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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. [ABSTRACT FROM AUTHOR]

This work presents a stochastic framework for the construction of rainfall intensity–time scale–return period relationships, which was applied in the recent regionalization of design rainfall curves over the Greek territory, described in a companion paper. The methodology outlined herein builds upon a widely-used mathematical framework, which has been recently revisited and upgraded, and incorporates two different versions: (a) a theoretically consistent stochastic model applicable for rainfall intensity over any scale of interest; and (b) a simplified version valid over small scales, which makes parameter estimation easier. Special attention is given to the presentation of the simplified version, which suffices for most engineering tasks. Parameter estimation approaches are presented in detail, including the K-moments framework that allows for reliable high-order moment estimation and handling of bias due to spatiotemporal dependence. [ABSTRACT FROM AUTHOR]

Cross-trait polygenic risk score (PRS) method has gained popularity for assessing genetic correlation of complex traits using summary statistics from biobank-scale genome-wide association studies (GWAS). However, empirical evidence has shown a common bias phenomenon that highly significant cross-trait PRS can only account for a very small amount of genetic variance (R2 can be < 1 % ) in independent testing GWAS. The aim of this paper is to investigate and address the bias phenomenon of cross-trait PRS in numerous GWAS applications. We show that the estimated genetic correlation can be asymptotically biased toward zero. A consistent cross-trait PRS estimator is then proposed to correct such asymptotic bias. In addition, we investigate whether or not SNP screening by GWAS p-values can lead to improved estimation and show the effect of overlapping samples among GWAS. We analyze GWAS summary statistics of reaction time and brain structural magnetic resonance imaging-based features measured in the Pediatric Imaging, Neurocognition, and Genetics study. We find that the raw cross-trait PRS estimators heavily underestimate the genetic similarity between cognitive function and human brain structures (mean R 2 = 1.32 % ), whereas the bias-corrected estimators uncover the moderate degree of genetic overlap between these closely related heritable traits (mean R 2 = 22.42 % ). Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

One of the properties of such nonparametric classes is that they are broad enough, so misspecification of Graph HT ht stops being an issue. The present paper studies much more narrow choices of Graph HT ht . The latter also highlights what choices of Graph HT ht is most appropriate. [Extracted from the article]

The Middle East region is recognized by observers as one of the most politically unstable areas worldwide. Due to the significant growth of foreign direct investment in the MENA region during the period before the recent political turmoil, this study empirically tests the impact of these political disturbances on foreign direct investment over time across Arab Spring countries. The study uses panel techniques to estimate the regression models by applying pooled OLS, fixed effect, and random effect. In addition, the Hausman test is used in order to choose the appropriate estimated model between fixed effects and random effects. The sample covers five countries in the MENA region that experienced the Arab Spring uprising during the period (2011–2014). The whole yearly data set ranges from 1980 to 2014. The data describes the periods before and during the Arab Spring turmoil (2011–2014). According to the appropriate fixed effect approach, results show that the economic, social, and political factors are all crucial contributors to FDI movements and volumes in Arab Spring countries. Interestingly, the Arab Spring era plays a very important role in the deterioration of FDI in these countries. The movement of FDI in the Arab Spring region is related to episodes of political instability. This political unrest has created an adverse impact on foreign investment, and this indicates a lack of trust of potential international investors. This study also confirms that other economic and social factors are significant contributors to FDI in the MENA region. [ABSTRACT FROM AUTHOR]

A new technique using impedance spectroscopy was developed to estimate the oxidation kinetics of the pyrolytic carbon interphase layer in SiCf/SiC ceramic matrix composites. The AC impedance Nyquist plot showed two semicircles that were curve-fitted using an equivalent circuit with two constant-phase element components. The analysis results revealed that the capacitance in the low-frequency region decreased with increasing oxidation time, whereas that in the high-frequency region remained relatively constant. This change in the capacitance in the low-frequency region was associated with the thickness of the oxidized SiO2 layer. This study might make a significant contribution to the literature, which has mostly focused on the characterization of ceramic matrix composites through measurements of mechanical properties. The limitations and potential applications of the developed method were also elucidated. [ABSTRACT FROM AUTHOR]

We sketch Friedrich Ackermann's research program following the concept of Imre Lakatos, with some historical key developments in the theory and application of aerotriangulation and image matching. The research program, with its core being statistical estimation theory, has decisively influenced photogrammetry since the 60s, is still fully alive, and a challenge for today's methods of image interpretation. We describe (1) Lakatos' concept of a scientific research program, with its negative and positive heuristics and (2) Ackermann's research program, clearly made explicit in his PhD, with its mathematical model, the ability to predict theoretical precision and reliability, the potential of analyzing rigorous and approximate method, and the role of testing. The development of aerotriangulation, later augmented by image matching techniques, is closely connected to Ackermann's successful attempts to integrate basic research and practical applications. [ABSTRACT FROM AUTHOR]

The study assesses the extent of nitrate pollution in the Densu Basin, because of its predominance in agriculture and urbanized vicinities, by employing robust techniques to estimate both the natural background and human-induced concentrations. Statistical methods used are the pre-selection method, graphical approach (probability plot), non-parametric approach (kernel density estimation), and parametric approach (Gaussian mixture model). The study shows that the Gaussian mixture model is robust enough in determining the spectral distribution and clustering of the NO3-N concentration in the basin. It estimated the natural background and human-induced concentration at 1.7 ± 1.3 and 9.8 ± 5.6 mg/L, respectively. The results show that the natural background concentration in the basin is more dominant and, hence, conducive for drinking. We found a 26% contribution from anthropogenic sources that had leaked into the natural groundwaters of the Basin. The data suggest multiple sources of NO3-N concentration in the groundwater. [ABSTRACT FROM AUTHOR]

One fundamental statistical question for research areas such as precision medicine and health disparity is about discovering effect modification of treatment or exposure by observed covariates. We propose a semiparametric framework for identifying such effect modification. Instead of using the traditional outcome models, we directly posit semiparametric models on contrasts, or expected differences of the outcome under different treatment choices or exposures. Through semiparametric estimation theory, all valid estimating equations, including the efficient scores, are derived. Besides doubly robust loss functions, our approach also enables dimension reduction in presence of many covariates. The asymptotic and non-asymptotic properties of the proposed methods are explored via a unified statistical and algorithmic analysis. Comparison with existing methods in both simulation and real data analysis demonstrates the superiority of our estimators especially for an efficiency improved version. for this article are available online. [ABSTRACT FROM AUTHOR]

We develop an expansion, similar in some respects to the Karhunen–Loève expansion, but which is more suitable for functional data indexed by spatial locations on a grid. Unlike the traditional Karhunen–Loève expansion, it takes into account the spatial dependence between the functions. By doing so, it provides a more efficient dimension reduction tool, both theoretically and in finite samples, for functional data with moderate spatial dependence. For such data, it also possesses other theoretical and practical advantages over the currently used approach. The article develops complete asymptotic theory and estimation methodology. The performance of the method is examined by a simulation study and data analysis. The new tools are implemented in an R package. for this article are available online. [ABSTRACT FROM AUTHOR]