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Tracking reentries of Starlink satellites during the rising phase of solar cycle 25
No full textThe exponential increase of low-Earth orbit (LEO) satellites in the past 5 years has brought into intense focus the need for reliable monitoring and reentry prediction to safeguard from space collisions and ground debris impacts. However, LEO satellites fly within the upper atmosphere region that exerts significant drag forces to their orbits, reducing their lifetimes, and increasing collision risks during dynamic events, like geomagnetic storms. Such conditions can become more severe during geomagnetic storms, particularly during extreme events. In this work, we use two-line element (TLE) satellite tracking data to investigate geomagnetic activity effects on the reentries of 523 Starlink satellites from 2020 to 2024. This period coincides with the rising phase of solar cycle 25, which has shown itself to be more intense than the previous solar cycle. We derive satellite altitudes and velocities from TLE files and perform a superposed epoch analysis, the first with hundreds of similar satellites. Even with limitedly accurate TLE data, our results indisputably show that satellites reenter faster with higher geomagnetic activity. This is explained by the fastest orbital decay rates (in km/day) of the satellites caused by increased drag forces. We also find that prediction errors, defined as the difference between the epochs of actual reentries and predicted reentries at reference altitudes, increase with geomagnetic activity. As a result, we clearly show that the intense solar activity of the current solar cycle has already had significant impacts on Starlink reentries. This is a very exciting time in satellite orbital drag research, since the number of satellites in LEO and solar activity are the highest ever observed in human history.The author(s) declare that financial support was received for the research and/or publication of this article. DO and EZ acknowledge financial support provided by NASA’s Space Weather Science Applications Operations 2 Research. DO thanks UMBC for providing financial support through the START (Strategic Awards for Research Transitions) program (grant # SR25OLIV).https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2025.1572313/ful
Radiation-driven Destruction of N-heterocycles in H₂O-ice mixtures
No full textEPSC-DPS Joint Meeting 2025, Helsinki, Finland, 7-12 September, 2025Simple nitrogen (N-) heterocycles are expected to be an abundant class of molecules within the interstellar medium (ISM). Computational and laboratory studies have demonstrated previously that these molecules likely form during the polymerization of acetylene in the presence of hydrogen cyanide (Ricca et al. 2001, Hamid et al. 2014), a process expected to occur in the stellar outflows of carbon-rich AGB stars. This production pathway is analogous to the formation of benzene, which has been detected in the presence of polyacetylenic chains (Cernicharo et al. 2001). Additionally, laboratory studies have demonstrated that N-heterocycles readily form during the irradiation of icy materials. Specifically, Materese et al. 2015 identified pyridine and isoquinoline in the room-temperature residues formed from photolyzed and warmed ices containing water, ammonia, and benzene or naphthalene. More recently, Wang et al. 2024 identified pyrrole and indole in electron-irradiated acetylene and ammonia ice mixtures.https://meetingorganizer.copernicus.org/EPSC-DPS2025/EPSC-DPS2025-172.htm
HARP2 pre-launch calibration: dealing with polarization effects of a wide field of view
No full textThe Hyper-Angular Rainbow Polarimeter (HARP2) is a wide-field-of-view (FOV) polarimeter built for the NASA Plankton Aerosol Cloud and Ocean Ecosystem (PACE) mission launched in early 2024. HARP2 measures the linear Stokes parameters across a 114° × 100° (along-track by cross-track) FOV. In the fall of 2022, HARP2 underwent calibration at NASA Goddard Space Flight Center (GSFC) Calibration Laboratory (Code 618). HARP2 was characterized for radiometric and polarimetric response across its FOV. We have used telecentric calibration methodology on prior iterations of HARP that involved the normalization of pixels across the FOV such that calibration parameters determined at the center of the charged coupled device (CCD) detector can be used across the entire scene. By using a dual-axis yaw–pitch motorized mount, we devised two scan patterns to evaluate this methodology for HARP2. The results show that pure intensity measurements do indeed vary minimally across the FOV and therefore can utilize the flat-field normalization (telecentric) technique. On the other hand, images of polarized targets change significantly across the FOV, and calibration parameters determined at the center of the detector used in the wide FOV perform significantly worse than calibration parameters determined at or near to the location of the test (up to 5 % mean absolute uncertainty in degree of linear polarization, DoLP). We evaluated the use of a paraboloid fit of the polarized calibration parameters, at discrete FOV locations, to determine those parameters at a pixel-level resolution. According to the wide-FOV results, this process shows a marked improvement for fully polarized (DoLP = 1) calibration data to less than 1 % uncertainty after using the paraboloid fit. These results are important for the development of any wide-FOV polarimeter, especially those like HARP2 which use a front lens which causes significant barrel distortion and a division of amplitude central optical element leveraging multiple reflections. Full characterization of the source of these optical effects remains a part of future work, but the improved methodology over the telecentric method is currently being implemented in the HARP2 L1B calibration pipeline pending internal review of the implementation in the HARP Image Processing Pipeline.The authors have been supported by the NASA FINESST (grant no. 80NSSC21K1600) on behalf of the Noah Sienkiewicz NASA PACE mission, the NASA ESTO InVest project, and the UMBC START award.https://amt.copernicus.org/articles/18/2447/2025
