Delay Variation Research Articles

An improving secure communication using multipath malicious avoidance routing protocol for underwater sensor network

The Underwater Sensor Network (UWSN) comprises sensor nodes with sensing, data processing, and communication capabilities. Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis. The characteristics of UWSN with underwater channels make them vulnerable to malicious attacks. UWSN communication networks are particularly susceptible to malicious attacks owing to high bit error rates, significant propagation delay variations, and low bandwidth. Moreover, because of the challenging and erratic underwater conditions, limited bandwidth, slow data transmission speed, and power constraints of underwater sensor nodes establishing secure communication in UWSN presents a significant challenge. To address the issues mentioned above, we have introduced the Multipath Malicious Avoidance Routing Protocol (M2ARP) and Foldable Matrix based Padding Rail Fence Encryption Scheme (FM-PRFES) methods to enhance secure communication in UWSNs. The proposed FM-PRFES method encrypts the input data to prevent unauthorized access during transmission within the network. Subsequently, the proposed Energy Efficiency Node Selection (EENS) method is used to identify the significant nodes in the network. Additionally, the Cuckoo Search Optimization (CSO) method is utilized to select the Cluster Head (CH) for data transmission. Subsequently, M2ARP is employed to analyze various routes and avoid adversarial nodes in the network. As a result, the proposed experimental analysis yields more efficient results regarding security, Packet Delivery Ratio (PDR), and throughput performance than traditional approaches.

Absolute antenna group delay variations: estimation, single station repeatability and their influence on coordinate and float ambiguity estimation in baseline positioning

Phase center offsets and variations (PCV) are common corrections applied to global navigation satellite system (GNSS) phase observations in the context of precise point positioning. Similar to PCV are group delay variations (GDV), which affect code observations. In this paper, absolute GDVs, which are independent of a reference antenna, are estimated as antenna- and frequency-specific for the frequencies 1575.42 MHz and 1176.45 MHz, where separation of receiving and transmitting antenna GDV is possible with non-rotating antennas. Nineteen receiving and transmitting antennas of GPS, Galileo, BeiDou, and QZSS satellites, using observations from static reference stations, are considered. The single station repeatability of the receiver antenna GDV i.e., the estimation of the GDV using only one station, is evaluated for all antenna-frequency combinations. The repeatability ranges from 1.1 to 7.6 cm at a zenith angle of 80°, showing significant differences between antennas and frequencies. The estimated GDV corrections are applied to multi-GNSS baseline positioning using a total of 116 baselines. Receiver antenna GDV corrections exhibit a significant non-zero mean effect on the code-based vertical coordinate estimate. Float ambiguities are estimated using observation periods of up to 20 min. The 0.95-quantile effect of the GDV corrections on narrow lane ambiguity estimates is 2.02 cycles at the first epoch and 0.54 cycles after 5 min using 30 s observation sampling. The effect on wide lane ambiguities is constant over the 20-min period considered, with the 0.95-quantile being around 0.1 cycles for the GNSS considered.

Influence of Lower Atmospheric Variability: An Investigation of Delayed Ionospheric Response to Solar Activity

AbstractThis study aims to examine the impact of lower atmospheric forcing on upper atmospheric variability using the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIEGCM). We conducted numerical experiments comparing induced variability due to Hough Mode Extension (HME) tides constrained by winds and temperatures from Ionospheric Connection Explorer‐Michelson Interferometer for Global High‐Resolution Thermospheric Imaging (ICON‐MIGHTI) observations. Our model comparisons focus on the changes in the composition of the thermosphere‐ionosphere and the delayed ionospheric response to the 27‐day solar EUV flux variations during periods of low solar activity. We report the results of model simulations with and without tidal forcing at the approximate 97 km lower boundary of the TIEGCM. The differences led to changes in thermosphere‐ionosphere parameters such as electron density, peak electron density, and the ratio. The results show that the impact of tidal forcing is mainly observed in the low‐ and mid‐latitude regions, affecting the correlation between and NmF2. This change in correlation affects the amount of ionospheric delay. When tidal forcing is included, the modeled delay improves compared to the observed delay during low solar activity. The spatial variation of ionospheric delay due to induced tidal effects highlights the importance of understanding lower atmospheric forcing in thermosphere‐ionosphere models. This is crucial for predicting and understanding the ionospheric response to solar flux.

