Academic literature on the topic 'Fixed-point observing systems'

Sustained in situ ice-ocean observations are sorely lacking in the Arctic, limiting research on climate, weather, ice-ocean processes, and geophysical hazards. A sustained network of advanced multipurpose underwater moorings and drifting buoys in the Nansen and Amundsen Basins that included acoustic and other instrumentation would make a substantial contribution to a high Arctic Ocean observing system. Such a network would provide point measurements of ocean parameters, large-scale temperature measurements using acoustic thermometry, acoustic geo-positioning of underwater floats and gliders, and passive acoustic measurements for detection of marine mammals, geohazards, and human generated noise. Optimal design of such a network of fixed moorings and drifting platforms requires accurate knowledge of the ice-ocean environment to determine the acoustic properties. Such a sustained network would build on the successful basin-wide coordinated arctic acoustic thermometry experiment (CAATEX). In this presentation, the focus will be on the oceanographic and acoustic characteristics of the Nansen and Amundsen Basins using observations made during CAATEX. The ability of several climate models and reanalysis products to describe the oceanographic characteristics as well as their usefulness in predicting low-frequency acoustic propagation will be evaluated.

The controversy between what lies amidst the spoken and written, and the gap of nonconformity constitutes a problematic dilemma in the Arabic language, a serious dilemma. This study seeks to identify this problem, trying to point out its features, causes and explanations by considering of the reasons derived from phonetic values, or from the origins that influenced the emergence of Arabic calligraphy becoming one of the fixed residues that define the Arabic systems of writing. The study reviews examples of this controversy being described s a problematic, so the study utilizes the instrument of induction, as well as the descriptive analytical method by observing and analyzing samples of the problem. The study does not aim to tally nor to monitor its details. As such, the study is limited to the features of deletion and addition. The study ended with a number of results regarding observing and dealing this controversy.

One of the most challenging tasks in physics has been understanding the route an out-of-equilibrium system takes to its thermalized state. This problem can be particularly overwhelming when one considers a many-body quantum system. However, several recent theoretical and experimental studies have indicated that some far-from-equilibrium systems display universal dynamics when close to a so-called non-thermal fixed point (NTFP), following a rescaling of both space and time. This opens up the possibility of a general framework for studying and categorizing out-of-equilibrium phenomena into well-defined universality classes. This paper reviews the recent advances in observing NTFPs in experiments involving Bose gases. We provide a brief introduction to the theory behind this universal scaling, focusing on experimental observations of NTFPs. We present the benefits of NTFP universality classes by analogy with renormalization group theory in equilibrium critical phenomena.

Abstract Some recent results showed that the renormalization group (RG) can be considered as a promising framework to address open issues in data analysis. In this work, we focus on one of these aspects, closely related to principal component analysis (PCA) for the case of large dimensional data sets with covariance having a nearly continuous spectrum. In this case, the distinction between ‘noise-like’ and ‘non-noise’ modes becomes arbitrary and an open challenge for standard methods. Observing that both RG and PCA search for simplification for systems involving many degrees of freedom, we aim to use the RG argument to clarify the turning point between noise and information modes. The analogy between coarse-graining renormalization and PCA has been investigated in Bradde and Bialek (2017 J. Stat. Phys. 167 462–75), from a perturbative framework, and the implementation with real sets of data by the same authors showed that the procedure may reflect more than a simple formal analogy. In particular, the separation of sampling noise modes may be controlled by a non-Gaussian fixed point, reminiscent of the behaviour of critical systems. In our analysis, we go beyond the perturbative framework using nonperturbative techniques to investigate non-Gaussian fixed points and propose a deeper formalism allowing us to go beyond power-law assumptions for explicit computations.

Correlation analysis serves as an easy-to-implement estimation approach for the quantification of the interaction or connectivity between different units. Often, pairwise correlations estimated by sliding windows are time-varying (on different window segments) and window size-dependent (on different window sizes). Still, how to choose an appropriate window size remains unclear. This paper offers a framework for studying this fundamental question by observing a critical transition from a chaotic-like state to a nonchaotic state. Specifically, given two time series and a fixed window size, we create a correlation-based series based on nonlinear correlation measurement and sliding windows as an approximation of the time-varying correlations between the original time series. We find that the varying correlations yield a state transition from a chaotic-like state to a nonchaotic state with increasing window size. This window size-dependent transition is analyzed as a universal phenomenon in both model and real-world systems (e.g., climate, financial, and neural systems). More importantly, the transition point provides a quantitative rule for the selection of window sizes. That is, the nonchaotic correlation better allows for many regression-based predictions.