Causal Feedback Discovery using Convergence Cross Mapping from Sea Ice Data
No full textSIGSPATIAL '25: The 33rd ACM International Conference on Advances in Geographic Information Systems, November 3 - 6, 2025, Minneapolis MN, USAIdentifying causal relationships in climate systems remains challenging due to nonlinear, coupled dynamics that limit the effectiveness of linear and stochastic causal discovery approaches. This study benchmarks Convergence Cross Mapping (CCM) against Granger causality, PCMCI, and VarLiNGAM using both synthetic datasets with ground truth causal links and 41 years of Arctic climate data (1979-2021). Unlike stochastic models that rely on autoregressive residual dependence, CCM leverages Takens' state-space reconstruction and delay-embedding to reconstruct attractor manifolds from time series. Cross mapping between reconstructed manifolds exploits deterministic signatures of causation, enabling the detection of weak and bidirectional causal links that linear models fail to resolve. Results demonstrate that CCM achieves higher specificity and fewer false positives on synthetic benchmarks, while maintaining robustness under observational noise and limited sample lengths. On Arctic data, CCM reveals significant causal interactions between sea ice extent and atmospheric variables like specific humidity, longwave radiation, and surface temperature with a p-value of 0.009, supporting ice-albedo feedbacks and moisture-radiation couplings central to Arctic amplification. In contrast, stochastic approaches miss these nonlinear dependencies or infer spurious causal relations. This work establishes CCM as a robust causal inference tool for nonlinear climate dynamics and provides the first systematic benchmarking framework for method selection in climate research.https://dl.acm.org/doi/10.1145/3764922.377117
Local Government Cybersecurity: A Theoretical Model
No full textLocal governments in the United States face growing cybersecurity threats but often operate with limited resources, inconsistent policy implementation, and varied technical capacities. Despite increasing attention to cybersecurity in the public sector, few empirical studies have examined the underlying relationships that shape cybersecurity management at the local level. This dissertation addresses that gap by using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) to identify and validate latent factors that characterize cybersecurity practices among local governments. Drawing on survey data collected in 2016 and 2022, this study empirically validates a six-factor model encompassing Training, Policies, Actions, Awareness, Support, and Tools. The findings suggest that cybersecurity readiness in local governments is best understood as a multi-dimensional construct driven by governance, including organizational behavior and leadership engagement, rather than by technology alone. Strong factor structures for Training, Awareness, and Support highlight the central role of human-centered factors, while weaker results for the Tools factor suggest variability in technical implementation and maturity. The findings have both theoretical and practical implications. Theoretically, this research advances the emerging field of local government cybersecurity management by providing a validated model structure. Practically, it identifies specific areas for improvement, including the need for more targeted and recurring training, stronger alignment between specific cybersecurity actions and policy development, more consistent adoption of tools, and greater cross-departmental collaboration. The dissertation also addresses limitations related to sampling, model modifications, and non-longitudinal design, and proposes directions for future research, including structural equation modeling and longitudinal studies, to further refine the theory of cybersecurity management among local governments
Iterative Random Training Sampling Approaches to Hyperspectral Image Classification with and without Background
No full textAs one of fundamental tasks in remote sensing, hyperspectral image classification (HSIC) has attracted considerable interest. Among existing techniques, 3D Convolutional Neural Networks (3D-CNNs) are considered as powerful spectral-spatial classification (SSC) approaches due to their ability in automatically learning hierarchical spectral-spatial features. However, their performance depends heavily on large labeled datasets and requires significant computational resources. In addition, the background (BKG) issue in HSIC has been largely overlooked, despite being a key challenge in real-world applications. The first contribution of this dissertation is the development of a novel deep learning framework, named Iterative Random Training Sampling Convolutional Neural Network (IRTS-CNN). This framework integrates iterative random training sampling spectral-spatial classification (IRTS-SSC) with CNNs, allowing CNN models to iteratively update spatial information through a feedback-based spatial filtering mechanism. IRTS-CNN serves as a generalizable structure that can enhance any baseline CNN classifier through an iterative refinement process. Experimental results show that IRTS-CNN significantly improves CNN classification performance, especially when the training sample size is small.
To overcome the computational inefficiencies of deep learning methods, the second contribution made in this dissertation introduces a concept of the Iterative Gaussian-Laplacian Pyramid Network (IGLPN). A traditional CNN consists of a series of feedforward layers which is composed of convolutional (CL) and pooling (PL) sublayers. This architecture can be interpreted through a Gaussian Pyramid (GP), where each layer uses low-pass filtering and downsampling. Additionally, a Laplacian Pyramid (LP) can be constructed to capture differential information between consecutive layers. IGLPN leverages these structures to realize CNN functionality more efficiently. This structure reduces computational demands while improving classification accuracy.