Role of delay in brain dynamics

Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study demonstrates that this disadvantage can be converted into a computational advantage using a network with a single output and M multiple delays between successive layers, thereby generating a polynomial time-series outputs with M. The proposed role of delay in brain dynamics (RoDiB) model, is capable of learning increasing number of classified labels using a fixed architecture, and overcomes the inflexibility of the brain to update the learning architecture using additional neurons and connections. Moreover, the achievable accuracies of the RoDiB system are comparable with those of its counterpart tunable single delay architectures with M outputs. Further, the accuracies are significantly enhanced when the number of output labels exceeds its fully connected input size. The results are mainly obtained using simulations of VGG-6 on CIFAR datasets and also include multiple label inputs. However, currently only a small fraction of the abundant number of RoDiB outputs is utilized, thereby suggesting its potential for advanced computational power yet to be discovered.

Prognostic impact of diagnostic and therapeutic time delays in breast cancer: an exploratory data analysis for patients at Parirenyatwa Hospital, Zimbabwe

Objective: This paper seeks to investigate factors related to time delays for diagnosis and treatment in breast cancer patients at Parirenyatwa Hospital in Zimbabwe and subsequently evaluate the effects of presentation and diagnosis delays on cancer stage. Methods: The study was done for 379 patients with histologically diagnosed invasive breast cancer, from 2015 through to 2029. The study sought to identify factors associated with the time delays ( months)using parametric and non-parametric methods, depending on whether underlying assumptions of such tests are met. A multiple logistic regression model was also used to analyse the association between factors, primary delay, secondary delay variables and cancer stage. Results: The median of the primary, secondary and treatment delay were found to be 7.6, 1 and 0.4 months respectively. Rural residence, Karnofsky Performance Score below 70%, hypertension comorbidity, tumor size (>5cm) and well differentiated tumors (grade 1) were significant factors for delayed presentation. Longer primary delay times and post-menopausal status were associated with secondary delay. Advanced cancer stage at diagnosis and those on medical aid were more likely to have a delay in treatment onset. Conclusion: Primary and secondary delay were predictive of advanced disease using logistic regression. Keywords: Breast cancer; primary delay; secondary delay; treatment delay; Zimbabwe.

An optimized BP neural network for modeling zenith tropospheric delay in the Chinese mainland using coupled particle swarm and genetic algorithm

ABSTRACT Tropospheric delay influences high-precision navigation positioning and precipitable water vapor retrieval with the Global Navigation Satellite System (GNSS). Existing Zenith Tropospheric Delay (ZTD) models often struggle to accurately capture the non-linear variations in tropospheric delay. Therefore, this study employs the coupled Particle Swarm Optimization (PSO) algorithm with the Genetic Algorithm Back Propagation (GABP) neural network, combined with ERA5 reanalysis meteorological data, to develop an optimized model (PSO-GABP) for ZTD in the Chinese mainland. Nevertheless, ZTD data at the target point are obtained through four different methods: the integration method, model method, and GPT3 models at varying resolutions (EZTD_P, EZTD_S, GPT3_1, and GPT3_5). The analysis reveals the following: (1) The Root Mean Square (RMS) errors of the ZTD values obtained through these different methods are 1.86 cm, 3.42 cm, 3.99 cm, and 4.09 cm, respectively, when verified against GNSS_ZTD data from 2016. The optimized model yields ZTD values with the RMS error of 0.98 cm, 1.96 cm, 2.34 cm, and 2.36 cm, representing improvements of 47.3%, 42.7%, 41.4%, and 42.3% compared to the pre-optimization results. These improvements are significant; (2) The predictive capability of the constructed ZTD model is evaluated using GNSS_ZTD data from 2019 as a reference. The PSO_EZTD_P model demonstrates excellent accuracy and practicality in the Chinese mainland. As a result, the tropospheric delay optimization model based on the PSO-GABP neural network can provide valuable references for real-time GNSS navigation positioning and precipitable water vapor detection in the Chinese mainland.

