties of the resulting matrices are studied and used to inspect the retrieval capabilities of the related models as a function of their control parameters.

From traditional to tech-savvy: the evolution of Nigerian libraries in the machine learning era

Purpose This study explores the implementation of personalised information access, driven by machine learning, in Nigerian public libraries. The purpose of this paper is to address existing challenges, enhance the user experience and bridge the digital divide by leveraging advanced technologies. Design/methodology/approach This study assesses the current state of Nigerian public libraries, emphasising challenges such as underfunding and lack of technology adoption. It proposes the integration of machine learning to provide personalised recommendations, predictive analytics for collection development and improved information retrieval processes. Findings The findings underscore the transformative potential of machine learning in Nigerian public libraries, offering tailored services, optimising resource allocation and fostering inclusivity. Challenges, including financial constraints and ethical considerations, are acknowledged. Originality/value This study contributes to the literature by outlining strategies for responsible implementation and emphasising transparency, user consent and diversity. The research highlights future directions, anticipating advancements in recommendation systems and collaborative efforts for impactful solutions.

Enhancing Bilingual Lexicon Induction via Bi-directional Translation Pair Retrieving

Most Bilingual Lexicon Induction (BLI) methods retrieve word translation pairs by finding the closest target word for a given source word based on cross-lingual word embeddings (WEs). However, we find that solely retrieving translation from the source-to-target perspective leads to some false positive translation pairs, which significantly harm the precision of BLI. To address this problem, we propose a novel and effective method to improve translation pair retrieval in cross-lingual WEs. Specifically, we consider both source-side and target-side perspectives throughout the retrieval process to alleviate false positive word pairings that emanate from a single perspective. On a benchmark dataset of BLI, our proposed method achieves competitive performance compared to existing state-of-the-art (SOTA) methods. It demonstrates effectiveness and robustness across six experimental languages, including similar language pairs and distant language pairs, under both supervised and unsupervised settings.

SAR sensing of the atmosphere: stack-based processing for tropospheric and ionospheric phase retrieval

ABSTRACT This paper is intended to summarize the research conducted during the first 2 years of the Dragon 5 project 59,332 (geophysical and atmospheric retrieval from Synthetic Aperture Radar (SAR) data stacks over natural scenarios). Monitoring atmospheric phenomena, encompassing both tropospheric and ionospheric conditions, holds pivotal significance for various scientific and practical applications. In this paper, we present an exploration of advanced techniques for estimating tropospheric and ionospheric phase screens using stacks of Synthetic Aperture Radar (SAR) images. Our study delves into the current state-of-the-art in atmospheric monitoring with a focus on spaceborne SAR systems, shedding light on their evolving capabilities. For tropospheric phase screen estimation, we propose a novel approach that jointly estimates the tropospheric component from all the images. We discuss the methodology in detail, highlighting its ability to recover accurate tropospheric maps. Through a series of quantitative case studies using real Sentinel-1 satellite data, we demonstrate the effectiveness of our technique in capturing tropospheric variability over different geographical regions. Concurrently, we delve into the estimation of ionospheric phase screens utilizing SAR image stacks. The intricacies of ionospheric disturbances pose unique challenges, necessitating specialized techniques. We dissect our approach, showcasing its capacity to mitigate ionospheric noise and recover precise phase information. Real data from the Sentinel-1 satellite are employed to showcase the efficacy of our method, unraveling ionospheric perturbations with improved accuracy. The integration of our techniques, though presented separately for clarity, collectively contributes to a comprehensive framework for atmospheric monitoring. Our findings emphasize the potential of SAR-based approaches in advancing our knowledge of atmospheric processes, thus fostering advancements in weather prediction, geophysics, and environmental management.