In the past, most of HSIC methods assume BKG is removed by using ground truth (GT),. Unfortunately, this is impractical in real-world scenarios. HSIC that perform well without background (HSIC-NB) typically do not work well when BKG is included. To address this, a third contribution made in this dissertation develops a new approach to HSIC with background included (HSIC-B), called Hierarchical One-Class Detection (HOCD) which extends One-Class Detection (OCD) to a hierarchical framework guided by Class Classification Priority (CCP). Experiments demonstrate that HOCD achieves robust accuracy with minimal degradation in the presence of background.
These three contributions advance HSIC by enhancing adaptability to limited labeled data samples, reducing resource demands, and addressing background challenges, as validated through extensive experiments
Lifelong and continual learning - A survey
No full textWhile traditional machine learning techniques involve one single iteration of the collation, preparation and training of a model with data, this handicaps models by being a ”no updates once deployed” approach. Using this approach not only severely limits the model’s performance with respect to real-world data that might change continually, but also makes several assumptions about the widespread availability of data, that might not always be the case. Data also has the tendency to constantly evolve, with several slight changes over time being adding up to make a large dent in the performance of static models. With these limitations in mind, it is in our best interests to create ”lifelong learners” - models that are both able to learn new tasks, as well as be good at the old ones. This survey paper aims to understand the basics behind the concept of lifelong and continual learning, as well as learn more about the leading ideas in the same field. Additionally, this paper also aims to understand the core concepts and ideology behind successful lifelong learners, and factors that must be taken into account when building one.https://falcon10056.github.io/continual_learning.pd
A critical examination of the applicability of teleworking as a part of business continuity planning in the UAE police force
No full textD.P.A. -- The University of Baltimore, 2025Thesis submitted to the School of Public Affairs of The University of Baltimore in partial fulfillment of the requirements for the degree
of Doctor of Public AffairsThis study determines the effect of teleworking as a component of the business continuity plan with specific reference to the planning of policing operations in the administrative and operational divisions of the police force. This was prompted by the COVID-19 pandemic and focused on its effect on efficiency, resilience, and continuity. It further explored whether teleworking as a component of the business continuity plan with specific reference to the planning of policing operations in the administrative and operational divisions of the police force can attain service parity plus its benefits and challenges and strategies for effective adoption. The paper takes a mixed-methods approach where the research integrates interview-based thematic analysis and survey data subjected to statistical analysis. Findings indicate that teleworking improved productivity and flexibility. The study focuses on the hybrid model, technological infrastructure, and on-going training and leadership in maximizing teleworking (as part of business continuity planning (BCP), with specific reference to policing operations planning in police force's administrative and operational divisions). Change management challenges, technology gaps, and risks of information security and technology infrastructure are areas of concern that have actionable recommendations to address. This research deems teleworking (as part of business continuity planning (BCP), with specific reference to policing operations planning in police force's administrative and operational divisions) as a vital component of organizational continuity planning and provides perspectives on the effective and sustainable integration of this planning into policing operations
Exploring Vocabulary Retention Through the Gamification of Foreign Language Learning Applications
No full textXRISM Spectroscopy of Accretion-Driven Wind Feedback in NGC 4151
No full textThe hottest, most ionized, and fastest winds driven by accretion onto massive black holes have the potential to reshape their host galaxies. Calorimeter resolution X-ray spectroscopy is the ideal tool to understand this feedback mode, as it enables accurate estimates of physical characteristics needed to
determine the wind’s kinetic power. We report on a photoionization analysis of five observations of the Seyfert-1.5 galaxy NGC 4151, obtained with XRISM/Resolve in 2023 and 2024. In the Fe K band, individual spectra require as many as six wind absorption components. Slow “warm absorbers” (WAs, vₒᵤₜ ∼ 100 − 1000 km s−1 ), very fast outflows (VFOs, vₒᵤₜ ∼ 10³ km s⁻¹ − 10⁴ km s⁻¹), and ultra-fast outflows (UFOs, vₒᵤₜ ∼ 104 km s⁻¹ − 10⁵ km s⁻¹ or 0.033 − 0.33 c) are detected simultaneously, and indicate a stratified, multiphase wind. Fast and variable emission components suggest that the wind is axially asymmetric. All of the wind components have mass flow rates comparable to or in excess of the mass accretion rate, though the slowest zones may be “failed” winds that do not escape. Two UFO components have kinetic luminosities that exceed the theoretical threshold of Lₖᵢₙ ≥ 0.5%Lₑ* necessary to strip the host bulge of gas and halt star formation, even after corrections for plausible filling factors. The bulk properties of the observed winds are consistent with magnetocentrifugal driving, where the density depends on radius as n ∝ r ⁻¹.⁵, but radiative driving and other mechanisms may also be important. Numerous complexities and variability require further analysis.
* = subscript ddhttp://arxiv.org/abs/2507.0921