Improved GPS tropospheric path delay estimation using variable random walk process noise

Accurate positioning using the Global Positioning System relies on accurate modeling of tropospheric delay. Estimated tropospheric delay must vary sufficiently to capture true variations; otherwise, systematic errors propagate into estimated positions, particularly the vertical. However, if the allowed delay variation is too large, the propagation of data noise into all parameters is amplified, reducing precision. Here we investigate the optimal choice of tropospheric constraints applied in the GipsyX software, which are specified by values of random walk process noise. We use the variability of 5-min estimated positions as a proxy for tropospheric error. Given that weighted mean 5-min positions closely replicate 24-h solutions, our ultimate goal is to improve 24-h positions and other daily products, such as precise orbit parameters. The commonly adopted default constraint for the zenith wet delay (ZWD) is 3 mm/√(hr) for 5-min data intervals. Using this constraint, we observe spurious wave-like patterns of 5-min vertical displacement estimates with amplitudes ~ 100 mm coincident with Winter Storm Ezekiel of November 27, 2019, across the central/eastern USA. Loosening the constraint suppresses the spurious waves and reduces 5-min vertical displacement variability while improving water vapor estimates. Further improvement can be achieved when optimizing constraints regionally, or for each station. Globally, results are typically optimized in the range of 6–12 mm/√(hr). Generally, we at least recommend loosening the constraint from the current default of 3 mm/√(hr) to 6 mm/√(hr) for ZWD every 300 s. Constraint values must be scaled by √(x/300) for alternative data intervals of x seconds.

Cervical cancer screening delay and associated factors among women with HIV in Lesotho: a mixed-methods study

Abstract Background Cervical cancer is the fourth most common cancer in women worldwide, and women with human immunodeficiency virus (HIV) are particularly at risk of developing it. Regular screening effectively prevents morbidity and mortality. This mixed-methods study quantitatively assessed cervical cancer screening uptake and qualitatively explored the process of undergoing cervical cancer screening to understand possible reasons for delayed screening among women with HIV in Lesotho. Methods Between October 2020 and March 2022, the Viral load Triggered ART care in Lesotho (VITAL) trial enrolled women aged 18 years and older with HIV who were taking antiretroviral therapy (ART). Cervical cancer screening delay was defined as reporting a screening that occurred more than two years ago or never having been screened. Cervical cancer screening uptake and the association between screening delay and sociodemographic variables were assessed using a multivariable mixed-effects logistic regression model accounting for clustering at clinic level. In-depth interviews were conducted with 16 women to obtain information on awareness, perceptions, and barriers to cervical cancer screening and were analyzed using thematic analysis. Results Quantitative data were available for 3790 women. Among them, cervical cancer screening was delayed in 1814 (47.9%), including 1533 (40.5%) who were never screened. Compared to women aged 25 to 39 years, women aged 18 to 24 years (adjusted odds ratio (aOR) 2.8; 95% confidence interval (CI) 2.1–3.7), women aged 40 to 59 years (aOR 1.3; CI 1.1–1.6), and women older than 60 years (aOR 3.9; CI 3.0-5.1) were at higher risk of screening delay. Furthermore, time on ART below 6 months (aOR 1.6; CI 1.1–2.3) compared to above 6 months was associated with screening delay. Qualitative data identified limited awareness of cervical cancer risks and screening guidelines, misconceptions and fears created by the influence of other women’s narratives, and low internal motivation as the main barriers to screening uptake. Conclusions Cervical cancer screening delay was common. Limited personal awareness and motivation as well as the negative influence of other women were the primary internal barriers to cervical cancer screening. Awareness and screening campaigns in Lesotho should consider these factors. Trial registration clinicaltrials.gov, NCT04527874, August 27, 2020.