Understanding the dynamics of coastal marine ecosystems is fundamental for assessing environmental health and addressing anthropogenic impacts. This study analyzes a decade-long dataset of high-resolution Chlorophyll fluorescence (ChlF) measurements collected hourly from August 2012 to December 2022 at the E1 meteo-oceanographic buoy (Böhm et al. 2016). The buoy is located in the Northern Adriatic Sea (44° 08.58' N; 12° 34.20' E), approximately 100 km south of the Po River delta and 7 km northeast of Rimini, Italy. As part of the "Delta del Po and Costa Romagnola" (Bergami and Riminucci 2025) research site within the Italian LTER network and the PNRR ITINERIS project, the E1 station provides a unique platform for multidisciplinary research and long-term environmental monitoring. ChlF data, collected by the WET Labs® ECO Triplet (now SeaBird Scientific), reveal significant seasonal and interannual variations in chlorophyll concentration, estimates from in situ ChlF, ranging from below detection limits to a maximum daily average of 41 μg/L. Together with ChlF, other meteorological, chemical, and physical parameters such as temperature (TEMP), dissolved oxygen (DO), salinity (SAL), turbidity (TURB), and wind speed (WS) were incorporated for this study (Riminucci et al. 2024, Riminucci et al. 2025). A total of 40 distinct algal bloom events were identified using ChlF concentration thresholds and growth rate dynamics (Trombetta et al. 2019). The analysis of bloom frequency revealed two main periods of bloom events per year (Fig. 1), with the identified blooms lasting an average of 13±10 days. During these events, ChlF concentration increased to an average of 6.5 μg/L, indicating substantial algal growth, compared to a baseline of 2.8 μg/L observed outside bloom periods. The most intense blooms occurred in spring, driven by nutrient inputs and increased sunlight, while summer blooms were weaker, less frequent, and shorter in duration due to thermal stratification. In contrast, autumn and winter saw a resurgence of bloom activity, influenced by freshwater inflow, nutrient resuspension, and strong mixing. These seasonal patterns underscore the dynamic interplay between temperature, wind, and nutrient availability in shaping phytoplankton dynamics.Principal Component Analysis (PCA) was conducted to identify key relationships among environmental variables and their influence on algal blooms (Fig. 2). The strong correlation between ChlF and DO during bloom periods reflects the role of photosynthesis in elevating oxygen levels. TEMP and SAL are linked due to seasonal stratification, while WS and TURB highlight wind-driven physical disturbances, such as sediment resuspension and waves. Overall, PCA captures both seasonal and biological processes, including bloom dynamics, as well as physical disturbances driven by wind and water movement. Seasonal factors govern bloom dynamics, with spring and late autumn/winter supporting phytoplankton growth due to favourable nutrient availability and stable conditions, while summer stratification limits blooms. These findings underscore the interplay of biological and physical drivers in shaping the ecosystem response over the decade. Two bloom types were identified: Single-Peak Blooms, typical in spring, characterized by rapid growth and short durations, and Multi-Peak Blooms, more common in autumn, with extended periods due to intermittent nutrient apportion. Nutrient-rich freshwater inputs from the Po River, nitrogen and phosphorus, and seasonal cycles of phytoplankton play a significant role in driving these blooms. Diatoms such as <em>Skeletonema marinoi</em> dominate the winter bloom, while spring and autumn blooms are diatom-driven, modulated by rainfall and nutrient runoff (Grilli et al. 2020, Totti et al. 2019). Summer, characterized by water column stratification, generally exhibits lower ChlF concentrations unless disturbed by storms or mixing events that reintroduce nutrients into surface waters. These findings emphasize the complex seasonal and environmental drivers shaping bloom dynamics. This study introduces a methodological framework for detecting coastal algal blooms by analyzing patterns derived from in-situ fluorescence measurements, offering insights into bloom dynamics. While challenges such as data gaps due to sensor maintenance and biofouling, the findings underscore the value of long-term, high-frequency observations in understanding environmental processes and managing coastal ecosystems. Future research should focus on integrating complementary datasets, such as nutrient concentrations or satellite observations, to deepen the understanding of bloom drivers. Additionally, expanding datasets temporally, by including more years, and spatially, by incorporating other monitoring systems (e.g., fixed-point stations), will further enhance the robustness and applicability of the framework.