Mobile-integrated SRR-based four-port MIMO antenna for broadband mm-wave 5G applications

ABSTRACT A low-profile, single-layered, compact-size, highly-isolated four-port MIMO antenna integrated within a 5 G smartphone has been proposed for a futuristic mm-wave broadband communication system. The proposed MIMO antenna configuration has been derived from a conventional inset-fed rectangular microstrip patch by introducing two parallel I-shaped slots on the patch surface for operating over a wide bandwidth of 4.91 GHz, that is, from 26.78 to 31.69 GHz. The antenna geometry has an overall dimension of 20.9 mm x 20.9 mm x 0.787 mm. The proposed configuration attained a maximum gain of 8.11 dBi and radiation efficiency of 90% at 28 GHz resonating frequency. The integration of the square-shaped SRR structure among closely packed antenna elements has significantly enhanced the overall isolation by 13 dB without compromising the antenna’s physical dimensions. The realisation of the S-parameters has also been carried out from the equivalent circuit model to agree with the results obtained from the electromagnetic simulation through CST software. The measured radiation patterns assured the minimal variation with simulated characteristics along the E and H planes. A comprehensive safety investigation of the proposed MIMO antenna has been carried out to assess power densities (<10 W/ m 2 ) across different usage modes, including data, reading, and talking in a mobile application perspective. Several diversity parameters, including ECC (<0.0001), DG (>9.999 dB), CCL (<0.12 bits/sec/Hz), MEG, and TARC, have also been evaluated as performance metrics. Furthermore, the linear transmission property of the proposed MIMO antenna has been validated through the variation of group delay (<0.5 ns) over operating frequency bands in the far-field region.

Relationship between duration of undiagnosed illness, clinical features and cognitive impairment in bipolar disorder

Purpose: It is believed that a delay in the diagnosis of bipolar disorder may adversely affect the clinical course and outcome. This study aimed to investigate the relationship between diagnostic delay and clinical variables, as well as neurocognitive and social cognitive disorders Materials and methods: Eighty-four patients with bipolar disorder in remission were included in the study. Participants were evaluated using a neuropsychological battery that assessed verbal memory and learning, visual memory and learning, verbal fluency, attention, processing speed, executive functions, working memory, and social cognition. Results: The duration of undiagnosed illness was longer in patients with bipolar II disorder, those without psychotic features, those with at least one suicide attempt, those whose first episode was depressive, and those currently on antidepressants. A significant positive correlation was found between the duration of undiagnosed illness and scores on the Controlled Oral Word Association Test, total number of episodes, hypomanic episodes, depressive episodes, and their respective durations. Conversely, a significant negative correlation was found between the duration of undiagnosed illness and both the number and duration of manic episodes. Conclusion: We found that a delay in diagnosis and treatment was associated with more recurrences in bipolar disorder, an increased number of depressive episodes, and at least one lifetime suicide attempt. However, the association between extended periods of untreated illness and poor clinical and functional outcomes did not align with cognitive impairment.

Development and verification of real-time hybrid model test delay compensation method for monopile-type offshore wind turbines

The real-time hybrid model (RTHM) test is adept at addressing the scale contradiction, the lack of fidelity in wind modelling in hydrodynamic testing facilities and spatial constraints inherent in conventional monopile-type offshore wind turbine (OWT) model testing methods, thus emerging as an effective avenue for conducting physical model tests of Monopile-type OWTs. This method entails the reproduction of aerodynamic loads or platform motions using loading device or vibration tables. Time delays in the physical attributes of the loading device and signal transmission processes within the system can result in error accumulation, with the potential to impact overall system stability. Moreover, time delay compensation algorithms for hybrid model test systems with force control loading can easily generate excessive noise, leading to system divergence. As a result, time delay has emerged as a technical challenge in the RTHM test. To address this issue, this paper has developed second-order and third-order polynomial extrapolation algorithms, alongside an adaptive compensation algorithm. The adaptive compensation algorithm employs the least squares method to identify parameters of the loading system, enabling it to address variations in the time delay of the experimental system caused by the nonlinearity of the loading system and changes in the physical properties of the model. The feasibility and effects of time delay compensation for various algorithms are validated through numerical simulation. Results indicate that the adaptive compensation algorithm surpasses second and third-order polynomial extrapolation compensation algorithms in terms of accuracy and compensation effectiveness. To validate the applicability of the adaptive compensation algorithm, a RTHM test was conducted. Across rotor thrust force (RotThrust) and tower top displacement, there was an average reduction of approximately 5 % and 9 % in the maximum and minimum synchronization errors, respectively. This highlights the efficacy of the delay compensation algorithm in practical applications, notably diminishing time delay errors within the experimental system. The adaptive compensation algorithm continuously adjusts and updates parameters, enhancing the adaptability of the compensation process to time-varying systems.

A Method by using predefined windows to improve the InSAR atmospheric correction model based on GNSS ZTD and its horizontal delay gradient

ABSTRACT Correcting the signal delay caused by electromagnetic waves passing through the atmosphere is crucial in InSAR. Atmospheric delays are significant enough to obscure the true deformation signal, making it difficult for InSAR to detect deformation signals at the millimetre scale. Therefore, it is imperative to mitigate atmospheric interference appropriately. To interpolate sparse GNSS networks and capture small-scale atmospheric delays, this study presents an enhanced method for constructing an atmospheric correction model based on GNSS Zenith Total Delay (ZTD) and its horizontal gradient, which is integrated within a predefined window. To validate the effectiveness of the improved model, we applied it to 27 InSAR short-time and short-spatial baseline unwrapped interferograms. These interferograms are composed of images observed by the Sentinel-1A satellite from January to December 2022 in Southern California. The corrective impact of the enhanced model is also compared against that of the initial inversion model and GACOS to confirm the viability of the approach. Results of the evaluation, considering root mean square error (RMSE), standard deviation (STD), and the correlation between phase and elevation, indicate that our enhanced method reduces correction errors by 35.10%, 20.05%, and 63.52%, respectively, compared to GACOS. Compared to the initial model, these reductions are 58.19%, 57.23%, and 4.34%, respectively. By integrating the outcomes from all three evaluation methods and analysing the corrected images, our enhanced model effectively resolves the inconsistency in correction effects resulting from the spatial variability of atmospheric delay. Moreover, incorporating the horizontal gradient in the inversion method has proven effective in capturing small-scale atmospheric delay signals. This highlights the feasibility and benefits of our enhanced model.

On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning

For over forty years, TCP has been the main protocol for transporting data on the Internet. To improve congestion control algorithms (CCAs), delay bounding algorithms such as Vegas, FAST, BBR, PCC, and Copa have been developed. However, despite being designed to ensure fairness between data flows, these CCAs can still lead to unfairness and, in some cases, even cause data flow starvation in WiFi networks under certain conditions. We propose a new CCA switching solution that works with existing TCP and WiFi standards. This solution is offline and uses Deep Reinforcement Learning (DRL) trained on features such as noncongestive delay variations to predict and prevent extreme unfairness and starvation. Our DRL-driven approach allows for dynamic and efficient CCA switching. We have tested our design preliminarily in realistic datasets, ensuring that they support both fairness and efficiency over WiFi networks, which requires further investigation and extensive evaluation before online deployment.