5G is the fifth generation of cellular networks and has been used in a lot of different areas. 5G often requires sudden rises in power consumption. To stabilize the power supply, a 5G system requires a lithium-ion battery (LIB) or a mechanism called AC main modernization to provide energy support during the power peak periods. The LIB approach is the best option in terms of simplicity and maintainability. Moreover, a 5G system requires not only high-performance energy but also the ability of tracking and prediction. Therefore, the requirement for a smart power supply for lithium-ion batteries with temporal monitoring and estimation is highly desirable. In this paper, we focus on artificial intelligence (AI) improvements to increase the accuracy of LIB state-of-health prediction. By observing the SeqInSeq nature of the battery data, our approach uses self-attention and fixed-point positional encoding. We also take advantage of autoregression to archive the trainable dependency from a non-linear branch and a linear branch in creating the final output. Compared with the current state-of-the-art (SOTA) method, our experimental results show that we provide better accuracy, compared with the baseline output using the NASA and CALCE datasets. From the same setting, we archive a reduction of 20.08% root mean square error (RMSE) and 29.01% mean absolute percentage error (MAPE) on NASA loss, compared to the SOTA approaches. On CALCE, the numbers are a 5.99% RMSE and 12.59% MAPE decrement, which is significant.

The expected amplitude of fixed-point sea surface temperature (SST) fluctuations induced by barotropic and baroclinic tidal flows is estimated from tidal current atlases and SST observations. The fluctuations considered are the result of the advection of pre-existing SST fronts by tidal currents. They are thus confined to front locations and exhibit fine-scale spatial structures. The amplitude of these tidally induced SST fluctuations is proportional to the scalar product of SST frontal gradients and tidal currents. Regional and global estimations of these expected amplitudes are presented. We predict barotropic tidal motions produce SST fluctuations that may reach amplitudes of 0.3 K. Baroclinic (internal) tides produce SST fluctuations that may reach values that are weaker than 0.1 K. The amplitudes and the detectability of tidally induced fluctuations of SST are discussed in the light of expected SST fluctuations due to other geophysical processes and instrumental (pixel) noise. We conclude that actual observations of tidally induced SST fluctuations are a challenge with present-day observing systems.

Direct ocean observing systems are a scarce set of data that build the backbone for understanding the current state of the ocean. These systems are limited in areas of high variability in temperature and salinity. Pioneering work of Wunsch (1977) proposed efforts to extract detailed hydrographic information using sound propagation through the ocean, providing the scaffolding to improve our understanding of the ocean's interior. The integrated nature of such measurements makes acoustic thermometry a powerful application for monitoring regional to basin-averaged hydrographic changes in the ocean, a measurement that is difficult to achieve by individual “point” measurements (moorings, ship-borne CTD casts, or autonomous floats) alone. This work models acoustic travel times corresponding to eigenrays between a fixed source and receiver from an evolving ocean state. Inclusive computation of acoustic travel times within a dynamically evolving modeled ocean state provide a novel approach to model data comparison within a general ocean circulation model. An adjoint assimilation framework combines observations with numerical models to estimate and update oceanic state variables Forget et al. (2015). This process provides the necessary operators for model data comparison of ocean acoustic observable quantities within a consistent state estimation framework allowing for data assimilation from acoustic tomography measurements.

The continuous sensing of water parameters is of great importance to the study of dynamic processes in the ocean, coastal areas, and inland waters. Conventional fixed-point and ship-based observing systems cannot provide sufficient sampling of rapidly varying processes, especially for small-scale phenomena. Acoustic tomography can achieve the sensing of water parameter variations over time by continuously using sound wave propagation information. A multi-station acoustic tomography experiment was carried out in a reservoir with three sound stations for water temperature observation. Specifically, multi-path propagation sound waves were identified with ray tracing using high-precision topography data obtained with ship-mounted ADCP. A new grid inverse method is proposed in this paper for water temperature profiling along a vertical slice. The progression of water temperature variation in three vertical slices between acoustic stations was mapped by solving an inverse problem. The reliability and adaptability of the grid method developed in this research are verified by comparison with layer-averaged water temperature results. The grid method can be further developed for the 3D mapping of water parameters over time, especially in small-scale water areas, where sufficient multi-path propagation sound waves can be obtained.

AbstractYaw resonances cause the rear-end of the vehicle to swing, which is related to the feeling of handling. As a basis for improving this motion, this paper considers the restoration of yaw resonance. The equilibrium position of the yaw resonance is the extension of the velocity vector at the “heading point,” where the vehicle median line is perpendicular to the turning radius in a steady state turn. Toward this position, the center of percussion at the rear-end of the vehicle travels. This travel is the restoration of yaw. Observing the vehicle behavior from the earth-fixed coordinate system at the moment when the heading point changes direction of travel, the heading point and the center of percussion travel in their respective directions. Each motion continues for a distance from the rear wheel to the heading point to reach the equilibrium position. This continues time equals “yaw lead time constant.” Therefore, when the yaw lead time constant is small, the vehicle is restored in a short time.