The Influence of Regulation, Audit Delay, Audit Fees, and Firm Size on Auditor Switching in Transportation and Logistics Entities Listed on The Idx from 2018 to 2022

This study aims to demonstrate the impact of regulations, audit delay, audit fees, and firm size on auditor switching. The subjects of this observation are transportation and logistics entities listed on the Indonesia Stock Exchange during the period 2018-2022. The sampling method used is purposive sampling, resulting in 55 observation samples. The analysis in this study employs logistic regression analysis. The tool used is Eviews version 13. The results of the study prove that the variables of regulation, audit delay, audit fees, and firm size do not influence auditor turnover individually. However, when tested together, these variables collectively influence auditor turnover with a percentage of 31.6%, while the remaining 68.4% is explained by variables other than those observed.

The NANOGrav 15 yr Data Set: Chromatic Gaussian Process Noise Models for Six Pulsars

Abstract Pulsar timing arrays (PTAs) are designed to detect low-frequency gravitational waves (GWs). GWs induce achromatic signals in PTA data, meaning that the timing delays do not depend on radio frequency. However, pulse arrival times are also affected by radio-frequency-dependent “chromatic” noise from sources such as dispersion measure (DM) and scattering delay variations. Furthermore, the characterization of GW signals may be influenced by the choice of chromatic noise model for each pulsar. To better understand this effect, we assess if and how different chromatic noise models affect the achromatic noise properties in each pulsar. The models we compare include existing DM models used by the North American Nanohertz Observatory for Gravitational waves (NANOGrav) and noise models used for the European PTA Data Release 2 (EPTA DR2). We perform this comparison using a subsample of six pulsars from the NANOGrav 15 yr data set, selecting the same six pulsars as from the EPTA DR2 six-pulsar data set. We find that the choice of chromatic noise model noticeably affects the achromatic noise properties of several pulsars. This is most dramatic for PSR J1713+0747, where the amplitude of its achromatic red noise lowers from log 10 A RN = − 14.1 − 0.1 + 0.1 to − 14.7 − 0.5 + 0.3 , and the spectral index broadens from γ RN = 2.6 − 0.4 + 0.5 to γ RN = 3.5 − 0.9 + 1.2 . We also compare each pulsar's noise properties with those inferred from the EPTA DR2, using the same models. From the discrepancies, we identify potential areas where the noise models could be improved. These results highlight the potential for custom chromatic noise models to improve PTA sensitivity to GWs.

Cortico-striatal beta oscillations as a reward-related signal

The value associated with reward is sensitive to external factors, such as the time between the choice and reward delivery as classically manipulated in temporal discounting tasks. Subjective preference for two reward options is dependent on objective variables of reward magnitude and reward delay. Single neuron correlates of reward value have been observed in regions, including ventral striatum, orbital, and medial prefrontal cortex. Brain imaging studies show cortico-striatal-limbic network activity related to subjective preferences. To explore how oscillatory dynamics represent reward processing across brain regions, we measured local field potentials of rats performing a temporal discounting task. Our goal was to use a data-driven approach to identify an electrophysiological marker that correlates with reward preference. We found that reward-locked oscillations at beta frequencies signaled the magnitude of reward and decayed with longer temporal delays. Electrodes in orbitofrontal/medial prefrontal cortex, anterior insula, ventral striatum, and amygdala individually increased power and were functionally connected at beta frequencies during reward outcome. Beta power during reward outcome correlated with subjective value as defined by a computational model fit to the discounting behavior. These data suggest that cortico-striatal beta oscillations are a reward signal correlated, which may represent subjective value and hold potential to serve as a biomarker and potential therapeutic target.

Improving stability and performance in IoT-Driven networked control systems

With the widespread adoption and benefits of the Internet of Things (IoT), this technology has expanded its reach into the realm of automation and control, giving rise to a branch known as industrial networks. The use of IoT technology is on the rise due to its daily increasing advantages and ease of use. However, it has also introduced new challenges in control systems, prompting researchers to conduct numerous studies to address these challenges and propose innovative solutions. One of these challenges, for example, is the limited bandwidth of the network, which leads to challenging issues such as variable random time delays and packet loss. In light of these challenges, using networked control systems instead of many traditional control methods can significantly improve the performance and stability of closed-loop systems. In this context, this paper aims to enhance system tolerance and reliability, along with permanent state error correction, by presenting methods for designing networked controllers that are influenced by random delays. To achieve this goal, we first propose a predictive control model with reduced prediction horizons, assuming constant delays in the input. This controller not only ensures stability but also tracks the reference input. Simulation results demonstrate that this controller can tolerate some variations in delay despite being designed for constant delays. However, it is not robust against significant variations in delay inherent to network environments. Therefore, in the second approach, a controller based on the Lyapunov function and linear matrix inequalities is designed for a system with time-varying input delays. This controller, in addition to stabilizing the system, tracks the reference input by minimizing the state error.

Effects of Waveform, Time Delay, and Vibration Axis on the Perception of Vibrotactile Compliance Illusions on Smartphone Touchscreens

A hard surface feels soft or elastic when carefully controlled vibrations are provided to a pressing finger. This compliance illusion method may be employed to augment graphical objects on smartphone screens with compliant tactile properties. However, product engineers encounter practical questions when attempting to use the compliance illusion method for their products. Should the waveform be sinusoidal? How much time delay is acceptable? Should the vibration axis be perpendicular to the surface? A series of experiments was conducted to address these questions. The first experiment revealed that the waveform could not be distinguished in the context of the compliance illusion method when applied to typical smartphones. The second experiment demonstrated that time delays greater than 25 ms and the use of the three vibration axes could be distinguished in the same context. The third experiment explored how the perceptual qualities of the compliance illusion change with variations in time delay and the axis of vibration. Time delay had significant effects on softness, smoothness, elasticity, and unpleasantness, while the vibration axis had significant effects on softness, smoothness, and elasticity.

Treating Tropospheric Phase Delay in Large-scale Sentinel-1 Stacks to Analyze Land Subsidence

Variations in the tropospheric phase delay pose a primary challenge to achieving precise displacement measurements in Interferometric Synthetic Aperture Radar (InSAR) analysis. This study presents a cluster-based empirical tropospheric phase correction approach to analyze land subsidence rates from large-scale Sentinel‑1 data stacks. Our method identifies the optimum number of clusters in individual interferograms for K‑means clustering, and segments extensive interferograms into areas with consistent tropospheric phase delay behaviors. It then performs tropospheric phase correction based on empirical topography-phase correlation, addressing stratified and broad-scale tropospheric phase delays. Applied to a six-year data stack along a 1000-km track in Iran, we demonstrate that this approach enhances interferogram quality by reducing the standard deviation by 50% and lowering the semivariance of the interferograms to 20 cm2 at distances up to 800 km in 97% of the interferograms. Additionally, the corrected time series of deformation shows a 40% reduction in the root mean square of residuals at the most severely deformed points. By analyzing the corrected interferograms, we show that our method improves the efficiency of country-scale InSAR surveys to detect and quantify present-day land subsidence in Iran, which is essential for groundwater management and sustainable water resource planning.

Entropy Analysis of FPGA Interconnect and Switch Matrices for Physical Unclonable Functions

Random variations in microelectronic circuit structures represent the source of entropy for physical unclonable functions (PUFs). In this paper, we investigate delay variations that occur through the routing network and switch matrices of a field-programmable gate array (FPGA). The delay variations are isolated from other components of the programmable logic, e.g., look-up tables (LUTs), flip-flops (FFs), etc., using a feature of Xilinx FPGAs called dynamic partial reconfiguration (DPR). A set of partial designs is created to fix the placement of a time-to-digital converter (TDC) and supporting infrastructure to enable the path delays through the target interconnect and switch matrices to be extracted by subtracting out common-mode delay components. Delay variations are analyzed in the different levels of routing resources available within FPGAs, i.e., local routing and across-chip routing. Data are collected from a set of Xilinx Zynq 7010 devices, and a statistical analysis of within-die variations in delay through a set of the randomly-generated and hand-crafted interconnects is presented.